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probability assignment function.
More specifically, this work considers a few broadcast protocols with silencing, differing for the probability assignment function.
The mapping m : 2 Θ → [0, 1] called Basic Probability Assignment Function (BPAF) is defined as: { m = 0 ∑ A ∈ Θ m A = 1 (11).
Finally, a different approach is considered when analyzing the class of probabilistic broadcast protocols, designed around the idea that each node forwards a received packet according to a characteristic probability assignment function (PAF), computed by each node in a distributed manner [10, 11].
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m(A) is defined as the basic probability assignment (BPA) function of hypothesis A. The belief and plausibility functions are derived from the BPA function, and are respectively defined by left{ begin{array}{l} {text{bel} phi) = 0}, {text{bel}(A) = sum_{Bsubset A}m(B), forall A subsetTheta} end{array} right.
According to the DS theory, uncertainty of the received signals caused by channel fading, SI, and noise can be characterized by the basic probability assignment (BPA) function, which is a likelihood measure and computed by the proposed probability density function (PDF) with the SI suppression considered.
The basic probability assignment (BPA) function is used here to take into account all the available evidence, and is defined as a mapping S from the power set 2 Ω of a finite set Ω={A 1,A 2,⋯,A N } to [0,1] that for any T∈2 Ω, and we have sum_{Tin2^{Omega}}S(T)=1,,S(T ge0 (13).
Then, the basic probability assignment (BPA) functions of changed and unchanged classes are obtained by the fuzzy membership values from the FCM algorithm.
This function is also called basic probability assignment.
However, any other probability assignment is allowed.
Any probability assignment that meets this condition is called an invariant assignment.
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