Multilevel MIMO Detection with Deep Learning
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Abstract (arXiv)
A quasi-static flat multiple-antenna channel is considered. We show how real multilevel modulation symbols can be detected via deep neural networks. A multi-plateau sigmoid function is introduced. Then, after showing the DNN architecture for detection, we propose a twin-network neural structure. Batch size and training statistics for efficient learning are investigated. Near-Maximum-Likelihood performance with a relatively reasonable number of parameters is achieved.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2018.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 1.
7 papers.
- 2021LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receiverscited by 7
- 2023Model-based Deep Learning for Beam Prediction based on a Channel Chartcited by 4
- 2022Graph Neural Network Aided MU-MIMO Detectorscited by 2
- 2021Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detectioncited by 0
- 2022Deep Learning-Aided Spatial Multiplexing with Index Modulationcited by 0
- 2022Deep Convolutional Learning-Aided Detector for Generalized Frequency Division Multiplexing with Index Modulationcited by 0
- 2024Differentiable High-Order Markov Models for Spectrum Predictioncited by 0
- 2024Differentiable High-Order Markov Models for Spectrum Prediction30 Nov 2024
- 2023Model-based Deep Learning for Beam Prediction based on a Channel Chart4 Dec 2023
- 2022Graph Neural Network Aided MU-MIMO Detectors19 Jun 2022
- 2022Deep Convolutional Learning-Aided Detector for Generalized Frequency Division Multiplexing with Index Modulation6 Feb 2022
- 2022Deep Learning-Aided Spatial Multiplexing with Index Modulation6 Feb 2022
- 2021LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers5 Feb 2021
- 2021Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection30 Jan 2021