Document Type

Journal Article

Publication Title

Sensors

Volume

23

Issue

23

PubMed ID

38067683

Publisher

MDPI

School

School of Engineering

RAS ID

64736

Funders

King Saud University

Comments

Hagras, E. A. A., Desouky, S. F., Aldosary, S., Khaled, H., & Hassan, T. M. (2023). Time reduction for SLM OFDM PAPR based on adaptive genetic algorithm in 5G IoT networks. Sensors, 23(23), article 9310. https://doi.org/10.3390/s23239310

Abstract

In this paper, a new peak average power and time reduction (PAPTR) based on the adaptive genetic algorithm (AGA) strategy is used in order to improve both the time reduction and PAPR value reduction for the SLM OFDM and the conventional genetic algorithm (GA) SLM-OFDM. The simulation results demonstrate that the recommended AGA technique reduces PAPR by about 3.87 dB in comparison to SLM-OFDM. Comparing the suggested AGA SLM-OFDM to the traditional GA SLM-OFDM using the same settings, a significant learning time reduction of roughly 95.56% is achieved. The PAPR of the proposed AGA SLM-OFDM is enhanced by around 3.87 dB in comparison to traditional OFDM. Also, the PAPR of the proposed AGA SLM-OFDM is roughly 0.12 dB worse than that of the conventional GA SLM-OFDM.

DOI

10.3390/s23239310

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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