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      Permanent link:
      https://www.ias.ac.in/article/fulltext/sadh/047/0242

    • Keywords

       

      OFDM; peak to average power ratio; bit error rate; signal to noise ratio; neural network; linear regression.

    • Abstract

       

      OFDM is a ubiquitous modulation scheme used to achieve high data rate during transmission and reception in broadband internet of things application. But the envelope aberration ofOFDMsignal leads to high peakto-average power ratio (PAPR) which finally results in overall transmitter inefficiency. This article proposes a neural network (NN) based gradient clipping approach at the transmitter and a linear regression model at the receiver to minimize the PAPR in OFDMsystems with reasonable computational complexity. The simulation result shows that when compared to the original OFDM signal, the proposed Neural network-based Regression assisted PAPR Reduction achieves PAPR reduction by 82.9% and 84.1% for 64QAM-OFDM and 16QAM OFDM signal respectivelywithout compromising on the bit error rate (BER) performance.

    • Author Affiliations

       

      A V MAYAKANNAN1 C ARVIND2 P DHINAKAR3 G SASIKALA4 B SATHYASRI4 K SRIHARI5 V K SHANMUGANATHAN6

      1. Sri Venkateswara College of Engineering and Technology, Thiruvallur, India
      2. Department of ECE, Karpagam College of Engineering, Coimbatore, India
      3. Department of ECE, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, India
      4. Department of ECE, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Chennai, India
      5. Department of CSE, SNS College of Technology, Coimbatore, India
      6. Department of Aeronautical Engineering, Tagore Engineering College, Chennai, India

    • Dates

       
      Published
      19 November 2022
      Manuscript received
      19 March 2022
      Manuscript revised
      1 October 2022
      Accepted
      24 October 2022

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