An intelligent autoencoder for reducing peak to average power in advanced optical 5G radio network for 64-QAM transmission schemes
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Nidhi Gour
Abstract
In this study, we present an intelligent autoencoder-based method to address the advanced optical 5G radio networks’ peak-to-average power ratio (PAPR) problem. Effective PAPR reduction strategies are necessary because high PAPR in multicarrier communication systems, like OFDM, causes power inefficiencies and signal distortion. By utilizing autoencoders’ potent representation learning capabilities, our approach decreases PAPR by encoding and decoding signals with less amplitude fluctuations. The autoencoder is trained using a sizable dataset of multicarrier signals, which helps the model learn how to compress and rebuild signals with as few peak excursions as possible. The suggested method is tested in a 64-QAM and 256 subcarrier 5G system that is representative of metropolitan settings with substantial line-of-sight components and operates in a Rician fading channel environment. The intelligent autoencoder considerably lowers PAPR while preserving signal integrity and overall system performance, according to simulation data. By comparing the suggested method’s performance to that of conventional PAPR reduction strategies, it is shown to be more effective in terms of both bit error rate (BER) and PAPR reduction. This study demonstrates how sophisticated machine learning approaches can be incorporated into next-generation optical 5G communication systems to enable more dependable and efficient 5G networks.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: Not applicable.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: Not applicable.
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Data availability: Not applicable.
References
1. Kumar, A, Gaur, N, Chakravarty, S, Nanthaamornphong, A. Reducing the PAPR of OTFS modulation using hybrid PAPR algorithms. Wirel Pers Commun 2023;133:2503–23. https://doi.org/10.1007/s11277-024-10885-y.Suche in Google Scholar
2. Kumar, KK, Babu, AY, Krishna, BT. Regressive group phase weighting in PTS combined with SLM approach for PAPR reduction in OFDM systems. Clust Comput 2019;22:10897–903. https://doi.org/10.1007/s10586-017-1222-8.Suche in Google Scholar
3. Kumar, A, Rajagopal, K, Alruwais, N, Alshahrani, HM, Mahgoub, H, Othman, KM PAPR reduction using SLM-PTS-CT hybrid PAPR method for optical NOMA waveform. Heliyon 2023;9. https://doi.org/10.1016/j.heliyon.2023.e20901.Suche in Google Scholar PubMed PubMed Central
4. Wei, Z, Wang, Z, Zhang, J, Qian, L, Zhang, J, Fu, HY. Evolution of optical wireless communication for B5G/6G Prog. Quant Electron 2022;83. https://doi.org/10.1016/j.pquantelec.2022.100398.Suche in Google Scholar
5. Alsharif, MH, Kelechi, AH, Albreem, MA, Chaudhry, SA, Zia, MS, Kim, S. Sixth generation (6G) wireless networks: vision, research activities, challenges and potential solutions. Symmetry 2020;12:676. https://doi.org/10.3390/sym12040676.Suche in Google Scholar
6. Kumar, A, Dhanagopal, R, Albreem, MA, Le, D-N. A comprehensive study on the role of advanced technologies in 5G based smart hospital. Alex Eng J 2021)2021;60:5527–36. https://doi.org/10.1016/j.aej.2021.04.016.Suche in Google Scholar
7. Kumar, A, Chakravarthy, S, Gaur, N, Nanthaamornphong, A. Hybrid approaches to PAPR, BER, and PSD optimization in 6G OTFS: implications for healthcare. J Commun Network 2024;26:308–20. https://doi.org/10.23919/jcn.2024.000027.Suche in Google Scholar
8. Gunturu, A, Godala, AR, Sahoo, AK, Chavva, AKR. Performance analysis of OTFS waveform for 5G NR mmWave communication system. In: Proceeding of IEEE conference on wireless communications and networking, 2021:1–6.10.1109/WCNC49053.2021.9417346Suche in Google Scholar
9. Mishra, HB, Singh, P, Prasad, AK, Budhiraja, R. OTFS channel estimation and data detection designs with superimposed pilots. IEEE Trans Wireless Commun 2022;21:2258–74. https://doi.org/10.1109/twc.2021.3110659.Suche in Google Scholar
10. Kumar, A, Gaur, N, Nanthaamornphong, A. Optimizing PAPR, BER, and PSD efficiency: using phase factors generated by bacteria foraging algorithm for PTS and SLM methods. IEEE Access 2024;12:54964–77. https://doi.org/10.1109/access.2024.3389823.Suche in Google Scholar
11. Kumar, A, Yamini, AP, Gaur, N, Nanthaamornphong, A. A mathematical analysis of the PAPR in the NOMA waveform using SLM algorithm for 64-QAM. J Interdiscipl Math 2024;27:223–32. https://doi.org/10.47974/JIM-1815.Suche in Google Scholar
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