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

TL;DR

This paper introduces a deep learning-based adaptive channel estimator for wideband mmWave systems that effectively addresses beam squint effects, outperforming existing methods in accuracy and efficiency.

Contribution

It proposes a novel LISTA-CE algorithm with hypernetwork integration for online adaptivity, improving channel estimation in wideband mmWave systems.

Findings

Significantly outperforms state-of-the-art algorithms

Lower complexity and fewer parameters

Rapid adaptation to new scenarios

Abstract

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.