Abstract
Unmanned aerial vehicle (UAV) plays an important role in wireless systems. It can be deployed flexibly to help improve communication quality. To solve the problem of energy limitation of UAV, we propose an energy-efficient offloading scheme with UAV. By jointly optimizing the energy consumption, user scheduling, UAV location and offloading ratio, we can minimize the total power consumption of the system under the premise of delay. We use K-means algorithm and user priority queue to deal with the real-time random mobility of users. Considering the complexity of the problem, we propose an offloading algorithm based on Deep Deterministic Policy Gradient (DDPG). In our framework, we can obtain the best computational offload policy in an complex and changeable environment. In addition, our energy-saving solution can extend the service time of the UAV. The simulation results show that our framework has better performance than conventional DDPG algorithm and other base solutions.
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Acknowledgement
This work was supported in part by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20222012, in part by the Fundamental Research Funds for the Central Universities, under Grant NS2023052, in part by the China National Key R &D Program during the 14th Five-year Plan Period under Grant No. 2022YFB2901600, and in part by the Fund of Prospective Layout of Scientific Research for NUAA.
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Yang, G., Zheng, H., Zhai, X.B., Zhu, J. (2023). Energy-Efficient Cellular Offloading Optimization for UAV-Aided Networks. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_21
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DOI: https://doi.org/10.1007/978-981-99-5844-3_21
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