摘要
设备到设备(D2D)通信能够以蜂窝设施为基础来提高资源利用率、用户吞吐量和节省电池能量。在D2D网络中,模式选择和资源分配是关键问题。为了提高D2D通信的和速率与频谱利用效率,提出一种联合模式选择、功率和资源块分配的方案。首先根据用户地理位置选定模式选择标准,帮助用户选择相应的通信模式;然后针对复用通信模式,使用基于深度强化学习的异步优势动作评价(A3C)算法为不同的D2D用户分配资源块和功率。仿真结果表明,本文提出的基于A3C算法的联合优化方案收敛速度快,并且性能相对于其他算法较好。
Device to device(D2D)communication can be based on cellular facilities to improve resource utilization,user throughput and save battery energy.In D2D network,mode selection and resource allocation are the key issues.In order to improve the sum rate and spectrum efficiency of D2D communication,a scheme of joint mode selection,power and resource block allocation is proposed.Firstly,the mode selection criteria are selected according to the user′s geographical location to help the user select the corresponding communication mode;Then,for the multiplexing communication mode,the asynchronous dominant action evaluation(A3C)algorithm based on deep reinforcement learning is used to allocate resource blocks and power to different D2D users.The simulation results show that the joint optimization scheme based on A3C algorithm proposed in this paper has fast convergence speed and better performance than other algorithms.
作者
沈国丽
李君
李正权
Shen Guoli;Li Jun;Li Zhengquan(Nanjing University of Information Science&Technology,Nanjing 210044,China;Wuxi University,Wuxi 214105,China;Key Laboratory of Advanced Control of Light Industry Process,Ministry of Education,Jiangnan University,Wuxi 214122,China;State Key Laboratory of Network and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《电子测量技术》
北大核心
2022年第24期76-84,共9页
Electronic Measurement Technology
基金
国家自然科学基金(61571108)
网络与交换技术国家重点实验室(北京邮电大学)开放课题资助项目(SKLNST-2020-1-13)资助
关键词
模式选择
功率分配
资源分配
D2D通信
深度强化学习
mode selection
power distribution
resource allocation
D2D communication
deep reinforcement learning
作者简介
沈国丽,硕士研究生,主要研究方向为无线通信、深度强化学习方向。E-mail:20201249231@nuist.edu.cn;通信作者:李正权,教授,主要研究方向为无线通信、信号处理、信道编码译码方向。E-mail:lzq722@jiangnan.edu.cn;李君,教授,主要研究方向为无线通信、资源分配、机器学习、编码译码等方向。E-mail:07a0303105@cjlu.edu.cn
