VAMP-based channel estimation for wideband beamspace massive MIMO systems

Millimeter-wave (mmWave) Massive multiple-input multiple-output (Massive MIMO) communication can provide high-speed network services for emerging application scenarios due to the abundant spectrum resources in the high-frequency band, which has emerged as a key technology for future wireless networks. Beamspace Massive MIMO systems equipped with lensed antenna arrays (LAA) have attracted considerable attention from industry and academic since it is an effective solution with low power and low cost. However, the beam squint effect causes beamspace channel estimation to be significantly complicated in wideband beamspace Massive MIMO systems. To address this problem, we investigate a channel estimator based on the vector approximate message passing (VAMP) algorithm to improve the estimation performance. Specifically, the wideband beamspace channel estimation is firstly considered as the two-dimensional (2D) image reconstruction problem. Subsequently, by the VAMP-based scheme, the 2D natural image is accurately sparse reconstructed from noisy linear measurements, which effectively solve the channel estimation problem. Simulation results verify the effectiveness of the proposed method and highlight its excellent performance in terms of the channel estimation.