Multi-agent deep reinforcement learning (MADRL) meets multi-user MIMO systems

• URL: http://arxiv.org/abs/2109.04986v1
• Date: Fri, 10 Sep 2021 16:50:45 GMT
• Title: Multi-agent deep reinforcement learning (MADRL) meets multi-user MIMO systems
• Authors: Heunchul Lee, Jaeseong Jeong
• Abstract summary: We present a MADRL-based approach that can jointly optimize precoders to achieve the outer-boundary, called pareto-boundary, of the achievable rate region. We will also address a phase ambiguity issue with the conventional complex baseband representation of signals widely used in radio communications. To the best of our knowledge, this is the first work to demonstrate that the MA-DDPG framework can jointly optimize precoders to achieve the pareto-boundary of achievable rate region.
• Score: 0.3883460584034765
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A multi-agent deep reinforcement learning (MADRL) is a promising approach to challenging problems in wireless environments involving multiple decision-makers (or actors) with high-dimensional continuous action space. In this paper, we present a MADRL-based approach that can jointly optimize precoders to achieve the outer-boundary, called pareto-boundary, of the achievable rate region for a multiple-input single-output (MISO) interference channel (IFC). In order to address two main challenges, namely, multiple actors (or agents) with partial observability and multi-dimensional continuous action space in MISO IFC setup, we adopt a multi-agent deep deterministic policy gradient (MA-DDPG) framework in which decentralized actors with partial observability can learn a multi-dimensional continuous policy in a centralized manner with the aid of shared critic with global information. Meanwhile, we will also address a phase ambiguity issue with the conventional complex baseband representation of signals widely used in radio communications. In order to mitigate the impact of phase ambiguity on training performance, we propose a training method, called phase ambiguity elimination (PAE), that leads to faster learning and better performance of MA-DDPG in wireless communication systems. The simulation results exhibit that MA-DDPG is capable of learning a near-optimal precoding strategy in a MISO IFC environment. To the best of our knowledge, this is the first work to demonstrate that the MA-DDPG framework can jointly optimize precoders to achieve the pareto-boundary of achievable rate region in a multi-cell multi-user multi-antenna system.

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