Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems can use different multiple access schemes to coordinate multi-user task offloading. However, it is still unknown which scheme is the most energy-efficient, especially when the offloading blocklength is finite. To answer this question, this paper minimizes and compares the MEC-related energy consumption of non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA)-based offloading schemes within UAV-enabled MEC systems, considering both infinite and finite blocklength scenarios. Through theoretically analysis of the minimum energy consumption required by these three schemes, two novel findings are presented. First, TDMA consistently achieves lower energy consumption than FDMA in both infinite and finite blocklength cases, due to the degrees of freedom afforded by sequential task offloading. Second, NOMA does not necessarily achieve lower energy consumption than FDMA when the offloading blocklength is finite, especially when the channel conditions and the offloaded task data sizes of two user equipments (UEs) are relatively symmetric. Furthermore, an alternating optimization algorithm that jointly optimizes the portions of task offloaded, the offloading times of all UEs, and the UAV location is proposed to solve the formulated energy consumption minimization problems. Simulation results verify the correctness of our analytical findings and demonstrate that the proposed algorithm effectively reduces MEC-related energy consumption compared to benchmark schemes that do not optimize task offloading portions and/or offloading times.

翻译：无人机赋能的移动边缘计算系统可采用不同的多址接入方案协调多用户任务卸载。然而，在卸载块长度有限的场景下，何种方案最具能效优势仍不明确。为探究此问题，本文在无人机移动边缘计算系统中，针对非正交多址接入、频分多址接入和时分多址接入三种卸载方案，分别考虑无限与有限块长度场景，最小化并比较其与移动边缘计算相关的能耗。通过对三种方案所需最小能耗的理论分析，得出两项创新性结论：首先，由于顺序任务卸载提供的自由度优势，时分多址接入在无限与有限块长度场景下均持续表现出比频分多址接入更低的能耗；其次，当卸载块长度有限时，非正交多址接入未必比频分多址接入更节能，特别是在两个用户设备的信道条件与卸载任务数据量相对对称的情况下。此外，本文提出一种交替优化算法，通过联合优化任务卸载比例、所有用户设备的卸载时隙及无人机部署位置，求解所构建的能耗最小化问题。仿真结果验证了理论分析的正确性，并表明相较于未优化任务卸载比例和/或卸载时隙的基准方案，所提算法能有效降低移动边缘计算相关能耗。