ISSN 1210-2512 (Print)

ISSN 1805-9600 (Online)



Proceedings of Czech and Slovak Technical Universities

About the Journal
Feature Articles
Editorial Board
Publishing Department
Society [CZ]

Log out
Your Profile

April 2024, Volume 33, Number 1 [DOI: 10.13164/re.2024-1]

Show all Hide all

X. Rao, Z. Y. Sun, H. H. Tao [references] [full-text] [DOI: 10.13164/re.2024.0001] [Download Citations]
Multi-Beam Associated Coherent Integration Algorithm for Weak Target Detection

Weak target detection is a great challenging in radar field. To detect the weak targets with beam migration, a novel tri-dimensional time model (i.e. fast time, slow time, and beam time) and a novel tri-dimensional signal model which based on the time model are set up firstly. Then, according to the presented models, we propose two multi-beam associated (MBA) coherent integration algorithms based on time-shared multi-beam (TSMB) and space-shared multi-beam (SSMB), respectively. The two proposed algorithms could both eliminate beam migration via associating multi-beam and realize coherent integration via discrete Fourier transform. According to different beam scanning modes, the subsequent analyses show that the MBA coherent integration algorithm based on SSMB (MBACIA-SSMB) may have a better detection performance than that based on TSMB (MBACIA-TSMB). Moreover, the capabilities to estimate the target’s radial velocity and tangency velocity are analyzed. Finally, some numerical experiments are given to verify the performances of MBACIA-TSMB and MBACIA-SSMB.

  1. CHEN, X. L., GUAN, J., LIU, N., et al. Maneuvering target detection via Radon-fractional Fourier transform-based long-time coherent integration. IEEE Transactions on Signal Processing, 2014, vol. 62, no. 4, p. 939–953. DOI: 10.1109/TSP.2013.2297682
  2. WEI, S., DAI, Y., ZHANG, Q. Weak and maneuvering target detection with long observation time based on segment fusion for narrowband radar. Sensors, 2022, vol. 22, no. 18, p. 1–24. DOI: 10.3390/s22187086
  3. ZHANG, Z., LIU, N., HOU, Y., et al. A coherent integration segment searching based GRT-GRFT hybrid integration method for arbitrary fluctuating target. Remote Sensing, 2022, vol. 14, no. 11, p. 1–20. DOI: 10.3390/rs14112695
  4. CAO, Y.-F., WANG, W.-Q., ZHANG, S. Long-time coherent integration for high-order maneuvering target detection via zerotrap line extraction. IEEE Transactions on Aerospace and Electronic Systems, 2021, vol. 57, no. 6, p. 4017–4027. DOI: 10.1109/TAES.2021.3082718
  5. TAO, R., LI, Y. L., WANG, Y. Short-time fractional Fourier transform and its applications. IEEE Transactions on Signal Processing, 2009, vol. 58, no. 5, p. 2568–2580. DOI: 10.1109/TSP.2009.2028095
  6. QI, L., TAO, R., ZHOU, S., et al. Detection and parameter estimation of multicomponent LFM signal based on the fractional Fourier transform. Science in China Series F: Information Science, 2004, vol. 47, no. 2, p. 184–198. DOI: 10.1360/02yf0456
  7. GUAN, J., CHEN, X. L., HUANG Y., et al. Adaptive fractional Fourier transform-based detection algorithm for moving target in heavy sea clutter. IET Radar, Sonar & Navigation, 2012, vol. 6, no. 5, p. 389–401. DOI: 10.1049/iet-rsn.2011.0030
  8. DAI, Z., ZHANG, X., FANG, H., et at. High accuracy velocity measurement based on keystone transform using entropy minimization. Chinese Journal of Electronics, 2016, vol. 25, no. 4, p. 774–778. DOI: 10.1049/cje.2016.06.009
  9. ZHANG, S., ZENG, T., LONG, T., et al. Dim target detection based on keystone transform. In IEEE International Radar Conference. Arlington (VA, USA), 2005, p. 889–894 DOI: 10.1109/RADAR.2005.1435953
  10. SU, J., XING, M., WANG, G., et al. High-speed multi-target detection with narrowband radar. IET Radar, Sonar & Navigation, 2010, vol. 4, no. 4, p. 595–603. DOI: 10.1049/iet-rsn.2008.0160
  11. XU, J., YU, J., PENG, Y. N., et al. Radon-Fourier transform for radar target detection, I: Generalized Doppler filter bank. IEEE Transactions on Aerospace and Electronic Systems, 2011, vol. 47, no. 2, p. 1186–1202. DOI: 10.1109/TAES.2011.5751251
  12. XU, J., YU, J., PENG, Y. N., et al. Radon-Fourier transform for radar target detection (II): Blind speed sidelobe suppression. IEEE Transactions on Aerospace and Electronic Systems, 2011, vol. 47, no. 4, p. 2473-2489. DOI: 10.1109/TAES.2011.6034645
  13. YU, J., XU, J., PENG, Y. N., et al. Radon-Fourier transform for radar target detection (III): Optimality and fast implementations. IEEE Transactions on Aerospace and Electronic Systems, 2012, vol. 48, no. 2, p. 991–1004. DOI: 10.1109/TAES.2012.6178044
  14. XU, J., XIA, X. G., PENG, S. B., et al. Radar maneuvering target motion estimation based on generalized Radon-Fourier transform. IEEE Transactions on Signal Processing, 2012, vol. 60, no. 12, p. 6190–6201. DOI: 10.1109/TSP.2012.2217137
  15. MA, B., ZHANG, S., JIA, W., et at. Fast implementation of generalized Radon–Fourier transform. IEEE Transactions on Aerospace and Electronic Systems, 2021, vol. 57, no. 6, p. 3758 to 3767. DOI: 10.1109/TAES.2021.3082717
  16. GAO, C., TAO, R., KANG, X. Weak target detection in the presence of sea clutter using Radon-Fractional Fourier transform canceller. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, vol. 14, p. 5818–5830. DOI: 10.1109/JSTARS.2021.3078723
  17. XIA, X. G. Discrete chirp-Fourier transform and its application to chirp rate estimation. IEEE Transactions on Signal Processing, 2000, vol. 48, no. 11, p. 3122–3133. DOI: 10.1109/78.875469
  18. WU, L., WEI, X., YANG, D., et al. ISAR imaging of targets with complex motion based on discrete chirp Fourier transform for cubic chirps. IEEE Transactions on Geoscience and Remote Sensing, 2012, vol. 50, no. 10, p. 4201–4212. DOI: 10.1109/TGRS.2012.2189220
  19. CARLSON, B. D., EVANS, E. D., WILSON, S. L. Search radar detection and track with the Hough transform. I. System concept. IEEE Transactions on Aerospace and Electronic Systems, 1994, vol. 30, no. 1, p. 102–108. DOI: 10.1109/7.250410
  20. CARLSON, B. D., EVANS, E. D., WILSON, S. L. Search radar detection and track with the Hough transform. II. Detection statistics. IEEE Transactions on Aerospace and Electronic Systems, 1994, vol. 30, no. 1, p. 109–115. DOI: 10.1109/7.250411
  21. CARLSON, B. D., EVANS, E. D., WILSON, S. L. Search radar detection and track with the Hough transform. III. Detection performance with binary integration. IEEE Transactions on Aerospace and Electronic Systems, 1994, vol. 30, no. 1, p. 116 to 125. DOI: 10.1109/7.250412
  22. CARRETERO-MOYA, J., GISMERO-MENOYO, J., ASENSIOLOPEZ, A., et al. Application of the Radon transform to detect small-targets in sea clutter. IET Radar, Sonar & Navigation, 2009, vol. 3, no. 2, p. 155–166. DOI: 10.1049/iet-rsn:20080123
  23. DENG, X., PI, Y., MORELANDE, M., et al. Track-before-detect procedures for low pulse repetition frequency surveillance radars. IET Radar, Sonar & Navigation, 2011, vol. 5, no. 1, p. 65–73. DOI: 10.1049/iet-rsn.2009.0245
  24. GROSSI, E., LOPS, M., VENTURINO, L. A novel dynamic programming algorithm for track-before-detect in radar systems. IEEE Transactions on Signal Processing, 2013, vol. 61, no. 10, p. 2608–2619. DOI: 10.1109/TSP.2013.2251338
  25. RAO, X., TAO, H. H., SU, J., et al. Axis rotation MTD algorithm for weak target detection. Digital Signal Processing, 2014, vol. 26, p. 81–86. DOI: 10.1016/j.dsp.2013.12.003
  26. KIRKLAND, D. Imaging moving targets using the second-order keystone transform. IET Radar, Sonar & Navigation, 2011, vol. 5, no. 8, p. 902–910. DOI: 10.1049/iet-rsn.2010.0304
  27. YANG, J. G., HUANG, X., THOMPSON, J., et al. Low-frequency ultra-wideband synthetic aperture radar ground moving target imaging. IET Radar, Sonar & Navigation, 2011, vol. 5, no. 9, p. 994–1001. DOI: 10.1049/iet-rsn.2010.0387
  28. SUN, G., XING, M. D., WANG, Y., et al. Improved ambiguity estimation using a modified fractional Radon transform. IET Radar, Sonar & Navigation, 2011, vol. 5, no. 4, p. 489–495. DOI: 10.1049/iet-rsn.2010.0246
  29. YAO, D., ZHANG. X., SUN, Z. Long-time coherent integration for maneuvering target based on second-order keystone transform and Lv’s distribution. Electronics, 2022, vol. 11, no. 13, p. 1–15. DOI: 10.3390/electronics11131961
  30. WU, L., WEI, X., YANG, D., et al. ISAR imaging of targets with complex motion based on discrete chirp Fourier transform for cubic chirps. IEEE Transactions on Geoscience and Remote Sensing, 2012, vol. 50, no. 10, p. 4201–4212. DOI: 10.1109/TGRS.2012.2189220
  31. RAO, X., TAO, H. H., SU, J., et al. Detection of constant radial acceleration weak target via IAR-FRFT. IEEE Transactions on Aerospace and Electronic Systems, 2015, vol. 54, no. 4, p. 3242 to 3253. DOI: 10.1109/TAES.2015.140739
  32. RAO, X., TAO, H. H., XIE J., et al. Long‐time coherent integration detection of weak maneuvering target via integration algorithm, improved axis rotation discrete chirp‐Fourier transform. IET Radar, Sonar & Navigation, 2015, vol. 9, no. 7, p. 917–926. DOI: 10.1049/iet-rsn.2014.0344
  33. SKOLNIK, M. I. Introduction to Radar System. 3rd ed. Columbus (OH): McGraw-Hill, 2002. ISBN: 9787121042072
  34. RAO, X., ZHONG, T., TAO, H. H., et al. Improved axis rotation MTD algorithm and its analysis. Multidimensional Systems and Signal Processing, 2019, vol. 30, no. 2, p. 885–902. DOI: 10.1007/s11045-018-0588-y

Keywords: Beam migration, coherent integration, multi-beam, weak target detection

Yu Wang, Xiang Zou, Jiantong Shi, Minhua Liu [references] [full-text] [DOI: 10.13164/re.2024.0012] [Download Citations]
YOLOv5-based Dense Small Target Detection Algorithm for Aerial Images Using DIOU-NMS

With the advancement of various aerial platforms, there is an increasing abundance of aerial images captured in various environments. However, the detection of densely packed small objects within complex backgrounds remains a challenge. To address the task of detecting multiple small objects, a multi-object detection algorithm based on distance intersection over union loss non-maximum suppression (DIOU-NMS) integrated with you only look once version 5 (YOLOv5) is proposed. Leveraging the YOLOv5s model as the foundation, the algorithm specifically addresses the detection of abundantly and densely packed targets by incorporating a dedicated small object detection layer within the network architecture, thus effectively enhancing the detection capability for small targets using an additional upsampling operation. Moreover, conventional non-maximum suppression is replaced with DIOU-based non-maximum suppression to alleviate the issue of missed detections caused by target density. Experimental results demonstrate the effectiveness of the proposed method in significantly improving the detection performance of dense small targets in complex backgrounds.

  1. BUGEAU A., PEREZ P. Detection and segmentation of moving objects in complex scenes. Computer Vision and Image Understanding, 2009, vol. 113, no. 4, p. 459–476. DOI: 10.1016/j.cviu.2008.11.005
  2. YIN, H., CHEN, B., CHAI, Y., et al. Review of vision-based object detection and tracking (in Chinese). Acta Automatica Sinica, 2016, vol. 42, no. 10, p. 1466–1489. DOI: 10.16383/j.aas.2016.c150823
  3. OSCO, L. P., DE ARRUDA, M. D. S., GONCALVES, D. N., et al. A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, vol. 174, p. 1–17. DOI: 10.1016/j.isprsjprs.2021.01.024
  4. SIVAKUMAR, A. N. V., LI, J. T., SCOTT, S., et al. Comparison of object detection and patch-based classification deep learning models on mid- to late-season weed detection in UAV imagery. Remote Sensing, 2020, vol. 12, no. 13, p. 2136–2140. DOI: 10.3390/rs12132136
  5. WANG, L., XIANG, L. R., TANG, L., et al. A convolutional neural network-based method for corn stand counting in the field. Sensors, 2021, vol. 21, no. 2, p. 507–510. DOI: 10.3390/s21020507
  6. AMMOUR, N., ALHICHRI, H., BAZI, Y., et al. Deep learning approach for car detection in UAV imagery. Remote Sensing, 2017, vol. 9, no. 4, p. 312–316. DOI: 10.3390/rs9040312
  7. LIU, Y., SHI, G., LI, Y., et al. M-YOLO based detection and recognition of highway surface oil filling with unmanned aerial vehicle. In Proceeding of 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP). Xi’an (China), 2022, p. 1884–1887. DOI: 10.1109/ICSP54964.2022.9778782
  8. DING, W., ZHANG, L. Building detection in remote sensing image based on improved YOLOV5. In Proceeding of 2021 17th International Conference on Computational Intelligence and Security (CIS). Chengdu (China), 2021, p. 133–136. DOI: 10.1109/CIS54983.2021.00036
  9. ZHANG, R., WEN, C. SOD‐YOLO: A small target defect detection algorithm for wind turbine blades based on improved YOLOv5. Advanced Theory and Simulations, 2022, vol. 5, no. 7, p. 2100631–2100635. DOI: 10.1002/adts.202100631
  10. GUO, J., XIE, J., YUAN, J., et al. Fault identification of transmission line shockproof hammer based on improved YOLO V4. In Proceeding of International Conference on Automation and Applications (ICAA). Nanjing (China), 2021, p. 826–833. DOI: 10.1109/ICAA53760.2021.00151
  11. LIU, C. Y., WU, Y. Q., LIU, J. J., et al. MTI-YOLO: A lightweight and real-time deep neural network for insulator detection in complex aerial images. Energies, 2021, vol. 14, no. 5, p. 1–19. DOI: 10.3390/en14051426
  12. SAMBOLEK, S., IVASIC-KOS, M. Automatic person detection in search and rescue operations using deep CNN detectors. IEEE Access, 2021, no. 9, p. 37905–37922. DOI: 10.1109/access.2021.3063681
  13. BOZIC-STUTIC, D., MARUSIC, Z., GOTOVAC, S. Deep learning approach in aerial imagery for supporting land search and rescue mission. International Journal of Computer Vision, 2019, vol. 127, no. 9, p. 1256–1278. DOI: 10.1007/s11263-019-01177-1
  14. DE OLIVEIRA, D. C., WEHRMEISTER, M. A. Using deep learning and low-cost RGB and thermal cameras to detect pedestrians in aerial images captured by multirotor UAV. Sensors, 2018, vol. 18, no. 7, p. 1–33. DOI: 10.3390/s18072244
  15. CHEN, C., LIU, M. Y., TUZEL, O., et al. R‑CNN for small object detection. In Proceeding of Asian Conference on Computer Vision (ACCV). Taipei (Taiwan), 2016, vol. 5, p. 214–230. DOI: 10.1007/978-3-319-54193-8_14
  16. YU, X., GONG, Y., JIANG, N., et al. Scale match for tiny person detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Snowmass Village (USA), 2020, p. 1246–1254. DOI: 10.1109/WACV45572.2020.9093394
  17. VIOLA, P., JONES, M. Robust real-time face detection. International Journal of Computer Vision, 2004, vol. 57, no. 2, p. 137–154. DOI: 10.1023/B:VISI.0000013087.49260.fb
  18. DALAL, N., TRIGGS, B. Histograms of oriented gradients for human detection. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Diego (USA), 2005, p. 1–8. DOI: 10.1109/cvpr.2005.177
  19. FELZENSZWALB, P. F., GIRSHICK, R. B., MCALLESTER, D., et al. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, vol. 32, no. 9, p. 1627–1645. DOI: 10.1109/TPAMI.2009.167
  20. GIRSHICK, R., DONAHUE, J., DARRELL, T., et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Columbus (USA), 2014, p. 580–587. DOI: 10.1109/CVPR.2014.81
  21. HE, K. M., ZHANG, X. Y., REN, S. Q., et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. In Fleet, D., Pajdla, T., Schiele, B., et al. (Eds.) Computer Vision – ECCV 2014, p. 346–361. DOI: 10.1007/978-3-319-10578-9_23
  22. GIRSHICK, R. Fast R-CNN. In Proceedings of IEEE International Conference on Computer Vision. Santiago (Chile), 2015, p. 1440–1448. DOI: 10.1109/ICCV.2015.169
  23. REN, S., HE, K., GIRSHICK, R., et al. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, vol. 39, no. 6, p. 1137–1149. DOI: 10.1109/TPAMI.2016.2577031
  24. REDMON, J., DIVVALA, S., GIRSHICK, R., et al. You Only Look Once: Unified, real-time object detection. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Seattle (USA), 2016, p. 779–788. DOI: 10.1109/CVPR.2016.91
  25. REDMON, J., FARHADI, A. YOLOv3: An incremental improvement. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City (USA), 2018, p. 1–6. DOI: 10.48550/arXiv.1804.02767
  26. BOCHKOVSKIY, A., WANG, C. Y., LIAO, H. Y. M. YOLOv4: Optimal speed and accuracy of object detection. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Seattle (USA), 2020. DOI: 10.48550/arXiv.2004.10934
  27. NELSON, J., SOLAWETZ J. YOLOv5 is Here: State-of-the-Art Object Detection at 140 fps. [Online] Available at: https://blog roboflow com/yolov5-is-here
  28. WANG, C. Y., LIAO, H. Y. M., WU, Y. H., et al. CSPNet: A new backbone that can enhance learning capability of CNN. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Washington (USA), 2020, p. 1571–1580. DOI: 10.1109/CVPRW50498.2020.00203
  29. LIN, T. Y., DOLLAR, P., GIRSHICK, R., et al. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu (USA), 2017, p. 936–944. DOI: 10.1109/CVPR.2017.106
  30. DENG, C., WANG, M., LIU, L., et al. Extended feature pyramid network for small object detection. IEEE Transactions on Multimedia, 2021, vol. 24, p. 1968–1979. DOI: 10.1109/TMM.2021.3074273
  31. YANG, C., HUANG, Z., WANG, N. QueryDet: Cascaded sparse query for accelerating high-resolution small object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans (USA), 2022, p. 13658-13667. DOI: 10.1109/CVPR52688.2022.01330
  32. ZHOU, L., DENG, G., LI, W., et al. A lightweight SE-YOLOv3 network for multi-scale object detection in remote sensing imagery. International Journal of Pattern Recognition and Artificial Intelligence, 2021, vol. 35, no. 13. DOI: 10.1142/S0218001421500373
  33. WANG, M., YANG, W., WANG, L., et al. FE-YOLOv5: Feature enhancement network based on YOLOv5 for small object detection. Journal of Visual Communication and Image Representation, 2023, vol. 90, p. 1–8. DOI: 10.1016/j.jvcir.2023.103752
  34. KIM, M., JEONG, J., KIM, S. ECAP-YOLO: Efficient channel attention pyramid YOLO for small object detection in aerial image. Remote Sensing, 2021, vol. 13, p. 1–20. DOI: 10.3390/rs13234851
  35. LUO, X., WU, Y., WANG, F. Target detection method of UAV aerial imagery based on improved YOLOv5. Remote Sensing, 2022, vol. 14, no. 19, p. 1–25. DOI: 10.3390/rs14195063
  36. SZEGEDY, C., LIU, W., JIA, Y., et al. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston (USA), 2015, p. 1–9. DOI: 10.1109/CVPR.2015.7298594
  37. SRIVASTAVA, N., HINTON, G., KRIZHEVSKY, A., et al. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 2014, vol. 15, no. 1, p. 1929–1958. DOI: 10.5555/2627435.2670313
  38. IOFFE, S., SZEGEDY, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning. Lille (France), 2015, p. 448–456. DOI: 10.48550/arXiv.1502.03167
  39. ZHANG, Z., SABUNCU, M. Generalized cross entropy loss for training deep neural networks with noisy labels. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS'18). Montreal (Canada), 2018, p. 8792 to 8802. DOI: 10.48550/arXiv.1805.07836
  40. LIU, S., QI, L., QIN, H., et al. Path aggregation network for instance segmentation. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City (USA), 2018, p. 8759–8768. DOI: 10.48550/arXiv.1803.01534
  41. WOO, S., PARK, J., LEE, J. Y., et al. CBAM: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV). Munich (Germany), 2018. DOI: 10.48550/arXiv.1807.06521
  42. GHIASI, G., LIN T. Y., LE, Q. V. DropBlock: A regularization method for convolutional networks. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS'18). Montreal (Canada), 2018, p. 10750–10760. DOI: 10.48550/ arXiv.1810.12890
  43. ZHU, P., WEN, L., DU, D., et al. Vision meets drones: Past, present and future. In Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Seattle (USA), 2020. DOI: 10.48550/arXiv.2001.06303

Keywords: Object Detection, YOLOv5, DIOU-NMS, Aerial Images, Small Object Detection, Complex Backgrounds.

