Both the Mobile edge computing (MEC)-based and fog computing (FC)-aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMA-based computation offloading scheme for vehicular platoons on highways, where the vehicles can offload their computing tasks to other platoon members. To cope with the rapidly fluctuating channel quality, we divide the timeline into successive time slots according to the channel's coherence time. Robust computing and offloading decisions are made for each time slot after taking the channel estimation errors into account. Considering a certain time slot, we first analytically characterize both the locally computed source data and the offloaded source data as well as the energy consumption of every vehicle in the platoons. We then formulate the problem of minimizing the long-term maximum task queue by optimizing the allocation of both the communication and computing resources. To solve the problem formulated, we design an online algorithm based on the classic Lyapunov optimization method and successive convex approximation (SCA) method. Finally, the numerical simulation results characterize the performance of our algorithm and demonstrate its advantages both over the local computing scheme and the orthogonal multiple access (OMA)-based offloading scheme.

翻译：基于移动边缘计算（MEC）和雾计算（FC）辅助的车联网（IoV）均构成满足低延迟普适计算需求的有前景范式。为此，我们针对高速公路上的车辆队列构建了一种基于动态NOMA的计算卸载方案，其中车辆可将计算任务卸载至队列内其他成员。为应对快速波动的信道质量，我们依据信道相干时间将时间轴划分为连续时隙。在考虑信道估计误差后，我们为每个时隙制定鲁棒的计算与卸载决策。针对特定时隙，首先解析表征了队列中每辆车的本地计算源数据、卸载源数据及能量消耗，进而将优化通信与计算资源分配的问题表述为最小化长期最大任务队列。为解决该问题，我们基于经典李雅普诺夫优化方法与逐次凸近似（SCA）方法设计了在线算法。最终，数值仿真结果表征了所提算法的性能，并证明其相较于本地计算方案及基于正交多址接入（OMA）的卸载方案具有优势。