Spectrum sensing by cognitive radios at very low SNR

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.Comment: IEEE Global Communications Conference 200

Technical challenges for cognitive radio in the tv white space spectrum

Abstract — The FCC recently issued the regulatory rules for cognitive radio use of the TV white space spectrum. These new rules provide an opportunity but they also introduce a number of technical challenges. The challenges require development of cognitive radio technologies like spectrum sensing as well as new wireless PHY and MAC layer designs. These challenges include spectrum sensing of both TV signals and wireless microphone signals, frequency agile operation, geo-location, stringent spectral mask requirements, and of course the ability to provide reliable service in unlicensed and dynamically changing spectrum. After describing these various challenges we will describe some of the possible methods for meeting these challenges. rules provide limits on out-of-band (OOB) emissions which impact the spectral mask of any cognitive radio network using this spectrum. The challenges of this strict spectral mask are described in Section VII. Finally, though not a strict FCC rule, it is important to provide reliable operation in these channels given the unlicensed nature of these cognitive radio devices. The ability to provide reliable operation is critical to the success of any wireless network. The challenges of providing reliable operation in this dynamic spectrum is described in Section VIII

Optimal Spectral Feature Detection for Spectrum Sensing at Very Low SNR

Spectrum sensing is one of the enabling functionalities for cognitive radio systems to operate in the spectrum white space. To protect the primary incumbent users from interference, the cognitive radio is required to detect incumbent signals at very low signal-to-noise ratio (SNR). In this paper, we study a spectrum sensing technique based on spectral correlation 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 this sensing technique is asymptotically equivalent to the likelihood ratio test (LRT) at very low SNR, but with less computational complexity. That is, the spectral correlation-based detector is asymptotically optimal according to the Neyman-Pearson criterion. 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 levels as low as -20 dB