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Empowering Intelligent Low-altitude Economy with Large AI Model Deployment

Zhonghao Lyu, Yulan Gao, Junting Chen, Hongyang Du, Jie Xu, Kaibin Huang, Dong In Kim

arXiv:2505.22343v2eess.SPcs.AI

TL;DR

LAIM deployment in low-altitude systems faces onboard resource constraints, mismatches with dynamic environments, and inefficiencies from decoupled SCC designs. The paper proposes a hierarchical architecture, enabling techniques, and a task-oriented pipeline, then reports real-world case studies of LAIM-enabled deployment.

Abstract

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Low-altitude economy (LAE) represents an emerging economic paradigm that redefines commercial and social aerial activities. Large artificial intelligence models (LAIMs) offer transformative potential to further enhance the intelligence of LAE services. However, deploying LAIMs in LAE poses several challenges, including the significant gap between their computational/storage demands and the limited onboard resources of LAE entities, the mismatch between lab-trained LAIMs and dynamic physical environments, and the inefficiencies of traditional decoupled designs for sensing, communication, and computation. To address these issues, we first propose a hierarchical system architecture tailored for LAIM deployment and present representative LAE application scenarios. Next, we explore key enabling techniques that facilitate the mutual co-evolution of LAIMs and low-altitude systems, and introduce a task-oriented execution pipeline for scalable and adaptive service delivery. Then, the proposed framework is validated through real-world case studies. Finally, we outline open challenges to inspire future research.

I. INTRODUCTION

The introduction frames LAIM deployment in low-altitude systems as a response to a growing need for intelligent services, constrained by onboard resources, dynamic environments, and inefficient decoupled SCC designs. It proposes an architecture, enabling techniques, a task-oriented pipeline, and real-world case studies.

I. INTRODUCTION

  • Onboard battery, storage, and processing limits make direct deployment of full-scale LAIMs infeasible, while pre-trained models may degrade on evolving, mission-specific tasks.Traditional decoupled sensing, communication, and computation designs can also waste resources when these functions are coupled and compete for constrained resources.Source: §I · p. 1, 3) Co-designing of sensing,… · p. 1

3) Co-designing of sensing, communication, and computation (SCC):

  • The proposed task-oriented ISCC framework co-designs sensing, communication, and computation around LAIM task requirements.The introduction identifies unified architectures, environment-aware design methods, and real-world prototypes as underexplored needs.Source: 3) Co-designing of sensing,… · pp. 1–2
  • The paper proposes a hierarchical LAIM deployment architecture and classifies representative LAE application scenarios.It also outlines open issues for future research.Source: 3) Co-designing of sensing,… · p. 2, Figure 1 · p. 2
  • The paper explores mutual benefits between LAIMs and LAE, including enabling techniques and real-world interaction loops that refine LAIM performance.It also proposes a task-oriented execution pipeline for LAIM-empowered services.Source: 3) Co-designing of sensing,… · p. 2
  • Real-world case studies examine LAIM-guided UAV placement and resource-aware deployment across air-ground platforms using real-world data and testbeds.The studies demonstrate framework feasibility under practical low-altitude conditions.Source: 3) Co-designing of sensing,… · p. 2

II. SYSTEM ARCHITECTURE AND APPLICATION SCENARIOS OF LAIM-EMPOWERED LAE

The paper proposes an aerial-native, mission-adaptive architecture for distributed AI execution across dynamic 3D topologies with fast-moving IAAs. Its ground, aerial, and cloud tiers support scalable and adaptive LAIM-enabled services.

II. SYSTEM ARCHITECTURE AND APPLICATION SCENARIOS OF LAIM-EMPOWERED LAE

  • The proposed three-tier framework comprises ground, aerial, and cloud layers for scalable, adaptive LAIM-enabled intelligence across low-altitude services.It responds to dynamic 3D topologies and fast-moving IAAs, unlike architectures relying on static terrestrial infrastructure.Source: §II · p. 2

A. Low-altitude System Architecture with LAIM Integration

  • Ground sensors and mobile user equipment collect data and perform preliminary processing, while base stations and edge servers provide stronger processing and communication capabilities.Limited devices may run lightweight LAIM components; semantically compressed embeddings or task-relevant raw data can be sent to higher tiers.Source: A. Low-altitude System Architecture… · p. 2
  • Aerial IAAs combine sensing, computing, communication, and service execution, using mobility for repositioning, coverage extension, and topology reconfiguration.They execute latency-sensitive tasks locally and offload compute-intensive tasks to the cloud; cooperative IAAs use dynamic routing, adaptive beamforming, and mesh reconfiguration.Source: A. Low-altitude System Architecture… · pp. 2–3
  • The cloud layer coordinates distributed LAIM training, inference, and cross-layer resource management, hosting full-scale models and partitioning execution across layers.Deployment is organized around environment-aware, task-specific QoS requirements such as urgency, channel conditions, and resource availability.Source: A. Low-altitude System Architecture… · p. 3

B. Typical Application Scenarios

The paper groups representative low-altitude economy applications by their dominant communication, sensing, or computation demands. It describes communication and sensing use cases centered on resilient links and adaptive environmental awareness.

