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Differential Evolution Constrained Optimization for Peak Reduction

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Artificial Intelligence (CICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13606))

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Abstract

Peak-to-average power ratio (PAPR) reduction is a major practical problem in Orthogonal Frequency Division Multiplexing (OFDM) systems. Many techniques, for example, use convex optimization to reduce the PAPR of OFDM signals. Existing methods either minimize the PAPR under the error vector magnitude (EVM) constraint or minimize the EVM under the PAPR constraint. Because of the complexities of these respective problems, researchers have proposed an interior point method with a computational complexity of \(O(N^3)\). In this paper, we define a new problem that aims to find a feasible solution under the EVM and PAPR constraints, and propose a Differential Evolution Peak Reduction (DEPR) algorithm to deal with it. The motivation of the new problem lies in the fact that in applications, finding a feasible solution efficiently is more desirable than finding an optimal one exhaustively. The proposed DE algorithm minimizes the PAPR through a parallel direct search in the noise vector space. The computational complexity of the new algorithm is \(O(N{\log _2}N)\). Simulation results show that the number of computational steps of the new approach are \(10\%\) less than those of the convex optimization approaches without degrading the bit error rate (BER). Three empirical values were also obtained to ensure the efficiency and effectiveness of our algorithm.

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Authors and Affiliations

  1. Southwest Petroleum University, Chengdu, 610500, China

    Min Wang, Ting Wen, Xiaoyu Jiang & Anan Zhang

Authors
  1. Min Wang
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  2. Ting Wen
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  3. Xiaoyu Jiang
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  4. Anan Zhang
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Corresponding author

Correspondence to Min Wang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Xiaomi Inc., Beijing, China

    Daniel Povey

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. JD Explore Academy, Beijing, China

    Tao Mei

  5. Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

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Wang, M., Wen, T., Jiang, X., Zhang, A. (2022). Differential Evolution Constrained Optimization for Peak Reduction. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_29

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  • DOI: https://doi.org/10.1007/978-3-031-20503-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20502-6

  • Online ISBN: 978-3-031-20503-3

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