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International Conference on Information and Communication Technology Convergence (ICTC)

Deep Reinforcement Learning Based Bit Flipping Algorithm for Hamming Codes

Hanbin Lee, Seoyoung Yu, Jeong Woo Lee

Published Oct 16, 2024• 2 citations
Publisher

Abstract

Hamming codes are effective for single-bit error correction but struggle with multiple-bit errors. While the bit-flipping (BF) algorithm can handle some multiple-bit errors, it also faces limitations. To address these issues, we propose a new decoding method using deep reinforcement learning (DRL) based BF algorithm. Simulation results show that, in a burst channel environment, the proposed method achieves approximately 10% better BER performance compared to the traditional BF algorithm for channel error probabilities ranging from 0.001 to 0.01.

DOI
10.1109/ictc62082.2024.10827687

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Hamming codes are effective for single-bit error correction but struggle with multiple-bit errors. While the bit-flipping (BF) algorithm can handle some multiple-bit errors, it also faces limitations. To address these issues, we propose a new decoding method using deep reinforcement learning (DRL) based BF algorithm. Simulation results show that, in a burst channel environment, the proposed method achieves approximately 10% better BER performance compared to the traditional BF algorithm for channel error probabilities ranging from 0.001 to 0.01.
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In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes. We start by reviewing several iterative decoding algorithms that involve a decision-making process at each step, including bit-flipping (BF) decoding, residual belief propagation, and anchor decoding. We then illustrate how such algorithms can be mapped to Markov decision processes allowing for data-driven learning of optimal decision strategies, rather than basing decisions on heuristics or intuition. As a case study, we consider BF decoding for both the binary symmetric and additive white Gaussian noise channel. Our results show that learned BF decoders can offer a range of performance-complexity trade-offs for the considered Reed-Muller and BCH codes, and achieve near-optimal performance in some cases. We also demonstrate learning convergence speed-ups when biasing the learning process towards correct decoding decisions, as opposed to relying only on random explorations and past knowledge.
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A new class of iterative bit flipping (BF) decoding algorithms adapted for low-density parity check (LDPC) convolutional codes is proposed. Compared with Gallager's original BF algorithm, the new BF algorithms improve both the coding gain and the error correction speed. At a bit error rate (BER) of 10 , the best of the new bit-flipping algorithms achieves, after only 6 iterations and with much simpler decoding hardware, a coding gain within 3.5 dB of that of the conventional min-sum belief propagation decoding algorithm.
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In this paper, a new method for improving hard-decision bit-flipping (BF) decoding is proposed for low-density parity-check codes. The flipping criterion is derived theoretically from the soft-decision belief-propagation decoding algorithm. The proposed BF decoding algorithm is channel independent and is improved by introducing a more efficient method for computing the reliability of the parity checks. Extensive simulations are provided to demonstrate the efficiency of the proposed algorithm. Simulation results show that the proposed algorithm can achieve about 1.5dB coding gain improvement at BER = 5×10−6while reducing up to 48% iterations for decoding and maintaining low decoding complexity, compared with the conventional weighted BF algorithm.