Multi-Agent Deep Reinforcement Learning Based User Association for Dense mmWave Networks

Mohamed Sana, A. Domenico, E. Strinati
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引用次数: 12

Abstract

Finding the optimal association between users and base stations that maximizes the network sum-rate is a complex task. This problem is combinatorial and non-convex, and is even more challenging in millimeter-wave networks due to beamforming, blockages, and severe path loss. Despite the interest that this problem has gained over the last years, the various solutions proposed so far in the literature still fail at being flexible, computationally effective, and suitable to the dynamic nature of mobile networks. This paper addresses these issues with a novel distributed algorithm based on multi-agent reinforcement learning. More specifically, we model each user as an agent, which, at each time step, maps its observations to an action corresponding to an association request to a base station in its coverage range. Our numerical results show that the proposed solution offers near optimal performance and thanks to its flexibility, provides large sum-rate gain with respect to the state-of-art approaches.
基于多智能体深度强化学习的密集毫米波网络用户关联
找到用户和基站之间的最佳关联,使网络总速率最大化是一项复杂的任务。该问题具有组合性和非凸性,在毫米波网络中由于波束形成、阻塞和严重的路径损耗而更具挑战性。尽管这个问题在过去几年里引起了人们的兴趣,但到目前为止,文献中提出的各种解决方案仍然在灵活性、计算效率和适合移动网络的动态性方面失败。本文提出了一种基于多智能体强化学习的分布式算法来解决这些问题。更具体地说,我们将每个用户建模为一个代理,在每个时间步,它将其观察映射到与对其覆盖范围内的基站的关联请求相对应的操作。我们的数值结果表明,所提出的解决方案提供了接近最佳的性能,并且由于其灵活性,相对于最先进的方法提供了较大的求和速率增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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