Optimized Min-Sum Decoding Algorithm for Low Density Parity Check Codes

Abstract

Low Density Parity Check (LDPC) code approaches Shannon-limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized minsum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Minsum algorithm. The optimization factor is obtained before decoding program, and the same factor is multiplied twice in one cycle. So the increased complexity is fairly low. Simulation results show that the proposed Optimized Min-Sum decoding algorithm performs very close to the Sum-Product decoding while preserving the main features of the Min-Sum decoding, that is low complexity and independence with respect to noise variance estimation errors.

Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs

“…7.7.4]. For the decoding of the received signal vector, we apply an optimized min-sum algorithm [48] with 50 iterations. In contrast to that, the subcode prediction uses only 5 iterations.…”