Region-Specific Coarse Quantization with Check Node Awareness in 5G LDPC Decoding

TL;DR

This paper introduces advanced quantization and check node awareness techniques for 5G LDPC decoders, significantly improving error correction performance and efficiency with novel modeling, grouping strategies, and schedule optimizations.

Contribution

It proposes a comprehensive set of methods for coarse quantization and check node awareness in 5G LDPC decoding, including alignment regions, degree-specific message separation, and schedule optimization.

Findings

Performance improvement up to 0.4 dB with grouping strategies

Reduced iteration count by up to 35% through schedule optimization

2-bit decoding doubles area efficiency compared to 4-bit decoding

Abstract

This paper presents novel techniques for improving the error correction performance and reducing the complexity of coarsely quantized 5G-LDPC decoders. The proposed decoder design supports arbitrary message-passing schedules on a base-matrix level by modeling exchanged messages with entry-specific discrete random variables. Variable nodes (VNs) and check nodes (CNs) involve compression operations designed using the information bottleneck method to maximize preserved mutual information between code bits and quantized messages. We introduce alignment regions that assign the messages to groups with aligned reliability levels to decrease the number of individual design parameters. Group compositions with degree-specific separation of messages improve performance by up to 0.4 dB. Further, we generalize our recently proposed CN-aware quantizer design to irregular LDPC codes and layered schedules. The method optimizes the VN quantizer to maximize preserved mutual information at the output of the subsequent CN update, enhancing performance by up to 0.2 dB. A schedule optimization modifies the order of layer updates, reducing the average iteration count by up to 35%. We integrate all new techniques in a rate-compatible decoder design by extending the alignment regions along a rate-dimension. Our complexity analysis shows that 2-bit decoding can double the area efficiency over 4-bit decoding at comparable performance.