Luca Davoli

@inproceedings{olmadafestmacano:2023:ew,

title = {Simulation-based Analysis of Experimental 5G NR Downlink CSI-RSRP-based Handover Performance},

author = {Eleonora Oliosi and Sunil Mathew and Luca Davoli and Gianluigi Ferrari and Nicolo Strozzi and Filippo Manghi and Fabio Casoli and Andrea Notari},

url = {https://ieeexplore.ieee.org/document/10477096},

year  = {2024},

date = {2024-03-20},

urldate = {2024-03-20},

booktitle = {28th European Wireless Conference (European Wireless 2023)},

pages = {8-13},

address = {Rome, Italy},

abstract = {In the last years, 5G New Radio (NR), introduced in 2019 in the 3rd Generation Partnership Project (3GPP) Release 15, has become the global radio standard for 5G networks. The increasing number of available 5G gNodeBs (gNBs) makes handover management crucial, especially from the point of view of the Quality of Experience (QoE) of a User Equipment (UE). In order to analyse the handover behaviour in 5G networks, we derive a Matlab-based simulator for location-based handover management. As a first step, an accurate simulation model of an experimental 5G NR communication system is derived. In detail, the performance results are expressed in terms of Reference Signal Received Power (RSRP) 5G NR signal quality metric, measured on the Channel State Information (CSI) Reference Signal (RS) on a DownLink (DL) transmission from a gNB to a mobile UE. In order to derive an accurate simulator, we rely on an experimental testbed based on the use of a commercial wireless 5G embedded modem positioned in a moving vehicle and collecting geolocalized RSRP values.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bedafe:2024:iiwot,

title = {A Cloud-Oriented Indoor-Outdoor Real-Time Localization IoT Architecture for Industrial Environments},

author = {Laura Belli and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/CCNC51664.2024.10454636},

issn = {2331-9860},

year  = {2024},

date = {2024-03-18},

urldate = {2024-03-18},

booktitle = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)},

pages = {1-6},

address = {Las Vegas, NV, USA},

abstract = {Localization services for precise and continuous monitoring of the locations of both humans and vehicles in industrial environments are among the most relevant applications in Industrial Internet of Things (IIoT) contexts, to maximize safety and optimize operational activities. Unfortunately, localization in industrial scenarios is particularly challenging because targets can generally move freely in both indoor and outdoor areas. In this paper, we propose a localization monitoring architecture based on a prototypical wearable IoT device equipped with Ultra-Wide Band (UWB), inertial, and GNSS/RTK technologies for seamless localization in heterogeneous environments. We focus on a Web of Things (WoT) approach, verifying suitability and limitations in a real use case scenario. Our approach shows that the proposed architecture can effectively enhance the safety of workers, detecting potentially dangerous events and triggering alarms (e.g., via smart buzzers or gas concentration warning devices) based on a cloud WoT architecture.},

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pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dabefeloaz:2024:iiwot,

title = {An Edge Computing-Oriented WoT Architecture for Air Quality Monitoring in Mobile Vehicular Scenarios},

author = {Luca Davoli and Laura Belli and Gianluigi Ferrari and Elisa Londero and Paolo Azzoni},

doi = {10.1109/CCNC51664.2024.10454646},

issn = {2331-9860},

year  = {2024},

date = {2024-03-18},

urldate = {2024-03-18},

booktitle = {2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)},

pages = {1-7},

address = {Las Vegas, NV, USA},

abstract = {Nowadays, the need to efficiently process information in Internet of Things (IoT)-oriented heterogeneous scenarios has increased significantly, e.g., in all scenarios where unobtrusive environmental monitoring is beneficial for the involved people (e.g., inside public transport vehicles, indoor workplaces and offices, large public infrastructures, etc.). This objective typically requires the combination of heterogeneous IoT systems, which need to efficiently share information, e.g., through the Web of Things (WoT) paradigm. In this paper, we propose an edge computing-oriented flexible WoT architecture, with distributed intelligence, for air quality monitoring and prediction inside a public transport bus. Our results show that the proposed architecture allows seamless integration of heterogeneous IoT systems according to a WoT perspective, exploiting the device/edge/fog computing continuum and using containerized and secure processing modules.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{maoldafestno:2023:icrvitm,

title = {On the Reduction of Unnecessary Handovers Using 5G Small Cells in a 5G NR Environment},

author = {Sunil Mathew and Eleonora Oliosi and Luca Davoli and Gianluigi Ferrari and Nicolò Strozzi and Andrea Notari},

