Energy‐efficient resource allocation in relay‐aided orthogonal frequency division multiplexing cognitive radio networks with quality of service provisioning. (31st July 2020)

• Record Type: Journal Article
• Authors: Yan, Feiyu; Zhao, Jihong; Qu, Hua; Xu, Xiguang
• Is Part Of: International journal of communication systems. Volume 33:Number 16(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-31
• Subjects: cognitive radio -- DF relays -- energy efficiency -- OFDM -- resource allocation -- QoS provisioning; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.4566
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: This paper investigates the energy‐efficient resource allocation problem in orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) networks with multiple decode‐and‐forward relays. In order to maximize the system energy efficiency (EE), we jointly optimize the relay selection, subcarrier pairing, and power allocation subject to the transmit power constraints, the interference thresholds at the primary system, and the quality of service (QoS) provisioning for the CR system. Using the fractional programming and the Lagrangian dual decomposition method, the optimization problem which is a mixed‐integer nonlinear program (MINLP) can be efficiently solved to obtain an asymptotically optimal solution. We also propose a suboptimal scheme to reduce the computational complexity at the expense of a little performance loss. Simulation results demonstrate the performance of proposed schemes and the effect of the constraints values. Abstract : In this paper, we jointly optimize the relay selection, subcarrier pairing and power allocation to maximize the energy efficiency in decoded‐and‐forward relay‐aided OFDM‐base cognitive radio networks with QoS provisioning. Using the fractional programming and the Lagrangian dual decomposition method, the optimization problem which is a mixed‐integer nonlinear program can be efficiently solved to obtain an asymptotically optimal solution. We also propose a suboptimal scheme to reduce the computational complexity at the expense of a little performance loss.