# Multilevel MIMO Detection with Deep Learning

arXiv: https://arxiv.org/abs/1812.01571 · HTML: https://mlsanity.com/paper/1812.01571/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers](https://mlsanity.com/paper/2102.02993/) (2021)
- [Model-based Deep Learning for Beam Prediction based on a Channel Chart](https://mlsanity.com/paper/2312.02239/) (2023)
- [Graph Neural Network Aided MU-MIMO Detectors](https://mlsanity.com/paper/2206.09381/) (2022)
- [Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection](https://mlsanity.com/paper/2102.00178/) (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)
- [Differentiable High-Order Markov Models for Spectrum Prediction](https://mlsanity.com/paper/2412.00328/) (2024)

## 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.