All Dates/Times are Australian Eastern Standard Time (AEST)

Technical Program

Paper Detail

Paper IDD3-S7-T2.4
Paper Title Deep Learning-Based Bit Reliability Based Decoding for Non-binary LDPC Codes
Authors Taishi Watanabe, Takeo Ohseki, Kosuke Yamazaki, KDDI Research, Inc., Japan
Session D3-S7-T2: Topics in Coding III
Chaired Session: Thursday, 15 July, 00:00 - 00:20
Engagement Session: Thursday, 15 July, 00:20 - 00:40
Abstract The bit reliability based (BRB) and weighted bit reliability based (wBRB) algorithms are non-binary low-density parity-check (LDPC) code decoding algorithms with an excellent tradeoff between computational complexity and performance. However, the performance of these algorithms needs further improvement. We apply deep learning to these algorithms. Weights are assigned to each edge of the Tanner graphs of the non-binary LDPC codes in the proposed algorithms. We demonstrate the effectiveness of applying deep learning to the BRB and wBRB algorithms in terms of implementation and performance. The proposed algorithms achieve an approximately 0.3 dB higher bit error rate performance than the original algorithms in the high SNR region. The increase in computational complexity and memory consumption does not significantly change the implementation of the algorithms.