Interference Model Similarity Index and Its Applications to mmWave Networks: Extended version

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 (mmWave) 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. We further use the proposed index to develop a simple interference model for mmWave networks that can significantly simplify design principles of the important procedures for wireless communication, such as beamforming, interference management, scheduling, and topology H. Shokri-Ghadikolaei and C. Fischione are with KTH Royal Institute of Technology, Stockholm, Sweden (E-mails: {hshokri, carlofi}@kth.se). E. Modiano is with the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, USA (E-mail: [email protected]). A preliminary version of this paper [1] has been accepted for presentation at the IEEE International Conference on Communications (ICC), 2016.