Uplink Performance Analysis in D2D-Enabled Millimeter-Wave Cellular Networks with Clustered Users

Esma Turgut, M. Cenk Gursoy

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


In this paper, an analytical framework is provided to analyze the uplink performance of device-To-device (D2D)-enabled millimeter-wave (mm-wave) cellular networks with clustered D2D user equipments (UEs). The locations of cellular UEs are modeled as a Poisson point process, while the locations of potential D2D UEs are modeled as a Poisson cluster process. Signal-To-interference-plus-noise ratio outage probabilities are derived for both cellular and D2D links using tools from stochastic geometry. The distinguishing features of mm-wave communications such as directional beamforming and having different path loss laws for the line-of-sight and non-line-of-sight links are incorporated into the outage analysis by employing a flexible mode selection scheme and Nakagami fading. Also, the effect of beamforming alignment errors on the outage probability is investigated to get insight into the performance in practical scenarios. Moreover, area spectral efficiency of the cellular and D2D networks is determined for both underlay and overlay types of sharing. Optimal spectrum partition factor is determined for overlay sharing by considering the optimal weighted proportional fair spectrum partition.

Original languageEnglish (US)
Article number8602443
Pages (from-to)1085-1100
Number of pages16
JournalIEEE Transactions on Wireless Communications
Issue number2
StatePublished - Feb 2019


  • Device-To-device (D2D) communication
  • Poisson cluster process
  • Poisson point process
  • SINR outage probability
  • Thomas cluster process
  • mode selection
  • stochastic geometry
  • uplink analysis of mm-wave cellular networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


Dive into the research topics of 'Uplink Performance Analysis in D2D-Enabled Millimeter-Wave Cellular Networks with Clustered Users'. Together they form a unique fingerprint.

Cite this