This letter studies an ultra-reliable low latency communication problem focusing on a vehicular edge computing network in which vehicles either fetch and synthesize images recorded by surveillance cameras or acquire the synthesized image from an edge computing server. The notion of risk-sensitive in financial mathematics is leveraged to define a reliability measure, and the studied problem is formulated as a risk minimization problem for each vehicle’s end-to-end (E2E) task fetching and offloading delays. Specifically, by resorting to a joint utility and policy estimation-based learning algorithm, a distributed risk-sensitive solution for task fetching and offloading is proposed. Simulation results show that our proposed solution achieves performance improvements up to 40% variance reduction and steeper distribution tail of the E2E delay over an averaged-based baseline.

中文翻译：

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车辆边缘计算的风险敏感任务获取和卸载

这封信研究了一个超可靠的低延迟通信问题，重点是车辆边缘计算网络，其中车辆要么获取和合成监控摄像头记录的图像，要么从边缘计算服务器获取合成图像。利用金融数学中风险敏感的概念来定义可靠性度量，并将研究的问题表述为每个车辆的端到端 (E2E) 任务获取和卸载延迟的风险最小化问题。具体来说，通过联合效用和基于策略估计的学习算法，提出了一种分布式风险敏感的任务获取和卸载解决方案。