Internet-of-Vehicles resource optimization method based on a non-orthogonal multiple access technology

Abstract

The invention relates to the field of resource optimization in an Internet of Vehicles, in particular to an Internet-of-Vehicles resource optimization method based on a non-orthogonal multiple accesstechnology, which comprises the following steps of: when a vehicle task is processed in an NOMA-assisted vehicle edge computing system, taking minimization of total energy consumption of the vehicle edge computing system as a principle; determining an unloading and caching decision of the system, calculating and allocating caching resources, namely considering random flow arrival and queue stability of a vehicle user, and defining as a random optimization problem through joint optimization calculation of the unloading decision and the content caching decision and calculation and allocation ofthe caching resources; and utilizing a Lyapunov optimization theory to propose a dynamic joint calculation unloading, content caching and resource allocation algorithm for solving the problem, decoupling the algorithm into two independent sub-problems, and utilizing 0-1 integer programming and linear programming to solve the two sub-problems. According to the invention, the computing resources ofthe mobile edge computing server can be effectively processed, and the energy consumption of the system is reduced.

Description

technical field [0001] The invention relates to the field of resource optimization in the Internet of Vehicles network, in particular to a resource optimization method in the Internet of Vehicles network based on non-orthogonal multiple access. Background technique [0002] With the rapid development of the Internet of Things and the increasing popularity of computing-intensive mobile applications, existing mobile user equipment cannot meet the corresponding challenges in terms of application processing or energy supply. At the same time, driven by mobile video, online social media, etc., big data traffic is increasing exponentially, making mobile networks face computational constraints. Although the wireless spectrum has been fully utilized by, for example, ultra-dense networks, the backhaul may become a new bottleneck, unable to bear the huge business traffic. Future wireless networks are expected to support a host of computationally intensive and latency-sensitive applic...

AI Technical Summary

Problems solved by technology

[0005] At present, the relevant literature has done a lot of research work on computing offloading and content caching, but rarely consider and solve the energy consumption of computing offloading and caching

In addition, in real communication systems, the mobile traffic generated by vehicles usually arrives randomly and dynamically, and the above work mainly performs calculation offloading and content caching in a static network, without considering the dynamic characteristics of data and cache queues and dynamically optimizing data traffic.

Therefore, unpredictability can make it difficult to obtain system performance related to literature

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