mlsanityData to 14 Oct 2026

Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning

arXiv 2509.14343 ↗First posted 17 Sep 2025eess.SY cs.AI cs.SYcited by 4

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Open-Radio Access Network (O-RAN) has become an important paradigm for 5G and beyond radio access networks. This paper presents an xApp called xSlice for the Near-Real-Time (Near-RT) RAN Intelligent Controller (RIC) of 5G O-RANs. xSlice is an online learning algorithm that adaptively adjusts MAC-layer resource allocation in response to dynamic network states, including time-varying wireless channel conditions, user mobility, traffic fluctuations, and changes in user demand. To address these network dynamics, we first formulate the Quality-of-Service (QoS) optimization problem as a regret minimization problem by quantifying the QoS demands of all traffic sessions through weighting their throughput, latency, and reliability. We then develop a deep reinforcement learning (DRL) framework that utilizes an actor-critic model to combine the advantages of both value-based and policy-based updating methods. A graph convolutional network (GCN) is incorporated as a component of the DRL framework for graph embedding of RAN data, enabling xSlice to handle a dynamic number of traffic sessions. We have implemented xSlice on an O-RAN testbed with 10 smartphones and conducted extensive experiments to evaluate its performance in realistic scenarios. Experimental results show that xSlice can reduce performance regret by 67% compared to the state-of-the-art solutions. Source code is available at https://github.com/xslice-5G/code.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 17 of its 78 references that are in the map. First posted on arXiv 17 Sep 2025.

Builds on

17 of 78 references.

References · 17

  1. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  2. 2020AM-GCN: Adaptive Multi-channel Graph Convolutional Networkscited by 121
  3. 2022Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challengescited by 55
  4. 2020Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networkscited by 45
  5. 2021ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platformscited by 35
  6. 2016srsLTE: An Open-Source Platform for LTE Evolution and Experimentationcited by 17
  7. 2020Intelligent O-RAN for Beyond 5G and 6G Wireless Networkscited by 14
  8. 2022Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicingcited by 14
  9. 2022Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learningcited by 13
  10. 2022Teal: Learning-Accelerated Optimization of WAN Traffic Engineeringcited by 13
  11. 2023Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Managementcited by 7
  12. 2023Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approachcited by 5
5 more
  1. 2024Safe and Robust Reinforcement Learning: Principles and Practicecited by 4
  2. 2023An Open, Programmable, Multi-vendor 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterfacecited by 3
  3. 2020LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networkscited by 2
  4. 2014A Utility Proportional Fairness Radio Resource Block Allocation in Cellular Networkscited by 1
  5. 2023Intelligent QoS aware slice resource allocation with user association parameterization for beyond 5G ORAN based architecture using DRLcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RANcited by 1
  2. 2026Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automationcited by 0
  3. 2026Place, Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNBcited by 0
  4. 2026Evolving Inspectable O-RAN Slicing xApps with LLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learning
  2. 2024Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN
  3. 2024Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications
  4. 2025Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
  5. 2025Graph Reinforcement Learning for QoS-Aware Load Balancing in Open Radio Access Networks
  6. 2025Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN
  7. 2025Generative Resource Allocation for 6G O-RAN with Diffusion Policies
  8. 2025Prioritizing Latency with Profit: A DRL-Based Admission Control for 5G Network Slices