Rician Modeling and Prediction for Wireless Packet Data Systems

Abstract

Wireless systems exploiting multi-user diversity require accurate channel state information. In this paper we examine channel prediction as a method to improve the performance of these systems. We model the outdoor channel by the Rician model, which combines diffuse spectra with a single specular component. Prediction accuracy is determined for the Rician model given various ratios (K factors) of specular to diffuse power. Simulation results show that accurate channel prediction further than one wavelength can be achieved at 30 dB SNR when the channel K factor is greater than ten. The predictor was evaluated on measured channels, and the Rician model was found to be appropriate as prediction intervals of the measured data are similar to those of synthetic data with same K factor. Using measurements some results regarding channel stationarity and optimal filter order are presented. We show for a proportionally fair scheduled downlink packet system that the predictor reduces fade margin at fixed packet outage leading to an improvement in throughput.