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A hybrid envelope fluctuations reduction approach using multilayer neural network for MIMO-OFDM signals

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Abstract

One of the major drawback in multi-carrier signals is large envelope fluctuations i.e., high peak-to-average power ratio (PAPR). The objective of this paper is to propose neural network based active gradient project sequence, a computationally efficient hybrid method to reduce PAPR in multiple-input multiple-output orthogonal frequency division multiplexing system without sacrificing BER performance. In this paper, a neural network based trained module of approximate gradient project scheme (AGP-NN) is combined in parallel with partial transmit sequence method. The Levenberg–Marquardt training algorithm is used to train neural network based AGP module. The simulation results show that the proposed technique not only outperforms other conventional techniques but also offers less computational complexity.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology, Guna, M.P., 473226, India

    Khushboo Pachori

  2. Department of Electronics and Communication Engineering, Thapar University, Patiala, Punjab, 147004, India

    Amit Mishra

Authors
  1. Khushboo Pachori
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  2. Amit Mishra
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Correspondence to Khushboo Pachori.

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Pachori, K., Mishra, A. A hybrid envelope fluctuations reduction approach using multilayer neural network for MIMO-OFDM signals. Wireless Netw 22, 2705–2712 (2016). https://doi.org/10.1007/s11276-015-1126-6

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  • DOI: https://doi.org/10.1007/s11276-015-1126-6

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