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Beyond Research: Translating AI-Enabled Telecommunications Innovation into National-Scale Implementation
Subject area: Science,Engineering and Technology · Area of research: Telecommunication
DOI: https://doi.org/10.64388/IREV9I8-1714078
Abstract
Artificial Intelligence (AI) has presented great opportunities in improving the telecommunication networks in terms of predictive analytics, automated fault management and intelligent resource allocation. Most AI-driven solutions, however, are limited to lab work or pilot projects, which do not have the potential to affect operational networks. The paper will focus on the urgent requirement to commercialize AI telecommunications ideas, the concept of experimental research to practical infrastructure at a national level. We concentrate on the use of Convolutional Neural Networks (CNNs) in extracting high-quality features on complex network traffic and signal data, and the use of Random Forest models in decision-making that is robust and interpretable, and thus can be used in real-time. The framework allows scalable, trustworthy, and interpretable AI functions in geographically split networks by combining these models into a hybrid framework. The planned solution can be used as a feasible roadmap to a nationwide implementation, increase network resiliency, service continuity, and regulatory-compliant operations, improving the modernization and operational intelligence of the U.S. telecommunications ecosystem.
Keywords
AI-based Telecommunication, Nationwide Implementation, Convolutional Neural Network, Random Forest, 5G, 6G, Network Automation, Infrastructure Resilience.
References
[1] Ahmad, I., Chen, Y., Imran, M. A., & Zoha, A. (2022). Artificial intelligence in 5G networks: Use cases, architectures, and future directions. IEEE Access, 10, 12345–12363. https://doi.org/10.1109/ACCESS.2022.3141598
[2] Alsharif, M. H., Basheri, M., & Zaidi, S. A. R. (2021). Machine learning for self-optimizing networks in 5G and beyond. Computer Networks, 192, 108054. https://doi.org/10.1016/j.comnet.2021.108054
[3] Chen, M., Saad, W., & Yin, C. (2020). Deep learning for wireless networks: Techniques, applications, and future challenges. IEEE Communications Surveys & Tutorials, 22(1), 15–50. https://doi.org/10.1109/COMST.2019.2930839
[4] Fang, Y., Wang, L., & Wang, Y. (2021). Self-optimization networks (SON) for 5G: Principles, architectures, and deployment strategies. IEEE Network, 35(2), 120–127. https://doi.org/10.1109/MNET.001.2000112
[5] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[6] Guo, S., He, R., Li, J., & Wang, C. (2021). Hybrid CNN and Random Forest models for network traffic prediction in 5G networks. IEEE Transactions on Network and Service Management, 18(4), 4256–4268. https://doi.org/10.1109/TNSM.2021.3085161
How to cite this paper
@article{1714078,
author = {Pratim Prakash Rai},
title = {Beyond Research: Translating AI-Enabled Telecommunications Innovation into National-Scale Implementation},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {8},
pages = {250-262},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1714078.pdf},
abstract = {Artificial Intelligence (AI) has presented great opportunities in improving the telecommunication networks in terms of predictive analytics, automated fault management and intelligent resource allocation. Most AI-driven solutions, however, are limited to lab work or pilot projects, which do not have the potential to affect operational networks. The paper will focus on the urgent requirement to commercialize AI telecommunications ideas, the concept of experimental research to practical infrastructure at a national level. We concentrate on the use of Convolutional Neural Networks (CNNs) in extracting high-quality features on complex network traffic and signal data, and the use of Random Forest models in decision-making that is robust and interpretable, and thus can be used in real-time. The framework allows scalable, trustworthy, and interpretable AI functions in geographically split networks by combining these models into a hybrid framework. The planned solution can be used as a feasible roadmap to a nationwide implementation, increase network resiliency, service continuity, and regulatory-compliant operations, improving the modernization and operational intelligence of the U.S. telecommunications ecosystem.},
keywords = {AI-based Telecommunication, Nationwide Implementation, Convolutional Neural Network, Random Forest, 5G, 6G, Network Automation, Infrastructure Resilience.},
month = {February},
doi = {https://doi.org/10.64388/IREV9I8-1714078}
}