The Internet of Vehicles (IoV) for fog computing (FC) addresses issues such as traffic congestion, transportation efficiency, and privacy. Non-orthogonal multiple access (NOMA) is a popular technology that enhances spectral efficiency and increases the network's access capability. The synchronisation between NOMA and FC radio access networks extends the application of augmented or vehicular networking and other promising uses. However, with the rapid increase in user vehicles and mobile data, the existing IoV has not succeeded in meeting the real-world and dependable communication needs of modern intelligent transportation due to its limited flexibility. To overcome this, we propose a user grouping-based hybrid optimistic framework for resource allocation in NOMA-based FC vehicular networks (FCVR), named the gradient average subtraction-based optimisation (GASBO). Initially, the NOMA-based FCVR is simulated. User grouping is performed based on GASBO using the signal-to-interference-plus-noise ratio and user distance. Finally, resource allocation is achieved using the proposed GASBO, which combines gradient descent optimisation and average subtraction-based optimisation. The analytic measures obtained for energy efficiency, throughput, sub-channel utility, capacity, and penalty function are 5,366,844,362.870 bits/joule, 883.411 Mbps, 82.031, 2316.337, and 0.011, respectively.

中文翻译：

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GASBO：基于用户分组的梯度平均减法优化基于 NOMA 的雾计算车载网络


用于雾计算 (FC) 的车联网 (IoV) 解决了交通拥堵、运输效率和隐私等问题。非正交多址（NOMA）是一种流行的技术，可以提高频谱效率并增加网络的接入能力。 NOMA 和 FC 无线接入网络之间的同步扩展了增强或车辆网络的应用以及其他有前景的用途。然而，随着用户车辆和移动数据的快速增长，现有车联网由于其灵活性有限，未能成功满足现代智能交通现实可靠的通信需求。为了克服这个问题，我们提出了一种基于用户分组的混合乐观框架，用于在基于 NOMA 的 FC 车辆网络（FCVR）中进行资源分配，称为基于梯度平均减法的优化（GASBO）。最初，模拟基于 NOMA 的 FCVR。用户分组是基于GASBO，使用信号干扰加噪声比和用户距离来进行的。最后，使用所提出的 GASBO 实现资源分配，该 GASBO 结合了梯度下降优化和基于平均减法的优化。获得的能量效率、吞吐量、子信道效用、容量和惩罚函数的分析测量值分别为 5,366,844,362.870 位/焦耳、883.411 Mbps、82.031、2316.337 和 0.011。