Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection

• URL: http://arxiv.org/abs/2102.00178v1
• Date: Sat, 30 Jan 2021 07:29:04 GMT
• Title: Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection
• Authors: Tz-Wei Mo, Ronald Y. Chang, Te-Yi Kan
• Abstract summary: This paper proposes a novel multiple-input multiple-output (MIMO) symbol detector that incorporates a deep reinforcement learning (DRL) agent into the Monte Carlo tree search (MCTS) detection algorithm.
• Score: 7.115948058516199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a novel multiple-input multiple-output (MIMO) symbol detector that incorporates a deep reinforcement learning (DRL) agent into the Monte Carlo tree search (MCTS) detection algorithm. We first describe how the MCTS algorithm, used in many decision-making problems, is applied to the MIMO detection problem. Then, we introduce a self-designed deep reinforcement learning agent, consisting of a policy value network and a state value network, which is trained to detect MIMO symbols. The outputs of the trained networks are adopted into a modified MCTS detection algorithm to provide useful node statistics and facilitate enhanced tree search process. The resulted scheme, termed the DRL-MCTS detector, demonstrates significant improvements over the original MCTS detection algorithm and exhibits favorable performance compared to other existing linear and DNN-based detection methods under varying channel conditions.

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