Study of Knowledge-Aided Iterative Detection and Decoding for Multiuser MIMO Systems

TL;DR

This paper proposes a knowledge-aided iterative detection and decoding scheme for multiuser MIMO systems that reduces latency and improves performance by employing refined algorithms and reweighted belief propagation decoding.

Contribution

It introduces a novel knowledge-aided IDD system with reweighted BP decoding algorithms that leverage graph structure knowledge to enhance decoding efficiency.

Findings

Outperforms prior methods in simulation results.

Requires fewer decoding iterations.

Achieves reduced latency in LDPC decoding for multiuser MIMO systems.

Abstract

In this work, we consider the problem of reduced latency of low-density parity-check (LDPC) codes with iterative detection and decoding (IDD) receiver in multiuser multiple-antenna systems. The proposed knowledge-aided IDD (KA-IDD) system employs a minimum mean-square error detector with refined iterative processing and a reweighted belief propagation (BP) decoding algorithm. We present reweighted BP decoding algorithms, which exploit the knowledge of short cycles in the graph structure and reweighting factors derived from the expansion of hypergraphs. Simulation results show that the proposed KA-IDD scheme and algorithms outperform prior art and require a reduced number of decoding iterations.