S. Kawdungta, D. Torrungrueng, H.-T. Chou [references] [full-text] [DOI: 10.13164/re.2024.0024] [Download Citations]
Split-Ring Coupled Low-Cost Antenna with Electromagnetic Bandgap (EBG) Superstrates to Produce Tri-bands and High Gains

In this paper, a novel tri-band low-cost antenna covering the desired frequencies is presented. The architecture is formed by a printed dipole coupled by a split-ring within an electromagnetic bandgap (EBG) structure for high radiation gains. The printed dipole is placed beneath two dielectric superstrates, and the coupling split-ring is placed on its top. The proposed antenna is excited by the printed dipole with a coaxial connector. It is placed in the middle cavity formed by two dielectric superstrates and a metal reflector as the simple EBG structure. The simulation results show three resonant frequencies at 1.42, 2.39 and 5.40 GHz respectively, with uni-directional radiation patterns and high gains enhanced by the EBG structure. Experimental measurements over an antenna prototype validate the results of reflection coefficients and radiation patterns. It is found that the gains are 8.50, 6.00 and 8.10 dBi at 1.42, 2.39 and 5.00 GHz respectively, which are sufficient for L-band and WiFi applications. In addition, simulation and measurement results are in good agreement.

  1. JIA, F., ZHENG, Z., WANG, Q., et al. A new multi-band multiarray antenna configuration with scattering suppression for radiation pattern distortion mitigation of base station. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 7, p. 6006–6011. DOI: 10.1109/TAP.2022.3175957
  2. CUI, Y., LUO, P., GONG, Q., et al. A compact tri-band horizontally polarized omnidirectional antenna for UAV applications. IEEE Antennas and Wireless Propagation Letters, 2019, vol. 18, no. 4, p. 601–605. DOI: 10.1109/LAWP.2019.2897380
  3. TA, S. X., CHOO, H., PARK, I., et al. Multi-band, wide-beam, circularly polarized, crossed, asymmetrically barbed dipole antennas for GPS applications. IEEE Transactions on Antennas and Propagation, 2013, vol. 61, no. 11, p. 5771–5775. DOI: 10.1109/TAP.2013.2277915
  4. PAUL, P. M., KANDASAMY, K., SHARAWI, M. S. A multi band SRR and strip loaded slot antenna. In 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting. Boston (MA, USA), 2018, p. 663–664. DOI: 10.1109/APUSNCURSINRSM.2018.8608217
  5. ELAVARASI, C., SHANMUGANATHAM, T. Multiband SRR loaded Koch star fractal antenna. Alexandria Engineering Journal, 2018, vol. 57, no. 3, p. 1549–1555. DOI: 10.1016/j.aej.2017.04.001
  6. PENDRY, J. B., HOLDEN, A. J., ROBBINS, D. J., et al. Magnetism from conductors and enhanced nonlinear phenomena. IEEE Transactions on Microwave Theory and Techniques, 1999, vol. 47, no. 11, p. 2075–2084. DOI: 10.1109/22.798002
  7. NUTAN REDDY, A., RAGHAVAN, S. Split ring resonator and its evolved structures over the past decade. In 2013 IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICECCN). Tirunelveli (India), 2013, p. 625–629. DOI: 10.1109/ICE-CCN.2013.6528575
  8. BAENA, J. D., BONACHE, J., MARTIN, F., et al. Equivalent circuit models for split-ring resonators and complementary split ring resonators coupled to planar transmission lines. IEEE Transactions on Microwave Theory and Techniques, 2005, vol. 53, no. 4, p. 1451–1461. DOI: 10.1109/TMTT.2005.845211
  9. KAWDUNGTA, S., JAIBANAUEM, P., PONGGA, R., et al. Superstrate-integrated switchable beam rectangular microstrip antenna for gain enhancement. Radioengineering, 2017, vol. 26, no. 2, p. 430–437. DOI: 10.13164/re.2017.0430
  10. NGUYEN-TRONG, N., TRAN, H. H., NGUYEN, T. K., et al. A compact wideband circular polarized fabry-perot antenna using resonance structure of thin dielectric slabs. IEEE Access, 2018, vol. 6, p. 56333–56339. DOI: 10.1109/ACCESS.2018.2872571
  11. KAWDUNGTA, S., TORRUNGRUENG, D., BOONPOONGA, A., CHOU, H.-T. Dual-band Huygens source antennas with partially reflective surfaces. Radio Science, 2022, vol. 57, no. 11, p. 1–13. DOI: 10.1029/2022RS007602
  12. TORRUNGRUENG, D., KAWDUNGTA, S., AKKARAEKTHALIN, P. An efficient analysis of the far-field radiation of an electric/magnetic Hertzian dipole embedded in electromagnetic bandgap structures of periodic lossless multilayers using the equivalent CCITL model. Journal of Electromagnetic Waves and Applications, 2016, vol. 30, no. 17, p. 2227–2240. DOI: 10.1080/09205071.2016.1243490
  13. WU, X. H., KISHK, A. A., GLISSON, A. W. A transmission line method to compute the far-field radiation of arbitrarily directed Hertzian dipoles in a multilayer dielectric structure: Theory and applications. IEEE Transactions on Antennas and Propagation, 2006, vol. 54, no. 10, p. 2731–2741. DOI: 10.1109/TAP.2006.882164
  14. DASSAULT SYSTÈMES. CST Studio Suite 2012, Dassault Systèmes, 2012, Retrieved from:

Keywords: Electromagnetic bandgap, split-ring, superstrates, tri-band

L. J. Ge, S. X. Niu, C. P. Shi, Y. C. Guo, G. J. Chen [references] [full-text] [DOI: 10.13164/re.2024.0034] [Download Citations]
Cascaded Deep Neural Network Based Adaptive Precoding for Distributed Massive MIMO Systems

In time-division duplex (TDD) distributed large-scale multiple input multiple output (DM-MIMO) systems, the traditional downlink channel precoding method is used to resist inter-user interference (IUI). However, when the Channel State Information (CSI) is incomplete, the performance loss is serious, not only the bit error rate is high, but also the complexity of the traditional precoding algorithm is high. In order to solve these problems, this paper proposes an adaptive precoding framework based on deep learning (DL) for joint training and split application deployment. First, we train a channel emulator deep neural network (CE-DNN) to learn and simulate the transmission process of the wireless communication channel. Then, we concatenate an untrained precoding DNN (P-DNN) with a trained CE-DNN and retrain the cascaded neural network to converge. The last step is to obtain the P-DNN, namely the adaptive precoding network, by dismantling the joint trained network. Simulation results show that, when CSI is imperfect, the proposed method is compared with Tomlinson-Harashima precoding (THP) and block diagonalization (BD) precoding. The proposed method has a lower mean square error (MSE) and higher spectrum efficiency, as well as a bit error rate (BER) performance close to the THP. The source codes and the neural network codes are available on request.

  1. MARZETTA, T. L. Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Transactions on Wireless Communications, 2010, vol. 9, no. 11, p. 3590–3600. DOI: 10.1109/TWC.2010.092810.091092
  2. RUSEK, F., PERSSON, D., LAU, B. K., et al. Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 2013, vol. 30, no. 1, p. 40–60. DOI: 10.1109/MSP.2011.2178495
  3. AGIWAL, M., ROY, A., SAXENA, N. Next generation 5G wireless networks: A comprehensive survey. IEEE Communications Surveys Tutorials, 2016, vol. 18, no. 3, p. 1617–1655. DOI: 10.1109/COMST.2016.2532458
  4. KAMGA, G. N., XIA, M., AISSA, S. Spectral-efficiency analysis of massive MIMO systems in centralized and distributed schemes. IEEE Transactions on Communications, 2016, vol. 64, no. 5, p. 1930–1941. DOI: 10.1109/TCOMM.2016.2519513
  5. SOOMRO, H., HABIB, A. Impact of remote radio head positions on the performance of distributed massive MIMO system with user mobility. In 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST). Islamabad (Pakistan), 2018, p. 789–794. DOI: 10.1109/IBCAST.2018.8312313
  6. LU, L., LI, G. Y., SWINDLEHURST, A. L., et al. An overview of massive MIMO: Benefits and challenges. IEEE Journal of Selected Topics in Signal Processing, 2014, vol. 8, no. 5, p. 742–758. DOI: 10.1109/JSTSP.2014.2317671
  7. GUPTA, A., JHA, R. K. A survey of 5G network: Architecture and emerging technologies. IEEE Access, 2015, vol. 3, p. 1206–1232. DOI: 10.1109/ACCESS.2015.2461602
  8. BUZZI, S., DANDREA, C., ZAPPONE, A., et al. User-centric 5G cellular networks: Resource allocation and comparison with the cell-free massive MIMO approach. IEEE Transactions on Wireless Communications, 2020, vol. 19, no. 2, p. 1250–1264. DOI: 10.1109/TWC.2019.2952117
  9. REN, H., LIU, N., PAN, C., et al. Energy efficiency optimization for MIMO distributed antenna systems. IEEE Transactions on Vehicular Technology, 2017, vol. 66, no. 3, p. 2276–2288. DOI: 10.1109/TVT.2016.2574899
  10. LI, Y., FAN, P., LIU, L., et al. Distributed MIMO precoding for in-band full-duplex wireless backhaul in heterogeneous networks. IEEE Transactions on Vehicular Technology, 2018, vol. 67, no. 3, p. 2064–2076. DOI: 10.1109/TVT.2017.2713413
  11. YANG, T. Distributed MIMO broadcasting: Reverse computeand-forward and signal-space alignment. IEEE Transactions on Wireless Communications, 2017, vol. 16, no. 1, p. 581–593. DOI: 10.1109/TWC.2016.2626360
  12. BRANDT, R., BENGTSSON, M. Distributed CSI acquisition and coordinated precoding for TDD multicell MIMO systems. IEEE Transactions on Vehicular Technology, 2016, vol. 65, no. 5, p. 2890–2906. DOI: 10.1109/TVT.2015.2432051
  13. YANG, M., WEI, H., WANG, D. Analysis of BD precoding for distributed large-scale MIMO systems with RF mismatches at UEs. In IEEE/CIC International Conference on Communications in China - Workshops (CIC/ICCC). Shenzhen (China), 2015, p. 48–51. DOI: 10.1109/ICCChinaW.2015.7961578
  14. KIM, M., CHOI, I. K., HONG, S. E., et al. Joint centralized and distributed precoding in scalable cell-free massive MIMO systems. In 13th International Conference on Information and Communication Technology Convergence (ICTC). Jeju Island (Korea), 2022, p. 1254–1257. DOI: 10.1109/ICTC55196.2022.9952405
  15. SOHRABI, F., ATTIAH, K., YU, W. Deep learning for distributed channel feedback and multiuser precoding in FDD massive MIMO. IEEE Transactions on Wireless Communications, 2021, vol. 20, no. 7, p. 4044–4057. DOI: 10.1109/TWC.2021.3055202
  16. KHAN, M. H. A., CHO, K. M., LEE, M. H., et al. A simple block diagonal precoding for multi-user MIMO broadcast channels. Eurasip Journal on Wireless Communications and Networking, 2014, vol. 2014, no. 95, p. 1–8. DOI: 10.1186/1687-1499-2014-95
  17. ELLIOTT, R. C., KRZYMIEN, W. A. Downlink scheduling via genetic algorithms for multiuser single-carrier and multicarrier MIMO systems with dirty paper coding. IEEE Transactions on Vehicular Technology, 2009, vol. 58, no. 7, p. 3247–3262. DOI: 10.1109/TVT.2008.2009059
  18. TSENG, F. S., WANG, Y. C. Codebook size design for RVQ based Tomlinson-Harashima precoded MIMO broadcast channels. IEEE Transactions on Vehicular Technology, 2015, vol. 64, no. 10, p. 4876–4881. DOI: 10.1109/TVT.2014.2367577
  19. LIANG, L., XU, W., DONG, X. Low-complexity hybrid precoding in massive multiuser MIMO systems. IEEE Wireless Communications Letters, 2014, vol. 3, no. 6, p. 653–656. DOI: 10.1109/LWC.2014.2363831
  20. AYACH, O. E., RAJAGOPAL, S., ABU-SURRA, S., et al. Spatially sparse precoding in millimeter wave MIMO systems. IEEE Transactions on Wireless Communications, 2014, vol. 13, no. 3, p. 1499–1513. DOI: 10.1109/TWC.2014.011714.130846
  21. YU, X., SHEN, J. C., ZHANG, J., et al. Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems. IEEE Journal of Selected Topics in Signal Processing, 2016, vol. 10, no. 3, p. 485–500. DOI: 10.1109/JSTSP.2016.2523903
  22. KERRET, P. D., GESBERT, D. Robust decentralized joint precoding using team deep neural network. In 15th International Symposium on Wireless Communication Systems (ISWCS). Lisbon (Portugal), 2018, p. 1–5. DOI: 10.1109/ISWCS.2018.8491209
  23. HOJATIAN, H., NADAL, J., FRIGON, J. F., et al. Decentralized beamforming for cell-free massive MIMO with unsupervised learning. IEEE Communications Letters, 2022, vol. 26, no. 5, p. 1042–1046. DOI: 10.1109/LCOMM.2022.3157161
  24. LIN, T., ZHU, Y. Beamforming design for large-scale antenna arrays using deep learning. IEEE Wireless Communications Letters, 2020, vol. 9, no. 1, p. 103–107. DOI: 10.1109/LWC.2019.2943466
  25. ELBIR, A. M., PAPAZAFEIROPOULOS, A. K. Hybrid precoding for multiuser millimeter wave massive MIMO systems: A deep learning approach. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 1, p. 552–563. DOI: 10.1109/TVT.2019.2951501
  26. WANG, Q., FENG, K., LI, X., et al. PrecoderNet: Hybrid beamforming for millimeter wave systems with deep reinforcement learning. IEEE Wireless Communications Letters, 2020, vol. 9, no. 10, p. 1677–1681. DOI: 10.1109/LWC.2020.3001121
  27. GE, L., QI, C., GUO, Y., et al. Classification weighted deep neural network based channel equalization for massive MIMO-OFDM systems. Radioengineering, 2022, vol. 31, no. 3, p. 346–356. DOI: 10.13164/re.2022.0346
  28. MINASIAN, A., SHAHBAZPANAHI, S., ADVE, R. S. Distributed massive MIMO systems with non-reciprocal channels: Impacts and robust beamforming. IEEE Transactions on Communications, 2018, vol. 66, no. 11, p. 5261–5277. DOI: 10.1109/TCOMM.2018.2859937
  29. YUN, S., KANG, J., KIM, I., et al. Deep artificial noise: Deep learning-based precoding optimization for artificial noise scheme. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 3, p. 3465–3469. DOI: 10.1109/TVT.2020.2965959
  30. ALBREEM, M. A., HABBASH, A. H. A., IKKI, S. S., et al. Overview of precoding techniques for massive MIMO. IEEE Access, 2021, vol. 9, p. 60764–60801. DOI: 10.1109/ACCESS.2021.3073325

Keywords: Distributed multiple-input multiple-output (D-MIMO), deep neural network, downlink precoding, channel state information (CSI)

W. Abd Alaziz, B. Abood, R. M. Muttasher, M. A. Fadhel, B. A. Jebur [references] [full-text] [DOI: 10.13164/re.2024.0045] [Download Citations]
Exact BER Performance Analysis of an Elementary Coding Techniques for NOMA System on AWGN Channel

Ultra-Reliable Low Latency Communication (URLLC) requirements of modern wireless communication systems have heightened the need for complexity reduction in data processing along with error detection and correction techniques. Motivated by this fact, we introduce a low-complexity coding scheme for Non-Orthogonal Multiple Access (NOMA). Furthermore, this work presents a comprehensive mathematical analysis of the proposed coded NOMA communication system and evaluates its Bit Error Rate (BER) performance in various scenarios. Our study showcases a precise match between practical and theoretical results, underlining the presented mathematical analysis precision. Moreover, we conduct a comparison between the proposed NOMA system and other coded and uncoded NOMA systems. This comparison highlights the superior performance of the proposed system, providing evidence of its potential to achieve the desired complexity reduction without compromising performance. Finally, in the same work environment, it is worth noting that the proposed system demonstrated superior performance compared to typical uncoded NOMA systems. It achieved a minimum improvement of 21 dB for the 1st user and a 17 dB improvement for the 2nd and 3rd users.

