As unmanned aerial vehicles (UAVs) are employed in various areas of modern communication, cellular communication with UAV assistance continues to gain attention. However, it can be significantly complex to optimize a system that considers both the link direction between UAVs and ground users, as well as the location of UAVs. Therefore, we introduce a novel hybrid throughput optimization strategy to address the tightly coupled problem involving adjustments to both UAV height and air-ground link direction. First, the link direction is represented as a table of boolean values and optimized within a discrete space with the heuristic algorithm, where the UAV location remains fixed. Second, we optimize the UAV height with a fixed link direction through the deep reinforcement learning method, which can strengthen the robustness of the algorithm. Finally, the desired result is achieved with a hybrid iteration of the previous two processes. Experimental results demonstrate the effectiveness of the proposed algorithm by achieving a 40% gain on the throughput of the transmission link.


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    Title :

    UAV-Assisted Hybrid Throughput Optimization Based on Deep Reinforcement Learning


    Contributors:
    Zhang, Zhilan (author) / Liu, Shuo (author) / Luo, Yizhe (author) / Wang, Yufeng (author) / Ding, Wenrui (author)


    Publication date :

    2023-10-13


    Size :

    2785786 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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