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Federated learning for efficient spectrum allocation in open RAN

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Abstract

In the evolving landscape of Open Radio Access Networks (Open RAN), the dynamic and unpredictable nature of network conditions presents significant challenges for traditional spectrum allocation strategies. This paper introduces an innovative framework that leverages Federated Learning (FL) to refine and enhance spectrum allocation within Open RAN. Utilizing the decentralized architecture of FL, our model introduces a system that is not only more adaptive to real-time changes but also offers enhanced robustness for spectrum management. We delve into the advantages of this approach, such as significant improvements in data traffic management, latency reduction, and overall network capacity enhancement. Additionally, we address potential implementation challenges, providing strategic countermeasures to ensure the successful deployment of our FL-based framework. Through this exploration, our paper underscores the transformative potential of integrating FL with Open RAN, marking a significant step forward in the application of AI technologies for optimizing wireless communication networks. This contribution opens new avenues for research in AI-driven spectrum allocation, setting a foundation for future empirical validations and the development of more efficient, intelligent telecommunication infrastructures.

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The original contributions presented in the study and included in the article; further inquiries can be directed to the corresponding author.

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Funding

This research was supported by the College of Technological Innovation, Zayed University (ZU), and the Technology Innovation Institute (TII) under grant numbers RIF-20130 & EU2205.

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Authors and Affiliations

  1. Department of Technological Innovation, Zayed University, 144534, Abu Dhabi, United Arab Emirates

    Muhammad Asad & Safa Otoum

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  1. Muhammad Asad
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  2. Safa Otoum
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Contributions

MA, as the lead author and postdoctoral researcher, was responsible for the conceptualization, methodology, data analysis, and drafting of the manuscript. SO, as the supervisor, provided guidance on the research direction, and significantly contributed to refining the research framework and manuscript review and editing.

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Correspondence to Muhammad Asad.

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Asad, M., Otoum, S. Federated learning for efficient spectrum allocation in open RAN. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04500-9

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