# DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations

arXiv: https://arxiv.org/abs/2010.16283 · HTML: https://mlsanity.com/paper/2010.16283/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier](https://mlsanity.com/paper/2201.05524/) (2022)
- [DeepTx: Deep Learning Beamforming with Channel Prediction](https://mlsanity.com/paper/2202.07998/) (2022)
- [Deep Learning Based Uplink Multi-User SIMO Beamforming Design](https://mlsanity.com/paper/2309.16603/) (2023)
- [HybridDeepRx: Deep Learning Receiver for High-EVM Signals](https://mlsanity.com/paper/2106.16079/) (2021)
- [Learning OFDM Waveforms with PAPR and ACLR Constraints](https://mlsanity.com/paper/2110.10987/) (2021)
- [Deep Learning-Based Pilotless Spatial Multiplexing](https://mlsanity.com/paper/2312.05158/) (2023)
- [EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver](https://mlsanity.com/paper/2602.11834/) (2026)
- [Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems](https://mlsanity.com/paper/2106.16074/) (2021)
- [Reducing the Environmental Impact of Wireless Communication via Probabilistic Machine Learning](https://mlsanity.com/paper/2311.12807/) (2023)
- [A Machine Learning Approach for Simultaneous Demapping of QAM and APSK Constellations](https://mlsanity.com/paper/2405.09909/) (2024)
- [Out-of-Distribution Detection via Channelwise Feature Aggregation in Neural Network-Based Receivers](https://mlsanity.com/paper/2505.15570/) (2025)
- [Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions](https://mlsanity.com/paper/2509.15950/) (2025)
- [A Transformer Inspired AI-based MIMO receiver](https://mlsanity.com/paper/2510.20363/) (2025)
- [Learning During Detection: Continual Learning for Neural OFDM Receivers via DMRS](https://mlsanity.com/paper/2602.20361/) (2026)

## Abstract (arXiv)

Recently, deep learning has been proposed as a potential technique for improving the physical layer performance of radio receivers. Despite the large amount of encouraging results, most works have not considered spatial multiplexing in the context of multiple-input and multiple-output (MIMO) receivers. In this paper, we present a deep learning-based MIMO receiver architecture that consists of a ResNet-based convolutional neural network, also known as DeepRx, combined with a so-called transformation layer, all trained together. We propose two novel alternatives for the transformation layer: a maximal ratio combining-based transformation, or a fully learned transformation. The former relies more on expert knowledge, while the latter utilizes learned multiplicative layers. Both proposed transformation layers are shown to clearly outperform the conventional baseline receiver, especially with sparse pilot configurations. To the best of our knowledge, these are some of the first results showing such high performance for a fully learned MIMO receiver.