Energy efficiency optimization-based resource allocation for underlay RF-CRN with residual energy and QoS guarantee

• Main Authors: Jie Tian, He Xiao, Yimao Sun, Dong Hou, Xianglu Li
• Format: Article
• Language: English
• Published: SpringerOpen 2020-10-01
• Series: EURASIP Journal on Wireless Communications and Networking
• Subjects: Energy harvesting; Cognitive radio network; Energy efficiency; Residual energy; QoS; Dinkelbach
• Online Access: http://link.springer.com/article/10.1186/s13638-020-01824-z

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.