Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis
This addresses the lack of end-to-end solutions for designing, deploying, and testing AI-based xApps in real O-RAN networks, which is an incremental advancement in domain-specific automation.
The paper tackles the slow progress in practical AI-based solutions for O-RAN network control by introducing an end-to-end design and evaluation procedure, developing a Reinforcement Learning-based xApp using two RL approaches for resource allocation.
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.