Performance evaluation of 64-QAM OFDM based FSO transmission for millimeter wave 5G applications
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Zainab F. Mohammad
Abstract
This article presents a simulation architecture to investigate the performance of high dynamic range orthogonal frequency division multiplexing (OFDM) as a modulation technique to transmit the quadrature amplitude modulation (QAM) basebands signal over free space optics (FSO) for the fifth generation/new radio (5G/NR) signals for millimeter wave (mm-wave) applications that broadcasting at 28 GHz. The evaluation study of this FSO system is carried out using the opti-system software (2024). The combination of FSO and 64QAM-OFDM systems has led to a high data rate at lower cost of wireless networks. The performance of the system has been analysed and compared with different values of the CW laser power, FSO range under different weather condition. Moreover, the FSO channel was modeled using both log-normal and gamma–gamma turbulence distributions. The performance analysis is based on bit error rate (BER),Q-factor, signal to noise ratio (SNR), error vector magnitude (EVM),optical spectrum analyses and cancellation diagram. According to the results obtained, the presented system can be considered as a good candidate for 5G and beyond mobile communication systems.
Acknowledgments
I would like to express my grateful thanks to my parents for their support, kindness and patience in all and every stage of my life. They devoted their heart and soul to advance my education and encourage me to love learning and hardworking.
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Research ethics: The corresponding author declares that this manuscript is their own work, and has not been published before in any journal and/or conference, and it is never been considered for publication or submitted to any other journals.
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Informed consent: Not applicable. This study does not involve human participants.
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Author contributions: I design and analysis the system and wrote the article and suitable editing, and reading the final version.
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Use of Large Language Models, AI and Machine Learning Tools: Not applicable. Large Language Models (LLMs) were not used in the preparation of this manuscript. No AI or machine learning tools were used in the research, analysis, or preparation of this manuscript.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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