Physically constrained unfolded multi-dimensional OMP for large MIMO systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908769385971712 |
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| author | Klaimi, Nay Elvira, Clément Mary, Philippe Magoarou, Luc Le |
| author_facet | Klaimi, Nay Elvira, Clément Mary, Philippe Magoarou, Luc Le |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10771 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physically constrained unfolded multi-dimensional OMP for large MIMO systems Klaimi, Nay Elvira, Clément Mary, Philippe Magoarou, Luc Le Signal Processing Machine Learning 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. |
| title | Physically constrained unfolded multi-dimensional OMP for large MIMO systems |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2601.10771 |