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List-Based Optimization of Proximal Decoding for LDPC Codes
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DOI：10.1109/LCOMM.2024.3458422.png)
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摘要
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In this letter, the proximal decoding algorithm is considered within the context of additive white Gaussian noise (AWGN) channels. An analysis of the convergence behavior of the algorithm shows that proximal decoding inherently enters an oscillating behavior of the estimate after a certain number of iterations. Due to this oscillation, frame errors arising during decoding can often be attributed to only a few remaining wrongly decoded bit positions. In this letter, an improvement of the proximal decoding algorithm is proposed by establishing an additional step, in which these erroneous positions are attempted to be corrected. We suggest an empirical rule with which the components most likely needing correction can be determined. Using this insight and performing a subsequent ML-in-the-list decoding, a gain of up to 1 dB is achieved compared to conventional proximal decoding, depending on the decoder parameters and the code.
Keyword:
Iterative decoding
Maximum likelihood decoding
Optimization
Oscillators
Polynomials
Linear programming
Convergence
Optimization-based decoding
proximal decoding
ML-in-the-list
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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