Paper Info
Title
A Fingerprint Localization Method In Collocated Massive Mimo-Ofdm Systems Using Clustering And Gaussian Process Regression
Abstract
Localization has been a notable feature in wireless communications due to the increasing demand for location information. Fingerprinting-based (FP) localization methods are promising for rich scattering environments due to their high reliability and accuracy. The Gaussian process regression (GPR) method could potentially be used as an FP-based localization method to facilitate localization and provide high accuracy. However, it is limited by high complexity, especially in a large-scale environment. In this paper, we propose an FP-based localization method in collocated massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems using the affinity propagation clustering (APC) algorithm and Gaussian process regression (GPR) to estimate the user's location. Fingerprints are extracted based on instantaneous channel state information (CSI) by taking full advantage of the high resolution in the angle and delay domains. Then, the training fingerprints are clustered using the (APC) algorithm to reduce matching complexity and computational complexity. Finally, the data distribution within each cluster is accurately modeled using GPR to provide excellent support for further localization. Simulation studies reveal that the proposed method improves localization performance significantly by reducing the location estimation error. Additionally, it reduces the matching complexity and computational complexity.
Year
DOI
Venue
2020
10.1109/VTC2020-Fall49728.2020.9348603
2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL)
Keywords
DocType
Citations 
Massive MIMO-OFDM, Localization, Machine Learning, Fingerprinting, Affinity propagation Clustering (APC), Gaussian process regression (GPR)
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Seyedeh Samira Moosavi100.34
Paul Fortier210117.51