# GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection

arXiv: https://arxiv.org/abs/2404.14886 · HTML: https://mlsanity.com/paper/2404.14886/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 16 of 16)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Revisiting Heterophily For Graph Neural Networks](https://mlsanity.com/paper/2210.07606/) (2022)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks](https://mlsanity.com/paper/1906.02174/) (2019)
- [Inference in Probabilistic Graphical Models by Graph Neural Networks](https://mlsanity.com/paper/1803.07710/) (2018)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection](https://mlsanity.com/paper/2007.00140/) (2020)
- [Graph Neural Networks for Massive MIMO Detection](https://mlsanity.com/paper/2007.05703/) (2020)
- [Graph Neural Network Aided Expectation Propagation Detector for MU-MIMO Systems](https://mlsanity.com/paper/2201.03731/) (2022)
- [Representation Learning on Heterophilic Graph with Directional Neighborhood Attention](https://mlsanity.com/paper/2403.01475/) (2024)
- [Deep Learning-Aided Tabu Search Detection for Large MIMO Systems](https://mlsanity.com/paper/1909.01683/) (2019)
- [GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection](https://mlsanity.com/paper/2205.10620/) (2022)
- [Leveraging Deep Neural Networks for Massive MIMO Data Detection](https://mlsanity.com/paper/2204.05350/) (2022)
- [A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems](https://mlsanity.com/paper/2110.14112/) (2021)
- [Graph Neural Network Aided MU-MIMO Detectors](https://mlsanity.com/paper/2206.09381/) (2022)

## Built on by (most cited first, 5 of 5)

- [The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges](https://mlsanity.com/paper/2407.09618/) (2024)
- [What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks](https://mlsanity.com/paper/2406.18854/) (2024)
- [Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning](https://mlsanity.com/paper/2409.05755/) (2024)
- [Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning](https://mlsanity.com/paper/2409.09888/) (2024)
- [GraphTARIF: Linear Graph Transformer with Augmented Rank and Improved Focus](https://mlsanity.com/paper/2510.10631/) (2025)

## Abstract (arXiv)

Massive MIMO (multiple-input multiple-output) detection is an important topic in wireless communication and various machine learning based methods have been developed recently for this task. Expectation Propagation (EP) and its variants are widely used for MIMO detection and have achieved the best performance. However, EP-based solvers fail to capture the correlation between unknown variables, leading to a loss of information, and in addition, they are computationally expensive. In this paper, we show that the real-valued system can be modeled as spectral signal convolution on graph, through which the correlation between unknown variables can be captured. Based on such analysis, we propose graph convolution-enhanced expectation propagation (GCEPNet). GCEPNet incorporates data-dependent attention scores into Chebyshev polynomial for powerful graph convolution with better generalization capacity. It enables a better estimation of the cavity distribution for EP and empirically achieves the state-of-the-art (SOTA) MIMO detection performance with much faster inference speed. To our knowledge, we are the first to shed light on the connection between the system model and graph convolution, and the first to design the data-dependent coefficients for graph convolution.