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Computation Offloading with Time-Varying Fading Channel in Vehicular Edge Computing

机译:使用时变衰落通道在车辆边缘计算中的计算卸载

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Vehicular edge computing (VEC) is considered as a novel paradigm to enhance the safety of automated vehicles and intelligent transportation systems. The computation offloading strategies are the key point of VEC, and the effect of time-varying channel cannot be ignored during the task transmission period. This paper investigates the utility maximization problem with task delay requirement constraints, in which the influence of time-varying channel on the task offloading strategies during the task offloading period is considered. Due to the influence of time-varying channel, location of the vehicle and the allocated bandwidth, the task transmission time is uncertain. In order to deal with it, we first utilize the fixed spectrum efficiency (SE) instead of the time-varying SE, and then propose a linearization based Branch and Bound (LBB) algorithm to solve the fixed SE problem. After that, a fixed SE based heuristic (FSEH) algorithm is proposed to solve the original problem. The simulation results are provided to show that the performance of FSEH algorithm has a small gap of 3.93% only to the upper bound, and increased by 20.8% compared with the Minimum Overhead Offloading Algorithm (MOOA), when the bandwidth grows from 5 MHz to 30 MHz.
机译:车辆边缘计算(VEC)被认为是一种新型范式,以增强自动车辆和智能运输系统的安全性。计算卸载策略是VEC的关键点,并且在任务传输时段期间不能忽略时变信道的效果。本文调查了任务延迟要求约束的实用最大化问题,其中考虑了在任务卸载期间对任务卸载策略上的时变信道对任务卸载策略的影响。由于时变通道,车辆位置和分配带宽的影响,任务传输时间是不确定的。为了处理它,我们首先利用固定频谱效率(SE)而不是时变SE,然后提出基于线性化的分支和绑定(LBB)算法来解决固定的SE问题。之后,提出了一种固定的SE的启发式(FSEH)算法来解决原始问题。提供了模拟结果表明,与最小架空卸载算法(Mooa)从5从5增长的最小开销卸载算法(Mooa)相比,FSEH算法的性能仅为3.93 %的小差距为3.93 %,并且增加了20.8% MHz到30 MHz。

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