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

TL;DR

This paper proposes a network slicing framework for eVTOLs in advanced air mobility, dynamically managing resources to improve task offloading efficiency amid high mobility and limited onboard computing.

Contribution

It introduces a novel Low-Altitude Intelligent Network (LAIN) framework utilizing 5G/6G slicing technologies with algorithms for optimal resource management and task offloading for eVTOLs.

Findings

Outperforms benchmarks in resource efficiency

Reduces operational and violation costs

Adapts effectively to high eVTOL velocities

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.