mlsanityData to 14 Oct 2026

Learning to Detect

arXiv 1805.07631 ↗First posted 19 May 2018cs.IT cs.LG math.ITcited by 63

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Abstract (arXiv)

In this paper we consider Multiple-Input-Multiple-Output (MIMO) detection using deep neural networks. We introduce two different deep architectures: a standard fully connected multi-layer network, and a Detection Network (DetNet) which is specifically designed for the task. The structure of DetNet is obtained by unfolding the iterations of a projected gradient descent algorithm into a network. We compare the accuracy and runtime complexity of the purposed approaches and achieve state-of-the-art performance while maintaining low computational requirements. Furthermore, we manage to train a single network to detect over an entire distribution of channels. Finally, we consider detection with soft outputs and show that the networks can easily be modified to produce soft decisions.

Key facts. Cited by 63 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 May 2018.

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Citing papers per year: 2021: 20; 2022: 16; 2023: 13; 2024: 7; 2025: 4; 2026: 3.

63 papers.

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  1. 2021Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approachcited by 110
  2. 2022CholecTriplet2021: A benchmark challenge for surgical action triplet recognitioncited by 44
  3. 2023CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Freecited by 39
  4. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  5. 2023Generative AI for Physical Layer Communications: A Surveycited by 17
  6. 2021Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeletoncited by 16
  7. 2021Autoregressive Belief Propagation for Decoding Block Codescited by 15
  8. 2021Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspectivecited by 12
  9. 2021LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receiverscited by 11
  10. 2021Model-Based Machine Learning for Communicationscited by 8
  11. 2023Score-based Source Separation with Applications to Digital Communication Signalscited by 8
  12. 2025Deep Unfolding: Recent Developments, Theory, and Design Guidelinescited by 7
38 more
  1. 2021RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detectioncited by 6
  2. 2022Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysiscited by 6
  3. 2022Bit Error and Block Error Rate Training for ML-Assisted Communicationcited by 6
  4. 2022PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarizationcited by 6
  5. 2024Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Reviewcited by 6
  6. 2021Fixed points of nonnegative neural networkscited by 5
  7. 2022Layered Depth Refinement with Mask Guidancecited by 5
  8. 2022Channel charting based beamformingcited by 5
  9. 2024GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detectioncited by 5
  10. 2021Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systemscited by 4
  11. 2021Learning to Estimate RIS-Aided mmWave Channelscited by 4
  12. 2021Learn to Communicate with Neural Calibration: Scalability and Generalizationcited by 4
  13. 2022Neural Decoding with Optimization of Node Activationscited by 4
  14. 2022CFARnet: deep learning for target detection with constant false alarm ratecited by 4
  15. 2022Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processingcited by 4
  16. 2023Model-based Deep Learning for Beam Prediction based on a Channel Chartcited by 4
  17. 2021Learning based signal detection for MIMO systems with unknown noise statisticscited by 3
  18. 2021CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexitycited by 3
  19. 2021HybridDeepRx: Deep Learning Receiver for High-EVM Signalscited by 3
  20. 2022A Model-based GNN for Learning Precodingcited by 3
  21. 2023InstMove: Instance Motion for Object-centric Video Segmentationcited by 3
  22. 2021End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channelscited by 2
  23. 2022Graph Neural Network Aided MU-MIMO Detectorscited by 2
  24. 2023Low PAPR MIMO-OFDM Design Based on Convolutional Autoencodercited by 2
  25. 2023Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMMcited by 2
  26. 2021Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detectioncited by 1
  27. 2021Conceptual Expansion Neural Architecture Search (CENAS)cited by 1
  28. 2021A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weightscited by 1
  29. 2021Adversarial Neural Networks for Error Correcting Codescited by 1
  30. 2022Neuroplastic graph attention networks for nuclei segmentation in histopathology imagescited by 1
  31. 2022Learning to Detect with Constant False Alarm Ratecited by 1
  32. 2022DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systemscited by 1
  33. 2023SICNN: Soft Interference Cancellation Inspired Neural Network Equalizerscited by 1
  34. 2024Deep Learning-Based Detection for Marker Codes over Insertion and Deletion Channelscited by 1
  35. 2024Maximum-Likelihood Estimation Based on Diffusion Model For Wireless Communicationscited by 1
  36. 2026EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receivercited by 1
  37. 2021Harnessing Tensor Structures -- Multi-Mode Reservoir Computing and Its Application in Massive MIMOcited by 0
  38. 2022MetaSSD: Meta-Learned Self-Supervised Detectioncited by 0

50 most cited of 63 All 63 (JSON)