Multipath intelligent transmission strategy for UAM dynamic networks

WANG Yuting; LENG Supeng; XIONG Kai

doi:10.11959/j.issn.1000-0801.2025099

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Multipath intelligent transmission strategy for UAM dynamic networks

Topic：Low-Altitude Intelligent Network & Low-Altitude Security | 更新时间：2025-04-24

- Multipath intelligent transmission strategy for UAM dynamic networks
- Telecommunications Science Vol. 41, Issue 3, Pages: 64-72(2025)
- 作者机构：
  
  电子科技大学信息与通信工程学院，四川成都 611731
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China Youth Project(62201122);Sichuan Science and Technology Program(2024YFHZ0321)
- DOI：10.11959/j.issn.1000-0801.2025099
  CLC： TP393
- Received：14 January 2025，
  
  Revised：2025-03-10，
  
  Published：20 March 2025
- 稿件说明：
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王煜婷,冷甦鹏,熊凯.面向高动态城市空中交通网络的多径智能传输策略[J].电信科学,2025,41(03):64-72.

WANG Yuting,LENG Supeng,XIONG Kai.Multipath intelligent transmission strategy for UAM dynamic networks[J].Telecommunications Science,2025,41(03):64-72.
王煜婷,冷甦鹏,熊凯.面向高动态城市空中交通网络的多径智能传输策略[J].电信科学,2025,41(03):64-72. DOI： 10.11959/j.issn.1000-0801.2025099.

WANG Yuting,LENG Supeng,XIONG Kai.Multipath intelligent transmission strategy for UAM dynamic networks[J].Telecommunications Science,2025,41(03):64-72. DOI： 10.11959/j.issn.1000-0801.2025099.

摘要

飞行汽车作为低空经济的重要载体，对于大力发展城市空中交通、构建高效的低空智联网具有十分重要的作用。然而，灵活和广泛的飞行范围导致飞行汽车网络通信链路不稳定，网络拓扑动态变化。针对这些问题，提出了一种多径智能传输策略，以实现高效的数据传输。首先提出了一种基于强化学习的多径智能路由算法，该算法不仅能有效提升路由效率、降低路由时延，还能够根据不同的传输需求动态划分数据包，以适应物理环境的变化。为进一步提高路由算法对动态环境的适应性，设计了一种环境检验机制来判断当前路由策略与动态网络的匹配程度，实现了在路由过程中自适应地调整路由策略。仿真实验表明，在不同情境下，基于强化学习的多径智能传输策略在有效降低端到端传输时延的同时，还提高了数据成功恢复的概率。

Abstract

As an important vehicle for the low-altitude economy

the flying cars play a crucial role in vigorously developing urban air mobility (UAM) and building an efficient low-altitude intelligent network. However

the high mobility of flying cars introduces instability in transmission links due to high-dynamic network changes. To address these challenges

a multipath intelligent transmission strategy was proposed to achieve efficient data transmission. Firstly

a multipath intelligent routing algorithm based on reinforcement learning was introduced

which was not only proven to effectively enhance routing efficiency and reduce routing latency but also capable of dynamically segmenting data packets according to different transmission requirements to adapt to changes in the physical environment. To further improve the adaptability of the routing algorithm to dynamic environments

an environmental validation mechanism was designed to evaluate the compatibility between current routing strategies and dynamic network

enabling self-adaptive adjustment of routing strategies during the transmission process. Simulation experiments demonstrate that

under various scenarios

the multipath intelligent routing strategy based on reinforcement learning (MIRSRL) effectively reduces end-to-end transmission delay while also increasing the probability of successful data recovery.

关键词

Keywords

references

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