Soft MIMO Detection Using Marginal Posterior Probability Statistics

TL;DR

This paper introduces a novel soft MIMO detection algorithm that transforms posterior probability distributions into Gaussian forms using optimal transport and order statistics, enabling efficient recovery of LLRs with improved performance in fading and correlated channels.

Contribution

It proposes a new MPPS-based soft MIMO detection method that simplifies posterior distributions and employs a lightweight neural network for low-complexity LLR recovery.

Findings

Significant performance improvement over traditional methods.

Effective in fading and correlated channel conditions.

Reduces computational complexity with a lightweight network.

Abstract

Soft demodulation of received symbols into bit log-likelihood ratios (LLRs) is at the very heart of multiple-input-multiple-output (MIMO) detection. However, the optimal maximum a posteriori (MAP) detector is complicated and infeasible to be used in a practical system. In this paper, we propose a soft MIMO detection algorithm based on marginal posterior probability statistics (MPPS). With the help of optimal transport theory and order statistics theory, we transform the posteriori probability distribution of each layer into a Gaussian distribution. Then the full sampling paths can be implicitly restored from the first- and second-order moment statistics of the transformed distribution. A lightweight network is designed to learn to recovery the log-MAP LLRs from the moment statistics with low complexity. Simulation results show that the proposed algorithm can improve the performance significantly with reduced samples under fading and correlated channels.