Joint Estimation of MIMO Channel Parameters Using Space–Time Correlation Matrix for Different Angular Distributions

Abstract

In this paper, we propose a new low-complexity method to jointly estimate the multiple-input multiple-output (MIMO) channel parameters namely the mean angle of arrival (AoA), the angular spread (AS) and the maximum Doppler spread (DS). We consider Gaussian and Laplacian angular distributions for the incoming AoAs in the case of a Rayleigh channel model. Our estimator is based on the magnitudes and phases of the space–time correlation functions of the received signals. To this end, closed-form expressions of the required functions were derived. Two different approaches are studied using these cross-correlation functions; first at a non zero time lag and second at two different time lags. To evaluate the robustness of the proposed estimator, the two Stage approach and the improved maximum likelihood method based on the Gauss Newton algorithm are taken as benchmarks for the mean AoA and the AS estimation. For the maximum DS, the two Rays and the auto-correlation based algorithms are chosen. Simulation results show that the proposed estimator offers more accurate estimates in almost all considered scenarios. We also compare our work to a recent joint estimator which exploits the Derivatives of the cross-correlation function (DCCF). Our method outperforms the DCCF algorithm at a lower computational cost.