  1. SAITO, Y., KISHIYAMA, Y., BENJEBBOUR, A., et al. Non-orthogonal multiple access (NOMA) for cellular future radio access. In IEEE 77th Vehicular Technology Conference (VTC-Spring). Dresden (Germany), 2013, p. 1–5. DOI: 10.1109/VTCSpring.2013.6692652
  2. DAI, L., WANG, B., HAN, S., et al. Non-orthogonal multiple access for 5G: Solutions, challenges, opportunities, and future research trends. IEEE Communications Magazine, 2015, vol. 53, no. 9, p. 74–81. DOI: 10.1109/MCOM.2015.7263349
  3. DING, Z., LIU, Y., CHOI, J., et al. Application of nonorthogonal multiple access in LTE and 5G networks. IEEE Communications Magazine, 2017, vol. 55, no. 2, p. 185–191. DOI: 10.1109/MCOM.2017.1500657CM
  4. KEATING, R., RATASUK, R., GHOSH, A. Investigation of nonorthogonal multiple access techniques for future cellular networks. In IEEE 86th Vehicular Technology Conference (VTC-Fall). Toronto (Canada), 2017, p. 1–5. DOI: 10.1109/VTCFall.2017.8288405
  5. CHUNLIN, Y., ZHIFENG, Y., WEIMIN, L., et al. Non-orthogonal multiple access schemes for 5G. ZTE Communications, 2019, vol. 14, no. 4, p. 11–16. DOI: 10.3969/j.issn.1673-5188.2016.04.002
  6. ARACHILLAGE, U. S. S. S., JAYAKODY, D. N. K., BISWASH, S. K., et al. Recent advances and future research challenges in nonorthogonal multiple access for 5G networks. In IEEE 87th Vehicular Technology Conference (VTC-Spring). Porto (Portugal), 2018, p. 1–6. DOI: 10.1109/VTCSpring.2018.8417843
  7. ZHU, J., WANG, J., HUANG, Y., et al. On optimal power allocation for downlink non-orthogonal multiple access systems. IEEE Journal on Selected Areas in Communications, 2017, vol. 35, no. 12, p. 2744–2757. DOI: 10.1109/JSAC.2017.2725618
  8. DING, Z., SCHOBER, R., POOR, H. V. Unveiling the importance of SIC in NOMA systems–Part 1: State of the art and recent findings. IEEE Communications Letters, 2020, vol. 24, no. 11, p. 2373–2377. DOI: 10.1109/LCOMM.2020.3012604
  9. DING, Z., SCHOBER, R., POOR, H. V. Unveiling the importance of SIC in NOMA systems–Part II: New results and future directions. IEEE Communications Letters, 2020, vol. 24, no. 11, p. 2378–2382. DOI: 10.1109/LCOMM.2020.3012601
  10. NARAYANASAMY, I., JAYASHREE, L. A survey on successive interference cancellation schemes in non-orthogonal multiple access for future radio access. Wireless Personal Communications, 2021, vol. 120, no. 2, p. 1057–1078. DOI: 10.1007/s11277-021-08504-1
  11. SUN, H., XIE, B., HU, R., et al. Non-orthogonal multiple access with SIC error propagation in downlink wireless MIMO networks. In IEEE 84th Vehicular Technology Conference (VTC-Fall). Montreal (Canada), 2016, p. 1–5. DOI: 10.1109/VTCFall.2016.7881111
  12. AL-ABBASI, Z., KHAMIS, M. Spectral efficiency (SE) enhancement of NOMA system through iterative power assignment. Wireless Networks, 2021, vol. 27, p. 1309–1317. DOI: 10.1007/s11276-020-02511-z
  13. KHAN, W., LIU, J., JAMEEL, F., et al. Spectral efficiency optimization for next generation NOMA-enabled IoT networks. IEEE Transactions on Vehicular Technology, 2020, vol. 69, no. 12, p. 15284–15297. DOI: 10.1109/TVT.2020.3038387
  14. WEI, X., Al-OBIEDOLLAH, H., CUMANAN, K., et al. Energy efficiency maximization for hybrid TDMA-NOMA system with opportunistic time assignment. IEEE Transactions on Vehicular Technology, 2022, vol. 71, no. 8, p. 8561–8573. DOI: 10.1109/TVT.2022.3173029
  15. SADIA, H., ZEESHAN, M., SHEIKH, S. Performance analysis of downlink power domain NOMA under fading channels. In 2018 ELEKTRO. Mikulov (Czech Republic), 2018, p. 1–6. DOI: 10.1109/ELEKTRO.2018.8398247
  16. SREENU, S., KALPANA, N. Innovative power allocation strategy for NOMA systems by employing the modified ABC algorithm. Radioengineering, 2022, vol. 31, no. 3, p. 312–322. DOI: 10.13164/re.2022.0312
  17. REZVANI, S., JORSWIECK, E., JODA, R., et al. Optimal power allocation in downlink multicarrier NOMA systems: Theory and fast algorithms. IEEE Journal on Selected Areas in Communications, 2022, vol. 40, no. 4, p. 1162–1189. DOI: 10.1109/JSAC.2022.3143237
  18. SINDHU, P., DEEPAK, S., ABDUL HAMEED, K. A novel low complexity power allocation algorithm for downlink NOMA networks. In IEEE Recent Advances in Intelligent Computational Systems (RAICS). Thiruvananthapuram (India), 2018, p. 36–40. DOI: 10.1109/RAICS.2018.8635048
  19. ABD-ALAZIZ, W., JEBUR, B., FAKHREY, H., et al. A low complexity coding scheme for NOMA. IEEE Systems Journal, 2023, vol. 17, no. 3, p. 4464–4473. DOI: 10.1109/JSYST.2023.3262174
  20. GOLDSMITH, A. Wireless Communications. Cambridge: Cambridge University Press, 2005. ISBN: 9780511841224
  21. GOLDSMITH, A. Principles of Modern Wireless Communication Systems. India: McGraw-Hill Education, 2015. ISBN: 9789339220037
  22. JEBUR, B. A., ALKASSAR, S. H., ABDULLAH, M. A. M., et al. Efficient machine learning-enhanced channel estimation for OFDM systems. IEEE Access, 2021, vol. 9, p. 100839–100850. DOI: 10.1109/ACCESS.2021.3097436

Keywords: NOMA, constructive interference, BER, repetitive code, channel coding

Y. Zhao, F. Yang, C. Wang, F. Ye, F. Zhu, Y. Liu [references] [full-text] [DOI: 10.13164/re.2024.0054] [Download Citations]
Inverse Synthetic Aperture Radar Imaging Based on the Non-Convex Regularization Model

Compressed Sensing (CS) has been shown to be an effective technique for improving the resolution of inverse synthetic aperture radar (ISAR) imaging and reducing the hardware requirements of radar systems. In this paper, our focus is on the l_p 0 p 1 model, which is a well-known non-convex and non-Lipschitz regularization model in the field of compressed sensing. In this study, we propose a novel algorithm, namely the Accelerated Iterative Support Shrinking with Full Linearization (AISSFL) algorithm, which aims to solve the l_p regularization model for ISAR imaging. The AISSFL algorithm draws inspiration from the Majorization-Minimization (MM) iteration algorithm and integrates the principles of support shrinkage and Nestrove's acceleration technique. The algorithm employed in this study demonstrates simplicity and efficiency. Numerical experiments demonstrate that AISSFL performs well in the field of ISAR imaging.

  1. HAJDUCH, G., GARELLO, R., LE CAILLEC, J.-M., et al. High resolution snapshot SAR/ISAR imaging of ship targets at sea. Proceedings of SPIE - SAR Image Analysis, Modeling, and Techniques V, 2002, vol. 4883, p. 39–47. DOI: 10.1117/12.461899
  2. LAZAROV, A. D., MINCHEV, C. N. ISAR Image reconstruction and autofocusing procedure over phase modulated signals. In International Radar Conference. Edinburgh (UK), 2002, p. 536–541. DOI: 10.1049/cp:20020344
  3. PRICKET, M. J., CHEN, C. C. Principle of inverse synthetic aperture radar (ISAR) imaging. In Electronics and Aerospace Systems Conference (EASCON). Arlington (USA), 1980, p. 340–345. ISSN: 0531-6863
  4. WEHNER, D. R. High-Resolution Radar. Norwood: Artech House, 1987. ISBN: 0890061947
  5. DONOHO, D. L. Compressed sensing. IEEE Transactions on Information Theory, 2006, vol. 52, no. 4, p. 1289–1306. DOI: 10.1109/TIT.2006.871582
  6. CANDES, E. J., ROMBERG, J. K., TAO, T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, vol. 52, no. 2, p. 489–509. DOI: 10.1109/TIT.2005.862083
  7. CANDES, E. J., ROMBERG, J. K., TAO, T. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 2006, vol. 59, no. 8, p. 1207–1223. DOI: 10.1002/cpa.20124
  8. BARANIUK, R., STEEGHS, P. Compressive radar imaging. In IEEE Radar Conference. Waltham (USA), 2007, p. 128–133. DOI: 10.1109/RADAR.2007.374203
  9. POTTER, L. C., PARKER, J. T. Sparsity and compressed sensing in radar imaging. Proceedings of the IEEE, 2010, vol. 98, no. 6, p. 1006–1020. DOI: 10.1109/JPROC.2009.2037526
  10. PATEL, V. M., EASLEY, G. R., HEALY, D. M. Compressed synthetic aperture radar. IEEE Journal of Selected Topics in Signal Processing, 2010, vol. 4, no. 2, p. 244–254. DOI: 10.1109/JSTSP.2009.2039181
  11. FENG, J., GONG, Z. ISAR imaging based on iterative reweighted Lp block sparse reconstruction algorithm. Progress In Electromagnetics Research M, 2016, vol. 48, p. 155–162. DOI: 10.2528/PIERM16041501
  12. WEI, S., LIANG, J., WANG, M., et al. CIST: An improved isar imaging method using convolution neural network. Remote Sensing, 2020, vol. 12, no. 16, p. 2641. DOI: 10.3390/rs12162641
  13. TROPP, J. A. Greed is good: Algorithmic results for sparse approximation. IEEE Transactions on Information Theory, 2004, vol. 50, no. 10, p. 2231–2242. DOI: 10.1109/TIT.2004.834793
  14. NATARAJAN, B. K. Sparse approximate solutions to linear systems. SIAM Journal on Computing, 1995, vol. 24, no. 2, p. 227–234. DOI: 10.1137/S0097539792240406
  15. DAUBECHIES, I., TESCHKE, G., VESE, L. A. Iteratively solving linear inverse problems under general convex constraints. Inverse Problems and Imaging, 2007, vol. 1, no. 1, p. 29–46. DOI: 10.3934/ipi.2007.1.29
  16. DONOHO, D. L. For most large underdetermined systems of linear equations the minimal ℓ1-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics, 2006, vol. 59, no. 6, p. 797–829. DOI: 10.1002/cpa.20132
  17. BOYD, S., VANDENBERGHE, L. Convex Optimization. Cambridge University Press, 2004. ISBN: 9780511804441, DOI: 10.1017/CBO9780511804441
  18. CANDES, E. J., ROMBERG, J. K., TAO, T. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 2006, vol. 59, no. 8, p. 1207–1223. DOI: 10.1002/cpa.20124
  19. ZHANG, L., QIAO, Z. J., XING, M. D. High-resolution ISAR imaging by exploiting sparse apertures. IEEE Transactions on Antennas and Propagation, 2012, vol. 60, no. 2, p. 997–1008. DOI: 10.1109/TAP.2011.2173130
  20. ZHANG, L., QIAO, Z. J., XING, M. D. High-resolution ISAR imaging with sparse stepped-frequency waveforms. IEEE Transactions on Geoscience and Remote Sensing, 2011, vol. 49, no. 11, p. 4630–4651. DOI: 10.1109/TGRS.2011.2151865
  21. HASHEMPOUR, H. R. Sparsity-driven ISAR imaging based on two-dimensional ADMM. IEEE Sensors Journal, 2020, vol. 20, no. 22, p. 13349–13356. DOI: 10.1109/JSEN.2020.3006105
  22. OZ, Y., ALP, Y. K., YAZGAN-ERER, I. ISAR imaging under group sparsity constraints using ADMM. In Signal Processing and Communications Applications Conference (SIU). Gaziantep (Turkey), 2020, p. 1–4. DOI: 10.1109/SIU49456.2020.9302303
  23. LI, S. D., CHEN, F. W., YANG, J., et al. A novel 2D complex FISTA for ISAR imaging. In IET International Radar Conference. Hangzhou (China), 2015, p. 1–4. DOI: 10.1049/cp.2015.0995
  24. CANDES, E. J., WAKIN, M. B., BOYD, S. P. Enhancing sparsity by reweighted ℓ1 minimization. Journal of Fourier Analysis and Applications, 2008, vol. 14, no. 5, p. 877–905. DOI 10.1007/s00041-008-9045-x
  25. CHARTRAND, R., STANEVA, V. Restricted isometry properties and nonconvex compressive sensing. Inverse Problems, 2008, vol. 24, no. 3, p. 1–14. DOI: 10.1088/0266-5611/24/3/035020
  26. FOUCART, S., LAI, M. J. Sparsest solutions of underdetermined linear systems via ℓ

Keywords: ISAR, compressed sensing, non-convex optimization, AISSFL algorithm

D. Huang, J. Tang, L. Xu, Y. Wu [references] [full-text] [DOI: 10.13164/re.2024.0062] [Download Citations]
Design and Performance Analysis of MCPC and P4 Waveforms for OFDM based Radar System

This study aimed to investigate the performance of Multicarrier Phase Coding (MCPC) and P4-encoded waveforms. Researchers explored the unique properties of these signals, focusing on aspects like phase distribution, autocorrelation, power spectral density for P4 encoding, and aperiodic autocorrelation and ambiguity function for MCPC signals. The findings identified optimal MCPC sequences with reduced peak-to-mean envelope power ratios (PMEPR), improving signal performance. Complementary codes based on permutation were also generated and analyzed for MCPC sequences. The study utilized an improved genetic algorithm to develop new and improved waveforms, underscoring the importance of techniques like optimal sequence permutation, complementary sequences, and classical window frequency weighting in enhancing signal performance.

  1. LARSSON, E. G., EDFORS, O., TUFVESSON, F., et al. Massive MIMO for next generation wireless systems. IEEE Communications Magazine, 2014, vol. 52, no. 2, p. 186–195. DOI: 10.1109/MCOM.2014.6736761
  2. STURM, C., ZWICK, T., WIESBECK, W., et al. An OFDM system concept for joint radar and communications operations. In VTC Spring 2009 - IEEE 69th Vehicular Technology Conference. Barcelona (Spain), 2013, p. 1–5. DOI: 10.1109/VETECS.2009.5073387
  3. CHENG, S. J., WANG, W. Q., SHAO, H. Z., et al. Spread spectrum-coded OFDM chirp waveform diversity design. IEEE Sensors Journal, 2015, vol. 15, no. 10, p. 5694–5700. DOI: 10.1109/JSEN.2015.2448617
  4. TIAN, X., SONG, Z. On radar and communication integrated system using OFDM signal. In IEEE Radar Conference (RadarConf). Seattle (USA), 2017, p. 318–0323. DOI: 10.1109/RADAR.2017.7944220
  5. LI, C., BAO, W., XU, L., et al. Radar communication integrated waveform design based on OFDM and circular shift sequence. Mathematical Problems in Engineering, 2017, vol. 2017, p. 1–10. DOI: 10.1155/2017/9840172
  6. ELLINGER, J., ZHANG, Z., WICKS, M., et al. Multi-carrier radar waveforms for communications and detection. IET Radar, Sonar & Navigation, 2017, vol. 11, no. 3, p. 444–452. DOI: 10.1049/iet-rsn.2016.0244
  7. LEVANON, N. Multifrequency radar signals. In Record of the IEEE International Radar Conference [Cat. No. 00CH37037]. Alexandria (USA), 2000, p. 683–688. DOI: 10.1109/RADAR.2000.851916
  8. LEVANON, N., MOZESON, E. Multicarrier radar signal-pulse train and CW. IEEE Transactions on Aerospace and Electronic Systems, 2002, vol. 38, no. 2, p. 707–720. DOI: 10.1109/TAES.2002.1009000
  9. MOZESON, E., LEVANON, N. Multicarrier radar signals with low peak-to-mean envelope power ratio. IEE Proceedings-Radar, Sonar and Navigation, 2003, vol. 150, no. 2, p. 71–77. DOI: 10.1049/ip-rsn:20030263
  10. DENG, B., SUN, B., WEI, X., et al. Ambiguity function analysis for MCPC radar signal. In International Conference on Industrial Mechatronics and Automation. Wuhan (China), 2010, p. 650–653. DOI: 10.1109/ICINDMA.2010.5538224
  11. FARNANE, K., MINAOUI, K., ROUIJEL, A., et al. Analysis of the ambiguity function for phase-coded waveforms. In IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA). Marrakech (Morocco), 2015, p. 1–4. DOI: 10.1109/AICCSA.2015.7507195
  12. RIHACZEK, A. W. Principles of High-Resolution Radar. McGraw-Hill Book Company, 1969. ISBN: 0754321069
  13. KAYVAN, R. Radar Signals. Press Institute of Imam Hussein University, 2023. ISBN: 9786222860905
  14. LEVANON, N. Radar Principles. Wiley, 1988. ISBN: 9780471858812
  15. MAHAFZA, B. R. Radar Systems Analysis and Design Using MATLAB. Chapman and Hall/CRC, 2022. ISBN: 9780367507930
  16. LEVANON, N. Stepped-frequency pulse-train radar signal. IEE Proceedings-Radar, Sonar and Navigation, 2003, vol. 149, no. 6, p. 297–309. DOI: 10.1049/ip-rsn:20020432
  17. GOLOMB, S. W., TAYLOR, H. Constructions and properties of Costas arrays. Proceedings of the IEEE, 1984, vol. 72, no. 9, p. 1143–1163. DOI: 10.1109/PROC.1984.12994
  18. LEVANON, N., MOZESON, E. Nullifying ACF grating lobes in stepped-frequency train of LFM pulses. IEEE Transactions on Aerospace and Electronic Systems, 2003, vol. 39, no. 2, p. 694–703. DOI: 10.1109/TAES.2003.1207275
  19. SHARMA, S., BICA, M., KOIVUNEN, V. Reduced PMEPR multicarrier radar waveform design. In 53rd Asilomar Conference on Signals, Systems, and Computers. Pacific Grove (USA), 2019, p. 2048–2052. DOI: 10.1109/IEEECONF44664.2019.9048815
  20. MOHSENI, R., SHEIKHI, A., SHIRAZI, M. A. M. Compression of multicarrier phase-coded radar signals with low sampling rate. In International Conference on Radar. Adelaide (Australia), 2008, p. 718–721. DOI: 10.1109/RADAR.2008.4654014
  21. MOHSENI, R., SHEIKHI, A., SHIRAZI, M. A. M. A new approach to compress multicarrier phase-coded signals. In IEEE Radar Conference. Rome (Italy), 2008, p. 1–6. DOI: 10.1109/RADAR.2008.4720800
  22. LI, J., ZHOU, J., WANG, W., et al. A radar waveform design of MCPC method for interrupted sampling repeater jamming suppression via fractional Fourier transform. Progress In Electromagnetics Research C, 2023, vol. 129, p. 1–15. DOI: 10.2528/PIERC22102001
  23. BICA, M., KOIVUNEN, V. Multicarrier radar-communications waveform design for RF convergence and coexistence. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton (UK), 2019, p. 7780–7784. DOI: 10.1109/ICASSP.2019.8683655
  24. BICA, M., KOIVUNEN, V. Generalized multicarrier radar: Models and performance. IEEE Transactions on Signal Processing, 2016, vol. 64, no. 17, p. 4389–4402. DOI: 10.1109/TSP.2016.2566610
  25. HASSAN, W. Textile dual band antenna printed on artificial heart bag for WBAN communications. Progress In Electromagnetics Research C, 2023, vol. 129, p. 273–287. DOI: 10.2528/PIERC23010904
  26. DONNET, B. J., LONGSTAFF, I. D. Combining MIMO radar with OFDM communications. In IEEE European Radar Conference (EURAD). Manchester (UK), 2006, p. 37–40. DOI: 10.1109/EURAD.2006.280267
  27. LELLOUCH, G., NIKOOKAR, H. On the capability of a radar network to support communications. In 14th IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT). Delft (Netherlands), 2007, p. 1–5. DOI: 10.1109/SCVT.2007.4436249
  28. GARMATYUK, D., SCHUERGER, J., MORTON, Y. T., et al. Feasibility study of a multi-carrier dual-use imaging radar and communication system. In IEEE European Microwave Conference (EURAD). Munich (Germany), 2007, p. 1473–1476. DOI: 10.1109/EURAD.2007.4404970
  29. WU, K., CHU, N., WU, D., et al. The Enkurgram: A characteristic frequency extraction method for fluid machinery based on multi-band demodulation strategy. Mechanical Systems and Signal Processing, 2021, vol. 155, p. 1–34. DOI: 10.1016/j.ymssp.2020.107564
  30. LIU, Z., ZHANG, Y., LUO, X. Performance analysis of radar communication shared signal based on OFDM. In Gao, H., Wun, J., Yin, J., et al. (eds.) Communications and Networking. ChinaCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2022, vol. 433, p. 250–263. DOI: 10.1007/978-3-030-99200-2_20
  31. MURALIDHARA, N., VINOD, V., SANTHOSHKUMAR, D., et al. Comparative analysis of polyphase codes for digital pulse compression applications. International Journal of Engineering Research & Technology, 2014, vol. 3, no. 10, p. 1–5. DOI: 10.17577/IJERTV3IS100764
  32. MOHSENI, R. SHEIKHI, A., MASNADI-SHIRAZI, M. A. Multicarrier constant envelope OFDM signal design for radar applications. AEU-International Journal of Electronics and Communications, 2010, vol. 64, no. 11, p. 999–1008. DOI: 10.1016/j.aeue.2009.10.008
  33. XIAO, Z., ZENG, Y. Waveform design and performance analysis for full-duplex integrated sensing and communication. IEEE Journal on Selected Areas in Communications, 2022, vol. 40, no. 6, p. 1823–1837. DOI: 10.1109/JSAC.2022.3155509
  34. POPOVIĆ, B. M. Complementary sets based on sequences with ideal periodic autocorrelation. Electronics Letters, 1990, vol. 26, no. 18, p. 1428–1430. DOI: 10.1049/el:19900916
  35. KOOPMAN, P. 32-bit cyclic redundancy codes for internet applications. In Proceedings International Conference on Dependable Systems and Networks. Washington (USA), 2002, p. 459–468. DOI: 10.1109/DSN.2002.1028931
  36. INDUMATHI, G., ANANTHAKIRUPA, V. A. A., RAMESH, M. Architectural design of 32 bit polar encoder. Circuits and Systems, 2016, vol. 7, no. 5, p. 1–11. DOI: 10.4236/cs.2016.75047
  37. FARNETT, E. C., STEVENS, G. H., SKOLNIK, M. Pulse compression radar. Chapter in Skolnik, M. I. Radar Handbook 2, 1990, p. 10–11. ISBN: 007057913X
  38. LEVANON, N. Multifrequency complementary phase-coded radar signal. IEE Proceedings-Radar, Sonar and Navigation, 2000, vol. 147, no. 6, p. 276–284. DOI: 10.1049/ip-rsn:20000734
  39. MIRJALILI, S. Evolutionary Algorithms and Neural Networks: Theory and Applications. Heidelburg (Australia): Springer International Publishing, 2019. ISBN: 9783030065720
  40. WHITLEY, D. A genetic algorithm tutorial. Statistics and Computing, 1994, vol. 4, p. 65–85. DOI: 10.1007/BF00175354
  41. LELLOUCH, G., TRAN, P., PRIBIC, R., et al. OFDM waveforms for frequency agility and opportunities for Doppler processing in radar. In IEEE Radar Conference. Rome (Italy), 2008, p. 1–6. DOI: 10.1109/RADAR.2008.4720798

Keywords: MCPC, P4 phase code, autocorrelation function, ambiguity function, improved gene algorithm, classical window frequency weighting

C. Chen, F. F. Yang, D. K. Waweru [references] [full-text] [DOI: 10.13164/re.2024.0075] [Download Citations]
Optimized-Goppa Codes Based on the Effective Selection of Goppa Polynomials for Coded-Cooperative Generalized Spatial Modulation Network

This paper proposes a novel optimized-Goppa-coded cooperative generalized spatial modulation (OGCC-GSM) scheme for short-to-medium information block transmission. In the proposed OGCC-GSM scheme, an efficient Goppa polynomial selection approach is designed to ensure that the selected Goppa codes applied in the source and relay nodes both have the largest minimum Hamming distance (MHD) and the optimal weight distribution. Compared to conventional coded cooperation (CC) with a single antenna, the proposed scheme employs the generalized spatial modulation (GSM) technique to achieve more diversity gains, where each node is equipped with multiple antennas and more than one transmit antenna (TA) is activated at each time-instant transmission. As a benchmark comparison, the OGCC spatial modulation (OGCC-SM) scheme is also investigated with a single TA active. Moreover, the reduced-complexity transmit antenna combination (RC-TAC) selection algorithm utilized in GSM is first developed with the aid of the channel state information (CSI) to reconcile computational complexity and system performance. In addition, joint decoding is conducted on the destination terminal to further enhance the performance of the proposed scheme. The simulated results indicate the performance of the proposed OGCC-GSM scheme is superior to that of its benchmark OGCC-SM scheme, with a substantial reduction in the number of TAs. Besides, Monte Carlo simulations demonstrate that the proposed OGCC-GSM scheme prevails over its counterparts by a margin of over 4.2 dB under identical conditions.

  1. XU, K., DU, Z. Y., JIANG, B. Dynamic coded cooperation with incremental redundancy: Throughput and diversity-multiplex tradeoff analysis. IEEE Communications Letters, 2020, vol. 24, no. 3, p. 506–509. DOI: 10.1109/LCOMM.2020.2969670
  2. HUNTER, T. E., NOSRATINIA, A. Diversity through coded cooperation. IEEE Transactions on Wireless Communications, 2006, vol. 5, no. 2, p. 283–289. DOI: 10.1109/TWC.2006.1611050
  3. ZENG, J. L., LIU, S.Y., WANG, H. Generalized distributed multiple turbo coded cooperative differential spatial modulation. KSII Transaction on Internet and Information Systems, 2023, vol. 17, no. 3, p. 999–1021. DOI: 10.3837/tiis.2023.03.017
  4. LIANG, H., LIU, A. J., LIU, X., et al. Construction and optimization for adaptive polar coded cooperation. IEEE Wireless Communications Letters, 2020, vol. 9, no. 8, p. 1187–1190. DOI: 10.1109/LWC.2020.2984738
  5. WANG, H., CHEN, Q. C. LDPC based network coded cooperation design for multi-way relay networks. IEEE Access, 2019, vol. 7, p. 62300–62311. DOI: 10.1109/ACCESS.2019.2915293
  6. ALMOLIKI, Y. M., ALDHAEEBI, M. A., ALMWALD, G. A., et al. The performance of RS and RSCC coded cooperation systems using higher order modulation schemes. In Proceedings Sixth International Conference on Intelligent Systems, Modelling and Simulation. Kuala Lumpur (Malaysia), 2015, p. 211–214. DOI: 10.1109/ISMS.2015.11
  7. GUO, P. C., YANG, F. F., ZHAO, C. L., et al. Jointly optimized design of distributed Reed-Solomon codes by proper selection in relay. Telecommunication Systems, 2021, vol. 78, no. 3, p. 391–403. DOI: 10.1007/s11235-021-00822-w
  8. LOPEZ, H. H., MATTHEWS, G. L. Multivariate Goppa codes. IEEE Transactions on Information Theory, 2023, vol. 69, no. 1, p. 126–137. DOI: 10.1109/TIT.2022.3201692
  9. WAWERU, D. K., YANG, F. F., ZHAO, C. L., et al. Design of optimized distributed Goppa codes and joint decoding at the destination. Telecommunication Systems, 2022, vol. 81, no. 3, p. 341–355. DOI: 10.1007/s11235-022-00948-5
  10. FENG, F. A., YANG, F. F., CHEN, C., et al. Jointly optimized design of distributed Goppa codes and decoding. Radioengineering, 2023, vol. 32, no. 1, p. 23–32. DOI: 10.13164/re.2023.0023
  11. CHEN, L., WANG, Z. Q., DU, Y., et al. Generalized transceiver beamforming for DFRC with MIMO radar and MU-MIMO communication. IEEE Journal on Selected Areas in Communications, 2022, vol. 40, no. 6, p. 1795–1808. DOI: 10.1109/JSAC.2022.3155515
  12. MESLEH, R. Y., HARALD, H., SINANOVIC, S., et al. Spatial modulation. IEEE Transactions on Vehicular Technology, 2008, vol. 57, no. 4, p. 2228–2241. DOI: 10.1109/TVT.2007.912136
  13. YOUNIS, A., SERAFIMOVSKI, N., MESLEH, R., et al. Spatial modulation. Generalised spatial modulation. In Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers. Pacific Grove (USA), 2010, p. 1498–1502. DOI: 10.1109/ACSSC.2010.5757786
  14. BEZZATEEV, S. V., NOSKOV, I. K. Patterson algorithm for decoding separable binary Goppa codes. In Wave Electronics and its Application in Information and Telecommunication Systems (WECONF). Saint Petersburg (Russia), 2019, p. 1–5. DOI: 10.1109/WECONF.2019.8840650
  15. XIAO, L. X., XIAO, P., XIAO, Y., et al. Transmit antenna combination optimization for generalized spatial modulation systems. IEEE Access, 2018, vol. 6, p. 41866–41882. DOI: 10.1109/ACCESS.2018.2859794
  16. MOON, J., PARK, J., LEE, J. Cyclic redundancy check code based high-rate error-detection code for perpendicular recording. IEEE Transactions on Magnetics, 2006, vol. 42, no. 5, p. 1626–1628. DOI: 10.1109/TMAG.2006.870444
  17. ZHAO, C. L., YANG, F. F., WAWERU, D. K., et al. Optimized distributed Goppa codes based on spatial modulation. IET Communications, 2023, vol. 17, no. 13, p. 1447–1464. DOI: 10.1049/cmu2.12634
  18. ZHAO, C. L., YANG, F. F., WAWERU, D. K., et al. Distributed Goppa-coded generalized spatial modulation: Optimized design and performance study. Electronics, 2023, vol. 12, no. 11. DOI: 10.3390/electronics12112404

Keywords: Optimized Goppa codes, Goppa polynomials, coded cooperation, generalized spatial modulation (GSM), spatial modulation (SM)

S. B. Harisha, E. Mallikarjun, M. Amit [references] [full-text] [DOI: 10.13164/re.2024.0089] [Download Citations]
Deep Learning Assisted Linear Sampling Method for the Reconstruction of Perfect Electric Conductors

In this study, a linear approach, linear sampling method (LSM) is used to reconstruct the shape of perfectly electric conductors (PEC) with the help of deep learning as a post-processing technique. In microwave imaging, the LSM is a simple and reliable linear inversion technique for determining the morphological features of unknown objects under investigation. However, the output of this method depends on the frequency of operation, the choice of regularization parameter,and it is unable to produce satisfactory results for objects with complex shapes. To overcome this drawback, a deep learning approach is used in this work, which can produce a better output in terms of accuracy, resolution. Here, the rough estimate of the PEC scatterer obtained using LSM is used to train the U-Net based convolutional neural network, which maps this output with the corresponding ground truth profiles. The proposed hybrid model is validated using several examples of synthetic and experimental data.

  1. LIU, T., ZHAO, Y., WEI, Y., et al. Concealed object detection for activate millimeter wave image. IEEE Transactions on Industrial Electronics, 2019, vol. 66, no. 12, p. 9909–9917. DOI: 10.1109/TIE.2019.2893843
  2. BRIQECH, Z., GUPTA, S., BELTAY, A. A., et al. 57-64 GHz imaging/detection sensor-part II: Experiments on concealed weapons and threatening materials detection. IEEE Sensors Journal, 2020, vol. 20, no. 18, p. 10833–10840. DOI: 10.1109/JSEN.2020.2997293
  3. ZHURAVLEV, A., RAZEVIG, V., CHIZH, M., et al. A new method for obtaining radar images of concealed objects in microwave personnel screening systems. IEEE Transactions on Microwave Theory and Techniques, 2021, vol. 69, no. 1, p. 357–364. DOI: 10.1109/TMTT.2020.3023443
  4. WANG, C., SHI, J., ZHOU, Z., et al. Concealed object detection for millimeter-wave images with normalized accumulation map. IEEE Sensors Journal, 2021, vol. 21, no. 5, p. 6468–6475. DOI: 10.1109/JSEN.2020.3040354
  5. OK, G., KIM, H. J., CHUN, H. S., et al. Foreign-body detection in dry food using continuous sub-terahertz wave imaging. Food Control, 2014, vol. 42, p. 284–289. DOI: 10.1016/j.foodcont.2014.02.021
  6. BOURGEOIS, J. R., SMITH, G. S. A complete electromagnetic simulation of the separated aperture sensor for detecting buried land mines. IEEE Transactions on Antennas and Propagation, 1998, vol. 46, no. 10, p. 1419–1426. DOI: 10.1109/8.725272
  7. BANSAL, R. Of mice and men [Microwave surfing]. IEEE Microwave Magazine, 2015, vol. 16, no. 11, p. 18–20. DOI: 10.1109/MMM.2015.2478088
  8. CHEW, W. C., OTTO, G. P. Microwave imaging of multiple conducting cylinders using local shape functions. IEEE Microwave and Guided Wave Letters, 1992, vol. 2, no. 7, p. 284–286. DOI: 10.1109/75.143396
  9. WEEDON, W. H., CHEW, W. C. Time-domain inverse scattering using the local shape function (LSF) method. Inverse Problems, 1993, vol. 9, no. 5, p. 551–564. DOI: 10.1088/0266-5611/9/5/005
  10. OTTO, G. P., CHEW, W. C. Microwave inverse scattering-local shape function imaging for improved resolution of strong scatterers. IEEE Transactions on Microwave Theory and Techniques, 1994, vol. 42, no. 1, p. 137–141. DOI: 10.1109/22.265541
  11. ZHOU, Y., LING, H. Electromagnetic inversion of IPswich objects with the use of the genetic algorithm. Microwave and Optical Technology Letters, 2002, vol. 33, no. 6, p. 457–459. DOI: 10.1002/mop.10349
  12. TAKENAKA, T., MENG, Z. Q., TANAKA, T., et al. Local shape function combined with genetic algorithm applied to inverse scattering for strips. Microwave and Optical Technology Letters, 1997, vol. 16, no. 6, p. 337–341. DOI: 10.1002/(SICI)1098-2760(19971220)16:6<337::AID-MOP5>3.0.CO;2-L
  13. QING, A. Microwave imaging of parallel perfectly conducting cylinders. International Journal of Imaging Systems and Technology, 2001, vol. 11, no. 6, p. 365–371. DOI: 10.1002/ima.10000
  14. YU, C., SONG, L. P., LIU, Q. H. Inversion of multi-frequency experimental data for imaging complex objects by a DTA-CSI method. Inverse Problems, 2005, vol. 21, no. 6, p. 165–178. DOI: 10.1088/0266-5611/21/6/S12
  15. AZARO, R., DONELLI, M., FRANCESCHINI, D., et al. Multiscaling reconstruction of metallic targets from TE and TM experimental data. Microwave and Optical Technology Letters, 2006, vol. 48, no. 2, p. 322–324. DOI: 10.1002/mop.21338
  16. SUN, S., KOOIJ, B. J., YAROVOY, A. G. A linear model for microwave imaging of highly conductive scatterers. IEEE Transactions on Microwave Theory and Techniques, 2018, vol. 66, no. 3, p. 1149–1164. DOI: 10.1109/TMTT.2017.2772795
  17. ROGER, A. Newton-Kantorovitch algorithm applied to an electromagnetic inverse problem. IEEE Transactions on Antennas and Propagation, 1981, vol. 29, no. 2, p. 232–238. DOI: 10.1109/TAP.1981.1142588
  18. QING, A. Electromagnetic inverse scattering of multiple two-dimensional perfectly conducting objects by the differential evolution strategy. IEEE Transactions on Antennas and Propagation, 2003, vol. 51, no. 6, p. 1251–1262. DOI: 10.1109/TAP.2003.811492
  19. QING, A. Electromagnetic inverse scattering of multiple perfectly conducting cylinders by differential evolution strategy with individuals in groups (GDES). IEEE Transactions on Antennas and Propagation, 2004, vol. 52, no. 5, p. 1223–1229. DOI: 10.1109/TAP.2004.827495
  20. CHIU, C. C., LIU, P. T. Image reconstruction of a perfectly conducting cylinder by the genetic algorithm. IEE Proceedings - Microwaves, Antennas and Propagation, 1996, vol. 143, no. 3, p. 249–253. DOI: 10.1049/ip-map:19960363
  21. CHIEN, W., HUANG, C. H., CHIU, C. C. Cubic-spline expansion for a two-dimensional periodic conductor in free space. International Journal of Applied Electromagnetics and Mechanics, 2006, vol. 24, no. 1–2, p. 105–114. DOI: 10.3233/jae-2006-780
  22. CHIEN, W., CHIU, C. C., LI, C. L. Cubic-spline expansion for a conducting cylinder buried in a slab medium. Electromagnetics, 2006, vol. 26, no. 5, p. 329–343. DOI: 10.1080/02726340600710783
  23. ZHOU, Y., LI, J., LING, H. Shape inversion of metallic cavities using hybrid genetic algorithm combined with tabu list. Electronics Letters, 2003, vol. 39, no. 3, p. 280–281. DOI: 10.1049/el:20030207
  24. CHIEN, W., CHIU, C. C. Using NU-SSGA to reduce the searching time in inverse problem of a buried metallic object. IEEE Transactions on Antennas and Propagation, 2005, vol. 53, no. 10, p. 3128–3134. DOI: 10.1109/TAP.2005.856362
  25. LITMAN, A., LESSELIER, D., SANTOSA, F. Reconstruction of a two-dimensional binary obstacle by controlled evolution of a level-set. Inverse Problems, 1998, vol. 14, no. 3, p. 685–706. DOI: 10.1088/0266-5611/14/3/018
  26. YE, X. Z., ZHONG, Y., CHEN, X. Reconstructing perfectly electric conductors by the subspace-based optimization method with continuous variables. Inverse Problems, 2011, vol. 27, no. 5, p. 1–14. DOI: 10.1088/0266-5611/27/5/055011
  27. SHEN, J., ZHONG, Y., CHEN, X., et al. Inverse scattering problems of reconstructing perfectly electric conductors with TE illumination. IEEE Transactions on Antennas and Propagation, 2013, vol. 61, no. 9, p. 4713–4721. DOI: 10.1109/TAP.2013.2271891
  28. COLTON, D., KIRSCH, A. A simple method for solving inverse scattering problems in the resonance region. Inverse Problems, 1996, vol. 12, no. 4, p. 383–39. DOI: 10.1088/0266-5611/12/4/003
  29. COLTON, D., MONK, P. Target identification of coated objects. IEEE Transactions on Antennas and Propagation, 2006, vol. 54, no. 4, p. 1232–1242. DOI: 10.1109/TAP.2006.872564
  30. CATAPANO, I., CROCCO, L., ISERNIA, T. Improved sampling methods for shape reconstruction of 3-D buried targets. IEEE Transactions on Geoscience and Remote Sensing, 2008, vol. 46, no. 10, p. 3265–3273. DOI: 10.1109/TGRS.2008.921745
  31. CATAPANO, I., CROCCO, L. An imaging method for concealed targets. IEEE Transactions on Geoscience and Remote Sensing, 2009, vol. 47, no. 5, p. 1301–1309. DOI: 10.1109/TGRS.2008.2010773
  32. CATAPANO, I., SOLDOVIERI, F., CROCCO, L. On the feasibility of the linear sampling method for 3-D GPR surveys. Progress In Electromagnetics Research, 2011, vol. 118, p. 185–203. DOI: 10.2528/PIER11042704
  33. CATAPANO, I., CROCCO, L. A qualitative inverse scattering method for through-the-wall imaging. IEEE Geoscience and Remote Sensing Letters, 2010, vol. 7, no. 4, p. 685–689. DOI: 10.1109/LGRS.2010.2045473
  34. BOZZA, G., BRIGNONE, M., PASTORINO, M. Application of the no-sampling linear sampling method to breast cancer detection. IEEE Transactions on Antennas and Propagation, 2010, vol. 57, no. 10, p. 2525–2534. DOI: 10.1109/TBME.2010.2055059
  35. SUN, J. An eigenvalue method using multiple frequency data for inverse scattering problems. Inverse Problems, 2012, vol. 28, no. 2, p. 1–15. DOI: 10.1088/0266-5611/28/2/025012
  36. GUZINA, B., CAKONI, F., BELLIS, C. On the multifrequency obstacle reconstruction via the linear sampling method. Inverse Problems, 2010, vol. 26, no. 12, p. 1–29. DOI: 10.1088/0266-5611/26/12/125005
  37. AYDIN, I., BUDAK, G., SEFER, A., et al. Recovery of impenetrable rough surface profiles via CNN-based deep learning architecture. International Journal of Remote Sensing, 2022, vol. 43, no. 15–16, p. 5658–5685. DOI: 10.1080/01431161.2022.2105177
  38. KUO, Y. H., KIANG, J. F. Deep-learning linear sampling method for shape restoration of multilayered scatterers. Progress In Electromagnetics Research C, 2022, vol. 124, p. 197–209. DOI: 10.2528/PIERC22081005.
  39. BELKEBIR, K., SAILLARD, M. Testing inversion algorithms against experimental data. Inverse problems, 2001, vol. 17, no. 6, p. 1565–1571. DOI: 10.1088/0266-5611/17/6/301
  40. FRANCHOIS, A., PICHOT, C. Microwave imaging-complex permittivity reconstruction with a levenberg-marquardt method. IEEE Transactions on Antennas and Propagation, 1997, vol. 45, no. 2, p. 203–215. DOI: 10.1109/8.560338
  41. PASTORINO, M. Microwave Imaging. John Wiley & Sons, 2010. ISBN: 780470278000
  42. PETERSON, A. F., RAY, S. L., MITTRA, R. Computational Methods for Electromagnetics. New York: Wiley-IEEE Press, 1998. ISBN: 9780780311220
  43. COLTON, D., HADDAR, H., PIANA, M. The linear sampling method in inverse electromagnetic scattering theory. Inverse Problems, 2003, vol. 19, no. 6, p. 105–137. DOI: 10.1088/0266-5611/19/6/057
  44. CATAPANO, I., CROCCO, L., ISERNIA, T. On simple methods for shape reconstruction of unknown scatterers. IEEE Transactions on Antennas and Propagation, 2007, vol. 55, no. 5, p. 1431–1436. DOI: 10.1109/TAP.2007.895563
  45. CATAPANO, I., CROCCO, L. An imaging method for concealed targets. IEEE Transactions on Geoscience and Remote Sensing, 2009. vol. 47, no. 5, p. 1301–1309. DOI: 10.1109/TGRS.2008.2010773
  46. CATAPANO, I., CROCCO, L., D’URSO, M., et al. 3D microwave imaging via preliminary support reconstruction: Testing on the fresnel 2008 database. Inverse Problems, 2009, vol. 25, no. 2, p. 1–23. DOI: 10.1088/0266-5611/25/2/024002
  47. BRIGNONE, M., BOZZA, G., ARAMINI, R., et al. A fully nosampling formulation of the linear sampling method for threedimensional inverse electromagnetic scattering problems. Inverse Problems, 2009, vol. 25, no. 1, p. 1–20. DOI: 10.1088/0266-5611/25/1/015014
  48. JIN, K. H., MCCANN, M. T., FROUSTEY, E., et al. Deep convolutional neural network for inverse problems in imaging. IEEE Transactions on Image Processing, 2017, vol. 26, no. 9, p. 4509–4522. DOI: 10.1109/TIP.2017.2713099
  49. RONNEBERGER, O., FISCHER, P., BROX, T. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Munich (Germany), 2015, p. 234–241. DOI: 10.1007/978-3-319-24574-4_28
  50. ALOM, M. Z., HASAN, M., YAKOPCIC, C., et al. Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. arXiv:1802.06955, 2018, p. 1–12. DOI: 10.48550/arXiv.1802.06955
  51. WEI, Z., CHEN, X. Deep-learning schemes for full-wave nonlinear inverse scattering problems. IEEE Transactions on Geoscience and Remote Sensing, 2018, vol. 57, no. 4, p. 1849–1860. DOI: 10.1109/TGRS.2018.2869221
  52. THATHAMKULAM, A. A., BENNY, R., CHERIAN, P., et al. Non-iterative microwave imaging solutions for inverse problems using deep learning. Progress In Electromagnetics Research M, 2021, vol. 102, p. 53–63. DOI: 10.2528/PIERM21021304
  53. KHOSHDEL, V., ASHRAF, A., LOVETRI, J. Enhancement of multimodal microwave-ultrasound breast imaging using a deep learning technique. Sensors, 2019, vol. 19, no. 18, p. 1–14. DOI: 10.3390/s19184050
  54. WEI, Z., LIU, D., CHEN, X. Dominant-current deep learning scheme for electrical impedance tomography. IEEE Transactions on Biomedical Engineering, 2019, vol. 66, no. 9, p. 2546–2555. DOI: 10.1109/TBME.2019.2891676
  55. HARISHA, S. B, MALLIKARJUN, E., AMIT, M. Deep learning assisted distorted born iterative method for solving electromagnetic inverse scattering problems. Progress In Electromagnetics Research C, 2023, vol. 133, p. 65–79. DOI: 10.2528/PIERC23040702
  56. GOODFELLOW, I., BENGOI, Y., COURVILLE, A. Deep Learning. MIT Press, 2016. ISBN: 0262035618
  57. KANDEL, I., CASTELLI, M. The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express, 2020, vol. 6, no. 4, p. 312–315. DOI: 10.1016/j.icte.2020.04.010

