Optimal Resource Allocation Using Support Vector Machine for Wireless Power Transfer in Cognitive Radio Networks

In this paper, we present an efficient allocation of power and channel so that power-drained primary users can harvest energy from secondary systems through wireless power transfer, and in return, the secondary system can have full access to the licensed spectrum for a certain length of time. Considering partial channel state information between primary and secondary systems, we formulate an optimization problem consisting of probabilistic constraint. By introducing a confidence level and using support vector machine, we convert the probabilistic constraint to deterministic form. We also implement a particle swarm optimization technique to solve the resource allocation optimization problem, while guaranteeing the required data rate for both primary and secondary systems. The results verify that the proposed scheme enables a higher number of primary users to efficiently harvest energy, and it also increases total primary capacity.

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