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    翁童, 袁伟娜. 一种基于SPSO算法降低FBMC系统PAPR的新方法[J]. 华东理工大学学报(自然科学版), 2020, 46(5): 702-708. DOI: 10.14135/j.cnki.1006-3080.20190731002
    引用本文: 翁童, 袁伟娜. 一种基于SPSO算法降低FBMC系统PAPR的新方法[J]. 华东理工大学学报(自然科学版), 2020, 46(5): 702-708. DOI: 10.14135/j.cnki.1006-3080.20190731002
    WENG Tong, YUAN Weina. A New Method Based on Scaled Particle Swarm Optimisation to Reduce the PAPR of FBMC System[J]. Journal of East China University of Science and Technology, 2020, 46(5): 702-708. DOI: 10.14135/j.cnki.1006-3080.20190731002
    Citation: WENG Tong, YUAN Weina. A New Method Based on Scaled Particle Swarm Optimisation to Reduce the PAPR of FBMC System[J]. Journal of East China University of Science and Technology, 2020, 46(5): 702-708. DOI: 10.14135/j.cnki.1006-3080.20190731002

    一种基于SPSO算法降低FBMC系统PAPR的新方法

    A New Method Based on Scaled Particle Swarm Optimisation to Reduce the PAPR of FBMC System

    • 摘要: 部分传输序列(Partial Transfer Sequence, PTS)是滤波器组多载波(Filter Bank Multicarrier,FBMC)降低峰均功率比(Peak-to-Average Power Ratio, PAPR)的有效方法之一,但PTS存在计算复杂度高等问题。本文提出了一种基于PTS的新方法,采用一种奇数分割法(Odd-PTS),并在此基础上引入了一种基于比例因子的粒子群优化(Scaled Particle Swarm Optimisation, SPSO)算法,通过加入比例因子克服PSO算法收敛速度不足等缺点。该方法不仅降低了系统的PAPR性能还降低了计算复杂度,并显著提高了频谱利用率。通过仿真验证了本文方法的有效性。

       

      Abstract: Partial transfer sequence (PTS) is an effective method for filter bank multicarrier (FBMC) to reduce peak-to-average power ratio (PAPR). However, PTS method selects the optimal phase factor via exhaustive search, which inevitable results in high computational complexity. In order to overcome this shortcoming, this paper presents a new PTS-based method. Firstly, based on the traditional PTS segmentation method, this paper introduces an odd-partial transfer sequence(OPTS) segmentation method, which uses staggered segmentation for odd subblocks and random segmentation for even subblocks. By combining the advantages of staggered segmentation and random segmentation, this proposed method can effectively improve the PAPR performance of the system, meanwhile, reduce the computational complexity. Secondly, this paper proposes a scaled particle swarm optimization (SPSO) algorithm by incorporating the scaling factor into the traditional PSO algorithm. It is known that the conventional particle swarm optimization (PSO) algorithm has a slow convergence rate in the iteration process and is prone to fall into local optimal in high-dimensional space. Aiming at the above shortcoming, this paper proposes an improved PSO algorithm, SPSO algorithm, whose main idea is to use the scaling factor to control the particle speed, and obtain better PAPR performance with lower computational complexity and faster convergence speed. It will slightly increase PAPR value via integrating SPSO algorithm into OPTS and can attain better performance in terms of complexity, and significantly improve the spectrum utilization. Finally, simulation results verify the effectiveness of the new method.

       

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