Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect. To solve the problem, we propose a learnable iterative shrinkage thresholding algorithm-based channel estimator (LISTA-CE) based on deep learning. The proposed channel estimator can learn to transform the beam-frequency mmWave channel into the domain with sparse features through training data. The transform domain enables us to adopt a simple denoiser with few trainable parameters. We further enhance the adaptivity of the estimator by introducing hypernetwork to automatically generate learnable parameters for LISTA-CE online. Simulation results show that the proposed approach can significantly outperform the state-of-the-art deep learning-based algorithms with lower complexity and fewer parameters and adapt to new scenarios rapidly.
@article{arxiv.2104.13656,
title = {Adaptive Channel Estimation Based on Model-Driven Deep Learning for Wideband mmWave Systems},
author = {Weijie Jin and Hengtao He and Chao-Kai Wen and Shi Jin and Geoffrey Ye Li},
journal= {arXiv preprint arXiv:2104.13656},
year = {2021}
}