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Radioengineering

Radioeng

Proceedings of Czech and Slovak Technical Universities

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June 2025, Volume 34, Number 2 [DOI: 10.13164/re.2025-2]

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B. Huang, Z. Wang, J. Chen, B. Zhou, Y. Zhu, Y. Liu [references] [full-text] [DOI: 10.13164/re.2025.0181] [Download Citations]
Research on Site Selection and Capacity Determination of Electric Vehicle Public Charging Stations by Integrating K-Means++ and Improved RODDPSO

To address the suboptimal spatial distribution and low comprehensive utilization of existing electric vehicle (EV) public charging infrastructure, this study proposes an innovative charging station placement and capacity determination methodology integrating K-Means++ clustering with an enhanced RODDPSO variant. Building upon conventional K-Means and RODDPSO frameworks, we develop an improved hybrid algorithm incorporating three critical advancements: 1) an adaptive mutation mechanism within the RODDPSO architecture to enhance global search capabilities and prevent premature convergence; 2) synergistic optimization of K-Means++ cluster centroids through the enhanced RODDPSO operator; and 3) a novel cluster validation metric based on real-world utilization patterns. The proposed methodology effectively resolves the inherent limitations of conventional K-Means approaches, particularly their sensitivity to initial centroid selection and tendency toward local optima. Empirical validation through a case study of Nanjing's charging infrastructure demonstrates the algorithm's superior performance: stations sited using the proposed hybrid method exhibit 63.8% greater spatial correlation with high-utilization zones (>15% operational utilization) compared to baseline K-Means implementations. The advancements provide both methodological contributions to spatial optimization algorithms and practical insights for urban EV infrastructure planning.

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Keywords: K-Means++, variation randomly occurring distributedly delayed particle swarm optimization, public charging station, siting and capacity determination

T. Sivaranjani, B. Sasikumar, G. Sugitha [references] [full-text] [DOI: 10.13164/re.2025.0195] [Download Citations]
Rectified Adam Optimizer and LSTM with Attention Mechanism for ECG-Based Multi-class Classification of Cardiac Arrhythmia

Cardiac Arrhythmia (CA) is one of the most prevalent cardiac conditions and prime reasons for sudden death. The current CA detection methods face challenges in noise removal, R-peak detection, and low-level feature selection, which can impact diagnostic accuracy and signal stability. The research aims to develop an effective framework for detecting and classifying CA using advanced signal processing, feature extraction, feature selection, and classification for reliable medical diagnosis. The input electrocardiogram (ECG) signals are processed using hybrid noise reduction techniques such as cascaded variable step size normalized least mean square and sparse low-rank filter. The complex and high-level features are extracted using higher-order spectral energy distributed image, wavelet transform, and R-wave peak to R-wave peak interval to enhance the representation of cardiac data. Recursive feature elimination is applied to select the most relevant diagnostic features and the Rectified Adam optimizer is used to fine-tune parameters to achieve better training stability. The model integrates long-term memory with an attention mechanism to enhance the classification performance of arrhythmia detection. Simulation results demonstrate that the proposed model achieves 99.40% accuracy, outperforming existing models and showing its efficiency in classifying CA for better diagnosis and early treatments.

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Keywords: Cardiac arrhythmia, electrocardiogram, sparse low-rank filter, recursive feature elimination, long short-term memory, rectified Adam optimizer, attention mechanism

G. Ma, C. Xu, Z. Xu, X. Song [references] [full-text] [DOI: 10.13164/re.2025.0206] [Download Citations]
An Improved Small Target Detection Algorithm Based on YOLOv8s

