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

Zhipeng Cheng; Minghui LiWangy; Ning Chen; Hongyue Lin; Zhibin Gao; Lianfen Huang

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

Networking and Internet Architecture 2020-04-20 v1

Authors: Zhipeng Cheng , Minghui LiWangy , Ning Chen , Hongyue Lin , Zhibin Gao , Lianfen Huang

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.

Keywords

resource allocation network protocol wireless network

Cite

@article{arxiv.2004.08194,
  title  = {Learning-Based Joint User-AP Association and Resource Allocation in Ultra Dense Network},
  author = {Zhipeng Cheng and Minghui LiWangy and Ning Chen and Hongyue Lin and Zhibin Gao and Lianfen Huang},
  journal= {arXiv preprint arXiv:2004.08194},
  year   = {2020}
}

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5 pages, 5 figures

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

Abstract

Keywords

Cite

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