Toward AI-Native 6G: Unveiling Online Optimization and Deep Reinforcement Learning for Autonomous Network Slicing

<p dir="ltr">The shift to AI-native 6G networks demands autonomous slicing strategies that can adapt to diverse and evolving edge and IoT service needs. Two paradigms have emerged: Learn to Slice (L2S), where AI optimizes network slicing for general services, and Slice to Learn (S2L)...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Amr Abo-eleneen (17032284) (author)
مؤلفون آخرون:	Menna Helmy (23073205) (author), Alaa Awad Abdellatif (17151163) (author), Mohamed Abdallah (3073191) (author), Amr Mohamed (3508121) (author), Aiman Erbad (14150589) (author)
منشور في:	2025
الموضوعات:	Engineering Communications engineering Information and computing sciences Artificial intelligence EXP3 DQN slicing AI zero-touch optimization Training Resource management Accuracy Adaptation models 6G mobile communication Optimization Internet of Things Computational modeling Data models
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Toward AI-Native 6G: Unveiling Online Optimization and Deep Reinforcement Learning for Autonomous Network Slicing

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