Multi-Objective Resource Allocation for Secure Communication in Cognitive Radio Networks with Wireless Information and Power Transfer (1403.0054v2)

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 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 multi-objective 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 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 requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The adopted multi-objective optimization problem is non-convex 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. Besides, 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-to-optimal performance of the proposed suboptimal schemes, but also unveil an interesting trade-off between the considered conflicting system design objectives.

• The paper introduces a multi-objective framework that minimizes transmit power, maximizes energy harvesting, and reduces interference leakage.
• It employs semidefinite programming relaxation and the weighted Tchebycheff method to derive globally optimal or near-optimal solutions under CSI uncertainties.
• Empirical results reveal that the optimized configurations significantly enhance physical layer security and energy efficiency in cognitive radio networks.

Secure Multi-Objective Resource Allocation in Cognitive Radio Networks

The research presented in this paper focuses on developing resource allocation strategies for cognitive radio (CR) networks which cater to the dual goals of secure communication and efficient power transfer. The paper proposes a multi-objective optimization framework addressing several conflicting system design objectives in such networks. The results obtained can lead to more efficient CR systems that enhance both physical layer security and energy efficiency, while minimizing interference.

Cognitive radio networks facilitate shared spectrum use between primary and secondary users. This paper considers multiuser multiple-input single-output (MISO) systems that leverage cognitive radio for secondary networks, wherein secondary receivers can harvest energy. The primary design challenge addressed is to simultaneously deliver secure information and optimize wireless power transfer in the presence of channel state information (CSI) uncertainties at the secondary and primary receivers, both regarded as potential eavesdroppers.

The innovation lies in the multi-objective optimization framework incorporating three key objectives: minimizing total transmit power, maximizing energy harvesting efficiency, and minimizing the interference power leakage-to-transmit power ratio (IPTR). The paper introduces a Pareto optimal resource allocation algorithm utilizing the weighted Tchebycheff method. It specifically caters to quality of service (QoS) requirements regarding communication secrecy and accounts for the imperfection in CSI.

The optimization challenges in obtaining a convex solution from the inherently non-convex multi-objective problem are addressed via semidefinite programming (SDP) relaxation. Importantly, the research demonstrates how to derive the global optimal solution by exploring both primal and dual solutions of the relaxed problem. The paper also outlines two suboptimal resource allocation schemes to be employed when the dual problem solution is not available for optimal construction. Numerical results exhibit that these suboptimal setups also perform closely to the optimal configurations, revealing intricate trade-offs between the diverse system design objectives.

Noteworthy claims from the paper highlight that minimizing transmit power correlates with reduced total interference empirically, while energy efficiency contrasts with high power demands. Thus, careful multi-objective optimization is necessary to balance these opposing factors for efficient CR network operation, with practical implementations possibly enhancing AI strategies for network management.

The implications of this work extend to both theoretical and practical domains. Theoretically, the proposed framework can be extended to various resource allocation problems in wireless networks with multiple competing objectives. Practically, this can revolutionize cognitive networks by providing guidelines for broadcasters and telecom operators to balance spectrum efficiency and user quality guarantees, ensuring information security in wireless environments.

In future research, advances in AI, particularly in machine learning models, could further refine these optimization strategies making them more adaptive to fast-changing network conditions. The application of AI could automate real-time Pareto optimal decision-making, improving both the spectrum utilization and energy efficiency, paving the way for smarter cognitive radio environments.