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

Abstract

A novel time-varying channel adaptive low-complexity chase (LCC) algorithm with low redundancy is proposed, where only the necessary number of test vectors (TVs) are generated and key equations are calculated according to the channel evaluation to reduce the decoding complexity. The algorithm evaluates the error symbol numbers by counting the number of unreliable bits of the received code sequence and dynamically adjusts the decoding parameters, which can reduce a large number of redundant calculations in the decoding process. We provide a simplified multiplicity assignment (MA) scheme and its architecture. Moreover, a multi-functional block that can implement polynomial selection, Chien search and the Forney algorithm (PCF) is provided. On this basis, a high-efficiency LCC decoder with adaptive error-correcting capability is proposed. Compared with the state-of-the-art LCC (TV = 16) decoding, the number of TVs of our decoder was reduced by 50.4% without loss of the frame error rate (FER) performance. The hardware implementation results show that the proposed decoder achieved 81.6% reduced average latency and 150% increased throughput compared to the state-of-the-art LCC decoder.

Resilience of Reed-Solomon Codes against Single-Frequency Electromagnetic Disturbances: Fault Mechanisms and Fault Elimination through Symbol Inversion

“…In recent years, new techniques have been emerged to improve the bit error rate performance of RS Codes over Additive White Gaussian Noise (AWGN) and Rayleigh channels such as multipath diversity approach [9]. Additionally, techniques based on deep learning models [10,11] as well as low complexity chase decoding [12,13] have been introduced to enhance the decoding capability of RS Codes.…”