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

TL;DR

This paper develops beamforming techniques to maximize secrecy energy efficiency in MISO cognitive radio networks with energy harvesting, considering various channel state information scenarios and robust design under uncertainties.

Contribution

It introduces novel optimization algorithms for SEE maximization under different CSI assumptions, including robust design with bounded uncertainties, using advanced mathematical techniques.

Findings

Proposed algorithms effectively maximize SEE compared to existing methods.

Robust designs perform well under channel uncertainties.

Maximum SEE can be found via a simple search in one dimension.

Abstract

In this paper, we investigate different secrecy energy efficiency (SEE) optimization problems in a multiple-input single-output underlay cognitive radio (CR) network in the presence of an energy harvesting receiver. In particular, these energy efficient designs are developed with different assumptions of channels state information (CSI) at the transmitter, namely perfect CSI, statistical CSI and imperfect CSI with bounded channel uncertainties. In particular, the overarching objective here is to design a beamforming technique maximizing the SEE while satisfying all relevant constraints linked to interference and harvested energy between transmitters and receivers. We show that the original problems are non-convex and their solutions are intractable. By using a number of techniques, such as non-linear fractional programming and difference of concave (DC) functions, we reformulate the original problems so as to render them tractable. We then combine these techniques with the Dinkelbach's algorithm to derive iterative algorithms to determine relevant beamforming vectors which lead to the SEE maximization. In doing this, we investigate the robust design with ellipsoidal bounded channel uncertainties, by mapping the original problem into a sequence of semidefinite programs by employing the semidefinite relaxation, non-linear fractional programming and S-procedure. Furthermore, we show that the maximum SEE can be achieved through a search algorithm in the single dimensional space. Numerical results, when compared with those obtained with existing techniques in the literature, show the effectiveness of the proposed designs for SEE maximization.