2024-03-29T11:24:01Z
https://zenodo.org/oai2d
oai:zenodo.org:4710561
2021-04-23T00:27:32Z
user-locus-project
Anjum, Bushra
2020-08-01
<p>In this interview, Ubiquity's senior editor Dr. Bushra Anjum chats with Dr. Gürkan Solmaz, a senior researcher at NEC Laboratories Europe, Germany, about his work focused on building new situation classification frameworks for smart cities (IoT). They then discuss the three major design aspects for such systems, namely, certainty, efficiency, and privacy.</p>
The Digital Library is published by the Association for Computing Machinery. Copyright © 2020 ACM, Inc.
https://doi.org/10.1145/3417343
oai:zenodo.org:4710561
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
smart cities
certainty
efficiency
privacy
A Conversation with Gürkan Solmaz: Situation classification in the internet of things (IoT)
info:eu-repo/semantics/article
oai:zenodo.org:5211278
2021-08-17T13:48:25Z
user-locus-project
Orlando, Danilo
Palamà, Ivan
Bartoletti, Stefania
Bianchi, Giuseppe
Blefari Melazzi, Nicola
2020-09-01
<p>In this letter, we propose three schemes designed to detect attacks over the air interface in cellular networks. These decision rules rely on the generalized likelihood ratio test, and are fed by data that can be acquired using common off- the-shelf receivers. In addition to more classical (barrage/smart) noise jamming attacks, we further assess the capability of the proposed schemes to detect the stealthy activation of a rogue base station. The evaluation is carried out through an experimentation of a LTE system concretely reproduced using Software-Defined Radios. Illustrative examples confirm that the proposed schemes can effectively detect air interface threats with high probability.</p>
https://doi.org/10.5281/zenodo.5211278
oai:zenodo.org:5211278
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.5211277
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
adaptive detection
anomaly detection
smart jammer
noise-like jammer
IMSI-catching
LTE
Design and Experimental Assessment of Detection Schemes for Air Interface Attacks in Adverse Scenarios
info:eu-repo/semantics/article
oai:zenodo.org:5017126
2021-06-23T13:48:21Z
user-locus-project
Lacruz, Jesús O.
García, Dolores
Jiménez, Pablo
Palacios, Joan
Widmer, Joerg
2021-06-23
<p>Millimeter-wave (mm-wave) communications have become an integral part of WLAN standards and 5G mobile networks and, as application data rate requirements increase, more and more traffic will move to these very high frequency bands. While for sub-6 GHz research there is an ample choice of powerful experimental platforms, building mm-wave systems is much more difficult due to the very high hardware requirements. To address the lack of suitable experimentation platforms, we propose mm-FLEX, a flexible and modular open platform with real- time signal processing capabilities that supports a bandwidth of 2 GHz and is compatible with current mm-wave standards. The platform is built around a fast FPGA processor and a 60 GHz phased antenna array front-end that can be reconfigured at nanosecond timescales. Together with its ease of use, this turns the platform into a unique tool for research on beam training in highly mobile scenarios and full-bandwidth mm-wave signal processing.</p>
https://doi.org/10.5281/zenodo.5017126
oai:zenodo.org:5017126
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.5017125
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
High-Speed Millimeter-Wave Mobile Experimentation on Software-Defined Radios
info:eu-repo/semantics/article
oai:zenodo.org:5040572
2021-06-29T13:48:14Z
user-locus-project
Barco, Raquel
Alvarez-Merino, Carlos S.
Luo-Chen, Hao Qiang
Khatib, Emil J.
2021-06-29
<p>Ultra-Wide Band (UWB) technology stands out as one of the most promising technologies for locating the user in indoor scenarios for the new 5G mobile generation. As a drawback, it requires a dense infrastructure. For this study, a simulation of a real environment with UWB and Long Term Evolution (LTE) base stations for positioning users is presented, tracked by an Extended Kalman Filter (EKF). The proposed method uses information that is unusable with UWB alone, and combines it with LTE location, improving the precision for the latter and enabling sparse infrastructure deployments.</p>
https://doi.org/10.1109/LCOMM.2021.3074960
oai:zenodo.org:5040572
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
UWB
Position control
Location fusion
Indoor positioning
Mobile network
Opportunistic fusion of ranges from different sources for indoor positioning
info:eu-repo/semantics/article
oai:zenodo.org:5211320
2021-08-17T13:48:25Z
user-locus-project
Torres, Renato
Fortes, Sergio
Baena, Eduardo
Barco, Raquel
2021-07-26
<p>Over time, load balancing systems in cellular networks have been key to avoiding overload problems in the network and to maintaining a correct resource allocation and performance. However, the classical approaches were not designed for the dynamism generated by user behavior. The big crowds of users at certain social venues are one of the main concerns of mobile operators due to the load imbalance generated. Additionally, the mobility of users who attend social events (e.g., sports events, concerts, etc.) greatly impacts network performance due to its high correlation with network traffic. The availability to inform about events, particularly regarding venue location (e.g., stadiums, concert halls, convention centers) is exponentially growing thanks to its proliferation in social networks through geolocation databases and other functionalities. Therefore, the present work proposes a novel load balancing system integrating a fuzzy logic controller algorithm with social-awareness, which considers the relative position between cell sites and the social event venue in order to configure the network parameters. This approach is evaluated for different configurations of load balancing methods simulated on an urban macro scenario, mitigating the impact of the number of users per cell without degrading the signal quality. In this way, results show that social event data information plus soft or aggressive transmission power changes in cells can help to maintain the balance in the number of users per cell during mass events.</p>
https://doi.org/10.1109/ACCESS.2021.3100459
oai:zenodo.org:5211320
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
social events
mobile communication
fuzzy control
load balancing
social-awareness
communication system operations and management
Social-Aware Load balancing System for Crowds in Cellular Networks
info:eu-repo/semantics/article
oai:zenodo.org:4072266
2020-10-08T00:26:58Z
user-locus-project
user-eu
Nicola Blefari-Melazzi
Stefania Bartoletti
Luca Chiaraviglio
Flavio Morselli
Eduardo Baena
Giacomo Bernini
Domenico Giustiniano
Mythri Hunukumbure
Gurkan Solmaz
Kostas Tsagkaris.
