# Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2202.06439 · HTML: https://mlsanity.com/paper/2202.06439/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2022.

## Built on by (most cited first, 13 of 13)

- [LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN](https://mlsanity.com/paper/2504.18062/) (2025)
- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning](https://mlsanity.com/paper/2509.14343/) (2025)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks](https://mlsanity.com/paper/2501.06242/) (2025)
- [Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks](https://mlsanity.com/paper/2604.01238/) (2026)
- [Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learning](https://mlsanity.com/paper/2401.08861/) (2024)
- [Slicing for AI: An Online Learning Framework for Network Slicing Supporting AI Services](https://mlsanity.com/paper/2411.02412/) (2024)
- [RAN Cortex: Memory-Augmented Intelligence for Context-Aware Decision-Making in AI-Native Networks](https://mlsanity.com/paper/2505.07842/) (2025)
- [DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks](https://mlsanity.com/paper/2605.23056/) (2026)
- [Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery](https://mlsanity.com/paper/2604.01049/) (2026)
- [When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System](https://mlsanity.com/paper/2605.20037/) (2026)
- [Privacy-Aware Agent Collaboration for Dynamic VR Slice Management in 6G SD-RAN](https://mlsanity.com/paper/2606.26123/) (2026)

## Abstract (arXiv)

The evolution of the future beyond-5G/6G networks towards a service-aware network is based on network slicing technology. With network slicing, communication service providers seek to meet all the requirements imposed by the verticals, including ultra-reliable low-latency communication (URLLC) services. In addition, the open radio access network (O-RAN) architecture paves the way for flexible sharing of network resources by introducing more programmability into the RAN. RAN slicing is an essential part of end-to-end network slicing since it ensures efficient sharing of communication and computation resources. However, due to the stringent requirements of URLLC services and the dynamics of the RAN environment, RAN slicing is challenging. In this article, we propose a two-level RAN slicing approach based on the O-RAN architecture to allocate the communication and computation RAN resources among URLLC end-devices. For each RAN slicing level, we model the resource slicing problem as a single-agent Markov decision process and design a deep reinforcement learning algorithm to solve it. Simulation results demonstrate the efficiency of the proposed approach in meeting the desired quality of service requirements.