论文标题
用于基于无人机的网络机载计算的动态编码分布式卷积
Dynamic Coded Distributed Convolution for UAV-based Networked Airborne Computing
论文作者
论文摘要
单个无人机（UAV）的计算资源和电池容量有限，因此很难处理计算密集型任务，例如许多深度学习应用中的卷积操作。基于无人机的网络机载计算（NAC）是应对这一挑战的一种有前途的技术。它允许在范围内的无人机通过UAV-TO-UAV通信链接共享彼此的资源，并以协作方式执行计算密集型任务。本文研究了NAC架构的矢量卷积问题。开发了一种具有隐私意识的新型动态编码卷积策略，以解决基于无人机的NAC的独特功能，包括节点异质性，经常变化的网络类型，随时间变化的通信和计算资源。仿真结果表明其对不确定的散落者的效率和韧性很高。
A single unmanned aerial vehicle (UAV) has limited computing resources and battery capacity, making it difficult to handle computationally intensive tasks such as the convolution operations in many deep learning applications. UAV-based networked airborne computing (NAC) is a promising technique to address this challenge. It allows UAVs within a range to share resources among each other via UAV-to-UAV communication links and carry out computation-intensive tasks in a collaborative manner. This paper investigates the vector convolution problem over the NAC architecture. A novel dynamic coded convolution strategy with privacy awareness is developed to address the unique features of UAV-based NAC, including node heterogeneity, frequently changing network typologies, time-varying communication and computation resources. Simulation results show its high efficiency and resilience to uncertain stragglers.
