Dynamic Spectrum Access Decisions. George F. Elmasry

Sweden, June 2000.

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      Notes

      1 1 Spread spectrum signals are especially hard to detect with simple energy detection.

      2 2 The definitions of false alarm and misdetection depend on the spectrum sensing goal, as clarified later in this chapter.

      3 3 As mentioned earlier, the definition of the probability of detection PD and the probability of false alarm PF are case dependent. In the case presented here, PD is the probability of hypothesizing the signal presence where the signal is actually present while PF is the probability of hypothesizing the signal presence but there was only noise power present.

      4 4 Notice that using the ROC model for DSA can require considering two main aspects. The first aspect is to estimate the decision threshold λE through analysis. The second aspect is the use of a DSA cognitive algorithm that can fine‐tune the value of λE based on deployment dynamics and design requirements relying on measured metrics.

      5 5 It is important to note how the ROC model uses energy instead of signal and noise amplitude sampling. Energy detection uses square values which extends the scale of decision making, leading to more accuracy, but creates a one‐dimensional positive axis only scale, as shown in Figure 3.1.

      6 6 For signals with low energy such as spread spectrum signals, SNIR is inheritably low, making energy detection based spectrum sensing more challenging.

      7 7 This machine learning technique can be local, distributed using cooperative techniques or centralized.

      8 8 Chapter 5 explains how to create metrics that measure the performance of DSA decision fusion results.

      9 9 Sometimes it is difficult for those who know digital communications and understand decision theory as it applies to symbol decoding to see how the fourth case in Table 3.1 is irrelevant. A simple way to conceptualize the ROC model, while being cognizant of the difference with symbol decoding, is to map the second and third rows in Table 3.1 to the case of symbol error decoding. The ROC model distinguishes between these two error types while the digital communications decoding model combines them as symbol error probability. The first row is simply mapped to the probability of decoding the correct symbol. The fourth column is irrelevant in both cases. With symbol decoding, not decoding a symbol that was never sent or the ROC model not hypothesizing the presence of a signal that is not present are both irrelevant cases.

      10 10 Following the same theme of correlating to symbols decoding: in symbol decoding, the probability of correctly decoding a symbol plus the probability of error decoding add to one. The reader has to be careful in using this resemblance between energy detection and symbol decoding as energy detection has no consideration of the signal dimensions and symbol decoding error can either be an erasure or can produce another known symbol.

      11 11 The FCC regulation states that “no modification to the incumbent system should be required to accommodate opportunistic use of the spectrum by secondary users”.

      12 12 This can only happen if the correlation process output in the absence of a preamble is high for some odd reason.

      13 13 Other constant envelope signals such as minimum shift keying signals have the same advantage.

      14 14 Other higher order PSK can also be used to increase the number of bits per symbol.

      15 15 The estimated received signal power can change due to factors such as mobility and adaptive power control.

      16 16 Problem 2 in the exercise section should lead the reader to approach this ROC model as an overlay case instead of attempting to create a complex ROC model analysis with two thresholds.

      17 17 The MANET nodes can be using omnidirectional antennas.

      18 18 Chapter 5 explains how DSA can become a set of cloud services that can be offered at any hierarchical entity in a large‐scale set of heterogeneous networks.

      19 19 This can increase the number of over‐the‐air relay hops but route around jammed areas.

      20 20 Military waveforms that can be utilized in some cases may be using unlicensed spectrum, transmitting over ultra‐wideband, using spread spectrum with high chip rate and low data rate and using other techniques such as fast frequency hopping to make the military signal seems as a background noise to other signals in the area or to an eavesdropping node. Military waveforms can also switch to an antijamming mode to overcome certain types of jammers.

      21 21 Notice that in military communications, the action of switching to a different