Notice how with the three energy detection techniques covered so far, the outcome is simple:
1 Signal energy level at the defined carrier frequency f0 and bandwidth W, and
2 Noise floor energy at the same carrier and bandwidth.
It is important to note that the hypothesizing and decision‐making processes covered in Chapter 3 can be tricky under certain circumstances, such as fading channels. While frequency domain energy detection can implement good techniques such as the sliding window explained above, distributed and centralized DSA techniques can have a view of spectrum sensing that is more comprehensive than a local node. Distributed and centralized DSA techniques can analyze spectrum sensing information per RF neighbor and further overcome the uncertainty that can result from fading channels.
While Section 2.3.1 introduced same‐channel in‐band sensing, Sections 2.3.2 and 3.3.3 introduced the most common forms of augmented sensing where the augmented sensor can be configured to sense a frequency band defined by the carrier frequency f0 and bandwidth W as illustrated in Figures 2.4 and 2.5. Augmented sensors can be built to sense a wide band of frequencies and sense multiple sub‐bands each defined by a carrier frequency f0 and a bandwidth W simultaneously. This simultaneous sensing utilizes parallel paths where each path starts with a bandpass filter configured for the carrier frequency f0 and bandwidth W for one of sub‐bands being sensed.
2.4 Signal Characteristics Spectrum Sensing
There are different signal characteristics that a spectrum sensor can detect. Here, we go beyond simple energy detection with no prior knowledge of the signal being sensed to having some prior knowledge of the signal and the ability to synthesize the detected signal to extract more information.
2.4.1 Matched Filter Based Spectrum Sensing
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.
2.4.2 Autocorrelation Based Spectrum Sensing
This spectrum sensing approach has more advantages than the matched filter approach described in the previous section when attempting to detect the presence of a narrow band signal. Autocorrelation estimation can result in a constant false alarm rate (CFAR) by employing techniques that reduce the dependency on noise power. With this sensing technique, the sensor can have a stored representation of the signal as samples S(t) = (s0, s1, …, sN − 1) or has a signal generator that can generate a copy of the sensed signal and sample it. The sensed signal S*(t) is sampled to produce
Figure 2.8 Signal detection using autocorrelation.
If the sensor uses a signal generator and a sampler instead of stored samples, as in Figure 2.8, the sampler can be broken down as shown in Figure 2.9 where down conversion and a low pass filter (LPF) is used before time sampling.11 In Figure 2.9, the signal is a complex baseband signal with center frequency fc. The LPF has a bandwidth of (−fbw, fbw) Hz and the sampling rate is
Figure 2.9 Signal sampling before autocorrelation.
The correlation technique attempts to produce unbiased estimation of the signal that can be expressed in terms of a variable l that is adaptively chosen to reduce noise power dependency. Different techniques can be employed to incorporate l in the correlation function, such as estimating a covariance matrix.
Autocorrelation based spectrum sensing can use a bank of signal generators, samplers, and correlators to detect