  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.
