Analog MIMO Communication for One-shot Distributed Principal Component Analysis

TL;DR

This paper introduces an analog MIMO transmission framework for one-shot distributed PCA in wireless networks, providing estimators that are robust to channel noise and do not require channel state information, with theoretical performance bounds and validation.

Contribution

It proposes a novel analog MIMO approach for one-shot DPCA, including two maximum-likelihood estimators that handle channel distortion without needing CSI, supported by theoretical analysis and simulations.

Findings

Both estimators have identical scaling laws for mean square subspace distance.

The proposed methods are robust to channel noise and do not require CSI.

Simulation results confirm theoretical bounds and latency advantages.

Abstract

A fundamental algorithm for data analytics at the edge of wireless networks is distributed principal component analysis (DPCA), which finds the most important information embedded in a distributed high-dimensional dataset by distributed computation of a reduced-dimension data subspace, called principal components (PCs). In this paper, to support one-shot DPCA in wireless systems, we propose a framework of analog MIMO transmission featuring the uncoded analog transmission of local PCs for estimating the global PCs. To cope with channel distortion and noise, two maximum-likelihood (global) PC estimators are presented corresponding to the cases with and without receive channel state information (CSI). The first design, termed coherent PC estimator, is derived by solving a Procrustes problem and reveals the form of regularized channel inversion where the regulation attempts to alleviate the effects of both receiver noise and data noise. The second one, termed blind PC estimator, is designed based on the subspace channel-rotation-invariance property and computes a centroid of received local PCs on a Grassmann manifold. Using the manifold-perturbation theory, tight bounds on the mean square subspace distance (MSSD) of both estimators are derived for performance evaluation. The results reveal simple scaling laws of MSSD concerning device population, data and channel signal-to-noise ratios (SNRs), and array sizes. More importantly, both estimators are found to have identical scaling laws, suggesting the dispensability of CSI to accelerate DPCA. Simulation results validate the derived results and demonstrate the promising latency performance of the proposed analog MIMO