TY - JOUR
T1 - A modified shuffled frog leaping algorithm for PAPR reduction in OFDM systems
AU - Zhou, Jie
AU - Dutkiewicz, Eryk
AU - Liu, Ren Ping
AU - Huang, Xiaojing
AU - Fang, Gengfa
AU - Liu, Yuanan
PY - 2015/12
Y1 - 2015/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84960293275&partnerID=8YFLogxK
U2 - 10.1109/TBC.2015.2459660
DO - 10.1109/TBC.2015.2459660
M3 - Article
AN - SCOPUS:84960293275
SN - 0018-9316
VL - 61
SP - 698
EP - 709
JO - IEEE Transactions on Broadcasting
JF - IEEE Transactions on Broadcasting
IS - 4
M1 - 7182786
ER -