Keywords: Deep Learning, linear sampling method, PEC, microwave imaging

X. Guo, M. Wang, C. Cheng, M. Zhou [references] [full-text] [DOI: 10.13164/re.2024.0100] [Download Citations]
Robust and Fair Multi-Objective Power Allocation Problem Based on Efficient and Healthy Cognitive Radio

Cognitive radio networks (CRNs) is a technology that can alleviate the scarcity of radio resources, improve communication efficiency, and reduce electromagnetic radiation pollution. However, traditional research mostly concentrates on a single optimization function, which is too constrained to achieve global consideration. We suggest a multi-objective optimization problem (MOP) with the objectives of transmission rate and power efficiency. Then, we introduce a fairness factor with the minimum protection rate to ensure the quality of data transfer for each secondary user(SU). We use the ellipsoid set to characterize the uncertain parameters under the actual channel state information (CSI). In the worst case, the semi-infinite programming (SIP) problem is transformed into a second-order cone programming (SOCP) problem. The original problem is linearly combined using the weighted-sum method to construct a single objective problem (SOP), which is then turned into a solvable convex optimization problem and resolved using the Lagrange dual algorithm and sub-gradient method. The simulation results demonstrate the ability of our proposed algorithm to balance power and transmission rate optimization by adjusting the weighting values, while maintaining good robustness.

  1. SINGH, K. K., YADAV, P., SINGH, A., et al. Cooperative spectrum sensing optimization for cognitive radio in 6G networks. Computers & Electrical Engineering, 2021, vol. 95, no. 5, p. 1–12. DOI: 10.1016/j.compeleceng.2021.107378
  2. SONG, Z., GAO, Y., TAFAZOLLI, R. A survey on spectrum sensing and learning technologies for 6G. IEICE Transactions on Communications, 2021, vol. 104, no. 10, p. 1207–1216. DOI: 10.1587/transcom.2020DSI0002
  3. BDRANY, A., SADKHAN, S. B. Decision making approaches in cognitive radio – status, challenges and future trends. In 2020 International Conference on Advanced Science and Engineering (ICOASE 2020). Duhok (Iraq), 2020, p. 195–198. DOI: 10.1109/ICOASE51841.2020.9436597
  4. DHURANDHER, S. K., KUMAR, B. A hybrid spectrum access approach for efficient channel allocation and power control in cognitive radio network. International Journal of Communication Systems, 2022, vol. 35, no. 6, p. 5070.1–5070.18. DOI: 10.1002/dac.5070
  5. ZHENG, K., LIU, X., ZHU, Y., et al. Total throughput maximization of cooperative cognitive radio networks with energy harvesting. IEEE Transactions on Wireless Communications, 2020, vol. 19, no. 1, p. 533–546. DOI: 10.1109/TWC.2019.2946813
  6. GOYAL, P., BUTTAR, A. S., GOYAL, M. An efficient spectrum hole utilization for transmission in cognitive radio networks. In 3rd International Conference on Signal Processing and Integrated Networks (SPIN). Noida (India), 2016, p. 322–327. DOI: 10.1109/SPIN.2016.7566712
  7. AMIN, O., BEDEER, E., AHMED, M. H., et al. Energy efficiency-spectral efficiency tradeoff: A multiobjective optimization approach. IEEE Transactions on Vehicular Technology, 2016, vol. 65, no. 4, p. 1975–1981. DOI: 10.1109/TVT.2015.2425934
  8. CUI, C., YANG, D., JIN, S. Robust spectrum-energy efficiency for green cognitive communications. Mobile Networks and Applications, 2021, vol. 26, no. 3, p. 1217–1224. DOI: 10.1007/s11036-019-01347-y
  9. RAMZAN, M. R., NAWAZ, N., AHMED, A., et al. Multiobjective optimization for spectrum sharing in cognitive radio networks: A review. Pervasive and Mobile Computing, 2017, vol. 41, p. 106–131. DOI: 10.1016/j.pmcj.2017.07.010
  10. BENMAMMAR, B., BENMOUNA, Y., KRIEF, F. A Pareto optimal multi-objective optimization for parallel dynamic programming algorithm applied in cognitive radio ad hoc networks. International Journal of Computer Applications in Technology, 2019, vol. 59, no. 2, p. 152–164. DOI: 10.1504/IJCAT.2019.10019443
  11. SASIKUMAR, S., JAYAKUMARI, J. A novel method for the optimization of spectral-energy efficiency tradeoff in 5G heterogeneous cognitive radio network. Computer Networks, 2020, vol. 180, no. 24, p. 1–7. DOI: 10.1016/j.comnet.2020.107389
  12. XU, L., CAI, L., GAO, Y., et al. Security-aware proportional fairness resource allocation for cognitive heterogeneous networks. IEEE Transactions on Vehicular Technology, 2018, vol. 67, no. 12, p. 11694–11704. DOI: 10.1109/TVT.2018.2873139
  13. XU, Y., SHU, F., HU, R., et al. Robust resource allocation in NOMA based cognitive radio networks. In International Conference on Communications in China (ICCC). Changchun (China), 2019, p. 243–248. DOI: 10.1109/ICCChina.2019.8855922
  14. ASKARI, M., VAKILI, V. T. Maximizing the minimum achievable rates in cognitive radio networks subject to stochastic constraints. AEU: International Journal of Electronics and Communications, 2018, vol. 92, p. 146–156. DOI: 10.1016/j.aeue.2018.04.025
  15. CHEN, Z., CAI, J., ZHU, F., et al. Modeling and robust continuous power allocation strategy with imperfect channel state information in cognitive radio networks. Journal of Physics: Conference Series, 2021, vol. 1764, no. 1, p. 1–5. DOI: 10.1088/1742-6596/1746/1/012013
  16. SUN, J., GUO, B., HU, Y., et al. Multi-objective optimization of spectrum sensing and power allocation based on improved Slime Mould Algorithm. Journal of Physics: Conference Series, 2021, vol. 1996, no. 1, p. 1–6. DOI: 10.1088/1742-6596/1966/1/012018
  17. RANJAN, R., AGRAWAL, N., JOSHI, S. Interference mitigation and capacity enhancement of cognitive radio networks using modified greedy algorithm/channel assignment and power allocation techniques. IET Communications, 2020, vol. 14, no. 9, p. 1502–1509. DOI: 10.1049/iet-com.2018.5950
  18. HE, X., SONG, Y., XUE, Y., et al. Resource allocation for throughput maximization in cognitive radio network with NOMA. Computers, Materials & Continua, 2022, vol. 70, no. 1, p. 195 to 212. DOI: 10.32604/cmc.2022.017105
  19. NASEER, S., MINHAS, Q. A., SALEEM, K., et al. A game theoretic power control and spectrum sharing approach using cost dominance in cognitive radio networks. PeerJ Computer Science, 2021, vol. 7, no. 4, p. 1–20. DOI: 10.7717/peerj-cs.617
  20. BAIDAS, M. W., ALSUSA, E., HAMDI, K. A. Performance analysis and SINR-based power allocation strategies for downlink NOMA networks. IET Communications, 2020, vol. 14, no. 5, p. 723–735. DOI: 10.1049/iet-com.2018.6112
  21. NGUYEN, X. X., KHA, H. H., THAI, P. Q., et al. Multi-objective optimization for information-energy transfer trade-offs in fullduplex multi-user MIMO cognitive networks. Telecommunication Systems: Modeling, Analysis, Design and Management, 2021, vol. 76, no. 1, p. 85–96. DOI: 10.1007/s11235-020-00696-4
  22. HAN, R., GAO, Y., YE, M. Spectrum allocation and power control with multi-objective optimization in cognitive radio network (in Chinese). Application Research Computer, 2019, vol. 36, no. 9, p. 2755–2759. DOI: 10.19734/j.issn.1001-3695.2018.03.0180
  23. ZHOU, M., YIN, H., WANG, H. Robust energy efficiency power allocation algorithm for cognitive radio networks with rate constraints. In 2017 IEEE 17th International Conference on Communication Technology (ICCT 2017). Chengdu (China), p. 849–854. DOI: 10.1109/ICCT.2017.8359755
  24. SUN, S., NI, W., ZHU, Y. Robust power control in cognitive radio networks: A distributed way. In 2011 IEEE International Conference on Communications. Kyoto (Japan), 2011, p. 1–6. DOI: 10.1109/icc.2011.5963428
  25. ZHOU, M., ZHAO, X. A robust energy efficiency power allocation algorithm in cognitive radio networks. China Communications, 2018, vol. 15, no. 10, p. 150–158. DOI: 10.1109/CC.2018.8485477
  26. XU, Y., YANG, M., YANG, Y., et al. Max-min energy-efficient optimization for cognitive heterogeneous networks with spectrum sensing errors and channel uncertainties. IEEE Wireless Communications Letters, 2022, vol. 11, no. 6, p. 1113–1117. DOI: 10.1109/LWC.2021.3130632
  27. AWAD, H., BAYOUMI, E. H. E., SOLIMAN, H. M., et al. Robust tracker of hybrid microgrids by the invariant-ellipsoid set. Electronics, 2021, vol. 10, no. 15, p. 1–20. DOI: 10.3390/electronics10151794
  28. ROBAT MILI, M., HAMDI, K. A., MARVASTI, F., et al. Joint optimization for optimal power allocation in OFDMA femtocell networks. IEEE Communications Letters, 2016, vol. 20, no. 1, p. 133–136. DOI: 10.1109/LCOMM.2015.2497697
  29. KHAN, W. U., JAMEEL, F., RISTANIEMI, T., et al. Joint spectral and energy efficiency optimization for downlink NOMA networks. IEEE Transactions on Cognitive Communications and Networking, 2020, vol. 6, no. 2, p. 645–656. DOI: 10.1109/TCCN.2019.2945802
  30. DREVES, A., FACCHINEI, F., KANZOW, C., et al. On the solution of the KKT conditions of generalized Nash equilibrium problems. Siam Journal on Optimization, 2015, vol. 21, no. 3, p. 1082–1108. DOI: 10.1137/100817000
  31. WANG, H., ZHU, M., ZHOU, M. A robust power allocation scheme in ad-hoc cognitive radio networks. International Journal of Online Engineering, 2017, vol. 13, no. 8, p. 45–59. DOI: 10.3991/ijoe.v13i08.6906

Keywords: Cognitive Radio Networks (CRNs), multi-objective optimization, robust power allocation, fairness factor, rate constraint, weighting coefficient

D. Chen, X. Xu, L. Guo, S. Xiong [references] [full-text] [DOI: 10.13164/re.2024.0111] [Download Citations]
Research on Locating Tunnel-Lining Defects Using Fast Synthetic Aperture Focusing Imaging Based on GPR

This paper proposes a method based on image processing algorithms for ground penetrating radar (GPR) to locate hidden defects in tunnel linings. Firstly, the fast synthetic aperture focusing imaging (Fast-SAFI) algorithm is used to accurately identify the morphology of tunnel-lining defects. Secondly, an iterative algorithm is used to determine the connected regions on the binary image, exclude background noise interference, and locate the centroid and vertices of the correct target connected regions to achieve the positioning of the depth of tunnel-lining defects. To verify the feasibility of the proposed positioning algorithm, a verification experiment was conducted on the experimental wall of the China Academy of Railway Sciences. The experimental results show that the proposed positioning algorithm is reliable and rapid for identifying and locating the morphology and depth of tunnel-lining defects.

