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Low-Complexity Chase Decoding of Reed-Solomon Codes Using Channel Evaluation.

Hao Wang1, Wei Zhang1, Yanyan Chang1

  • 1School of Microelectronics, Tianjin University, Tianjin 300072, China.

Entropy (Basel, Switzerland)
|March 25, 2022
PubMed
Summary
This summary is machine-generated.

A new adaptive algorithm reduces decoding complexity for low-complexity chase (LCC) decoders by optimizing test vector generation. This novel approach significantly cuts latency and boosts throughput without compromising error correction performance.

Keywords:
Reed–Solomon (RS) codesVLSIalgebraic soft-decision decoding (ASD)low latencylow-complexity chase (LCC)multiplicity assignment

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Area of Science:

  • Digital Communications
  • Error Correction Coding
  • Signal Processing

Background:

  • Low-Complexity Chase (LCC) algorithms are crucial for efficient decoding in communication systems.
  • Existing LCC decoders often involve redundant calculations, increasing complexity and latency.
  • Adaptive decoding strategies are needed to optimize performance based on channel conditions.

Purpose of the Study:

  • To propose a novel time-varying channel adaptive low-complexity chase (LCC) algorithm.
  • To reduce decoding complexity and improve hardware implementation efficiency.
  • To enhance adaptive error-correcting capabilities in LCC decoders.

Main Methods:

  • Developed a low-redundancy adaptive LCC algorithm that dynamically adjusts decoding parameters.
  • Implemented a simplified multiplicity assignment (MA) scheme and a multi-functional polynomial selection, Chien search, and Forney algorithm (PCF) block.
  • Generated only necessary test vectors (TVs) based on channel evaluation to minimize computations.

Main Results:

  • Reduced the number of test vectors (TVs) by 50.4% compared to state-of-the-art LCC decoding (TV=16) with no loss in frame error rate (FER) performance.
  • Achieved an 81.6% reduction in average latency through hardware implementation.
  • Increased throughput by 150% compared to existing LCC decoders.

Conclusions:

  • The proposed adaptive LCC algorithm offers significant improvements in decoding efficiency and hardware performance.
  • Dynamic adjustment of decoding parameters and optimized TV generation effectively reduce complexity and latency.
  • The novel decoder provides a high-efficiency solution with adaptive error-correcting capabilities for modern communication systems.