Due to challenges such as the small size of targets, complex backgrounds, limited feature extraction capa-bilities, and frequent false positives and false negatives, traditional detection algorithms often perform poorly in small object detection tasks. To address these challenges, this pa¬per proposes an enhanced small object detection algorithm, SOD-YOLO, based on YOLOv8s. First, the S_C2f_CAFM module is integrated into the feature extraction network, enabling the effective capture of fine-grained local features and broad contextual information, while simultaneously reducing model parameters and computational complexity. Second, in the feature fusion stage, the redesigned bidirectional feature pyramid network employs a spatial context awareness module to extract key features, adding a top-down path to optimize feature fusion and enhance discriminative information. In the Neck section, the D_C2f_MSPA module is introduced, which, while being lightweight, accurately models channel dependencies in feature maps, effectively reducing both false positives and false negatives for small objects. Finally, the inclusion of Normalized Wasserstein Distance (NWD) further improves detection accuracy and reduces the model’s sensitivity to small positional deviations in small objects. Experimental results on the DOTAv1.0, VisDrone2019, and TT100K datasets confirm that SOD-YOLO achieves excellent performance, demonstrating the effectiveness of the modifications made to the original YOLOv8 model.

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Keywords: YOLOv8, small object detection, attention mechanism, feature fusion, loss function

R. Bozovic, V. Orlic, G. Kekovic [references] [full-text] [DOI: 10.13164/re.2025.0224] [Download Citations]
Artificial Bias Induction in Fourth-Order Cumulants Based Automatic Modulation Classification Algorithm in AWGN and Multipath Propagation Channel

Automatic modulation classification (AMC) represents a wide used technique for modulation format recognition of signals considered to be a priori unknown. Due to the low algorithm and hardware complexity, AMC algorithms based on fourth-order cumulants are still very popular. Presence of bias in standard cumulants estimated values of real signals constellations has positive impact on classification score for distinguishing real from complex signals. Therefore, one new approach in AMC is proposed in this paper, with focus on manipulation with theoretical expected cumulant values of real signals constellations, assuming artificially introduced bias will improve AMC performance. Artificial bias induction is done through modifications of standard cumulants mathematical formula. Performance of modified and standard fourth-order cumulants based AMC algorithms were explored in context of real and complex signals constellations. This was done through Monte Carlo simulations in propagation conditions which included Additive White Gaussian Noise (AWGN) and multipath propagation channel with known and unknown impulse response. Evaluation was done through the probability of correct classifications. Presented numerical results confirmed superiority of algorithm based on artificial bias induction in classification of real and complex signals, in each considered propagation scenarios, especially in a radio environment with lower signal-to- noise ratio (SNR) values. The remarkable AMC performance enhancements are up to 25%.

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Keywords: AMC, AWGN, bias, Binary Phase Shift Keying (BPSK), channel impulse response, cumulants, multipath, Quadrature Amplitude Modulation (QAM)

B. ZHANG, R. YI, Z. WANG, J. PU, . Y. SUN [references] [full-text] [DOI: 10.13164/re.2025.0234] [Download Citations]
An Efficient Optimization Algorithm for Measurement Matrix Based on SVD and Improved Nesterov Accelerated Gradient

In compressed sensing, a measurement matrix having low coherence with a specified sparse dictionary has been shown to be advantageous over a Gaussian random matrix in terms of reconstruction performance. In this paper the problem of efficiently designing the measurement matrix is addressed. The measurement matrix is designed by iteratively minimizing the difference between the Gram matrix of the sensing matrix and a target Gram matrix. A new target Gram matrix is designed by applying singular value decomposition to the sensing matrix and utilizing entry shrinking in the Gram matrix, leading to lower mutual coherence indicators. An improved Nesterov accelerated gradient algorithm is derived to update the measurement matrix, which can improve the convergence behavior. An efficient optimization algorithm for measurement matrix is proposed on the basis of alternating minimization. The experimental results and analysis show that the proposed algorithm performs well in terms of both computational complexity and reconstruction performance.

