International Journal of Communication Systems最新文献

The rapid evolution of fifth-generation (5G) networks has significantly increased the demand for high-speed data transmission, ultra-low latency, and massive device connectivity. However, these advancements introduce new challenges in resource allocation and energy efficiency, particularly for user equipment (UE) that must handle complex communication tasks under limited power budgets. Conventional resource management methods often suffer from inefficient spectrum utilization, static power allocation, and high interference, leading to degraded network performance and excessive energy consumption. Moreover, as 5G systems integrate features like carrier aggregation (CA) and multi-node connectivity, maintaining a balance between throughput and energy efficiency becomes increasingly complex. These limitations motivate the need for a holistic approach that can adapt intelligently to varying network conditions while ensuring sustainable power usage. To overcome these challenges, this research proposes an energy-efficient resource allocation strategy (EERAS), a unified framework designed to optimize spectrum utilization and minimize power consumption simultaneously. EERAS integrates three key components: the OptiGrabber (OPG) algorithm for dynamic allocation of radio resource blocks (RRBs), the ActiState Loop (ASL) algorithm for reinforcement learning–based power control according to signal-to-noise ratio (SNR) variations, and the PowerNap reception (PNRX) technique for intelligent idle-state management. Collectively, these modules enable adaptive, context-aware optimization, enhancing throughput while conserving energy. Simulation results validate the framework's effectiveness, achieving 3.5–4.5-W power savings per UE, a 9.4% reduction in latency, and a throughput improvement of 90%–95%, marking a significant step toward sustainable, energy-conscious 5G and future 6G networks.

The rapid advancement in vehicular communication systems has necessitated the development of efficient and reliable power control algorithms to support 5G millimeter-wave (mmWave) networks. This research evaluates the performance of the Adaptive Sidelink Open Loop Power Control (AS-OLPC) algorithm in vehicular communication scenarios. Highlights the potential of 5G mmWave communication for meeting high-speed, high-bandwidth, and dependable communication needs for vehicle-to-infrastructure (V2I) applications in high-mobility traffic scenarios. Using SUMO and ns-3, we build a simulation-based experimental setup to test and evaluate proposed scenarios. Three 5G millimeter wave use cases were offered for research. These use cases included greater CV penetration, dynamic mobility, and V2I packet size and data rate requirements. AS-OLPC dynamically adjusts transmission power to channel conditions and vehicle motions to minimize interference and improve communication. This optimizes communication. The study uses mmWave channel models, sidelink communication protocols, and realistic vehicle motion patterns. In their 5G mmWave vehicle communication simulations, AS-OLPC uses several key metrics. These measures include latency, packet loss, throughput, and signal-to-interference-plus-noise ratio (SINR).

The traffic management in the network, which is able to serve latency-sensitive applications such as video conferencing in a very efficient manner, is provided by a software-defined networking (SDN) system architecture that is centralized and programmable. On the other hand, regular SDN methods typically show that the adaptations to dynamically changing network situations are too conservative and slow. In addition, as a result of insufficient management of long-range traffic dependencies, they frequently suffer from jitter, latency spikes, and packet loss. To address these limitations, this paper proposes an intelligent SDN-based routing framework employing a hybrid Alex Quantum Dilated Convolutional Neural Network (AQDCNN). The proposed AQDCNN combines the spatial–temporal feature extraction capability of AlexNet with the long-term dependency modeling and quantum-enhanced computational efficiency of QDCNN. The framework is implemented within a multicontroller SDN environment using Mininet and OpenFlow, enabling dynamic network endpoint provisioning, QoS-aware traffic prioritization, and fault-tolerant failure adaptation. The experimental results show that the model outperforms the tested algorithms in terms of minimum setup and fast recovery time (1.90 and 2.10 ms) with low recovery overhead (2.70 ms), as well as high FTI equal to 8.50 and the lowest path change frequency equal to 4.20, which are considered significant stability and service continuity results for experiments conducted on a real-life network topology. The proposed AQDCNN framework is against existing CNN, GN-DQN, DRL-SDN, IFRA-GLB, and MTF-WMSSA–based routing methods, demonstrating superior results in fault tolerance, rerouting time, and flow setup efficiency.

