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June 2023, Volume 32, Number 2 [DOI: 10.13164/re.2023-2]

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Muhammad HAROON AURANGZEB, Faisal AKRAM, Imran RASHID, Attiq AHMED [references] [full-text] [DOI: 10.13164/re.2023.0187] [Download Citations]
Pilots Optimization and Surface Area Effects on Channel Estimation in RIS Aided MIMO System

Reconfigurable intelligent surface (RIS) is an emerging tool for 5G and wireless communication technologies that have attracted researchers' interest. However, the passive nature and the high number of reflecting elements in RIS result in a large pilot overhead, which makes channel estimation challenging in multi-user multiple-input multiple-output (MU-MIMO) wireless communication systems. Previous works have shown an improvement in reducing the pilot overhead by exploiting the structured sparsity in rows and columns, which was further improved by compensating offset among users in angular cascaded channels of RIS aided system. To further reduce the pilot overhead, we analyze and adopt coherence-optimized pilots for channel estimation and propose an algorithm to analyze the combined effect of low-coherence pilots with an optimum size of RIS elements for a given number of users, transmit antennas, and normalized error threshold performance. The simulation results illustrate better NMSE performance as compared to contemporary techniques.

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Keywords: Channel estimation, compressed sensing, reconfigurable intelligent surface, mm-wave mimo communication, sparse channel

L. J. Ge, Z. C. Wang, L. Qian, P. Wei [references] [full-text] [DOI: 10.13164/re.2023.0197] [Download Citations]
Sparsity Adaptive Compressive Sensing based Two-stage Channel Estimation Algorithm for Massive MIMO-OFDM Systems

Massive multi-input multioutput (MIMO) coupled with orthogonal frequency division multiplexing (OFDM) has been utilized extensively in wireless communication systems to investigate spatial diversity. However, the increasing need for channel estimate pilots greatly increases spectrum consumption and signal overhead in massive MIMO-OFDM systems. This paper proposes a two-stage channel estimation algorithm based on sparsity adaptive compressive sensing (CS) to address this issue. To estimate the channel state information (CSI) for pilot locations in Stage 1, we provide a geometry mean-based block orthogonal matching pursuit (GBMP) method. By calculating the geometric mean of the energy in the support set of the channel response, the GBMP method, when compared to conventional CS methods, can drastically reduce the number of iterations and effectively increase the convergence rate of channel reconstruction. Stage 2 involves estimating the CSI for nonpilot locations using a time-frequency correlation interpolation method, which can increase the accuracy of the channel estimation and is dependent on the estimated results from Stage 1. According to the simulation results, the proposed two-stage channel estimation algorithm greatly reduces the running time with little error performance degradation when compared to traditional channel estimating algorithms.

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Keywords: Channel estimation, compressive sensing, MIMOOFDM, time-frequency correlation

M. Kumar, A. J. Mondal [references] [full-text] [DOI: 10.13164/re.2023.0207] [Download Citations]
An Improved Latch for SerDes Interface: Design and Analysis under PVT and AC Noise

Digital subsystem prefers CMOS process, but it is difficult to manage speed and average power (Pavg) trade-off in each era with power supply voltage (Vdd) scaling. Current mode logic (CML) has emerged as an alternative to design the fundamental block of a SerDes, namely, the latch. However, available CML circuits consume significant Pavg and suffer from rapid input slewing. Typically, fast switching inputs enable current flow to effective supply voltage VP and overcharges output. In fact, VP is different than externally applied Vdd and oscillates with time as and when an abrupt current is drawn. This affects delay td and introduces jitter. The topic presents a new latch for SerDes interface using a new current steering circuit and coupled to a power delivery network (PDN). The significant point is to attain an almost constant td in comparison to conventional designs while the Vdd changes. The post-layout results at 0.09-μm CMOS and 1.1 V Vdd indicate that the Pavg and td are 339.5 µW and 61.9 ps, respectively, at 27OC. Surprisingly, the td variation is noted to be minimum and the power supply noise induced jitter is around 1.5 ns when VP close to the circuit varies due to sudden current.

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Keywords: PDN, latch, figure of merit, Monte Carlo, output noise, jitter

D. Zhang, X. Chen, S. Qi, H. Zhang [references] [full-text] [DOI: 10.13164/re.2023.0221] [Download Citations]
SIW-Based Frequency-Tunable Self-Oscillating Active Integrated Antenna

A frequency-tunable self-oscillating active integrated antenna (AIA) mainly composed of active circuit and 1×2 substrate integrated waveguide (SIW) antenna array is proposed in this paper. Manipulating bias voltage to the varactors loaded on SIW antenna could offer electronic control of oscillation frequency. The DC bias circuit of the varactors integrated in SIW cavity can provide compact structure. Due to the load effect of the high Q SIW cavity, the designed antenna exhibits low phase noise. According to the measured results, the effective isotropic radiated power (EIRP) ranges from 4.4 to 12.9 dBm which is superior to previous reports with the frequency tuning range of about 20 MHz. The phase noise is -92.7 dBc/Hz at 100 kHz offset. The measured results also show that the cross-polarization levels are almost 20 dB lower than the co-polarized one in the main beam direction at 5.698 GHz.

