Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective

TL;DR

This paper proposes an O-RAN-based framework for low-altitude economy applications, enabling AI-driven, real-time coordination of aerial nodes in complex environments, with a focus on semantic understanding and trajectory planning.

Contribution

It introduces an open RAN-enabled LAE architecture with AI-optimized control, including a semantic-aware rApp and reinforcement learning xApp for UAV swarm management.

Findings

Feasibility demonstrated through a semantic-aware rApp and RL-enabled xApp

Enhanced real-time trajectory planning for UAV swarms

Identification of key research challenges and standardization needs

Abstract

Despite the growing interest in low-altitude economy (LAE) applications, including UAV-based logistics and emergency response, fundamental challenges remain in orchestrating such missions over complex, signal-constrained environments. These include the absence of real-time, resilient, and context-aware orchestration of aerial nodes with limited integration of artificial intelligence (AI) specialized for LAE missions. This paper introduces an open radio access network (O-RAN)-enabled LAE framework that leverages seamless coordination between the disaggregated RAN architecture, open interfaces, and RAN intelligent controllers (RICs) to facilitate closed-loop, AI-optimized, and mission-critical LAE operations. We evaluate the feasibility and performance of the proposed architecture via a semantic-aware rApp that acts as a terrain interpreter, offering semantic guidance to a reinforcement learning-enabled xApp, which performs real-time trajectory planning for LAE swarm nodes. We survey the capabilities of UAV testbeds that can be leveraged for LAE research, and present critical research challenges and standardization needs.