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Deep Reinforcement Learning Based Bit Flipping Algorithm for Hamming Codes
- Lee, Hanbin;
- Yu, Seoyoung;
- Lee, Jeong Woo
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1초록
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. © 2024 IEEE.
키워드
bit flipping decoding; Hamming codes; reinforcement learning
- 제목
- Deep Reinforcement Learning Based Bit Flipping Algorithm for Hamming Codes
- 저자
- Lee, Hanbin; Yu, Seoyoung; Lee, Jeong Woo
- 발행일
- 2024-10
- 유형
- Conference paper
- 저널명
- International Conference on ICT Convergence
- 권
- 2024
- 페이지
- 1022 ~ 1023