2020-10-07
<p>Location information and context-awareness are essential for a variety of existing and emerging 5G-based applications. Nevertheless, navigation satellite systems are denied in in-door environments, current cellular systems fail to provide high-accuracy localization, and other local localization technologies (e.g., Wi-Fi or Bluetooth) imply high deployment, maintenance and integration costs. Raw spatiotemporal data are not sufficient by themselves and need to be integrated with tools for the analysis of the behavior of physical targets, to extract relevant features of interests. In this paper, we present LOCUS, an H2020 project (https://www.locus-project.eu/) funded by the European Commission, aiming at the design and implementation of an innovative location management layered platform which will be able to: i) improve localization accuracy, close to theoretical bounds, as well as localization security and privacy, ii) extend localization with physical analytics, iii) extract value out from the combined interaction of localization and analytics, while guaranteeing users’ privacy.</p>
https://doi.org/10.1109/EuCNC48522.2020.9200961
oai:zenodo.org:4072266
Zenodo
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
LOCUS: Localization and analytics on-demand embedded in the 5G ecosystem
info:eu-repo/semantics/article
oai:zenodo.org:4017397
2020-10-08T11:41:24Z
user-locus-project
user-eu
Flavio Morselli
Stefania Bartoletti
Andrea Conti
2020-07-21
<p>Sensor radar networks (SRNs) employing ultrawideband (UWB) signals are a prominent solution for accurate localization and tracking in indoor environments. However, tracking device-free targets via SRNs is challenging, especially in environments heavily affected by clutter. Clutter characterization is vital to derive performance benchmarks as well as to design inference algorithms for SRNs. Examples of clutter statistical characterization have been provided in the literature for conventional SRNs employing narrowband signals in outdoor scenarios. However, considerably less effort has been devoted for SRNs employing UWB signals in indoor environments. This paper proposes an approach to characterize the clutter-plus-noise component after mitigation filtering in UWB SRNs. In particular, the statistical properties of the residual clutter-plus-noise are derived by applying statistical tests on measurements gathered in an indoor environment via UWB sensor radar networks.</p>
https://doi.org/10.1109/iccworkshops49005.2020.9145358
oai:zenodo.org:4017397
Zenodo
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Indoor Residual Clutter Characterization for UWB Sensor Radar Networks
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6396204
2022-06-29T07:37:09Z
user-locus-project
Morselli, Flavio
Bartoletti, Stefania
Z. Win, Moe
Conti, Andrea
2021-08-13
<p>Location awareness is essential in 5th generation (5G) ecosystem to enable location-based services and to efficiently manage the network. This paper presents a method for efficient localization based on the fusion of heterogeneous observations gathered with different technologies in the 5G ecosystem. In particular, a soft information (SI)-based approach is developed for hybrid localization fusing 5G and Wi-Fi measurements. Results obtained in an indoor environment compliant with 3rd Generation Partnership Project standards quantify the benefits of hybrid localization via SI with respect to the case of a single technology.</p>
https://doi.org/10.1109/SPAWC51858.2021.9593139
oai:zenodo.org:6396204
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Location awareness
5G
Wi-Fi
machine learning
wireless networks
Localization in 5G Ecosystem with Wi-Fi
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4147647
2021-02-27T00:27:15Z
user-ieee
user-locus-project
user-eu
Maurizio Rea
Domenico Giustiniano
Joerg Widmer
2020-12-10
<p>Inertial sensors embedded in mobile devices, such as accelerometers and gyroscopes, have shown great potential to study human motion. In this paper, we propose to estimate the device movement without any access to physical inertial sensors in the mobile. Our idea is to infer the movements of the mobile through radio measurements, a concept we call “virtual inertial sensors”. We propose a method for estimating the rotation of a user that uses only WiFi Fine Time Measurements (FTM) to infer the rotation speed. We evaluate and demonstrate the proposed approach with experiments, using commodity 802.11ac Access Point (AP)s for Channel State Information (CSI) and FTMs measurements, and a Google Pixel 3 smartphone as mobile terminal. While FTM works with only one single antenna, it achieves better performance than a CSI-based estimator that exploits four antennas and multiple sub-carriers at the AP, but is limited by the typical one single WiFi antenna at the smartphone side. Together with walking speed estimation of a<br>
user, we envision that virtual inertial sensors can be leveraged by location systems and sensing mechanisms, including 5G, to improve localization accuracy, infer user behavior, and design better and more secure communication.</p>
https://doi.org/10.5281/zenodo.4147647
oai:zenodo.org:4147647
eng
Zenodo
https://zenodo.org/communities/ieee
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4147646
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE MASS 2020, 10-13 December 2020
Virtual Inertial Sensors with Fine Time Measurements
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4073056
2020-10-15T13:34:20Z
user-locus-project
user-eu
Lorenzo Mucchi
Risto Vuohtoniemi
Hasnain Virk
Andrea Conti
Matti Hämäläinen
Jari Iinatti
Moe Z. Win
2020-06-01
<p>Emerging healthcare radio technologies are designed to operate in the 2:4GHz industrial, scientific and medical</p>
<p>(ISM) band. Since both standardized (Bluetooth and Wi-Fi) and non-standardized (proprietary) devices use the same frequency</p>
<p>band, the aggregate interference may significantly affect the performance of medical wireless systems. This paper characterizes</p>
<p>the spatiotemporal spectrum occupancy and proposes models for the aggregate interference in hospital environments.</p>
<p>In particular, time–frequency and cluster-based statistical models for the aggregate interference are developed based on network</p>
<p>experimentation. The proposed models enable the design of wireless networks for e-health applications and medical services.</p>
https://doi.org/10.1109/twc.2020.2995116
oai:zenodo.org:4073056
Zenodo
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Spectrum Occupancy and Interference Model based on Network Experimentation in Hospital
info:eu-repo/semantics/article
oai:zenodo.org:4710611
2021-04-23T00:27:32Z
user-ieee
user-locus-project
Villegas, Javier
Baena, Eduardo
Fortes, Sergio
Barco, Raquel
2021-03-12
<p>It has been long established that crowds generated by social events (e.g., sports matches, parades, fairs...) produce a high impact on cellular network service. However, to estimate such an impact, it is necessary to use data sources classically outside the mobile operator control. In this way, and following a social-aware approach, the forecasting mechanisms should be able to combine both social and network information to obtain reliable predictions. To this end, the present work develops a complete system for its use in the prediction of cellular metrics (e.g., connections, throughput...). The performance of the proposed solution is evaluated in a real cellular network, showing the capabilities of the approach to provide accurate forecasting.</p>
Prior knowledge without using data from other partners
https://doi.org/10.1109/LCOMM.2021.3065812
oai:zenodo.org:4710611
eng
Zenodo
https://zenodo.org/communities/ieee
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Cellular communications
self-organizing networks
social events
network intelligence
forecasting
Social-Aware Forecasting for Cellular Networks Metrics
info:eu-repo/semantics/article
oai:zenodo.org:4580438
2021-04-22T17:04:48Z
user-ieee
user-locus-project
user-eu
Chiaraviglio, Luca
Rossetti, Simone
Saida, Sara
Bartoletti, Stefania
Blefari Melazzi, Nicola
2021-02-04
<p>According to a very popular belief - very widespread among non-scientific communities - the exploitation of narrow beams, a.k.a. ‘‘pencil beamforming’’, results in a prompt increase of exposure levels radiated by 5G Base Stations (BSs). To face such concern with a scientific approach, in this work we propose a novel localization-enhanced pencil beamforming technique, in which the traffic beams are tuned in accordance with the uncertainty localization levels of User Equipment (UE). Compared to currently deployed beamforming techniques, which generally employ beams of fixed width, we exploit the localization functionality made available by the 5G architecture to synthesize the direction and the width of each pencil beam towards each served UE. We then evaluate the effectiveness of pencil beamforming in terms of ElectroMagnetic Field (EMF) exposure and UE throughput levels over different realistic case- studies. Results, obtained from a publicly released open-source simulator, dispel the myth: the adoption of localization-enhanced pencil beamforming triggers a prompt reduction of exposure w.r.t. other alternative techniques, which include e.g., beams of fixed width and cellular coverage not exploiting beamforming. The EMF reduction is achieved not only for the UE that are served by the pencil beams, but also over the whole territory (including the locations in proximity to the 5G BS). In addition, large throughput levels - adequate for most of 5G services - can be guaranteed when each UE is individually served by one dedicated beam.</p>
Simulated data from CNIT without using data from other partners
https://doi.org/10.5281/zenodo.4580438
oai:zenodo.org:4580438
eng
Zenodo
https://zenodo.org/communities/ieee
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4580437
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
5G cellular networks
5G localization service
pencil beam management
EMF analysis
throughput analysis
Pencil Beamforming Increases Human Exposure to ElectroMagnetic Fields: True or False?