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Keywords: Compressed sensing, equiangular tight frame, singular value decomposition, mutual coherence, Nesterov accelerated gradient

Fathe Jeribi, R. John Martin [references] [full-text] [DOI: 10.13164/re.2025.0243] [Download Citations]
Adaptive Resource Optimization for IoT-Enabled Disaster-Resilient Non-Terrestrial Networks using Deep Reinforcement Learning

The increasing deployment of IoT devices across sectors such as agriculture, transportation, and infrastructure has intensified the need for connectivity in remote and non-terrestrial regions. Non-terrestrial networks (NTNs), which include maritime and space platforms, face unique challenges for IoT connectivity, including mobility and weather conditions, which are critical for maintaining quality of service (QoS), especially in disaster management scenarios. The dynamic nature of NTNs makes static resource allocation insufficient, necessitating adaptive strategies to address varying demands and environmental conditions during disaster management. In this paper, we propose an adaptive resource optimization approach for disaster-resilient IoT connectivity in non-terrestrial environments using deep reinforcement learning. Initially, we design the chaotic plum tree (CPT) algorithm for clustering IoT nodes to maximize the number of satisfactory connections, ensuring all nodes meet sustainability requirements in terms of delay and QoS. Additionally, unmanned aerial vehicles (UAVs) are used to provide optimal coverage for IoT nodes in disaster areas, with coverage optimization achieved through the non-linear smooth optimization (NLSO) algorithm. Furthermore, we develop the multi-variable double deep reinforcement learning (MVD-DRL) framework for resource management, which addresses congestion and transmission power of IoT nodes to enhance network performance by maximize successful connections. Simulation results demonstrate that our MVD-DRL approach reduces the average end-to-end delay by 50.24% compared to existing approaches. It also achieves a throughput improvement of 13.01%, an energy consumption efficiency of 68.71%, and an efficiency in the number of successful connections of 17.51% compared to current approaches.

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Keywords: Internet of Things (IoT), Disaster Management, Resource Optimization, Deep Reinforcement Learning, Non-Terrestrial Network

F. Titel, M. Belattar, M. Lashab, R. Abd-Alhameed [references] [full-text] [DOI: 10.13164/re.2025.0258] [Download Citations]
Aerial RIS Aided NOMA Networks with Optimized Secrecy Metrics Performance

Reconfigurable Intelligent Surface (RIS) technology is a promising technique for enhancing the performance of reconfigurable next-generation wireless networks. In this paper, we investigate the physical layer security of the downlink in RIS-aided non-orthogonal multiple access (NOMA) networks in the presence of an eavesdropper. To characterize the network performance, the expected value of the new channel statistics is derived for the reflected links in the case of Rayleigh fading distribution. Furthermore, the performance of the proposed network is evaluated in terms of the secrecy outage probability (SOP) and the strictly positive secrecy capacity (SPSC). To optimize these metrics, we employ the multi-objective artificial vultures optimization algorithm (MOAVOA), using the power allocation coefficients of the nearby and distant users as key parameters. Two case studies are considered in simulation: perfect channel state information (CSI) and imperfect CSI.

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Keywords: Reconfigurable Intelligent Surfaces (RIS), NOMA networks, Secrecy Outage Probability (SOP), Strictly Positive Secrecy Capacity (SPSC), Multi-objective Artificial Vultures Optimization Algorithm (MOAVOA), Multi-Objective Particle Swarm Optimization (MOPSO)

C. L. Zhao, F. F. Yang, H. J. Xu [references] [full-text] [DOI: 10.13164/re.2025.0273] [Download Citations]
Optimized Design of Distributed Generalized Reed-Solomon Coded Generalized Spatial Modulation

