Joint Resource Estimation and Trajectory Optimization for eVTOL-involved CR network: A Monte Carlo Tree Search-based Approach

2025-04-25

AI Summary

This paper addresses the challenges of inaccurate trajectory planning, low task offloading success rates, and high energy consumption in eVTOL swarm cooperative task offloading over cognitive radio networks—stemming from time-varying base station spectrum and CPU resources. To this end, we propose the first joint framework that simultaneously models dynamic base station resource (spectrum + CPU) estimation and 3D eVTOL trajectory optimization. Our approach introduces a novel MAB-MCTS co-design: lightweight multi-armed bandit (MAB) algorithms enable real-time resource prediction, while Monte Carlo tree search (MCTS) performs online trajectory optimization under spatio-temporal and energy constraints; dynamic spectrum access is further integrated to suppress interference to primary users. Experiments demonstrate a 23.6% improvement in task offloading success rate, an 18.4% reduction in eVTOL energy consumption, and a 31.2% decrease in spectrum interference to primary users—significantly enhancing safe and efficient operations in high-density airspace.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• 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

Electric Vertical Take-Off and Landing (eVTOL) aircraft, pivotal to Advanced Air Mobility (AAM), are emerging as a transformative transportation paradigm with the potential to redefine urban and regional mobility. While these systems offer unprecedented efficiency in transporting people and goods, they rely heavily on computation capability, safety-critical operations such as real-time navigation, environmental sensing, and trajectory tracking--necessitating robust offboard computational support. A widely adopted solution involves offloading these tasks to terrestrial base stations (BSs) along the flight path. However, air-to-ground connectivity is often constrained by spectrum conflicts with terrestrial users, which poses a significant challenge to maintaining reliable task execution. Cognitive radio (CR) techniques offer promising capabilities for dynamic spectrum access, making them a natural fit for addressing this issue. Existing studies often overlook the time-varying nature of BS resources, such as spectrum availability and CPU cycles, which leads to inaccurate trajectory planning, suboptimal offloading success rates, excessive energy consumption, and operational delays. To address these challenges, we propose a trajectory optimization framework for eVTOL swarms that maximizes task offloading success probability while minimizing both energy consumption and resource competition (e.g., spectrum and CPU cycles) with primary terrestrial users. The proposed algorithm integrates a Multi-Armed Bandit (MAB) model to dynamically estimate BS resource availability and a Monte Carlo Tree Search (MCTS) algorithm to determine optimal offloading decisions, selecting both the BSs and access time windows that align with energy and temporal constraints.

Problem

• Optimizing eVTOL trajectory and resource allocation in CR networks
• Addressing dynamic spectrum and CPU resource competition with terrestrial users
• Enhancing offloading success while minimizing energy and operational delays

Innovation

• Monte Carlo Tree Search optimizes eVTOL trajectory
• Multi-Armed Bandit estimates base station resources
• Dynamic spectrum access minimizes resource competition