Learning-Aided Channel Estimation for Wideband mmWave MIMO Systems With Beam Squint

Accurate channel state information is fundamental for fully unleashing the potential of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, two primary challenges hinder its acquisition. The first challenge is the insufficient research on hybrid beamformer design during the channel estimation period, contrasted with the extensive focus on the data transmission period. Secondly, conventional channel estimation schemes overlook practical effects present in mmWave wideband systems, namely beam squint. To tackle these obstacles, this paper presents a learning-aided joint optimization framework for beamformers and the geometric parameter estimation function, customized for specific environments. Inspired by the analogous operations between a neural network and a reshaped signal model, the early stages of the network are trained to emulate the reshaped signal model. After completing training, the early weight matrices are extracted to shape the beamformers, while the remainder constitutes the function. Essentially, joint optimization is accomplished by decomposing a single trained network into multiple components under specific constraints. Following the initial search for geometric parameters facilitated by these components, they undergo beam squint-specialized refinement for precise channel reconstruction. Numerical results demonstrate the adaptability of the proposed beamformers to the environment through heightened effective SNR levels. Furthermore, the superior performance of the proposed method over existing methods in channel estimation is proven.