Towards Fine-Grained Indoor Localization based on Massive MIMO-OFDM System: Experiment and Analysis

TL;DR

This paper develops a massive MIMO-OFDM indoor localization system that leverages multipath components and fingerprinting techniques, achieving centimeter-level accuracy through experimental validation.

Contribution

It introduces a novel fingerprinting approach based on MPCs extracted via SAGE, demonstrating high-precision indoor localization with a small training set.

Findings

Achieves 1.63-2.5 cm mean absolute error in localization

Distributed uniform linear array yields highest accuracy

Effective with small training datasets

Abstract

Fine-grained indoor localization has attracted attention recently because of the rapidly growing demand for indoor location-based services (ILBS). Specifically, massive (large-scale) multiple-input and multiple-output (MIMO) systems have received increasing attention due to high angular resolution. This paper presents an indoor localization testbed based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system, which supports physical-layer channel measurements. Instead of exploiting channel state information (CSI) directly for localization, we focus on positioning from the perspective of multipath components (MPCs), which are extracted from the CSI through the space-alternating generalized expectation-maximization (SAGE) algorithm. On top of the available MPCs, we propose a generalized fingerprinting system based on different single-metric and hybrid-metric schemes. We evaluate the impact of the varying antenna topologies, the size of the training set, the number of antennas, and the effective signal-to-noise ratio (SNR). The experimental results show that the proposed fingerprinting method can achieve centimeter-level positioning accuracy with a relatively small training set. Specifically, the distributed uniform linear array obtains the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from the high spatial resolution.