A physical model for wireless channels to provide insights for long range prediction

Abstract

Algorithms that predict the wireless channel for up to a few wavelengths cannot be adequately tested with stationary models. Ray-tracing or FDTD methods do not provide insights into the relationship between reflector configurations and the performance of long-range prediction. Therefore, we present a novel model that: (1) creates non-stationary datasets to test our previously proposed adaptive long range prediction algorithm, which enables practical realization of adaptive transmission techniques; (2) classifies the reflector geometries that have typical or most severe parameter variations, so that the reflector configurations for test datasets can be appropriately chosen; (3) provides 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.; (4) illuminates the origins of the temporal and statistical properties of measured data. The algorithm performs similarly on channels given by the physical model or actual measured data, but differently on a channel simulated by the stationary Jakes model. The insights of the model accurately describe the performance of the algorithm in several scattering environments when prediction is employed with adaptive power control and adaptive modulation. Moreover, we study limits of the long-range prediction at frequencies other than the observed frequency, of importance in correlated uplink and downlink transmission, orthogonal frequency division multiplexing (OFDM) and frequency-hopping systems.