Urban cell partitioning for improved statistical propagation modeling

Urban environments present significant challenges to wireless system designers since radiated signals reflect off of buildings, cars, and other objects consisting of diverse materials and having very complex surfaces. Out of necessity, details of this environment are obscured so that a reasonably compact representation may be used to design and evaluate new systems. The continuum of models ranges from statistical models, which take into account only broad classifications of the propagation environment (dense urban, suburban, etc.), to ray-tracing methods, which require detailed knowledge of objects between transmitter and receiver. Taking the standard deviation of the prediction error as a measure of the model accuracy, one would expect the accuracy to improve as more information on the environment is folded into the model. In this paper, we discuss a modification to the traditional statistical models that significantly improves the estimation error with a minimal increase in complexity. Results are presented for measurements taken from the control channel of an active cellular base station transmitting at 880 MHz.