Abstract
How to achieve energy-efficient transmission in radio frequency energy harvesting cognitive radio network (RF-CRN) is of great importance when nodes in CRN are self-maintained. This paper presents a radio frequency (RF) energy harvesting hardware-based underlay cognitive radio network (RH-CRN) structure, where a secondary transmitter (ST) first harvests energy from RF signals source originating from the primary network, and then communicates with a secondary receiver (SR) in underlay mode by using the harvested energy. The total consumed energy by the secondary user (SU) must be equal to or less than the total harvested energy referred to as energy causality constraint, In addition, the ST possesses some initial energy which may be the residual energy from the former transmission blocks, and we consider the energy loss of energy harvesting circuit as a systematic factor as well. Our goal is to achieve the maximum energy efficiency (EE) of the secondary network by jointly optimizing transmitting time and power. To guarantee the quality of service (QoS) of secondary transceiver, a minimum requirement of throughput constraint is imposed on the ST in the process of EE maximization. As the EE maximization is a nonlinear fractional programming problem, a quick iterative algorithm based on Dinkelbach’s method is proposed to achieve the optimal resource allocation. Simulation results show that the proposed strategy has fast convergence and can improve the system EE greatly while ensuring the QoS.
Highlights
Radio frequency (RF) energy harvesting-based (EH) cognitive radio network (RF-Cognitive radio networks (CRN)), which has emerged as a promising way to address the problems of spectrum scarcity and energy efficiency while consistent with the call for green communication at the same time, has received extensive attention over the recent years [1]
Wireless powered communication networks (WPCN) in which distributed wireless devices are powered via dedicated wireless energy transfer (WET) by the hybrid access point (H-Access point (AP)) in the downlink (DL) for wireless information transmission (WIT) in the uplink (UL) [4,5,6,7,8,9], unlike previous studies on simultaneous wireless information and power transfer (SWIPT) in which the wireless information and energy are included in downlink RF signals at the same time [10,11,12]
The throughput threshold is selected to meet the quality of service (QoS) requirements according to different application scenarios and is reflected in the corresponding constraints of the optimization problem
Summary
In [29], a wireless powered underlay CRN where SUs first harvest energy in the DL wireless power transfer (WPT) phase and use the energy in the TDM UL wireless information transmission phase is introduced These works optimize SE for cognitive WPCN, so they mainly focus on maximizing the sum rate of the SU system under different constraints without considering energy efficiency. : : : C(t, Ps) ≥ Cmin 0
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