doi = {10.1109/IC-RVITM60032.2023.10435126},

year  = {2024},

date = {2024-02-21},

urldate = {2023-01-01},

booktitle = {2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM)},

pages = {1-6},

address = {Bangalore, India},

abstract = {Nowadays, the deployment of 5G Non-StandAlone (NSA) networks has led to significant enhancements in the User Equipment (UE) experience, in particular in terms of latency reduction and throughput increase. To this end, 5G gNodeBs (gNBs) offer broad coverage but may face challenges in areas with low signal strength, resulting in a Quality of Service (QoS) degradation and too short connections to distant gNBs (denoted as Unnecessary HandOvers, UHOs) due to geographic peculiarities. In order to tackle these issues, in this paper the use of cost-effective 5G small cells in critical areas is considered, aiming at (i) boosting coverage, (ii) enhancing UE QoS, and (iii) avoiding UHOs, thus improving HO performance and UE QoS.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stpadani:23:icar,

title = {Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics},

author = {Nikolaos Stathoulopoulos and Emanuele Pagliari and Luca Davoli and George Nikolakopoulos},

doi = {10.1109/ICAR58858.2023.10406402},

issn = {2572-6919},

year  = {2024},

date = {2024-02-01},

urldate = {2023-12-01},

booktitle = {2023 21st International Conference on Advanced Robotics (ICAR)},

pages = {121-127},

address = {Abu Dhabi, United Arab Emirates},

abstract = {This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi finger-printing in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system's overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{madapafe:2023:iotais,

title = {Accurate Classification of Sport Activities Under Tiny Deployability Constraints},

author = {Armin Mazinani and Luca Davoli and Danilo Pietro Pau and Gianluigi Ferrari},

doi = {10.1109/IoTaIS60147.2023.10346056},

issn = {2832-1383},

year  = {2023},

date = {2023-12-14},

urldate = {2023-01-01},

booktitle = {2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)},

pages = {261-267},

address = {Bali, Indonesia},

abstract = {Human Activity Recognition (HAR) plays a prominent role in various domains, such as healthcare, surveillance, and sports. In this paper, our goal is to identify the most accurate Deep Learning (DL) algorithm under tiny deployability constraints. Our results show that a Recurrent Neural Network (RNN) given by the combination of a one-dimensional Convolutional Neural Network (ID-CNN) with Bi-directional Gated Recurrent Unit (Bi-GRU) is the most attractive solution, with respect to Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the recently introduced Legendre Memory Unit (LMU). The algorithm performance is investigated over a publicly available dataset consisting of 19 different daily activities. According to the obtained results, 1D-CNN-BiGRU has an average accuracy within 0.2% of that of BiGRU (the RNN with highest accuracy) with an execution time more than 4 times shorter.},

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pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{madafe:2023:brains,

title = {Deep Learning-Based Cryptocurrency Price Prediction: A Comparative Analysis},

author = {Armin Mazinani and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/BRAINS59668.2023.10317011},

year  = {2023},

date = {2023-11-17},

urldate = {2023-01-01},

booktitle = {2023 5th Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS)},

pages = {1-8},

address = {Paris, France},

abstract = {In recent years, cryptocurrencies have gained a lot of popularity in the financial markets and now, in addition to investing on them, it is possible to use them as a common currency to meet daily needs. Given the complex nature of financial markets and their reliance on different parameters to determine stocks' and assets' prices, the ability to predict prices is important for investment decisions, especially with respect to cryptocurrencies. To this end, Deep Learning (DL)-based algorithms can be viable solutions, owing to their use as time series forecasting tools. In this paper, we investigate the applicability of DL algorithms to forecast the prices of three cryptocurrencies, namely Bitcoin, Ethereum, and Ripple. We evaluate the performance of the proposed approach, in terms of short-term and long-term prediction accuracy (considering proper error metrics).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokeyd,

title = {Simulation-based Analysis of Experimental 5G NR Downlink CSI-RSRP-based Handover Performance},

author = {Eleonora Oliosi and Sunil Mathew and Luca Davoli and Nicolò Strozzi and Filippo Manghi and Fabio Casoli and Andrea Notari and Gianluigi Ferrari},

year  = {2023},

date = {2023-10-02},

urldate = {2023-10-02},

booktitle = {Proceedings of the 28th European Wireless},

address = {Rome, Italy},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{madapafe:2023:icitri,

