汪汉新 李鹏威.基于遗传退火的密集D2D网络资源分配算法[J].中南民族大学学报自然科学版,2020,39(1):79-84
基于遗传退火的密集D2D网络资源分配算法
Resource allocation scheme for dense D2D networks based on genetic annealing algorithm
  
DOI:10.12130/znmdzk.20200115
中文关键词: 关键词: 终端直通通信  干扰图  资源分配  接入率  系统容量
英文关键词: Keywords: Device-to-Device(D2D)communication  interference graph  resource allocation  access rate  system capacity
基金项目:国家自然科学基金项目(61571467,61671483);湖北省自然科学基金重点项目(2016CFA089)
作者单位
汪汉新 李鹏威 中南民族大学 电子信息工程学院智能无线通信湖北省重点实验室武汉430074 
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中文摘要:
      针对高密集场景下传统D2D资源分配算法接入率较低的问题,提出了基于遗传退火的密集D2D网络资源分配算法。首先根据小区内用户之间的干扰情况构建干扰图;然后根据干扰图为D2D用户构建候选信道集合;最后设计适应度函数,并通过遗传退火算法寻找适应度最高的信道分配方案,以提高系统总吞吐量和D2D接入率。仿真结果显示,提出的算法与图着色算法和随机分配算法相比,系统总吞吐量平均增幅为6.2%和21.8%,D2D用户接入率平均增幅为14.7%和44.5%,表明该算法在提高系统总容量的同时,还能有效提升D2D对的接入率。
英文摘要:
      Abstract: Aiming at the problem of low access rate of traditional D2D resource allocation algorithm in high dense scenes, a resource allocation scheme based for Dense D2D Networks on genetic annealing algorithm was proposed. Firstly, the interference graph was constructed according to the interference situation between users in the cell. And then, the candidate channel set was constructed for the D2D users according to the interference graph. Finally, the fitness function was designed, and the most adaptive channel allocation scheme was found by genetic annealing algorithm to improve the total system throughput and D2D access rate. The simulation results show that the total system throughput increases by 6.2% and 21.8%, the average D2D user access rate increases by 14.7% and 44.5%, compared with the graph coloring and random allocation algorithms. The proposed algorithm can improve the total system capacity and increase the access rate of D2D pairs.
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