Encyclopedia of Wireless Networks

Living Edition
| Editors: Xuemin (Sherman) Shen, Xiaodong Lin, Kuan Zhang

Area Spectral Efficiency of Ultradense Networks

  • Guoqiang Mao
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32903-1_44-1

Synonyms

Definition

Area spectral efficiency refers to the data rate that can be achieved per unit bandwidth and in a unit area of the wireless network. It has the unit of b/s/Hz/m2 or b/s/Hz/km2.

Historical Background

Network densification has been the main driver of wireless network capacity increase in the past and will play an even more crucial role in the development of the next generation mobile communication systems (5G). Network densification refers to the deployment of more base stations (BSs) and wireless access points per unit area and the associated technological advances to support such densification. There are three primary ways of increasing the wireless network capacity: (1) adding more spectrum, (2) enhancing spectral efficiency through advanced communication techniques, and (3) enhancing spatial reuse of frequency spectrum through network densification. The area spectral efficiency (ASE) is a major metric to measure the...

This is a preview of subscription content, log in to check access.

References

  1. Andrews JG, Baccelli F, Ganti RK (2011) A tractable approach to coverage and rate in cellular networks. IEEE Trans Commun 59(11):3122–3134Google Scholar
  2. Andrews JG, Gupta AK, Dhillon HS (2016a) A primer on cellular network analysis using stochastic geometry. eprint arXiv:1604.03183Google Scholar
  3. Andrews JG, Zhang X, Durgin GD, Gupta AK (2016b) Are we approaching the fundamental limits of wireless network densification? IEEE Commun Mag 54(10):184–190Google Scholar
  4. Ding M, Lopez-Perez D, Mao G, Wang P, Lin Z (2015) Will the area spectral efficiency monotonically grow as small cells go dense? In: IEEE GLOBECOM, pp 1–7Google Scholar
  5. Ding M, Perez DL (2016) Please lower small cell antenna heights in 5G. In: IEEE Globecom, pp 1–6Google Scholar
  6. Ding M, Wang P, López-Pérez D, Mao G, Lin Z (2016a) Performance impact of LoS and NLoS transmissions in dense cellular networks. IEEE Trans Wirel Commun 15(3):2365–2380Google Scholar
  7. Ding T, Ding M, Mao G, Lin Z, López-Pérez D (2016b) Uplink performance analysis of dense cellular networks with LoS and NLoS transmissions. In: IEEE ICC, pp 1–6Google Scholar
  8. Ge X, Tu S, Mao G, Wang CX, Han T (2016) 5G ultra-dense cellular networks. IEEE Wirel Commun 23(1):72–79Google Scholar
  9. Gruber M (2016) Scalability study of ultra-dense networks with access point placement restrictions. In: IEEE ICC workshops, pp 650–655Google Scholar
  10. Haenggi M, Andrews JG, Baccelli F, Dousse O, Franceschetti M (2009) Stochastic geometry and random graphs for the analysis and design of wireless networks. IEEE J Sel Areas Commun 27(7):1029–1046Google Scholar
  11. Henderson W (2007) Webb report, ofcom.Google Scholar
  12. Kutty S, Sen D (2016) Beamforming for millimeter wave communications: an inclusive survey. IEEE Commun Surv Tutor 18(2):949–973Google Scholar
  13. Liu J, Sheng M, Liu L, Li J (2016a) Effect of densification on cellular network performance with bounded pathloss model. IEEE Commun Lett 21(2):346–349Google Scholar
  14. Liu J, Sheng M, Liu L, Li J (2016b) How dense is ultra-dense for wireless networks: from far- to near-field communications. eprint arXiv:1606.04749Google Scholar
  15. Small Cell Forum (2016) Small cell market status report, May 2016Google Scholar
  16. Zhang X, Andrews JG (2015) Downlink cellular network analysis with multi-slope path loss models. IEEE Trans Commun 63(5):1881–1894CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Technology SydneySydneyAustralia