International Journal of Communication Systems最新文献

Wireless personal communication is becoming more and more popular due to the rapid development of 5G communication networks. Modern wireless personal communication systems can be difficult to optimize due to the criteria for transmission speed and quality of service. In this manuscript, a cooperative resource allocation using optimized heterogeneous context-aware graph convolutional networks in 5G wireless networks (CRA-HCAGCN-5GWN) is proposed. Here, the cooperative resource allocation is used for channel information on a small scale rather than typical resource allocation when the channel environment is rapidly changing. HCAGCN fails to specify optimization techniques to identify optimal parameters for accurate cooperative resource allocation. Therefore, the Giant Trevally Optimizer (GTO) is employed to optimize the HCAGCN, which accurately optimizes resource allocation. The proposed CRA-HCAGCN-5GWN is implemented, and the performance metrics, like mean square error (MSE), minimum mean square error (MMSE), mean absolute error (MAE), root mean square error (RMSE), throughput, energy efficiency, and consumption time, are analyzed. The performance of the CRA-HCAGCN-5GWN approach attains 17.20%, 25.81%, and 32.18% lower mean square error; 16.40%, 28.81%, and 30.18% higher throughput; and 18.30%, 25.41%, and 31.08% lower energy efficiency when analyzed with existing methods.

This research article explores a novel category of multichannel cognitive radio (CR) wireless retrial queueing network. The channels (radio spectrums) are shared between secondary users (SUs) and primary users (PUs), with PUs enjoying preemptive priority. The PUs are originated from a finite number, $��N�$, of identical sources. In addition to PUs holding absolute priority over SUs, the impact of repeated attempts of PUs has been implemented. To optimize radio spectrum utilization and improve transmission quality, a waiting line buffer for SUs is deployed. For this intricate system, the ergodic condition and joint stationary probability distribution of SUs and PUs are derived using matrix-geometric methods. Various essential performance metrics of the system have been evaluated. Additionally, vital probabilistic descriptors, including successful, vain, and ideal retrials, are thoroughly examined in the context of the CR wireless network. By exploring the first-step principle, the first-passage time to attain a specific critical level in the waiting line buffer of SUs and associated average measures are discussed. Finally, comprehensive numerical results and graphical analyses are presented to provide insights into the proposed scheme.

Reconfigurable intelligent surfaces are a transformative technological advancement in wireless networks toward enhancing coverage and signal strength by exploiting intelligent, programmable surfaces. This paper discusses the application of metaheuristic optimization to configure reconfigurable intelligent surfaces for performance enhancement in wireless communications. Reconfigurable intelligent surface (RIS) technology based on programmable surfaces dynamically adjusts a radio environment, thereby having an impact on signal quality enhancement and coverage. We test several optimization algorithms, including genetic algorithm (GA), cuckoo search (CS), harmony search (HS), ant colony optimization (ACO), differential evolution (DE), dual annealing, gradient descent, tabu search, and the bee algorithm. The results show that GA explores the solution space. CS and HS balance well between exploration and exploitation. ACO and DE find paths and parameters efficiently, and dual annealing excels in escaping local optima. Tabu search and the bee algorithm also provide strong performance in the avoidance of local minima. RIS elements can integrate highly into the network to enhance its performance, leading to better-received signal strength and improved coverage. The results have the potential to optimize wireless networks by combining the use of RIS technology with advanced algorithms such as those described here and call for further research in hybrid optimization approaches and complex network scenarios.

In this paper, we address the challenge of limited channel capacity of wireless fronthaul links in cloud radio access networks (CRANs). A novel architecture is proposed in which the base band unit (BBU) is equipped with a massive multiple-input multiple-output (MIMO) antenna array to communicate with a set of remote radio heads (RRHs) that serve multiple user equipments (UEs). Moreover, an optimal signal quantization strategy is also designed at the BBU to accommodate for the limited fronthaul capacity. To this end, a joint optimization of power allocation at the BBU, quantization noise covariance, and RRH beamforming vectors is formulated via maximizing the achievable sum-rate subject to power constraints at the BBU and RRHs. The resulting highly nonconvex problem is reformulated as a semidefinite relaxation (SDR) problem by using the difference of convex (DC) programming approach. It is also deduced analytically that the solution yielded by this SDR problem is always of rank one. Numerical results confirm the potential of the proposed system in improving the capacity of the CRAN systems with wireless fronthauling. In particular, the proposed design outperforms two proposed benchmark schemes.

