# Soft MIMO Detection Using Marginal Posterior Probability Statistics

arXiv: https://arxiv.org/abs/2208.08045 · HTML: https://mlsanity.com/paper/2208.08045/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2022.

## Built on by (most cited first, 2 of 2)

- [Invariant Transformation and Resampling based Epistemic-Uncertainty Reduction](https://mlsanity.com/paper/2602.23315/) (2026)
- [Dual-Attention Based 3D Channel Estimation](https://mlsanity.com/paper/2604.01769/) (2026)

## Abstract (arXiv)

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.