Learning Quantization in LDPC Decoders
Abstract
Finding optimal message quantization is a key requirement for low complexity belief propagation (BP) decoding. To this end, we propose a floating-point surrogate model that imitates quantization effects as additions of uniform noise, whose amplitudes are trainable variables. We verify that the surrogate model closely matches the behavior of a fixed-point implementation and propose a hand-crafted loss function to realize a trade-off between complexity and error-rate performance. A deep learning-based method is then applied to optimize the message bitwidths. Moreover, we show that parameter sharing can both ensure implementation-friendly solutions and results in faster training convergence than independent parameters. We provide simulation results for 5G low-density parity-check (LDPC) codes and report an error-rate performance within 0.2 dB of floating-point decoding at an average message quantization bitwidth of 3.1 bits. In addition, we show that the learned bitwidths also generalize to other code rates and channels.
Cite
@article{arxiv.2208.05186,
title = {Learning Quantization in LDPC Decoders},
author = {Marvin Geiselhart and Ahmed Elkelesh and Jannis Clausius and Fei Liang and Wen Xu and Jing Liang and Stephan ten Brink},
journal= {arXiv preprint arXiv:2208.05186},
year = {2022}
}
Comments
6 Pages, 11 Figures, submitted to IEEE for possible publication