تخصيص منابع براي محاسبات لبه موبايل مبتني بر NOMA در شبكه هاي IoV

Internet of Vehicles (IoV), a classic application of the Internet of Things in the field of intelligent transportation systems. Through IoV technologies, vehicles can execute internal services an‎d safety applications such as vehicle tracking, autonomous driving, an‎d road traffic management, ensuring the safety of drivers an‎d passengers. The sixth generation (6G), the main wireless technology used for next-generation IoV, will provide ubiquitous connectivity, secure data sharing, efficient energy transfer, an‎d fast computing. One of the main challenges in IoV communications in 6G is the limitation of energy an‎d spectrum resources. In this regard, NOMA has emerged as a promising technology that allows multiple users to communicate simultaneously over the same time/frequency resources. Additionally, some IoV applications require significant computational resources, an‎d some are sensitive to latency. Therefore, in addition to providing high-quality an‎d reliable services in IoV systems, the issue of computational processes an‎d latency is also addressed in these systems. To address this problem, Mobile Edge Computing (MEC) is presented as a suitable solution to meet low-latency requirements by providing cloud computing capabilities at the edge of the mobile network. Another essential aspect in IoV networks is the study of effective resource allocation methods. This is because using a suitable solution for resource allocation has a significant impact on improving the efficiency of the network in question. Considering the provided explanations an‎d in order to overcome the challenges mentioned in this thesis, an IoV network based on MEC with support for NOMA technology is proposed an‎d then modeled. Subsequently, the problem of allocating channels, power, an‎d computational resources available in the proposed model is formulated with the aim of increasing the spectral efficiency of the network an‎d managing interference, an‎d the formulated problem is solved using metaheuristic algorithms. Simulation results show the improvement in system performance in terms of spectral efficiency. It is also shown that the proposed algorithms for solving the optimization problem have a good performance in achieving the optimal solution of the problem.

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