Prototyping O-RAN Enabled UAV Experimentation for the AERPAW Testbed

TL;DR

This paper presents a scalable open-source O-RAN experimentation platform integrated with the AERPAW UAV testbed, enabling advanced wireless network research and UAV connectivity optimization through experimental validation and AI-generated data.

Contribution

It introduces a novel scalable architecture and prototype for O-RAN experimentation within AERPAW, including integration of FlexRIC and AI-based data generation for UAV network research.

Findings

Successful integration of O-RAN with UAV testbed

Evaluation of FlexRIC for network monitoring and optimization

Demonstration of AI-generated data for digital twin support

Abstract

The Open Radio Access Network (O-RAN) architecture is reshaping the telecommunications landscape by enhancing network flexibility, openness, and intelligence. This paper establishes the requirements, evaluates the design tradeoffs, and introduces a scalable architecture and prototype of an open-source O-RAN experimentation platform within the Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW), an at scale testbed that integrates unmanned aerial vehicles (UAVs) with advanced wireless network technologies, offering experimentation in both outdoor testbed and emulation via a custom digital twin (DT). Through a series of aerial experiments, we evaluate FlexRIC, an open-source RAN Intelligent Controller, within the AERPAW hardware-software platform for network data monitoring, providing valuable insights into the proposed integration and revealing opportunities for leveraging O-RAN to create custom service based optimizations for cellular connected UAVs. We discuss the challenges and potential use cases of this integration and demonstrate the use of a generative artificial intelligence model for generating realistic data based on collected real-world data to support AERPAW's DT.