Abstract-In this paper, we study resource allocation for multiuser multiple-input-single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (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 is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-tooptimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well. Index Terms-Cognitive radio (CR), physical (PHY)-layer security, robust beamforming, wireless information and power transfer.
For example, the author in REF developed an MOO framework in cognitive radio networks with SWIPT incorporating three system design objectives: total transmit power minimization, EHE maximization, and interference-power-leakage-totransmit-power ratio minimization.
Multiobjective Resource Allocation for Secure Communication in Cognitive Radio Networks With Wireless Information and Power Transfer
IEEE Transactions on Vehicular Technology
IEEE Transactions on Vehicular Technology
Computer Science