Aşiri Yoǧun Heterojen Aǧlarda Poisson Boşluk Sureci ile Enerji Verimli Baz Istasyonu Yerleşimi

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

3 Atıf (Scopus)

Özet

In recent years, stochastic geometry tools have been widely used for the deployment of base stations (BSs) in ultradense multi-tier heterogeneous networks. One of these tools, the independent Poisson point process (PPP), is favored due to its tractability, but in reality, the BSs are not completely independent of each other within tier and across tiers. In this study, a two-tier cellular network model including macro and pico base stations (MBSs and PBSs), is proposed. In this model, MBSs are distributed based on the PPP, while the PBSs are deployed according to the Poisson hole process. Then, the effect of the deployment and the density of BSs on energy efficiency are investigated. The minimum achievable data rate for each layer is formulated and the minimum achievable throughput for the entire cellular network is obtained. Through the optimized BS distribution, it is shown with simulations that energy efficiency can be maximized.

Tercüme edilen katkı başlığıEnergy efficient base station deployment in ultra dense heterogeneous networks via poisson hole process
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı27th Signal Processing and Communications Applications Conference, SIU 2019
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781728119045
DOI'lar
Yayın durumuYayınlandı - Nis 2019
Etkinlik27th Signal Processing and Communications Applications Conference, SIU 2019 - Sivas, Turkey
Süre: 24 Nis 201926 Nis 2019

Yayın serisi

Adı27th Signal Processing and Communications Applications Conference, SIU 2019

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???event.eventtypes.event.conference???27th Signal Processing and Communications Applications Conference, SIU 2019
Ülke/BölgeTurkey
ŞehirSivas
Periyot24/04/1926/04/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Energy efficiency
  • Poisson hole process
  • Stochastic geometry
  • Ultra dense heterogeneous networks

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