Learned Trimmed-Ridge Regression for Channel Estimation in Millimeter-Wave Massive MIMO
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929450737729536 |
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| author | Wu, Pengxia Cheng, Julian Eldar, Yonina C. Cioffi, John M. |
| author_facet | Wu, Pengxia Cheng, Julian Eldar, Yonina C. Cioffi, John M. |
| contents | Channel estimation poses significant challenges in millimeter-wave massive multiple-input multiple-output systems, especially when the base station has fewer radio-frequency chains than antennas. To address this challenge, one promising solution exploits the beamspace channel sparsity to reconstruct full-dimensional channels from incomplete measurements. This paper presents a model-based deep learning method to reconstruct sparse, as well as approximately sparse, vectors fast and accurately. To implement this method, we propose a trimmed-ridge regression that transforms the sparse-reconstruction problem into a least-squares problem regularized by a nonconvex penalty term, and then derive an iterative solution. We then unfold the iterations into a deep network that can be implemented in online applications to realize real-time computations. To this end, an unfolded trimmed-ridge regression model is constructed using a structural configuration to reduce computational complexity and a model ensemble strategy to improve accuracy. Compared with other state-of-the-art deep learning models, the proposed learning scheme achieves better accuracy and supports higher downlink sum rates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02934 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learned Trimmed-Ridge Regression for Channel Estimation in Millimeter-Wave Massive MIMO Wu, Pengxia Cheng, Julian Eldar, Yonina C. Cioffi, John M. Information Theory Signal Processing Channel estimation poses significant challenges in millimeter-wave massive multiple-input multiple-output systems, especially when the base station has fewer radio-frequency chains than antennas. To address this challenge, one promising solution exploits the beamspace channel sparsity to reconstruct full-dimensional channels from incomplete measurements. This paper presents a model-based deep learning method to reconstruct sparse, as well as approximately sparse, vectors fast and accurately. To implement this method, we propose a trimmed-ridge regression that transforms the sparse-reconstruction problem into a least-squares problem regularized by a nonconvex penalty term, and then derive an iterative solution. We then unfold the iterations into a deep network that can be implemented in online applications to realize real-time computations. To this end, an unfolded trimmed-ridge regression model is constructed using a structural configuration to reduce computational complexity and a model ensemble strategy to improve accuracy. Compared with other state-of-the-art deep learning models, the proposed learning scheme achieves better accuracy and supports higher downlink sum rates. |
| title | Learned Trimmed-Ridge Regression for Channel Estimation in Millimeter-Wave Massive MIMO |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2408.02934 |