In all, the effects of power control on energy consumption and activity probability of MBS are greater than the activity probability of FAP.
Besides the activity probability of BS, access policy also impacts MUE coverage probability.
From (8), we find that the coverage probability of FUE depends on the activity probability of FAP.
In order to facilitate the analysis of the effects of activity probability of BS, here, we assume that MBS in activity 1 with probability represented by the x-axis, while activity 0 otherwise.
So in order to see the contrast clearly, here, we define λ f =10λ m =0.01/m 2, β m =1, and the activity probability of each FAP is 0.5.
Figure 5 plots the relationships among power consumption and activity probability of MBS (FAP) and the coefficient of power control (β m ) on the condition that σ 2=0.
Visualizing the curve allows to determine if activity probability variations are important enough to induce the selection of a threshold for prospective virtual screenings.
Here, we do not consider the concrete effect of activity level, so the activity probability of MBS (FAP) is denoted as q m (q f ) no matter what kind of sleeping strategy.
On the circumstance of our proposed sleeping strategy, according to (17) and (19), the relationships among energy consumption and activity probability and the coefficient of power control are the same as the random sleeping strategy.
We proposed the use of the predictiveness plots, R v) versus v, to describe the predictive capacity of a VS method, where R v) is the activity probability associated with the vth quantile of the VS scores: R v) = P[ A= 1 | Y = F −1 v ], and F is the CDF of VS scores.
