On-line and Dynamic Estimation of Rician Fading Channels in GSM-R Networks

The on-line and dynamic estimation algorithm for Rician fading channels in GSM-R networks is proposed, which is an expansion of local mean power estimation of Rayleigh fading channels. The proper length of statistical interval and required number of averaging samples are determined which are adaptive to different propagation environments. It takes advantage of the sampling signals and Rician fading parameters of last estimation to reduce measurement overhead. The performance of this method was evaluated by measurement experiment along the Beijing-Shanghai high-speed railway. When it is NLOS propagation, the required sampling intervals can be increased from 1.1 lambda in Lee's method to 3.7 lambda of the on-line and dynamic algorithm. And the sampling interval can be set up to 12 lambda although the length of statistical interval decreases when there is LOS signal, which can reduce the measurement overhead significantly. The algorithm can be applied in coverage assessment with lower measurement overhead, and in dynamic and adaptive allocation of wireless resource.