Policy-Guided MCTS for near Maximum-Likelihood Decoding of Short Codes

TL;DR

This paper introduces a neural network-guided Monte Carlo Tree Search decoder that achieves near maximum-likelihood decoding for short codes with significantly reduced complexity and latency.

Contribution

It presents a novel policy-guided MCTS decoding method that outperforms traditional algorithms in accuracy and efficiency for short block codes.

Findings

Achieves near ML decoding performance for short codes.

Reduces search complexity by 95% compared to non-GE OSD.

Provides lower decoding latency at high SNRs.

Abstract

In this paper, we propose a policy-guided Monte Carlo Tree Search (MCTS) decoder that achieves near maximum-likelihood decoding (MLD) performance for short block codes. The MCTS decoder searches for test error patterns (TEPs) in the received information bits and obtains codeword candidates through re-encoding. The TEP search is executed on a tree structure, guided by a neural network policy trained via MCTS-based learning. The trained policy guides the decoder to find the correct TEPs with minimal steps from the root node (all-zero TEP). The decoder outputs the codeword with maximum likelihood when the early stopping criterion is satisfied. The proposed method requires no Gaussian elimination (GE) compared to ordered statistics decoding (OSD) and can reduce search complexity by 95\% compared to non-GE OSD. It achieves lower decoding latency than both OSD and non-GE OSD at high SNRs.