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Novel architecture of FFT implementation for 5G module using machine learning algorithms

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Abstract

This proposed work combines R2B, R4B, and R8B using signal delay feedback FFT architecture. R2B SDF FFT architecture has complex and large computations, and many stages are being used. To overcome this problem, Radix-4 and Radix-8 FFT are built to increase efficiency and decrease computational steps to incorporate in DUC and DDC filters and OFDM modules of 5G based modules. Signal delay feedback FFT architecture has stages that can have one clock per cycle. Each phase consists of a butterfly-type diagram in which the twiddle factors and rotators are used. In the proposed joined R2B, R4B, and R8B combined FFT, 5 phases of R2B FFT have been used with standard machine learning algorithms. When combined with complex R2B FFT, the combined R2B, R4B, and R8B FFT are less computational than the current technique. In the proposed method, the **linx tool is used to execute the introduced model.

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All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India

    Nagarjuna Malladhi

  2. Department of Electronics and Communication Engineering, Sreyas Institute of Engineering and Technology, Thatti Annaram, Hyderabad, Telangana, India

    K. Narsimha Reddy

  3. Bayview Asset Management, LLC, Coral Gables, FL, USA

    Rajeev Ratna Vallabhuni

Authors
  1. Nagarjuna Malladhi
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  2. K. Narsimha Reddy
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  3. Rajeev Ratna Vallabhuni
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Correspondence to K. Narsimha Reddy.

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Malladhi, N., Reddy, K.N. & Vallabhuni, R.R. Novel architecture of FFT implementation for 5G module using machine learning algorithms. Int J Syst Assur Eng Manag 14, 2387–2394 (2023). https://doi.org/10.1007/s13198-023-02087-9

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  • DOI: https://doi.org/10.1007/s13198-023-02087-9

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