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Keywords: Substrate Integrated Waveguide (SIW), Active Integrated Antenna (AIA)

B. N. Tran-Thi, T. T. Nguyen-Ly, T. Hoang [references] [full-text] [DOI: 10.13164/re.2023.226] [Download Citations]
Further Improvements in Decoding Performance for 5G LDPC Codes Based on Modified Check-Node Unit

One of the most important units of Low-Density Parity-Check (LDPC) decoders is the Check-Node Unit. Its main task is to find the first two minimum values among incoming variable-to-check messages and return check-to-variable messages. This block significantly affects the decoding performance, as well as the hardware implementation complexity. In this paper, we first propose a modification to the check-node update rule by introducing two optimal offset factors applied to the check-to-variable messages. Then, we present the Check-Node Unit hardware architecture which performs the proposed algorithm. The main objective of this work aims to improve further the decoding performance for 5th Generation (5G) LDPC codes. The simulation results show that the proposed algorithm achieves essential improvements in terms of error correction performance. More precisely, the error-floor does not appear within Bit-Error-Rate (BER) of 10^(-8), while the decoding gain increases up to 0.21 dB compared to the baseline Normalized Min-Sum, as well as several state-of-the-art LDPC-based Min-Sum decoders.

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Keywords: Bit error rate, CNU architecture, LDPC codes, low computational complexity, Min-Sum algorithm, Normalized Min-Sum

M. Y. Onay, O. Ertug [references] [full-text] [DOI: 10.13164/re.2023.0236] [Download Citations]
Ambient Backscatter Communication Based Cooperative Relaying for Heterogeneous Cognitive Radio Networks

In this paper, a new network model is proposed to improve the performance of the secondary channel in cognitive radio networks (CRNs) based ambient backscatter communication systems. This model is considered as a cooperative system with multi-secondary transmitter (ST) and multi-relay. The ST backscatters data to both the secondary receiver (SR) and relay. Also it harvests energy from the signal emitted by the primary transmitter (PT) during the busy period. The relay activated by the ST user forwards the information from ST to SR. During the idle period, the PT broadcast is interrupted and ST also performs active data transmission using the energy it has harvested. We aim to maximize the number of data transmitted to the SR. Therefore, how long the ST will perform backscattering, energy harvesting and active data transmission is a problem to be solved. In such cooperative systems with multiple users, the solution of the problem becomes more complex. Therefore, the system model has been mathematically modeled and transformed into an optimization problem, considering that users are transmitting data using time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) techniques. Numerical results showed that higher data rates were achieved in NOMA. Additionally, It has been seen that the proposed model performs better when compared to the existing approaches in the literature, where the ST can only harvest energy and transmit data actively or only transmit data with ambient backscatter communication.

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Keywords: Ambient backscatter communication, cognitive radio networks, cooperative system, relay, energy harvesting, convex optimization

K. Chen, M. Gu, Z. Chen [references] [full-text] [DOI: 10.13164/re.2023.0248] [Download Citations]
Radar-Based Human Motion Recognition by Using Vital Signs with ECA-CNN

Radar technologies reserve a large latent capacity in dealing with human motion recognition (HMR). For the problem that it is challenging to quickly and accurately classify various complex motions, an HMR algorithm combing the attention mechanism and convolution neural network (ECA-CNN) using vital signs is proposed. Firstly, the original radar signal is obtained from human chest wall displacement. Chirp-Z Transform (CZT) algorithm is adopted to refine and amplify the narrow band spectrum region of interest in the global spectrum of the signal, and accurate information on the specific band is extracted. Secondly, six time-domain features were extracted for the neural network. Finally, an ECA-CNN is designed to improve classification accuracy, with a small size, fast speed, and high accuracy of 98%. This method can improve the classification accuracy and efficiency of the network to a large extent. Besides, the size of this network is 100 kb, which is convenient to integrate into the embedded devices.

