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异构网络中基于被囊群算法的D2D通信资源分配 被引量:1

D2D Communication Resource Allocation Based on Tunicate Swarm Algorithm in Heterogeneous Networks
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摘要 针对异构网络中D2D用户通信过程中复用蜂窝用户传输信道产生的资源分配优化问题,提出将改进被囊群优化算法用于频谱资源载波分配的能效优化问题中.首先,针对被囊群算法的收敛精度不高,易早熟以及局部最优等缺点,提出一种基于反向差分的被囊群算法(ODTSA).其次,通过为边缘D2D用户建立中继链路,大大提高了网络资源利用率.最后,在满足用户QoS的前提下,以能效最大化作为优化目标,用改进的被囊群算法进行资源分配.并将算法与TSA算法、YSGA算法、GWO以及PSO算法进行比较.仿真结果表明,所提算法相比于文中所对比算法明显提高了系统传输速率. Aiming at the resource allocation optimization problem caused by the multiplexing of cellular user channels in the process of D2D user communication in heterogeneous networks,an improved encapsulated group optimization algorithm was proposed to optimize the energy efficiency of spectrum resource carrier allocation.Firstly,a reverse difference based encapsulation algorithm(ODTSA)is proposed to solve the shortcomings of encapsulation algorithm,such as low convergence accuracy,precocity and local optimum.Secondly,the network resource utilization is greatly improved by establishing relay links for edge D2D users.Finally,on the premise of satisfying the user′s QoS,the energy efficiency maximization is taken as the optimization goal,and the improved encapsulated group algorithm is used to allocate resources.The algorithm is compared with TSA algorithm,YSGA algorithm,GWO and PSO algorithm.The simulation results show that the proposed algorithm significantly improves the transmission rate of the system compared with the algorithms in this paper.
作者 秦维娜 张达敏 张琳娜 尹德鑫 蔡朋宸 QIN Wei-na;ZHANG Da-min;ZHANG Lin-na;YIN De-xin;CAI Peng-chen(College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China;College of Mechanical Engineering,Guizhou University,Guiyang 550025,China)
出处 《小型微型计算机系统》 CSCD 北大核心 2023年第10期2144-2150,共7页 Journal of Chinese Computer Systems
基金 国家自然科学基金项目(62062021,61872034)资助 贵州省科学技术基金项目(黔科合基础[2020]1Y254)资助 贵州省科学技术基金项目(黔科合基础[2019]1064)资助.
关键词 D2D通信 资源分配 被囊群算法 能效 反向差分 Device-to-Device(D2D)communication resource allocation tunicate swarm algorithm energy efficiency opposition-based learning and differential evolution
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