Joint velocity estimation and symbol detection in non-stationary fading channels by particle filtering

This paper addresses the problem of joint velocity estimation and data detection over nonstationary fast fading channels. A realistic scenario is considered where mobile velocity changes continuously, resulting in non-stationary fading channels. A time-varying AR model and a Gauss-Markov model are used to describe the respective fading channel and variation of velocity. A connection is shown between the coefficients of the TVAR model and mobile velocity which makes the joint estimation and detection possible. A hierarchical dynamic state space model is formed for the problem, and a particle filtering algorithm is proposed. In particular, a hybrid importance function and the mixture Kalman filter are used to achieve efficient implementation of particle filtering. Simulation results are provided that show the performance of the particle filtering algorithm.