  1. JIANG, Y., ZHANG, X., TANIGUCHI, T. Quantitative condition inspection and assessment of tunnel lining. Automation in Construction, 2019, vol. 102, p. 258–269. DOI: 10.1016/j.autcon.2019.03.001
  2. YE, F., QIN, N., LIANG, X., et al. Analyses of the defects in highway tunnels in China. Tunnelling and Underground Space Technology, 2021, vol. 107, p. 1–17. DOI: 10.1016/j.tust.2020.103658
  3. MURTHY, A., PUKAZHENDHI, D., VISHNUVARDHAN, S., et al. Performance of concrete beams reinforced with GFRP bars under monotonic loading. Structures, 2020, vol. 27, p. 1274–1288. DOI: 10.1016/j.istruc.2020.07.020
  4. LEI, M., LIU, L., SHI, C., et al. A novel tunnel-lining crack recognition system based on digital image technology. Tunnelling and Underground Space Technology, 2021, vol. 108, p. 1–13. DOI: 10.1016/j.tust.2020.103724
  5. ASADI, P., GINDY, M., ALVAREZ, M., et al. A computer vision based rebar detection chain for automatic processing of concrete bridge deck GPR data. Automation in Construction, 2020, vol. 112, p. 1–12. DOI: 10.1016/j.autcon.2020.103106
  6. LOUPOS, K., DOULAMIS, A., STENTOUMIS, C., et al. Autonomous robotic system for tunnel structural inspection and assessment. International Journal of Intelligent Robotics and Applications. 2018, vol. 2, no. 1, p. 43–66. DOI: 10.1007/s41315-017-0031-9
  7. OKAZAKI, Y., OKAZAKI, S., ASAMOTO, S., et al. Applicability of machine learning to a crack model in concrete bridges. Computer Aided Civil and Infrastructure Engineering, 2020, vol. 35, no. 8, p. 775–792. DOI: 10.1111/mice.12532
  8. LEI, W., HOU, F., XI, J., et al. Automatic hyperbola detection and fitting in GPR B-scan image. Automation in Construction, 2019, vol. 106, p. 1–14. DOI: 10.1016/j.autcon.2019.102839
  9. JENSSEN, R., JACOBSEN, S. Measurement of snow water equivalent using drone-mounted ultra-wide-band radar. Remote Sensing, 2021, vol. 13, no. 13, p. 1–17. DOI: 10.3390/rs13132610
  10. OZDEMIR, C., DEMIRCI, S., YIGIT, E. Practical algorithms to focus B-scan GPR images: Theory and application to real data. Progress In Electromagnetics Research B, 2008, vol. 6, p. 109–122. DOI: 10.2528/PIERB08031207
  11. BEKTAS, H., OZDEMIR, O., ORHAN, M., et al. An experimental investigation of F-K migration and SAR algorithm using beam space MUSIC for UWB through-the-wall imaging. In IEEE Radar Methods and Systems Workshop (RMSW). Kiev (Ukraine), 2016, p. 70–75. DOI: 10.1109/RMSW.2016.7778553
  12. WU, J., HU, H., SONG, Y., et al. Ultrasonic phased array phase shift migration imaging of irregular surface components using attenuation compensation and anti-aliasing technique. NDT & E International, 2023, vol. 133, p. 1–13. DOI: 10.1016/j.ndteint.2022.102759
  13. SMITHA, N., BHARADWAJ, U., ABILASH, S. et al. Kirchhoff and F-K migration to focus ground penetrating radar images. International Journal of Geo-Engineering, 2016, vol. 7, no. 1, p. 1–12. DOI: 10.1186/s40703-016-0019-6
  14. MORROW, I., GENDEREN, P. A 2D polarimetric backpropagation algorithm for ground-penetrating radar applications. Microwave and Optical Technology Letters, 2001, vol. 28, no. 1, p. 1–4. DOI: 10.1002/1098-2760(20010105)28:1<1::AIDMOP1>3.0.CO;2-D
  15. BRADFORD, J., PRIVETTE, J., WILKINS, D., et al. Reverse-time migration from rugged topography to image ground-penetrating radar data in complex environments. Engineering, 2018, vol. 4, no. 5, p. 661–666. DOI: 10.1016/j.eng.2018.09.004
  16. GIANNAKIS, I., TOSTI, F., LANTINI, L., et al. Diagnosing emerging infectious diseases of trees using ground penetrating radar. IEEE Transactions on Geoscience and Remote Sensing, 2019, vol. 58, no. 2, p. 1146–1155. DOI: 10.1109/TGRS.2019.2944070
  17. ALANI, A., GIANNAKIS, I., ZOU, L., et al. Reverse-time migration for evaluating the internal structure of tree-trunks using ground penetrating radar. NDT & E International, 2020, vol. 115, p. 1–10. DOI: 10.1016/j.ndteint.2020.102294
  18. TANYER, S. High-resolution radar in inhomogeneous media. In Fourth International Conference on Signal Processing Proceedings (ICSP). Beijing (China), 1998, p. 381–384. DOI: 10.1109/ICOSP.1998.770231
  19. MARASCO, G., ROSSO, M., AIELLO, S., et al. Ground penetrating radar fourier pre-processing for deep learning tunnel defects’ automated classification. In International Conference on Engineering Applications of Neural Networks (EANN). Hersonissos (Greece), 2022, p. 165–176. DOI: 10.1007/978-3-031-08223-8_14
  20. ROSSO, M., ALOISIO, A., RANDAZZO, V., et al. Comparative deep learning studies for indirect tunnel monitoring with and without Fourier pre-processing. Integrated Computer-Aided Engineering, 2023, vol. Pre-press, p. 1–20. DOI: 10.3233/ICA-230709
  21. HUANG, J., YANG, X., ZHOU, F., et al. A deep learning framework based on improved self-supervised learning for ground-penetrating radar tunnel lining inspection. Computer-Aided Civil and Infrastructure Engineering, 2023, Early View, p. 1–20. DOI: 10.1111/mice.13042
  22. JOHANSSON, E., MAST, J. Three-dimensional ground-penetrating radar imaging using synthetic aperture time-domain focusing. Advanced Microwave and Millimeter-Wave Detectors, 1994, vol. 2275, p. 205–214. DOI: 10.1117/12.186717
  23. HUANG, S., AHMADI, M., SID-AHMED, M. An edge based thresholding method. In IEEE International Conference on Systems, Man and Cybernetics. Taipei (Taiwan), 2006, p. 1603–1608. DOI: 10.1109/ICSMC.2006.384947
  24. CAO, M., SUN, D. Infrared weak target detection based on improved morphological filtering. In Chinese Control And Decision Conference (CCDC). Hefei (China), 2020, p. 1808–1813. DOI: 10.1109/CCDC49329.2020.9164372
  25. SINGH, S., PAL, K., NIGAM, M. Fuzzy edge detection based on maximum entropy thresholding. IETE Journal of Research, 2011, vol. 57, no. 4, p. 325–330. DOI: 10.4103/0377-2063.86281
  26. OZDEMIR, C., DEMIRCI, S., YIGIT, E., et al. A review on migration methods in B-scan ground penetrating radar imaging. Mathematical Problems in Engineering, 2014, vol. 2014, p. 1–16. DOI: 10.1155/2014/280738

Keywords: Image processing, ground penetrating radar, fast synthetic aperture focusing imaging, tunnel-lining defects, iterative method

L. Guo, Y. J. Wei, Y. P. Shi, X. J. Zou, G. M. Wang [references] [full-text] [DOI: 10.13164/re.2024.0121] [Download Citations]
A High Directivity Microstrip Coupler Based on Reflective Resistors

In this paper, a novel high-directivity microstrip coupler based on reflective resistors is presented. The proposed coupler consists of three pairs of coupled-line sections and one pair of resistors. Generally, the coupling degree can be controlled by the coupled-lines, while the resistors are employed to adjust the amplitude of the reflected signal to cancel out the leakage signal. The mechanism of high directivity is derived and the S-parameters are presented. To verify the design concept, a 20 dB microstrip coupler operating at 2 GHz is processed and measured. The measured results indicate that the return loss of input and output ports is more than 28.5 dB with a typical insert loss of 0.3 dB, while that of coupled and isolated ports is more than 15 dB. And the directivity is more than 20 dB with a maximum 53.1 dB at 2.01 GHz in a fractional bandwidth of 22.5%.

  1. MONGIA, R., BAHL, I., BHARTIA, P. RF and Microwave Coupled-Line Circuits. 2nd ed. London (UK): Artech House, 2007. ISBN: 9780890068304
  2. CRIPPS, S. Coupler talk. IEEE Microwave Magazine, 2006, vol. 7, no. 5, p. 32–37. DOI: 10.1109/MW-M.2006.247908
  3. JORGESEN, D., MARKI, C. Directivity and VSWR Measurements. 8 pages. Morgan Hill (USA): Marki Microwave, Inc., 2010. [Online] Cited 2023-08-10. Available at:
  4. MARCH, S. L. Phase velocity compensation in parallel-coupled microstrip line. In IEEE MTT-S International Microwave Symposium Digest. Dallas (USA), 1982, p. 410–412. DOI: 10.1109/MWSYM.1982.1130739
  5. DYDYK, M. Microstrip directional couplers with ideal performance via single-element compensation. IEEE Transactions on Microwave Theory and Techniques, 1999, vol. 47, no. 6, p. 956 to 964. DOI: 10.1109/22.769332
  6. GRUSZCZYNSKI, S., WINCZA, K. Generalized methods for the design of quasi-ideal symmetric and asymmetric coupled-line sections and directional couplers. IEEE Transactions on Microwave Theory and Techniques, 2011, vol. 59, no. 7, p. 1709–1718. DOI: 10.1109/TMTT.2011.2138155
  7. LEE, S., LEE, Y. An inductor-loaded microstrip directional coupler for directivity enhancement. IEEE Microwave and Wireless Components Letters, 2009, vol. 19, no. 6, p. 362–364. DOI: 10.1109/LMWC.2009.2020014
  8. HA, J., SHIN, W., LEE, Y. An inductive-loading method for directivity enhancement of microstrip coupled-line couplers. IEEE Microwave and Wireless Components Letters, 2017, vol. 27, no. 4, p. 356–358. DOI: 10.1109/LMWC.2017.2678422
  9. SHELEG, B., SPIELMAN, B. E. Broad-band directional couplers using microstrip with dielectric overlays. IEEE Transactions on Microwave Theory and Techniques, 1974, vol. 22, no. 12, p. 1216–1220. DOI: 10.1109/TMTT.1974.1128466
  10. KLEIN, J. L., CHANG, K. Optimum dielectric overlay thickness for equal even- and odd-mode phase velocities in coupled microstrip circuits. Electronics Letters, 1990, vol. 26, no. 5, p. 274–276. DOI: 10.1049/el:19900182
  11. PODELL, A. A high directivity microstrip coupler technique. In G-MTT 1970 International Microwave Symposium. Newport Beach (USA), 1970, p. 33–36. DOI: 10.1109/GMTT.1970.1122761
  12. MULLER, J., JACOB, A. F. Advanced characterization and design of compensated high directivity quadrature coupler. In IEEE MTTS International Microwave Symposium Digest. Anaheim (USA), 2010, p. 724–727. DOI: 10.1109/MWSYM.2010.5517872
  13. OHTA, I., KAWAI, T., FUJII, T., et al. Directivity improvement of microstrip coupled line couplers based on equivalent admittance approach. In IEEE MTT-S International Microwave Symposium Digest. Philadelphia (USA), 2003, vol. 1, p. 43–46. DOI: 10.1109/MWSYM.2003.1210879
  14. CHANG, S. F., CHEN, J. L., JENG, Y. H., et al. New high directivity coupler design with coupled spurlines. IEEE Microwave and Wireless Components Letters, 2004, vol. 14, no. 2, p. 65–67. DOI: 10.1109/LMWC.2003.822565
  15. SOHN, S. M., GOPINATH, A., VAUGHAN, J. T. A compact, high power capable, and tunable high directivity microstrip coupler. IEEE Transactions on Microwave Theory and Techniques, 2016, vol. 64, no. 10, p. 3217–3223 DOI: 10.1109/TMTT.2016.2602835
  16. SAZANOV, D. M., GRIDIN, A. N., MISHUSTIN, B. A. Microwave Circuits. Moscow (Russia): Mir, 1982. ISBN: 0828523118
  17. ABELE, T.-A. Uber die Streumatrix Allgemein Zusammengeschalteter Mehrpole. (The scattering matrix of a general interconnection of multipoles). Archive der Elektrischen Ubertragung, 1960, vol. 14, p. 262–268.
  18. POURZADI, A., ATTARI, A. R., MAJEDI, M. S. A directivity enhanced directional coupler using epsilon negative transmission line. IEEE Transactions on Microwave Theory and Techniques, 2012, vol. 60, no. 11, p. 3395–3402. DOI: 10.1109/TMTT.2012.2216283
  19. WANG, L., WANG, G., SIDEN, J. Design of high-directivity wideband microstrip directional coupler with fragment-type structure. IEEE Transactions on Microwave Theory and Techniques, 2015, vol. 63, no. 12, p. 3962–3970. DOI: 10.1109/TMTT.2015.2490671
  20. MATTHAEI, G. L. Short-step Chebyshev impedance transformers. IEEE Transactions on Microwave Theory and Techniques, 1966, vol. 14, no. 8, p. 372–383. DOI: 10.1109/TMTT.1966.1126277
  21. LIU, M., LIN, F. Two-section broadband couplers with wide-range phase differences and power-dividing ratios. IEEE Microwave and Wireless Components Letters, 2021, vol. 31, no. 2, p. 117–120. DOI: 10.1109/LMWC.2020.3041256

Keywords: High directivity, microstrip coupler, S-parameters

V. S. Nguyen, T. V. Chien, D. K. Hoa, D. D. Hung, G. N. Hoai, Q. L. Chi, L. T. Tu [references] [full-text] [DOI: 10.13164/re.2024.0127] [Download Citations]
On the Performance of Multi-Robot Wireless-based Networks

The performance of the multi-robot wireless-based networks is investigated in this paper. Particularly, we derive the outage probability (OP) and potential throughput (PT) of the worst terminal in the closed-form expressions under two scenarios, with and without direct transmission from the centre robot to all terminal robots. The considered system is complicated since it involves many random variables (RVs) and they are correlated owing to the common link from the central robot to the relay one. To overcome such correlations, our approach is to first derive the performance of the considered metric condition on the correlated link, we then take the average over the common link. Numerical results based on the Monte-Carlo method are given to verify the accuracy of the derived framework as well as to identify the behaviors of two metrics with respect to some key parameters such as the transmit power at both central and immediate robots.

  1. JAWHAR, I., MOHAMED, N., WU, J., et al. Networking of multi-robot systems: Architectures and requirements. Journal of Sensor and Actuator Networks, 2018, vol. 7, no. 4, p. 1–16. DOI: 10.3390/jsan7040052
  2. LI, R., XIAO, Y., YANG, P., et al. UAV-aided two-way relaying for wireless communications of intelligent robot swarms. IEEE Access, 2020, vol. 8, p. 56141–56150. DOI: 10.1109/ACCESS.2020.2979478
  3. ZHONG, X., ZHOU, Y. Establishing and maintaining wireless communication coverage among multiple mobile robots using a radial basis network controller trained via reinforcement learning. In IEEE International Conference on Robotics and Biomimetics (ROBIO). Shenzhen (China), 2013, p. 1353–1359. DOI: 10.1109/ROBIO.2013.6739653
  4. KRESTOVNIKOV, K., CHERSKIKH, E., SAVELIEV, A. Structure and circuit solution of a bidirectional wireless power transmission system in applied robotics. Radioengineering, 2021, vol. 30, no. 1, p. 142–149. DOI: 10.13164/re.2021.0142
  5. ZHANG, Y., LIU, T., ZHANG, H., et al. LEACH-R: LEACH relay with cache strategy for mobile robot swarms. IEEE Wireless Communications Letters, 2021, vol. 10, no. 2, p. 406–410. DOI: 10.1109/LWC.2020.3033039
  6. SHARMA, H., RAJESH, M. Design and simulation of WSN for ZigBee based communication in multi-robot system. In International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). Chennai (India), 2017, p. 1353–1355. DOI: 10.1109/ICECDS.2017.8389664
  7. CHUDE-OKONKWO, U. A. K., NUNOO, S., NGAH, R. Time varying UWB channel model for mobile robot-to-robot communication systems. In IEEE 11th Malaysia International Conference on Communications (MICC). Kuala Lumpur (Malaysia), 2013, p. 1–6. DOI: 10.1109/MICC.2013.6805789
  8. SUGIYAMA, H., TSUJIOKA, T., MURATA, M. Integrated operations of multi-robot rescue system with ad hoc networking. In 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE) . Aalborg (Denmark), 2009, p. 535–539. DOI: 10.1109/WIRELESSVITAE.2009.5172502
  9. NGUYEN, N. P., THANH, T. L., DUONG, Q. T., et al. Secure communications in cognitive underlay networks over Nakagamim channel. Physical Communication, 2017, vol. 25, p. 610–618. DOI: 10.1016/j.phycom.2016.05.003
  10. TU, L.-T., DI RENZO, M. On the energy efficiency of heterogeneous cellular networks with renewable energy sources: A stochastic geometry framework. IEEE Transactions on Wireless Communications, 2020, vol. 19, no. 10, p. 6752–6770. DOI: 10.1109/TWC.2020.3005618
  11. LIU, J., SHENG, M., LIU, L., et al. Effect of densification on cellular network performance with bounded pathloss model. IEEE Communications Letters, 2016, vol. 21, no. 2, p. 346–349. DOI: 10.1109/LCOMM.2016.2615298
  12. DI RENZO, M., TU, L.-T., ZAPPONE, A., et al. A tractable closed form expression of the coverage probability in Poisson cellular networks. IEEE Wireless Communications Letters, 2018, vol. 8, no. 1, p. 249–252. DOI: 10.1109/LWC.2018.2868753
  13. DI RENZO, M., ZAPPONE, A., TU, L.-T., et al. Spectral-energy efficiency pareto front in cellular networks: A stochastic geometry framework. IEEE Wireless Communications Letters, 2018, vol. 8, no. 2, p. 424–427. DOI: 10.1109/LWC.2018.2874642
  14. ZHANG, Z., WU, Q., WANG, J. ARQ protocols in cognitive decode-and-forward relay networks: Opportunities gain. Radioengineering, 2015, vol. 24, no. 1, p. 54–63. DOI: 10.13164/re.2015.0054
  15. LIU, P., PEROVIC, N. S., SPRINGER, A. The impact of user effects on the performance of dual receive antenna diversity systems in flat Rayleigh fading channels. Radioengineering, 2014, vol. 23, no. 1, p. 286–299. ISSN: 1805-9600
  16. CHITRA, M., YASHASWINI, S., DHANASEKARAN, S. Performance analysis of cooperative underlay NOMA-assisted cognitive radio networks. IEEE Wireless Communications Letters, 2023, Early Access, p. 1–5. DOI: 10.1109/LWC.2023.3325240
  17. 3RD GENERATION PARTNERSHIP PROJECT (3GPP). TS 36.101: Evolved Universal Terrestrial Radio Access (EUTRA); User Equipment (UE) radio transmission and reception (18.3.0 ed.). 2023. Cited 2023-10-30. Available at:
  18. KESHAVAMURTHY, B., BLISS, M. A., MICHELUSI, N. MAESTRO-X: Distributed orchestration of rotary-wing UAV relay swarms. IEEE Transactions on Cognitive Communications and Networking, 2023, vol. 9, no. 3, p. 794–810. DOI: 10.1109/TCCN.2023.3248859
  19. SELVAM, P. D., VISHVAKSENAN, K. S. Antenna selection and power allocation in massive MIMO. Radioengineering, 2019, vol. 28, no. 1, p. 340–346. DOI: 10.13164/re.2019.0340
  20. TRINH, V. C., NGUYEN, C. T., BJORNSON, E., et al. Power control in cellular massive MIMO with varying user activity: A deep learning solution. IEEE Transactions on Wireless Communications, 2020, vol. 19, no. 9, p. 5732–5748. DOI: 10.1109/TWC.2020.2996368
  21. TRINH, V. C., PAPAZAFEIROPOULOS, A. K., TU, L.-T., et al. Outage probability analysis of IRS-assisted systems under spatially correlated channels. IEEE Wireless Communications Letters, 2021, vol. 10, no. 8, p. 1815–1819. DOI: 10.1109/LWC.2021.3082409
  22. TRINH, V. C., TU, L.-T., TRAN, D. H., et al. Controlling smart propagation environments: Long-term versus short-term phase shift optimization. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Singapore, 2022, p. 5348–5352. DOI: 10.1109/ICASSP43922.2022.9746584
  23. NGUYEN, N.-L., TU, L.-T., NGUYEN, T. N., et al. Performance on cognitive broadcasting networks employing fountain codes and maximal ratio transmission. Radioengineering, 2023, vol. 32, no. 1, p. 1–10. DOI: 10.13164/re.2023.0001

Keywords: Multi-robot networks, Outage probability, Selection combining, Performance analysis, Potential throughput

Z. Guo, Y. Zhou, H. Yang, S. Li, T. Li, X. Cao [references] [full-text] [DOI: 10.13164/re.2024.0136] [Download Citations]
The Design of Broadband Circularly Polarized Multi-beam Antenna with Linearly Polarized Feeding Source and Transmitarray Unit

Among the existing circularly polarized multi-beam transmitarray antennas, circularly or linearly-to-circularly polarized transmitarray units are always required. Naturally, designing circularly polarized units is more challenging than linear polarization. To simplify design and increase operation bandwidth, a novel method using linearly polarized transmitarray units and feeding sources is proposed for designing the circularly polarized multi-beam antenna. Broadband circular polarization is realized by utilizing sequential rotation technology and 1-bit phase compensation of linearly polarized transmitarray units. Meanwhile, beam scanning is achieved by using linearly polarized feeding sources offset. To validate the design, two multi-beam antenna samples are demonstrated. Simulated and measured results show that the designed multi-beam transmitarray antennas can realize beam scanning to 0°, ±10°, and ±20° in E-plane and H-plane. Moreover, two antennas maintain -10-dB impedance bandwidth and 3-dB axial ratio (AR) bandwidth at 8.5-10.4 GHz and 8.5-10.5 GHz, respectively. The proposed circularly polarized multi-beam transmitarray antennas have the advantages of broadband operation, simple design, and low cost.

