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

TL;DR

This paper proposes a multi-agent deep Q-learning approach to optimize user association and resource allocation in ultra dense networks, effectively addressing inter-site interference issues.

Contribution

It introduces a novel multi-agent deep Q-learning method with a deep Q-network for joint optimization in UDNs, ensuring convergence and improved performance.

Findings

Effective interference mitigation demonstrated

Superior performance over baseline methods

Robustness under various simulation settings

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.