Open Access

ARTICLE

PAPR Reduction in NOMA by Using Hybrid Algorithms

Mohit Kumar Sharma, Arun Kumar*
Department of Electronics and Communication Engineering, JECRC University, Jaipur, 303905, India
* Corresponding Author: Arun Kumar. Email:

Computers, Materials & Continua 2021, 69(1), 1391-1406. https://doi.org/10.32604/cmc.2021.017666

Received 06 February 2021; Accepted 02 April 2021; Issue published 04 June 2021

Abstract

Non-orthogonal multiple access (NOMA) is gaining considerable attention due to its features, such as low out-of-band radiation, signal detection capability, high spectrum gain, fast data rate, and massive D2D connectivity. It may be considered for 5G networks. However, the high peak-to-average power ratio (PAPR) is viewed as a significant disadvantage of a NOMA waveform, and it weakens the quality of signals and the throughput of the scheme. In this article, we introduce a modified NOMA system by employing a block of wavelet transform, an alternative to FFT (Fast Fourier transform). The modified system combines the details of fractional frequency and time analysis of NOMA signals. In this correspondence, we utilize an advanced partial transmission scheme (PTS), and selective mapping (SLM), and present a genetic algorithm (GA) for SLM to investigate the peak power performance of a WT-based NOMA system. The performance of WT-SLM, WT-PTS, and WT-SLM-GA methods is compared with that of the traditional NOMA-based SLM and PTS methods. The simulation results demonstrate that the proposed system effectively reduces PAPR in comparison with the traditional schemes.

Keywords

PAPR; wavelet transform; NOMA; PTS; SLM; 5G

Cite This Article

M. Kumar Sharma and A. Kumar, "Papr reduction in noma by using hybrid algorithms," Computers, Materials & Continua, vol. 69, no.1, pp. 1391–1406, 2021.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1272

    View

  • 1051

    Download

  • 0

    Like

Share Link

WeChat scan