Spectrum sensing by cognitive radios at very low SNR

TL;DR

This paper introduces a spectral correlation-based spectrum sensing method for cognitive radios that effectively detects TV signals at extremely low SNR levels, ensuring minimal interference with primary users.

Contribution

It proposes an asymptotically optimal sensing technique based on spectral correlation at very low SNR and provides insights on selecting spectral features for reliable detection.

Findings

Detects TV signals at SNR as low as -20 dB

Spectral correlation-based sensing is asymptotically optimal at low SNR

Guidelines for choosing spectral features for improved sensing performance

Abstract

Spectrum sensing is one of the enabling functionalities for cognitive radio (CR) systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the CR is required to detect incumbent signals at very low signal-to-noise ratio (SNR). In this paper, we present a spectrum sensing technique based on correlating spectra for detection of television (TV) broadcasting signals. The basic strategy is to correlate the periodogram of the received signal with the a priori known spectral features of the primary signal. We show that according to the Neyman-Pearson criterion, this spectral correlation-based sensing technique is asymptotically optimal at very low SNR and with a large sensing time. From the system design perspective, we analyze the effect of the spectral features on the spectrum sensing performance. Through the optimization analysis, we obtain useful insights on how to choose effective spectral features to achieve reliable sensing. Simulation results show that the proposed sensing technique can reliably detect analog and digital TV signals at SNR as low as -20 dB.