Over-the-Air Statistical Estimation

TL;DR

This paper introduces analog joint estimation-communication schemes for distributed statistical estimation over Gaussian MACs, demonstrating they outperform digital schemes by leveraging physical layer properties and approaching theoretical lower bounds.

Contribution

The paper develops novel analog schemes for distributed estimation over Gaussian MACs and establishes their near-optimality through information-theoretic lower bounds.

Findings

Analog schemes outperform digital schemes in estimation accuracy.

Proposed schemes achieve risk within a logarithmic factor of lower bounds.

Physical layer exploitation significantly reduces estimation error.

Abstract

We study schemes and lower bounds for distributed minimax statistical estimation over a Gaussian multiple-access channel (MAC) under squared error loss, in a framework combining statistical estimation and wireless communication. First, we develop "analog" joint estimation-communication schemes that exploit the superposition property of the Gaussian MAC and we characterize their risk in terms of the number of nodes and dimension of the parameter space. Then, we derive information-theoretic lower bounds on the minimax risk of any estimation scheme restricted to communicate the samples over a given number of uses of the channel and show that the risk achieved by our proposed schemes is within a logarithmic factor of these lower bounds. We compare both achievability and lower bound results to previous "digital" lower bounds, where nodes transmit errorless bits at the Shannon capacity of the MAC, showing that estimation schemes that leverage the physical layer offer a drastic reduction in estimation error over digital schemes relying on a physical-layer abstraction.