Generalized Phase Shift Pilots for Massive MIMO-OFDM Channel Estimation

We investigate a non-orthogonal pilot design for massive multiple-input multiple-output (MIMO) channel estimation with orthogonal frequency division multiplexing (OFDM) modulation. With beam based channel model, we formulate a signal model for channel acquisition with a comb-type pilot structure and derive its simplified equivalent form. Then, we propose generalized phase shift pilots (GPSPs) and reveal their relevant properties. We prove that the inter-user interference with the proposed pilot is negligible when the weight sequence length is large enough, validating its feasibility, especially when the statistical channel state information (CSI) is unavailable. By leveraging the properties of the GPSPs, we provide an efficient implementation for channel estimation with the Gaussian generalized approximate message passing sparse Bayesian learning (GGAMP-SBL) algorithm, where the number of complex multiplications is significantly reduced. Simulation results indicate that the proposed pilot enables low-complexity channel estimation while satisfactory performance is ensured.