Performance analysis of spatially distributed LTE-U/NR-U and Wi-Fi networks: An analytical model for coexistence study

Baswade, Anand M. and Reddy, Mohith and Franklin, Antony and Tamma, Bheemarjuna Reddy (2021) Performance analysis of spatially distributed LTE-U/NR-U and Wi-Fi networks: An analytical model for coexistence study. Journal of Network and Computer Applications, 191. pp. 1-15. ISSN 1084-8045

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Abstract

To access an unlicensed channel, Wi-Fi follows the Listen Before Talk (LBT) mechanism, whereas LTE-U (and future New Radio in Unlicensed (NR-U) Patriciello et al., 2020; Lagen et al., 2019) adopts the ON–OFF duty-cycled mechanism to fairly share the channel with Wi-Fi. These different mechanisms result in quite different performance for Wi-Fi and LTE-U/NR-U based on their relative deployment and density in the environment. In this work, we present an analytical model for the characterization of achievable throughputs of Wi-Fi and LTE-U networks in spatially distributed scenarios with downlink-prevalent traffic. The proposed model is used to study how LTE-U and Wi-Fi coexist with each other in spatially distributed scenarios. Our extensive simulation results show that it is a reliable model for estimating the throughput of both Wi-Fi and LTE-U in a spatially distributed deployment. The impact of various network parameters such as energy detection and carrier sense threshold on Wi-Fi and LTE-U coexistence is explored as a byproduct and corroborated via a National Instrument experimental test bed. Our model gives very high accuracy in throughput estimation and the mean normalized error is less than 3% for a 80-node scenario in which 50% of nodes belong to each of Wi-Fi and LTE-U network. The analytical model is also used to conduct coexistence studies of LTE-U and Wi-Fi. © 2021

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IITH Creators:
IITH CreatorsORCiD
Franklin, AntonyUNSPECIFIED
Tamma, Bheemarjuna Reddyhttps://orcid.org/0000-0002-4056-7963
Item Type: Article
Additional Information: Virmani & Taneja (2018), proponen una Técnica de Preprocesamiento de Texto (TPIR), para mejorar la eficiencia de las tareas de preprocesamiento, la cual consta de 4 fases: División del texto en párrafos u oraciones y extracción de la información sintáctica a través de tokenización; Combinación de tokens similares; Separación de palabras vacías y tokens; y aplicación de un algoritmo de stemming basado en corrección ortográfica. La evaluación se realizó sobre The International Monetary Fund, obteniendo mejores resultados en comparación con el preprocesamiento tradicional, al reducir tokens y espacios de almacenamiento. Gupta, Patel, & Bramhe (2018), proponen un enfoque de recuperación de documentos que utiliza un índice Hash teniendo en cuenta características de los términos (term features). La metodología propuesta utiliza 5 pasos: Preprocesamiento, donde cada documento se convierte en un vector de características, y se eliminan las palabras vacías, se compara con un vector de palabras clave para encontrar el vector de características del documento, luego, un conjunto de características positivas y negativas se utiliza para asignar el ID del término, se genera un número Hash para el documento y el valor de índice asignado da la identificación del documento, finalmente, las consultas del usuario reciben el mismo procesamiento, de forma que los id de los términos son la clave para la función hash, la cual recupera los documentos que coinciden. Los experimentos se realizaron con datasets reales y artificiales.
Uncontrolled Keywords: CSAT; Inter-RAT coexistence; LBT; LTE-U; NR-U; Performance analysis; Wi-Fi
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 13 Sep 2022 05:55
Last Modified: 13 Sep 2022 05:55
URI: http://raiithold.iith.ac.in/id/eprint/10551
Publisher URL: http://doi.org/10.1016/j.jnca.2021.103157
OA policy: https://v2.sherpa.ac.uk/id/publication/11381
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