Time Varying Wireless Channel Modeling, Estimation, Identification, and Power Control from Measurements

This paper is concerned with a time-varying wireless channel modeling, its parameter estimation, system identification, and optimal power control from measurement data. The channel model is represented in state space form, while the Expectation Maximization algorithm and Kalman filtering are used in the channel parameter and state estimation, respectively. The inphase and quadrature components of the wireless channel and its parameters are estimated from received signal measurements. The proposed algorithm is tested using measurement data, and the results are presented. Moreover, an optimal power control algorithm based on the estimated parameters and channel states is proposed. Numerical results indicate that a significant gain in performance can be achieved using the proposed algorithms.