AI-Based Mimo Antenna Optimization
Keywords:
AI-based Beamforming, Smart Antenna Systems, Next-Generation Wireless Networks, 5G and 6G Technologies, Machine Learning in Wireless Communication, Signal Processing Optimization, Real-time Network Adaptation.Abstract
The rapid progression of wireless communication technology has profoundly influenced contemporary culture, enabling unparalleled connectedness and data transmission. As the need for elevated data rates, improved dependability, and reduced latency escalates, the constraints of current network infrastructures become more apparent. Next-generation wireless networks, including 5G and the impending 6G, seek to address these difficulties by using sophisticated technology such as AI-driven beamforming and intelligent antenna systems. Beamforming amplifies signal strength and reduces interference by targeting wireless signals to certain receiving devices, therefore markedly enhancing service quality in metropolitan environments. Smart antenna systems enhance network performance by dynamically modifying their patterns according to real-time circumstances. The use of artificial intelligence (AI) in these systems facilitates advanced real-time analysis, forecasting, and decision-making capacities, crucial for overseeing the intricate and evolving characteristics of next-generation networks. This study examines the use of AI-driven beamforming and intelligent antenna design in next-generation wireless networks. The paper illustrates the efficacy of AI-driven strategies in improving network performance via comprehensive theoretical analysis and practical applications, including case studies. Significant results indicate that AI-augmented models attain up to 95% accuracy in fault identification, a 30% enhancement in process optimization, and a 20% decrease in maintenance expenses relative to conventional techniques. The research underscores the pragmatic advantages and possibilities of incorporating AI into semiconductor production processes. This study enhances the development and optimization of wireless communication technologies by addressing both technical and practical factors, so supporting the overarching objective of establishing ubiquitous and seamless connection.
Downloads
References
Li, Y., & Wang, T. (2021). "Deep learning-based beamforming in mmWave massive MIMO systems." IEEE Transactions on Communications, 69(7), 4703-4713. https://doi.org/10.1109/TCOMM.2021.3073146
Zhang, J., & Zhang, H. (2022). "AI-enhanced adaptive beamforming for high-mobility mmWave communications." IEEE Wireless Communications, 29(4), 92-99. https://doi.org/10.1109/MWC.2022.9965493
Shen, X., & Tang, J. (2020). "Machine learning for beam management in millimeter-wave massive MIMO: A comprehensive survey." IEEE Communications Surveys & Tutorials, 22(3), 2011-2048. https://doi.org/10.1109/COMST.2020.2991603
Li, Q., & Qi, L. (2023). "AI-based coordinated beamforming for multi-user massive MIMO networks." Sensors, 23(5), 2772. https://doi.org/10.3390/s23052772
Wu, Y., & Wang, X. (2021). "Deep reinforcement learning for hybrid beamforming in mmWave MIMO systems." IEEE Transactions on Vehicular Technology, 70(8), 7842-7853. https://doi.org/10.1109/TVT.2021.3094842
Xu, C., & Zhang, X. (2020). "AI-driven dynamic beamforming for 5G and beyond networks." IEEE Network, 34(5), 250-257. https://doi.org/10.1109/MNET.011.2000142
Han, S., & Lee, J. (2022). "Machine learning techniques for smart antenna design in next-generation wireless
networks." IEEE Antennas and Propagation Magazine, 64(2), 40-51. https://doi.org/10.1109/MAP.2022.3159014
Liu, Y., & Zhou, M. (2021). "Artificial intelligence for intelligent reflecting surface and smart antenna design." IEEE Journal on Selected Areas in Communications, 39(12), 3815-3831. https://doi.org/10.1109/JSAC.2021.3109503
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
