{"ID":2823414,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00257","arxiv_id":"2601.00257","title":"Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective","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.","short_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...","url_abs":"https://arxiv.org/abs/2601.00257","url_pdf":"https://arxiv.org/pdf/2601.00257v1","authors":"[\"Aly Sabri Abdalla\",\"Vuk Marojevic\"]","published":"2026-01-01T08:22:38Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.AI\",\"cs.CV\",\"cs.MA\",\"cs.NI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}