Generalized Mutual Information-Maximizing Quantized Decoding of LDPC Codes with Layered Scheduling

TL;DR

This paper introduces a generalized mutual information-maximizing quantized decoding framework for LDPC codes, utilizing simple mappings and fixed-point operations, applicable to various degree distributions and compatible with belief propagation or min-sum algorithms.

Contribution

It develops a novel MIM density evolution method to design LUTs for node updates and introduces layered scheduling for faster convergence and reduced memory in quantized LDPC decoding.

Findings

Outperforms state-of-the-art LUT decoders in the waterfall region.

Achieves faster convergence with fewer iterations.

Approaches floating-point layered BP performance within 0.3 dB at high SNR.

Abstract

In this paper, we propose a framework of the mutual information-maximizing (MIM) quantized decoding for low-density parity-check (LDPC) codes by using simple mappings and fixed-point additions. Our decoding method is generic in the sense that it can be applied to LDPC codes with arbitrary degree distributions, and can be implemented based on either the belief propagation (BP) algorithm or the min-sum (MS) algorithm. In particular, we propose the MIM density evolution (MIM-DE) to construct the lookup tables (LUTs) for the node updates. The computational complexity and memory requirements are discussed and compared to the LUT decoder variants. For applications with low-latency requirement, we consider the layered schedule to accelerate the convergence speed of decoding quasi-cyclic LDPC codes. In particular, we develop the layered MIM-DE to design the LUTs based on the MS algorithm, leading to the MIM layered quantized MS (MIM-LQMS) decoder. An optimization method is further introduced to reduce the memory requirement for storing the LUTs. Simulation results show that the MIM quantized decoders outperform the state-of-the-art LUT decoders in the waterfall region with both 3-bit and 4-bit precision over the additive white Gaussian noise channels. For small decoding iterations, the MIM quantized decoders also achieve a faster convergence speed compared to the benchmarks. Moreover, the 4-bit MIM-LQMS decoder can approach the error performance of the floating-point layered BP decoder within 0.3 dB in the moderate-to-high SNR regions, over both the AWGN channels and the fast fading channels.