Simultaneous Channel Estimation and Localization of Terahertz Massive MIMO Systems via Bayesian Tensor Decomposition

Abstract

In this letter, a Bayesian tensor-based scheme is proposed for simultaneous channel estimation and localization (SCEAL) in wideband terahertz (THz) massive multiple-input multiple-output (MIMO) systems with hybrid analog-digital architectures. Considering the beam squint effect, we construct the received training signal into a sixth-order tensor model, including the angle-of-arrival (AOA)/angle-of-departure (AOD), time delay, and path gains. Then, a real-valued Bayesian inference algorithm is developed to fit the constructed tensor model for SCEAL. The proposed algorithm achieves SCEAL with and without line-of-sight (LOS) path, and offers higher accuracy compared to the state-of-the-art algorithms. Simulations verify the effectiveness of the proposed algorithm.