A belief-based approach to spectrum occupancy mapping

Abstract

Maintaining a map of signal power levels is one approach to dynamic spectrum management, but generating it in a complex environment with unknown emitters using sensor measurements is challenging. Precise representation of signal levels using sparse sensors is not feasible in realistic conditions where measurements may be inaccurate and the propagation conditions are uncertain. The goal is to use sensor power measurements to identify regions where the signal level exceeds, or falls below, a given threshold, and to provide a level of confidence in those determinations. In this work, a belief-based method using Dempster-Shafer analysis is developed, which can accommodate uncertainties due to the propagation conditions and sensor inaccuracies and combines evidence from different sensors to give a belief value for each state. The method is illustrated first for a simplistic, flat-earth model, then the impacts of parameter uncertainties, shadowing and sensor errors are incorporated. A real operating environment is emulated using a propagation prediction program, and it is demonstrated that the new approach is able to provide useful input to the spectrum management function and enables a sophisticated interpretation to support context-specific decision-making.