Adaptive RF Planning Strategies for 5G Networks Using Machine Learning Techniques

Aqsa Sayed

doi:10.5281/zenodo.14838531

Adaptive RF Planning Strategies for 5G Networks Using Machine Learning Techniques

Author(s)	Aqsa Sayed
Country	United States
Abstract	As 5G networks continue to evolve, the complexity of managing Radio Frequency (RF) resources and optimizing network performance has increased. The traditional methods for RF planning, which are typically static and require significant manual intervention, are proving to be inadequate for the dynamic and highly diverse demands of 5G networks. This paper explores the application of machine learning (ML) techniques for adaptive RF planning in 5G networks. By leveraging machine learning algorithms, it becomes possible to create more responsive, data-driven models that can optimize RF resource allocation, improve coverage, and ensure efficient spectrum utilization. We discuss various ML methods, including supervised, unsupervised, and reinforcement learning, and examine their potential to address the challenges of RF planning in 5G networks. Furthermore, the paper outlines practical use cases, challenges, and future directions for deploying ML-based RF planning strategies in real-world 5G environments.
Keywords	Machine Learning, Adaptive Strategies, Spectrum Optimization, Coverage Optimization, Reinforcement Learning, Supervised Learning, Unsupervised Learning
Field	Engineering
Published In	Volume 4, Issue 11, November 2023
Published On	2023-11-03
Cite This	Adaptive RF Planning Strategies for 5G Networks Using Machine Learning Techniques - Aqsa Sayed - IJLRP Volume 4, Issue 11, November 2023. DOI 10.5281/zenodo.14838531
DOI	https://doi.org/10.5281/zenodo.14838531
Short DOI	https://doi.org/g84g38

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Adaptive RF Planning Strategies for 5G Networks Using Machine Learning Techniques

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