Joint Angle and Delay Estimation for 3D Massive MIMO Systems Based on Parametric Channel Modeling

Abstract

Mobile data traffic is predicted to have an exponential growth in the future. In order to meet the challenge as well as the form factor limitation on the base station, 3D massive MIMO has been proposed as one of the enabling technologies to significantly increase the spectral efficiency of a wireless system. In massive MIMO systems, a base station will rely on the uplink sounding signals from mobile stations to figure out the spatial information to perform MIMO beam-forming. Accordingly, multi-dimensional parameter estimation of a MIMO wireless channel becomes crucial for such systems to realize the predicted capacity gains. In this thesis, we study separated and joint angle and delay estimation for 3D massive MIMO systems in mobile wireless communications. To be specific, we first introduce a separated low complexity time delay and angle estimation algorithm based on unitary transformation and derive the mean square error (MSE) for delay and angle estimation in the millimeter wave massive MIMO system. Furthermore, a matrix-based ESPRIT-type algorithm is applied to jointly estimate delay and angle, the mean square error (MSE) of which is also analyzed. Finally, we found that azimuth estimation is more vulnerable compared to elevation estimation. Simulation results suggest that the dimension of the underlying antenna array at the base station plays a critical role in determining the estimation performance. These insights will be useful for designing practical massive MIMO systems in future mobile wireless communications.