Precoder Design for Correlated Data Aggregation via Over-the-Air Computation in Sensor Networks

TL;DR

This paper introduces a low-complexity precoder design for over-the-air computation in sensor networks, leveraging data correlations to improve aggregation efficiency and reduce communication costs.

Contribution

It presents a non-iterative precoder design that incorporates data correlations and reduces dimensionality, enhancing AirComp performance in sensor networks.

Findings

Outperforms conventional methods with many sensors and fewer receive antennas.

Requires no iterative processes, lowering computational costs.

Provides effective data dimensionality reduction.

Abstract

Over-the-air computation (AirComp) enables efficient wireless data aggregation in sensor networks by simultaneous processing of calculation and communication. This paper proposes a novel precoder design for AirComp that incorporates statistical properties of sensing data, spatial correlation and heterogeneous data correlation. The proposed design of the precoder requires no iterative processes so that it can be realized with low computational costs. Moreover, this method provides dimensionality reduction of sensing data to reduce communication costs per sensor. We evaluate performance of the proposed method in terms of various system parameters. The results show the superiority of the proposed method to conventional non-iterative methods in cases where there are a large number of sensors and where the number of receive antennas at the aggregator is less than that of the total transmit antennas at the sensors.