Reduced Complexity RPA Decoder for Reed-Muller Codes
Jiajie Li, Syed Mohsin Abbas, Thibaud Tonnellier, Warren J. Gross · 2021
The recursive projection-aggregation (RPA) decoder is a recently proposed near maximum likelihood (ML) decoder for Reed-Muller (RM) codes with low rates and short code lengths. However, the high computational complexity of RPA decoding is a major bottleneck for using RPA in applications that have a limited resource and energy budget. In this work, syndrome-based early stopping techniques as well as a scheduling scheme are proposed for the RPA decoder, which help in reducing the computational complexity while keeping similar decoding performance. Comparing to the baseline RPA decoder, the proposed techniques result in a 69−98% reduction in the average computational complexity for a target frame error rate (FER) of 10−5. Additionally, this work introduces hardware-friendly approximation functions to replace the RPA’s computationally expensive transcendental projection function.