{"title":"Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications","authors":"Mohamed Kamel Benbraika, Okba Kraa, Yassine Himeur, Khaled Telli, Shadi Atalla, W. Mansoor","doi":"10.3390/computers13020044","DOIUrl":null,"url":null,"abstract":"Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"31 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/computers13020044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions.
设备到设备（D2D）通信是一种新兴技术，对未来的蜂窝网络（包括 5G 及其他）至关重要。其潜力在于提高系统吞吐量、卸载网络核心和提高频谱效率。因此，优化资源和功率分配以减少同信道干扰对于利用这些优势至关重要。在本文中，我们对元启发式算法进行了比较研究，采用了遗传算法（GA）、粒子群优化算法（PSO）、蜜蜂生命算法（BLA）以及匹配技术与 BLA 的新型组合，以实现联合信道和功率分配优化。仿真结果凸显了生物启发算法在应对这些挑战方面的有效性。此外，在吞吐量、收敛速度和实现实用解决方案方面，拟议的匹配算法与 BLA 的组合优于其他元启发式算法，即 PSO、BLA 和 GA。
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