info:eu-repo/semantics/article
oai:zenodo.org:5760966
2021-12-16T11:49:32Z
user-locus-project
Bartoletti, Stefania
Wymeersch, Henk
Mach, Tomasz
Brunnegård, Oliver
Giustiniano, Domenico
Hammarberg, Peter
Furkan Keskin, Musa
O. Lacruz, Jesus
Modarres Razavi, Sara
Rönnblom, Joakim
Tufvesson, Fredrik
Widmer, Joerg
Blefari Melazzi, Nicola
2021-11-01
<p>This paper presents a shared vision among stake- holders across the value chain on the use of radio positioning and sensing for road safety in the 5G ecosystem. The key enabling technologies and architectural functionalities are explored, focus- ing on the extremely stringent localization and communication requirements. A case study for joint radar and communication using experimental data showcases the potential of the new enablers that are paving the way towards enhanced road safety in Beyond 5G scenarios.</p>
https://doi.org/10.5281/zenodo.5760966
oai:zenodo.org:5760966
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.5760965
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Vehicular
road safety
positioning
joint radar and communication
5G
Positioning and Sensing for Vehicular Safety Applications in 5G and Beyond
info:eu-repo/semantics/article
oai:zenodo.org:5017613
2021-06-23T14:31:59Z
user-locus-project
Blanco Pizarro, Alejandro
Palacios Beltrán, Joan
Cominelli, Marco
Gringoli, Francesco
Widmer, Joerg
2021-06-24
<p>WiFi location systems are remarkably accurate, with decimeter- level errors for recent CSI-based systems. However, such high accuracy is achieved under Line-of-Sight (LOS) conditions and with an access point (AP) density that is much higher than that typically found in current deployments that primarily target good coverage. In contrast, when many of the APs within range are in Non-Line- of-Sight (NLOS), the location accuracy degrades drastically.</p>
<p>In this paper we present UbiLocate, a WiFi location system that copes well with common AP deployment densities and works ubiquitously, i.e., without excessive degradation under NLOS. UbiLocate demonstrates that meter-level median accuracy NLOS localization is possible through (i) an innovative angle estimator based on a Nelder-Mead search, (ii) a fine-grained time of flight ranging system with nanosecond resolution, and (iii) the accuracy improvements brought about by the increase in bandwidth and number of antennas of IEEE 802.11ac. In combination, they provide superior resolvability of multipath components, significantly improving location accuracy over prior work. We implement our location system on off-the-shelf 802.11ac devices and make the implementation, CSI-extraction tool and custom Fine Timing Measurement design publicly available to the research community. We carry out an extensive performance analysis of our system and show that it outperforms current state-of-the-art location systems by a factor of 2-3, both under LOS and NLOS.</p>
https://doi.org/10.1145/3458864.3468850
oai:zenodo.org:5017613
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Indoor localization
CSI
802.11ac
AoA
ToF
Wireless Networks
Accurate Ubiquitous Localization with Off-the-Shelf IEEE 802.11ac Devices
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5016963
2021-06-23T13:48:21Z
user-locus-project
Fortes, Sergio
Barco, Raquel
Baena, Carlos
Villegas, Javier
Baena, Eduardo
Zeeshan Asghar, Muhammad
2021-02-22
<p>Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is consid- ered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost.</p>
https://doi.org/10.3390/s21041501
oai:zenodo.org:5016963
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cellular networks
location-awareness
positioning
failure management
Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
info:eu-repo/semantics/article
oai:zenodo.org:4018516
2020-09-09T00:59:25Z
user-locus-project
Ivan Palamà
Francesco Gringoli
Giuseppe Bianchi
Nicola Blefari Melazzi
2020-09-08
<p>The goal of this paper is to assess how different User Terminals react to IMSI-catching attacks, namely location privacy attacks aiming at gathering the user’s International Mobile Subscriber Identity (IMSI). After having implemented two different attack techniques over two different Software-Defined-Radio (SDR) platforms (OpenAirInterface and srsLTE), we have tested these attacks over different versions of the mobile phone brands, for a total of 19 different radio modems tested. We show that while the majority of devices surrender almost immediately, iPhones seem to implement some cleverness that resembles proper countermeasures. We also bring about evidence that the two chosen SDR platforms implement different signaling procedures that differentiate their ability as IMSI-catchers. We finally analyse IMSI-catchers’ behaviors agains subscribers of different operators, showing that successfulness of the attack depends only on the chipset and the SDR tool.We believe that our analysis may be useful either to practitioners that need to experiment with mobile security, as well as engineers for improving the design of mobile modems.</p>
https://doi.org/10.1145/3411276.3412191
oai:zenodo.org:4018516
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
The diverse and variegated reactions of different cellular devices to IMSI catching attacks
info:eu-repo/semantics/article
oai:zenodo.org:6322752
2022-03-02T13:48:58Z
user-locus-project
Solmaz, Gürkan
Baranwal, Pankaj
Cirillo, Flavio
2022-03-02
<p>The widespread use of pervasive sensing technolo- gies such as wireless sensors and street cameras allows the de- ployment of crowd estimation solutions in smart cities. However, existing Wi-Fi-based systems do not provide highly accurate crowd size estimation. Furthermore, these systems do not adapt to the dynamic changes in-the-wild, such as unexpected crowd gatherings. This paper presents a new adaptive machine learning system, called CountMeIn, to address the crowd estimation problem using polynomial regression and neural networks. The approach transfers the calibration task from cameras to machine learning after a short training with people counting from stereo- scopic cameras, Wi-Fi probe packets, and temporal features. After the training, CountMeIn calibrates Wi-Fi using the trained model and maintains high accuracy for a longer duration without cameras. We test the approach in our pilot study in Gold Coast, Australia, for about five months. CountMeIn achieves 44% and 72% error reductions in minutely and hourly crowd estimations compared to the state-of-the-art methods.</p>
https://doi.org/10.5281/zenodo.6322752
oai:zenodo.org:6322752
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.6322751
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Crowd mobility
machine learning
neural networks
CountMeIn: Adaptive Crowd Estimation with Wi-Fi in Smart Cities
info:eu-repo/semantics/article
oai:zenodo.org:5760913
2022-01-27T11:21:00Z
user-locus-project
Bartoletti, Stefania
Palamà, Ivan
Orlando, Danilo
Bianchi, Giuseppe
Blefari Melazzi, Nicola
2021-12-06
<p>Location based services are expected to play a major role in future generation cellular networks, starting from the incoming 5G systems. At the same time, localization technologies may be severely affected by attackers capable to deploy low cost fake base stations and use them to alter localization signals. In this paper, we concretely focus on two classes of threats: noise-like jammers, whose objective is to reduce the signal-to- noise ratio, and spoofing/meaconing attacks, whose objective is to inject false or erroneous information into the receiver. Then, we formulate the detection problems as binary hypothesis tests and solve them resorting to the generalized likelihood ratio test design procedure as well as the Latent Variable Models, which involves the expectation-maximization algorithm to estimate the unknown data distribution parameters. The proposed techniques can be applied to a large class of location data regardless the subsumed network architecture. The performance analysis is conducted over simulated data generated by using measurement models from the literature and highlights the effectiveness of the proposed approaches in detecting the aforementioned classes of attacks.</p>
