# Deep MIMO Detection

## TL;DR

This paper introduces a deep neural network-based detector for MIMO systems that achieves state-of-the-art accuracy with lower complexity and robustness, applicable to both fixed and varying channel conditions.

## Contribution

It proposes a novel neural network architecture for MIMO detection, capable of handling both constant and varying channels, outperforming traditional methods in accuracy and complexity.

## Key findings

- Deep neural networks achieve state-of-the-art accuracy in MIMO detection.
- The proposed method is robust against ill-conditioned channels.
- It offers lower computational complexity compared to existing techniques.

## Abstract

In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and propose a modern neural network architecture suitable for this detection task. First, we consider the case in which the MIMO channel is constant, and we learn a detector for a specific system. Next, we consider the harder case in which the parameters are known yet changing and a single detector must be learned for all multiple varying channels. We demonstrate the performance of our deep MIMO detector using numerical simulations in comparison to competing methods including approximate message passing and semidefinite relaxation. The results show that deep networks can achieve state of the art accuracy with significantly lower complexity while providing robustness against ill conditioned channels and mis-specified noise variance.