Secure Energy Efficiency Optimization for MISO Cognitive Radio Network with Energy Harvesting

TL;DR

This paper proposes a novel secure energy efficiency optimization framework for MISO cognitive radio networks with energy harvesting, addressing security, energy constraints, and interference management using advanced non-convex optimization techniques.

Contribution

It introduces a new transmit covariance matrix design method for secure energy efficiency maximization in MISO CR networks with energy harvesting, employing fractional programming and DC approaches.

Findings

Proposed algorithms effectively optimize secure energy efficiency.

Algorithms converge reliably in simulations.

Enhanced security and energy harvesting performance achieved.

Abstract

This paper investigates a secure energy efficiency (SEE) optimization problem in a multiple-input single-output (MISO) underlay cognitive radio (CR) network. In particular, a multi-antenna secondary transmitter (SU-Tx) simultaneously sends secured information and energy to a secondary receiver (SU-Rx) and an energy receiver (ER), respectively, in the presence of a primary receiver (PU-Rx). It is assumed that the SU-Rx, ER and PU-Rx are each equipped with a single antenna. In addition, the SU-Tx should satisfy constraints on maximum interference leakage to the PU-Rx and minimum harvested energy at the ER. In this CR network, we consider the transmit covariance matrix design with the assumption of perfect channel state information (CSI) at the SU-Tx. In addition, it is assumed that the ER is a potential passive eavesdropper due to broadcast nature of wireless transmission. On the other hand, we consider the worst-case scenario that ER's energy harvesting requirement is only satisfied when it performs only energy harvesting without intercepting or eavesdropping information intended for the SU-Rx. We formulate this transmit covariance matrix design as a SEE maximization problem which is a non-convex problem due the non-linear fractional objective function. To realize the solution for this non-convex problem, we utilize the non-linear fractional programming and difference of concave (DC) functions approaches to reformulate into a tractable form. Based on these techniques and the Dinkelbach's method, we propose iterative algorithms to determine the solution for the original SEE maximization problem. Numerical simulation results are provided to demonstrate the performance of the proposed transmit covariance matrix design and convergence of the proposed algorithms.