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

• URL: http://arxiv.org/abs/2601.00257v1
• Date: Thu, 01 Jan 2026 08:22:38 GMT
• Title: Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective
• Authors: Aly Sabri Abdalla, Vuk Marojevic,
• Abstract summary: This paper introduces an open radio access network (O-RAN)-enabled low-altitude economy (LAE) framework.<n>We evaluate the feasibility and performance of the proposed architecture via a semantic-aware rApp that acts as a terrain interpreter.<n>We survey the capabilities of UAV testbeds that can be leveraged for LAE research, and present critical research challenges and standardization needs.
• Score: 2.3920356798957436
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• IMAGINE: Intelligent Multi-Agent Godot-based Indoor Networked Exploration [0.0]
  This paper implements Multi-Agent Reinforcement Learning (MARL) to address challenges in a 2D indoor environment.<n>Policy training aims to achieve emergent collaborative behaviours and decision-making under uncertainty.
  arXiv  Detail & Related papers  (2026-02-02T22:08:41Z)
• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Agentic AI Meets Edge Computing in Autonomous UAV Swarms [3.9444299467643025]
  Agentic AI, powered by large language models (LLMs), with autonomous reasoning, planning, and execution, opens new operational possibilities.<n>However, infrastructure constraints, dynamic environments, and the computational demands of multi-agent coordination limit real-world deployment.<n>This paper investigates the integration of LLM-based agentic AI and edge computing to realize scalable and resilient autonomy in UAV swarms.
  arXiv  Detail & Related papers  (2026-01-20T19:45:33Z)
• Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL [37.79695337425523]
  Low-altitude intelligent networks (LAINs) can support mission-critical applications such as disaster response, environmental monitoring, and real-time sensing.<n>These systems face key challenges, including energy-constrained UAVs, task arrivals, and heterogeneous computing resources.<n>We propose an integrated air-ground collaborative network and formulate a time-dependent integer nonlinear programming problem that jointly optimize UAV trajectory planning and task offloading decisions.
  arXiv  Detail & Related papers  (2025-12-05T08:14:45Z)
• Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm [85.7583231789615]
  6G positions intelligence as a native network capability, transforming the design of radio access networks (RANs)<n>Within this vision, Semantic-native communication and agentic intelligence are expected to play central roles.<n>Agentic intelligence endows distributed RAN entities with goal-driven autonomy, reasoning, planning, and multi-agent collaboration.
  arXiv  Detail & Related papers  (2025-12-04T03:09:33Z)
• AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios [64.51320327698231]
  We introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios.<n>We develop an innovative semi-automated collaborative agent-based labeling assistant framework.<n>We also propose HawkEyeTrack, a novel method that collaboratively enhances vision-language representation learning.
  arXiv  Detail & Related papers  (2025-11-26T04:44:27Z)
• AI-Open-RAN for Non-Terrestrial Networks [2.558515208305258]
  We propose the concept of AIO-RAN-NTN, a unified all-in-one Radio Access Network (RAN) for Non-Terrestrial Networks (NTNs)<n>This approach advances interoperability, flexibility, and intelligence in next-generation telecommunications.
  arXiv  Detail & Related papers  (2025-11-14T23:41:52Z)
• Security-Aware Joint Sensing, Communication, and Computing Optimization in Low Altitude Wireless Networks [83.84711311344918]
  Integrated sensing, communications, and computing (I SCC) is one of the core parts of Low-Altitude Wireless Networks (LAWNs)<n>This paper studies joint performance optimization of I SCC while considering secrecyness of the communications.<n>We propose a deep Q-network (DQN)-based multi-objective evolutionary algorithm, which adaptively selects evolutionary operators according to the evolving optimization objectives.
  arXiv  Detail & Related papers  (2025-11-03T11:06:41Z)
• Agentic AI Reasoning for Mobile Edge General Intelligence: Fundamentals, Approaches, and Directions [74.35421055079655]
  Large language models (LLMs) have enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.<n>Mobile Edge General Intelligence (MEGI) brings real-time, privacy-preserving reasoning to the network edge.<n>We propose a joint optimization framework for efficient LLM reasoning deployment in MEGI.
  arXiv  Detail & Related papers  (2025-09-27T10:53:48Z)
• The LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network [0.0]
  We present the concept of the Large Language Model (LLM)-RAN Operator.<n>LLM is embedded into the RAN control loop to translate high-level human intents into optimal network actions.<n>This paper aims to bridge the gap between AI theory and wireless systems engineering in the NextG era.
  arXiv  Detail & Related papers  (2025-08-27T20:47:45Z)
• Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches [76.12691010182802]
  This survey focuses on enabling agentic artificial intelligence (AI) in satellite-augmented low-altitude economy and terrestrial networks (SLAETNs)<n>We introduce the architecture and characteristics of SLAETNs, and analyze the challenges that arise in integrating satellite, aerial, and terrestrial components.<n>We examine how these models empower agentic functions across three domains: communication enhancement, security and privacy protection, and intelligent satellite tasks.
  arXiv  Detail & Related papers  (2025-07-19T14:07:05Z)
• From Turbulence to Tranquility: AI-Driven Low-Altitude Network [17.660082508775957]
  Low Altitude Economy (LAE) networks own transformative potential in urban mobility, emergency response, and aerial logistics.<n>These networks face significant challenges in spectrum management, interference mitigation, and real-time coordination across dynamic and resource-constrained environments.<n>This study explores three core elements for enabling intelligent LAE networks as follows machine learning-based spectrum sensing and coexistence, artificial intelligence (AI)-optimized resource allocation and trajectory planning, and testbed-driven validation and standardization.
  arXiv  Detail & Related papers  (2025-06-02T07:12:44Z)
• The Future of Aerial Communications: A Survey of IRS-Enhanced UAV Communication Technologies [2.8002534443865987]
  The advent of Intelligent Reflecting Surfaces (IRS) and Unmanned Aerial Vehicles (UAVs) is setting a new benchmark in the field of wireless communications. IRS, with their groundbreaking ability to manipulate electromagnetic waves, have opened avenues for substantial enhancements in signal quality, network efficiency, and spectral usage. UAVs have emerged as dynamic, versatile elements within communication networks, offering high mobility and the ability to access and enhance coverage in areas where traditional, fixed infrastructure falls short.
  arXiv  Detail & Related papers  (2024-06-02T09:58:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.