title = {Air Quality Estimation with Embedded AI-Based Prediction Algorithms},

author = {Armin Mazinani and Luca Davoli and Danilo Pietro Pau and Gianluigi Ferrari},

doi = {10.1109/ICITRI59340.2023.10249864},

year  = {2023},

date = {2023-09-19},

urldate = {2023-09-19},

booktitle = {2023 International Conference on Information Technology Research and Innovation (ICITRI)},

pages = {87-92},

address = {Jakarta, Indonesia},

abstract = {Air pollution is one of the main criticalities in cities with large populations. Therefore, accurate air quality prediction is crucial to control the environmental pollution and to maintain healthy living conditions for the citizens. To this end, particulate matters (e.g., PM2.5) have been recognised as one of the most important pollutants with a detrimental impact on human health. In this paper, we investigate the trade-off between estimation accuracy and computational complexity of Machine Learning (ML) and Deep Learning (DL) algorithms in predicting air pollution (in terms of PM2.5 concentration), in order to investigate their applicability to Internet of Things (IoT)-oriented applications. Six DL methods are implemented and evaluated, considering various time lags. DL approaches are shown to outperform ML approaches—in the DL case, two distinct optimizers, namely ADAM and Root Mean Squared Propagation (RMSProp), are considered. Among all algorithms evaluated, GRU had a RMSE of 20.02, while SimpleRNN reduced the MACs number by 98.90% over GRU and with an accuracy drop of 7.5%.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stprbobedafevi:2023:foodops,

title = {Selection of 4.0 sensors for small holders: the compromise between the advantages and the costs of the implementation},

author = {Roberta Stefanini and Luca Preite and Eleonora Bottani and Laura Belli and Luca Davoli and Gianluigi Ferrari and Giuseppe Vignali},

doi = {10.46354/i3m.2023.foodops.007},

year  = {2023},

date = {2023-09-19},

urldate = {2023-01-01},

booktitle = {Proceedings of the 9th International Food Operations and Processing Simulation Workshop (FoodOPS 2023)},

pages = {1-7},

address = {Athens, Greece},

abstract = {The agricultural sector involves various environmental impacts, related to soil exploitation, water consumption and greenhouse gasses emissions. The advent of 4.0 technologies could help reduce them, e.g., by using sensors that constantly control the field. However, these solutions are often implemented by big producers that can easily bear their costs. Thence, in the case of small holders, can the benefits achievable with 4.0 technologies justify their implementation costs? To answer this question, an Italian field with three rows of tomatoes has been investigated as a case study. A row with a traditional irrigation system has been compared to two rows with a 60% irrigation scenario monitored with 4.0 sensors. Overall, one environmental sensor, three crop analysis sensors, three flowmeters, three valves and one network infrastructure have been selected and introduced. The key findings of the work allow for quantifying the amount of water that small holders can save; the positive Net Present Value recommends the investment, with a Pay Back Period of 1.9 years. In the next steps, additional 4.0 sensors will be tested in the agricultural supply chain of some selected small holders in the Mediterranean area, to check whether the 4.0 implementation could not only reduce water consumption, but also improve storage conditions and reduce wastage.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{madabefera:2023:aeit,

title = {Thermal Camera-based Driver Monitoring in the Automotive Scenario},

author = {Veronica Mattioli and Luca Davoli and Laura Belli and Gianluigi Ferrari and Riccardo Raheli},

doi = {10.23919/AEITAUTOMOTIVE58986.2023.10217235},

year  = {2023},

date = {2023-08-23},

urldate = {2023-08-23},

booktitle = {2023 AEIT International Conference on Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)},

pages = {1-6},

address = {Modena, Italy},

abstract = {Driver monitoring in the automotive scenario is a key task in preventing hazardous events, such as road accidents. To this purpose, driver monitoring systems aim at providing prompt assistance in case of anomalies, such as alterations of the driver’s psycho-physiological status. The driving performance may, indeed, be negatively affected by high levels of stress and workload, that might be caused by different factors.In this paper, a novel in-vehicle monitoring system to extract the skin temperature of a driver, as one of the main indicators of his/her psycho-physiological status, is proposed. Thermal imaging techniques and video processing algorithms are jointly employed to record and extract temperature-related information in a non-invasive and contactless way. Temperature variations detected on the subject’s face are regulated by the activity of the Autonomic Nervous System (ANS) and represent, indeed, an important index of perceived stress levels. The feasibility of the proposed method is assessed both in simulated and real driving scenarios on the basis of experimental results.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{padacife:2020:isaect,

