An Online Joint Optimization Approach for QoE Maximization in UAV-Enabled Mobile Edge Computing
<|reference_start|>An Online Joint Optimization Approach for QoE Maximization in UAV-Enabled Mobile Edge Computing: Given flexible mobility, rapid deployment, and low cost, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) shows great potential to compensate for the lack of terrestrial edge computing coverage. However, limited battery capacity, computing and spectrum resources also pose serious challenges for UAV-enabled MEC, which shorten the service time of UAVs and degrade the quality of experience (QoE) of user devices (UDs) {\color{b} without effective control approach}. In this work, we consider a UAV-enabled MEC scenario where a UAV serves as an aerial edge server to provide computing services for multiple ground UDs. Then, a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) is formulated to maximize the QoE of UDs under the UAV energy consumption constraint. To solve the JTRTOP that is proved to be a future-dependent and NP-hard problem, an online joint optimization approach (OJOA) is proposed. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) by using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes.<|reference_end|>
@article{he2024an,
    title={An Online Joint Optimization Approach for QoE Maximization in
  UAV-Enabled Mobile Edge Computing},
    author={Long He, Geng Sun, Zemin Sun, Pengfei Wang, Jiahui Li, Shuang Liang,
  Dusit Niyato},
    journal={arXiv preprint arXiv:2404.02166},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02166},
    primaryClass={cs.IT math.IT}
}