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Learning-Based Joint User-AP Association and Resource Allocation in Ultra Dense Network

Research paper by Zhipeng Cheng, Minghui LiWangy, Ning Chen, Hongyue Lin, Zhibin Gao, Lianfen Huang

Indexed on: 20 Apr '20Published on: 17 Apr '20Published in: arXiv - Computer Science - Networking and Internet Architecture



Abstract

With the advantages of Millimeter wave in wireless communication network, the coverage radius and inter-site distance can be further reduced, the ultra dense network (UDN) becomes the mainstream of future networks. The main challenge faced by UDN is the serious inter-site interference, which needs to be carefully addressed by joint user association and resource allocation methods. In this paper, we propose a multi-agent Q-learning based method to jointly optimize the user association and resource allocation in UDN. The deep Q-network is applied to guarantee the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method and different performances under different simulation parameters are evaluated.