https://doi.org/10.5281/zenodo.5760913
oai:zenodo.org:5760913
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.5760912
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
5G
Anomaly Detection
Change Detection
Generalized Likelihood Ratio Test
Location Security
Meaconing
Noise-Like Jamming
Spoofing
Anomaly Detection Algorithms for Location Security in 5G Scenarios
info:eu-repo/semantics/article
oai:zenodo.org:6396157
2022-06-29T07:37:11Z
user-locus-project
Chraiti, Mohaned
Conti, Andrea
Z. Win, Moe
2021-08-13
<p>Mobile communication at millimeter-waves (mmWaves) is affected by the rapid and random variations of the wireless environment. This requires accurate channel estimation at the receiver to compensate for the channel dynamics. The required channel training overhead has been shown to occupy a considerable fraction of the mmWave transmission bandwidth, especially when the mobile device (MD) moves at high-speed. We cope with the issue of the rapid variation of the channel by introducing a coherent transmission/detection technique, whose mechanism requires that MD has prior knowledge of its position as an alternative to the channel estimate. In particular, we consider the case of road side links with high-speed MDs and derive a closed-form expression for the optimal detector. We show that the proposed approach reduces the training overhead significantly at the expense of a signal-to-noise ratio (SNR) increase for a given bit error rate.</p>
Special session on Advanced localization algorithms toward beyond 5G
https://doi.org/10.1109/SPAWC51858.2021.9593116
oai:zenodo.org:6396157
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
mmWave
mobile networks
road side links
localization
spatial modulation
mmWave Communications for High Mobility Devices: The Case of Road Side Links
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5734174
2021-11-29T13:48:43Z
user-locus-project
Zhao, Yuxin
Shrestha, Deep
2021-07-01
<p>UE localization has proven its implications on mul- titude of use cases ranging from emergency call localization to new and emerging use cases in industrial IoT. To support plethora of use cases Radio Access Technology (RAT)-based positioning has been supported by 3GPP since Release 9 of its specifications that featured basic positioning methods based on Cell Identity (CID) and Enhanced-CID (E-CID). Since then, multiple positioning techniques and solutions are proposed and integrated in to the 3GPP specifications. When it comes to evaluating performance of the positioning techniques, achievable accuracy (2-Dimensional or 3-Dimensional) has, so far, been the primary metric. With an advent of Release 16 New Radio (NR) positioning, it is possible to configure Positioning Reference Signal (PRS) with wide bandwidth that naturally helps improving the positioning accuracy. However, the improvement is evident when the conditions are ideal for positioning. In practice where the conditions are non-ideal and the positioning accuracy is severely impacted, estimating the uncertainty in position estimation be- comes important and can provide significant insight on how reliable a position estimation is.</p>
<p>In order to determine the uncertainty in position estimation we resort to Machine Learning (ML) techniques that offer ways to determine the uncertainty/reliability of the predictions for a trained model. Hence, in this work we propose to combine ML methods such as Gaussian Process (GP) and Random Forest (RF) with RAT-based positioning measurements to predict the location of a UE and in the meantime also assess the uncertainty of the estimated position. The results show that both GP and RF not only achieve satisfactory positioning accuracy but also give a reliable uncertainty assessment of the predicted position of the UE.</p>
https://doi.org/10.5281/zenodo.5734174
oai:zenodo.org:5734174
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.5734173
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
positioning
uncertainty
machine learning
Gaussian Process
Random Forest
Uncertainty in Position Estimation Using Machine Learning
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5760907
2021-12-16T11:49:35Z
user-locus-project
Baena, Eduardo
Fortes, Sergio
Alay, Özgü
Xie, Min
Lønsethagen, Håkon
Barco, Raquel
2021-05-13
<p>Although log processing of network equipment is a common technique in cellular network management, several factors make said task challenging, especially during mass attendance events. The present paper assesses classic methods for cellular network measurement and acquisition, showing how the use of on-the-field user probes can provide relevant capabilities to the analysis of cellular network performance. Therefore, a framework for the deployment of this kind of system is proposed here based on the development of a new hardware virtualization platform with radio frequency capabilities. Accordingly, an analysis of the characteristics and requirements for the use of virtual probes was performed. Moreover, the impact that social events (e.g., sports matches) may have on the service provision was evaluated through a real cellular scenario. For this purpose, a long-term measurement study during crowded events (i.e., football matches) in a stadium has been conducted, and the performances of different services and operators under realistic settings has been evaluated. As a result, several considerations are presented that can be used for better management of future networks.</p>
https://doi.org/10.3390/ s21103404
oai:zenodo.org:5760907
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cellular performance
operators
measurements
probe
crowds
Cellular Network Radio Monitoring and Management through Virtual UE Probes: A Study Case Based on Crowded Events
info:eu-repo/semantics/article
oai:zenodo.org:4710641
2021-04-23T00:27:32Z
user-ieee
user-locus-project
Hunukumbure, Mythri
Kolawole, Oluwatayo
Wu, Shangbin
Qi, Yinan
2020-08-04
<p>Drone based 5G services can be particularly attractive in emergency scenarios, with their rapid deployment and good SNR capabilities. We propose the use of multiple drones to conduct 3D indoor positioning in emergency situations in this paper. We estimate the performance metrics for such a system, when complying with 3GPP rel. 16 NR- positioning specifications. We also propose new designs for the PRS (positioning reference signal) used in the standards, when there are different SCS (sub-carrier spacing) involved. We develop a mapping function based on neural networks to map the multiple SNR from the drone base stations to the positioning error. The key finding from this work is that the common OTDOA (observed time difference of arrival) based positioning methods alone will not be able to meet the strict accuracy and reliability thresholds for this emergency use case. Adaptation and/or combination with other 3GPP and non- 3GPP positioning methods will be required in this regard.</p>
https://doi.org/10.5281/zenodo.4710641
oai:zenodo.org:4710641
