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arXiv · 2004.08194

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
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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.

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Zhipeng Cheng, Minghui LiWangy, Ning Chen, Hongyue Lin, Zhibin Gao, Lianfen Huang. 2020-04-17. Learning-Based Joint User-AP Association and Resource Allocation in Ultra Dense Network. https://arxiv.org/abs/2004.08194

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