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

2026-05-21

AI Summary

This study addresses the challenges of ultra-low latency and high bandwidth demands posed by immersive applications such as virtual reality (VR) in multi-slice 6G networks, where conventional resource allocation and caching mechanisms struggle to dynamically meet heterogeneous quality-of-service requirements. To tackle this issue, the authors propose a deep reinforcement learning–based intelligent edge caching and dynamic resource allocation framework within the O-RAN architecture. For the first time, a Deep Q-Network (DQN) agent is integrated into the control plane to enable joint optimization and adaptive scheduling across eMBB, URLLC, and the emerging MBRLLC network slices. Simulation results demonstrate that the proposed approach significantly outperforms traditional schemes, simultaneously reducing end-to-end latency and enhancing throughput, thereby substantially improving the reliability and responsiveness of 6G networks for critical VR services.

Abstract

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.

Problem

• 6G networks
• network slicing
• edge caching
• resource allocation
• VR services

Innovation

• Deep Reinforcement Learning
• Edge Caching
• Network Slicing
• O-RAN
• MBRLLC