118 research outputs found
Deep Learning-based Fingerprinting for Outdoor UE Positioning Utilising Spatially Correlated RSSs of 5G Networks
Outdoor user equipment (UE) localisation has attracted a significant amount of attention due to its importance in many location-based services. Typically, in rural and open areas, global navigation satellite systems (GNSS) can provide an accurate and reliable localisation performance. However, in urban areas GNSS localisation accuracy is significantly reduced due to shadowing, scattering and signal blockages. In this work, the UE positioning assisted by deep learning in 5G and beyond networks is investigated in an urban area environment. We study the impact of utilising the spatial correlation in the received signal strengths (RSSs) on the UE positioning accuracy and how to utilise such correlation with deep learning algorithms to improve the localisation accuracy. Numerical results showed the importance of utilising the spatial correlation in the RSS to improve the prediction accuracy for all of the considered models. In addition, the impact of varying the number of access points (APs) transmitters on the localisation accuracy is also investigated. Numerical results showed that a lower number of APs may be sufficient when not considering uncertainties in RSS measurements. Moreover, we study how much the degrading effect of RSS uncertainty can be compensated for by increasing the number of APs.Peer reviewe
A CNN Approach for 5G mmWave Positioning Using Beamformed CSI Measurements
The advent of Artificial Intelligence (AI) has impacted all aspects of human life. One of the concrete examples of AI impact is visible in radio positioning. In this article, for the first time we utilize the power of AI by training a Convolutional Neural Network (CNN) using 5G New Radio (NR) fingerprints consisting of beamformed Channel State Information (CSI). By observing CSI, it is possible to characterize the multipath channel between the transmitter and the receiver, and thus provide a good source of spatiotemporal data to find the position of a User Equipment (UE). We collect ray-tracing-based 5G NR CSI from an urban area. The CSI data of the signals from one Base Station (BS) is collected at the reference points with known positions to train a CNN. We evaluate our work by testing: a) the robustness of the trained network for estimating the positions for the new measurements on the same reference points and b) the accuracy of the CNN-based position estimation while the UE is on points other than the reference points. The results prove that our trained network for a specific urban environment can estimate the UE position with a minimum mean error of 0.98 m.Peer reviewe
Role of Reconfigurable Intelligent Surfaces in 6G Radio Localization: Recent Developments, Opportunities, Challenges, and Applications
Reconfigurable intelligent surfaces (RISs) are seen as a key enabler low-cost
and energy-efficient technology for 6G radio communication and localization. In
this paper, we aim to provide a comprehensive overview of the current research
progress on the RIS technology in radio localization for 6G. Particularly, we
discuss the RIS-assisted radio localization taxonomy and review the studies of
RIS-assisted radio localization for different network scenarios, bands of
transmission, deployment environments, as well as near-field operations. Based
on this review, we highlight the future research directions, associated
technical challenges, real-world applications, and limitations of RIS-assisted
radio localization
ML-Enabled Outdoor User Positioning in 5G NR Systems via Uplink SRS Channel Estimates
Cellular user positioning is a promising service provided by Fifth Generation
New Radio (5G NR) networks. Besides, Machine Learning (ML) techniques are
foreseen to become an integrated part of 5G NR systems improving radio
performance and reducing complexity. In this paper, we investigate ML
techniques for positioning using 5G NR fingerprints consisting of uplink
channel estimates from the physical layer channel. We show that it is possible
to use Sounding Reference Signals (SRS) channel fingerprints to provide
sufficient data to infer user position. Furthermore, we show that small
fully-connected moderately Deep Neural Networks, even when applied to very
sparse SRS data, can achieve successful outdoor user positioning with
meter-level accuracy in a commercial 5G environment.Comment: 6 pages, 8 figures, Accepted to be published in IEEE International
Conference on Communications 2023, Rome, Ital
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
The commercial availability of low-cost millimeter wave (mmWave)
communication and radar devices is starting to improve the penetration of such
technologies in consumer markets, paving the way for large-scale and dense
deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the
same time, pervasive mmWave access will enable device localization and
device-free sensing with unprecedented accuracy, especially with respect to
sub-6 GHz commercial-grade devices. This paper surveys the state of the art in
device-based localization and device-free sensing using mmWave communication
and radar devices, with a focus on indoor deployments. We first overview key
concepts about mmWave signal propagation and system design. Then, we provide a
detailed account of approaches and algorithms for localization and sensing
enabled by mmWaves. We consider several dimensions in our analysis, including
the main objectives, techniques, and performance of each work, whether each
research reached some degree of implementation, and which hardware platforms
were used for this purpose. We conclude by discussing that better algorithms
for consumer-grade devices, data fusion methods for dense deployments, as well
as an educated application of machine learning methods are promising, relevant
and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys &
Tutorials (IEEE COMST
Positioning in 5G and 6G Networks—A Survey
Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios
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