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Interference Model Similarity Index and Its Applications to Millimeter-Wave Networks
KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik.ORCID-id: 0000-0001-6737-0266
KTH, Skolan för elektroteknik och datavetenskap (EECS), Nätverk och systemteknik.ORCID-id: 0000-0001-9810-3478
2018 (engelsk)Inngår i: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 17, nr 1, s. 71-85Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In wireless communication networks, interference models are routinely used for tasks, such as performance analysis, optimization, and protocol design. These tasks are heavily affected by the accuracy and tractability of the interference models. Yet, quantifying the accuracy of these models remains a major challenge. In this paper, we propose a new index for assessing the accuracy of any interference model under any network scenario. Specifically, it is based on a new index that quantifies the ability of any interference model in correctly predicting harmful interference events, that is, link outages. We consider specific wireless scenario of both conventional sub-6 GHz and millimeter-wave networks and demonstrate how our index yields insights into the possibility of simplifying the set of dominant interferers, replacing a Nakagami or Rayleigh random fading by an equivalent deterministic channel, and ignoring antenna sidelobes. Our analysis reveals that in highly directional antenna settings with obstructions, even simple interference models (such as the classical protocol model) are accurate, while with omnidirectional antennas, more sophisticated and complex interference models (such as the classical physical model) are necessary. Our new approach makes it possible to adopt the simplest interference model of adequate accuracy for every wireless network.

sted, utgiver, år, opplag, sider
IEEE, 2018. Vol. 17, nr 1, s. 71-85
Emneord [en]
Wireless communications, interference model, performance analysis, millimeter-wave networks
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-222454DOI: 10.1109/TWC.2017.2762667ISI: 000422945400006Scopus ID: 2-s2.0-85032305190OAI: oai:DiVA.org:kth-222454DiVA, id: diva2:1181635
Merknad

QC 20180209

Tilgjengelig fra: 2018-02-09 Laget: 2018-02-09 Sist oppdatert: 2018-02-20bibliografisk kontrollert

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