Wireless sensor networks (WSNs) are crucial for applications such as environmental monitoring, smart cities, and disaster detection, but they face significant challenges from malicious nodes, outliers, and routing inefficiencies. These issues hinder network performance and reliability, while existing solutions often fall short due to high computational demands and limited scalability. This paper introduces the SmartRoute optimization network: anomaly-aware energy-efficient routing for real-time landslide monitoring in WSNs (SRON), an innovative framework designed to enhance reliability and energy efficiency in WSNs, especially for landslide monitoring. The SRON framework integrates three core methodologies: dynamic position bias for malicious node detection, grouped query attention for precise outlier management, and SmartPPO for optimized routing. The dynamic position vias module identifies and isolates malicious nodes to prevent false data from disrupting network accuracy. The grouped query attention mechanism improves outlier detection by clustering sensor readings, thereby filtering anomalies and reducing false alarms. Furthermore, the integration of PPO with the attention sink mechanism enhances routing efficiency, enabling energy-aware, reliable data transmission while focusing on critical data points. The developed SRON framework is rigorously tested in Python using more complex simulation parameters, achieving a malicious node detection accuracy of 96%, an outlier detection accuracy of 95%, and a routing efficiency improvement of 94%. These results underscore SRON's capability to address modern WSN challenges, offering a comprehensive, adaptive, and resource-efficient solution for real-time landslide detection and other critical monitoring applications in complex environments.

A wireless sensor network (WSN) is deployed to monitor agricultural environments, helping to overcome issues like suboptimal crop selection that can result in lower yields and diminished quality. To enhance crop recommendation, this work proposes the Kronecker LeNet Forward Harmonic Net (KLeFHNet). The workflow begins with WSN simulation and energy prediction using a DRNN, after which clusters are formed using the Dung Namib Optimizer (DNO) and routing is handled by the Fractional DNO (FDNO). The combination of data normalization, Wave–Hedges metric-based feature selection, and oversampling for data augmentation boosts the performance of the model. The integration of the Deep Kronecker Network, LeNet, and Forward Harmonic Network within KLeFHNet boosts energy efficiency and prolongs network longevity. Experimental results show that FDNO achieves an energy consumption of 0.353 J, a network lifetime of 74.271, and a trust of 86.266, whereas KLeFHNet attains a TPR of 0.900, TNR of 0.895, and FPR of 0.105, supporting sustainable, data-driven agriculture.

The recent technologies in IoT-based smart healthcare services are gaining attention because of their quality of service and reliability. These remote health monitoring systems offer enhanced quality of comfort to individuals and also meet emergency situation management requirements. Automatic data recording, processing, and communication with a third party like a doctor, caretaker, or hospital wirelessly enables the system to supersede conventional healthcare techniques. This paper focuses on the major challenging issues faced in the implementation of wireless body area networks (WBANs) in a dynamic environment, and the performance evaluation of the currently used systems. As the data collected by the sensor nodes are highly critical, a machine learning algorithm, integrating a rule-based screening and a bidirectional LSTM, is proposed to detect data manipulation by intruders. The performance of the network is then evaluated with the help of various parameters. The study results provide better insight into system performance and optimization of system parameters to achieve better reliability and throughput in the presence of false data.

Channel state information (CSI) is necessary for wireless systems supported by reconfigurable intelligent surfaces (RIS) to regulate wireless channels and maximize bandwidth as well as energy effectiveness. In this paper, an Adaptive Multilayer Contrastive Graph Neural Networks for Channel Estimation in Reconfigurable Intelligent Surface-Aided MIMO Schemes (RIS-MIMO-CE-MCGNN) is proposed. Using reconfigurable intelligent surfaces, multiuser millimeter wave big MIMO systems with lower overhead may estimate frequency flat and frequency-selective cascaded channels. Then uses a double-structured sparsity property of the angular cascaded channel matrices and the common sparsity property among the different subcarriers since distinct angle cascaded channels that are visible to various users have completely shared nonzero rows together with user-specific column supports. Support Detection using Adaptive Multilayer Contrastive Graph Neural Networks (AMCGNN). Then, the Channel estimation is performed using the Wader Hunt Optimization Algorithm (WHOA) technique. Finally, the system seeks to minimize the quantity of pilot overhead needed for precise channel estimate. The proposed technique attains 6.18%, 5.58% and 7.29% higher SNR and 7.88%, 5.88%, and 7.19% higher PSNR comparing with the existing methods: Channel estimation for reconfigurable intelligent surface-dependent full-duplex MIMO with hardware impairments (CE-RIS-MIMO-HI), RIS-supported Multiuser MIMO Systems Exploiting Extreme Learning Machine (RIS-MU-MIMO-EELM), and RIS-assisted mmWave-MIMO channel estimation utilizing DL and compressive sensing (RIS-MIMO-CE-CS), respectively.

