Iterative Detection and Decoding for MIMO Systems with Knowledge-Aided Message Passing Algorithms

TL;DR

This paper introduces innovative belief propagation decoding algorithms for iterative detection and decoding in MIMO systems, leveraging graph cycle knowledge and hypergraph reweighting to enhance performance.

Contribution

The paper proposes two novel BP decoding algorithms that exploit graph cycle information and hypergraph reweighting, improving IDD performance in MIMO systems.

Findings

Proposed BP algorithms outperform existing methods in simulations.

The algorithms achieve better decoding with fewer iterations.

Enhanced detection accuracy in multi-antenna systems.

Abstract

In this paper, we consider the problem of iterative detection and decoding (IDD) for multi-antenna systems using low-density parity-check (LDPC) codes. The proposed IDD system consists of a soft-input soft-output parallel interference (PIC) cancellation scheme with linear minimum mean-square error (MMSE) receive filters and two novel belief propagation (BP) decoding algorithms. The proposed BP algorithms exploit the knowledge of short cycles in the graph structure and the reweighting factors derived from the hypergraph's expansion. Simulation results show that when used to perform IDD for multi-antenna systems both proposed BP decoding algorithms can consistently outperform existing BP techniques with a small number of decoding iterations.