International Journal of Communication Systems最新文献

{"title":"Enhancing WBAN Data Transmission: A Lightweight Approach With Fixed Binary Golomb Compression and Symmetric Mutual Authentication Protocol","authors":"Kavya Sabapathy,&nbsp;Praveen Kumar Ramajayam","doi":"10.1002/dac.70110","DOIUrl":"https://doi.org/10.1002/dac.70110","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless body area networks (WBAN) containing wearable sensing medical devices have enticed huge attention by providing high-quality medical services to people without restraint in their day-to-day activities. WBAN monitors elderly people or patients suffering from any long-lasting diseases from their place without being hospitalized, saving critical time transportation delays and admission costs. Wireless medical devices are attached or implanted in the human body to sense medical-related data and further transmit it for medical services in an unsecured wireless medium. These sensing medical devices are miniature-sized with a limited battery source, so energy should be exploited carefully. The sensing devices deplete their energy more during data transmission. Efficient energy exploitation and assuring data security and privacy in WBAN are highly recommended. Data to be transmitted from the sensing device are compressed before transmission to reduce the number of data transmissions and save energy. In this paper, to attain efficient energy exploitation, Lightweight Fixed Binary Golomb (LFBG) data compression is performed at each sensor before transmission, and further to guarantee the privacy and security of the data, Lightweight Symmetric Mutual Authentication (LSMA) protocol is implemented. The LFBG compression with the least computation saves up to 84% of energy, and LSMA with the least computation and transmission authenticates and also shares the session key securely.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Adaptive Crocodile Optimization Algorithm Based Deep Elman Recurrent Neural Network for Channel Estimation With Hybrid Precoder in MIMO-OFDM System","authors":"S. Santhi Jabarani,&nbsp;Jaison Jacob","doi":"10.1002/dac.70116","DOIUrl":"https://doi.org/10.1002/dac.70116","url":null,"abstract":"<div>\u0000 \u0000 <p>Due to the massive usage of smartphones, frequent usage of the IoT, and wireless visual streaming services, data traffic in the wireless network and data explosion has increased over the next years. System modeling and channel estimation are the two main challenges while designing the wireless 5G MIMO communication system. A 2 × 2 MIMO-SFBC system is proposed to enhance the spectral efficiency and capacity of wireless communication systems by exploiting spatial diversity and frequency diversity. The SFBC coding technique gives a low bit error rate (BER) and high signal-to-noise ratio (SNR). Channel modeling and channel estimation are very difficult tasks in the complex propagation characteristics of highly dynamic channels. This paper proposes an improved ERNN-LSTM network to enhance the accuracy and efficiency of channel modeling and estimation in wireless communication systems. Initially, a least squares estimator is employed to obtain an initial estimate of the historical channel responses of a pilot block. These initial estimates are subsequently utilized to train an Elman recurrent neural network (ERNN). The weights of the ERNN's channel parameters are optimized using the Adaptive Crocodile Algorithm. Simulation results show that the proposed ACO-DERNN method achieves a BER of 10⁻⁵ at 30 dB SNR, outperforming conventional methods.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement Learning Algorithm for Improving Spectral Energy Efficiency Using Large Intelligent Surfaces","authors":"Jai A. Desai,&nbsp;Shriram D. Markande","doi":"10.1002/dac.70111","DOIUrl":"https://doi.org/10.1002/dac.70111","url":null,"abstract":"<div>\u0000 \u0000 <p>The Spectral Energy Efficiency (<i>SEE</i>) is the concrete feature of future generations of wireless systems. It is in turn dependent upon the System User-Achievable-Data Rate (<i>SAR</i>). The <i>SAR</i> of the current generation systems can be enhanced by use of Large Intelligent Surfaces (LIS). They implement a pane of reflecting antennas made up of meta-materials. These panels are mounted on any architectural structure like apartments, schools/colleges etc. The beauty of LIS is that they can be trained by means of machine learning models to reflect the incoming electro-magnetic signal towards the required direction that can increase the received signal strength at the receiver. This increased signal strength at the receiver further boosts the Signal to Noise ratio (<i>SNR</i>) and SAR. This paper implements a Reinforcement Learning (RiL) based customized loss model in a Recurrent Neural Network (RNN) model to enhance the <i>SEE</i> of the LIS based systems. The dataset required for training and validation of DL model is produced from the publicly available ray tracing based DeepMIMO generator. The simulation findings demonstrate that the suggested RNN-RiL model exhibits an enhancement of 1.14 bps/Hz in <i>SAR</i>, and an improvement of 2.75 Mbits/J enhancement in the <i>SEE</i> when compared to the baseline technique. This rise in the <i>SEE</i> can be useful in inculcating more number of users per sec while maintaining the Quality of Service (QoS) thus enabling energy harvesting in LIS.