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 contextaware orchestration of aerial nodes with limited integration of articial 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.
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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 contextaware orchestration of aerial nodes with limited integration of articial 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 ca
The low-altitude economy (LAE) is an emerging paradigm that leverages different types of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) to transform sectors such as logistics, agriculture, surveillance, and public safety. By enabling low-cost, high-efficiency services, including precision sensing, data collection, and rapid delivery, LAE enhances operational reach in both urban and remote areas. LAE supports real-time feedback and reduced environmental impact compared to traditional transportation and delivery methods; it expands accessibility to critical services, such as emergency response, healthcare delivery, and environmental monitoring. Despite its potential, the large-scale deployment of aerial services faces several challenges, including regulatory restrictions, technological constraints, and infrastructure limitations. A unified framework is required to ensure safe and scalable operations across heterogeneous airspaces and aerial platforms, providing real-time sensing and decisionmaking, collaborative multi-modal navigation, and adaptive flight scheduling and airspace management [1].
Existing cellular and ad-hoc mesh networks present several limitations for supporting LAE operations, including: (i) lack of mission-conditioned orchestration under partial observability; (ii) single-timescale control loops; (iii) limited network adaptability to new service demands; and (iv) limited coverage and communications scalability in 3D. The open radio access network (O-RAN) introduces a new network architecture that promotes openness, intelligence, virtualization, and interoperability. It disaggregates traditional RAN components and introduces open interfaces, enabling multivendor RAN deployment, service scalability, and innovation through AI-driven RAN intelligent controllers (RICs) [2]. The O-RAN near-real time (Near-RT) and Non-RT RICs support closed-loop, multi-timescale optimization and policy-driven orchestration of network resources and user services. This aligns with the requirements of scalable LAE deployments, where real-time decision-making, autonomous coordination, efficient resource utilization, and seamless coexistence of aerial and terrestrial communications are essential.
Enabled by O-RAN, this paper introduces an AI native orchestration framework that fuses semantic priors with online metrics and operates over multi-timescale control loops to stabilize real-time decisions in partially observed 3D airspace. By contrast, existing literature such as [3], [4] operates purely on link-level metrics, lacks mission-conditioned semantics and environment uncertainty awareness, focuses on aerial coverage expansion, and integrates AI into existing systems rather than devising AI native communications and control. Early O-RAN-UAV studies stop at architectural feasibility and key performance indicator (KPI) reporting; they consider singletimescale policy control loops as opposed to Non-RT and Near-RT RIC orchestration and do not account for LAEspecific missions/conditions [5], [6]. To the best of our knowledge, this is the first O-RAN-enabled LAE framework that integrates environmental semantics and mission-conditioned dual-timescale control for LAE swarms.
The rest of the paper is organized as follows: Section II introduces the LAE use cases and the requirements associated with each use case. Section III discusses the potential of O-RAN as a key enabler for LAE operations. Section IV presents a proof-of-concept use case validating the interplay between the RICs for LAE swarm navigation.
TECHNICAL REQUIREMENTS LAE spans multiple industry sectors and applications, each with distinct operational goals, quality of services (QoS) requirements, and constraints, which are captured in Table I for representative LAE use cases along with performance metrics, and O-RAN enabled functionalities. The QoS requirements are derived from UAV use cases and applications as defined by 3GPP standardization working groups [7].
Urban Air Mobility (UAM) is one of the most demanding LAE scenarios, requiring less than 10 ms end-to-end latency and high reliability (≥99.999%) to ensure safe and autonomous navigation across congested, multi-tier airspaces. This requires predictive mobility models to maintain communications continuity with precise localization of UAVs. Coordinated trajectory optimization must also incorporate dynamic edge-compute offloading for onboard path recalculation and traffic management. At scale, UAM scenarios demand orchestration of hundreds of UAVs, each having a high propulsion energy consumption and needing context-aware policy adaptation.
LAE nodes for disaster response are deployed under infrastructure-deficient conditions for tasks such as search and rescue, damage assessment, and delivery of essential supplies. These missions require aerial nodes to operate under rapidly evolving conditions, such as limited network coverage and unpredictable mobility patterns. Effective UAV-assist
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