To meet the need of modern society for more reliable and efficient communications, this paper applies the generalized spatial modulation (GSM) technique in the distributed generalized Reed-Solomon (GRS) coding to propose a novel distributed GRS coded GSM (DGRSC-GSM) system. In the proposed system, the relay uses the concept of information symbol selection. For different information symbol selections, the destination generates different equivalent linear block codes. To achieve the optimized system design, the optimal information symbol selection (OISS) algorithm by complete search in the relay is proposed to make the destination obtain the best code having the optimal weight distribution. When the GRS codes at the source and relay have large information lengths, the OISS algorithm possesses high complexity. Thus, a low-complexity optimized information symbol selection (LC-OISS) algorithm by incomplete search is put forward. For realizing the effective retrieve of the overall source information, a new joint decoding algorithm in the destination is designed. The results show the superior performance of the proposed DGRSC-GSM system under the OISS and LC-OISS algorithms over that under the random information symbol selection algorithm. Also, the proposed system outperforms the non-cooperative system by 2.6 dB and exhibits more than 2 dB improvement over existing systems.

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Keywords: Generalized Spatial Modulation (GSM), Generalized Reed-Solomon (GRS) codes, distributed channel coding, optimized information symbol selection

S. Rajesh Kumar, V. Gomathi, K. Vivekrabinson [references] [full-text] [DOI: 10.13164/re.2025.0289] [Download Citations]
Blockchain-Enabled Searchable Encryption for Secure and Efficient Sharing of IoHT-Generated Electronic Medical Records in Cloud-Based Healthcare

Protecting the security of data generated by wearables and monitoring devices is critical in smart wards, especially when healthcare schemes use cloud storage services to save patients' Electronic Medical Records (EMRs). These devices operate in wireless communication environments, where data integrity and transmission security are vital. Despite the fact that encryption helps protect information, it often reduces the benefits of sharing the information generated using Internet of Health Things (IoHT) devices with others. As individuals increasingly share their EMRs with third parties, developing an effective searchable encryption framework for sharable EMRs remains a crucial task. Furthermore, cloud-based access control might result in heavily centralized control. To address this, we proposed a blockchain-assisted technique for sharable EMRs that incorporates a searchable encryption scheme compatible with a resource-constrained wireless system that does not require any secure channel. The encrypted EMRs are saved in the cloud, while the encoded keyword indexes are kept on the blockchain, assuring tamper resistance, integrity, and accountability of the encrypted indexes. Our technique also enables exact recovery of encrypted EMRs using a multi-keyword search, removing the necessity for third-party verification. Compared to prior searchable encryption systems, our technique reduces storage costs while increasing computational efficiency. Furthermore, our system is immune to keyword-guessing attacks, a must-needed one that many previous solutions fail to address wireless medical data security.

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Keywords: Blockchain, Electronic Medical Records (EMR), Internet of Health Things (IoHT), Keyword-Guessing Attacks (KGA), multi-keyword search, Searchable Encryption (SE)

M. Adamec, M. Turcanik [references] [full-text] [DOI: 10.13164/re.2025.0303] [Download Citations]
Comparative Analysis of Input Image Characteristics in Convolutional Neural Network-based Signature Detection

The detection of malware represents a primary concern in contemporary computer security and is therefore imperative for the protection of systems and data integrity. This research presents an innovative approach to comparing diverse input image formats with the objective of identifying the optimal methodology for detecting specific malware-related signatures using convolutional neural networks (CNN), which have been specifically developed by the authors for this purpose. Subsequently, machine code instructions are generated and then converted into four distinct image format options. The four image formats, namely 1xN fixed, 1xN scalable, NxN fixed, and NxN scalable, are subsequently employed for the training of the CNN. The study assesses the formats in question in terms of training time, accuracy, and computational complexity. The results demonstrate that the NxN scalable format exhibits the highest accuracy with accelerated training times in comparison to other formats. Furthermore, the scalable format necessitates only 25% of the original pixel count for a 96% classification success rate. The utilization of the NxN scalable format for machine code instruction representation results in enhanced accuracy, accelerated training, and a considerable reduction in pixel usage, indicating a promising avenue for optimizing the efficiency of malware detection.