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wireless Based Network Dynamic Handover Decision Analysis Using Convolution Enhanced Evolving Attention Network","authors":"B. Sakthivel,&nbsp;J. Nafeesa Begum,&nbsp;M. Ramamoorthy,&nbsp;P. Selvi,&nbsp;S. Sakthivel,&nbsp;M. Sathya","doi":"10.1002/dac.70100","DOIUrl":"https://doi.org/10.1002/dac.70100","url":null,"abstract":"<div>\u0000 \u0000 <p>Dynamic handover decisions are among the most important in such a way that the transmission should be seamless with the preservation of quality of service (QoS) in wireless networks. Traditional handover mechanisms have various limitations, like high latency with increased packet loss, resulting in network congestion and inefficient resource distribution. These challenges become even more difficult because modern networks are extremely complex. To address these challenges, this paper proposes a dynamic handover decision analysis using convolution enhanced evolving attention network for improved QoS in wireless networks (CEEAN-DHOD-QoS-WN). This methodology incorporates a piranha foraging optimization algorithm (PFOA) to enhance the route selection based on some parameters, such as delay, bit error rate, jitter, energy consumption, average bit rate, and received signal strength. Then, an optimal decision is carried out to manage vertical handoff using convolution enhanced evolving attention network (CEEAN). The experimental results demonstrate significant improvements in throughput, latency, and overall network efficiency compared with the existing methods. The simulation outputs prove that the CEEAN-DHOD-QoS-WN technique attains 0.69, 0.65, and 0.61 ns lower delay and 20.12, 17.10, and 13.11 J lower energy consumption compared with existing methods: QOS aware vertical handover procedure in heterogeneous wireless network (QoS-VHO-HWN), a novel handover scheme for millimeter wave network: a method of integrating reinforcement learning and optimization (HOS-MWN-RLA), and user preference-dependent heterogeneous network management scheme for vertical handover (UP-HNMS-VH), respectively.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Legitimacy of IoT Devices Based on an Energy-Efficient Trust Management Scheme in Information-Centric Networking","authors":"Sachin Sharma,&nbsp;Mohammad Yahya,&nbsp;Anupriya Jain,&nbsp;Ahmed I. Alutaibi,&nbsp;Abdullah Baihan,&nbsp;Papiya Dutta,&nbsp;Ahed Abugabah","doi":"10.1002/dac.70085","DOIUrl":"https://doi.org/10.1002/dac.70085","url":null,"abstract":"<div>\u0000 \u0000 <p>A rapidly developing future technology, wireless sensor networks (WSNs) have promise for a wide range of military and business applications. Potential safety issues could affect WSN technology because it combines wireless communications with processing capacity. A novel networking architecture on the Internet of Things (IoT) called information-centric networking (ICN) provides more security than standard Internet Protocol (IP) networks. However, it still experiences a lot of security issues, particularly from internal attacks. Applying trust management technologies is an effective way to secure against internal threats. Therefore, an evolutionary particle swarm optimization energy-efficient trust management scheme (EPSO-EETMS) is proposed to evaluate the legitimacy of IoT devices and nodes. The data regarding routing pathways using trust can identify various types of attacked solutions. The proposed solution is thoroughly evaluated using some networking parameters, such as the distance between devices, energy use, and information loss during data transmission. These practical factors include energy consumption when transmitting data between nodes, message delivery to previous or subsequent nodes, and distance between two devices. According to the evaluation results, the proposed strategy outperforms standard techniques in terms of response time, authentication delays, and the number of requests from fake nodes. The accuracy of the proposed technique is obtained to be 98.66% for 100 nodes, which is higher than that of existing routing techniques.