Geometry-Based Phase and Time Synchronization for Multi-Antenna Channel Measurements

TL;DR

This paper presents a geometry-based method for estimating and compensating phase and time offsets in multi-antenna systems, enabling improved synchronization crucial for massive MIMO applications and machine learning transferability.

Contribution

It introduces novel algorithms leveraging antenna position information to estimate phase and time offsets under multipath conditions, addressing a key challenge in distributed massive MIMO synchronization.

Findings

Algorithms successfully estimate offsets in real-world datasets

Enhanced synchronization improves array processing accuracy

Method enables better transfer of machine learning models across environments

Abstract

Synchronization of transceiver chains is a major challenge in the practical realization of massive MIMO and especially distributed massive MIMO. While frequency synchronization is comparatively easy to achieve, estimating the carrier phase and sampling time offsets of individual transceivers is challenging. However, under the assumption of phase and time offsets that are constant over some duration and knowing the positions of several transmit and receive antennas, it is possible to estimate and compensate for these offsets even in scattering environments with multipath propagation components. The resulting phase and time calibration is a prerequisite for applying classical antenna array processing methods to massive MIMO arrays and for transferring machine learning models either between simulation and deployment or from one radio environment to another. Algorithms for phase and time offset estimation are presented and several investigations on large datasets generated by an over-the-air-synchronized channel sounder are carried out.