Over-the-Air Gaussian Process Regression Based on Product of Experts

TL;DR

This paper introduces AirComp GPR, a distributed Gaussian process regression method utilizing over-the-air computation to enhance efficiency in wireless network data analysis, especially for large datasets.

Contribution

It presents a novel over-the-air computation approach for distributed GPR based on the product-of-experts approximation, reducing computation time while maintaining communication efficiency.

Findings

Speeds up GPR computation time significantly.

Maintains constant communication cost regardless of data and node size.

Effective in radio map construction tasks.

Abstract

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.