# Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks

arXiv: https://arxiv.org/abs/2210.03911 · HTML: https://mlsanity.com/paper/2210.03911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 8 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Learned Factor Graphs for Inference from Stationary Time Sequences](https://mlsanity.com/paper/2006.03258/) (2020)
- [Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test](https://mlsanity.com/paper/2012.04379/) (2020)
- [Unitary Approximate Message Passing for Sparse Bayesian Learning](https://mlsanity.com/paper/2101.09954/) (2021)
- [Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO](https://mlsanity.com/paper/2002.06380/) (2020)
- [Hierarchical Codebook based Multiuser Beam Training for Millimeter Wave Massive MIMO](https://mlsanity.com/paper/2203.06438/) (2022)
- [Residual Neural Networks for Digital Predistortion](https://mlsanity.com/paper/2005.05655/) (2020)
- [Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches](https://mlsanity.com/paper/2006.08894/) (2020)
- [Extreme Learning Machine-Based Receiver for MIMO LED Communications](https://mlsanity.com/paper/1903.01551/) (2019)
- [Massive MIMO As an Extreme Learning Machine](https://mlsanity.com/paper/2007.00221/) (2020)

## Built on by (most cited first, 1 of 1)

- [Message Passing Based Two-Timescale Bayesian Learning for Joint Channel and Memory Hardware Impairments Tracking](https://mlsanity.com/paper/2607.01660/) (2026)

## Abstract (arXiv)

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical applications. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to "model" the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods.