Complexity Reduction in AMP Iterative Detection: A New Approach with Error Function-Aided Mechanism and Convergence-Based Termination

Approximate message passing (AMP) iterative detection is recognized as a reliable and practical approach for multiple-input multiple-output (MIMO) systems. However, existing AMP detection algorithms face a critical challenge: high computational complexity due to redundant iterations, making them impractical for the coming 6G networks with increased data throughput demands. This paper addresses this challenge by investigating the mutual information (MI) update flow in AMP iterative MIMO detection and introducing a precise MI computation mechanism based on the error function, referred to as the EFA mechanism. Leveraging the EFA mechanism, we propose a convergence-based termination (CT) scheme to accurately track the convergent iteration number and eliminate redundant iterations in AMP iterative detection. Numerical results demonstrate that the MI flow calculated using the EFA mechanism is consistent with the convergence behavior of AMP iterative MIMO detection across different iterations and signal-to-noise ratios (SNRs). Specifically, the EFA mechanism can precisely identify the convergent iteration number and corresponding SNR. Additionally, the CT scheme achieves up to a 80% reduction in complexity compared to original AMP detection, while maintaining the expected BER performance.