Tensor-Based Channel Estimation for Millimeter-Wave Massive MIMO by Exploiting Sparsity in Delay-Angular Domain

Millimeter-wave massive multiple-input multiple-output employing a large-scale antenna array is a promising technology for 5G and 6G cellular networks. It also provides strong support for high-speed, low-latency communications and diverse applications. In order to enhance the accuracy and efficiency of channel estimation, in this paper, we formulate a tensor-based channel estimation model with sparse regularization aiming at characterizing the sparse structure of a large-scale channel in the delay-angular domain. An efficient subspace Newton least squares algorithm is designed to solve the nonconvex discontinuous tensor-based model, operating in restricted subspaces and being capable of handling singularities of the Hessian matrix. Our proposed algorithm is also proved to enjoy global and linear (or sublinear) convergence. Some numerical simulations are performed, which demonstrate the feasibility of our proposed model and the time validity of our algorithm.