title = {A Modular Multi-interface Gateway for Heterogeneous IoT Networking},

author = {Emanuele Pagliari and Luca Davoli and Antonio Cilfone and Gianluigi Ferrari},

doi = {10.1109/ISAECT50560.2020.9523689},

year  = {2021},

date = {2021-09-01},

urldate = {2021-09-01},

booktitle = {2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT)},

pages = {1-6},

address = {Marrakech, Morocco},

abstract = {The massive deployment of Internet of Things (IoT) architectures and applications, in many fields over the last decade, has accelerated research efforts on low-power wireless connectivity protocols. Moreover, many standards have been introduced, highlighting the need to make data flow among different communication protocols and network interfaces feasible. To this end, devices like Gateways (GWs) play a crucial role in many IoT applications and will impact future developments and possibilities. In this paper, the design and deployment of a new modular and scalable GW architecture solution, suitable for a wide plethora of use case scenarios and useful as a starting point for many possible improvements and applications, is proposed. Experimental performance results are discussed, showing the roles of different interfaces in specific use cases in which the proposed this solution may be applied.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cocidafe:2020:metroagrifor,

title = {AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting},

author = {Gaia Codeluppi and Antonio Cilfone and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/MetroAgriFor50201.2020.9277553},

year  = {2020},

date = {2020-12-08},

booktitle = {2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},

pages = {348--353},

address = {Trento, Italy},

abstract = {Controlling and forecasting environmental variables (e.g., air temperature) is usually a key and complex part in a greenhouse management architecture. Indeed, a greenhouse inner micro-climate, which is the result of an extensive set of inter-related environmental variables influenced by external weather conditions, has to be tightly monitored, regulated, and, some-times, forecast. Nowadays, Wireless Sensor Networks (WSNs) and Machine Learning (ML) are two of the most successful technologies to deal with this challenge. In this paper, we discuss how a Smart Gateway (GW), acting as a collector for sensor data coming from a WSN installed in a greenhouse, could be enriched with a Neural Network (NN)-based prediction model allowing to forecast a greenhouse’s inner air temperature. In the case of missing sensor data coming from the WSN, the proposed prediction algorithm, fed with meteorological open data (gathered from the DarkSky repository), is run on the GW in order to predict the missing values. Despite the model is especially designed to be lightweight and executable by a device with constrained capabilities, it can be adopted either at Cloud or at GW level to forecast future air temperature’s values, in order to support the management of a greenhouse. Experimental results show that the NN-based prediction algorithm can forecast greenhouse air temperature with a Root Mean Square Error (RMSE) of 1.50°C, a Mean Absolute Percentage Error (MAPE) of 4.91%, and a R2 score of 0.965.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{klkrda:2020,

title = {LoRaWAN with HSM as a Security Improvement for Agriculture Applications},

author = {Reinhard Kloibhofer and Erwin Kristen and Luca Davoli},

editor = {António Casimiro and Frank Ortmeier and Erwin Schoitsch and Friedemann Bitsch and Pedro Ferreira},

doi = {10.1007/978-3-030-55583-2_13},

isbn = {978-3-030-55583-2},

year  = {2020},

date = {2020-09-08},

booktitle = {Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops},

volume = {12235},

pages = {176-188},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The digital future in agriculture has started a long time ago, with Smart Farming and Agriculture 4.0 being synonyms that describe the change in this domain. Digitalization stands for the needed technology to realize the transformation from conventional to modern agriculture. The continuously monitoring of all environmental data and the recording of all work parameters enables data collections, which are used for precise decision making and the planning of in-time missions. To guarantee secure and genuine data, appropriate data security measures must be provided.



This paper will present a research work in the EU AFarCloud project. It introduces the important LoRaWAN data communication technology for the transmission of sensor data and to present a concept for improving data security and protection of sensor nodes. Data and device protection are becoming increasingly important, particularly around LoRaWAN applications in agriculture.



In the first part, a general assessment of the security situation in modern agriculture, data encryption methods, and the LoRaWAN data communication technology, will be presented.



Then, the paper explains the security improvement concept by using a Hardware Secure Module (HSM), which not only improves the data security but also prevents device manipulations. A real system implementation (Security Evaluation Demonstrator, SED) helps to validate the correctness and the correct function of the advanced security improvement.