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing Intelligent Reflecting Surface (IRS)–Aided NOMA Networks in Covert Wireless Communication Using Jammer","authors":"Maryam Najimi","doi":"10.1002/dac.70108","DOIUrl":"https://doi.org/10.1002/dac.70108","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, an intelligent reflecting surface (IRS)–assisted non-orthogonal multiple access (NOMA) network is proposed in a multi-user scenario where the transmitter communicates with its receiver covertly by helping a friendly jammer. In this network, an adversary detects the communication existence of the users in the frequency band while the jammer sends the jamming signals to the adversary to degrade its performance. In this case, the analytical expressions for the secrecy outage probability (SOP), false alarm probability, and the missed detection probability at adversary are obtained. Rayleigh fading channel is assumed as the channel model while the covert communication performance is improved. For this purpose, the total effective rates are maximized by optimization of the transmission power, power allocation to multiple users, IRS reflection matrix, and also transmission probability adjustment with constraints on the detection performance and SOP. The problem is non-convex; therefore, we present the genetic algorithm (GA) method to find the suboptimal solution for the problem with lower complexity. Numerical results show the performance improvement of the proposed algorithm in comparison to the benchmark algorithms.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Based on Antennas Modeling for 5G and 6G Communication Systems: A Systematic Review","authors":"Karrar Shakir Muttair,&nbsp;Oras Ahmed Shareef,&nbsp;Hazeem Baqir Taher","doi":"10.1002/dac.70105","DOIUrl":"https://doi.org/10.1002/dac.70105","url":null,"abstract":"<div>\u0000 \u0000 <p>Artificial intelligence (AI)-aided communications have gained significant traction in recent years due to the widespread application of machine learning (ML) and deep learning (DL) machines with algorithms to solve math problems in wireless communications. This study offers an overview of the use of ML models in antenna design and optimization. This incorporates DL on ML frameworks, categories, and structure to get practical and broad insights using ML techniques for high throughput, quick data analysis, and prediction. This article also comprehensively reviews recent research papers on antenna design via ML. This includes an analysis of several ML algorithms that have been applied to produce antenna parameters such as the reflection coefficient (<i>S</i>-parameters), efficiency and gain values, and radiation patterns of the antennas. However, the current antenna design's structure, variables, and external factors remain complex. In addition, the expense of time and processing resources is inescapable and unacceptable to most designers. ML-based antennas have been created to increase antenna modeling efficiency and accuracy to solve these challenges. Techniques for modeling data may be used to predict the performance of an antenna for a certain set of antenna factors of design. As a result, this study highlights the most sophisticated applied ML techniques that have been presented to increase antenna modeling efficiency and accuracy. The results demonstrate that AI, ML, and DL may minimize simulation needs, predict antenna behavior, and reduce time with high accuracy.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rain Optimization Algorithm–Based Optimized Reconfigurable Antenna for Satellite Communication","authors":"S. Parasuraman,&nbsp;S. Yogeeswaran","doi":"10.1002/dac.70084","DOIUrl":"https://doi.org/10.1002/dac.70084","url":null,"abstract":"<div>\u0000 \u0000 <p>Reconfigurable antennas have played a substantial role in the growth of wireless communication technology due to their low cost, high speed, and compact size. However, one of the key challenges in satellite communication is the efficient use of bandwidth and maintaining high-quality signal reception under varying environmental conditions. Traditional satellite communication systems rely on fixed antennas that may not adapt effectively to changing atmospheric conditions, signal interference, or satellite position variations, leading to suboptimal performance. To overcome these limitations, reconfigurable antennas (RAs) have emerged as a promising solution, offering flexibility to adjust their operating parameters. This research aims to address this challenge by employing a rain optimization algorithm (ROA) to optimize the parameters of a reconfigurable antenna for satellite communication. The optimization-based reconfigurable antenna by the intrusion of Flame Retardant-4 (FR4) substrate is proposed with a thickness of 1.4 mm, a permittivity of 4.4, and a tangent loss of 0.019, respectively. Here, the L-shaped slots have been embedded in the ground plane of dimension 