Blockage Prediction Based UAV Anti-blockage Trajectory Design in Millimeter-Wave Communication Network

MA Xue; XIE Xie; DONG Yangrui; LI Xiaoya; HE Chen; FAN Jianping

doi:10.11999/JEIT240970

Volume 47 Issue 4

Apr. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(4): 1093-1103

MA Xue, XIE Xie, DONG Yangrui, LI Xiaoya, HE Chen, FAN Jianping. Blockage Prediction Based UAV Anti-blockage Trajectory Design in Millimeter-Wave Communication Network[J]. Journal of Electronics & Information Technology, 2025, 47(4): 1093-1103. doi: 10.11999/JEIT240970

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MA Xue, XIE Xie, DONG Yangrui, LI Xiaoya, HE Chen, FAN Jianping. Blockage Prediction Based UAV Anti-blockage Trajectory Design in Millimeter-Wave Communication Network[J]. Journal of Electronics & Information Technology, 2025, 47(4): 1093-1103. doi: 10.11999/JEIT240970

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Blockage Prediction Based UAV Anti-blockage Trajectory Design in Millimeter-Wave Communication Network

doi: 10.11999/JEIT240970 cstr: 32379.14.JEIT240970

MA Xue¹,
XIE Xie²,
DONG Yangrui¹,
LI Xiaoya¹,
HE Chen^{1, 3
,
,},
FAN Jianping¹

1.
School of Information Science and Technology, Northwest University, Xi’an 710127, China
2.
Northwest Institute of Mechanical and Electrical Engineering, Xianyang 712099, China
3.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (61901375, U24B20130), Shaanxi Innovation Team (2023-CX-TD-04), The Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (2025D05)

Received Date: 2024-10-29
Rev Recd Date: 2025-03-31

Available Online: 2025-04-07

Publish Date: 2025-04-01

Abstract

Abstract

Objective Unmanned Aerial Vehicle (UAV)-assisted millimeter-Wave (mmWave) communication enables high-speed data transmission in diverse on-demand service and emergency scenarios. However, mmWave signals are inherently sensitive to blockage, leading to significant path loss that adversely affects system throughput. Existing anti-blockage strategies primarily rely on the probabilistic blockage model for UAV deployment, which is often limited in accurately reflecting real-time blockage status. To address this issue, a UAV anti-blockage trajectory planning method based on blockage prediction is proposed for scenarios where the geographic information of building obstacles (e.g., location, shape, and size) is unknown and mobile user positioning contains errors. This method enables accurate prediction of the blockage status for UAVs and users at any location, including unvisited areas. A three-Dimensional (3D) UAV trajectory is then designed through an iterative process that alternates between trajectory optimization and blockage prediction to mitigate blockage effects, thereby improving user throughput. Methods Utilizing the locations of the UAV and users, the link blockage is predicted by designing a geometric feature vector that incorporates Taylor expansion terms to account for position errors. Based on this prediction, the UAV’s 3D trajectory is optimized iteratively to avoid blockages and enhance user throughput. A Double Deep Q-Network (DDQN)-based deep reinforcement learning algorithm is employed to address this challenge. During decision-making, the UAV selects an action based on the current Q-value estimate and blockage prediction while maintaining an exploratory capability through a greedy strategy. As the UAV provides communication services, it continuously collects new blockage status data and periodically updates the blockage prediction model, improving prediction accuracy. The iterative interaction between action selection and prediction accuracy refinement enhances overall system performance. Results and Discussions The proposed anti-blockage UAV 3D trajectory design algorithm alternates between blockage prediction and trajectory optimization. Simulation results indicate that the designed feature vector for user positioning errors improves blockage prediction accuracy (Fig. 5). This improvement arises because incorporating Taylor expansion terms yields a feature vector that better approximates the actual user position in the presence of errors compared to one without these terms. Examples of UAV 3D trajectories in mmWave communication networks are demonstrated in two different urban environments (Fig. 6). The UAV selects actions based on user locations, resulting in an irregular flight path that aligns with user distribution. The relationship between user throughput and UAV transmit power under different blockage status methods when applying the proposed trajectory planning algorithm is illustrated (Fig. 7). In all cases, user throughput increases with higher UAV transmit power, as greater power enhances communication performance. Notably, employing the proposed blockage prediction model achieves higher throughput and closely approximates the performance of the method using real blockage status. This is because the prediction model reduces blockage uncertainty by accurately predicting blockages, thereby better matching the actual environment. The algorithm complexity comparison is presented in Table 3. The RBS+DDQN (Real Blockage Status + DDQN-Based Path Planning) benchmark algorithm requires complete prior knowledge of the 3D geographic information of buildings, which may pose challenges in real-world applications due to complex data processing and potential latency issues. Compared with existing algorithms that use the probabilistic blockage model, the proposed algorithm, although relatively more complex, does not require building geographic information and achieves higher throughput despite errors in mobile user positioning. Its performance is close to the ideal algorithm with real blockage status, where full geographic information is available (Fig. 8 and Fig. 9). Therefore, the proposed algorithm achieves a balance between complexity and performance. Conclusions This study proposes an anti-blockage UAV 3D trajectory design algorithm for scenarios where prior building information is unavailable and mobile users have positioning errors. By incorporating a Taylor expansion error term into the feature vector, blockage prediction accuracy is enhanced using UAV and user location data. The blockage prediction model accurately determines blockage status at any position, including unvisited areas. Subsequently, the DDQN algorithm optimizes the UAV trajectory to avoid building blockages, thereby maximizing user throughput. Simulation results demonstrate that introducing the Taylor expansion feature vector improves blockage prediction accuracy in the presence of mobile user positioning errors. Furthermore, although the proposed algorithm has relatively high complexity, it achieves higher user throughput, effectively balancing complexity and performance.
- MmWave UAV communication,
- Mobile user positioning error,
- Blockage prediction,
- UAV 3D trajectory design,
- Deep reinforcement learning

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References(26)

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