MIMO Over-the-Air Computation for High-Mobility Multi-Modal Sensing

TL;DR

This paper advances over-the-air computation for high-mobility multi-modal sensor networks by developing beamforming and feedback techniques, leveraging differential geometry and Grassmann manifolds to optimize multi-function data aggregation.

Contribution

It introduces a novel beamforming solution based on Grassmann-centroids and establishes a duality with multicast-beamforming, enabling efficient multi-function AirComp in high-mobility scenarios.

Findings

Optimal beamforming is a Grassmann-centroid problem.

The solution reduces computational complexity compared to semidefinite relaxation.

A new channel-feedback technique improves data aggregation efficiency.

Abstract

In future Internet-of-Things networks, sensors or even access points can be mounted on ground/aerial vehicles for smart-city surveillance or environment monitoring. To support the high-mobility sensing with low network latency, a technique called over-the-air-computation (AirComp) was recently developed which enables an access-point to receive a desired function of sensing-data from concurrent-transmissions by exploiting the superposition property of a multi-access-channel. This work aims at further developing AirComp for next-generation multi-antenna multi-modal sensor networks. Specifically, we design beamforming and channel-feedback techniques for multi-function AirComp. Given the objective of minimizing sum-mean-squared-error of computed functions, the optimization of receive-beamforming for multi-function AirComp is a NP-hard problem. The approximate problem based on tightening transmission-power constraints, however, is shown to be solvable using differential-geometry. The solution is proved to be the weighted-centroid of points on a Grassmann-manifold, where each point represents the subspace spanned by the channel matrix of a sensor. As a by-product, the beamforming problem is found to have the same form as the classic problem of multicast-beamforming, establishing the AirComp-multicasting-duality. Its significance lies in making the said Grassmannian-centroid solution transferable to the latter problem which otherwise is solved using the computation-intensive semidefinite-relaxation-technique. Last, building on the AirComp-beamforming solution, an efficient channel-feedback technique is designed for an access-point to receive the beamformer from distributed sensor transmissions of designed signals that are functions of local channel-state-information.