Time Reduction for SLM OFDM PAPR Based on Adaptive Genetic Algorithm in 5G IoT Networks

Esam A A Hagras; Sameh F Desouky; Saad Aldosary; Haitham Khaled; Tarek M Hassan

doi:10.3390/s23239310

Time Reduction for SLM OFDM PAPR Based on Adaptive Genetic Algorithm in 5G IoT Networks

Sensors (Basel). 2023 Nov 21;23(23):9310. doi: 10.3390/s23239310.

Authors

Esam A A Hagras¹, Sameh F Desouky², Saad Aldosary³, Haitham Khaled⁴, Tarek M Hassan¹

Affiliations

¹ Faculty of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt.
² Independent Researcher, Ottawa, ON K1S 5B6, Canada.
³ Department of Computer Science, Community College, King Saud University, Riyadh 11437, Saudi Arabia.
⁴ Department of Electronics and Communications, School of Engineering, Edith Cowan University, Perth, WA 6027, Australia.

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.

Keywords: adaptive genetic algorithm; peak-to-average power ratio; selected mapping.

Grants and funding

RSP2023R260/King Saud University