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A modified shuffled frog leaping algorithm for PAPR reduction in OFDM systems

机译:OFDM系统中降低PAPR的改进的改组蛙跳算法

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Reducing 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-PTS). The MCCSFLA-PTS is inspired by natural clonal selection of frog colony and based on chaos theory. Simulation results show that the proposed MCCSFLA-PTS achieves better PAPR reduction than genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms.
机译:降低峰值平均功率比(PAPR)是正交频分复用(OFDM)系统中的实现挑战。减少PAPR的一种方法是将一组选定的部分传输序列(PTS)应用于发射信号。然而,PTS选择是一个高度复杂的NP - 硬质问题,并且当在OFDM系统中使用大量子载波时,计算复杂度非常高。在本文中,我们提出了一种新的启发式PTS选择方法,该方法,改进的混沌克隆混洗青蛙跳跃算法(MCCSFLA-PTS)。 MCCSFLA-PTS受到青蛙殖民地的自然克隆选择,并基于混沌理论的启发。仿真结果表明,所提出的MCCSFLA-PTS实现比遗传,量子进化和选择性映射算法更好的PAPR减少。此外,所提出的算法比遗传和量子进化算法更快地收敛。

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