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

Volume 48 Issue 9

Sep. 2026

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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, 2026, 48(9): 3822-3832. 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, 2026, 48(9): 3822-3832. 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, 2026, 48(9): 3822-3832. 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: The 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

Publish Date: 2026-09-18

Abstract

Abstract

Objective Accurate prediction of Air-to-Ground (A2G) channel parameters is essential for adaptive transmission and resource optimization in Unmanned Aerial Vehicle (UAV) communications. In urban Millimeter-Wave (mmWave) scenarios, A2G links are highly sensitive to blockage, reflection, scattering, and rapidly changing geometry among the transmitter, receiver, and surrounding buildings. The channel therefore shows strong spatial and temporal nonstationarity. Conventional pilot- or feedback-based methods can become ineffective because channel estimates are rapidly outdated. Recent data-driven approaches have shown potential. However, many rely heavily on historical channel observations or directly use raw images as network inputs, which may introduce redundant visual information and reduce physical interpretability. To address these limitations, this paper proposes a vision-assisted mmWave A2G channel prediction method. Low-dimensional geometric features are extracted from the propagation environment instead of using raw visual data directly. The method preserves key structural information governing channel evolution, reduces irrelevant redundancy, and improves the accuracy of channel prediction. Methods An Integrated Sensing and Communication (ISAC) dataset with strict spatial and temporal alignment is established for mmWave UAV A2G channel prediction. On the sensing side, a high-fidelity Three-Dimensional (3D) urban scenario containing 23 buildings, roads, and intersections is constructed in Unreal Engine 4.27. Synchronized RGB and depth images are collected in 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 2 160 valid visual samples (Fig. 1, Fig. 2). On the communication side, the same scene is reconstructed in Wireless InSite. The transmitter-receiver positions are updated synchronously along the same trajectories to ensure frame-level alignment between visual and channel data (Fig. 3). A cross-modal spatial feature extraction scheme is developed to obtain compact and physically meaningful environmental representations. Buildings are first detected from RGB images using YOLOv8 (Fig. 4). The detected regions are then registered with depth images to reconstruct 3D point clouds. After Euclidean clustering and axis-aligned bounding-box fitting, key geometric attributes are extracted, including planar position, height, and volume. These features are combined with the transmitter-receiver distance to form the spatial feature vector for each frame. Their relevance to path loss, received power, and Root Mean Square (RMS) delay spread is evaluated through cosine-similarity-based correlation analysis (Fig. 6). Based on the extracted features, a hybrid Transformer-Multilayer Perceptron (MLP) network is designed for channel prediction (Fig. 5). Building features are first projected into a latent space. A stacked Transformer encoder is then used to capture global interactions among buildings through masked multi-head self-attention. Masked average pooling aggregates building-level representations into a scene-level environmental descriptor. This descriptor is concatenated with the link-distance feature and fed into an MLP 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). This result indicates that compact building descriptors can effectively characterize the propagation environment. Among these descriptors, building height shows the strongest correlation with all three channel parameters. This finding confirms the key role of building height in blockage, attenuation, and multipath propagation in urban mmWave 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 in received power with high fidelity (Fig. 9). By contrast, the benchmark model captures only the overall trend and shows larger deviations at peaks, valleys, and abrupt-change intervals. Residual analysis further demonstrates the superiority of the proposed method. Across all three tasks, its errors are more concentrated around zero and fluctuate within narrower ranges than those of the benchmark model (Fig. 10). Quantitatively, both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are reduced (Fig. 11). Compared with the benchmark model, the proposed method improves MAE by 48.2%, 55.9%, and 53.1% for path loss, RMS delay spread, and received power, respectively. The corresponding RMSE improvements are 41.7%, 49.3%, and 45.2% (Table 3). The model also maintains acceptable complexity, with about 5.5 M parameters and a single-frame inference delay of about 3.4 ms. These results indicate its potential for real-time deployment. Conclusions A vision-assisted mmWave A2G channel prediction method is proposed for UAV communications. A strictly aligned ISAC dataset is constructed, and low-dimensional spatial features with clear physical meaning are extracted from RGB-depth visual data. The method establishes an effective mapping from environmental geometry to channel parameters. The proposed Transformer-MLP framework accurately predicts path loss, received power, and RMS delay spread. It also provides 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(19)

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