In this article, scarecrow-shaped antenna with a Rogers RT6002 substrate with a permittivity of 2.94 and a thickness of 1 mm is presented. It is operating from 3.5 to 12 GHz frequency band. The next generation of wireless communication networks will make extensive use of machine learning (ML). It is anticipated that the growth of various communication-based applications will improve coverage and spectrum efficiency when compared with traditional systems. A wide range of domains, including antennas, can benefit from the application of ML to generate solutions. Scarecrow-shaped antenna is optimized using machine learning algorithms decision tree, random forest, XGBoost regression, K-nearest neighbor (KNN), and light gradient boosting regression (LGBR). The antenna's return loss, gain, and directivity were predicted in this work. The KNN achieved the highest accuracy in the prediction of return loss. Hence, proposed antenna is suitable for flexible wireless communication systems, IoT, 5G, and 6G.

Optimizing waveforms in radar and sonar systems is crucial for enhancing spectral efficiency and target detection capabilities, yet this process often faces challenges like computational complexity and convergence issues. This research introduces a novel method that leverages the frequency-domain exponential cross ambiguity function (FECAF) within multi-tone sinusoidal frequency modulated (MTSFM) waveforms, combined with the Conjugate Gradient Method with Efficient Line Search (CGM-ELS) for optimization. The optimization of MTSFM waveforms is achieved by combining the generalized integrated sidelobe level (GISL) and peak-to-mean power envelope ratio (PMEPR) metrics. The GISL metric controls the main lobe and sidelobe structure of the waveform's autocorrelation function (ACF), quantifying unwanted energy in sidelobes to find an optimal compromise. PMEPR ensures efficient operation of radar or sonar transmitters by minimizing energy variations in the waveform's envelope, which is crucial for peak power-limited systems. To optimize these metrics, the CGM-ELS algorithm is employed, ensuring efficient convergence through iterative adjustment of waveform parameters based on gradient information and penalty functions. The proposed method transforms the optimization problem into an unconstrained format, reducing computational complexity and improving convergence rates. Experimental results have shown significant enhancements in computational efficiency and convergence rate, demonstrating the effectiveness of the CGM-ELS algorithm in synthesizing waveforms with optimal ambiguity function characteristics for radar and sonar applications.

This study introduces a quad-port multi-input multi-output antenna design specifically suited for fifth-generation wireless technology. The antenna geometry is characterized by a flower-shaped configuration, featuring five petals complemented by a circular slot, positioned in the ground plane to improve operational bandwidth. The effective realization of wide-band characteristics has been studied utilizing the analysis of characteristic modes. The design antenna demonstrates simulated performance parameters that cover the millimeter-wave frequency band from 25.6 to 32.4 GHz, delivering a maximum gain of 6.1 dBi and ensuring minimum port isolation of > 15 dB across all ports. In addition, a circular stub is purposefully positioned over the slot to facilitate circular polarization radiation behavior at 26.3 GHz, exhibiting an axial ratio bandwidth of 0.5 GHz (26–26.5 GHz). Further, the proposed antenna design is subjected to validation encompassing diversity metrics. The proposed antenna structure is successfully fabricated, and its performance is experimentally validated. A comprehensive comparative analysis is then conducted to evaluate its alignment with the simulated results.

