AI-Optimized Network Slicing for Adaptive Bandwidth Management in 5G and Beyond
Keywords:
network slicing, adaptive bandwidth management, machine learningAbstract
The introduction of 5G and beyond network architectures has revolutionized the telecommunication sector, enabling faster, more reliable, and efficient data transmission. Network slicing is a crucial feature of 5G, offering a method to allocate resources dynamically and efficiently by creating multiple virtual networks within a single physical infrastructure. AI-based optimization techniques are emerging as a solution to further enhance the flexibility and scalability of network slicing, particularly in managing bandwidth for diverse use cases. This paper explores the integration of AI in optimizing network slicing for adaptive bandwidth management in 5G and beyond. It investigates how machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques can improve network slicing operations, such as dynamic resource allocation, traffic management, and QoS (Quality of Service) optimization. The challenges and limitations of implementing AI-optimized network slicing are also discussed, along with potential future directions for its deployment. The paper highlights the transformative potential of AI in the 5G ecosystem, addressing both the opportunities and obstacles in the path of implementing AI-optimized adaptive bandwidth management.
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