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

arXiv: https://arxiv.org/abs/2509.14343 · HTML: https://mlsanity.com/paper/2509.14343/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 17 of 17)

- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [AM-GCN: Adaptive Multi-channel Graph Convolutional Networks](https://mlsanity.com/paper/2007.02265/) (2020)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [srsLTE: An Open-Source Platform for LTE Evolution and Experimentation](https://mlsanity.com/paper/1602.04629/) (2016)
- [Intelligent O-RAN for Beyond 5G and 6G Wireless Networks](https://mlsanity.com/paper/2005.08374/) (2020)
- [Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing](https://mlsanity.com/paper/2208.01736/) (2022)
- [Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.06439/) (2022)
- [Teal: Learning-Accelerated Optimization of WAN Traffic Engineering](https://mlsanity.com/paper/2210.13763/) (2022)
- [Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management](https://mlsanity.com/paper/2301.06510/) (2023)
- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [Safe and Robust Reinforcement Learning: Principles and Practice](https://mlsanity.com/paper/2403.18539/) (2024)
- [An Open, Programmable, Multi-vendor 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface](https://mlsanity.com/paper/2310.17062/) (2023)
- [LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks](https://mlsanity.com/paper/2009.03771/) (2020)
- [A Utility Proportional Fairness Radio Resource Block Allocation in Cellular Networks](https://mlsanity.com/paper/1406.2630/) (2014)
- [Intelligent QoS aware slice resource allocation with user association parameterization for beyond 5G ORAN based architecture using DRL](https://mlsanity.com/paper/2311.01594/) (2023)

## Built on by (most cited first, 4 of 4)

- [EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN](https://mlsanity.com/paper/2602.09206/) (2026)
- [Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automation](https://mlsanity.com/paper/2605.10648/) (2026)
- [Place, Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNB](https://mlsanity.com/paper/2608.23824/) (2026)
- [Evolving Inspectable O-RAN Slicing xApps with LLMs](https://mlsanity.com/paper/2609.27337/) (2026)

## 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.