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PTS-PAPR Reduction Technique for 5G Advanced Waveforms Using BFO Algorithm

Arun Kumar1, Manoj Gupta1, Dac-Nhuong Le2,3,*, Ayman A. Aly4

1 Department of Electronics and Communication Engineering, JECRC University, Jaipur 303905, India
2 Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam
3 Faculty of Information Technology, Duy Tan University, Danang 550000, Vietnam
4 Department of Mechanical Engineering, College of Engineering, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia

* Corresponding Author: Dac-Nhuong Le. Email: email

(This article belongs to this Special Issue: Recent Advances in Intelligent Systems and Communication)

Intelligent Automation & Soft Computing 2021, 27(3), 713-722.


Non-orthogonal multiple access (NOMA) will play an imperative part in an advanced 5G radio arrangement, owing to its numerous benefits such as improved spectrum adeptness, fast data rate, truncated spectrum leakage, and, so on. So far, NOMA undergoes from peak to average power ratio (PAPR) problem, which shrinks the throughput of the scheme. In this article, we propose a hybrid method, centered on the combination of advanced Partial transmission sequence (PTS), Selective mapping (SLM), and bacteria foraging optimization (BFO), known as PTS-BFO and SLM-PTS. PTS and SLM are utilized at the sender side and divide the NOMA into several sub-blocks. An optimal phase factor is achieved by the BFA and combined with the NOMA sub-block, where a low peak power value is obtained. Further, we estimate the bit error rate (BER) and PAPR of BFA in the SLM and PTS technique. The simulation outputs reveal that the PTS-BFO outperforms the traditional peak power minimization approaches and moderates the complexity of the system. It is concluded that the proposed algorithm is not explored for the NOMA waveform.


Cite This Article

A. Kumar, M. Gupta, D. Le and A. A. Aly, "Pts-papr reduction technique for 5g advanced waveforms using bfo algorithm," Intelligent Automation & Soft Computing, vol. 27, no.3, pp. 713–722, 2021.


cc 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.
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