eng
Zenodo
https://zenodo.org/communities/ieee
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.4710640
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Critical communications
Drone RRH
Indoor positioning
Neural networks
OTDOA positioning
Indoor 3D localization in emergency scenarios through drone based rapid 5G deployment
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:4926020
2021-06-23T12:10:06Z
user-locus-project
Khatib, Emil J.
Perles Roselló, María Jesús
Miranda-Páez, Jesús
Giralt, Victoriano
Barco, Raquel
2021-05-14
<p>The year 2020 was marked by the emergence of the COVID-19 pandemic. After months of uncontrolled spread worldwide, a clear conclusion is that controlling the mobility of the general population can slow down the propagation of the pandemic. Tracking the location of the population enables better use of mobility limitation policies and the prediction of potential hotspots, as well as improved alert services to individuals that may have been exposed to the virus. With mobility in their core functionality and a high degree of penetration of mobile devices within the general population, cellular networks are an invaluable asset for this purpose. This paper shows an overview of the possibilities offered by cellular networks for the massive tacking of the population at different levels. The major privacy concerns are also reviewed and a specific use case is shown, correlating mobility and number of cases in the province of Málaga (Spain).</p>
This article belongs to the Special Issue Human Activity Detection and Recognition
https://doi.org/10.3390/s21103424
oai:zenodo.org:4926020
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
location
cellular networks
COVID-19
pandemic
Mass Tracking in Cellular Networks for the COVID-19 Pandemic Monitoring
info:eu-repo/semantics/article
oai:zenodo.org:5645025
2021-11-29T10:00:49Z
user-locus-project
NADDEH, Nathalie
BEN JEMAA, Sana
ELAYOUBI, Salah Eddine
CHAHED, Tijani
2021-06-15
<p>Ultra-Reliable Low Latency Communications (URLLC) is a key service in fifth generation (5G) networks, that requires stringent Quality of Service (QoS) in terms of latency and reliability. As URLLC services may require specific numerology and/or specific channel access and re-transmission strategies, network slicing has been proposed as a solution for multiplexing them with other services such as enhanced Mobile Broadband (eMBB). Once the URLLC slice is configured and resources are dimensioned and allocated to it, URLLC performance targets should be attained thanks to the 5G New Radio (NR) low latency and high reliability features. However, in vehicular services such as safety message exchange, URLLC slice resource dimensioning cannot be static due to the varying number of vehicles in the cell. We show in this paper how the delay for slice reconfiguration alters the URLLC performance and propose a proactive resource reservation scheme that anticipates slice needs and allows ensuring URLLC targets. In order to reduce the impact of this proactive reservation on eMBB performance, we make use of vehicle trajectory prediction and show that limiting anticipated reservation to fewer cells allows reaching the target URLLC QoS with a limited degradation of the network capacity.</p>
https://doi.org/10.1109/VTC2021-Spring51267.2021.9448703
oai:zenodo.org:5645025
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
5G
RAN Slicing
Vehicular URLLC
Resource Reservation
Proactive RAN Resource Reservation for URLLC Vehicular Slice
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5157830
2021-08-04T13:48:21Z
user-locus-project
user-eu
Domenico Giustiniano
Giuseppe Bianchi
Andrea Conti
Stefania Bartoletti
Nicola Blefari-Melazzi
2021-08-04
<p>The COVID-19 pandemic has suddenly raised the need for technological solutions capable to trace contacts of people and provide location-based analytics. Several countries have adopted proximity-based (short- range) technologies, such as Bluetooth, which however appear hindered by deployment issues, security leakages, lack of reliability, and data governance concerns. This paper posits that 5G and beyond can play a primary role in contact tracing and group movement monitoring. Contact tracing based on 5G location-based analytics benefits from the pervasive deployment of cellular networks, the several years of effort to design cellular standards for localization and analytics, and the best practices of cellular operators to handle location data.</p>
https://doi.org/10.5281/zenodo.5157830
oai:zenodo.org:5157830
Zenodo
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5157829
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
5G and Beyond for Contact Tracing
info:eu-repo/semantics/article
oai:zenodo.org:5644849
2021-11-04T13:48:31Z
user-locus-project
Calvo-Palomino, Roberto
Bhattacharya, Arani
Bovet, Gérôme
Giustiniano, Domenico
2021-11-04
<p>GNSS/GPS is a positioning system widely used nowadays in our lives for real-time localization in Earth. This technology is highly vulnerable to spoofing/jamming attacks caused by malicious intruders. In the recent years, commodity and low-cost radio-frequency hardware have been used to interfere with the legitimate GPS signal. Existing spoofing detection solutions use costly receivers and computationally expensive algorithms which limit the large-scale deployment. In this work we propose a GNSS spoofing detection system that can run on spectrum sensors with Software-Defined Radio (SDR) capabilities and cost in the order of 20 euros. Our approach exploits the predictability of the Doppler characteristics of the received GPS signals to determine the presence of anomalies or malicious attackers. We propose an artificial recurrent neural network (RNN) based on Long short-term memory (LSTM) for anomaly detection. We use data received by low-cost SDR receivers that are processed locally by low-cost embedded machines such as Nvidia Jetson Nano to provide inference capabilities. We show that our solution predicts very accurately the Doppler shift of GNSS signals and can determine the presence of a spoofing transmitter.</p>
https://doi.org/10.5281/zenodo.5644849
oai:zenodo.org:5644849
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.5644848
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
LSTM-based GNSS Spoofing Detection Using Low-cost Spectrum Sensors
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6777821
2022-06-29T07:37:03Z
user-locus-project
Meneghello, Francesca
Garlisi, Domenico
Dal Fabbro, Nicolo`
Tinnirello, Ilenia
Rossi, Michele
2022-06-23
<p>In this article we present SHARP, an original approach for obtaining human activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi) devices. SHARP grants the possibility to discern the activities of different persons, across different time-spans and environments. To achieve this, we devise a new technique to clean and process the channel frequency response (CFR) phase of the Wi-Fi channel, obtaining an estimate of the Doppler shift at a radio monitor device. The Doppler shift reveals the presence of moving scatterers in the environment, while not being affected by (environment-specific) static objects. SHARP is trained on data collected as a person performs seven different activities in a single environment. It is then tested on different setups, to assess its performance as the person, the day and/or the environment change with respect to those considered at training time. In the worst-case scenario, it reaches an average accuracy higher than 95%, validating the effectiveness of the extracted Doppler information, used in conjunction with a learning algorithm based on a neural network, in recognizing human activities in a subject and environment independent way. The collected CFR dataset and the code are publicly available for replicability and benchmarking purposes.</p>
