@inproceedings {42705,
	title = {Coverage and Performance Analysis of 5G Non-Standalone Deployments},
	journal = {Proceedings of the 16th ACM Workshop on Wireless Network Testbeds, Experimental evaluation \& CHaracterization},
	year = {2022},
	pages = {61-68},
	publisher = {ACM},
	abstract = {Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry. In this paper, we present the first large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two Mobile Network Operators (MNOs) in Rome, Italy, to first, study network deployment and radio cov- erage aspects, and second, explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). Results show that 5G NSA can provide higher downlink throughput and slightly lower latency compared to Fourth Generation (4G) systems. However, performance is infuenced by several factors, including propagation conditions, system configurations, and handover events, ultimately highlighting the need for further system optimization.},
	author = {Kousias, Konstantinos and Rajiullah, Mohammad and Caso, Giuseppe and Alay, {\"O}zg{\"u} and Brunstr{\"o}m, Anna and De Nardis, Luca and Neri, Marco and Ali, Usman and Di Benedetto, Maria-Gabriella}
}
@article {42702,
	title = {Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks},
	journal = {Future Internet},
	volume = {14},
	year = {2022},
	pages = {239},
	publisher = {Multidisciplinary Digital Publishing Institute},
	abstract = {The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3GPP). In the past, channel characterization for 5G has been addressed mainly based on the measurements performed on dedicated links in experimental setups. This paper presents a state-of-the-art contribution to the characterization of the outdoor-to-indoor radio channel in the 3.5 GHz band, based on experimental data for commercial, deployed 5G networks, collected during a large scale measurement campaign carried out in the city of Rome, Italy. The analysis presented in this work focuses on downlink, outdoor-to-indoor propagation for two operators adopting two different beamforming strategies, single wide-beam and multiple synchronization signal blocks (SSB) based beamforming; it is indeed the first contribution studying the impact of beamforming strategy in real 5G networks. The time and power-related channel characteristics, i.e., mean excess delay and Root Mean Square (RMS) delay spread, path loss, and K-factor are studied for the two operators in multiple measurement locations. The analysis of time and power-related parameters is supported and extended by a correlation analysis between each pair of parameters. The results show that beamforming strategy has a marked impact on propagation. A single wide-beam transmission leads, in fact, to lower RMS delay spread and lower mean excess delay compared to a multiple SSB-based transmission strategy. In addition, the single wide-beam transmission system is characterized by a smaller path loss and a higher K-factor, suggesting that the adoption of a multiple SSB-based transmission strategy may have a negative impact on downlink performance.},
	author = {Ali, Usman and Caso, Giuseppe and De Nardis, Luca and Kousias, Konstantinos and Rajiullah, Mohammad and Alay, {\"O}zg{\"u} and Neri, Marco and Brunstr{\"o}m, Anna and Di Benedetto, Maria-Gabriella}
}
@inproceedings {42704,
	title = {Implications of handover events in commercial 5G non-standalone deployments in Rome},
	journal = {ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases},
	year = {2022},
	pages = {22-27},
	publisher = {ACM},
	abstract = {Fifth Generation (5G) cellular networks are gaining popularity and are soon expected to become the norm in the global telecommunications industry. In this paper, we follow an experimental approach to study key aspects of Handover (HO) events in commercial 5G Non Standalone (NSA) deployments. We collect our dataset by conducting a large-scale measurement campaign in the city of Rome, Italy. With our analysis, we aim to first, empirically evaluate the current HO strategies followed in 5G NSA, and second, to show the impact of HOs on end-user application performance. Our results show an increased number of intra-RAT HOs, i.e., HOs between cells with the same Radio Access Technology (RAT), in situation where signal quality conditions between multiple candidate cells is on par. In addition, we show that inter-RAT HOs, i.e., HOs between cells with different RATs, can be detrimental to the application performance.},
	doi = {10.1145/3538394.3546041},
	url = {https://doi.org/10.1145/3538394.3546041},
	author = {Kousias, Konstantinos and Rajiullah, Mohammad and Caso, Giuseppe and Alay, {\"O}zg{\"u} and Brunstr{\"o}m, Anna and De Nardis, Luca and Neri, Marco and Ali, Usman and Di Benedetto, Maria-Gabriella}
}
@article {42703,
	title = {Large-Scale Dataset for the Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks},
	journal = {Data},
	volume = {7},
	year = {2022},
	pages = {34},
	publisher = {MDPI},
	abstract = {Understanding radio propagation characteristics and developing channel models is fundamental to building and operating wireless communication systems. Among others uses, channel characterization and modeling can be used for coverage and performance analysis and prediction. Within this context, this paper describes a comprehensive dataset of channel measurements performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. Previous efforts to analyze outdoor-to-indoor propagation characteristics in this band were made by using measurements collected on dedicated, mostly single-link setups. Hence, measurements performed on deployed and operational 5G networks still lack in the literature. To fill this gap, this paper presents a dataset of measurements performed over commercial 5G networks. In particular, the dataset includes measurements of channel power delay profiles from two 5G networks in Band n78, i.e., 3.3{\textendash}3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy by using the Rohde \& Schwarz (R\&S) TSMA6 network scanner during several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics toward the definition and refinement of empirical channel propagation models.},
