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The combination of Terahertz (THz) and massive multiple-input multiple-output(MIMO) is promising to meet the increasing data rate demand of future wirelesscommunication systems thanks to the huge bandwidth and spatial degrees offreedom. However, unique channel features such as the near-field beam spliteffect make channel estimation particularly challenging in THz massive MIMOsystems. On one hand, adopting the conventional angular domain transformationdictionary designed for low-frequency far-field channels will result indegraded channel sparsity and destroyed sparsity structure in the transformeddomain. On the other hand, most existing compressive sensing-based channelestimation algorithms cannot achieve high performance and low complexitysimultaneously. To alleviate these issues, in this paper, we first adoptfrequency-dependent near-field dictionaries to maintain good channel sparsityand sparsity structure in the transformed domain under the near-field beamsplit effect. Then, a deep unfolding-based wideband THz massive MIMO channelestimation algorithm is proposed. In each iteration of the unitary approximatemessage passing-sparse Bayesian learning algorithm, the optimal update rule islearned by a deep neural network (DNN), whose structure is customized toeffectively exploit the inherent channel patterns. Furthermore, a mixedtraining method based on novel designs of the DNN structure and the lossfunction is developed to effectively train data from different systemconfigurations. Simulation results validate the superiority of the proposedalgorithm in terms of performance, complexity, and robustness.

Deep Unfolding-Based Channel Estimation for Wideband TeraHertz Near-Field Massive MIMO Systems