https://doi.org/10.1109/TMC.2022.3185681
oai:zenodo.org:6777821
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/restrictedAccess
Wi-Fi sensing
contactless indoor monitoring
human activity recognition
neural networks
CSI
CFR
IEEE 802.11ac
SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points
info:eu-repo/semantics/article
oai:zenodo.org:4073068
2020-10-15T13:29:44Z
user-locus-project
user-eu
Maurizio Rea
Domenico Giustiniano
Guillermo Bielsa
Danilo De Donno
Joerg Widmer
2020-06-17
<p>Beam training in dynamic millimeter-wave (mm-wave) networks with mobile devices is highly challenging as devices must scan a large angular domain to maintain alignment of their directional antennas under mobility. Exploiting the fact that mobile devices will typically integrate multiple chipsets, we study a set of non-mmwave input data that can be leveraged jointly to provide faster beam search and better data rate. We leverage these findings to introduce SLASH, an algorithm that adaptively narrows the sector search space and accelerates link establishment, link maintenance and handover between mm-wave devices. We evaluate SLASH both with simulations and experiments in a 60-GHz testbed. SLASH can increase the data rate by more than 64% for link establishment and 67% for link maintenance with respect to prior work.</p>
https://doi.org/10.1109/MedComNet49392.2020.9191675
oai:zenodo.org:4073068
Zenodo
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Beam Search Strategy for MillimeterWave Networks with Out-of-Band Input Data
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5017501
2021-06-23T13:48:21Z
user-locus-project
Lacruz, Jesus O.
Ruiz Ortiz, Rafael
Widmer, Joerg
2021-06-23
<p>The performance of wireless communication systems is evolving rapidly, making it difficult to build experimentation platforms that meet the hardware requirements of new standards. The bandwidth of current systems ranges from 160 MHz for IEEE 802.11ac/ax to 2 GHz for Millimeter-Wave (mm-wave) IEEE 802.11ad/ay, and they support up to 8 spatial MIMO streams. Mobile 5G and beyond systems have a similarly diverse set of requirements.</p>
<p>To address this, we propose a highly configurable wireless plat- form that meets such requirements and is both affordable and scal- able. It is implemented on a single state-of-the-art FPGA board that can be configured from 4x4 mm-wave MIMO with 2 GHz channels to 8x8 MIMO with 160 MHz channels in sub-6 GHz bands. In ad- dition, multi-band operation will play an important role in future wireless networks and our platform supports mixed configurations with simultaneous use of mm-wave and sub-6 GHz. Finally, the platform supports real-time operation, e.g., for closed-loop MIMO beam training with low-latency, by implementing suitable hard- ware/software accelerators. We demonstrate the platform’s perfor- mance in a wide range of experiments. The platform is provided as open-source to build a community to use and extend it.</p>
https://doi.org/10.1145/3458864.3466868
oai:zenodo.org:5017501
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Millimeter Wave
Testbed
FPGA
MIMO
Phased Antenna Array
Multi-band
A Real-Time Experimentation Platform for sub-6 GHz and Millimeter-Wave MIMO Systems
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5760946
2021-12-16T11:49:33Z
user-locus-project
Posluk, Maria
Ahlander, Jesper
Shrestha, Deep
Modarres Razavi, Sara
Lindmark, Gustav
Gunnarsson, Fredrik
2021-05-20
<p>Indoor positioning is currently recognized as one of the important features in emergency, commercial and industrial applications. The 5G network enhances mobility, flexibility, reliability, and security to new higher levels which greatly benefit the IoT and industrial applications. Industrial IoT (IIoT) use-cases are characterized by ambitious system requirements for positioning accuracy in many verticals. For example, on the factory floor, it is important to locate assets and moving objects such as forklifts. The deployment design for different IIoT environments has a significant impact on the positioning performance in terms of both accuracy and availability of the service. Indoor factory (InF) and indoor open office (IOO) are two available and standardized Third Generation Partnership Project (3GPP) scenarios for evaluation of indoor channel models and positioning performance in IIoT use cases. This paper aims to evaluate the positioning performance in terms of accuracy and availability while considering different deployment strategies. Our simulation-based evaluation shows that deployment plays a vital role when it comes to achieving high accuracy positioning performance. It is for example favorable to deploy the 5G Transmission and Reception Points (TRPs) on the walls of the factory halls than deploying them attached to the ceiling.</p>
https://doi.org/10.5281/zenodo.5760946
oai:zenodo.org:5760946
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.5760945
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
5G
indoor positioning
CRLB
GDOP
DL-TDOA
NLOS conditions
accuracy
availability
deployment strategies
5G Deployment Strategies for High Positioning Accuracy in Indoor Environments
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5215796
2021-10-08T13:48:33Z
user-locus-project
Guidi, Francesco
Dardari, Davide
2021-01-25
<p>Next 5G and beyond applications have attracted a tremendous interest towards systems using antenna arrays with an extremely large number of antennas where the technology conceived for communication might also be exploited for high-accuracy positioning applications. In this paper, we investigate the possibility to infer the position of a single antenna transmitter using a single asynchronous receiving node by retrieving information from the incident spherical wavefront. To this end, we consider the adoption of a suitable mix of processing at electromagnetic (EM) and signal levels, as a lower complexity alternative to classical massive array systems where the processing is done entirely at signal level. Thus, we first introduce a dedicated general model for different EM processing architectures, entailing the use or not of a lens that can have either a reconfigurable or a fixed phase profile, and successively we investigate their attainable positioning performance. The effect of the interference is also investigated to evaluate the robustness of the considered system to the presence of multiple simultaneous transmitting sources. Results, obtained for different apertures of the exploited lens/array, confirm the possibility to achieve interesting positioning performance using a single antenna array with a limited aperture.</p>
https://doi.org/10.1109/TWC.2021.3052053
oai:zenodo.org:5215796
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Spherical wavefront
near-field
holographic positioning
massive array
lens array
mm-wave
Radio Positioning With EM Processing of the Spherical Wavefront
info:eu-repo/semantics/article
oai:zenodo.org:5734194
2021-12-16T11:49:35Z
user-locus-project
Bartoletti, Stefania
Bianchi, Giuseppe
Orlando, Danilo
Palamà, Ivan
Blefari-Melazzi, Nicola
2021-08-17
