Abstract
Cognitive radio networks have been gaining widespread attraction among researchers especially with the increasing demand for radio frequency spectrum whose availability is quite scarce. Cognitive radio networks provide an ideal solution to allocate spectrum to users on an intelligent basis through a series of spectrum sensing and decision making. A metaheuristic soft computing framework is proposed and implemented in this research work by using powerful optimization concepts of evolutionary algorithm, namely ant colony algorithm, coupled with graph-cut modeling of given wireless network to provide the expected precision of detection. Channel characteristics have been taken as the feature vectors which are modeled as n-tuple graph to decide upon the maximization of channel allocation probability based on availability in an opportunistic basis. Exhaustive experimentations have been conducted and optimal performance justified against other benchmark algorithms.
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Padmanaban, B., Sathiyamoorthy, S. A metaheuristic optimization model for spectral allocation in cognitive networks based on ant colony algorithm (M-ACO). Soft Comput 24, 15551–15560 (2020). https://doi.org/10.1007/s00500-020-04882-z
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DOI: https://doi.org/10.1007/s00500-020-04882-z