Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV Communications

With the increasing development of 6th-generation (6G) air-to-ground (A2G) communications, the combination of millimeter-wave (mmWave) and multiple-input multiple-output (MIMO) technologies can offer unprecedented bandwidth and capacity for unmanned aerial vehicle (UAV) communications. The introduct...

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Main Authors: Borui Huang, Zhichao Xin, Fan Yang, Yuyang Zhang, Yu Liu, Jie Huang, Ji Bian
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Sensors
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Online Access:https://www.mdpi.com/1424-8220/25/12/3731
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author Borui Huang
Zhichao Xin
Fan Yang
Yuyang Zhang
Yu Liu
Jie Huang
Ji Bian
author_facet Borui Huang
Zhichao Xin
Fan Yang
Yuyang Zhang
Yu Liu
Jie Huang
Ji Bian
author_sort Borui Huang
collection DOAJ
description With the increasing development of 6th-generation (6G) air-to-ground (A2G) communications, the combination of millimeter-wave (mmWave) and multiple-input multiple-output (MIMO) technologies can offer unprecedented bandwidth and capacity for unmanned aerial vehicle (UAV) communications. The introduction of new technologies will also make the UAV channel characteristics more complex and variable, posing higher requirements for UAV channel modeling. This paper presents a novel predictive channel modeling method based on Transformer architecture by integrating data-driven approaches with UAV air-to-ground channel modeling. By introducing the mmWave and MIMO into UAV communications, the channel data of UAVs at various flight altitudes is first collected. Based on the Transformer network, the typical UAV channel characteristics, such as received power, delay spread, and angular spread, are then predicted and analyzed. The results indicate that the proposed predictive method exhibits excellent performance in prediction accuracy and stability, effectively addressing the complexity and variability of channel characteristics caused by mmWave bands and MIMO technology. This method not only provides strong support for the design and optimization of future 6G UAV communication systems but also lays a solid communication foundation for the widespread application of UAVs in intelligent transportation, logistics, and other fields in the future.
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publishDate 2025-06-01
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spelling doaj-art-d3b016aa03af447bb8c8f5f60c795c982025-08-20T03:16:39ZengMDPI AGSensors1424-82202025-06-012512373110.3390/s25123731Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV CommunicationsBorui Huang0Zhichao Xin1Fan Yang2Yuyang Zhang3Yu Liu4Jie Huang5Ji Bian6School of Integrated Circuits, Shandong University, Jinan 250101, ChinaSchool of Integrated Circuits, Shandong University, Jinan 250101, ChinaSchool of Integrated Circuits, Shandong University, Jinan 250101, ChinaSchool of Integrated Circuits, Shandong University, Jinan 250101, ChinaSchool of Integrated Circuits, Shandong University, Jinan 250101, ChinaNational Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing 211189, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 250399, ChinaWith the increasing development of 6th-generation (6G) air-to-ground (A2G) communications, the combination of millimeter-wave (mmWave) and multiple-input multiple-output (MIMO) technologies can offer unprecedented bandwidth and capacity for unmanned aerial vehicle (UAV) communications. The introduction of new technologies will also make the UAV channel characteristics more complex and variable, posing higher requirements for UAV channel modeling. This paper presents a novel predictive channel modeling method based on Transformer architecture by integrating data-driven approaches with UAV air-to-ground channel modeling. By introducing the mmWave and MIMO into UAV communications, the channel data of UAVs at various flight altitudes is first collected. Based on the Transformer network, the typical UAV channel characteristics, such as received power, delay spread, and angular spread, are then predicted and analyzed. The results indicate that the proposed predictive method exhibits excellent performance in prediction accuracy and stability, effectively addressing the complexity and variability of channel characteristics caused by mmWave bands and MIMO technology. This method not only provides strong support for the design and optimization of future 6G UAV communication systems but also lays a solid communication foundation for the widespread application of UAVs in intelligent transportation, logistics, and other fields in the future.https://www.mdpi.com/1424-8220/25/12/3731channel characteristic predictionunmanned aerial vehicle (UAV) air-to-ground channelsmillimeter-wave (mmWave)multiple-input multiple-output (MIMO)
spellingShingle Borui Huang
Zhichao Xin
Fan Yang
Yuyang Zhang
Yu Liu
Jie Huang
Ji Bian
Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV Communications
Sensors
channel characteristic prediction
unmanned aerial vehicle (UAV) air-to-ground channels
millimeter-wave (mmWave)
multiple-input multiple-output (MIMO)
title Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV Communications
title_full Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV Communications
title_fullStr Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV Communications
title_full_unstemmed Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV Communications
title_short Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV Communications
title_sort transformer based air to ground mmwave channel characteristics prediction for 6g uav communications
topic channel characteristic prediction
unmanned aerial vehicle (UAV) air-to-ground channels
millimeter-wave (mmWave)
multiple-input multiple-output (MIMO)
url https://www.mdpi.com/1424-8220/25/12/3731
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