B. Typical Application Scenarios

  • The paper categorizes LAE applications by their dominant functional demand: communication, sensing, or computation.Figure 1 presents use cases including smart manufacturing, smart grids, vehicular networks, logistics, and precision agriculture, with their corresponding SCC functions.Source: B. Typical Application Scenarios · p. 3

1) Communication-centric Applications:

  • Communication-centric applications use LAIM-enabled IAAs for coverage, dynamic spectrum access, self-organized mesh networking, and predictive channel estimation.Multi-sensor channel measurement and modeling support 3D beamforming and interference mitigation in dynamic, resource-limited environments.Source: 1) Communication-centric Applications · p. 3

2) Sensing-centric Applications:

  • Sensing-centric applications use multimodal IAA sensors and adapt sensing modes and trajectories to real-time environmental feedback.Examples include agriculture, pollution assessment, disaster response, and obstacle avoidance in low-visibility or changing conditions.Source: 2) Sensing-centric Applications · p. 3

3) Computation-centric Applications:

Computation-centric LAE applications distribute AI tasks across ground, aerial, and cloud nodes, from latency-sensitive processing to large-scale analysis. The paper also introduces enabling techniques, including ISAC, and a task-oriented execution pipeline for LAIM-enabled services.

3) Computation-centric Applications:

  • LAE entities act as distributed computation infrastructure for latency-sensitive tasks and computation-intensive analysis, while continuous real-world fine-tuning is needed to narrow the lab-to-field performance gap.Examples include smart-grid anomaly detection, UAV path replanning, environment-map reconstruction, traffic forecasting, and airspace coordination.Source: 3) Computation-centric Applications · p. 3
  • The paper presents representative LAIM-enabled LAE use cases and their corresponding sensing, communication, and computation functions.The examples include smart manufacturing, smart grids, intelligent vehicular networks, smart logistics, and precision agriculture.Source: 3) Computation-centric Applications · p. 3

III. MUTUAL BENEFITS OF DEPLOYING LAIMS IN LAE

  • The paper frames LAIM–LAE integration around mutual benefits and a task-oriented execution pipeline for service delivery.It identifies ISAC, ICC, and ISCC as core enabling techniques for deployment in dynamic, resource-constrained low-altitude environments.Source: §III · p. 3, A. Key Enabling Techniques for… · p. 3

1) ISAC for LAIM-empowered LAE:

  • ISAC lets aerial and ground entities reuse spectrum and hardware for sensing and data transmission through dual-purpose waveform design.Joint channel-parameter estimation and physical-environment perception support environment-aware channel modeling and real-time CSI acquisition.Source: 1) ISAC for LAIM-empowered LAE · pp. 3–4

2) ICC for LAIM-empowered LAE:

Communication and computation are co-designed to reduce latency and support task-relevant information exchange, while ISCC jointly aligns sensing, communication, and computation with mission goals. This coordination supports distributed LAIM training and resource-aware inference across low-altitude entities.

2) ICC for LAIM-empowered LAE:

  • Semantic communications transmit compressed task-relevant features, while AirComp aggregates distributed data over multiple-access channels for collaborative tasks.Together, they support compute-while-communicate services in resource-constrained low-altitude environments.Source: 2) ICC for LAIM-empowered LAE · p. 4

3) ISCC for LAIM-empowered LAE:

  • Optimizing sensing resolution or transmission rate independently can degrade task performance because sensing, communication, and computation have coupled contributions.ISCC instead co-optimizes these resources for mission-level accuracy, latency, energy, and control goals.Source: 3) ISCC for LAIM-empowered LAE · p. 4
  • Distributed LAIM training can place model segments and training tasks across ground devices, IAAs, and cloud nodes, exchanging model or gradient information rather than raw data.LoRA can accelerate fine-tuning, while federated learning supports semantic-head generalization through periodic aggregation.Source: 3) ISCC for LAIM-empowered LAE · pp. 4–5
  • ISCC supports split or co-inference across ground, aerial, and cloud nodes, coordinating compression, offloading, IAA placement, and resource allocation.Compression choices must balance accuracy, latency, and energy; high-reasoning tasks may require more model capacity despite higher energy and delay costs.Source: 3) ISCC for LAIM-empowered LAE · p. 5

B. LAE for Continual LAIM Evolution

Low-altitude systems provide real-world multimodal data and interactive task environments that can support continual LAIM evolution. The section describes representation learning, reinforcement learning, and multi-task adaptation as complementary routes.