Finally, an outlook on necessary future works declares what should be done in order to make the digital agriculture safe and secure in the same extent as Industrial Control Systems (ICSs) will be today.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cidafe:2019:isaect,

title = {Virtualizing LoRaWAN Nodes: a CoAP-based Approach},

author = { Antonio Cilfone and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/ISAECT47714.2019.9069691},

year  = {2019},

date = {2019-11-29},

booktitle = {2019 International Symposium on Advanced Electrical and Communication Technologies (ISAECT)},

pages = {1-6},

publisher = {IEEE},

abstract = {In the near future, Internet of Things (IoT) will play a relevant role in people’s lives and one of the main challenges will be the integration of heterogeneous networks. Among widely adopted network technologies, the development of the so-called Low-Power Wide-Area Networks (LPWANs), in particular Long Range WAN (LoRaWAN) is attracting a significant interest from both academic and industrial worlds. The integration of LoRaWAN with other communication technologies represents a fundamental requirement for a successful rapid and large-scale diffusion of IoT paradigms, such as Smart Farming, Smart Factory, and Smart City. The aim of this paper is two-fold: we propose (i) a mechanism for automatic discovery of the sensors a LoRa device is equipped with; and (ii) a novel networking architecture, based on cloud computing and node virtualization, to enable the interaction of LoRaWAN end-nodes with other IP-based IoT devices. Our solution does not impact LoRaWAN networking and enables a seamless interaction between LoRaWAN end-nodes and other Constrained Application Protocol (CoAP)-based nodes.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cocidafe:2019:metroagrifor,

title = {VegIoT Garden: a modular IoT Management Platform for Urban Vegetable Gardens},

author = {Gaia Codeluppi and Antonio Cilfone and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/MetroAgriFor.2019.8909228},

isbn = {978-1-7281-3611-0},

year  = {2019},

date = {2019-11-21},

booktitle = {2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},

pages = {121-126},

publisher = {IEEE},

address = {Portici, Italy},

abstract = {Nowadays, the agricultural sector is facing challenges especially because of an extensive range of grueling trends. In this context, new highly technological applications—such as Internet of Things (IoT), Precision Agriculture (PA), and blockchain—are enabling Smart Agriculture (SA), which holds the promise to support future needs. In this extended abstract, a low-cost, modular, and energy-efficient IoT platform for SA, denoted as VegIoT Garden, based on Commercial-Off-The-Shelf (COTS) devices, adopting short- and long-range communication protocols (IEEE 802.11 and LoRa), and aiming at enhancing the management of vegetable gardens through the collection, monitoring, and analysis of sensor data, related to relevant parameters of growing plants (i.e., air and soil humidity and temperature), is presented. The infrastructure is completed with an Internet-enabled Home Node (HN) and an iOS-based mobile App, developed in order to simplify data visualization and plants’ status monitoring. The proposed IoT system has been validated in a real scenario (a vegetable garden) for more than a week: the collected data highlighted possible causes for a disease contracted by vegetables (namely, tomato’s blossom-end root), thus validating VegIoT Garden.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cadamaciyuwafe:2019:iswcs,

title = {RSSI-based Methods for LOS/NLOS Channel Identification in Indoor Scenarios},

author = {Fabrizio Carpi and Luca Davoli and Marco Martalò and Antonio Cilfone and Yingjie Yu and Yi Wang and Gianluigi Ferrari},

doi = {10.1109/ISWCS.2019.8877315},

isbn = {978-1-7281-2527-5},

year  = {2019},

date = {2019-10-22},

booktitle = {2019 16th International Symposium on Wireless Communication Systems (ISWCS)},

pages = {171-175},

publisher = {IEEE},

address = {Oulu, Finland},

abstract = {In this paper, we investigate classification methods aiming at identifying the Line-Of-Sight (LOS) or Non-LOS (NLOS) condition of a wireless channel. Our approach is based on the computation of statistical features over N consecutive channel measurements at the receiver (namely, N Received Signal Strength Indicator, RSSI, values). First, threshold classification criteria, on the considered features, are derived in order to perform LOS/NLOS identification. The thresholds’ values are tuned according to the "behaviour" of the statistical features in the considered environment. This method is compared to a sample-based (whose aim is to detect the data distribution) and a machine learning-based approaches. Although our approach is general, we present experimental results for IEEE 802.11 indoor channels. Our results show that simple threshold-based classification criteria on the considered statistical features may yield approximately 85÷90% LOS/NLOS classification accuracy, making them an attractive strategy for future 5G systems.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}