面向用户体验质量的超密集异构网络资源分配算法

RESOURCE ALLOCATION ALGORITHM FOR HETEROGENEOUS ULTRA DENSE NETWORK ORIENTED TO USER EXPERIENCE QUALITY

  • 摘要: 针对超密集异构网络(Heterogeneous Ultra Dense Network,HUDN)环境复杂时变的特性,研究在HUDN下的非正交多址接入(Non-Orthogonal Multiple Access,NOMA)无线资源分配问题。为提升网络用户的服务体验质量(Quality of Experience,QoE),综合考虑用户-基站匹配、信道分配和功率分配问题,以最大化全网用户平均意见得分(Mean Opinion Score,MOS)累加和(SUM-MOS)为目标,提出并实现一种基于双深度Q网络(Double Deep Q-Network,DDQN)的无线网络资源分配算法。仿真结果表明,与DQN(Deep Q-Network)和Q-Learning等算法相比,该算法在有效提升全网用户QoE的同时,也提高了无线资源的利用率。

     

    Abstract: Aimed at the complex time-varying characteristics of heterogeneous ultra dense network (HUDN), the problem of non-orthogonal multiple access (NOMA) wireless resource allocation under HUDN is studied. In order to improve the quality of experience (QoE) of network users, the problems of user-base station matching, channel allocation and power allocation were considered comprehensively, and a wireless network resource allocation algorithm based on double deep Q-network (DDQN) was proposed and implemented with the goal of maximizing the SUM of the mean opinion score (MOS) of the whole network users. Simulation results show that, compared with DQN and Q-Learning algorithms, the proposed algorithm not only effectively improves the QoE of all network users, but also improves the utilization of wireless resources.

     

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