Wireless Networks
Neda Sedighian; Abbas Karimi; Javad Mohammadzadeh; Faraneh Zarafshan
Abstract
Background and Objectives: Vehicular Ad Hoc Networks (VANETs) face significant challenges due to high mobility and rapid topology changes. One of the most critical issues in this context is the clustering process, which directly impacts delay reduction, cluster stability, and overall network efficiency. ...
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Background and Objectives: Vehicular Ad Hoc Networks (VANETs) face significant challenges due to high mobility and rapid topology changes. One of the most critical issues in this context is the clustering process, which directly impacts delay reduction, cluster stability, and overall network efficiency. However, traditional clustering methods such as K-Means and MFO, which mainly rely on simple metrics like distance or signal strength, fail to deliver optimal performance in dynamic environments with variable network density. The primary objective of this study is to design and evaluate an advanced clustering algorithm called AI_MCA (Artificial Intelligence Multi Clustering Algorithm), leveraging artificial intelligence and multi-criteria decision-making. By considering factors such as signal strength, relative speed, node density, and vehicle movement direction, the proposed algorithm forms clusters with higher stability and efficiency in dynamic and high-density environments.Methods: This study uses simulations to evaluate AI_MCA in VANETs, which facilitate vehicle-to-vehicle communication and are characterized by high mobility and rapid position changes.Results: Simulations in NS3 and SUMO show that AI_MCA reduces latency by 20% (12ms vs. 15ms in MFO) and improves cluster stability by 30% (lifetime of 45s vs. 33s in K-Means) within a 600m range. At a 1000m range with 300 nodes, delay increases to 14ms and PDR drops to 88%.Conclusion: AI_MCA outperforms traditional methods like K-Means and MFO, offering a scalable solution for VANET clustering.
Wireless Networks
F. Rahdari; M. Sheikh-Hosseini; M. Jamshidi
Abstract
Background and Objectives: This research addresses the performance drop of edge users in downlink non-orthogonal multiple access (NOMA) systems. The challenging issue is pairing the users, which becomes more critical in the case of edge users due to poor signal quality as well as the similarity of users' ...
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Background and Objectives: This research addresses the performance drop of edge users in downlink non-orthogonal multiple access (NOMA) systems. The challenging issue is pairing the users, which becomes more critical in the case of edge users due to poor signal quality as well as the similarity of users' channel gains.Methods: To study this issue, the capabilities of intelligent reflecting surface (IRS) technology are investigated to enhance system performance by modifying the propagation environment through intelligent adjusting of the IRS components. In doing so, an optimization problem is formulated to determine the optimal user powers and phase shifts of IRS elements. The objective is to maximize the system sum rate by considering the channel gain difference constraint. Additionally, the study addresses the effect of the IRS location in the cell on system performance.Results: The proposed approach is evaluated for various scenarios and compared with benchmarks in terms of average bit error rate (BER) and sum rate. The numerical results show that IRS-assisted NOMA improves the performance of edge users and distributes resources more fairly compared to conventional NOMA.Conclusion: Simulation results demonstrate that using IRS-assisted NOMA can effectively address the issue of edge users. By modifying the channel between the BS and the edge users using IRS, the channel gain difference of the users is increased, thereby enhancing the overall system performance. Particularly, the proposed IRS-NOMA system offers a gain of about 4 dB at a BER of 0.01 and 3 dB at the sum rate of 0.1 bps/Hz compared to conventional NOMA. In addition, it was observed that the location of the IRS in the cell affects the system's performance.
