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用于系统级仿真的无人机路径损耗预测和信道模型研究综述。

A Survey of Path Loss Prediction and Channel Models for Unmanned Aerial Systems for System-Level Simulations.

机构信息

School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., Zografou, 15773 Athens, Greece.

出版信息

Sensors (Basel). 2023 May 15;23(10):4775. doi: 10.3390/s23104775.

DOI:10.3390/s23104775
PMID:37430690
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10222034/
Abstract

Unmanned aerial systems (UAS) have recently gained popularity, and they are envisioned as an integral parts of the current and future wireless and mobile-radio networks. Despite the exhaustive research on air-to-ground channels, there are insufficient studies, experimental campaigns and general channel models related to air-to-space (A2S) and air-to-air (A2A) wireless links. This paper presents a comprehensive review of the available channel models and path-loss prediction for A2S and A2A communications. Specific case studies attempting to extend current models' parameters and provide important knowledge of the channel behavior in combination with UAV flight characteristics are also provided. A time-series rain-attenuation synthesizer is also presented that describes quite accurately the impact of the troposphere at frequencies above 10 GHz. This specific model can be also applied to both A2S and A2A wireless links. Finally, scientific challenges and gaps that can be used for future research on the upcoming 6G networks are highlighted.

摘要

无人航空系统(UAS)最近越来越受欢迎,它们被视为当前和未来无线和移动无线电网络的组成部分。尽管对空对地信道进行了详尽的研究,但与空对空(A2S)和空对空(A2A)无线链路相关的研究、实验活动和通用信道模型还不够充分。本文对 A2S 和 A2A 通信的可用信道模型和路径损耗预测进行了全面回顾。还提供了特定的案例研究,试图扩展当前模型的参数,并结合无人机飞行特性提供对信道行为的重要了解。还提出了一个时间序列降雨衰减合成器,该合成器可以非常准确地描述在 10GHz 以上频率下对流层的影响。该特定模型也可应用于 A2S 和 A2A 无线链路。最后,突出了未来 6G 网络研究中可以使用的科学挑战和差距。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/bf23a90f2e9c/sensors-23-04775-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/33eb66267c8d/sensors-23-04775-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/7c958450351c/sensors-23-04775-g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/dc33c29682f3/sensors-23-04775-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/740cb867be5a/sensors-23-04775-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/136f3499e4bd/sensors-23-04775-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/d2955a4f751d/sensors-23-04775-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/b8118dd93284/sensors-23-04775-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/bf23a90f2e9c/sensors-23-04775-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/631845d38e75/sensors-23-04775-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/ff1e64983822/sensors-23-04775-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/338b5d4ab670/sensors-23-04775-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/33eb66267c8d/sensors-23-04775-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/7c958450351c/sensors-23-04775-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/d04127e77606/sensors-23-04775-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/dc33c29682f3/sensors-23-04775-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/740cb867be5a/sensors-23-04775-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/136f3499e4bd/sensors-23-04775-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/d2955a4f751d/sensors-23-04775-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/b8118dd93284/sensors-23-04775-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/626d/10222034/bf23a90f2e9c/sensors-23-04775-g012.jpg

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