  1. ZHU, X. W., GAO, J., CAO, X. Y., et al. A novel low-RCS and wideband circularly polarized patch array based on metasurfece. Radioengineering, 2019, vol. 29, no. 1, p. 99–107. DOI: 10.13164/re.2019.0099
  2. YANG, B., YU, Z., LAN, J., et al. Digital beamforming-based massive MIMO transceiver for 5G millimeter-wave communications. IEEE Transactions on Microwave Theory and Techniques, 2018, vol. 66, no. 7, p. 3403–3418. DOI: 10.1109/TMTT.2018.2829702
  3. ZHANG, Z., ZHAO, Y., JI, L., et al. Design of multi-beam antenna based on Rotman lens. International Journal of RF and Microwave Computer-Aided Engineering, 2018, vol. 28, no. 2, p. 1–10. DOI: 10.1002/mmce.21192
  4. CHENG, Y. J., CHEN, P., HONG, W., et al. Substrate-integrated waveguide beamforming networks and multibeam antenna arrays for low-cost satellite and mobile systems. IEEE Antennas & Propagation Magazine, 2011, vol. 53, no. 6, p. 18–30. DOI: 10.1109/MAP.2011.6157710
  5. TENGAH, Z., ABD RAHMAN, N. H., YAMADA, Y. Design of bifurcated beam using convex bent array feed for satellite mobile earth station application. Radioengineering, 2022, vol. 31, no. 4, p. 541–552. DOI: 10.13164/re.2022.0541
  6. CAMERON, T. R., ELEFTHERIADES, G. V. Analysis and characterization of a wide-angle impedance matching metasurface for dipole phased arrays. IEEE Transactions on Antennas and Propagation, 2015, vol. 63, no. 9, p. 3928–3938. DOI: 10.1109/TAP.2015.2448231
  7. BENINI, A., MARTINI, E., MONNI, S., et al. Phase-gradient meta-dome for increasing grating-lobe-free scan range in phased arrays. IEEE Transactions on Antennas and Propagation, 2018, vol. 66, no. 8, p. 3973–3982. DOI: 10.1109/TAP.2018.2835575
  8. GANDINI, E., SILVESTRI, F., BENINI, A., et al. A dielectric dome antenna with reduced profile and wide scanning capability. IEEE Transactions on Antennas and Propagation, 2021, vol. 69, no. 2, p. 747–759. DOI: 10.1109/TAP.2020.3022960
  9. MONTI, A., VELLUCCI, S., BARBUTO, M., et al. Quadratic gradient metasurface-dome for wide-angle beam-steering phased array with reduced gain loss at broadside. IEEE Transactions on Antennas and Propagation, 2023, vol. 71, no. 2, p. 2022–2027. DOI: 10.1109/TAP.2022.3222716
  10. CHENG, X., YAO, Y., TOMURA, T., et al. A compact multibeam end-fire circularly polarized septum antenna array for millimeter-wave applications. IEEE Access, 2018, vol. 6, p. 62784 to 62792. DOI: 10.1109/ACCESS.2018.2876872
  11. KUMAR, S., VISHWAKARMA, R. K., KUMAR, R., et al. Slotted circularly polarized microstrip antenna for RFID application. Radioengineering, 2017, vol. 26, no. 4, p. 1025–1032. DOI: 10.13164/re.2017.1025
  12. CAO, Y., YAN, S., LI, J., et al. A pillbox based dual circularly polarized millimeter-wave multi-beam antenna for future vehicular radar applications. IEEE Transactions on Vehicular Technology, 2022, vol. 71, no. 7, p. 7095–7103. DOI: 10.1109/TVT.2022.3162299
  13. NADI, M., RAJABALIPANAH, H., CHELDAVI, A., et al. Flexible manipulation of emitting beams using single-aperture circularly polarized digital metasurface antennas: Multi-beam radiation toward vortex beam generation. Advanced Theory and Simulations, 2020, vol. 3, no. 4, p. 1–11. DOI: 10.1002/adts.201900225
  14. LI, S. J., LI, Z. Y., HAN, B. W., et al. Multifunctional coding metasurface with left and right circularly polarized and multiple beams. Frontiers in Materials, 2022, vol. 9, p. 1–9. DOI: 10.3389/fmats.2022.854062
  15. JEONG, M. G., KIM, J. H., BAE, S. H., et al. Miniaturised multibeam-controlled circular eight-port beamforming network for long-range UHF RFID hemispheric coverage. IET Microwaves Antennas & Propagation, 2018, vol. 12, no. 2, p. 154–159. DOI: 10.1049/iet-map.2017.0416
  16. JOO, T., KIM, Y., HWANG, C., et al. Design of multi-beam active phased array antenna system for aerial communications (in Korean). Journal of Korean Institute of Electromagnetic Engineering and Science, 2021, vol. 32, no. 4, p. 334–343. DOI: 10.5515/KJKIEES.2021.32.4.334
  17. AL-SADOON, M., PATWARY, M., ZAHEDI, Y., et al. A new beamforming approach using 60 GHz antenna arrays for multibeam 5G applications. Electronics, 2022, vol. 11, no. 11, p. 1–22. DOI: 10.3390/electronics11111739
  18. JIANG, Z. H., ZHANG, Y., XU, J., et al. Integrated broadband circularly polarized multibeam antennas using berry-phase transmit-arrays for Ka-band applications. IEEE Transactions on Antennas and Propagation, 2020, vol. 68, no. 2, p. 859–872. DOI: 10.1109/TAP.2019.2944547
  19. DUDEK, A., KANIOS, P., STASZEK, K., et al. Octave-band four-beam antenna arrays with stable beam direction fed by broadband 4 × 4 Butler matrix. Electronics, 2021, vol. 10, no. 21, p. 1–14. DOI: 10.3390/electronics10212712
  20. ANSARI, A. E., DAS, S., TABAKH, I., et al. Design and realization of a broadband multi-beam 1×2 array antenna based on 2×2 Butler matrix for 2.45 GHz RFID reader applications. Journal of Circuits, Systems and Computers, 2022, vol. 31, no. 17, p. 1–20. DOI: 10.1142/S0218126622503054
  21. CHEN, P., HONG, W., KUAI, Z., et al. A double layer substrate integrated waveguide Blass matrix for beamforming applications. IEEE Microwave and Wireless Components Letters, 2009, vol. 19, no. 6, p. 374–376. DOI: 10.1109/lmwc.2009.2020020
  22. VANI, T. D., SUBHASHINI, K. R. Design approach of multibeam using phased array antenna aided with Butler matrix for a fixed coverage area. Progress in Electromagnetics Research B, 2018, vol. 80, p. 133–149. DOI: 10.2528/PIERB18011012
  23. LIMA, E. B., MATOS, S. A., COSTA, J. R., et al. Circular polarization wide-angle beam steering at Ka-band by in-plane translation of a plate lens antenna. IEEE Transactions on Antennas and Propagation, 2015, vol. 63, no. 12, p. 5443–5455. DOI: 10.1109/TAP.2015.2484419
  24. LEE, C. H., HOANG, T. V., CHI, S. W., et al. Low-profile quad-beam circularly polarised antenna using transmissive metasurface. IET Microwaves, Antennas and Propagation, 2019, vol. 13, no. 10, p. 1690–1698. DOI: 10.1049/iet-map.2018.6056
  25. JIANG, Z. H., KANG, L., YUE, T., et al. Wideband transmit arrays based on anisotropic impedance surfaces for circularly polarized single-feed multibeam generation in the Q-band. IEEE Transactions on Antennas and Propagation, 2020, vol. 68, no. 1, p. 217–229. DOI: 10.1109/TAP.2019.2943343
  26. YANG, Y., BAN, Y. L., YANG, Q., et al. Millimeter wave wide-angle scanning circularly polarized antenna array with a novel polarizer. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 2, p. 1077–1086. DOI: 10.1109/TAP.2021.3111255
  27. YU, Z. Y., ZHANG, Y. H., HE, S. Y., et al. A wide-angle coverage and low scan loss beam steering circularly polarized folded reflectarray antenna for millimeter-wave applications. IEEE Transactions on Antennas and Propagation, 2022, vol. 70, no. 4, p. 2656–2667. DOI: 10.1109/TAP.2021.3118790
  28. ZHANG, P. P., ZHU, X. C., HU, Y., et al. A wideband circularly polarized folded reflect array antenna with linearly polarized feed. IEEE Antennas and Wireless Propagation Letters, 2022, vol. 21, no. 5, p. 913–917. DOI: 10.1109/LAWP.2022.3151622
  29. CHEN, J. H., LI, G. W., GE, Y. H., et al. Broadband circularly polarized multi-beam folded transmit array antenna. International Journal of RF and Microwave Computer-Aided Engineering, 2022, vol. 32, no. 7, p. 1–9. DOI: 10.1002/mmce.23161
  30. WANG, H., DONG, X. F., SHEN, J., et al. Fan-beam antenna design based on metasurface lenses. International Journal of RF and Microwave Computer-Aided Engineering, 2021, vol. 31, no. 4, p. 1–8. DOI: 10.1002/mmce.22582
  31. HALL, P. S. Application of sequential feeding to wide bandwidth, circularly polarised microstrip patch arrays. IEE Proceedings H (Microwaves, Antennas and Propagation), 1989, vol. 136, no. 5, p. 390–398. DOI: 10.1049/ip-h-2.1989.0070
  32. HALL, P. S., SMITH, M. S. Sequentially rotated arrays with reduced sidelobe levels. IEE Proceedings - Microwaves, Antennas and Propagation, 1994, vol. 141, no. 4, p. 321–325. DOI: 10.1049/ip-map_19941193
  33. SMOLDERS, A. B., VISSER, H. J. Low side-lobe circularly polarized phased arrays using a random sequential rotation technique. IEEE Transactions on Antennas and Propagation, 2014, vol. 62, no. 12, p. 6476–6481. DOI: 10.1109/TAP.2014.2359476

Keywords: Circular polarization, multi-beam antenna, spatial feed, transmitarray antenna

A. M. Pereira de Lucena, R. de Lima Florindo [references] [full-text] [DOI: 10.13164/re.2024.0145] [Download Citations]
Modified Costas Loop for Carrier Phase Tracking in GPS Receivers

The carrier phase received at the receivers of the Global Positioning System (GPS) links is used to detect navigation data and to precisely determine the position, speed and time corresponding to the user's equipment. Therefore, subsystems for carrier phase tracking are crucial parts in all GPS receivers. When the propagation conditions are favorable, the method frequently used for phase tracking is based on Digital Phase-Locked Loop (DPLL)) and implemented through the discrete Costas loop operating under the modulated L1 carrier, in the case of a GPS receiver. This technique is quite simple, well known and very suitable for implementation in low-cost receivers. In this article, we revisit the traditional Costas loop design and point out some issues that affect the phase tracking performance of this loop. In order to overcome these problems, we propose some modifications to the traditional Costas loop. The resulting architecture presents better performance and complexity equivalent to the original loop. Another contribution of this work is the mathematical analysis to evaluate the performance of the new architecture when operating on an Additive White Gaussian Noise (AWGN) channel. Various results from computational simulations carried out with the two architectures, in different operating scenarios, including AWGN, dynamic stress and ionospheric scintillation are presented and discussed. We conclude that the new architecture outperforms the traditional Costas loop in terms of the variance of the estimated phase error, root mean squared error of the estimated phase and robustness to cycle-slip and loss of lock.

  1. KAPLAN, E. D., HEGARTY, C. J. Understanding GPS/GNSS: Principles and Applications. 3rd ed. Artech House, 2017. ISBN: 1630810584
  2. ZHANG, L., MORTON, Y., VAN GRAAS, F., et al. Characterization of GNSS signal parameters under ionosphere scintillation conditions using software-based tracking algorithms. In IEEE/ION Position, Location and Navigation Symposium. Indian Wells (CA, USA), 2010, p. 264–275. DOI: 10.1109/PLANS.2010.5507209
  3. VILA-VALLS, J., CLOSAS, P., NAVARRO, M., et al. Are PLLs dead? A tutorial on Kalman filter-based techniques for digital carrier synchronization. IEEE Aerospace and Electronic Systems Magazine, 2017, vol. 32, no. 7, p. 28–45. DOI: 10.1109/MAES.2017.150260
  4. HUMPHREYS, T. E., PSIAKI, M. L., KINTNER, P. M., et al. GPS carrier tracking loop performance in the presence of ionospheric scintillations. In Proceedings of the 18th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2005). Long Beach (CA, USA), 2005, p. 156–167.
  5. CORTES, I., VAN DER MERWE, J. R., NURMI, J., et al. Evaluation of adaptive loop-bandwidth tracking techniques in GNSS receivers. Sensors, 2021, vol. 21, no. 2, p. 1–39. DOI: 10.3390/s21020502
  6. FOHLMEISTER, F., ANTREICH, F., NOSSEK, J. A. Dual Kalman filtering based GNSS phase tracking for scintillation mitigation. In 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS). Monterey (CA, USA), 2018, p. 1151–1158. DOI: 10.1109/PLANS.2018.8373499
  7. VILÀ-VALLS, J., LINTY, N., CLOSAS, P., et al. Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation. Navigation: Journal of the Institute of Navigation, 2020, vol. 67, no. 3, p. 511–535. DOI: 10.1002/navi.379
  8. VILÀ-VALLS, J., CLOSAS, P., FERNANDEZ-PRADES, C., et al. On the mitigation of ionospheric scintillation in advanced GNSS receivers. IEEE Transactions on Aerospace and Electronic Systems, 2018, vol. 54, no. 4, p. 1692–1708. DOI: 10.1109/TAES.2018.2798480
  9. VILÀ-VALLS, J., FERNANDEZ-PRADES, C., ARRIBAS, J., et al. On-line model learning for adaptive GNSS ionospheric scintil-lation estimation and mitigation. In 2018 IEEE/ION Position, Lo-cation and Navigation Symposium (PLANS). Monterey (CA, USA), 2018, p. 1167–1172. DOI: 10.1109/PLANS.2018.8373501
  11. LOPES, R. A. M., ANTREICH, F., FOHLMEISTER, F., et al. Ionospheric scintillation mitigation with Kalman PLLs employing radial basis function networks. IEEE Transactions on Aerospace and Electronic Systems, 2023, vol. 59, no. 5, p. 6878–6893. DOI: 10.1109/TAES.2023.3281431
  12. LAWAL, A. B. Fundamentals of Satellite Navigation Systems: How to Design GPS/GNSS Receivers Book 2, 3, 4 & 5: The Principles, Applications & Markets. Independently published (A. B. Lawal), 2018. ISBN: 979-8712615612
  13. LOPEZ-SALCEDO, J. A., DEL PERAL-ROSADO, J. A., SECO-GRANADOS, G. Survey on robust carrier tracking techniques. IEEE Communications Surveys & Tutorials, 2014, vol. 16, no. 2, p. 670–688. DOI: 10.1109/SURV.2013.082713.00228
  14. MAO, W.-L., CHEN, A.-B. Mobile GPS carrier phase tracking using a novel intelligent dual-loop receiver. International Journal of Satellite Communications and Networking, 2008, vol. 26, no. 2, p. 119–139. DOI: 10.1002/sat.898
  15. RAZAVI, A., GEBRE-EGZIABHER, D., AKOS, D. M. Carrier loop architectures for tracking weak GPS signals. IEEE Transactions on Aerospace and Electronic Systems, 2008, vol. 44, no. 2, p. 697–710. DOI: 10.1109/TAES.2008.4560215
  16. SUN, P., TANG, X., SU, Y., et al. Evaluation of carrier tracking performance for BeiDou signals in presence of ionospheric scintillation. In 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). Harbin (China), 2016, p. 812–817. DOI: 10.1109/IMCCC.2016.40
  17. LINDSEY, W. C., CHIE, C. M. A survey of digital phase-locked loops. Proceedings of the IEEE, 1981, vol. 69, no. 4, p. 410–431. DOI: 10.1109/PROC.1981.11986
  18. BRAASCH, M. S., VAN DIERENDONCK, A. J. GPS receiver architectures and measurements. Proceedings of the IEEE, 1999, vol. 87, no. 1, p. 48–64. DOI: 10.1109/5.736341
  19. HUMPHREYS, T. E., PSIAKI, M. L., KINTNER, P. M. Modeling the effects of ionospheric scintillation on GPS carrier phase tracking. IEEE Transactions on Aerospace and Electronic Systems, 2010, vol. 46, no. 4, p. 1624–1637. DOI: 10.1109/TAES.2010.5595583
  20. LEON-GARCIA, A. Probability, Statistics, and Random Processes for Electrical Engineering. 3rd ed. Upper Saddle River (NJ): Pearson/Prentice Hall, 2007. ISBN: 9780131471221
  21. MENGALI, U. Synchronization Techniques for Digital Receivers. Springer, 1997. ISBN: 978-0306457258
  22. DE LUCENA, A. M. P., DA SILVA, F. DE A. T. F., DA SILVA, A. S. Scintillation effects in S-band telemetry link of INPE's earth station in Cuiaba-Brazil. Radioengineering, 2021, vol. 30, no. 4, p. 739–748. DOI: 10.13164/re.2021.0739
  23. HUMPHREYS, T. E., PSIAKI, M. L., HINKS, J. C., et al. Simulating ionosphere-induced scintillation for testing GPS receiver phase tracking loops. IEEE Journal of Selected Topics in Signal Processing, 2009, vol. 3, no. 4, p. 707–715. DOI: 10.1109/JSTSP.2009.2024130

Keywords: Carrier phase tracking, GPS receiver, Costas loop, phase recovery

S. Yoon, B. Kim, S. Kim [references] [full-text] [DOI: 10.13164/re.2024.0155] [Download Citations]
A Robust Super-resolution Algorithm in a Low SNR Environment for Vital Sign Radar

We propose a robust super-resolution algorithm for vital sign radar in a low signal to noise ratio (SNR) environment. Conventional approaches, such as fast Fourier transform and super-resolution based algorithms, suffered to provide reliable results due to the limited data length and high noise level. To overcome these limitations, our proposed algorithm utilizes a low-complexity least mean square (LMS) filter and relaxation (RELAX) techniques to achieve robust performance in low SNR environments. To evaluate the effectiveness of our algorithm, we conducted both simulation and experimental studies. Our results show that the proposed method significantly outperforms conventional methods, with Monte-Carlo simulations of respiration and heartbeat achieving an RMSE approximately 7 and 120 times lower than that of the conventional method, respectively. Overall, our algorithm provides a promising solution for robust vital sign detection in challenging low SNR environments.

