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• W4210366687 abstract "Abstract Recently, the emerging computation-intensive in-vehicle applications and the exponentially growing data have posed a serious challenge to resource-limited in-vehicle devices. By integrating the mobile edge computing (MEC) into the vehicle networks, vehicular edge computing (VEC) is envisioned as a promising solution. In this paper, we divide the VEC system into small time slots, which effectively simulates dense small-scale computing scenarios in the rapidly varying channel environment over time. Furthermore, to alleviate the shortage of spectrum resources, vehicles can offload part of computing tasks to the edge server not only through licensed channels but also through unlicensed channels. However, dynamic topology changes and complex communication characteristics of the vehicle networks lead to the strategies of task offloading and resource allocation being difficult to obtain. By decoupling the non-convex optimizing problem and splitting it into multiple sub-problems, we design a joint power, spectrum and computing resource allocation strategy with low complexity, in order to minimize the total energy consumption of all vehicles for processing tasks. Theoretical analysis and simulation results validate the effectiveness of the proposed scheme that employing licensed and unlicensed access in VEC networks can reduce the energy consumption of vehicles for processing tasks in comparison with baseline schemes." @default.
• W4210366687 created "2022-02-08" @default.
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• W4210366687 date "2022-02-02" @default.
• W4210366687 modified "2023-10-16" @default.
• W4210366687 title "Unlicensed assisted transmission in vehicular edge computing networks" @default.
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• W4210366687 doi "https://doi.org/10.1186/s13634-022-00840-z" @default.
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