Millimeter-Wave Air-to-Ground Channel Prediction Assisted by Visual Information of the Propagation Environment

CHENG Yuanxun; HU Qingsong; ZHANG Xiaomin; WANG Xuesong

doi:10.11999/JEIT260274

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CHENG Yuanxun, HU Qingsong, ZHANG Xiaomin, WANG Xuesong. Millimeter-Wave Air-to-Ground Channel Prediction Assisted by Visual Information of the Propagation Environment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260274

Citation:

CHENG Yuanxun, HU Qingsong, ZHANG Xiaomin, WANG Xuesong. Millimeter-Wave Air-to-Ground Channel Prediction Assisted by Visual Information of the Propagation Environment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260274

Citation:

CHENG Yuanxun, HU Qingsong, ZHANG Xiaomin, WANG Xuesong. Millimeter-Wave Air-to-Ground Channel Prediction Assisted by Visual Information of the Propagation Environment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260274

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Millimeter-Wave Air-to-Ground Channel Prediction Assisted by Visual Information of the Propagation Environment

doi: 10.11999/JEIT260274 cstr: 32379.14.JEIT260274

China University of Mining and Technology, School of Information and Control Engineering, Xuzhou 221116, China

Funds: National Natural Science Foundation of China (52474185)

Received Date: 2026-03-16
Accepted Date: 2026-05-15
Rev Recd Date: 2026-05-15

Available Online: 2026-06-02

Abstract

Abstract

Objective Accurate prediction of air-to-ground (A2G) channel states is essential for adaptive transmission and resource optimization in unmanned aerial vehicle (UAV) communications. In urban millimeter-wave scenarios, however, A2G links are highly sensitive to blockage, reflection, scattering, and the rapidly changing geometric relationship among the transmitter, the receiver, and surrounding buildings. As a result, the channel exhibits strong spatial and temporal nonstationarity, and conventional pilot- or feedback-based acquisition methods may become ineffective because the obtained channel state information is easily outdated. Recent data-driven approaches have shown potential, but many of them rely heavily on historical channel observations or directly use raw images as network inputs, which may introduce redundant visual information and weaken physical interpretability. To address these limitations, this paper proposes a vision-assisted millimeter-wave A2G channel prediction method that extracts low-dimensional geometric features from the propagation environment instead of using raw visual data directly. The objective is to preserve the key structural information governing channel evolution while reducing irrelevant redundancy, thereby improving the prediction of channel. Methods A communication-and-sensing integrated dataset with strict spatial and temporal alignment is established for millimeter-wave UAV A2G channel prediction. On the sensing side, a high-fidelity three-dimensional urban scenario containing 23 buildings, roads, and intersections is constructed in Unreal Engine 4.27, where synchronized RGB and depth images are collected through AirSim using a multirotor UAV equipped with RGB and depth cameras. The UAV flies along 10 preset trajectories at a height of 55 m with a spatial sampling interval of 1 m, yielding 2160 valid visual samples (Fig. 1, Fig. 2). On the communication side, the same scene is reconstructed in Wireless InSite, and the transmitter-receiver positions are synchronously updated along the same trajectories to ensure frame-level alignment between visual and channel data (Fig. 3). To obtain compact and physically meaningful environmental representations, a cross-modal spatial feature extraction scheme is developed. Buildings are first detected from RGB images using YOLO-V8 (Fig. 4), and the detected regions are then registered with depth images to reconstruct three-dimensional point clouds. After Euclidean clustering and axis-aligned bounding-box fitting, key geometric attributes, including planar position, height, and volume, are extracted. These features are combined with the transmitter-receiver distance to form the spatial feature vector of each frame, and their relevance to path loss, received power, and RMS delay spread is evaluated through cosine-similarity-based correlation analysis (Fig. 6). Based on the extracted features, a hybrid Transformer-MLP network is designed for channel prediction (Fig. 5). Building features are first projected into a latent space, and a stacked Transformer encoder is employed to capture global interactions among buildings through masked multi-head self-attention. Masked average pooling is then used to aggregate building-level representations into a scene-level environmental descriptor, which is concatenated with the link distance feature and fed into a multilayer perceptron regressor to predict the three target channel parameters. Results and Discussions The results confirm the effectiveness of the proposed spatial feature representation. Correlation analysis shows that the extracted geometric features are consistently related to path loss, received power, and RMS delay spread under different aggregation strategies (Fig. 6), indicating that compact building descriptors can effectively characterize the propagation environment. Among them, building height exhibits the strongest correlation with all three channel parameters, highlighting its important role in blockage, attenuation, and multipath propagation in urban millimeter-wave A2G channels. In prediction experiments, the proposed method accurately tracks the variation trends of all three targets. It remains effective in deep-fading and sharp-fluctuation regions for path loss prediction (Fig. 7), achieves high consistency with the ground truth for RMS delay spread (Fig. 8), and follows rapid local fluctuations of received power with good fidelity (Fig. 9). In contrast, the benchmark model only captures the general trend and shows larger deviations in peaks, valleys, and abrupt-changing intervals. Residual analysis further demonstrates the superiority of the proposed method. Its errors are more concentrated around zero and fluctuate within narrower ranges than those of the benchmark model across all three tasks (Fig. 10). Quantitatively, both the mean absolute error and the root mean squared error are reduced (Fig. 11). In addition, the model maintains acceptable complexity, with about 5.5 M parameters and a single-frame inference delay of about 3.4 ms, indicating good potential for real-time deployment. Conclusions A vision-assisted millimeter-wave A2G channel prediction method for UAV communications is proposed. By constructing a strictly aligned communication-and-sensing dataset and extracting low-dimensional spatial features with clear physical meaning, the method establishes an effective mapping from environmental geometry to channel parameters. The proposed Transformer-MLP framework achieves accurate prediction of path loss, received power, and RMS delay spread, while offering better interpretability, robustness, and efficiency than the benchmark model.
- Integrated sensing and communications,
- Air-to-ground communication,
- Channel prediction,
- Spatial features

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References(20)

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