Joint Computation Offloading and Trajectory Planning for UAV-Assisted Edge Computing
Joint Computation Offloading and Trajectory Planning for UAV-Assisted Edge Computing
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DOI：
10.1109/twc.2021.3067163
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发表时间：
2021-08-01
影响因子：
10.4
通讯作者：
Wang, Xin
中科院分区：
文献类型：
--
作者：
Sun, Chao;Ni, Wei;Wang, Xin
无人机（无人机）具有出色的灵活性，可以充当机载计算服务器，协助智能终端（ST）完成计算密集型和延迟敏感型任务。本文提出了一种新的无人机辅助边缘计算框架，该框架联合优化了固定翼无人机的轨迹和CPU频率，以及卸载计划，以最大限度地减少无人机的能耗。关键的想法是，我们揭示了凸的优化条件，当无人机飞行的线性轨迹。在这种条件下，交替优化和连续凸近似（SCA）为基础的算法开发，有效地实现全局最优的线性轨迹，CPU配置和卸载时间表。另一个重要方面是，我们证明了当所揭示的条件不满足或无人机在二维水平飞行时，基于SCA的算法可以实现满足Karush-Kuhn-Tucker（KKT）条件的局部最优解。通过分析KKT条件，我们还揭示了最佳CPU频率和卸载计划的基本模式。大量的模拟验证的模式和证实我们的计划的优点。
With excellent flexibility, unmanned aerial vehicles (UAVs) can act as airborne computing servers to assist smart terminals (STs) with their computationally-intense and delay-sensitive tasks. This paper presents a new UAV-assisted edge computing framework, which jointly optimizes the trajectory and CPU frequency of a fixed-wing UAV, and the offloading schedule to minimize the energy consumption of the UAV. The key idea is that we reveal the condition for the convexity of the optimization, when the UAV flies a linear trajectory. Under the condition, alternating optimization- and successive convex approximation (SCA)-based algorithms are developed to efficiently achieve the globally optimal linear trajectory, CPU configuration, and offloading schedule. Another important aspect is that we prove the SCA-based algorithm can achieve a local optimum satisfying the Karush-Kuhn-Tucker (KKT) conditions, when the revealed condition is unmet or the UAV flies horizontally in two dimensions. By analyzing the KKT conditions, we also unveil the underlying patterns for the optimal CPU frequency and offloading schedule. Extensive simulations validate the patterns and corroborate the merits of our schemes.