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

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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Bibliographic Details
Main Author: Amr Abo-eleneen (17032284) (author)
Other Authors: Menna Helmy (23073205) (author), Alaa Awad Abdellatif (17151163) (author), Mohamed Abdallah (3073191) (author), Amr Mohamed (3508121) (author), Aiman Erbad (14150589) (author)
Published: 2025
Subjects:
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
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