Evolving CSP Algorithm in Predicting the Path Loss of Indoor Propagation Models
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The Branch and bound algorithm is used to optimize the constraint satisfaction problem and finds the optimum set of parameter frequency, floor attenuation factor, and path loss coefficient at which the path loss is minimum.Abstract:
Constraint programming is the study of system which is based on constraints. The solution of a constraint satisfaction problem is a set of variable value assignments, which satisfies all members of the set of constraints in the CSP. In this paper the application of constraint satisfaction programming is used in predicting the path loss of various indoor propagation models using chronological backtrack algorithm, which is basic algorithm of CSP. After predicting the path loss at different set of parameters such as frequencies (f), floor attenuation factor (FAF), path loss coefficient (n), we find the optimum set of parameter frequency (f), floor attenuation factor (FAF), path loss coefficient(n) at which the path loss is minimum. The Branch and bound algorithm is used to optimize the constraint satisfaction problem.read more
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