Multiobjective Resource Allocation for Secure Communication in Cognitive Radio Networks With Wireless Information and Power Transfer - 2016

PROJECT TITLE :

Multiobjective Resource Allocation for Secure Communication in Cognitive Radio Networks With Wireless Information and Power Transfer - 2016

ABSTRACT:

In this paper, we study resource allocation for multiuser multiple-input-single-output secondary Communication systems with multiple system design objectives. We tend to consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency after they are idle. The secondary system provides simultaneous wireless power and secure info transfer to the secondary receivers. We have a tendency to propose a multiobjective optimization framework for the look of a Pareto-optimal resource allocation algorithm primarily based on the weighted Tchebycheff approach. In specific, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting potency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a top quality-of-service (QoS) requirement concerning Communication secrecy in the secondary system and therefore the imperfection of the channel state data (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It's shown that the world optimal solution of the initial downside can be created by exploiting each the primal and the twin optimal solutions of the SDP-relaxed downside. Moreover, 2 suboptimal resource allocation schemes for the case when the answer of the twin problem is unavailable for constructing the optimal resolution are proposed. Numerical results not solely demonstrate the close-to-optimal performance of the proposed suboptimal schemes however unveil an fascinating tradeoff among the thought of conflicting system design objectives yet.

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