How is Machine Learning Transforming Network Management: Evaluating Advancements, Overcoming Challenges, and Anticipating Future Directions?
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Machine learning (ML) is transforming network management by addressing the challenges of new-generation cellular networks and improving performance. ML is being used to support a wide range of virtual services with diverse performance requirements . It is also being applied to forecast future traffic demands and characterize traffic features, enabling the optimization of network control mechanisms such as load balancing, routing, and scheduling . ML algorithms are being used for effective resource management in 5G and beyond wireless networks, providing improved quality of service and reducing computational burden . Additionally, ML is being used to predict the network performance of massive MIMO HetNet systems, aiding in network planning and investment decisions . However, there are still open issues and challenges in implementing AI/ML approaches in network management for future networks .
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| The provided paper does not discuss how machine learning is transforming network management. It focuses on using machine learning to predict the performance of a massive MIMO HetNet system. | |
| The provided paper does not specifically address the question of how machine learning is transforming network management. The paper focuses on ML-based radio resource management in 5G and beyond networks. | |
| The provided paper does not specifically discuss how machine learning is transforming network management. | |
| The provided paper does not directly address the question about how machine learning is transforming network management. The paper focuses on the ML-as-a-Service (MLaaS) platform in the context of 5G networks and its application in network slice provisioning and management. | |
| The provided paper does not directly address the question of how machine learning is transforming network management. |
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