Dynamic Spectrum Access Decisions. George F. Elmasry

Dynamic Spectrum Access Decisions - George F. Elmasry


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but the price for that is the need for more computational power.

      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.

      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

Schematic illustration of the signal detection using matched filters.

       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

Schematic illustration of the signal detection using autocorrelation. Schematic illustration of the 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


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