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

arXiv: https://arxiv.org/abs/2103.14863 · HTML: https://mlsanity.com/paper/2103.14863/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2021.

## Built on by (most cited first, 3 of 3)

- [Model-based Implicit Neural Representation for sub-wavelength Radio Localization](https://mlsanity.com/paper/2506.06387/) (2025)
- [Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition](https://mlsanity.com/paper/2604.26793/) (2026)
- [Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model](https://mlsanity.com/paper/2610.18946/) (2026)

## Abstract (arXiv)

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.