Читать книгу Dynamic Spectrum Access Decisions - George F. Elmasry - Страница 33

This technique requires pre‐knowledge of many aspects of the sensed signal such as bandwidth, operating frequency, modulation type and order, pulse shaping, and frame format. The spectrum sensor can quickly detect the presence of the sensed signal with high accuracy. This technique can be used before discovering more detailed signal characteristics such as spreading code and hopping pattern.

The matched filter will accentuate the targeted signal S(t) and will suppress other signals and noise. Notice that signals other than the targeted signal S(t) are essentially noise with respect to S(t). The impact of the suppressed signals and noise are referred to as W(t). The design of this matched filter includes:

1 Creating a contrast between S(t) and W(t) such that when S(t) is present at a time t, the output of the filter will have a large peak

2 Minimizing the probability of error. This can be achieved by considering the energy of the signal and the energy of the noise over a time T instead of considering the signal and noise amplitude. Energy calculation uses the square of the amplitude.

Notice that with wireless communications systems where we decode symbols, minimizing the probability of symbol error also uses signal and noise energy. However, the probability of error in spectrum sensing has two folds. With spectrum sensing, we have a probability of false alarm where the matched filter decides that S(t) is detected but S(t) was absent and the probability of misdetection where the matched filter decides that S(t) is absent but S(t) was present.⁹

Figure 2.7 shows the use of a matched filter to detect the presence of a signal S(t). Notice that a spectrum sensor can sense more than one signal type using a bank of matched filters. Once the spectrum sensor decides the signal type, other signal synthesizing techniques can be used to discover characteristics such as spreading code or orthogonality.

Figure 2.7 Signal detection using matched filters.

Some of the disadvantages of the matched filter sensing technique include the following:

 The implementation complexity may not be practical to implement for a large set of signals. Consider the detection of all types of commercial cellular signals and other known commercial but not cellular signals.

 Large power consumption is needed to execute the various receiver algorithms.