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.
RAS ID
64736
Document Type
Journal Article
Date of Publication
12-1-2023
Volume
23
Issue
23
Funding Information
King Saud University
PubMed ID
38067683
School
School of Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
MDPI
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