Authors | G. Caso, Ö. Alay, L. De Nardis, A. Brunström, M. Neri and M. Di Benedetto |
Title | Empirical Models for NB-IoT Path Loss in an Urban Scenario |
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics |
Status | Published |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | IEEE Internet of Things Journal |
Volume | 8 |
Issue | 17 |
Pagination | 13774-13788 |
Publisher | IEEE |
Keywords | Cellular Internet of Things, massive Machine Type Communications, Narrowband Internet of Things, Path Loss Empirical Models |
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. |
URL | https://ieeexplore.ieee.org/document/9383771 |
DOI | 10.1109/JIOT.2021.3068148 |
Citation Key | 27792 |