DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks
The paper discusses a framework for optimizing resource allocation in 6G networks, particularly for Virtual Reality services. It utilizes Deep Q-Network learning to enhance edge caching and resource provisioning across multiple network slices. Simulation results indicate that this approach significantly reduces latency and improves throughput compared to traditional methods.
- ▪The framework is designed for 6G O-RAN networks to support ultra-low latency and high bandwidth for VR services.
- ▪It incorporates Deep Q-Network learning for optimizing edge caching and dynamic resource allocation.
- ▪Simulation results show that the DQN-based framework outperforms traditional methods in terms of latency and throughput.
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Computer Science > Networking and Internet Architecture arXiv:2605.23056 (cs) [Submitted on 21 May 2026] Title:DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks Authors:Khaled M. Naguib, Soumaya Cherkaoui, Mahmoud M. Elmessalawy, Ahmed M. Abd El-Haleem, Ibrahim I. Ibrahim View a PDF of the paper titled DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks, by Khaled M. Naguib and 3 other authors View PDF HTML (experimental) Abstract:Virtual Reality (VR) services delivered over 6G networks demand ultra-low latency and high bandwidth to ensure seamless user experiences.
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