<p>Most localization systems rely on measurements gathered from signals emitted by stations whose position is assumed known as ground truth, namely anchors. As demonstrated by a significant bulk of experimental research, location security is threatened when an attacker becomes able to tamper either the signals emitted by the stations, or convince the user that the anchor station is in a different position than the true one. With this paper, we first propose a for- mal threat model which captures the above-mentioned wide class of attacks, and permits to quantitatively evaluate how tampering of one or more anchor locations undermines the user’s localization accuracy. We specifically derive a Cramér Rao Bound for the local- ization error, and we assess a number of example scenarios. We believe that our study may provide a useful formal benchmark for the design and analysis of detection and mitigation solutions.</p>
https://doi.org/10.1145/3465481.3470098
oai:zenodo.org:5734194
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Location security
spoofing
tampering
localization
Cramér Rao bound
Location Security under Reference Signals' Spoofing Attacks: Threat Model and Bounds
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5602888
2021-11-04T10:54:20Z
user-locus-project
Rea, Maurizio
Giustiniano, Domenico
Jimenéz Mateo, Pablo
Lizarribar, Yago
Widmer, Joerg
2021-08-04
<p>Beam training in dynamic millimeter-wave (mm-wave) networks with mobile devices is highly challenging as devices must scan a large angular domain to maintain alignment of their directional beams under mobility. In this work, we exploit the trend of multiple chipsets integrated in the same mobile device to study a set of non-mmwave input data that can be leveraged jointly to provide faster beam search and better data rate. We leverage these findings to introduce SLASH, an algorithm that adaptively narrows the sector search space and accelerates link establishment, link maintenance and handover between mm-wave devices. We experimentally evaluate SLASH with commodity hardware, including a 60 GHz testbed, commercial sub-6 GHz WiFi APs and smartphones. SLASH can increase the median data rate by more than 22% for link establishment and 25% for link maintenance with respect to prior work.</p>
https://doi.org/10.5281/zenodo.5602888
oai:zenodo.org:5602888
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.5602887
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Beam Searching for mmWave Networks with sub-6 GHz WiFi and Inertial Sensors Inputs: an experimental study
info:eu-repo/semantics/article
oai:zenodo.org:5760994
2021-12-16T11:49:31Z
user-locus-project
Álvarez-Merino, Carlos S.
Luo-Chen, Hao Qiang
Jatib Khatib, Emil
Barco, Raquel
2021-10-23
<p>High-precision indoor localisation is becoming a necessity with novel location-based ser- vices that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the oppor- tunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.</p>
https://doi.org/10.5281/zenodo.5760994
oai:zenodo.org:5760994
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.5760993
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
indoor positioning
fusion technologies
UWB
WiFi fine time measurement
LTE
maximum likelihood estimator
WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
info:eu-repo/semantics/article
oai:zenodo.org:4017435
2020-10-08T10:36:24Z
user-locus-project
user-eu
Linjie Yan
Pia Addabbo
Yuxuan Zhang
Chengpeng Hao
Jun Liu
Jian Li
Danilo Orlando
2020-04-22
<p>In this paper, we address the problem of detecting multiple Noise-Like Jammers (NLJs) through a radar system equipped with an array of sensors. To this end, we develop an elegant and systematic framework wherein two architectures are devised to jointly detect an unknown number of NLJs and to estimate their respective angles of arrival. The followed approach relies on the likelihood ratio test in conjunction with a cyclic estimation procedure which incorporates at the design stage a sparsity promoting prior. As a matter of fact, the problem at hand owns an inherent sparse nature which is suitably exploited. This methodological choice is dictated by the fact that, from a mathematical point of view, classical maximum likelihood approach leads to intractable optimization problems (at least to the best of authors' knowledge) and, hence, a suboptimum approach represents a viable means to solve them. Performance analysis is conducted on simulated data and shows the effectiveness of the proposed architectures in drawing a reliable picture of the electromagnetic threats illuminating the radar system.</p>
https://doi.org/10.1109/TAES.2020.2988960
oai:zenodo.org:4017435
Zenodo
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
A Sparse Learning Approach to the Detection of Multiple Noise-Like Jammers
info:eu-repo/semantics/article
oai:zenodo.org:4073084
2020-10-16T00:26:51Z
user-locus-project
user-eu
Zehao Yu
Zhenyu Liu
Florian Meyer
Andrea Conti
Moe Z. Win
2020-06-08
<p>Location-awareness using wireless signals is a key enabler for numerous emerging applications. Inspired by the recently proposed soft information (SI)-based localization, this paper develops a localization algorithm based on estimates of the channel impulse response (CIR), which inherently contains position information. We propose a delay-origin uncertainty model for describing the conditional distribution of the delays in CIR given node positions. A scalable localization algorithm is designed using belief propagation (BP) on a factor graph that incorporates the uncertainty model. The performance of the developed algorithm is quantified for mmWave signals using QuaDriGa channel simulator, showing decimeter-level localization accuracy in typical indoor environments.</p>
https://doi.org/10.1109/PLANS46316.2020.9110161
oai:zenodo.org:4073084
Zenodo
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
info:eu-repo/semantics/restrictedAccess
Localization Based on Channel Impulse Response Estimates
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:5644925
2021-11-04T13:48:31Z
user-locus-project
Mach, Tomasz
Tsoukaneri, Galini
Warren, Daniel
2021-08-06
<p>With mobile networks expected to support services with stringent reliability, availability, latency and throughput metrics, resulting in a more complex concept of Quality of Service (QoS), the ability to predict QoS variation and adapt the flow of traffic accordingly has become a critical requirement for some use cases. For example Connected and Automated Mobility applications could use QoS prediction to reduce the speed of an autonomous vehicle if network performance is going to deteriorate, and critical information needs to be conveyed. Current state-of-the-art approaches on QoS prediction are mostly focused on core network (CN), which is complex and suboptimal in some scenarios. In this paper, we introduce the concept of UE-based QoS prediction, discuss its motivation, and propose novel lightweight device-to-device (D2D)-based coverage prediction framework in RAN, based on a generalized D2D use case, applicable to multiple industries. We discuss how the proposed mechanism may be complementary to the CN-based prediction, analyse its performance and provide simulation results of the proposed framework to showcase its advantages. Finally, we study how the D2D prediction information could be used to trigger 5G RAN protocol adaptations, such as predictive data scheduling in MAC.</p>
https://doi.org/10.1109/ICC42927.2021.9500300
oai:zenodo.org:5644925
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
V2X
Predictive QoS,
D2D
B5G
RAN
RRC
MAC
D2D-based QoS prediction analysis in beyond 5G V2X
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6322777
2022-03-02T13:48:57Z
user-locus-project
Garcia, Dolores
Lacruz, Jesus O.
