A Simplified Interference Model for Outdoor Millimeter-wave Networks

  • Xiaolin JiangEmail author
  • Hossein Shokri-Ghadikolaei
  • Carlo Fischione
  • Zhibo Pang


Industry 4.0 is the emerging trend of the industrial automation. Millimeter-wave (mmWave) communication is a prominent technology for wireless networks to support the Industry 4.0 requirements. The availability of tractable accurate interference models would greatly facilitate performance analysis and protocol development for these networks. In this paper, we investigate the accuracy of an interference model that assumes impenetrable obstacles and neglects the sidelobes. We quantify the error of such a model in terms of statistical distribution of the signal to noise plus interference ratio and of the user rate for outdoor mmWave networks under different carrier frequencies and antenna array settings. The results show that assuming impenetrable obstacle comes at almost no accuracy penalty, and the accuracy of neglecting antenna sidelobes can be guaranteed with sufficiently large number of antenna elements. The comprehensive discussions of this paper provide useful insights for the performance analysis and protocol design of outdoor mmWave networks.


Millimeter-wave networks Interference model Simplicity-accuracy tradeoff Interference model accuracy index 


  1. 1.
    Schwab K (2016) The fourth industrial revolution. World Economic Forum, GenevaGoogle Scholar
  2. 2.
    Mishra AK, Nigam Y, Singh DR (2017) Controlled blasting in a limestone mine using electronic detonators: a case study. J Geol Soc India, Springer 89(1):87–90CrossRefGoogle Scholar
  3. 3.
    Yan Y, Qian Y, Sharif H, Tipper D (2013) A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun Surv Tuts 15(1):5–20CrossRefGoogle Scholar
  4. 4.
    Willig A, Matheus K, Wolisz A (2005) Wireless technology in industrial networks. Proc IEEE 93 (6):1130–1151CrossRefGoogle Scholar
  5. 5.
    Luvisotto M, Pang Z, Dzung D (2017) Ultra high performance wireless control for critical applications: challenges and directions. J IEEE Trans Ind Informat 13(3):1448–1459CrossRefGoogle Scholar
  6. 6.
    Pang Z, Luvisotto M, Dzung D (2017) High performance wireless communications for critical control applications. J IEEE Ind Electron MagGoogle Scholar
  7. 7.
    Rangan S, Rappaport TS, Erkip E (2014) Millimeter-wave cellular wireless networks: potentials and challenges. J Proc IEEE 102(3):366–385CrossRefGoogle Scholar
  8. 8.
    Akdeniz MR, Liu Y, Samimi MK, Sun S, Rangan S, Rappaport TS, Erkip E (2014) Millimeter wave channel modeling and cellular capacity evaluation. J IEEE J Sel Areas Commun 32(6):1164–1179CrossRefGoogle Scholar
  9. 9.
    Park C, Rappaport TS (2007) Short-range wireless communications for next-generation networks: UWB, 60 GHz millimeter-wave WPAN, and ZigBee. J IEEE Wirel Commun 14(4):70–78CrossRefGoogle Scholar
  10. 10.
    Singh S, Ziliotto F, Madhow U, Belding E, Rodwell M (2009) Blockage and directivity in 60 GHz wireless personal area networks: from cross-layer model to multihop MAC design. J IEEE J Sel Areas Commun 27 (8):1400–1413CrossRefGoogle Scholar
  11. 11.
    Shokri-Ghadikolaei H, Fischione C (2016) The transitional behavior of interference in millimeter wave networks and its impact on medium access control. J IEEE Trans Commun 62(2):723–740CrossRefGoogle Scholar
  12. 12.
    Thornburg A, Bai T, Heath RW (2015) Interference statistics in a random mmWave ad hoc network. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2904–2908Google Scholar
  13. 13.
    Singh S, Kulkarni MN, Ghosh A, Andrews JG (2015) Tractable model for rate in self-backhauled millimeter wave cellular networks. J IEEE J Sel Areas Commun 33(10):2196–2211CrossRefGoogle Scholar
  14. 14.
    Bai T, Heath RW (2015) Coverage and rate analysis for millimeter-wave cellular networks. J IEEE Trans Wirel Commun 14(2):1100–1114CrossRefGoogle Scholar
  15. 15.
    Di Renzo M (2015) Stochastic geometry modeling and analysis of multi-tier millimeter wave cellular networks. J IEEE Trans Wireless Commun 14(9):5038–5057CrossRefGoogle Scholar
  16. 16.
    Niu Y, Li Y, Jin D, Su L, Wu D (2015) Blockage robust and efficient scheduling for directional mmWave WPANs. J IEEE Trans Veh Technol 64(2):728–742CrossRefGoogle Scholar
  17. 17.
    Shokri-Ghadikolaei H, Fischione C, Modiano E (2016) On the accuracy of interference models in wireless communications. In: IEEE international conference on communications. Kuala Lumpur, pp 1–6Google Scholar
  18. 18.
    El Ayach O, Rajagopal S, Abu-Surra S, Pi Z, Heath RW (2014) Spatially sparse precoding in millimeter wave MIMO systems. J IEEE Trans Wireless Commun 13(3):1499–1513CrossRefGoogle Scholar
  19. 19.
    El Ayach O, Heath RW, Abu-Surra S, Rajagopal S, Pi Z (2012) The capacity optimality of beam steering in large millimeter wave MIMO systems. In: 13th IEEE international workshop on signal processing advances in wireless communications. Cesme, pp 100–104Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.KTH Royal Institute of TechnologyStockholmSweden
  2. 2.ABB Corporate ResearchVästeråsSweden

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