Dynamic estimation of local mean power in GSM-R networks

The 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 signal samples and Rician fading parameters of last estimation to reduce measurement overhead. The performance of this method was evaluated by measurement experiments along Beijing–Shanghai high-speed railway. When it is NLOS propagation, the required sampling intervals can be increased from 1:1k in Lee’s method to 3:7k of the dynamic algorithm. The sampling intervals can be set up to 12k although the length of statistical intervals decrease 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.