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Semantic-Oriented Image Transmission and Resource Allocation for UAV Networks
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DOI：10.1109/tcomm.2026.3717042.png)
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Abstract
En 中文
Autonomous aerial vehicles (UAVs) demonstrate significant potential for enhancing next-generation communication networks due to their flexible deployment, high adaptability, and collaborative service provision. However, the limitation of energy and communications resources hinders their widespread applications, especially for transmission of large files, e.g., high-quality images and videos. In this paper, we introduce the semantic communication technology to UAV networks for image transmission, which can extract the key semantic information and perform the maximum compression. We mathematically formulate a semantic transmission delay minimization problem, taking into account the quality standards for semantic information transmission, the limitation on network resources, and the energy consumption of each UAV. This problem is characterized as a non-convex, multi-timescale, and mixed-integer programming problem. Then, we put forth a semantic-oriented trajectory and resource allocation multi-agent reinforcement learning (SOTRA-MARL) algorithm to solve this problem, which explores the coordination of UAV trajectory, ground users’ (GUs) association strategy, and semantic information selection. The proposed algorithm allows each UAV to collaborate with others through centralized training on global information, while enabling each UAV to make distributed decisions on resource allocation during execution. Thus, this approach facilitates convergence toward a high-quality solution with fewer training iterations. Simulation results revel that our proposed algorithms significantly outperform benchmark approaches, particularly in reducing semantic information transmission delay and improving image transmission accuracy.
Keywords:
Semantic communication
resource allocation
multi-agent deep reinforcement learning
UAV networks
image transmission
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