Mobile edge computing (MEC) is extensively utilized for supporting diverse mobile applications and the Internet of Things (IoT). One of MEC's prime operations is utilizing unmanned aerial vehicles (UAVs) included with the MEC servers for providing computational aids for offloaded tasks by mobile users in temporal hotspot regions or a few emerging situations like sports areas or environmental disaster regions. However, despite the various merits of UAVs executed with MEC servers, it is constrained by their insufficient sensible energy consumption and computational resources. Furthermore, owing to the complication of UAV-aided MEC systems, energy consumption optimizations and computation resource optimizations cannot be obtained better in conventional optimization schemes. In this research, the kookaburra jellyfish algorithm (KJA) is presented for task offloading in a UAV-enabled MEC network. The main objective is to enhance the efficiency of task offloading in UAV-enabled MEC networks by optimizing energy consumption, computational resources, and communication time using the KJA. Initially, the UAV-enabled MEC network model is simulated. Next, task computation is performed, and thereafter, task uploading is carried out. Then, task offloading is executed using KJA with consideration of multiobjective models, namely, energy consumption, communication time, and cost. Moreover, KJA is devised by integrating kookaburra optimization algorithm (KOA) with jellyfish search optimizer (JSO). Afterward, the task offloading process and data transmission are conducted. In addition, KJA obtained minimum energy, load, and time of 0.448 J, 0.122, and 1.036 s.

Digital filter multiple access passive optical network (DFMA-PON) is adopted in recent years as it offers the high bandwidth, elastic network slicing, low latency, fixed data rate, and massive end-user comparability. To overcome limited system capacity, low transmission rate and fiber fault in existing DFMA-PONs, a wheel architecture based full-duplex 8 × 200 Gbps DFMA-PON integrated system using fiber and free space optics (FSO) link is presented in this paper. Results depict that maximum FSO channel range can be attained up to 2600 m together with 50 km fiber range under weak-to-strong turbulent, haze, rain, fog, and snow conditions. Besides, faithful fiber reach of 200 km can be obtained with fixed 100 m FSO distance at receiver sensitivity of −17.2 dBm and 1.5 dB power penalty. System can support 230 numbers end units at high optical signal to noise ratio of 29–88 dB with 50–200 Gbps throughput, together with high split ratio of 256 to −3.2 dB high gain as well as 2 dB noise figure. In addition, the comparative analysis with the previous work reveals that the proposed architecture offers optimum results than existing PONs. This system helps to enhance the disaster resilience with high network security, reliability, and survivability of 5G based networks.

The Internet of Things (IoT) has transformed vehicular ad hoc networks (VANETs), leading to the Internet of Vehicles (IOV). VANETs are wireless networks without fixed infrastructure, designed to improve traffic safety in real time, supporting intelligent transportation systems (ITS). Due to their unpredictable nature, VANETs face major challenges like frequent link failures, scalability, reliability, network layout issues, quality of service (QoS), and security, all of which are complex and difficult to solve (NP-hard problems). Traditional protocols are unsuitable for VANETs due to their unique properties. To accomplish the optimal number of clusters and achieve stability in VANETs within a dynamic environment, we propose a swarm-based metaheuristic algorithm called the rat swarm optimization (RSO) algorithm. The RSO algorithm employs a clustering technique to optimize the network performance and ensure efficient communication in VANETs. The RSO algorithm optimizes load based on node transmission range (Tx range) through effective resource utilization and coordination. RSO organizes the unstructured network into cluster structures and generates near-optimal clusters and CHs to reduce network randomness and maintain stability with lower communication costs. By keeping the number of clusters at an optimal level, the RSO algorithm enhances cluster lifetime and overall network performance. To assess the effectiveness and efficiency of the RSO algorithm, numerous experiments are performed by using various grid sizes, Tx ranges, and nodes in the network. The generated results demonstrate that the RSO algorithm stimulates 50.96%, 33.15%, 88.73%, and 96.70% optimal number of clusters when contrasted with the clustering algorithm–based on ant colony optimization (CACONET), moth flame clustering algorithm for IoV (MFCA-IoV), the whale optimization algorithm for clustering in vehicular ad hoc networks (WOACNET), and grasshoppers' optimization-based node clustering technique for VANETs (GOA) when the Tx range and nodes are taken into consideration. But, when the grid size is considered, the RSO generates 32.31%, 15.23%, 47.04%, and 58.33% optimal number of clusters when compared to cutting-edge algorithms. Hence, the quantitative results and the statistical representation show the proposed RSO algorithm's effectiveness over cutting-edge algorithms under the unpredictable nature of VANETs.