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

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 This paper has been presented in part at the IEEE Wireless Communications and Signal Processing (WCSP), October 11-13, 2017 [1]. M. Zhang is with the ). Z. Ding is with the School of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK (email: zhiguo.ding@manchester.ac.uk). O. A. Dobre is with the 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.

Outage Constrained Robust Secrecy Energy Efficiency Maximization for EH Cognitive Radio Networks

“…Nowadays, the exponential growth of data traffic and mobile computing have brought the increasing demands for wireless communication, such as scarce spectrum resources and energy supply [1]. As a promising approach to simultaneously improving both spectrum efficiency (SE) and energy efficiency (EE) in fifth generation (5G) and beyond wireless networks, the energy harvesting (EH) cognitive radio network (CRN) has recently attracted great research attention [2]. In contrast to other natural-resource EH techniques (such as solar, wind and thermal), the radio frequency (RF)-based EH technology is more suitable for stable energy provision [3].…”

Spectral-Energy Efficiency Trade-Off-Based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems

“…To exploit different potential benefits, NOMA has been recently integrated with different technologies such as cognitive radio (CR) [13], millimeter-wave [14] [15], multiple-antenna techniques [16], [17], [18] and conventional OMA techniques [19]. In particular, the combination of NOMA with spatial domain multiple access (SDMA) offered by multipleantenna can provide additional benefits, by jointly utilizing both the spatial and power domains.…”

Energy Efficiency in RF Energy Harvesting-Powered Distributed Antenna Systems for the Internet of Things

“…In [ 22 ], the system goal was to maximize secrecy EE by jointly optimizing the confidential signal’s power, the artificial noise power, and the power-splitting ratio. In [ 23 ], the goal was to design a beamforming technique maximizing the secrecy EE, where an efficient iterative algorithm was presented.…”

“… Although some existing works investigated the EE for DAS with RF EH, only the constraint of power control was considered, where the user’s information rate requirement was not involved; see, e.g., [ 17 , 18 ], while in our work, the system EE is maximized with the user’s minimal information rate requirement, which is much closer to the users’ demands. Different from the most similar existing works [ 22 , 23 ] where the linear EH model was used, in this work, the nonlinear EH model is adopted, as it was reported that the nonlinear EH one was obtained with real measurement data [ 26 , 27 ]. Thereby, our work may be much closer to the practical situation.…”

“…Different from the most similar existing works [ 22 , 23 ] where the linear EH model was used, in this work, the nonlinear EH model is adopted, as it was reported that the nonlinear EH one was obtained with real measurement data [ 26 , 27 ]. Thereby, our work may be much closer to the practical situation.…”