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IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

A Reinforcement Learning Based Decoding Method of Short Polar Codes

Jian Shu Gao, Kai Niu

Published Mar 29, 2021• 3 citations
Publisher

Abstract

In this paper, reinforcement learning (RL) is applied to find effective decoding strategies for short polar codes. We first illustrate how to map the step-decision decoding to a Markov decision process. Then, we choose the signal reliability to formulate the reward strategy, and the path metrics are taken as the Q-values of the state-action pairs. Finally, an adaptive Q-table approach is proposed for data-driven learning of optimal decision strategies. Compared with conventional Q-learning and deep Q-learning networks, the adaptive Q-table results in low computational and storage complexity. Simulation results show that the proposed RL decoding with adaptive Q-table achieves comparable performance to learned bit-flipping decoding and SC decoding with low complexity.

DOI
10.1109/wcncw49093.2021.9420004

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This paper explores the application of reinforcement learning techniques to improve the performance of bit-flipping decoders and finding their optimal decisions. We begin by providing an overview of bit-flipping based decoders and reinforcement learning algorithms. We then outline the methodology for mapping the iterative decoding process into Markov Decision Processes (MDPs). We propose a feedback-based method to exploit and enhance the performance of existing well-established decoders by applying reinforcement learning algorithms after a selected decoder. In essence, we modify the MDP to reduce the number of states, making reinforcement learning algorithms feasible for low-rate and long-length codes. In addition, focusing on the correction of dominant error patterns of a selected decoder improves its error correction capability. Finally, we present experimental results for the Binary Symmetric Channel (BSC) to demonstrate the efficiency of the proposed methods.
A traditional successive cancellation (SC) decoding algorithm produces error propagation in the decoding process. In order to improve the SC decoding performance, it is important to solve the error propagation. In this paper, we propose a new algorithm combining reinforcement learning and SC flip (SCF) decoding of polar codes, which is called a Q-learning-assisted SCF (QLSCF) decoding algorithm. The proposed QLSCF decoding algorithm uses reinforcement learning technology to select candidate bits for the SC flipping decoding. We establish a reinforcement learning model for selecting candidate bits, and the agent selects candidate bits to decode the information sequence. In our scheme, the decoding delay caused by the metric ordering can be removed during the decoding process. Simulation results demonstrate that the decoding delay of the proposed algorithm is reduced compared with the SCF decoding algorithm, based on critical set without loss of performance.