| 1. |
- Caso, Giuseppe, et al.
(författare)
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Empirical Models for NB-IoT Path Loss in an Urban Scenario
- 2021
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 8:17, s. 13774-13788
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Tidskriftsartikel (refereegranskat)abstract
- The lack of publicly available large scale measurements 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 conceived 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 NBIoT 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.
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| 2. |
- Ali, Usman, et al.
(författare)
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Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
- 2022
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Ingår i: Future Internet. - : MDPI. - 1999-5903. ; 14:8, s. 1-27
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Tidskriftsartikel (refereegranskat)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.
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| 3. |
- Ali, Usman, et al.
(författare)
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Large-Scale Dataset for the Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
- 2022
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Ingår i: Data. - : MDPI. - 2306-5729. ; 7:3, s. 34-34
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Tidskriftsartikel (refereegranskat)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., 3.3–3.8 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.
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| 4. |
- Caso, Giuseppe, et al.
(författare)
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An Initial Look into the Performance Evolution of 5G Non-Standalone Networks
- 2023
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Ingår i: TMA 2023 - Proceedings of the 7th Network Traffic Measurement and Analysis Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350325676 - 9783903176584 ; , s. 1-4
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Konferensbidrag (refereegranskat)abstract
- Fifth Generation (5G) networks have been operational worldwide for a couple of years. To reveal how the 5G system evolution (e.g., changes in network conditions, deployment, and configurations) affects user performance, empirical long-term analyses are required. This paper presents preliminary insights from our ongoing large-scale measurement study of the commercial 5G non-standalone (NSA) networks deployed in Rome, Italy. An initial comparison between the measurements in 2020-2021 vs. 2023 shows a decrease in throughput and latency performance, calling for deeper analyses toward understanding the root causes and deriving proper optimization solutions.
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| 5. |
- Caso, Giuseppe, et al.
(författare)
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User-Centric Radio Access Technology Selection : A Survey of Game Theory Models and Multi-Agent Learning Algorithms
- 2021
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 9, s. 84417-84464
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Tidskriftsartikel (refereegranskat)abstract
- User-centric radio access technology (RAT) selection is a key communication paradigm, given the increased number of available RATs and increased cognitive capabilities at the user end. When considered against traditional network-centric approaches, user-centric RAT selection results in reduced network-side management load, and leads to lower operational costs for RATs, as well as improved quality of service (QoS) and quality of experience (QoE) for users. The complex between-users interactions involved in RAT selection require, however, specific analyses, toward developing reliable and efficient schemes. Two theoretical frameworks are most often applied to user-centric RAT selection analysis, i.e., game theory (GT) and multi-agent learning (MAL). As a consequence, several GT models and MAL algorithms have been recently proposed to solve the problem at hand. A comprehensive discussion of such models and algorithms is, however, currently missing. Moreover, novel issues introduced by next-generation communication systems also need to be addressed. This paper proposes to fill the above gaps by providing a unified reference for both ongoing research and future research directions in the field. In particular, the review addresses the most common GT and MAL models and algorithms, and scenario settings adopted in user-centric RAT selection in terms of utility function and network topology. Regarding GT, the review focuses on non-cooperative models, because of their widespread use in RAT selection; as for MAL, a large number of algorithms are described, ranging from game-theoretic to reinforcement learning (RL) schemes, and also including most recent approaches, such as deep RL (DRL) and multi-armed bandit (MAB). Models and algorithms are analyzed by comparatively reviewing relevant literature. Finally, open challenges are discussed, in light of ongoing research and standardization activities.
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| 6. |
- De Nardis, Luca, et al.
(författare)
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Positioning by fingerprinting with multiple cells in NB-IoT networks
- 2022
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Konferensbidrag (refereegranskat)abstract
- Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, thanks to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from Long Term Evolution (LTE) are not yet widely available in existing networks, and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning, based on fingerprinting, that use coverage and radio information from multiple cells. The proposed strategies are evaluated on a large-scale dataset that includes experimental data from two NB-IoT operators. Results show that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell finger-printing, with a minimum average positioning error of about 20 meters, consistent across different network scenarios, vs. about 70 meters for current state-of-the-art.
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| 7. |
- De Nardis, Luca, et al.
(författare)
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Positioning by Multicell Fingerprinting in UrbanNB-IoT Networks
- 2023
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 23:9
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Tidskriftsartikel (refereegranskat)abstract
- Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments.
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| 8. |
- De Nardis, Luca, et al.
(författare)
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Robustness of time reversal versus all-rake transceivers in multiple access channels
- 2018
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Ingår i: Wireless Communications and Mobile Computing. - : Hindawi Limited. - 1530-8669 .- 1530-8677. ; 2018
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Tidskriftsartikel (refereegranskat)abstract
- Copyright © 2018 Luca De Nardis et al. Time reversal (TR) is an effective solution in both single user and multiuser communications for moving complexity from the receiver to the transmitter, in comparison to traditional postfiltering based on Rake receivers. Imperfect channel estimation may, however, affect pre- versus postfiltering schemes in a different way; this paper analyzes the robustness of time reversal versus All-Rake (AR) transceivers, inmultiple access communications, with respect to channel estimation errors. Two performance indicators are adopted in the analysis: symbol error probability and spectral efficiency. Analytic expressions for both indicators are derived and used as the basis for simulation-based performance evaluation. Results show that while TR leads to slight performance advantage over AR when channel estimation is accurate, its performance is severely degraded by large channel estimation errors, indicating a clear advantage for AR receivers in this case, in particular when extremely short impulsive waveforms are adopted. Results however also show a stronger non-Gaussianity of interference in the TR case suggesting that the adoption of a receiver structure adapted to non-Gaussian interference might tilt the balance towards TR.
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| 9. |
- Kousias, Konstantinos, et al.
(författare)
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A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements
- 2024
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Ingår i: IEEE Communications Magazine. - : IEEE. - 0163-6804 .- 1558-1896. ; 62:5, s. 44-49
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Tidskriftsartikel (refereegranskat)abstract
- Mobile networks are highly complex systems. Therefore, it is crucial to examine them from an empirical perspective to better understand how network features affect performance, so to suggest additional improvements. To this aim, this paper presents a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT), and 5G New Radio (NR) connectivity. We collected our dataset during seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization.
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| 10. |
- Kousias, Konstantinos, et al.
(författare)
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Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild
- 2020
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Ingår i: IEEE Communications Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 0163-6804 .- 1558-1896. ; 58:9, s. 39-45
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Tidskriftsartikel (refereegranskat)abstract
- Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising technology for massive machine type communication. 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 article 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.
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