A Physical Model for Wireless Channels to Understand and Test Long Range Prediction of Flat Fading

Algorithms [1 7] that predict the wireless channel for up to a few wavelengths cannot be adequately tested with stationary models, such as the Jakes model [8]. Moreover, ray-tracing or finite difference time domain (FDTD) methods do not provide insights into the relationship between the reflector configurations and the performance of the long-range prediction. A novel model is required to: (1) create non-stationary datasets to test our previously proposed adaptive long range prediction algorithm, which enables practical realization of adaptive transmission techniques, including modulation, adaptive coding, power control, sentient transmitter diversity, etc. (2) provide limits on the speed of adaptation needed for an algorithm to predict the channel significantly into the future, and thereby reveal the timing of future deep fades, etc. (3) classify the reflector geometries that will have the typical or the most severe parameter variations, so that the reflector configurations for test datasets can be appropriately chosen and (4) illuminate the origins of the temporal and statistical properties of measured data. We present a model that satisfies these criteria. It provides insights and test data for fading over relatively small spatial regions, as required for prediction. Therefore, it does not incorporate significant long-range fading or diffusive propagation (although it does utilize diffraction and can handle shadowing). It could be incorporated into the later stages of a long-range propagation model. We validate the performance of our adaptive prediction algorithm using channels given by the physical model or actual measured data. The performance is similar for both types of channel, and different from the performance when the channel is given by the Jakes model. Moreover, we demonstrate improvement of prediction performance when recursive least squares (RLS) adaptive tracking of the model coefficients is utilized, and show that when prediction is employed with adaptive power control, the accuracy depends on the scattering environment.

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