20 × 20 to enhance the antenna performance. ROA is established to optimize the parameter of the designed antenna. Uniformly, the high-pass filter (HPF) is initiated in the antenna design to eliminate the undesired frequencies. The parameters of the designed antenna, such as current distribution, gain, return loss, directivity, and radiation pattern, are simulated by the high-frequency structure simulator (HFSS) tool. For satellite applications, the proposed optimized antenna achieves a gain of 8.75 dB, a bandwidth of 9.70 MHz, and a return loss of −16.03 and −18.982 at the resonance frequencies of 9.7 and 29.5 GHZ. Furthermore, by altering the antenna's length and width, the parametric analysis is assessed in terms of the reflection coefficient and VSWR. Additionally, a hardware analysis is conducted.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revolutionizing Congestion Control Protocols for Robust WSN Routing Dynamics Through Optimized Dual Aggregated Attention Capsule Network","authors":"Vijayalakshmi Khagga,&nbsp;N. Sangeetha Priya,&nbsp;A. M. Prasad","doi":"10.1002/dac.70103","DOIUrl":"https://doi.org/10.1002/dac.70103","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless sensor networks (WSNs) have a vital part in real-time data distribution in areas like intelligent cities and tracking the environment. However, challenges like network congestion often result in packet loss, delays, and energy inefficiencies, severely impacting performance. Existing congestion control methods struggle with unpredictable traffic patterns and fluctuating node energy levels, leading to incorrect routing decisions and shortened network lifespans. This highlights the need for an intelligent congestion control protocol that dynamically manages traffic flow and adapts to network conditions. This study introduces a novel framework, “Revolutionizing Congestion Control Protocols for Robust WSN Routing Dynamics through Optimized Dual Aggregated Attention Capsule Network (DAT-G²ACN-GTAO),” which leverages an Advanced Atomic Orbital Search Paradigm to organize nodes and select cluster heads, thus enhancing complexity management and extending network lifespan. The framework employs a Dual Aggregation Transformer-based Gated Graph Attention Capsule Network to accurately detect congestion, steering data toward less congested routes to optimize transmission. Additionally, the Giant Trevally Adaptive Optimization (GTAO) method fine-tunes network parameters in real time, enhancing throughput and minimizing energy consumption. Experimental results demonstrate that the DAT-G²ACN-GTAO protocol significantly outperforms traditional methods, achieving a packet delivery ratio exceeding 99.2%, maintaining network throughput stability above 99.5%, and ensuring 99.3% accuracy in congestion detection and prioritized data transmission. This robust congestion control framework marks a substantial improvement over conventional approaches, significantly boosting WSN efficiency and network longevity, making it a critical enabler for deploying reliable, energy-efficient WSNs across diverse applications.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Robustness of OFDM Systems Using LSTM-Based Autoencoders","authors":"Rajarajan P,&nbsp;Madona B. Sahaai","doi":"10.1002/dac.70090","DOIUrl":"https://doi.org/10.1002/dac.70090","url":null,"abstract":"<div>\u0000 \u0000 <p>The ability of orthogonal frequency division multiplexing (OFDM) to counteract frequency-selective fading channels has made it a popular modem technology in contemporary communication systems. But maintaining dependable signaling is still difficult, especially when the signal-to-noise ratio (SNR) is low. In order to increase the dependability of OFDM systems, this study presents an enhanced LSTM-based autoencoder architecture. The suggested autoencoder efficiently utilizes temporal dependencies and reduces the impacts of channel distortion by encoding and decoding OFDM signals utilizing one-hot encoding employing long short-term memory (LSTM) networks. The outcomes of the simulation show notable gains in performance indicators. The average block error rate (BLER) of the suggested model is 0.0150, as opposed to 0.0296 for traditional autoencoders and 0.0886 for convolutional OFDM systems. Comparably, the average packet error rate (PER) is decreased to 0.0017, surpassing convolutional OFDM systems' 0.2260 and traditional autoencoders' 0.0070. These outcomes highlight the LSTM-based autoencoder's efficacy in enhancing OFDM systems' dependability, especially in demanding settings. This study lays the groundwork for employing cutting-edge deep learning methods to create reliable and effective communication systems.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 9","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}