Irregularly Clipped Sparse Regression Codes

被引：2
作者：
Li, Wencong [1 ]
Liu, Lei [1 ]
Kurkoski, Brian M. [1 ]
机构：
[1] Japan Adv Inst Sci & Technol, Nomi 9231292, Japan
来源：
2021 IEEE INFORMATION THEORY WORKSHOP (ITW) | 2021年
关键词：
SUPERPOSITION CODES; SIGNAL RECOVERY; PERFORMANCE;
D O I：
10.1109/ITW48936.2021.9611458
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Recently, it was found that clipping can significantly improve the section error rate (SER) performance of sparse regression (SR) codes if an optimal clipping threshold is chosen. In this paper, we propose irregularly clipped SR codes, where multiple clipping thresholds are applied to symbols according to a distribution, to further improve the SER performance of SR codes. Orthogonal approximate message passing (OAMP) algorithm is used for decoding. Using state evolution, the distribution of irregular clipping thresholds is optimized to minimize the SER of OAMP decoding. As a result, optimized irregularly clipped SR codes achieve a better tradeoff between clipping distortion and noise distortion than regularly clipped SR codes. Numerical results demonstrate that irregularly clipped SR codes achieve 0.4 dB gain in signal-to-noise-ratio (SNR) over regularly clipped SR codes at code length approximate to 2.5x10(4) and SER approximate to 10(-5). We further show that irregularly clipped SR codes are robust over a wide range of code rates.
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页数：6
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