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[2602.20178] Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis
Summary
This paper presents a data-driven approach to Multiuser Multiple-Input Multiple-Output (MU-MIMO) detection, introducing a novel architecture called GNNSIC that enhances performance while reducing complexity and training requirements.
Why It Matters
The study addresses critical challenges in MU-MIMO systems, particularly in symbol detection amidst inter-user interference and Channel State Information (CSI) uncertainty. By proposing a new architecture that leverages graph neural networks, it offers a more efficient solution that could significantly impact wireless communication technologies.
Key Takeaways
- GNNSIC improves upon DeepSIC by reducing the number of trainable parameters while maintaining performance.
- The architecture utilizes a graph-based message-passing process, enhancing sample efficiency.
- The theoretical analysis reveals improved generalization capabilities with fewer training samples.
- Simulation results indicate comparable or better Symbol Error Rate (SER) performance than existing models.
- This research could lead to advancements in wireless communication systems and their efficiency.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.20178 (eess) [Submitted on 13 Feb 2026] Title:Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis Authors:Yongwei Yi, Xinping Yi, Wenjin Wang, Xiao Li, Shi Jin View a PDF of the paper titled Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis, by Yongwei Yi and 3 other authors View PDF HTML (experimental) Abstract: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 Acycli...
