# DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks

arXiv: https://arxiv.org/abs/2605.23056 · HTML: https://mlsanity.com/paper/2605.23056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 5 of 5)

- [Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.06439/) (2022)
- [Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks](https://mlsanity.com/paper/2206.11328/) (2022)
- [ORANSlice: An Open-Source 5G Network Slicing Platform for O-RAN](https://mlsanity.com/paper/2410.12978/) (2024)
- [Open RAN Slicing with Quantum Optimization](https://mlsanity.com/paper/2502.00142/) (2025)
- [Intelligible Protocol Learning for Resource Allocation in 6G O-RAN Slicing](https://mlsanity.com/paper/2312.07362/) (2023)

## Built on by (most cited first, 1 of 1)

- [Privacy-Aware Agent Collaboration for Dynamic VR Slice Management in 6G SD-RAN](https://mlsanity.com/paper/2606.26123/) (2026)

## Abstract (arXiv)

Virtual Reality (VR) services delivered over 6G networks demand ultra-low latency and high bandwidth to ensure seamless user experiences. This paper presents an intelligent resource allocation and edge caching framework for 6G O-RAN networks, leveraging Deep Q-Network (DQN) learning for optimizing edge caching and dynamic resource provisioning across multiple network slices within an O-RAN-compliant architecture. By incorporating DRL agents into the network control plane, the proposed system enables proactive and adaptive content distribution as well as real-time computational resource allocation that meets the quality-of-service demands of eMBB, URLLC, and especially the emerging MBRLLC slices essential for VR. Simulation results demonstrate that the DQN-based framework consistently outperforms traditional methods in reducing latency and improving throughput, leading to more reliable and responsive support for immersive VR applications in 6G environments.