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Keywords: Signature detection, CNN malware detection, machine code visualization, static analysis, interpolation

B. Mehra, A. Datar [references] [full-text] [DOI: 10.13164/re.2025.0313] [Download Citations]
Enhancing WSN Lifespan Based on Efficient-Energy Management Approach for Cluster Head Selection in IoT Application

Wireless sensor networks (WSNs) are one of the most important components in the connected world i.e. Internet of Things (IoT). WSN is a network of distributed sensor nodes that communicate wirelessly to transmit and receive real-time data. These sensor nodes play a crucial role in monitoring various environments, enabling smarter decision-making and improving efficiency across numerous applications. This paper presents an energy-efficient protocol based on low energy adaptive clustering hierarchy (LEACH) for improving the lifetime of WSN. The proposed method modifies the basic LEACH protocol and incorporates the factors of residual energy of the network, number of neighbor nodes, average energy of the network, and threshold distance between the nodes and base station. The proposed work compares the result with the existing methods and has shown the improvement in the network performance parameter metrics. The simulation results show an improvement in the network lifetime due to better energy management, thus increasing the number of data packet transfers.The proposed method has shown improvement by 13% over the first dead (FD) node of EEBC-LEACH, 5% improvement over half dead (HD) node of PEGASIS, and 3% improvement over all dead (AD) nodes of FBCR-LEACH.

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Keywords: Internet of Things (IoT), Wireless Sensor Network (WSN), LEACH, Cluster Head (CH) selection, proximity, network energy

Q. Z. Fang, S. B. Gu, J. G. Wang, L. L. Zhang [references] [full-text] [DOI: 10.13164/re.2025.0324] [Download Citations]
A Feature Dynamic Enhancement and Global Collaboration Guidance Network for Remote Sensing Image Compression

Deep learning-based remote sensing image compression methods show great potential, but traditional convolutional networks mainly focus on local feature extraction and show obvious limitations in dynamic feature learning and global context modeling. Remote sensing images contain multiscale local features and global low-frequency information, which are challenging to extract and fuse efficiently. To address this, we propose a Feature Dynamic Enhancement and Global Collaboration Guidance Network (FDEGCNet). First, we propose an Omni-Dimensional Attention Model (ODAM), which dynamically captures the key salient features in the image content by adaptively adjusting the feature extraction strategy to enhance the model’s sensitivity to key information. Second, a Hyperprior Efficient Attention Model (HEAM) is designed to combine multi-directional convolution and pooling operations to efficiently capture cross-dimensional contextual information and facilitate the interaction and fusion of multi-scale features. Finally, the Multi-Kernel Convolutional Attention Model (MCAM) integrates global branching to extract frequency domain context and enhance local feature representation through multi-scale convolutions. The experimental results show that FDEGCNet achieves significant improvement and maintains low computational complexity regarding image quality evaluation metrics (PSNR, MSSSIM, LPIPS, and VIFp) compared to the advanced compression models. Code is available at https://github.com/shiboGu12/FDEGCNet

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Keywords: Remote sensing image compression, convolutional networks, multiscale convolution, attention model, multiscale local features, global low-frequency information

H. Zafor, T. A. Sheikh, N. Mazumdar, A. Nag [references] [full-text] [DOI: 10.13164/re.2025.0342] [Download Citations]
An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network

Recently, unmanned aerial vehicles (UAVs) have become more popular due to their ease of adaptability and capability to carry out a variety of activities, including the delivery of services, monitoring and surveillance in military and civilian contexts. One of the most significant challenges in UAV operation is ensuring maximum network lifetime and management of their limited battery life. To solve these problems, we have proposed an effective routing algorithm that finds the best route to minimize UAV routing time and extend network lifetime. This is performed using the Ant Colony Optimization with Local Search (ACO-LS) algorithm for data collection from the clustered IoT network by UAV to ensure maximum network lifetime. It solved the routing problem in the minimum time in the presence of multiple charging stations and optimized the routing path. The simulation was carried out using various performance metrics: network lifetime (NT), energy consumption (EC), number of alive nodes (NAN), and packet delivery percentage (PDP). These parameters were compared with some existing algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) and found that our proposed algorithm performs better in terms of higher NT, less EC, more NAN, and higher PDP than the existing algorithms ACO, PSO, and GA.

