Spectrum sensing for cognitive radio: A signal-processing perspective on signal-statistics exploitation

Abstract

Future cognitive radios will require use of both established emitter databases and local spectrum sensing to optimize their performance. We view these techniques as ways of estimating an RF environment map (RFEM), which characterizes the position, directivity, power, and modulation type of all relevant RF emitters in a geographical region of interest. Cognitive radios will make their best decisions when they have the best RFEM information available. Good RFEM estimates are facilitated by spectrum-sensing algorithms that exploit the complex statistics of modern communication signals rather than relying on energy detection. We illustrate some of the ways that such statistics can be exploited using collected modern communications signals.

Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks

“…Thus, REMs permitting the characterization of the position, directivity, power, and modulation type of PUs have become a challenging task in cognitive radio network (CRN) design [20]. Indeed, in [21], REMs were used to locate relevant PUs in a geographic region of interest, characterizing their positions, directivities, powers, and modulation types. Likewise, in [22], REMs were used to sense the spectrum based on an adaptive compressed spectrum-sensing algorithm, contributing spatial information to the network capable of adapting to the radio environment.…”