	author = {Ali, Usman and Caso, Giuseppe and De Nardis, Luca and Kousias, Konstantinos and Rajiullah, Mohammad and Alay, {\"O}zg{\"u} and Neri, Marco and Brunstr{\"o}m, Anna and Di Benedetto, Maria-Gabriella}
}
@article {27792,
	title = {Empirical Models for NB-IoT Path Loss in an Urban Scenario},
	journal = {IEEE Internet of Things Journal},
	volume = {8},
	year = {2021},
	pages = {13774-13788},
	publisher = { IEEE},
	abstract = {The lack of publicly available large scale measure- ments has hindered the derivation of empirical path loss (PL) models for Narrowband Internet of Things (NB-IoT). Therefore, simulation-based investigations currently rely on models con- ceived for other cellular technologies, which are characterized, however, by different available bandwidth, carrier frequency, and infrastructure deployment, among others. In this paper, we take advantage of data from a large scale measurement campaign in the city of Oslo, Norway, to provide the first empirical characterization of NB-IoT PL in an urban scenario. For the PL average term, we characterize Alpha-Beta-Gamma (ABG) and Close-In (CI) models. By analyzing multiple NB- IoT cells, we propose a statistical PL characterization, i.e., the model parameters are not set to a single, constant value across cells, but are randomly extracted from well-known distributions. Similarly, we define the PL shadowing distribution, correlation over distance, and inter-site correlation. Finally, we give initial insights on outdoor-to-indoor propagation, using measurements up to deep indoor scenarios. The proposed models improve PL estimation accuracy compared to the ones currently adopted in NB-IoT investigations, enabling more realistic simulations of urban scenarios similar to the sites covered by our measurements.\&nbsp;},
	keywords = {Cellular Internet of Things, massive Machine Type Communications, Narrowband Internet of Things, Path Loss Empirical Models},
	doi = {10.1109/JIOT.2021.3068148},
	url = {https://ieeexplore.ieee.org/document/9383771},
	author = {Caso, Giuseppe and Alay, {\"O}zg{\"u} and De Nardis, Luca and Brunstr{\"o}m, Anna and Neri, Marco and Di Benedetto, Maria-Gabriella}
}
@article {27661,
	title = {NB-IoT Random Access: Data-driven Analysis and ML-based Enhancements},
	journal = {IEEE Internet of Things Journal},
	volume = {8},
	year = {2021},
	note = {Supplementary Materials, Results, and Dataset available at\&nbsp;https://mosaic-simulamet.com/nbiot-randomaccess/\&nbsp;},
	month = {01/2021},
	pages = {11384-11399},
	publisher = { IEEE},
	abstract = {In the context of massive Machine Type Communications (mMTC), the Narrowband Internet of Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored Random Access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This paper presents the first data-driven analysis of NB-IoT RA, exploiting a large scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators{\textquoteright} configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. Comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a Machine Learning (ML) approach, and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in a RA enhanced scheme, and show that optimized configurations enable a power consumption reduction of at least 50\%. We also make our dataset available for further exploration, toward the discovery of new insights and research perspectives.},
	keywords = {Cellular Internet of Things, Empirical Analysis, massive Machine Type Communications, Narrowband Internet of Things, Random Access},
	doi = {10.1109/JIOT.2021.3051755},
	url = {https://ieeexplore.ieee.org/document/9324758},
	author = {Caso, Giuseppe and Kousias, Konstantinos and Alay, {\"O}zg{\"u} and Brunstr{\"o}m, Anna and Neri, Marco}
}
@article {27377,
	title = {Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild},
	journal = {IEEE Communications Magazine},
	volume = {58},
	year = {2020},
	note = {Preprint available here:\&nbsp;https://arxiv.org/abs/2005.02341},
	month = {09/2020},
	pages = {39-45},
	publisher = { IEEE},
	abstract = {Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising technology for massive Machine Type Communication (mMTC). Given that its deployment is rapidly progressing worldwide, measurement campaigns and performance analyses are needed to better understand the system and move toward its enhancement. With this aim, this paper presents a large scale measurement campaign and empirical analysis of NB-IoT on operational networks, and discloses valuable insights in terms of deployment strategies and radio coverage performance. The reported results also serve as examples showing the potential usage of the collected dataset, which we make open- source along with a lightweight data visualization platform.},
	doi = {10.1109/MCOM.001.2000131},
	url = {https://ieeexplore.ieee.org/document/9214385},
	author = {Kousias, Konstantinos and Caso, Giuseppe and Alay, {\"O}zg{\"u} and Brunstr{\"o}m, Anna and De Nardis, Luca and Di Benedetto, Maria-Gabriella and Neri, Marco}
}
