Low-Complexity Tensor-Based Channel Parameter Estimation for mmWave MIMO-Assisted Systems

This paper presents a low-complexity tensor-based channel estimation framework for downlink mmWave massive MIMO systems using passive Butler-matrix beamforming. By modeling received signals as a third-order PARAFAC tensor, we exploit known pilot structures via bilinear alternating least-squares (BALS) optimization to significantly reduce computational overhead. The decoupled spatial factors are then processed via 1D and 2D ESPRIT to extract angles of arrival and departure with automatic pairing. Additionally, we derive the Cramér--Rao lower bound (CRLB) for this architecture. Simulations confirm the proposed estimator outperforms conventional least-squares, achieving accurate channel reconstruction and approaching the CRLB at moderate-to-high signal-to-noise ratios.