B. LAE for Continual LAIM Evolution

  • Low-altitude systems continuously generate real-world multimodal, spatiotemporal data under physical constraints, enabling task-oriented fine-tuning of pre-trained LAIMs.Such systems can advance LAIMs as well as use them for services.Source: B. LAE for Continual LAIM Evolution · p. 5

1) Real-World Data for Robust Representation Learning:

  • Diverse low-altitude sensing data, including contextual correlations, varying noise, and outliers, can foster robust representations and refine digital-twin world models and semantic knowledge bases.The passage links continuous exposure to improved feature extraction, contextual adaptation, and multimodal fusion.Source: 1) Real-World Data for Robust… · p. 5

2) Reinforcement Learning (RL) in Real-world Loops:

  • Real-world interaction lets RL-type LAIM agents observe feedback and iteratively improve policies, with expert demonstrations available to guide policy optimization.The passage says continual interaction can develop reusable policies that generalize to unseen conditions.Source: 2) Reinforcement Learning (RL) in… · p. 5

3) Multi-Task LAIM Adaptation:

  • Multi-task LAIMs can adapt across tasks such as visual inspection and path planning using shared attention, cross-modal encoders, and task-specific output modules.Task failures can trigger on-the-fly adjustment and retraining.Source: 3) Multi-Task LAIM Adaptation · p. 5

C. Execution Pipeline

The execution pipeline combines offline initialization with online service stages. Its initial plan is built against a digital twin or virtual world model and requires later refinement because simulations can mismatch real-world conditions.

1) Offline System Initialization:

  • Offline initialization jointly optimizes IAA positioning, resource allocation, cooperation, and scheduling under task objectives and QoS constraints.Convex or non-convex optimization, RL, or generative AI can produce coarse initial setups that online adaptation later refines.Source: 1) Offline System Initialization · p. 5

2) Multi-modal Data Sensing:

  • Task-oriented multimodal sensing uses spatially selective and temporally adaptive sampling, including continuous or event-triggered collection, to provide data for inference and control.Ground terminals and IAAs collect raw data or semantic features according to region-of-interest characteristics and mission requirements.Source: 2) Multi-modal Data Sensing · p. 5

3) Information Transmission:

  • LAE networks exchange sensing and control information horizontally within layers and vertically across layers, choosing bit-level or semantic-level transmission according to urgency, topology, and resources.These exchanges support consensus and collaborative task execution.Source: 3) Information Transmission · p. 5

4) Downstream Task Execution and Control:

LAIM execution is partitioned across ground, aerial, and cloud entities according to task complexity and latency sensitivity, with feedback used to update the system. The case studies describe real-world validation and a channel-measurement implementation in low-altitude conditions.

4) Downstream Task Execution and Control:

  • Distributed LAIM execution assigns components such as semantic heads, transformer encoders, and global decision modules to ground nodes, IAAs, and cloud according to task demands.This supports responsiveness for latency-critical operations and centralized processing for computationally intensive tasks.Source: 4) Downstream Task Execution and Control · p. 5

5) Closed-loop Control Adaptation:

  • Environmental feedback, signal-quality changes, task outcomes, and disturbances inform real-time updates to resource allocation, IAA deployment, and task execution.The closed loop also supports performance evaluation, strategy adjustment, and LAIM refinement.Source: 5) Closed-loop Control Adaptation · pp. 5–6

IV. CASE STUDY

  • The case studies focus on LAIM-guided UAV placement and resource-aware LAIM deployment across LAE platforms.They are described as real-world-data and testbed studies of framework feasibility in practical low-altitude environments.Source: §IV · p. 6

A. Real-world Implementation Details

  • The channel-measurement implementation used a DJI M300 RTK UAV at 112 meters and 10 m/s to collect 2.6 GHz, 10 MHz I/Q samples over an approximately 530 × 400 meter area.The BS used a 4 × 8 dual-polarized array transmitting seven SSB beams; RSRP was sampled at 10 Hz, with centimeter-level RTK localization, and Fig. 3 shows the prototype in low-altitude scenarios.Source: A. Real-world Implementation Details · p. 6, Figure 3 · p. 6

B. LAIM-guided UAV Deployment

The case studies evaluate LAIM-guided UAV placement using real-world RSRP measurements and resource-aware LAIM deployment across air-ground platforms. They report stronger UAV-placement performance than conventional optimization and best performance from co-inference.

