Spectrum Sensing Techniques For Cognitive Radio Networks

In this chapter, we present the state of the art of the spectrum sensing techniques for cognitive radio networks as well and their comparisons. The rest of the chapter is organized as below: Section I.1, Section I.2, and Section I.3 present the spectrum management problem and the cognitive radio cycle as well as the compressive sensing solution; Section II.1 describes the spectrum sensing model; Section II.2 presents the existing spectrum sensing techniques, including energy, autocorrelation, Euclidian distance, wavelet, and matched filter based sensing. Finally, a conclusion is given at the end of the chapter. I.1 Spectrum Management and Cognitive Radio Wireless networks and information traffic have grown exponentially over the last decade, which has resulted in an excessive demand for the radio spectrum resources [1][2]. The radio spectrum is a limited resource controlled by regulations and the recognized authorities, such as the federal communications commission (FCC) in the US. The current radio spectrum allocation policy consists of assigning the channels to specific users with licenses for specific wireless technologies and services. Those licensed users have access to that spectrum portions to transmit/receive their data, while others are forbidden even when those spectrum portions is unoccupied [3]. Recent studies reported that the spectrum utilization ranges from 15% to 85% in the US under the fixed spectrum allocation (FSA) policy [4]. FCC measurements also show that some channels are heavily used while others are sparsely used as illustrated in Figure 1 [5].