Soft information boosts hard-decision decoding for product-like codes
Four-Level Quantized Soft Information Aided Hard-Decision Decoder for Product-like Codes
Information Theory
Summary
Digital communications often use codes to detect and fix errors, but decoding them can be complex or error-prone. This paper introduces a method that combines simple decoding steps with smart use of limited reliability information about each bit to improve error correction. The authors show this method helps fix more errors without a big jump in complexity. Their tests confirm better performance than traditional approaches and match methods needing more detailed information.
What this means in practice
- •For communication system engineers: Improve error correction in coded data transmissions while keeping decoding hardware simple and efficient.
- •For chip designers for data storage: Implement efficient low-complexity decoders that use limited reliability data to enhance storage error recovery.
Authors
Xiaowei Wu, Min Qiu, Yueying Zhan, Lei Yang
Abstract
We propose a novel low-complexity soft-aided hard-decision decoding scheme for product-like codes, termed iterative bounded-distance decoding (iBDD) with graded scaled reliability and bit-flipping (GSRF). This scheme operates on four-level quantized soft information. It incorporates a graded scaled reliability rule to assign decoding confidence to the bounded-distance decoding result, and employs a bit-flipping rule based on bit-level and component-code-level reliability. A selective re-decoding rule is designed to further facilitate the error performance while keeping the number of BDD attempts in reasonably low levels. The decoding parameter design of iBDD-GSRF is investigated for product codes and staircase codes, respectively. Simulation results on both codes verify that iBDD-GSRF achieves a gain of more than 0.4 dB over the conventional iBDD, comparable to several existing unquantized soft-aided decoders.