Learned Trimmed-Ridge Regression for Channel Estimation in Millimeter-Wave Massive MIMO
<|reference_start|>Learned Trimmed-Ridge Regression for Channel Estimation in Millimeter-Wave Massive MIMO: 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.<|reference_end|>
@article{wu2024learned,
    title={Learned Trimmed-Ridge Regression for Channel Estimation in
  Millimeter-Wave Massive MIMO},
    author={Pengxia Wu, Julian Cheng, Yonina C. Eldar, John M. Cioffi},
    journal={arXiv preprint arXiv:2408.02934},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02934},
    primaryClass={cs.IT eess.SP math.IT}
}