Learning Quantization in LDPC Decoders

TL;DR

This paper introduces a trainable surrogate model for optimizing message quantization in LDPC decoders, achieving near floating-point performance with low bitwidths and demonstrating generalization across codes and channels.

Contribution

It proposes a novel trainable surrogate model for message quantization, combined with deep learning optimization and parameter sharing, to improve LDPC decoding efficiency.

Findings

Achieves within 0.2 dB of floating-point decoding performance.

Average message quantization of 3.1 bits.

Learned bitwidths generalize across code rates and channels.

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.