An Analytical Performance Evaluation Framework for NB-IoT

Autor: Jonathan Prados-Garzon, Jorge Navarro-Ortiz, Pablo Ameigeiras, Pilar Andres-Maldonado, Juan M. Lopez-Soler
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Digibug. Repositorio Institucional de la Universidad de Granada
instname
Digibug: Repositorio Institucional de la Universidad de Granada
Universidad de Granada (UGR)
ISSN: 2016-7679
Popis: Narrowband Internet of Things (NB-IoT) technology emerged in Release 13 as one of the solutions to provide cellular IoT connectivity. NB-IoT is designed to achieve better indoor coverage, support of a massive number of low-throughput devices, with relaxed delay requirements, and lower energy consumption. Particularly, the extensive coverage of NB-IoT poses a great challenge. The goal is to cover devices in areas previously inaccessible by cellular networks due to penetration losses or remote locations. To solve this, NB-IoT utilizes bandwidth reduction and repetitions. However, for the targeted low range of signal to noise ratio (SNR), the coverage gain due to repetitions can be significantly limited by the performance of the channel estimator. In this paper, we provide an analytical evaluation framework to study the performance of NB-IoT. Our analysis includes the limitations due to realistic channel estimation (CE) and delves into the estimation of the SNR. Additionally, the conducted evaluation shows the impact of the coverage extension in the final performance of the NB-IoT user equipment (UE) in terms of uplink packet transmission latency and battery lifetime. Specifically, regarding UE’s battery lifetime, for a maximum coupling loss (MCL) of 164 dB, realistic CE evaluations obtain a battery lifetime reduction of approximately 90% compared to ideal CE.
This work was supported in part by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under Project TEC2016-76795-C6-4-R and in part by the Spanish Ministry of Education, Culture and Sport under Grant FPU 13/04833.
Databáze: OpenAIRE