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Decoder-in-the-Loop: Genetic Optimization- Based LDPC Code Design

delete2019-01-01
delete12
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OA
AI
A
Ahmed Elkelesh *
M
Moustafa Ebada
S
Sebastian Cammerer
L
Laurent Schmalen
S
Stephan ten Brink
DOI:10.1109/ACCESS.2019.2942999delete
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摘要

摘要

En 中文
LDPC code design tools typically rely on asymptotic code behavior and are affected by an unavoidable performance degradation due to model imperfections in the short length regime. We propose an LDPC code design scheme based on an evolutionary algorithm, the Genetic Algorithm (GenAlg), implementing a decoder-in-the-loop concept. It inherently takes into consideration the channel, code length and the number of iterations while optimizing the error-rate of the actual decoder hardware architecture. We construct short length LDPC codes (i.e., the parity-check matrix) with error-rate performance comparable to, or even outperforming that of well-designed standardized short length LDPC codes over both AWGN and Rayleigh fading channels. Our proposed algorithm can be used to design LDPC codes with special graph structures (e.g., accumulator-based codes) to facilitate the encoding step, or to satisfy any other practical requirement. Moreover, GenAlg can be used to design LDPC codes with the aim of reducing decoding latency and complexity, leading to coding gains of up to 0.325 dB and 0.8 dB at BLER of 10(5) for both AWGN and Rayleigh fading channels, respectively, when compared to state-of-the-art short LDPC codes. Also, we analyze what can be learned from the resulting codes and, as such, the GenAlg particularly highlights design paradigms of short length LDPC codes (e.g., codes with degree-1 variable nodes obtain very good results).
Keyword:
Parity check codes
Decoding
Design tools
Encoding
Genetic algorithms
Belief propagation
Evolutionary computation
Artificial intelligence
Complexity theory
LDPC codes
belief propagation decoding
short LDPC code design
EXIT charts
genetic algorithm
evolutionary algorithms
artificial intelligence
decoding complexity
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期刊

IEEE Access 封面图
IEEE Access
IF:
3.6
论文数:
9.8W
被引数:
29.4W

机构

U
University of Stuttgart
学者数:
1.1W
论文数: 9.4K
被引数: 1.3W
H
Helmholtz Association
学者数:
13.2W
论文数: 10.7W
被引数: 145
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