Читать книгу Dynamic Spectrum Access Decisions - George F. Elmasry - Страница 30

With this technique, the spectrum sensor has to rely on using bandpass filters. The spectrum sensor is given a center frequency f₀ and a bandwidth W to define the frequency range to sense. The spectrum sensor inputs the signal s(t) through the bandpass filter followed by a squaring device and an integrator, as shown in Figure 2.5. The details of how to build such a sensor are beyond the scope of this book. However, these sensors have the ability to define bandpass filters for any given center frequency f₀and any given bandwidth W in the broad‐spectrum range they are designed for. These sensors also have the ability to perform energy detection on a wide band of frequency divided into smaller sub‐bands in parallel.

Figure 2.5 Time domain energy detection.

Notice the importance of T in the integrator in Figure 2.5. A signal with weak power spectral density such as a spread spectrum signal would needs a longer time period T. With augmented sensors, the bandpass filter has a critical transfer function that can be expressed as follows:

(2.2)

The reason for emphasizing Equation (2.2) is that the augmented sensor needs to estimate the noise one‐sided power spectral density N₀, which is a challenge when the augmented spectrum sensor may have no prior knowledge of the signal it is sensing and hence has no means to directly estimate the noise floor, as in the case of same‐channel in‐band sensing. Instead, the augmented sensor relies on normalizing for the noise power and uses this normalization to compute the probability of a false alarm and the probability of detection as detailed in Chapter 3. If we conceptualize how the sensor creates an energy detection sample every T seconds, then in a large number of samples we have a high probability that the signal being sensed was not transmitted during the entire time period T. Thus, the noise floor becomes the energy sample collected with the minimum energy detection. The importance of Equation (2.2) is that it expresses the noise one‐sided power spectral density that correlates to the noise floor.

In time domain spectrum sensing, the time duration that the sensed signal remaining in a particular state can affect the outcome of the spectrum sensor. This time duration is referred to as the dwell time. The spectrum sensor observation time length should correlate to dwell time. Chapter 3 shows that one advantage of same‐channel in‐band sensing is that the in‐band signal is sensed during a known state and the sensing technique does not observe the signal during multiple states within a single sensing window. That is, the sensing technique can have knowledge of whether the transmitted signal is present or not during the entire sensing window. On the other hand, augmented sensors using time domain energy detection, where the sensed signal may change state during the observation time, can lend a higher probability of false alarm.