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

arXiv: https://arxiv.org/abs/2601.00257 · HTML: https://mlsanity.com/paper/2601.00257/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 1 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [Toward Next Generation Open Radio Access Network--What O-RAN Can and Cannot Do!](https://mlsanity.com/paper/2111.13754/) (2021)
- [Learning to Extract Building Footprints from Off-Nadir Aerial Images](https://mlsanity.com/paper/2204.13637/) (2022)
- [Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions](https://mlsanity.com/paper/2504.21583/) (2025)
- [Open-Source Software Radio Platform for Research on Cellular Networked UAVs -- It Works!](https://mlsanity.com/paper/2112.09067/) (2021)
- [Terrain-aware Low Altitude Path Planning](https://mlsanity.com/paper/2505.07141/) (2025)
- [Wireless Communication for Low-Altitude Economy with UAV Swarm Enabled Two-Level Movable Antenna System](https://mlsanity.com/paper/2505.22286/) (2025)

## Abstract (arXiv)

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.