Method for collaborative discrimation between authentic and spurious signals in a wireless cognitive network

摘要

A WRAN discriminates between authentic incumbent and spurious/malicious signals by collaboratively sensing the frequency environment, classifying and fusing the sensed results, and categorizing each signal as valid or invalid. Embodiments categorize signals according to reports from at least two nodes, thereby increasing detection confidence and resisting denial-of-service attacks. A “voting-rule” can be applied whereby a signal is authentic only if it is detected by a specified percentage of the nodes. Some embodiments categorized signals by comparing sensed analog signal properties, such as amplitude, bandwidth, pulse width, mean, variance, modulation, standard deviation, moments, cumulants, and rise and fall times, with properties of known incumbents and/or known incumbent types. Sensed results can be weighted according to known node locations and/or local topology, sensed signal strengths, and comparisons of sensed analog features with corresponding features of known incumbents and/or known incumbent types in the same class.