Dynamic Spectrum Access Decisions. George F. Elmasry

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Название Dynamic Spectrum Access Decisions
Автор произведения George F. Elmasry
Жанр Отраслевые издания
Серия
Издательство Отраслевые издания
Год выпуска 0
isbn 9781119573791



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we could create the following three metrics:

      (3.12)equation

      where PD is the probability of hypothesizing the presence of the sensed signal given that the sensed signal is present, PF is the probability of hypothesizing the presence of the sensed signal given that the sensed signal was not present, and Pm is the probability of hypothesizing the absence of the sensed signal given that the sensed signal was present.

Signal presence Hypotheses Evaluation metric
Y Y P D
Y N P F
N Y P m
N N N/A

      The PD, PF, and Pm metrics can be used to measure the efficiency of the decision‐making process given some design requirements. Notice that:

      Although10 Equations (3.10)(3.13) can apply to different systems, the system under design should influence how a machine‐learning algorithm would estimate λE. Let us consider the following two cases:

       Case 1: A commercial communication system of a secondary user attempting to opportunistically use the primary user spectrum. In this case, a higher probability of false alarm can be acceptable as the higher probability of misdetection can cause the secondary user to interfere with the primary user.11 With this case, the design of the machine learning algorithm would accept a higher probability of false alarm to minimize the probability of misdetection.

       Case 2: A military MANET system that can operate in an antijamming mode and the formed MANET can switch to a different waveform type only if the interference level is too high. With this case, a higher probability of misdetection may be acceptable since the antijamming waveform can operate in the presence of some level of interference. With this case, the design of the machine learning algorithm may target a higher probability of misdetection to minimize the probability of false alarm.

      The goal of same‐channel in‐band sensing has two folds. The first is to hypothesize for the presence of an interfering signal plus noise or hypothesize the presence of only noise when the communications signal is known to be absent. The second is when the communications signal is known to be present, the ROC model would then hypothesize if the interfering signal and noise power are too high to warrant a change of the operating frequency. The ROC model explained in the previous section has to be adapted to consider two different thresholds instead of one threshold. With same‐channel in‐band sensing, Equation (3.1) becomes:

      where r(n) is the interfering signal.

      When the same‐channel in‐band demodulation finds a preamble and it is demodulating and decoding an over‐the‐air frame, the same‐channel in‐band energy detection process is faced with the following two hypotheses:

      When the same‐channel in‐band demodulation does not find a preamble and it is not demodulating and decoding an over‐the‐air frame, the same‐channel in‐band energy detection process is faced with the following two hypotheses:

      Same‐channel in‐band sensing and the presence of the communications signal's marks such as preambles should lead the ROC model to minimizing PF, with very low probability that the receiver falsely decides that the preamble exist when it does not exist.12 With same‐channel in‐band detection, PF is the probability of deciding an interfering signal r exists when it does not exist. The key here is to have a good estimation of the noise floor energy and a good estimation of the communication signal energy in order to effectively hypothesize the presence or the absence of an interfering signal.