Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection
This work addresses MIMO detection for wireless communication systems, presenting an incremental improvement by enhancing an existing algorithm with deep reinforcement learning.
The paper tackled the problem of MIMO symbol detection by incorporating a deep reinforcement learning agent into the Monte Carlo tree search algorithm, resulting in a DRL-MCTS detector that shows significant improvements over the original MCTS and competitive performance against other methods under varying channel conditions.
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.