Physically constrained unfolded multi-dimensional OMP for large MIMO systems

2026-01-15

AI Summary

This work addresses the limitations of conventional sparse recovery methods in massive MIMO systems, which suffer from model mismatch and high computational complexity due to large-dimensional dictionaries, leading to reduced reliability. To overcome these challenges, the paper proposes MOMPnet—a novel sparse recovery framework that integrates deep unfolding with data-driven multi-dictionary learning. By employing multiple compact, independent dictionaries, MOMPnet enables low-complexity multidimensional orthogonal matching pursuit while embedding physical constraints to ensure interpretability and robustness against hardware impairments. Experimental results on real-world channel data demonstrate that MOMPnet significantly outperforms existing baseline methods, offering a compelling combination of computational efficiency, adaptability, and practical deployment potential.

Technology Category

• Machine Learning: Multimodal Learning
• Search and Optimization: Non-convex Optimization
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Sparse recovery methods are essential for channel estimation and localization in modern communication systems, but their reliability relies on accurate physical models, which are rarely perfectly known. Their computational complexity also grows rapidly with the dictionary dimensions in large MIMO systems. In this paper, we propose MOMPnet, a novel unfolded sparse recovery framework that addresses both the reliability and complexity challenges of traditional methods. By integrating deep unfolding with data-driven dictionary learning, MOMPnet mitigates hardware impairments while preserving interpretability. Instead of a single large dictionary, multiple smaller, independent dictionaries are employed, enabling a low-complexity multidimensional Orthogonal Matching Pursuit algorithm. The proposed unfolded network is evaluated on realistic channel data against multiple baselines, demonstrating its strong performance and potential.

Problem

• sparse recovery
• large MIMO
• channel estimation
• computational complexity
• physical model

Innovation

• deep unfolding
• dictionary learning
• sparse recovery
• large MIMO
• multidimensional OMP