International Journal of Communication Systems最新文献

An integrated terrestrial-satellite network with UAV relay nodes can offer augmented connectivity, especially for users in shadowed areas like suburban and mountainous areas. With the advent of sixth-generation (6G) wireless communication, non-terrestrial networks (NTN) shall evolve as the Center of attention to achieve seamless global coverage with better data transmission rates and ultra-reliable low-latency communication. Non-orthogonal multiple access (NOMA) also enhances spectral efficiency with large-scale connectivity through power-domain multiplexing. In this work, the performance of a satellite-UAV-terrestrial integrated network based on the amplify-and-forward (AF) relaying protocol at the UAV relay node is analyzed. Using Monte Carlo simulations, it is shown that the proposed system effectively alleviates outage probability compared to a no-relay system. Moreover, deployment of a cluster of UAV relays with partial relay selection, which maximizes performance and computational complexity, enhances reliability through the selection of the best-performing relay path based on channel conditions. Imperfect channel state information (CSI) and erroneous successive interference cancellation (SIC) are included in the analysis for practical implementation. Furthermore, integration of NTN with NOMA opens new routes to enhance spectral efficiency and coverage in 6G networks. The results confirm that the proposed system offers an efficient and effective communication system with superior performance under different parameter variations while addressing major challenges in next-generation wireless networks.

Vehicular ad-hoc networks (VANETs) play a critical role in designing intelligent transportation systems (ITS) to enable spontaneous communication among vehicles (V2V) and between vehicles and roadside units (V2I). The VANETs issue revolves around providing safe, effective communication within the evolving scenario when security and performance are compromised through malicious attacks, node misbehavior, and forgery of information. The review of this study is to evaluate various trust-based models in VANETs, analyze their effectiveness in improving security and efficiency, identify challenges in real-world implementations, and propose recommendations for enhancing trust management to ensure reliable and secure vehicular communication. Types of trust models include reputation-based, behavior-based, and hybrid models. Methodologies like machine learning and algorithms like trust propagation are used. Findings show that trust-based models in VANETs improve security by detecting malicious nodes, enhancing communication efficiency, and ensuring reliable data exchange. They balance security and performance for optimal network operation. Future research in trust-based models for VANETs could focus on integrating machine learning for dynamic trust evaluation, enhancing scalability, developing robust attack detection mechanisms, and improving real-time decision-making for secure vehicular communication.

This paper addresses the limitations of current swarm intelligence optimization algorithms, which often suffer from poor solution accuracy and slow convergence when applied to the challenges of sidelobe level suppression and null control for the pattern synthesis of sparse linear array antennas. To overcome these issues, we propose the fox optimization algorithm based on cubic chaotic mapping, adaptive spiral flight, and Gaussian stochastic wandering strategy (CSGFOX). First, cubic chaotic mapping is used to initialize the population, enhancing its diversity. The spiral flight strategy is then employed to adaptively update the fox's position, thereby improving the algorithm's global exploration capability. Finally, a Gaussian stochastic wandering strategy is incorporated, balancing the algorithm's local search ability with its global search capability. To validate the performance of the proposed algorithm, we evaluated it using benchmark functions. Additionally, the algorithm is applied to the optimization of sparse linear array antennas. The simulation results demonstrate that the proposed algorithm exhibits faster convergence and higher solution accuracy in suppressing the sidelobe level and controlling the nulls of the directional pattern of linear array antennas, compared to traditional optimization methods, including the fox algorithm, genetic algorithm, gray wolf algorithm, whale algorithm, and particle swarm optimization algorithm. At the same solution quality threshold, CSGFOX converged 61.5% faster than the original algorithm, achieving a 100% success rate and significantly outperforming FOX (95%) and other algorithms (10%–65%), thus confirming the proposed algorithm's effectiveness.

Wireless body sensor networks (WBSNs) are increasingly used in healthcare for remote monitoring of patients. Although these systems improve access to medical care, they also face serious challenges related to data security and patient authentication. This study proposes a lightweight and secure authentication framework based on a Three-Tier Secure Message Authentication Code (TTSMAC) protocol. The framework combines three key techniques: Factorized RSA (FRSA) for efficient key generation, Length Pearson Hashing (LPH) for secure token management, and Dual Secret Key Elliptic Curve Cryptography (DSK-ECC) for protecting stored data. Experimental results showed that the proposed framework reduces encryption/decryption time, lowers key setup overhead, and achieves higher throughput compared with existing methods. Also, the performance evaluations showed substantial improvements in encryption/decryption times and throughput, demonstrating the framework's suitability for resource-constrained, battery-powered wearable sensors. Overall, the framework enhances security, maintains patient data privacy, and ensures reliable authentication for WBSN-based healthcare applications.

Wireless sensor networks (WSNs) are widely utilized in remote and inaccessible environments to monitor and collect critical data. However, ensuring secure and efficient communication from sensor nodes to the base station remains a key challenge due to energy constraints and network vulnerabilities. Even with the advancement of numerous multipath routing protocols, it is still very difficult to achieve optimal energy consumption with low error rates and minimal end-to-end latency. Unreliable routing and hostile intrusions tend to reduce the overall network performance and lifetime, thus prompting the requirement of an intelligent, secure, and energy-aware routing protocol. This work advances the Adaptive Blockchain-based Q-value Regularized Bayesian Asymmetric Quantized Neural Network with Pine Cone Optimization (Q-RBANNet-PCO) for cluster-based energy-efficient routing in WSNs. The model starts with Cluster Head (CH) selection using the Human Memory Optimization Algorithm (HMOA)-based Cluster Head selection and uses Q-RBANNet with PCO for identifying secure routing paths, which are combined with a decentralized ABSP-HC blockchain layer. The method proposed has a PDR of 99.9%, packet loss of 0.1%, transmission latency of 0.6 s, and throughput of ~20 kbps, with the network lifetime lasting up to 1400 rounds. Therefore, Q-RBANNet-PCO appreciably enhances security, lowers energy consumption, and enhances scalability and reliability, making it appropriate for contemporary WSN applications in dynamic and critical environments.

