학술논문

Guessing Random Additive Noise Decoding with Quantized Soft Information
Document Type
Conference
Source
2023 IEEE Globecom Workshops (GC Wkshps) Globecom Workshops (GC Wkshps), 2023 IEEE. :1698-1703 Dec, 2023
Subject
Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
Quantization (signal)
Conferences
Computational modeling
Memory management
Numerical models
Error correction
Dynamic programming
GRAND
Soft Decision
Quantization
Statistical Model
Language
Abstract
In this work, we introduce discretized soft GRAND (DS-GRAND) based on dynamic programming (DP), which utilizes quantized soft information. Typical quantization values for per-information bit soft information in decoding chips range from 3 to 5 bits. Our simulations indicate that DSGRAND performs within 0.25 dB and 0.1 dB of maximum-likelihood (ML) decoding with 2 and 3 bit soft information quantizers, respectively. We analyze the memory requirements and computational complexity of DSGRAND, demonstrating that for the CA-SCL Polar decoder with a list size of 128, which closely approaches DSGRAND performance, DSGRAND outperforms CA-SCL by an order of magnitude in time complexity and two orders of magnitude in space complexity.