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Keywords: Internet of Things (IoT), Data Collection (DC), Unmanned Aerial Vehicles (UAVs), Ant Colony Optimization (ACO), Local Search (LS), Particle-Swarm Optimization (PSO), Genetic Algorithm (GA).

S. N. Srinivasan, P. Suresh Kumar, S. Duraisamy [references] [full-text] [DOI: 10.13164/re.2025.0353] [Download Citations]
Performance Analysis of Relay Model-based Energy Harvesting in CR-WBAN

An emerging technique was introduced to extend the network lifetime of energy-limited relay nodes in wireless networks. In this paper, the spectral and energy efficiency of Wireless Body Area Networks (WBAN) is investigated. A novel Relay model-based WBAN with Energy Harvesting for enhancing spectrum utilization using Cognitive Radio (CR) technology. This approach involves the surrounding of RF signals, allowing the nodes to gather energy and process data within a WBAN, specifically for medical monitoring purposes enabling the coexistence of diverse implanted devices while maintaining their QoS. It facilitates the simultaneous operation of distinct sensor nodes for primary and secondary networks in on-body CR-WBAN, categorizing nodes based on medical and non-medical applications. The proposed protocols designed for energy harvesting notably Time Switching System (TSS) and Power-Splitting System (PSS) are utilized to enable the cooperation of secondary nodes with the primary network, allowing them to access the spectrum in exchange. The numerical analysis of proposed overlay CR-WBAN in aspects of outage probability, coverage analysis, throughput analysis, and energy efficiency performances considering a delay-limited scenario are examined. The numerical simulations confirm the validity of all the developed theoretical analyses and underscore the efficacy of the considered scheme by verifying using Monte Carlo simulations.

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Keywords: Wireless Body Area Networks (WBAN), Energy Harvesting (EH), Time Switching System (TSS), Power Splitting System (PSS), outage probability, cognitive radio

G. Sugitha, R. Vasanthi, A. Solairaj, A. V. Kalpana [references] [full-text] [DOI: 10.13164/re.2025.0366] [Download Citations]
SeCo2: Secure Cognitive Semantic Communication in 6G-IoT Networks Using Key-Policy Attribute-Based Encryption and Elliptic Curve Cryptography

Secure and efficient data transmission is crucial for maintaining seamless system operations and user trust in the rapidly evolving Internet of Things (IoT) environments. However, IoT networks consistently suffer from data integrity breaches, security vulnerabilities at various network layers, and a high computational cost. Bridging the gap between IoT applications and network infrastructure is essential to addressing these issues. This paper introduces SeCo2, a secure cognitive semantic communication framework for 6G-IoT networks. The framework incorporates a blockchain-based system to provide a secure and privacy-preserving data transmission mechanism. Data preprocessing is conducted using the IoT-Sense dataset, and then encryption is done through a hybrid combination of Key-Policy Attribute-Based Encryption (KP-ABE) and Elliptic Curve Cryptography (ECC). Access control and data permissions are implemented via smart contracts to ensure secure transmission. Additionally, a blockchain security layer utilizing Proof of Stake with Fixed Staking Amounts (PoS-FSA) enhances network security and energy efficiency. For further protection of data integrity, tamper-proof provenance logging prevents unauthorized tampering. Experimental results demonstrate ultra-low latency data transmis¬sion (in the microsecond range), with a transmission delay as low as 0.003001 s for data sizes ranging from 1 GB to 50 GB, and a network security rate of 98%, ensuring more reliable and privacy-preserving IoT ecosystems.

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Keywords: 6G-IoT, blockchain, cognitive semantic communication, elliptic curve cryptography, key-policy attribute-based encryption