# Deep MIMO Detection

arXiv: https://arxiv.org/abs/1706.01151 · HTML: https://mlsanity.com/paper/1706.01151/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2017.

## Built on by (most cited first, 26 of 26)

- [Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective](https://mlsanity.com/paper/2105.01696/) (2021)
- [Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues](https://mlsanity.com/paper/2303.08106/) (2023)
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- [Knowledge Distillation Based Semantic Communications For Multiple Users](https://mlsanity.com/paper/2311.13789/) (2023)
- [GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection](https://mlsanity.com/paper/2404.14886/) (2024)
- [Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays](https://mlsanity.com/paper/2411.15144/) (2024)
- [Deep Learning Based Uplink Multi-User SIMO Beamforming Design](https://mlsanity.com/paper/2309.16603/) (2023)
- [Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork](https://mlsanity.com/paper/2101.04348/) (2021)
- [The Next Decade of Telecommunications Artificial Intelligence](https://mlsanity.com/paper/2101.09163/) (2021)
- [CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity](https://mlsanity.com/paper/2102.12756/) (2021)
- [Soft Graph Transformer for MIMO Detection](https://mlsanity.com/paper/2509.12694/) (2025)
- [Model-based Deep Learning for High-Dimensional Periodic Structures](https://mlsanity.com/paper/2309.12223/) (2023)
- [In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory](https://mlsanity.com/paper/2506.15176/) (2025)
- [MoE-CE: Enhancing Generalization for Deep Learning based Channel Estimation via a Mixture-of-Experts Framework](https://mlsanity.com/paper/2509.15964/) (2025)
- [Robust MIMO Detection using Hypernetworks with Learned Regularizers](https://mlsanity.com/paper/2110.07053/) (2021)
- [To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?](https://mlsanity.com/paper/2112.14011/) (2021)
- [Deep Learning-Aided Spatial Multiplexing with Index Modulation](https://mlsanity.com/paper/2202.02856/) (2022)
- [Deep Convolutional Learning-Aided Detector for Generalized Frequency Division Multiplexing with Index Modulation](https://mlsanity.com/paper/2202.02876/) (2022)
- [MetaSSD: Meta-Learned Self-Supervised Detection](https://mlsanity.com/paper/2205.15271/) (2022)
- [Truncated Polynomial Expansion-Based Detection in Massive MIMO: A Model-Driven Deep Learning Approach](https://mlsanity.com/paper/2402.12595/) (2024)
- [Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning](https://mlsanity.com/paper/2410.16760/) (2024)
- [A CNN-based End-to-End Learning for RIS-assisted Communication System](https://mlsanity.com/paper/2503.13976/) (2025)
- [Channel Estimation by Infinite Width Convolutional Networks](https://mlsanity.com/paper/2504.08660/) (2025)
- [Model-based Implicit Neural Representation for sub-wavelength Radio Localization](https://mlsanity.com/paper/2506.06387/) (2025)
- [A Transformer Inspired AI-based MIMO receiver](https://mlsanity.com/paper/2510.20363/) (2025)
- [Soft Graph Diffusion Transformer for MIMO Detection](https://mlsanity.com/paper/2605.00449/) (2026)

## Abstract (arXiv)

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.