Title: An improved artificial bee colony-partial transmit sequence algorithm for PAPR reduction in OFDM systems

Authors: Xiangyu Yu; Shuai Li; Zucong Zhu; Tao Zhang

Addresses: School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China ' School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China; School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China ' China Mobile Group Corporation, Guangdong Co., Ltd., Maoming Branch, Maoming, Guangdong, China ' School of Electronic and Communication Engineering, Guiyang University, Guiyang, Guizhou, China

Abstract: Orthogonal frequency division multiplexing (OFDM) is one of the most popular multi-carrier modulation techniques used in wireless communications and other applications. One of the major problems encountered in OFDM systems is the high peak to average power ratio (PAPR) of the transmitted signal, which will introduce nonlinear signal distortion and lead to high adjacent channel interference and make system performance worse. In this paper, a modified ABC-PTS (artificial bee colony-partial transmit sequence) PAPR reduction approach is proposed. Inspired by the idea of particle swarm optimisation algorithm, global best solution is introduced into the original ABC algorithm, and the updating equation is modified with the introduction of learning factor to consider the balance between the ability of exploration and exploitation of the algorithm. Simulation results have showed that the proposed approach has more reduction than the traditional ABC-PTS algorithm with the same time consuming while having lower bit error rate.

Keywords: ABC; artificial bee colony; OFDM; orthogonal frequency division multiplexing; PTS; partial transmit sequences; PAPR; peak-to-average power ratio; multi-carrier modulation; wireless communications; nonlinear signal distortion; channel interference; particle swarm optimisation; PSO; global best solution; learning factor; simulation.

DOI: 10.1504/IJWMC.2013.057395

International Journal of Wireless and Mobile Computing, 2013 Vol.6 No.5, pp.473 - 480

Received: 21 May 2013
Accepted: 27 Jun 2013
Published online: 16 Oct 2014 *

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