A Comparative Performance Evaluation of Machine Learning Algorithms for Fingerprinting Based Localization in DM-MIMO Wireless Systems Relying on Big Data Techniques

Walaa Y Al-Rashdan,Ashraf Tahat

doi:10.1109/access.2020.3001912

Walaa Y Al-Rashdan, Ashraf Tahat

Open Access

https://doi.org/10.1109/access.2020.3001912

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 42	License type: CC BY 4.0

Affiliation: Princess Sumaya University for Technology

Abstract

Mobile terminal (MT) localization based on the fingerprint approach is a strong contender solution for utilization in microcells urban environments and indoor settings that suffer from severe multipath and signal degradation. In this paper, we investigate and evaluate the performance of thirteen machine learning (ML) algorithms (including multi-target algorithms) employed in conjunction with fingerprint based MT localization for distributed massive multiple input multiple-output (DM-MIMO) wireless systems configurations. The fingerprints will rely solely on the received signal strengths (RSS) from the single-antenna MT collected at each of the receive antenna elements of the massive MIMO base station. The performance is evaluated through numerical simulations incorporating practical millimeter-wave signal propagation models suited for 5G wireless systems in combination with ray-tracing techniques, and in conjunction with the 3D OpenStreetMap to replicate real-life environments. In addition, the ML computational platform, and implementation of the proposed framework was selected with a focus on efficiently handling the anticipated big data that could be generated from a typical 5G network with expected large subscriber cell density (1 million/km 2 ). To that end, an Apache Spark based ML platform is proposed and employed. Several DM-MIMO system topologies and configuration parameters combinations affecting MT localization were investigated to analyze performance. Numerical simulation results demonstrated that the location of a MT could be effectively predicted by means of a subset of the collection of considered ML algorithms. The obtained results of MT localization performance evaluation metrics served to identify an optimum ML algorithm and methodology for employment in DM-MIMO systems.

Highlights

Over the last two decades, location based services (LBSs) have attained a great deal of popularity, where most consumer gadgets and goods are equipped with user location feature [1]
We set the EIRP of the Mobile terminal (MT) to be 1W (30 dBm)
We evaluated the performance of MT localization in our distributed massive multiple input multiple-output (DM-multiple-input multiple-output (MIMO)) using the common simulation parameters of Table 6, while we change the training data size fed to the machine learning (ML) algorithms

Summary

Introduction

Over the last two decades, location based services (LBSs) have attained a great deal of popularity, where most consumer gadgets and goods are equipped with user location feature [1]. There is an exponential increase of applications incorporating user location awareness on smart mobile terminals (MTs), and are important as traditional vehicular navigation. The majority of localization applications stem in urban settings, where the commonly utilized global positioning satellite systems endure deterioration in accuracy as a consequence of diminished satellite signals in the absence of line-of-sight (LOS) propagation to MTs in the vicinity of high-rise buildings and shadowed locale, in addition to the large consumption of power on a MT. Massive MIMO, which is a fundamental framework for the preeminent 5G technology, is established on the utilization of large-scale antenna arrays at base stations (BSs) or access points (APs)

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