Actor-critic Network For O-RAN Resource Allocation: Xapp Design, Deployment, And Analysis
2022 · Mohammadreza Kouchaki, Vuk Marojevic
Abstract
Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture that enables openness, intelligence, and automated control. The RAN Intelligent Controller (RIC) provides the platform to design and deploy RAN controllers. xApps are the applications which will take this responsibility by leveraging machine learning (ML) algorithms and acting in near-real time. Despite the opportunities provided by this new architecture, the progress of practical artificial intelligence (AI)-based solutions for network control and automation has been slow. This is mostly because of the lack of an endto-end solution for designing, deploying, and testing AI-based xApps fully executable in real O-RAN network. In this paper we introduce an end-to-end O-RAN design and evaluation procedure and provide a detailed discussion of developing a Reinforcement Learning (RL) based xApp by using two different RL approaches and considering the latest released O-RAN architecture and interfaces.
Authors
(none)
Tags
Stats
Related papers
- Dynamics Of Resource Allocation In O-rans: An In-depth Exploration Of On-policy And Off-policy Deep Reinforcement Learning For Real-time Applications (2024)2.26
- Sim2real For Reinforcement Learning Driven Next Generation Networks (2022)0.00
- Safe And Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach (2023)11.29
- Generalization In Reinforcement Learning For Radio Access Networks (2025)0.00
- FORLORN: A Framework For Comparing Offline Methods And Reinforcement Learning For Optimization Of RAN Parameters (2022)0.00
- Practical Policy Distillation For Reinforcement Learning In Radio Access Networks (2025)0.00
- Average Reward Reinforcement Learning For Wireless Radio Resource Management (2025)2.26
- Interpretable Attention-based Multi-agent PPO For Latency Spike Resolution In 6G RAN Slicing (2026)0.00