Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912799875137536 |
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| author | Abdalla, Aly Sabri Marojevic, Vuk |
| author_facet | Abdalla, Aly Sabri Marojevic, Vuk |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_00257 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective Abdalla, Aly Sabri Marojevic, Vuk Systems and Control Artificial Intelligence Computer Vision and Pattern Recognition Multiagent Systems Networking and Internet Architecture 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. |
| title | Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective |
| topic | Systems and Control Artificial Intelligence Computer Vision and Pattern Recognition Multiagent Systems Networking and Internet Architecture |
| url | https://arxiv.org/abs/2601.00257 |