# Learning to Detect

arXiv: https://arxiv.org/abs/1805.07631 · HTML: https://mlsanity.com/paper/1805.07631/

## Key facts

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

## Built on by (most cited first, 30 of 63)

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- [Deep Unfolding: Recent Developments, Theory, and Design Guidelines](https://mlsanity.com/paper/2512.03768/) (2025)
- [RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection](https://mlsanity.com/paper/2110.02219/) (2021)
- [Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis](https://mlsanity.com/paper/2207.10939/) (2022)
- [Bit Error and Block Error Rate Training for ML-Assisted Communication](https://mlsanity.com/paper/2210.14103/) (2022)
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- [Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review](https://mlsanity.com/paper/2401.11790/) (2024)
- [Fixed points of nonnegative neural networks](https://mlsanity.com/paper/2106.16239/) (2021)
- [Layered Depth Refinement with Mask Guidance](https://mlsanity.com/paper/2206.03048/) (2022)
- [Channel charting based beamforming](https://mlsanity.com/paper/2212.12340/) (2022)
- [GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection](https://mlsanity.com/paper/2404.14886/) (2024)
- [Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems](https://mlsanity.com/paper/2107.11958/) (2021)
- [Learning to Estimate RIS-Aided mmWave Channels](https://mlsanity.com/paper/2107.12631/) (2021)
- [Learn to Communicate with Neural Calibration: Scalability and Generalization](https://mlsanity.com/paper/2110.00272/) (2021)
- [Neural Decoding with Optimization of Node Activations](https://mlsanity.com/paper/2206.00786/) (2022)
- [CFARnet: deep learning for target detection with constant false alarm rate](https://mlsanity.com/paper/2208.02474/) (2022)
- [Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing](https://mlsanity.com/paper/2208.09287/) (2022)
- [Model-based Deep Learning for Beam Prediction based on a Channel Chart](https://mlsanity.com/paper/2312.02239/) (2023)
- [Learning based signal detection for MIMO systems with unknown noise statistics](https://mlsanity.com/paper/2101.08435/) (2021)
- [CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity](https://mlsanity.com/paper/2102.12756/) (2021)

## 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.