Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis

TL;DR

This paper presents an end-to-end design, deployment, and analysis framework for AI-based xApps in O-RAN, demonstrating reinforcement learning approaches within the latest architecture for improved network automation.

Contribution

It introduces a comprehensive procedure for developing, deploying, and testing RL-based xApps in real O-RAN environments, filling a gap in practical AI solutions for RAN control.

Findings

Successful deployment of RL-based xApps in real O-RAN networks

Comparison of two RL approaches for resource allocation

Enhanced automation and control in RAN environments

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.