# Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis

arXiv: https://arxiv.org/abs/2602.20178 · HTML: https://mlsanity.com/paper/2602.20178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 23 of 23)

- [Spectrally-normalized margin bounds for neural networks](https://mlsanity.com/paper/1706.08498/) (2017)
- [Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks](https://mlsanity.com/paper/1703.02930/) (2017)
- [Norm-Based Capacity Control in Neural Networks](https://mlsanity.com/paper/1503.00036/) (2015)
- [Size-Independent Sample Complexity of Neural Networks](https://mlsanity.com/paper/1712.06541/) (2017)
- [Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks](https://mlsanity.com/paper/1905.13210/) (2019)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation](https://mlsanity.com/paper/1905.03684/) (2019)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Adaptive Neural Signal Detection for Massive MIMO](https://mlsanity.com/paper/1906.04610/) (2019)
- [ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection](https://mlsanity.com/paper/1905.10750/) (2019)
- [DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection](https://mlsanity.com/paper/2002.03214/) (2020)
- [RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection](https://mlsanity.com/paper/2007.00140/) (2020)
- [Generalization Bounds for Neural Networks via Approximate Description Length](https://mlsanity.com/paper/1910.05697/) (2019)
- [Online Meta-Learning For Hybrid Model-Based Deep Receivers](https://mlsanity.com/paper/2203.14359/) (2022)
- [Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems](https://mlsanity.com/paper/2302.06896/) (2023)
- [Modular Hypernetworks for Scalable and Adaptive Deep MIMO Receivers](https://mlsanity.com/paper/2408.11920/) (2024)
- [Generalization Bounds for Neural Belief Propagation Decoders](https://mlsanity.com/paper/2305.10540/) (2023)
- [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)
- [On Purely Data-Driven Massive MIMO Detectors](https://mlsanity.com/paper/2401.07515/) (2024)
- [Understanding Deep MIMO Detection](https://mlsanity.com/paper/2105.05044/) (2021)
- [Graph Neural Network-Enhanced Expectation Propagation Algorithm for MIMO Turbo Receivers](https://mlsanity.com/paper/2308.11335/) (2023)
- [Deep Learning for Near-Field XL-MIMO Transceiver Design: Principles and Techniques](https://mlsanity.com/paper/2309.09575/) (2023)

## Abstract (arXiv)

In practical Multiuser Multiple-Input Multiple-Output (MU-MIMO) systems, symbol detection remains challenging due to severe inter-user interference and sensitivity to Channel State Information (CSI) uncertainty. In contrast to the mostly studied belief propagation-type model-driven methods, which incur high computational complexity, Soft Interference Cancellation (SIC) strikes a good balance between performance and complexity. To further address CSI mismatch and nonlinear effects, the recently proposed data-driven deep neural receivers, such as DeepSIC, leverage the advantages of deep neural networks for interference cancellation and symbol detection, demonstrating strong empirical performance. However, there is still a lack of theoretical underpinning for why and to what extent DeepSIC could generalize with the number of training samples. This paper proposes inspecting the fully data-driven DeepSIC detection within a Network-of-MLPs architecture, which is composed of multiple interconnected MLPs via outer and inner Directed Acyclic Graphs (DAGs). Within such an architecture, DeepSIC can be upgraded as a graph-based message-passing process using Graph Neural Networks (GNNs), termed GNNSIC, with shared model parameters across users and iterations. Notably, GNNSIC achieves excellent expressivity comparable to DeepSIC with substantially fewer trainable parameters, resulting in improved sample efficiency and enhanced user generalization. By conducting a norm-based generalization analysis using Rademacher complexity, we reveal that an exponential dependence on the number of iterations for DeepSIC can be eliminated in GNNSIC due to parameter sharing. Simulation results demonstrate that GNNSIC attains comparable or improved Symbol Error Rate (SER) performance to DeepSIC with significantly fewer parameters and training samples.