Learning Quantization in LDPC Decoders

• URL: http://arxiv.org/abs/2208.05186v1
• Date: Wed, 10 Aug 2022 07:07:54 GMT
• Title: Learning Quantization in LDPC Decoders
• Authors: Marvin Geiselhart, Ahmed Elkelesh, Jannis Clausius, Fei Liang, Wen Xu, Jing Liang and Stephan ten Brink
• Abstract summary: We propose a floating-point surrogate model that imitates quantization effects as additions of uniform noise. A deep learning-based method is then applied to optimize the message bitwidths. We report an error-rate performance within 0.2 dB of floating-point decoding at an average message quantization bitwidth of 3.1 bits.
• Score: 14.37550972719183
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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