Dynamic propagation characteristics estimation and tracking based on an EM-EKF algorithm in time-variant MIMO channel

In cognitive radio (CR), it is important to understand the multi-dimension and multi-scale characteristics of the dynamic multiple-input multiple-output (MIMO) channels obtained from observations. In this paper, to estimate and track propagation path parameters in time-variant MIMO channels, we propose an expectation maximization-extended Kalman filter (EM-EKF) in the frequency domain. The proposed algorithm can capture the dynamic channel with a high accuracy and a low time consumption. We use the EKF in the frequency domain to detect existed paths and to continue to track how the paths evolve over time. Then, we formulate a frequency-domain EM method to estimate the new paths in the dynamic propagation channel. Simulation results demonstrate that the proposed approach has an improved performance in terms of parametric estimation and a lower time consumption compared with the comparative space-alternating generalized expectation-maximization algorithm (SAGE).