mlsanityData to 7 Oct 2026

Deep MIMO Detection

arXiv 1706.01151 ↗First posted 4 Jun 2017stat.ML cs.IT cs.LGcited by 26

Not on the map. Why?

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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jun 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 4; 2023: 4; 2024: 4; 2025: 7; 2026: 1.

26 papers.

Sort:

By area:

  1. 2021Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspectivecited by 12
  2. 2023Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issuescited by 12
  3. 2022Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel chartingcited by 10
  4. 2023Knowledge Distillation Based Semantic Communications For Multiple Userscited by 5
  5. 2024GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detectioncited by 5
  6. 2024Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrayscited by 5
  7. 2023Deep Learning Based Uplink Multi-User SIMO Beamforming Designcited by 4
  8. 2021Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetworkcited by 3
  9. 2021The Next Decade of Telecommunications Artificial Intelligencecited by 3
  10. 2021CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexitycited by 3
  11. 2025Soft Graph Transformer for MIMO Detectioncited by 3
  12. 2023Model-based Deep Learning for High-Dimensional Periodic Structurescited by 1
14 more
  1. 2025In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theorycited by 1
  2. 2021Robust MIMO Detection using Hypernetworks with Learned Regularizerscited by 0
  3. 2021To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?cited by 0
  4. 2022Deep Learning-Aided Spatial Multiplexing with Index Modulationcited by 0
  5. 2022Deep Convolutional Learning-Aided Detector for Generalized Frequency Division Multiplexing with Index Modulationcited by 0
  6. 2022MetaSSD: Meta-Learned Self-Supervised Detectioncited by 0
  7. 2024Truncated Polynomial Expansion-Based Detection in Massive MIMO: A Model-Driven Deep Learning Approachcited by 0
  8. 2024Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learningcited by 0
  9. 2025A CNN-based End-to-End Learning for RIS-assisted Communication Systemcited by 0
  10. 2025Channel Estimation by Infinite Width Convolutional Networkscited by 0
  11. 2025Model-based Implicit Neural Representation for sub-wavelength Radio Localizationcited by 0
  12. 2025MoE-CE: Enhancing Generalization for Deep Learning based Channel Estimation via a Mixture-of-Experts Frameworkcited by 0
  13. 2025A Transformer Inspired AI-based MIMO receivercited by 0
  14. 2026Soft Graph Diffusion Transformer for MIMO Detectioncited by 0