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  1. arXiv eVTOL预印本历史资料

    Stacked Intelligent Metasurfaces-Aided eVTOL Delay Sensitive Communications

    With rapid urbanization and increasing population density, urban traffic congestion has become a critical issue, and traditional ground transportation methods are no longer sufficient to address it effectively. To tackle this challenge, the concept of Advanced Air Mobility (AAM) has emerged, aiming to utilize low-altitude airspace to establish a three-dimensional transportation system. Among various components of the AAM system, electric vertical take-off and landing (eVTOL) aircraft plays a pivotal role due to their flexibility and efficiency. However, the immaturity of Ultra Reliable Low Latency Communication (URLLC) technologies poses significant challenges to safety-critical AAM operations. Specifically, existing Stacked Intelligent Metasurfaces (SIM)-based eVTOL systems lack rigorous mathematical frameworks to quantify probabilistic delay bounds under dynamic air traffic patterns, a prerequisite for collision avoidance and airspace management. To bridge this gap, we employ network calculus tools to derive the probabilistic upper bound on communication delay in the AAM system for the first time. Furthermore, we formulate a complex non-convex optimization problem that jointly minimizes the probabilistic delay bound and the propagation delay. To solve this problem efficiently, we propose a solution based on the Block Coordinate Descent (BCD) algorithm and Semidefinite Relaxation (SDR) method. In addition, we conduct a comprehensive analysis of how various factors impact regret and transmission rate, and explore the influence of varying load intensity and total delay on th（预印本；同行评审状态请核对原文。）

2025年6月4日周三
2025年4月25日周五
  1. arXiv eVTOL预印本历史资料

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

    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 C（预印本；同行评审状态请核对原文。）

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