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

• URL: http://arxiv.org/abs/2605.23056v1
• Date: Thu, 21 May 2026 21:44:27 GMT
• Title: DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks
• Abstract summary: Virtual reality (VR) services delivered over 6G networks demand ultra-low latency and high bandwidth to ensure seamless user experiences.<n>This paper presents an intelligent resource allocation and edge caching framework for 6G O-RAN networks, leveraging Deep Q-Network (DQN) learning.
• Score: 5.468429759077718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks [49.19977685617281]
  Industrial 6G networks require ultra-reliable, low-latency, and energy-efficient connectivity in dynamic and blockage-prone environments.<n>We propose a RIS-enabled Open-RAN framework for integrated terrestrial/non-terrestrial (TN/NTN) industrial 6G networks.<n>We show improvements of up to 75% in data rate, 25% latency reduction, and 16% energy savings compared with state-of-the-art learning-based and non-RIS baselines.
  arXiv  Detail & Related papers  (2026-05-31T16:18:25Z)
• Deep Unfolded Fractional Optimization for Maximizing Robust Throughput in 6G Networks [5.855866479962828]
  6G wireless communication networks aim to leverage artificial intelligence tools for efficient and robust network optimization.<n>This paper considers a multi-antenna base station serving multiple users simultaneously through transmit beamforming in downlink mode.<n>To account for robustness, this work proposes an uncertainty-injected deep unfolded fractional programming framework for weighted sum rate (WSR) case.
  arXiv  Detail & Related papers  (2026-01-27T09:56:38Z)
• Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization [79.27012080083603]
  Large language models (LLMs) offer promising tools to enhance reinforcement learning in wireless networks.<n>We propose an LLM-assisted state representation and semantic extraction to enhance the multi-agent reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-01-15T01:42:39Z)
• Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks [43.88530200050682]
  This paper presents a graph neural network (GNN)-based multicast routing framework that jointly minimizes total transmission cost and supports user-specific video quality requirements.<n>The proposed method closely approximates optimal dynamic programming-based solutions while significantly reducing computational complexity.<n>The results also confirm strong generalization to large-scale and dynamic network topologies, highlighting the method's potential for real-time deployment in 6G multimedia delivery scenarios.
  arXiv  Detail & Related papers  (2025-10-13T08:00:45Z)
• Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning [19.02610605908148]
  This paper presents an xApp called xSlice for the Near-Real-Time (Near-RT) RAN Intelligent Controller (RIC) of 5G O-RANs.<n>xSlice is an online learning algorithm that adaptively adjusts MAC-layer resource allocation in response to dynamic network states.<n> Experimental results show that xSlice can reduce performance regret by 67% compared to the state-of-the-art solutions.
  arXiv  Detail & Related papers  (2025-09-17T18:20:04Z)
• DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation [58.62766376631344]
  We propose a customized wireless network intent (WNI-G) model to address different state variations of wireless communication networks. Extensive simulation achieves greater stability in spectral efficiency and variations of traditional DRL models in dynamic communication systems.
  arXiv  Detail & Related papers  (2024-10-18T14:04:38Z)
• Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning [69.00997996453842]
  We propose a deep Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for virtual network embedding. We show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue.
  arXiv  Detail & Related papers  (2024-06-25T07:42:30Z)
• Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks [44.37047471448793]
  In this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL) We propose an innovative PSL framework, namely, efficient parallel split learning (EPSL) to accelerate model training. We show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy.
  arXiv  Detail & Related papers  (2023-03-26T16:09:48Z)
• Intelligent Load Balancing and Resource Allocation in O-RAN: A Multi-Agent Multi-Armed Bandit Approach [4.834203844100679]
  We propose a multi-agent multi-armed bandit for load balancing and resource allocation (mmLBRA) scheme. We also present the mmLBRA-LB and mmLBRA-RA sub-schemes that can operate independently in non-realtime RAN intelligent controller (Non-RT RIC) and near-RT RIC, respectively.
  arXiv  Detail & Related papers  (2023-03-25T04:42:30Z)
• A Graph Neural Networks based Framework for Topology-Aware Proactive SLA Management in a Latency Critical NFV Application Use-case [0.34376560669160383]
  Recent advancements in 5G and 6G have led to the emergence of latency-critical applications delivered via a Network-series (NFV) enabled paradigm. We propose a proactive SLA management framework leveraging Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL) to balance the trade-off between efficiency and reliability.
  arXiv  Detail & Related papers  (2022-11-10T23:22:05Z)
• IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode [49.734868032441625]
  This paper proposes a bandwidth aware multi domain virtual network embedding algorithm (BA-VNE) The algorithm is mainly aimed at the problem that users need a lot of bandwidth in wireless communication mode. In order to improve the performance of the algorithm, we introduce particle swarm optimization (PSO) algorithm.
  arXiv  Detail & Related papers  (2022-02-03T03:34:06Z)
• Deep Adaptive Inference Networks for Single Image Super-Resolution [72.7304455761067]
  Single image super-resolution (SISR) has witnessed tremendous progress in recent years owing to the deployment of deep convolutional neural networks (CNNs) In this paper, we take a step forward to address this issue by leveraging the adaptive inference networks for deep SISR (AdaDSR) Our AdaDSR involves an SISR model as backbone and a lightweight adapter module which takes image features and resource constraint as input and predicts a map of local network depth.
  arXiv  Detail & Related papers  (2020-04-08T10:08:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.