Mutual Information Analysis for Factor Graph-based MIMO Iterative Detections through Error Functions

TL;DR

This paper introduces an error functions-aided analysis mechanism to accurately analyze mutual information flow in factor graph-based iterative MIMO detection, providing insights into convergence and performance bounds.

Contribution

It proposes a novel EF-AA mechanism for precise mutual information computation in FG-based MIMO detection, enhancing convergence analysis accuracy.

Findings

The EF-AA mechanism's theoretical results match BER performance.

It reveals exact convergence iteration number and SNR value.

Provides a performance bound for FG-based iterative MIMO detection.

Abstract

The factor graph (FG) based iterative detection is considered an effective and practical method for multiple-input and multiple-out (MIMO), particularly massive MIMO (m-MIMO) systems. However, the convergence analysis for the FG-based iterative MIMO detection is insufficient, which is of great significance to the performance evaluation and algorithm design of detection methods. This paper investigates the mutual information update flow for the FG-based iterative MIMO detection and proposes a precise mutual information computation mechanism with the aid of Gaussian approximation and error functions, i.e., the error functions-aided analysis (EF-AA) mechanism. Numerical results indicate that the theoretical result calculated by the EF-AA mechanism is completely consistent with the bit error rate performance of the FG-based iterative MIMO detection. Furthermore, the proposed EF-AA mechanism can reveal the exact convergent iteration number and convergent signal-to-ratio value of the FG-based iterative MIMO detection, representing the performance bound of the MIMO detection.