  1. KIM, S., KIM, B., JIN, Y., et al. Extrapolation-RELAX estimator based on spectrum partitioning for DOA estimation of FMCW radar. IEEE Access, 2019, vol. 7, p. 98771–98780. DOI: 10.1109/ACCESS.2019.2930102
  2. WU, Q., MEI, Z., LAI, Z., et al. A non-contact vital signs detection in a multi-channel 77GHz LFMCW radar system. IEEE Access, 2021, vol. 9, p. 49614–49628. DOI: 10.1109/ACCESS.2021.3068480
  3. XIA, W., LI, Y., DONG, S. Radar-based high-accuracy cardiac activity sensing. IEEE Transactions on Instrumentation and Measurement, 2021, vol. 70, p. 1–13. DOI: 10.1109/TIM.2021.3050827
  4. KHAN, F., AZOU, S., YOUSSEF, R., et al. IR-UWB radar-based robust heart rate detection using a deep learning technique intended for vehicular applications. Electronics, 2022, vol. 11, p. 1–15. DOI: 10.3390/electronics11162505
  5. KWON, H., CHOI, S., LEE, D., et al. Attention-based LSTM for non-contact sleep stage classification using IR-UWB radar. IEEE Journal of Biomedical and Health Informatics, 2021, vol. 25, no. 10, p. 3844–3853. DOI: 10.1109/JBHI.2021.3072644
  6. KWON, H., SON, D., LEE, D., et al. Hybrid CNN-LSTM network for real-time apnea-hypopnea event detection based on IR-UWB radar. IEEE Access, 2022, vol. 10, p. 17556–17564. DOI: 10.1109/ACCESS.2021.3081747
  7. FALLATAH, A., BOLIC, M., MACPHERSON, M., et al. Monitoring respiratory motion during VMAT treatment delivery using ultra-wideband radar. Sensors, 2022, vol. 22, no. 6, p. 1–16. DOI: 10.3390/s22062287
  8. MA, Y., WANG, P., XUE, H., et al. Non-contact vital states identification of trapped living bodies using ultra-wideband bio-radar. IEEE Access, 2021, vol. 9, p. 6550–6559. DOI: 10.1109/ACCESS.2020.3048381
  9. TANG, D., RODRIGUES, D. V. Q., BROWN, M. C., et al. Dual null detection points removal and time-domain sensitivity analysis of a self-injection-locked radar for small-amplitude motion sensing. IEEE Transactions on Microwave Theory and Techniques, 2022, vol. 70, no. 9, p. 4263–4272. DOI: 10.1109/TMTT.2022.3186299
  10. LI, Z., JIN, T., LI, L., et al. Spatiotemporal processing for remote sensing of trapped victims using 4-D imaging radar. IEEE Transactions on Geoscience and Remote Sensing, 2023, vol. 61, p. 1–12. DOI: 10.1109/TGRS.2023.3266039
  11. DOGRU, S., MARQUES, L. Through-wall mapping using radar: Approaches to handle multipath reflections. IEEE Sensors Journal, 2021, vol. 21, no. 10, p. 11674–11683. DOI: 10.1109/JSEN.2021.3067721
  12. PHAM, L., PAUL, M., PRASAD, P. Noncontact detection of cardiopulmonary activities of trapped humans in rescue relief events. IEEE Access, 2022, vol. 10, p. 75680–75692. DOI: 10.1109/ACCESS.2022.3190902
  13. WANG, Y., SHUI, Y., YANG, X., et al. Multi-target vital signs detection using frequency-modulated continuous wave radar. EURASIP Journal on Advances in Signal Processing, 2021, p. 1 to 19. DOI: 10.1186/s13634-021-00812-9
  14. LIN, Y., LEE, T. Max-MUSIC: A low-complexity high-resolution direction finding method for sparse MIMO radars. IEEE Sensors Journal, 2020, vol. 20, no. 24, p. 14914–14923. DOI: 10.1109/JSEN.2020.3009426
  15. HU, Y., DENG, W., ZHANG, X., et al. FDA-MIMO radar with long-baseline transmit array using ESPRIT. IEEE Signal Processing Letters, 2021, vol. 28, p. 1530–1534. DOI: 10.1109/LSP.2021.3095612
  16. SUN, R., ZHANG, W., YAO, B. Frame arrival detection for low SNR frequency selective fading channels. In 2018 IEEE/CIC Inter-national Conference on Communications in China (ICCC). Beijing (China), 2018, p. 374–378. DOI: 10.1109/ICCChina.2018.8641184
  17. LI, C., LIN, J. Random body movement cancellation in Doppler radar vital sign detection. IEEE Transactions on Microwave Theory and Techniques, 2008, vol. 56, no. 12, p. 3143–3152. DOI: 10.1109/TMTT.2008.2007139 [18] ARDALAN, S., MOGHADAMI, S., JAAFARI, S. Motion noise cancelation in heartbeat sensing using accelerometer and adaptive filter. IEEE Embedded Systems Letters, 2015, vol. 7, no. 4, p. 101 to 104. DOI: 10.1109/LES.2015.2457933
  18. LI, Y., GU, C., NIKOUBIN, T., et al. Wireless radar devices for smart human-computer interaction. In IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS). College Station (TX, USA), 2014, p. 65–68. DOI: 10.1109/MWSCAS.2014.6908353
  19. TOTH-LAUFER, E., VARKONYI-KOCZY, A. Personal-statistics-based heart rate evaluation in anytime risk calculation model. IEEE Transactions on Instrumentation and Measurement, 2015, vol. 64, no. 8, p. 2127–2135. DOI: 10.1109/TIM.2014.2376111
  20. AKAIKE, H. Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics, 1969, vol. 21, p. 243–247. DOI: 10.1007/BF02532251
  21. RISSANEN, J. Modeling by shortest data description. Automatica, 1978, vol. 14, p. 465–471. DOI: 10.1016/0005-1098(78)90005-5

Keywords: Vital sign radar, LMS filter, RELAX, low SNR, low complexity

Y. H. Hu, J. Z. Zhang, Y. X. Liu, G. J. Liu, G. K. Chen [references] [full-text] [DOI: 10.13164/re.2024.0163] [Download Citations]
Spectrum Map Construction Method Based on Dynamic Window Size Tensor Ring Low-rank Factors

Spectrum maps can model the received signal strength over a geographical region and will play a pivotal role in the intended spectrum management scheme. Traditional spectrum map construction methods cannot fully utilize the spatial-temporal correlation characteristics of observed spectrum data in a time-varying spectrum situation. The computational complexity for real-time scenes is unaffordable, and the current spectrum situation cannot be estimated promptly. To address this problem, we first model the spatial-temporal spectrum data by tensors. Then, based on the low-rank statistical characteristic of the spectrum map, we apply the tensor ring low-rank factors (TRLRF) algorithm to recover the missing spectrum data. Finally, a dynamic window mechanism is introduced to reduce the computational complexity further. The simulation results show that the proposed dynamic window size tensor ring low-rank factors (DW-TRLRF) algorithm yields higher accuracy than other state-of-the-art algorithms with significantly lower complexity.

  1. LEE, D., KIM, S. J., GIANNAKIS, G. B. Channel gain cartography for cognitive radios leveraging low rank and sparsity. IEEE Transactions on Wireless Communications, 2017, vol. 16, no. 9, p. 5953–5966. DOI: 10.1109/TWC.2017.2717822
  2. ZHAO, Y., ZHU, Q., LIN, Z., et al. Temporal prediction for spectrum environment maps with moving radiation sources. IET Communications, 2023, vol. 17, no. 5, p. 538–548. DOI: 10.1049/cmu2.12560
  3. LU, J., ZHA, S., HUANG, J., et al. The iterative completion method of the spectrum map based on the difference of measurement values. In 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP). Shenzhen (China), 2018, p. 255–259. DOI: 10.1109/SIPROCESS.2018.8600488
  4. WANG, C., WU, Y., ZHOU, F., et al. Accurate spectrum map construction using an intelligent frequency-spatial reasoning approach. In IEEE Global Communications Conference (GLOBECOM 2022). Rio de Janeiro (Brazil), 2022, p. 3460–3465. DOI: 10.1109/GLOBECOM48099.2022.10001024
  5. ZHANG, G. Y., WANG, J., CHEN, X. N., et al. Spectrum situation generation from sparse spatial sampling: Model and algorithm (in Chinese). Scientia Sinica Information, 2022, vol. 52, p. 2011–2036. DOI: 10.1360/SSI-2021-0382
  6. CHEN, G., LIU, Y., ZHANG, T., et al. A graph neural network based radio map construction method for urban environment. IEEE Communications Letters, 2023, vol. 27, no. 5, p. 1327–1331. DOI: 10.1109/LCOMM.2023.3260272
  7. SUN, H., CHEN, J. Propagation map reconstruction via interpolation assisted matrix completion. IEEE Transactions on Signal Processing, 2022, vol. 70, p. 6154–6169. DOI: 10.1109/TSP.2022.3230332
  8. BOCCOLINI, G., HERNANDEZ-PENALOZA, G., BEFERULLLOZANO, B. Wireless sensor network for spectrum cartography based on kriging interpolation. In IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). Sydney, (Australia), 2012, p. 1565 to 1570. DOI: 10.1109/PIMRC.2012.6362597
  9. SHEN, F., WANG, Z., DING, G., et al. 3D compressed spectrum mapping with sampling locations optimization in spectrum heterogeneous environment. IEEE Transactions on Wireless Communications, 2022, vol. 21, no. 1, p. 326–338. DOI: 10.1109/TWC.2021.3095342
  10. ZHANG, G., FU, X., WANG, J., et al. Spectrum cartography via coupled block-term tensor decomposition. IEEE Transactions on Signal Processing, 2020, vol. 68, p. 3660–3675. DOI: 10.1109/TSP.2020.2993530
  11. BASSER, P. J., MATTIELLO, J., LEBIHAN, D. MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 1994, vol. 66, no. 1, p. 259–267. DOI: 10.1016/S0006-3495(94)80775-1
  12. LEBIHAN, D., MANGIN, J. F., POUPON, C., et al. Diffusion tensor imaging: Concepts and applications. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2001, vol. 13, no. 4, p. 534–546. DOI: 10.1002/jmri.1076
  13. ACAR, E., DUNLAVY, D. M., KOLDA, T. G., et al. Scalable tensor factorizations for incomplete data. Chemometrics and Intelligent Laboratory Systems, 2011, vol. 106, no. 1, p. 41–56. DOI: 10.1016/j.chemolab.2010.08.004
  14. YUAN, L., LI, C., MANDIC, D., et al. Tensor ring decomposition with rank minimization on latent space: An efficient approach for tensor completion. In Proceedings of the AAAI conference on Artificial Intelligence. Honolulu (USA), 2019, vol. 33, no. 1, p. 9151–9158. DOI: 10.1609/aaai.v33i01.33019151
  15. HAN, D. R. A survey on some recent developments of alternating direction method of multipliers. Journal of the Operations Research Society of China, 2022, vol. 10, p. 1–52. DOI: 10.1007/s40305-021-00368-3
  16. WANG, W., AGGARWAL, V., AERON, S. Efficient low rank tensor ring completion. In Proceedings of the IEEE International Conference on Computer Vision. Venice (Italy), 2017, p. 5697 to 5705. DOI: 10.1109/iccv.2017.607
  17. YUAN, L., CAO, J., ZHAO, X., et al. Higher-dimension tensor completion via low-rank tensor ring decomposition. In 2018 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). Honolulu, (USA), 2018, p. 1071–1076. DOI: 10.23919/APSIPA.2018.8659708
  18. TANG, M., DIND, G., WU, Q., et al. A joint tensor completion and prediction scheme for multi-dimensional spectrum map construction. IEEE Access, 2016, vol. 4, p. 8044–8052. DOI: 10.1109/ACCESS.2016.2627243
  19. TANG, M., DING, G., XUE, Z., et al. Multi-dimensional spectrum map construction: A tensor perspective. In 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP). Yangzhou (China), 2016, p. 1–5. DOI: 10.1109/WCSP.2016.7752600
  20. ZHANG, G., WANG, J., PENG, Q., et al. Dynamic spectrum cartography via canonical polyadic tensor decomposition. Signal Processing, 2021, vol. 188, p. 1–13. DOI: 10.1016/j.sigpro.2021.108208
  21. DING, Z., ZHANG, J., LIU, Y., et al. Spectrum map construction based on optimized sensor selection and adaptive kriging model. Radioengineering, 2022, vol. 31, no. 3, p. 422–430. DOI: 10.13164/re.2022.0422
  22. GUDMUNDSON, M. Correlation model for shadow fading in mobile radio systems. Electronics Letters, 1991, vol. 27, no. 23, p. 2145–2146. DOI: 10.1049/el:19911328
  23. CAI, J. F., CANDES, E. J., SHEN, Z. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 2010, vol. 20, no. 4, p. 1956–1982. DOI: 10.1137/080738970
  24. SRINIVASAN, B. V., DURAISWAMI, R., MURTUGUDDE, R. Efficient Kriging for Real-Time Spatio-Temporal Interpolation. 8 pages. [Online] Cited 2010-01-18. Available at:
  25. HU, W., LIU, H., PENG, C., et al. A construction technology of electromagnetic spectrum map based on the kriging algorithm (in Chinese). Journal of Air Force Engineering University (Natural Science Edition), 2022, vol. 23, no. 3, p. 26–33. DOI: 10.3969/j.issn.1009-3516.2022.03.005
  26. AHARON, M., ELAD, M., BRUCKSTEIN, A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 2006, vol. 54, no. 11, p. 4311–4322. DOI: 10.1109/TSP.2006.881199

Keywords: Spatial-temporal spectrum data, spectrum situation construction, spectrum map, mobile radiation source

V. Abbasi, M. G. Shayesteh [references] [full-text] [DOI: 10.13164/re.2024.0173] [Download Citations]
Differential Spatial Modulation Using New Index Bits

Spatial modulation (SM) has the potential to meet the requirements of 5G and beyond communication systems with features such as reduced hardware complexity and good trade-off between spectral efficiency and energy efficiency. In this study, an efficient non-square differential spatial modulation (DSM) scheme is presented in which the number of time slots is one more than the number of transmit antennas. The introduced scheme includes one empty time slot. At the other time slots, the time slots of the conventional DSM (CDSM) are used (the Gray code order (GCO) can also be used). There is one active antenna at each time slot of the proposed scheme. The index of empty time slot conveys information. Thus, in comparison with CDSM (or GCO), for the same number of transmit antennas, the introduced scheme has more energy-free bits (index bits). It is free of pilot overhead, channel estimation complexity, and potential channel state information (CSI) estimation errors. Further, a detector with no error propagation is presented. Analytical expressions for the bit error rate (BER) are derived at high signal-to-noise ratios (SNRs) and high SNRs per bit (SNRbs). Simulation results verify the theoretical evaluation and demonstrate the efficiency of the proposed scheme.

  1. MANDLOI, M., GURJAR, D., PATTANAYAK, P., et al. 5G and Beyond Wireless Systems. Springer, 2021. DOI: 10.1007/978-981-15-6390-4
  2. CHENG, X., ZHANG, M., WEN, M., et al. Index modulation for 5G: Striving to do more with less. IEEE Wireless Communications, 2018, vol. 25, no. 2, p. 126–132. DOI: 10.1109/MWC.2018.1600355
  3. TUSHA, S. D., TUSHA, A., BASAR, E., et al. Multidimensional index modulation for 5G and beyond wireless networks. Proceedings of the IEEE, 2020, vol. 109, no. 2, p. 170–199. DOI: 10.1109/JPROC.2020.3040589
  4. FAZELI, A., NGUYEN, H. H., TUAN, H. D., et al. Non-coherent multi-level index modulation. IEEE Transactions on Communications, 2022, vol. 70, no. 4, p. 2240–2255. DOI: 10.1109/TCOMM.2022.3142159
  5. JOSE, D., SAMEER, S. Differential transmission schemes for generalized spatial modulation. IEEE Transactions on Vehicular Technology, 2021, vol. 70, no. 12, p. 12640–12650. DOI: 10.1109/TVT.2021.3118457
  6. ZOU, H., QUAN, D., JIN, X., et al. Differential space time media‐based modulation system. IET Communications, 2022, vol. 16, no. 9, p. 942–950. DOI: 10.1049/cmu2.12396
  7. ISHIKAWA, N., SUGIURA, S. Unified differential spatial modulation. IEEE Wireless Communications Letters, 2014, vol. 3, no. 4, p. 337–340. DOI: 10.1109/LWC.2014.2315635
  8. DWARIKA, K., XU, H. Differential full diversity spatial modulation using amplitude phase shift keying. Radioengineering, 2018, vol. 27, no. 1, p. 151–158. DOI: 10.13164/re.2018.0151
  9. DWARIKA, K., XU, H. Power allocation and low complexity detector for differential full diversity spatial modulation using two transmit antennas. Radioengineering, 2017, vol. 26, no. 2, p. 461 to 469. DOI: 10.13164/re.2017.0461
  10. LI, J., WEN, M., CHENG, X., et al. Differential spatial modulation with Gray coded antenna activation order. IEEE Communications Letters, 2016, vol. 20, no. 6, p. 1100–1103. DOI: 10.1109/LCOMM.2016.2557801
  11. XIAO, L., XIAO, Y., YANG, J., et al. Space-time block coded differential spatial modulation. IEEE Transactions on Vehicular Technology, 2017, vol. 66, no. 10, p. 8821–8834. DOI: 10.1109/TVT.2017.2696380
  12. XIAO, L., CHEN, D., HEMADEH, I. A., et al. Graph theory assisted bit-to-index-combination Gray coding for generalized index modulation. IEEE Transactions on Wireless Communications, 2020, vol. 19, no. 12, p. 8232–8245. DOI: 10.1109/TWC.2020.3020692
  13. BIAN, Y., CHENG, X., WEN, M., et al. Differential spatial modulation. IEEE Transactions on Vehicular Technology, 2015, vol. 64, no. 7, p. 3262–3268. DOI: 10.1109/TVT.2014.2348791
  14. MARTIN, P. A. Differential spatial modulation for APSK in time varying fading channels. IEEE Communications Letters, 2015, vol. 19, no. 7, p. 1261–1264. DOI: 10.1109/LCOMM.2015.2426172
  15. LIU, J., DAN, L., YANG, P., et al. High-rate APSK-aided differential spatial modulation: Design method and performance analysis. IEEE Communications Letters, 2016, vol. 21, no. 1, p. 168–171. DOI: 10.1109/LCOMM.2016.2610962
  16. ISHIKAWA, N., SUGIURA, S. Rectangular differential spatial modulation for open-loop noncoherent massive-MIMO downlink. IEEE Transactions on Wireless Communications, 2017, vol. 16, no. 3, p. 1908–1920. DOI: 10.1109/TWC.2017.2657497
  17. XIAO, L., XIAO, P., RUAN, H., et al. Differentially-encoded rectangular spatial modulation approaches the performance of its coherent counterpart. IEEE Transactions on Communications, 2020, vol. 68, no. 12, p. 7593–7607. DOI: 10.1109/TCOMM.2020.3021117
  18. WEI, R. Y., CHANG, C. W. A low-complexity soft-output detector for differential spatial modulation. IEEE Wireless Communications Letters, 2022, vol. 11, no. 5, p. 1077–1081. DOI: 10.1109/LWC.2022.3156889
  19. PROAKIS, J. G., SALEHI, M. Digital Communications. 5th ed. McGraw-Hill, Education, 2007. ISBN: 9780072957167
  20. HORN, R. A., JOHNSON, C. R. Matrix Analysis. Cambridge University Press, 1985. DOI: 10.1017/CBO9780511810817
  21. LOYKA, S., GAGNON, F. On accuracy and efficiency of Monte Carlo BER simulations for fading channel. In 2007 International Symposium on Signals, Systems and Electronics. Montreal (Canada), 2007, p. 255–258. DOI: 10.1109/ISSSE.2007.4294461

Keywords: Index modulation, 5G, time index modulation, differential spatial modulation (DSM), single active antenna, error propagation, diversity, signal to noise per bit (SNRb)