Many mobile and sensor nodes comprised wireless sensor networks (WSN). Yet, it is quite challenging to locate these sensor and mobile nodes. Because of the time-varying movements, analysis of the current positions of sensor nodes in WSN is quite challenging. Because of locating all known sources in unknown nodes, the typical localization approaches are used to find the position of these nodes, producing a lot of inaccuracy when forecasting the distance between the source and unknown nodes. Also, it is very expensive to use Global Positioning System (GPS) technology for node detection. Although numerous localization procedures for WSNs in a three-dimensional topology have been proposed, it is still important to create and refine new localization algorithms to further increase the accuracy of the node positioning method. In this research work, an advanced heuristic algorithm and a deep learning technique are developed for localizing the unknown nodes in a three-dimensional wireless sensor network (3D-WSN). Initially, the distance between the unknown node as well as the anchor node is evaluated using efficient hybrid deep learning techniques named bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU). Hybrid position of mine blast and chameleon swarm (HP-MBCS) is developed for tuning the parameters in deep learning techniques. An objective function of minimizing the average localization error (ALE) on node localization is obtained by optimally selecting the position of unknown nodes with the support of computed distance from the developed Bi-LSTM-GRU technique. The experimental simulation is carried out between the proposed and traditional models to show that the proposed model is efficient in minimizing localization error. The resultant outcome shows that the MEP value of the proposed HP-MBCS-Bi-LSTM-GRU model is 24.191, which is better than the other existing algorithms like EHO, EOO, MBO, and CSO, respectively. Thus, it was confirmed that the proposed Bi-LSTM-GRU not only improves the precision and effectiveness of node localization but also enhances the overall energy efficiency.

WSNs are essential in industrial and environmental areas, where they can be used to monitor things and track objects in real time. In WSNs that achieve accurate target localization, it is a primary problem in object tracking, primarily in dynamic environments. Additionally, tracking of objects is complicated due to the motion of objects being nonlinear in real-life conditions, which may negatively affect the performance of the tracking process. To address it, the current research will introduce a new hybrid tracking algorithm, which is a combination of Extended Kalman Filters (EKF) and Particle Swarm Optimization (PSO) to measure target state and adjust covariance parameters in WSNs. In this case, the adaptive parameter tuning will be carried out by PSO, whereas EKF modulates the real-time alterations dynamically, which will increase the monitoring efficiency under unpredictable circumstances. This combination enhances precision in tracking whereby filter parameters are constantly diminished with respect to the changing motion of the target. EKF has been applied to predict the state of a target in real-life situations because of its success in estimating the state of a free nonlinear system with Gaussian noise. A static filter configuration can, however, be inadequate in dynamic environments. PSO guarantees reliable tracking of objects regardless of the unpredictable situations by addressing this limitation by modulating EKF parameters in real time. In order to evaluate the performance of the proposed EKF-PSO approach, the study is applied in MATLAB using two network topologies: Random Topology (RT) and Hybrid Topology (HT). The various parameters that are employed to quantify the workability of the suggested approach include power usage, delay, and tracking error. The results of simulations for 100 repeated runs indicate that root mean square error decreases by 28% and energy savings increase by 15% compared with conventional EKF. The algorithm is dynamic in conditions of nonlinear motion and motion noise. From the results, it is shown that HT-EKF-PSO significantly outperforms RT-EKF-PSO with all metrics.