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Channel estimation in wideband millimeter-wave (mmWave) systems is verychallenging due to the beam squint effect. To solve the problem, we propose alearnable iterative shrinkage thresholding algorithm-based channel estimator(LISTA-CE) based on deep learning. The proposed channel estimator can learn totransform the beam-frequency mmWave channel into the domain with sparsefeatures through training data. The transform domain enables us to adopt asimple denoiser with few trainable parameters. We further enhance theadaptivity of the estimator by introducing hypernetwork to automaticallygenerate learnable parameters for LISTA-CE online. Simulation results show thatthe proposed approach can significantly outperform the state-of-the-art deeplearning-based algorithms with lower complexity and fewer parameters and adaptto new scenarios rapidly.

Adaptive Channel Estimation Based on Model-Driven Deep Learning for Wideband mmWave Systems