## 3 Spectrum Sensing for Cognitive Radio  
A CR dynamically alters its own frequency assignment after sensing its local spectrum and ultimately does not impact the performance of the primary network [4]. Physical (PHY) layer issues include spectrum sensing algorithms, low SNR signal detection, wideband or narrowband sensing, adaptive modulation and coding, waveform shaping, ADC, programmable filters. Medium access control (MAC) issues include coordination of quiet periods, spectrum sensing management, contiguous multichannel operation, inter-channel synchronization, real-time dynamic resource allocation, and multi-channel access. Spectrum Sensing for Narrowband Cognitive Radio  
An exhaustive review of spectrum sensing in IEEE 802.22 is given in [39]. A good survey of cognitive radio is [40]. Energy detection, pilot detection and collaborative detection are experimentally studied in the context of DTV [79]. Dynamically selecting one out of six (1.75 MHz) channels within 225-600 MHz was field tried within DARPA's XG program [80, 81]. Pilot-based sensing is used for fine sensing in IEEE 802.22, but is non-blind (ATSC -specific). A distributed approach is used [82]. Cyclostationary SensingThe inherent spectral redundancy (in cyclostationary signals [83]) caused by the use of a cyclic prefix in OFDM signals may be exploited, first by [11, 44] and then [84, 85, 86]. A unified approach to the recognition of signals belonging to the three basis air interfaces categories: single carrier TDMA, OFDM systems, single carrier CDMA systems [87]. It is also used to WCDMA [88]. It has been used in a framework of overlay/underlay cognitive radio [23, 24]. This unified approach may be the most promising, if there is some _prior_ information about communication such as modulation format [83]. Although valid for some commercial systems above, this is generally not true for DoD systems. Higher-order statistics (HOS) of the cyclostationary signals is needed. Figure 6: Digital TV Signal. Pilot is noticed in the left. Non-Gaussian Nature of the Frequency Domain SignalsNo prior knowledge of the type of signals present in the spectrum is often known. Signal detection in Gaussian noise can be carried out using HOS [6]. The fact that the cumulants of the order higher than two for a Gaussian process are zero can be used to detect the signals in the Gaussian noise. The received waveform samples can be grouped into segments, and higher order cumulants for each of these segments can be estimated. The detection thresholds are defined after a period of learning the distributions of the moments and cumulants, and a decision is made whether a particular segment of the received samples contains meaningful information or not. This is a test (also known as bi-coherency, tri-coherency, etc.) to determine if the received waveform belongs to the DTV signal or noise. In the time domain, DTV signals show Gaussian characteristics. In the frequency domain, however, they are non-Gaussian. We will exploit such characteristics to detect the signals in AWGN. Spectrum Sensing for Wideband Cognitive Radio  
Wideband (e.g., from 0 to 3 GHz) spectrum sensors scanning multiple licensed bands may not be able to include all feature detection algorithms necessary to identify all incumbents operating in the measured band [43]. In this case, it may be preferred to use energy detection. Other wideband sensing techniques are needed. At the cognitive radio (CR) transmitter, this sensing and transmission function is performed over the widest possible bandwidth to give the highest probability of detecting unused spectra--opportunistic transmission [17]. The unique sensing function forces the front-end to have several GHz sampling rate with high resolution (of 12 or more bits), if GHz bandwidths are to be searched [18]. The main limitation in a radio front-end's ability to detect weak signals is its dynamic range, which dictates the requirement for number of bits in A/D converter.