Experimental Evidence for Heavy Tailed Interference in the IoT

Clavier, Laurent; Pedersen, Troels; Rodriguez, Ignacio; Lauridsen, Mads; Egan, Malcolm

Type de document :

Pré-publication ou Document de travail

Titre :

Experimental Evidence for Heavy Tailed Interference in the IoT

Auteur(s) :

Clavier, Laurent [Auteur]

Circuits Systèmes Applications des Micro-ondes - IEMN [CSAM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Pedersen, Troels [Auteur]
Department of Electronic Systems - Aalborg University
Rodriguez, Ignacio [Auteur]
Aalborg University [Denmark] [AAU]
Lauridsen, Mads [Auteur]
Nokia Bell Labs
Egan, Malcolm [Auteur]
Software and Cognitive radio for telecommunications [SOCRATE]

Mot(s)-clé(s) en anglais :

Interference
IoT
statistical models
subexpo- nential distributions
heavy tails

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]

Résumé en anglais : [en]

5G and beyond sees an ever increasing density of connected things. As not all devices are coordinated, there are limited opportunities to mitigate interference. As such, it is crucial to characterize the interference in ...
Lire la suite >5G and beyond sees an ever increasing density of connected things. As not all devices are coordinated, there are limited opportunities to mitigate interference. As such, it is crucial to characterize the interference in order to understand its impact on coding, waveform and receiver design. While a number of theoretical models have been developed for the interference statistics in communications for the IoT, there is very little experimental validation. In this paper, we address this key gap in understanding by performing statistical analysis on recent measurements in the unlicensed 863 MHz to 870 MHz band in different regions of Aalborg, Denmark. In particular, we show that the measurement data suggests the distribution of the interference power is heavy tailed, confirming predictions from theoretical models.Lire moins >

Langue :

Anglais

Collections :