Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV Communications
Borui Huang, Zhichao Xin, Fan Yang, Yuyang Zhang, Yu Liu, Jie Huang, Ji Bian
TL;DR
This paper introduces a Transformer-based method to predict air-to-ground mmWave channel characteristics for 6G UAV communications.
Contribution
A novel predictive channel modeling method using Transformer architecture for UAV communications is proposed.
Findings
The proposed method accurately predicts UAV channel characteristics like received power and angular spread.
The method effectively handles the complexity and variability introduced by mmWave and MIMO technologies.
It supports the design of future 6G UAV communication systems and their applications in transportation and logistics.
Abstract
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…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · UAV Applications and Optimization · Telecommunications and Broadcasting Technologies