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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23056 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | iwRpUan-u4Wa |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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