GASBO: User grouping–based gradient average subtraction–based optimisation for NOMA-based fog computing vehicular network

Abstract

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.