Wireless sensor networks (WSN) include numerous sensor nodes deployed to collect data efficiently. Energy constraints and unreliable communication remain critical challenges in WSNs. This research proposes a Fuzzy Trust-based Hybrid Levy Snake Optimization model to improve energy efficiency, scalability, and reliability in WSNs. The Fuzzy Trust-based Hybrid Levy Snake Optimization model includes cluster formation, optimal cluster head selection using combined levy flight and snake optimization, and a fuzzy trust mechanism to enhance data security and minimize energy consumption. The algorithm identifies secure and energy-efficient routing paths by evaluating trust factors and optimizing cluster head selection based on parameters like energy and delay. Extensive experiments validate the Fuzzy Trust-based Hybrid Levy Snake Optimization model, demonstrating superior performance compared to traditional approaches. The Fuzzy Trust-based Hybrid Levy Snake Optimization model achieves a packet delivery ratio of 98.5%, throughput of 98.7%, and network lifetime of 9500 ms while reducing energy consumption to 83 J and end-to-end delay to 0.01 ms. These findings highlight the Fuzzy Trust-based Hybrid Levy Snake Optimization model's effectiveness in addressing energy and trust challenges in WSNs, offering a robust solution for secure and efficient data transmission.

Atmospheric duct interference (ADI) significantly impacts wireless long-distance communications, radar systems, and naval activities via anomalous signal propagation in tropospheric ducting layers. The previously used predictive methods, anchored on physical models and meteorological data, proved not to be effective in capturing the complex spatiotemporal features of ADI. This paper introduces an Improved Whale Optimization Algorithm-enhanced CNN-GRU model (IWOA-CNN-GRU) that addresses these limitations through three key innovations: (1) a hybrid convolutional and recurrent network for joint spatiotemporal feature extraction, (2) an enhanced WOA with Lévy flight (where heavy-tailed jumps mitigate local optima with 10% probability) and adaptive parameter optimization (dynamically balancing exploration/exploitation) to effectively optimize hyperparameters, and (3) robust generalization over a variety of atmospheric conditions over tropical coastal regions. Experimental results demonstrate the improved performance of the model with 98.50% validation accuracy and significant improvements in precision (0.95 ± 0.02), recall (0.96 ± 0.01), and F1-score (0.95 ± 0.01), a 12%–15% improvement over baselines. The IWOA optimizer performed improved convergence (p < 0.01 in Wilcoxon rank-sum tests) and reduced premature convergence cases by 37%, with the efficiency of computation being boosted by 14.2% during training time without compromising stability (σ² < 0.005 for accuracy variance). These gains confirm the algorithm's suitability for real-time prediction of ADI in operational networks, including radar and communication networks. This work establishes a new benchmark in machine learning–based ADI prediction, with significant implications for enhancing the reliability of wireless systems under changing atmospheric conditions.

The integration of Machine Learning with Low Power Wide Area Network (LPWAN) technologies, particularly LoRaWAN (Long Range Wide Area Network), is transforming the Internet of Things (IoT) landscape by enhancing network performance, scalability, and intelligence. LoRaWAN, an open LPWAN standard developed by the LoRa Alliance, enables long-range communication with minimal power consumption. Although LoRaWAN provides low-power and long-range communication capabilities, it still faces several challenges, including scalability limitations, energy efficiency concerns, resource allocation issues, limited coverage, and susceptibility to interference. Recent advancements in machine learning, deep learning, and federated learning have introduced innovative solutions to address these challenges, fostering the development of intelligent, adaptive, and efficient LoRaWAN networks. This survey also presents a comparative analysis of various LPWAN technologies by examining key parameters such as bandwidth, data rate, coverage, and other critical factors. Furthermore, it explores the performance metrics and practical applications of LoRaWAN across various domains, emphasizing the impact of ML-based approaches. By synthesizing recent research findings and real-world implementations, this survey provides a comprehensive understanding of how ML can significantly enhance the performance and capabilities of LoRaWAN networks.

Millimeter-wave (mmWave) communication has wide applications in 5G broadband cellular communication, wireless backhaul connections, wireless personal area networks, vehicular area networks, and mobile ad hoc networks. Major issues in using mmWave communication for multicasting are blockage and penetration losses in signal propagation. To overcome these losses, the proposed system model includes decode-and-forward (DF) relay nodes between source and destination nodes in the multicast network. The performance of the relay network is further improved by adopting physical layer network coding (PLNC) at the relay node. In addition, spatial modulation (SM) is adopted as a modulation technique due to its inherent advantage of improved spectral efficiency. In this paper, a multicast relay network using SM and PLNC in mmWave communications is proposed for indoor line-of-sight (LoS) environments. The error performance analysis of the proposed system is investigated in terms of deriving analytical expressions by considering orthogonal channel conditions among the nodes. The overall end-to-end pairwise error probability (PEP) and symbol error rate (SER) performances of the proposed system are derived from the performances at the links between the source nodes to relay and destination nodes during the first time slot and relay nodes to destination nodes during the second time slot. The advantage of the PLNC-based system is identified by comparing it with the non-PLNC-based multicast system. Analytical results are compared with Monte Carlo simulations.