11SIGNAL CLASSIFICATION IN WIDEBAND COGNITIVE RADIOS
For a wideband cognitive radio, the ability to accurately infer RF environment is critical to achieving its performance objectives. Each of the spectrum subbands may contain multiple signals from multiple systems. Comprehending spectrum state requires not only the ability to detect RF signals at arbitrary carrier frequencies within each subband, as we already discussed in Chapter 10, but also being able to identify these signals.
In this chapter, we investigate how a cognitive radio can identify spectral activities detected in the sensed subband. Referring to the spectral knowledge acquisition architecture of Figure 2.4, at the first stage of spectrum sensing, sensed observations from a subband are used to detect spectral activities as discussed in the previous chapter. Once a set of spectral activities are detected, these are passed on to the signal classification module at the next stage of spectrum sensing chain in the cognitive engine as shown in Figure 2.4.
Note that spectrum sensing algorithms for DSA-based cognitive radios1 scan a desired channel corresponding to one of the known (primary) systems to determine whether that particular signal is ON or OFF. For instance, a cognitive radio used for DSA over the idle TV bands treats the TV signals as the primary user signals. Hence, spectral characteristics (e.g., carrier frequency and bandwidth) of the signals of interest can normally be taken as known. ...
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