Robust Localization in OFDM-Based Massive MIMO through Phase Offset Calibration

TL;DR

This paper investigates the impact of phase offsets on OFDM-based massive MIMO localization accuracy and proposes a calibration method that significantly improves performance, validated through theoretical analysis and real-world experiments.

Contribution

It introduces a phase offset calibration framework for massive MIMO localization, addressing phase incoherence issues with theoretical insights and practical validation.

Findings

Spatial phase offsets severely degrade localization accuracy.

Frequency phase offsets have minor impact in the studied system.

Calibration reduces localization RMSE from 5 m to 1.2 cm.

Abstract

Accurate localization in Orthogonal Frequency Division Multiplexing (OFDM)-based massive Multiple-Input Multiple-Output (MIMO) systems depends critically on phase coherence across subcarriers and antennas. However, practical systems suffer from frequency-dependent and (spatial) antenna-dependent phase offsets, degrading localization accuracy. This paper analytically studies the impact of phase incoherence on localization performance under a static User Equipment (UE) and Line-of-Sight (LoS) scenario. We use two complementary tools. First, we derive the Cram\'er-Rao Lower Bound (CRLB) to quantify the theoretical limits under phase offsets. Then, we develop a Spatial Ambiguity Function (SAF)-based model to characterize ambiguity patterns. Simulation results reveal that spatial phase offsets severely degrade localization performance, while frequency phase offsets have a minor effect in the considered system configuration. To address this, we propose a robust Channel State Information (CSI) calibration framework and validate it using real-world measurements from a practical massive MIMO testbed. The experimental results confirm that the proposed calibration framework significantly improves the localization Root Mean Squared Error (RMSE) from 5 m to 1.2 cm, aligning well with the theoretical predictions.