B. LAIM-guided UAV Deployment

  • A prompt-driven workflow uses real-world RSRP values and task constraints to generate UAV coordinates and estimated communication rates.The comparison is against a conventional sum-rate optimization using a location-dependent LoS channel model.Source: B. LAIM-guided UAV Deployment · p. 6
  • LAIM-based UAV placement significantly outperforms traditional optimization, with the performance gap increasing as the number of UAVs grows.The passages attribute this to real-world channel measurements capturing interference and environmental conditions missed by simplified LoS models.Source: B. LAIM-guided UAV Deployment · p. 6, Figure 4 · p. 7

C. Resource-aware LAIM Deployment

  • The resource-aware scheme prunes a pre-trained LAIM and partitions it between an IAA and the cloud for collaborative execution.Experiments use BART on CNN/DailyMail with magnitude-based pruning and compare on-cloud, on-IAA, and co-inference paradigms.Source: C. Resource-aware LAIM Deployment · p. 7
  • Co-inference consistently achieves the best performance among the resource-aware deployment paradigms.The design jointly optimizes pruning ratio, transmit power, and computation frequency to balance inference quality, delay, and energy consumption.Source: C. Resource-aware LAIM Deployment · p. 7

V. CONCLUSIONS AND FUTURE DIRECTIONS

The conclusion frames LAIM deployment as a path toward air-ground intelligence in resource-constrained, dynamic low-altitude environments. It highlights future challenges in grounding LAIMs in airspace rules and enabling multi-agent coordination under strategic constraints.

V. CONCLUSIONS AND FUTURE DIRECTIONS

  • The proposed architecture, task-oriented pipeline, and real-world case studies address LAIM deployment in dynamic, resource-constrained low-altitude environments.The conclusion describes an air-ground intelligence paradigm combining real-time perception, communication, adaptive decision-making, and continual LAIM refinement.Source: §V · p. 7

1) Fusion of Airspace Knowledge Graphs and LAIMs:

  • Airspace-rule compliance remains difficult because knowledge graphs adapt poorly to rapid changes while LAIMs lack domain-specific regulatory reasoning.The proposed direction is to fuse real-time sensing with knowledge-graph-grounded LAIM reasoning.Source: 1) Fusion of Airspace Knowledge Graphs… · p. 7

2) Multi-agent Gaming:

  • Multi-agent coordination must balance distributed autonomy with global policy compliance amid partial observability, strategic uncertainty, and restricted data sharing.Execution delays and opaque LAIM-driven decisions also hinder synchronization and regulatory trust.Source: 2) Multi-agent Gaming · pp. 7–8

3) Sustainable LAIM Deployment in LAE:

Sustainable LAIM deployment in LAE faces energy constraints, while future systems must address ecosystem interoperability, security, and standardization challenges.

3) Sustainable LAIM Deployment in LAE:

  • Long-range satellite-based wireless power transfer may be feasible, but intermittent renewable energy and continuous AI workloads also call for energy-aware design, task-adaptive execution, and robust power management.The passage contrasts satellite-based transfer with terrestrial wireless power transfer, whose end-to-end efficiency may be much lower in long-range scenarios.Source: 3) Sustainable LAIM Deployment in LAE · p. 8

4) Open Ecosystem Development:

  • Open standards and architectures are proposed to address incompatible device protocols, AI-model and hardware upgrade-cycle mismatches, and commercial data silos.These barriers challenge interoperability and industry-wide knowledge sharing.Source: 4) Open Ecosystem Development · p. 8

5) Security Issues:

  • Low-power sensor uploads and backhaul links may expose IAAs to intentional jamming, motivating layered, integrated primary and secondary control links.The links are to be designed under the proposed hierarchical network infrastructure and task-oriented ISCC paradigm.Source: 5) Security Issues · p. 8

6) Standardization Alignment:

  • LAE-NTN deployments need unified architectures, interoperability protocols, and standardized interfaces, alongside delay-tolerant relay protocols, dynamic spectrum access, and service continuity.The passage frames alignment with ongoing 3GPP efforts as critical as LAE increasingly relies on IAAs and non-terrestrial networks.Source: 6) Standardization Alignment · p. 8
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