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Non-orthogonal Multiple Access-based Task Processing and Energy Optimization in Vehicular Edge Computing Networks
[发布时间:2024-07-10  阅读次数: 218]

作者:Lei Shi, Zepeng Li, Shuangliang Zhao, Yuqi Fan, Dingjun Qian发表刊物:Concurrency and Computation Practice and Experience

年份:July 2024

摘要:Vehicular edge computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks, where vehicles can choose to upload tasks to nearby edge nodes for processing. However, since the communication between vehicles and edge nodes is via wireless network, which means the channel condition is complex. Moreover, in reality, the arrival time of each vehicle task is stochastic, so efficient communication methods should be designed for VEC. As one of the key communication technologies in 5G, non-orthogonal multiple access (NOMA) can effectively increase the number of simultaneous transmission tasks and enhance transmission performance. In this article, we design a NOMA-based task allocation scheme to improve the VEC system. We first establish the mathematical model and divide the allocation of tasks into two processes: the transmission process and the computation process. In the transmission process, we adopt the NOMA technique to upload the tasks in batches. In the computation process, we use a high response-ratio strategy to determine the computation order. Then we define the optimization objective as maximizing task completion rate and minimizing task energy consumption, which is an integer nonlinear problem with lots of integer variables and cannot be solved directly. Through further analysis, we design a heuristics algorithm which we name as the AECO (average energy consumption optimization) algorithm. By using the AECO, we obtain the optimal allocation strategy by constantly adjusting the optimal variables. Simulation results demonstrate that our algorithm has a significant number of advantages.

参考文献拷贝字段:Lei Shi, Zepeng Li, Shuangliang Zhao, Yuqi Fan, Dingjun Qian. Non-orthogonal Multiple Access-based Task Processing and Energy Optimization in Vehicular Edge Computing Networks [J]. Concurrency and Computation,36(22), 2024: e8222.DOI: http://doi.org/10.1002/cpe.8222


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