# Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection

arXiv: https://arxiv.org/abs/2102.00178 · HTML: https://mlsanity.com/paper/2102.00178/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 30 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection](https://mlsanity.com/paper/2002.03214/) (2020)
- [Multilevel MIMO Detection with Deep Learning](https://mlsanity.com/paper/1812.01571/) (2018)
- [Learning to Search for MIMO Detection](https://mlsanity.com/paper/1909.07858/) (2019)
- [Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach](https://mlsanity.com/paper/1903.12546/) (2019)
- [Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling](https://mlsanity.com/paper/1909.06943/) (2019)
- [Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach](https://mlsanity.com/paper/1903.07831/) (2019)

## Built on by (most cited first, 1 of 1)

- [Automatic Hybrid-Precision Quantization for MIMO Detectors](https://mlsanity.com/paper/2208.05880/) (2022)

## Abstract (arXiv)

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.