A Modified Shuffled Frog Leaping Algorithm for PAPR Reduction in OFDM Systems

• Published in: IEEE transactions on broadcasting Vol. 61; no. 4; pp. 698 - 709
• Main Authors: Jie Zhou, Dutkiewicz, Eryk, Ren Ping Liu, Xiaojing Huang, Gengfa Fang, Yuanan Liu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Chaos theory; clonal selection algorithm; Convergence; Evolutionary algorithms; Frogs; Genetics; Markov processes; OFDM; Optimization; Orthogonal Frequency Division Multiplexing; PAPR; Peak to average power ratio; Reduction; shuffled frog leaping algorithm; clonal selection algorithm; OFDM; shuffled frog leaping algorithm; PAPR
• DOI: 10.1109/TBC.2015.2459660
• ISSN: 0018-9316

Significant reduction of the peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA). MCCSFLA is inspired by natural clonal selection of a frog colony, it is based on the chaos theory. We also analyze MCCSFLA using the Markov chain theory and prove that the algorithm can converge to the global optimum. Simulation results show that the proposed algorithm achieves better PAPR reduction than using others genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms.