Pilot Contamination Resilient Dual-Space Channel Estimation for Massive MIMO-HBF Systems

In this paper, a novel dual-space (DS) two-stage channel estimation scheme is proposed for massive multiple-input multiple-output hybrid beamforming (m-MIMO-HBF) systems with pilot contamination. This is the first work in m-MIMO which takes into account both HBF and pilot contamination. The proposed channel estimation scheme consists of two stages. In Stage I, a coarse sparse channel estimation is conducted in the beamspace based on the proposed variable-stepsize adaptive matching pursuit (VS-AMP) algorithm, which enhances the channel recovery accuracy by carefully selecting and setting the scaling factor and step size. In Stage II, the impact of pilot contamination from interfering users is initially mitigated through the beamspace separability, while coarse channel estimate is further refined into a quasi-sparse format by subspace projection. Simulation results show that the proposed channel estimation scheme outperforms the state-of-the-art schemes in terms of normalized mean square error (NMSE) of channel estimation and bit error rate. The NMSE of the proposed channel estimation scheme also exhibits higher resilience to interference intensity while maintaining comparable computational complexity.