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相关概念视频

Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

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Updated: Jun 16, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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基于深度学习的链路质量估计,用于RIS辅助无人机支持的无线通信系统.

Belayneh Abebe Tesfaw1, Rong-Terng Juang2, Li-Chia Tai3

  • 1Department of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新模型,使用一个封闭的循环单元 (GRU) 准确估计无线链接质量,用于无人机辅助通信网络的地面用户,辅助可重配置智能表面 (RIS). 该模型在复杂的环境中提高了性能.

关键词:
封闭的经常性单位 (GRU)链接质量估计链接质量估计可重新配置的智能表面 (RIS)无人驾驶飞行器 (UAV) 是一种无人驾驶飞行器.

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科学领域:

  • 无线通信工程 无线通信工程
  • 网络中的人工智能
  • 空中通信系统 空中通信系统

背景情况:

  • 无人机 (UAV) 越来越多地用于无线通信,但由于功率限制和非视线 (NLOS) 连接,在城市环境中面临挑战.
  • 可重新配置的智能表面 (RIS) 提供了一个有前途的解决方案,通过优化信号传播来改善无人机通信.
  • 在动态RIS辅助无人机网络中,估计地面用户的链接质量仍然是一个重大挑战.

研究的目的:

  • 为多用户RIS辅助无人机支持的无线通信系统提出一个新的链接质量估计模型.
  • 准确评估动态和复杂环境中的个人地面用户的通信链路质量.
  • 利用先进的机器学习技术,提高空中通信网络的性能.

主要方法:

  • 开发一种链接质量估计模型,使用一个门式循环单元 (GRU),一种循环神经网络.
  • 该GRU模型处理时间序列数据,包括用户通道信息和RIS相位转移配置.
  • 模拟多用户RIS辅助无人机支持的无线通信系统,以验证该模型的有效性.

主要成果:

  • 提出的基于GRU的模型证明了有效和准确的地面用户链接质量的估计.
  • 该框架成功地处理了动态RIS配置和无人机移动性的复杂性.
  • 模拟结果证实了该模型在增强通信性能预测方面的能力.

结论:

  • 在具有挑战性的RIS辅助无人机通信场景中,GRU模型为链路质量估计提供了强大的解决方案.
  • 准确的链接质量估计对于优化资源配置和确保可靠的通信至关重要.
  • 这项研究通过人工智能驱动的道评估,为智能空中通信网络的发展做出了贡献.