计算机与数字工程2023，Vol.51Issue(11)：2475-2482,2489,9.DOI:10.3969/j.issn.1672-9722.2023.11.001
D2D通信网络中基于深度强化学习的能效优化
Energy Efficiency Optimization Based on Deep Reinforcement Learning in D2D Communication Network
摘要
Abstract
In order to solve the problems of spectrum resource shortage,serious inter-link interference and high energy con-sumption of mobile communication networks,for the Device to Device(D2D)network,a deep reinforcement learning(RL)algo-rithm based on a distributed framework is considered,which uses double Q and dueling architecture to solve the problems of exces-sive state space and Q value overestimation.Simultaneous wireless information and power transfer(SWIPT)are used to effectively compensate for system energy consumption.Under the non-linear constraints of the minimum required throughput of cellular users and the power splitting ratio of D2D user,based on the changing location and channel state information(CSI),synchronously allo-cating resource blocks(RB),D2D transmitted power and power splitting ratio to achieve energy efficiency(EE)optimization.The simulation results show that under the premise of ensuring sum rate of cellular users,the proposed scheme is better than the base-line algorithm in terms of the total EE of the D2D links,and is more robust in communication networks with large-scale mobile us-ers.关键词
无线携能技术/能效/资源块分配/功率分流比/深度强化学习Key words
SWIPT/EE/RB allocation/power splitting ratio/DRL分类
信息技术与安全科学引用本文复制引用
仲星,李君,李正权,朱明浩,沈国丽,张茜茜..D2D通信网络中基于深度强化学习的能效优化[J].计算机与数字工程,2023,51(11):2475-2482,2489,9.基金项目
国家自然科学基金项目(编号:61571108) （编号:61571108）
网络与交换技术国家重点实验室(北京邮电大学)开放课题(编号:SKLNST-2020-1-13)资助. （北京邮电大学）
