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Volume 43 Issue 1
Jan.  2021
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Haiqiang CHEN, Yaoling WANG, Wenjuan WEI, Bingxu JIANG, Youming SUN, Xiangcheng LI, Tuanfa QIN. Two Low-complexity Symbol Flipping Decoding Algorithms for Non-binary LDPC Codes[J]. Journal of Electronics & Information Technology, 2021, 43(1): 51-59. doi: 10.11999/JEIT191008
Citation: Haiqiang CHEN, Yaoling WANG, Wenjuan WEI, Bingxu JIANG, Youming SUN, Xiangcheng LI, Tuanfa QIN. Two Low-complexity Symbol Flipping Decoding Algorithms for Non-binary LDPC Codes[J]. Journal of Electronics & Information Technology, 2021, 43(1): 51-59. doi: 10.11999/JEIT191008

Two Low-complexity Symbol Flipping Decoding Algorithms for Non-binary LDPC Codes

doi: 10.11999/JEIT191008
Funds:  The National Natural Science Foundation of China (61761006, 61961004, 61662004), The Natural Science Foundation of Guangxi (2017GXNSFAA198263, 2017GXNSFAA198276, 2018GXNSFAA138079)
  • Received Date: 2019-12-18
  • Rev Recd Date: 2020-11-23
  • Available Online: 2020-11-26
  • Publish Date: 2021-01-15
  • Two low-complexity symbol flipping decoding algorithms, the Improved weighted-Algorithm B algorithm (Iwtd-AlgB) and the Truncation-based Distance-Symbol-Flipping-Decoding with Prediction (TD-SFDP) algorithm, are presented for non-binary Low Density Parity Check (LDPC) codes. For the Iwtd-AlgB algorithm, the scaling factor of the flipping metric can be replaced by the simple sums of the extrinsic information and the distance-based parameter, which can avoid the multiplication operations in the iterations and thus can reduce the decoding complexity. For the presented TD-SFDP algorithm, the variable nodes and the finite field symbols are truncated and classified based on the extrinsic information frequency and the flipping function. Only those nodes/symbols that satisfy the designed conditions can be involved in the message updating process. Simulations and numeric results show that, the presented two decoding algorithms can reduce the computational complexity at each iteration with a controllable performance degradation, thus can make efficient trade-offs between performance and complexity.
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