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Keywords: Human motion recognition, vital signs, Efficient Channel Attention enabled Convolutional Neural Network (ECA-CNN), radar

B. Velichkovska, A. Cholakoska, V. Atanasovski [references] [full-text] [DOI: 10.13164/re.2023.0256] [Download Citations]
Machine Learning Based Classification of IoT Traffic

With the rapid expansion and widespread adoption of the Internet of Things (IoT), maintaining secure connections among active devices can be challenging. Since IoT devices are limited in power and storage, they cannot perform complex tasks, which makes them vulnerable to different types of attacks. Given the volume of data generated daily, detecting anomalous behavior can be demanding. However, machine learning (ML) algorithms have proven successful in extracting complex patterns from big data, which has led to active applications in IoT. In this paper, we perform a comprehensive analysis, including 4 ML algorithms and 3 neural networks (NNs), and propose a pipeline which analyzes the influence data reduction (loss) has on the performance of these algorithms. We use random undersampling as a data reduction technique, which simulates reduced network traffic data. The pipeline investigates several degrees of data loss. The results show that models trained on the original data distribution obtain accuracy that verges on 100%. XGBoost performs best from the classic ML algorithms. From the deep learning models, the 2-layered NN provides excellent results and has sufficient depth for practical application. On the other hand, when the models are trained on the undersampled data, there is a decrease in performance, most notably in the case of NNs. The most prominent change is seen in the 4-layered NN, where the model trained on the original dataset detects attacks with a success of 93.53%, whereas the model trained on the maximally reduced data has a success of only 39.39%.

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Keywords: Machine learning, deep learning, Internet of Things (IoT), intrusion detection, traffic modelling

B. Cseppento, A. Retzler, Z. Kollar [references] [full-text] [DOI: 10.13164/re.2023.0264] [Download Citations]
Optimization of the Crest Factor for Complex-Valued Multisine Signals

Multisine signals are commonly used in the measurement of dynamic systems and wireless channels. For optimal measurements with a high dynamic range, a low Crest Factor (CF) excitation signal is required. In this paper, a modified approach to optimize the crest factor for complex-valued multisine signals is presented. The approach uses a nonlinear optimization method where the real and imaginary parts can also be optimized for low CF. Furthermore, extensions of the real-valued multisine CF optimization methods are presented for complex-valued cases. The proposed methods are validated and compared using simulations. Based on the results it is shown that the novel approach can lead to more optimal signal design and lower CF compared to other techniques for complex-valued multisine signals.

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Keywords: Multisine, crest factor, PAPR, optimization, complex signal, channel estimation, OFDM

L. Kirasamuthranon, P. Wardkein, J. Koseeyaporn [references] [full-text] [DOI: 10.13164/re.2023.0273] [Download Citations]
Coding and Coherent Decoding techniques for Continuous Single Slope Cyclic Shift Chirp Signal

Chirp signals are currently widely used in broadband and spread spectrum communications due to their advantageous features, such as immunity to fading noise, low power consumption, consistent long-range transmission, and constant bandwidth. As a result, they are applied at the physical layer of the Internet-of-Things (IoT). This study proposes two techniques for encoding and decoding 4-cyclic shift chirp symbols, based on addition and subtraction operations. The proposed techniques have simple structures that can be easily implemented using analog circuits. The proposed encoding techniques reveal the relationship between cyclic-shift chirp symbols and pulse modulating signals (PWM, PPM, and PAM), which has rarely been discussed in prior research. Moreover, the circuits for encoding and decoding of the proposed techniques are implemented by discrete commercial devices at low frequency (25-35kHz) which is suitable for sonar and communication under water. However, this proposed technique is not limited to only low frequency but can also be used in high-frequency bands. Experimental and simulation results also show good agreement to theoretical analysis.

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Keywords: chirp signal, chirp symbol, cyclic-shift chirp modulation and demodulation, chirp signal spectrum, chirp spread spectrum.

S. Xiao, H. Tao, X. Shen, L. Zhang, M. Hu [references] [full-text] [DOI: 10.13164/re.2023.0287] [Download Citations]
Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio

Multitarget tracking (MTT) for image processing in low signal-to-noise ratio (SNR) is difficult and computationally expensive because the distinction between the target and the background is small. Among the current MTT algorithms, Random Finite Set (RFS) based filters are computationally tractable. However, the probability hypothesis density (PHD) filter, despite its low computational complexity, is not suitable for MTT in low SNR. The generalized labeled multi-Bernoulli (GLMB) filter and its fast implementation are unsuitable for realtime MTT due to their high computational complexity. To achieve realtime MTT in low SNR, a joint PHD filter and Hungarian assignment algorithm is first proposed in this work. The PHD filter is used for preliminary tracking of targets while the Hungarian assignment algorithm is employed to complete the association process. To improve the tracking performance in low SNR, a new track must undergo a trial period and a valid track will be terminated only if it is not detected for several frames. The simulation results show that the proposed MTT algorithm can achieve stable tracking performance in low SNR with small computational complexity. The proposed filter can be applied to MTT in low SNR that require realtime implementation.

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Keywords: Hungarian assignment algorithm, PHD filter, multitarget tracking (MTT), low signal-to-noise ratio (SNR)