Performance of Reduced Rank Adaptive Estimation for Joint Spatial Division and Multiplexing
- Author(s): Zhao, Yue
- Advisor(s): ayanoglu, ender
- et al.
Without doubt, an unprecedented number of devices is anticipated in the near future accord- ing to the developing of generations of networks. In the incoming 5th Generation Wireless Systems, 5G in short, which is expected to accommodate billions of wireless devices, the massive multiple-input-multiple-output (MIMO) systems are prominent candidates. In such systems, the acquisition of channel state information (CSI) is of great importance to enhance spectrum efficiency (SE) and energy efficiency (EE) significantly. In the thesis, the perfor- mance of three channel estimation algorithms, Reduced-Rank Least Mean Square (RR-LMS) estimation, Reduced-Rank Recursive Least Square (RR-RLS) estimation and Reduced-Rank (RR-) Kalman Filter estimation, is presented. The optimum parameters of RR-LMS and RR-RLS are provided respectively in a first-order autoregressive (AR(1)) channel model. In a second-order autoregressive (AR(2)) channel, we focus on the α pair’s feasible range where RR-LMS and RR-RLS works. In terms of the RR-Kalman Filter algorithm, we first study the impact of parameter mismatching on estimation performance, then we present a method to estimate the channel fading coefficients α and channel variance σh2 .