Jiménez Mateo, Pablo
Palacios, Joan
Ruiz, Rafael
Widmer, Joerg
2022-03-02
<p>mm-wave communications use analog beamforming techniques, which steer the signal energy in a desired direction, to overcome the high path-loss at such frequencies. To determine the direction in which to steer, mm-wave standards such as IEEE 802.11ad specify beam training mechanisms for both access points as well as client stations. However, the overhead of the beam training limits scalability as the density of network deployments increases and mobile devices that require constant re-training are supported. We design SPIDER, a low-overhead beam-training mechanism where only access points actively participate in the training and stations perform passive compressive estimation of the angle-of-arrival. To this end, stations carry out phase-coherent measurements by switching through multiple receive beam patterns on a time-scale of tens of nanoseconds when receiving a packet preamble. Since no suitable testbed platforms exist that support such fast antenna reconfiguration, we design a high-performance, full-bandwidth FPGA-based testbed platform for flexible mm-wave experimentation, that we make available as open source. The performance analysis with this testbed shows that our algorithm achieves highly accurate angle estimation used to drive the beam steering decisions and reduces overhead by an order of magnitude compared to IEEE 802.11ad beam training.</p>
https://doi.org/10.5281/zenodo.6322777
oai:zenodo.org:6322777
eng
Zenodo
https://zenodo.org/communities/locus-project
https://doi.org/10.5281/zenodo.6322776
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Beamtraining
Compressive Sensing
FPGA
IEEE 802.11ad
Phase-Coherent
Phased Antenna Array
Scalable Phase-Coherent Beam-Training for Dense Millimeter-wave Networks
info:eu-repo/semantics/article
oai:zenodo.org:5012088
2022-09-06T12:32:32Z
user-locus-project
Rea, Maurizio
Giustiniano, Domenico
2021-06-09
<p>The advent of the fourth Industrial Revolution (Industry 4.0) requires wireless networked solutions to connect machines. Flexibility needed by production in Industry 4.0 and deep automation of machine operations requires the massive introduction of automated transport systems. However, the industrial environment is notorious for being averse to wireless communication, with traditional wireless resource mechanisms prone to errors. In this work, we propose to exploit the knowledge of location to derive context information to dynamically allocate wireless resources in time and space to target devices. We exploit the spatial geometry of the Access Points (APs) and a statistical model that maps the user position's spatial distribution to an angle error distribution to derive a hypothesis test to declare if the link is under metallic blockage or not. In order to avoid changes to the client-side and operate with a single interface radio, we use the same wireless network both for positioning and scheduling. We experimentally show that our system can localize four mobile robots deployed in a very harsh environment with metal obstacles and reflections. Context information applied to wireless resources protocol help increasing up to 40% the network throughput in the above industrial-like scenario.</p>
https://doi.org/10.1109/TMC.2021.3088058
oai:zenodo.org:5012088
eng
Zenodo
https://zenodo.org/communities/locus-project
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Indoor localization system
wireless communication
industrial environment
context information
wireless protocol
Location-aware Wireless Resource Allocation in Industrial-like Environment
info:eu-repo/semantics/article
oai:zenodo.org:4073029
2020-10-08T12:26:53Z
user-locus-project
user-eu
JUAN L. BEJARANO-LUQUE
MATÍAS TORIL
MARIANO FERNÁNDEZ-NAVARRO
ANTONIO J. GARCÍA
SALVADOR LUNA-RAMÍREZ
2020-06-01
<p> In mobile networks, detecting and eliminating areas with poor performance is key to optimize</p>
<p>end-user experience. In spite of the vast set of measurements provided by current mobile networks, cellular</p>
<p>operators have problems to pinpoint problematic locations because the origin of such measurements (i.e., user</p>
<p>location) is not registered in most cases. At the same time, social networks generate a huge amount of data that</p>
<p>can be used to infer population density. In this paper, a data-driven methodology is proposed to detect the best</p>
<p>sites for new small cells to improve network performance based on attributes of connections, such as radio</p>
<p>link throughput or data volume, in the radio interface. Unlike state-of-the-art approaches, based on data from</p>
<p>only one source (e.g., radio signal level measurements or social media), the proposed method combines data</p>
<p>from radio connection traces stored in the network management system and geolocated posts from social</p>
<p>networks. This information is enriched with user context information inferred from trafc attributes. The</p>
<p>method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database</p>
<p>of geotagged messages from two social networks (Twitter and Flickr).</p>
https://doi.org/10.1109/ACCESS.2020.2998918
oai:zenodo.org:4073029
Zenodo
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
A Context-Aware Data-Driven Algorithm for Small Cell Site Selection in Cellular Networks
info:eu-repo/semantics/article
oai:zenodo.org:4072930
2020-10-08T12:26:52Z
user-locus-project
user-eu
Gürkan Solmaz
Jonathan Fürst
Samet Aytaç
Fang-Jing Wu
2020-07-15
<p>This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.</p>
https://doi.org/10.1109/IPSN48710.2020.00-38
oai:zenodo.org:4072930
Zenodo
https://zenodo.org/communities/locus-project
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Group-In: Group Inference from Wireless Traces of Mobile Devices
info:eu-repo/semantics/conferencePaper