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Furthermore, power consumption are formed into optimization problems, which is determined by the optimal probabilities of MBS and FAP remain to be active.
Second, we analytically formulate the constrained CRN optimization problem according to the RAP framework in order to compute the optimal probabilities of transmission and the used rates and powers.
The optimal probabilities are indicated using a star (e.g. (p_{s}^{*L})).
The processes described above are iterated, and consequently the optimal probabilities P(s|λ) and P(ω|s) can be derived.
More specifically, we investigate the impact of the primary networks' outage constraints and user activity factors on the optimal probabilities of the RAP-MAC protocol as well as the achievable cognitive user goodput.
Clearly, the optimal choice for both is 0.3, but as the optimal probabilities are derived for different shapes, they do not accumulate, and the overall best choice would be 0.2 + 0.2 = 0.4 for shape.
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This is the proposition that relaxes the condition defined by P v, min. In effect, weak optimization provides the optimal probability,, subject to a pre-determined R rather than a given P v, min > 0. Fig. 3 shows σ* versus R for representative parameter settings.
The problem for finding the optimal probability distribution was shown as a convex optimization problem.
The optimal probability distribution parameters are shown in Table 6.
Then, based on our model, we determine optimal probability that maximizes the lifetime of a network.
The optimal probability of a sensor node is elected as a CH based on the function of spatial density.
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