Network Slice-based Low-Altitude Intelligent Network for Advanced Air Mobility

2025-01-28

AI Summary

To address the challenges of dynamic task offloading and joint resource management for electric vertical take-off and landing (eVTOL) aircraft in low-altitude advanced air mobility (AAM)—stemming from high mobility, resource constraints, and restricted flight paths—this paper proposes a Low-Altitude Intelligent Network (LAIN) tailored for AAM. We innovatively design a low-altitude cooperative framework leveraging 5G/6G network slicing, integrating pre-scheduled access pairing, multi-dimensional resource pre-evaluation, and a deep reinforcement learning (DRL)-driven slice orchestration mechanism to jointly optimize communication, sensing, and edge computing resources. Simulation results demonstrate that, compared with baseline schemes, our approach improves average resource allocation efficiency by 32% across varying eVTOL speeds, reduces operational and regulatory violation costs by 27%, and significantly enhances system real-time performance and robustness.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Advanced Air Mobility (AAM) is transforming transportation systems by extending them into near-ground airspace, offering innovative solutions to mobility challenges. In this space, electric vertical take-off and landing vehicles (eVTOLs) perform a variety of tasks to improve aviation safety and efficiency, such as collaborative computing and perception. However, eVTOLs face constraints such as compacted shape and restricted onboard computing resources. These limitations necessitate task offloading to nearby high-performance base stations (BSs) for timely processing. Unfortunately, the high mobility of eVTOLs, coupled with their restricted flight airlines and heterogeneous resource management creates significant challenges in dynamic task offloading. To address these issues, this paper introduces a novel network slice-based Low-Altitude Intelligent Network (LAIN) framework for eVTOL tasks. By leveraging advanced network slicing technologies from 5G/6G, the proposed framework dynamically adjusts communication bandwidth, beam alignment, and computing resources to meet fluctuating task demands. Specifically, the framework includes an access pairing method to pre-schedule optimal eVTOL-BS-slice assignments, a pre-assessment algorithm to avoid resource waste, and a deep reinforcement learning-based slice orchestration mechanism to optimize resource allocation and lifecycle management. Simulation results demonstrate that the proposed framework outperforms existing benchmarks in terms of resource allocation efficiency and operational/violation costs across varying eVTOL velocities. This work provides valuable insights into intelligent network slicing for future AAM transportation systems.

Problem

• eVTOLs
• Advanced Air Mobility (AAM)
• Task Allocation and Resource Management

Innovation

• LAIN Intelligent Network
• 5G/6G Technology
• Resource Management for eVTOLs