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

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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5 · Who and what money

Authors and funding

5 authors.

Esam A A HagrasFaculty of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt.

Sameh F DesoukyIndependent Researcher, Ottawa, ON K1S 5B6, Canada.

Saad AldosaryDepartment of Computer Science, Community College, King Saud University, Riyadh 11437, Saudi Arabia.ORCID 0000-0001-6168-4141

Haitham KhaledDepartment of Electronics and Communications, School of Engineering, Edith Cowan University, Perth, WA 6027, Australia.ORCID 0000-0001-5865-3686

Tarek M HassanFaculty of Engineering, Delta University for Science and Technology, Gamasa 35712, Egypt.ORCID 0000-0003-0278-3937

Funding

King Saud University RSP2023R260

6 · The paper itself

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.

Indexed as

adaptive genetic algorithmpeak-to-average power ratioselected mapping

Identifiers

PMID38067683

DOI10.3390/s23239310

PMCPMC10708528

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.