论文标题
基于专家的产品的高空高斯流程回归
Over-the-Air Gaussian Process Regression Based on Product of Experts
论文作者
论文摘要
本文提出了带有空中计算的分布式高斯过程回归（GPR），称为AIRCOMP GPR，用于通过无线网络进行通信和计算有效的数据分析。 GPR是一种非参数回归方法，可以灵活地对目标进行建模。但是，随着数据数量的增加，它的计算复杂性和通信效率往往很大。 AIRCOMP GPR专注于基于Experts的GPR的GPR近似于分布式节点报告的值的总和。我们介绍了AirComp进行培训和预测步骤，以允许节点同时传输其本地计算结果；介绍了沟通策略，包括基于完美和统计渠道状态信息案例的分布式培训。应用于无线电图构造任务，我们证明AIRCOMP GPR会加快计算时间，同时保持训练常数的通信成本，而不管数据和节点的数量如何。
This paper proposes a distributed Gaussian process regression (GPR) with over-the-air computation, termed AirComp GPR, for communication- and computation-efficient data analysis over wireless networks. GPR is a non-parametric regression method that can model the target flexibly. However, its computational complexity and communication efficiency tend to be significant as the number of data increases. AirComp GPR focuses on that product-of-experts-based GPR approximates the exact GPR by a sum of values reported from distributed nodes. We introduce AirComp for the training and prediction steps to allow the nodes to transmit their local computation results simultaneously; the communication strategies are presented, including distributed training based on perfect and statistical channel state information cases. Applying to a radio map construction task, we demonstrate that AirComp GPR speeds up the computation time while maintaining the communication cost in training constant regardless of the numbers of data and nodes.
