Deep Reinforcement Learning Based User Association and Resource Allocation for D2D-enabled Wireless Networks
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DOI码：10.1109/ICCC52777.2021.9580261
所属单位：School of ComputerScience and Information Engineering, Hefei University of Technology
发表刊物：2021 IEEE/CIC International Conference on Communications in China
项目来源：National Natural Science Foundation of China under Grants 61971176 and 61901156, Anhui Provincial Na
关键字：D2D communication, resource allocation, user association, ultra-dense network, deep reinforcement learning.
摘要：With the ultra-dense deployment of small-cell base stations (SBSs), it is common today to find a user locates within the coverage area of several SBSs. In this paper, we investigate the joint user association and resource allocation problem of D2D pairs in ultra-dense cellular networks. Specifically, we formulate an optimization problem for D2D pairs that are within the overlapping coverage areas of several SBSs. By jointing optimizing the user association and resource allocation of such D2D pairs, we maximize the overall data rate of both cellular users and D2D pairs. After that, the double-dueling-deep Qnetwork (D3QN) algorithm is adopted to address the formulated problem, in which we consider the central controller in the network as an agent, and let it interact with the environment to find the optimal user association and resource allocation strategy. Numerical results validate that our proposed D3QN algorithm could achieve near-optimal performance, and is superior to other schemes.
合写作者：Xiaowei Meng,Linsheng Mei,Wei Huang
第一作者：Caihong Kai
论文类型：CCF会议论文
学科门类：工学
文献类型：C
ISSN号：2377-8644
是否译文：否
发表时间：2021-11-13
收录刊物：EI