# RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection

arXiv: https://arxiv.org/abs/2007.00140 · HTML: https://mlsanity.com/paper/2007.00140/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 20 of 20)

- [Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems](https://mlsanity.com/paper/2302.06896/) (2023)
- [Solving Linear Inverse Problems using Higher-Order Annealed Langevin Diffusion](https://mlsanity.com/paper/2305.05014/) (2023)
- [Fast-FNet: Accelerating Transformer Encoder Models via Efficient Fourier Layers](https://mlsanity.com/paper/2209.12816/) (2022)
- [GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection](https://mlsanity.com/paper/2404.14886/) (2024)
- [Soft Graph Transformer for MIMO Detection](https://mlsanity.com/paper/2509.12694/) (2025)
- [Graph Neural Network Aided MU-MIMO Detectors](https://mlsanity.com/paper/2206.09381/) (2022)
- [Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder](https://mlsanity.com/paper/2301.05017/) (2023)
- [Deep-Learning-Aided Alternating Least Squares for Tensor CP Decomposition and Its Application to Massive MIMO Channel Estimation](https://mlsanity.com/paper/2305.13947/) (2023)
- [SICNN: Soft Interference Cancellation Inspired Neural Network Equalizers](https://mlsanity.com/paper/2308.12591/) (2023)
- [Deep Learning-Based Pilotless Spatial Multiplexing](https://mlsanity.com/paper/2312.05158/) (2023)
- [ReQuestNet: A Foundational Learning model for Channel Estimation](https://mlsanity.com/paper/2508.08790/) (2025)
- [Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems](https://mlsanity.com/paper/2106.16074/) (2021)
- [Robust MIMO Detection using Hypernetworks with Learned Regularizers](https://mlsanity.com/paper/2110.07053/) (2021)
- [Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning](https://mlsanity.com/paper/2212.12329/) (2022)
- [Generative Diffusion Model Driven Massive Random Access in Massive MIMO Systems](https://mlsanity.com/paper/2505.12382/) (2025)
- [Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network](https://mlsanity.com/paper/2510.02108/) (2025)
- [Learning Successive Interference Cancellation for Low-Complexity Soft-Output MIMO Detection](https://mlsanity.com/paper/2601.16586/) (2026)
- [Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis](https://mlsanity.com/paper/2602.20178/) (2026)
- [Soft Graph Diffusion Transformer for MIMO Detection](https://mlsanity.com/paper/2605.00449/) (2026)
- [Learning-to-Transition for Large-scale and High-Order MIMO Detection](https://mlsanity.com/paper/2608.14511/) (2026)

## Abstract (arXiv)

In this paper, we present a novel neural network for MIMO symbol detection. It is motivated by several important considerations in wireless communication systems; permutation equivariance and a variable number of users. The neural detector learns an iterative decoding algorithm that is implemented as a stack of iterative units. Each iterative unit is a neural computation module comprising of 3 sub-modules: the likelihood module, the encoder module, and the predictor module. The likelihood module injects information about the generative (forward) process into the neural network. The encoder-predictor modules together update the state vector and symbol estimates. The encoder module updates the state vector and employs a transformer based attention network to handle the interactions among the users in a permutation equivariant manner. The predictor module refines the symbol estimates. The modular and permutation equivariant architecture allows for dealing with a varying number of users. The resulting neural detector architecture is unique and exhibits several desirable properties unseen in any of the previously proposed neural detectors. We compare its performance against existing methods and the results show the ability of our network to efficiently handle a variable number of transmitters with high accuracy.