Over-the-Air Computation with Spatial-and-Temporal Correlated Signals

TL;DR

This paper develops an over-the-air computation framework that accounts for spatial and temporal correlations in sensor signals, optimizing policies to minimize mean-squared error in wireless sensor networks.

Contribution

It introduces the first optimal and low-complexity AirComp policies considering correlated signals, improving data aggregation accuracy in IoT applications.

Findings

Optimal policy achieves minimum MSE using current and past signals.

Closed-form low-complexity policy approaches optimal performance.

Enhanced data aggregation accuracy with correlated sensor signals.

Abstract

Over-the-air computation (AirComp) leveraging the superposition property of wireless multiple-access channel (MAC), is a promising technique for effective data collection and computation of large-scale wireless sensor measurements in Internet of Things applications. Most existing work on AirComp only considered computation of spatial-and-temporal independent sensor signals, though in practice different sensor measurement signals are usually correlated. In this letter, we propose an AirComp system with spatial-and-temporal correlated sensor signals, and formulate the optimal AirComp policy design problem for achieving the minimum computation mean-squared error (MSE). We develop the optimal AirComp policy with the minimum computation MSE in each time step by utilizing the current and the previously received signals. We also propose and optimize a low-complexity AirComp policy in closed form with the performance approaching to the optimal policy.