In recent years, there has been a significant interest in ubiquitous coverage, high data rate connectivity, and mobile edge computing (MEC) as crucial services within the future sixth-generation (6G) wireless networks. These services are regarded as essential components, exemplifying the advancements anticipated in 6G technology. Nevertheless, the successful implementation of these services in MEC-enabled vehicular networks significantly relies on the availability of robust network coverage and telecommunication framework. Unfortunately, in far-flung and isolated regions, such framework is often lacking, posing significant challenges in achieving uninterrupted connectivity, comprehensive coverage, and efficient computation offloading. Targeting the aforementioned horizon, in this paper, an uplink non-orthogonal multiple access (NOMA)-MEC-enabled aerial-vehicular network operating at millimeter wave (mmWave) is proposed in which the ground vehicles are provided edge computing services by an aerial autonomous vehicle, i.e., a high-altitude platform (HAP). We present a system model in which a HAP equipped with MEC servers offers computation offloading capabilities to a vehicular network. Our main objective is to minimize the transaction time of a NOMA cluster offloading its data to MEC servers located at the HAP. We devise a dual-layer optimization scheme for optimizing the transmission power and computational resource allocation by using the Lagrange multipliers method and then attain convergence by implementing a sub-gradient approach. We further extend our work by proposing a data-aware NOMA clustering scheme. The simulation results demonstrate the efficacy of our proposed approach, showing a notable reduction in the transaction time in comparison to the baseline scheme. The optimal power allocation enhances the data rates which subsequently reduces the transmission time, and the optimal cores assignment effectively minimizes the computation time. Additionally, the data-aware NOMA clustering scheme shows promising results by enhancing the system effective throughput and the spectral efficiency in comparison to the conventional NOMA clustering.

中文翻译：

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利用毫米波功能在支持 NOMA-MEC 的飞行器网络中进行计算卸载

近年来，人们对无处不在的覆盖、高数据速率连接和移动边缘计算 (MEC) 作为未来第六代 (6G) 无线网络中的关键服务产生了浓厚的兴趣。这些服务被视为重要组成部分，体现了 6G 技术的预期进步。然而，这些服务在支持 MEC 的车载网络中的成功实施在很大程度上依赖于强大的网络覆盖和电信框架的可用性。不幸的是，在偏远和偏远的地区，往往缺乏这样的框架，这对实现不间断连接、全面覆盖和高效计算卸载提出了重大挑战。针对上述视野，本文提出了一种以毫米波（mmWave）运行的上行链路非正交多址（NOMA）-MEC支持的空中车辆网络，其中地面车辆由空中自主飞行器提供边缘计算服务车辆，即高空平台（HAP）。我们提出了一个系统模型，其中配备 MEC 服务器的 HAP 为车辆网络提供计算卸载功能。我们的主要目标是最大限度地缩短 NOMA 集群将数据卸载到位于 HAP 的 MEC 服务器的事务时间。我们设计了一种双层优化方案，通过使用拉格朗日乘子方法来优化传输功率和计算资源分配，然后通过实施次梯度方法来实现收敛。我们通过提出数据感知 NOMA 聚类方案进一步扩展了我们的工作。模拟结果证明了我们提出的方法的有效性，与基线方案相比，交易时间显着减少。最佳功率分配提高了数据速率，从而减少了传输时间，并且最佳核心分配有效地最小化了计算时间。此外，与传统的 NOMA 聚类相比，数据感知的 NOMA 聚类方案通过提高系统有效吞吐量和频谱效率，显示出有希望的结果。