Multi-User Massive MIMO Channel Estimation Based on Kalman Filters

Graduation date: 2017In wireless communication, channel state information (CSI) is essential for data detection. Fast fading coefficients estimation is important in order to acquire accurate CSI. Kalman filters (KF) are widely used for real time parameter estimation and can be used to estimate the fast fading coefficients of a mobile communication channel. Previous attempts at applying the KF to estimate fast fading coefficients of a massive multiple input multiple output (MIMO) channel assume that the channel autocorrelation is constant or varies weakly. Due to the fact that the carrier frequency of 5G massive MIMO systems reach tens of giga hertz, the channel autocorrelation could vary more acutely. The large number of antennas used in massive MIMO also increases the size of channel coefficients matrix. Therefore, some previous approaches based on nonlinear KF lead to high computational complexity. In order to improve system robustness of a nonlinear time varying channel and ease the computational demand, a combined channel coefficient and autocorrelation estimator based on the KF is presented in this thesis. With the substantially improved receiver channel diversity provided by the massive MIMO system, a fairly accurate channel autocorrelation estimate can be achieved with linear estimator. Compared to previous non-linear estimators, the proposed method is more practical because the computational complexity is reduced substantially. It is shown through simulation that our combined channel coefficients and autocorrelation estimator can improve the mean square error (MSE) for all possible variations of channel autocorrelation