Advertisement

Improving Spectrum Efficiency in Heterogeneous Networks Using Granular Identification

  • Rohit SinghEmail author
  • Douglas Sicker
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 261)

Abstract

Given the ever-increasing demand for wireless services and the pending explosion of the Internet of Things (IoT), demand for radio spectrum will only become more acute. Setting aside (but not ignoring) the need for additional allocations of spectrum, the existing spectrum needs to be used more efficiently so that it can meet the demand. Other than providing more spectrum there are other factors (like, transmit power, antenna angles, QoS, bandwidth, and others) that can be adjusted to cater to the demand and at the same time increase the spectrum efficiency. With heterogeneity and densification these factors are so varied it becomes necessary that we have some tool to monitor these factors so as to optimize our outcome. Here we propose a PHY layer granular identification that monitors the physical and logical parameters associated with a device/antenna. Through a simple optimization problem, we show how the proposed identification mechanism can further the cause of spectrum efficiency and ease coordination among devices in a heterogeneous network (HetNet) to assign resources more optimally. Compared to received signal strength (RSS) way of assigning resources the proposed approach shows a \(138\%\) to \(220\%\) increase (depending on the requested QoS) in spectrum efficiency. Ultimately, this research is aimed at assisting the regulators in addressing future spectrum related efficiency and enforcement issues.

Keywords

Spectrum efficiency Identification Heterogeneous networks Spectrum sharing Optimization Radio resource management 

References

  1. 1.
    Cisco Visual Network Index: Global Mobile Traffic Forecast Update 2016–2021. Technical report, Cisco, USA (2017)Google Scholar
  2. 2.
    Connecting America: The National Broadband Plan. Technical report, Federal Communications Commission (2010)Google Scholar
  3. 3.
    Song, H.J., Nagatsuma, T.: Present and future of terahertz communications. IEEE Trans. Terahertz Sci. Technol. 1(1), 256–263 (2011).  https://doi.org/10.1109/TTHZ.2011.2159552CrossRefGoogle Scholar
  4. 4.
    McHenry, M.A., McCloskey, D., Roberson, D.A., MacDonald, J.T.: Spectrum occupancy measurements, Chicago, Illinois, 16–18 November 2005. Technical report, Shared Spectrum Company Report (2005)Google Scholar
  5. 5.
    Wang, J., et al.: Spectral efficiency improvement with 5G technologies: results from field tests. IEEE J. Sel. Areas Commun. 35(8), 1867–1875 (2017).  https://doi.org/10.1109/JSAC.2017.2713498CrossRefGoogle Scholar
  6. 6.
    Debaillie, B., et. al.: In-band full-duplex transceiver technology for 5G mobile networks. In: 41st IEEE European Solid-State Circuits Conference (ESSCIRC), Graz, Austria, pp. 84–87, (2015).  https://doi.org/10.1109/ESSCIRC.2015.7313834
  7. 7.
    Ding, M., Perez, D.L.: Performance impact of base station antenna heights in dense cellular networks. IEEE Trans. Wirel. Commun. 16(12), 8147–8161 (2017).  https://doi.org/10.1109/TWC.2017.2757924CrossRefGoogle Scholar
  8. 8.
    Sung, D.H., Baras, J.S., Zhu, C.: coordinated scheduling and power control for downlink cross-tier interference mitigation in heterogeneous cellular networks. In IEEE Global Communications Conference (GLOBECOM 2013), Atlanta, GA, USA, pp. 3809–3813 (2013).  https://doi.org/10.1109/GLOCOM.2013.6831666
  9. 9.
    Xu, X., Kutrolli, G., Mathar, R.: Dynamic downlink power control strategies for LTE femtocells. In: 7th International Conference on Next Generation Mobile Apps, Services and Technologies, Prague, Czech Republic, pp. 181–186 (2013)Google Scholar
  10. 10.
    Li, Q., Hu, R.Q., Xu, Y., Qian, Y.: Optimal fractional frequency reuse and power control in the heterogeneous wireless networks. IEEE Trans. Wirel. Commun. 12(6), 2658–2668 (2013)CrossRefGoogle Scholar
  11. 11.
    Nam, W., Bai, D., Lee, J., Kang, I.: Advanced interference management for 5G cellular networks. IEEE Commun. Mag. 5G Wirel. Commun. Syst.: Prospect. Challenges 52(5), 52–60 (2014)Google Scholar
  12. 12.
    Report of the Spectrum Efficiency Working Group. Technical report, Federal Communications Commission Spectrum Policy Task Force (2002)Google Scholar
  13. 13.
    Singh, R., et. al.: A method for evaluating coexistence of LTE and radar altimeters in the 4.2–4.4 GHz band. In: 17th Wireless Telecommunications Symposium (WTS), Chicago, IL, USA, pp. 1–9 (2017)Google Scholar
  14. 14.
    Propagation Data and Prediction Models for the Planning of Indoor Radiocommunication Systems and Radio Local Area Networks in the Frequency Range 900 MHz to 100 GHz. Technical report, International Telecommunication Union (ITU), RRecommendation ITU-R P.1238 (1997)Google Scholar
  15. 15.
    Sun, S., et. al.: Propagation path loss models for 5G urban micro- and macro-cellular scenarios. In: 83rd IEEE Vehicular Technology Conference (VTC 2016-Spring), Nanjing, China, pp. 1–6 (2016).  https://doi.org/10.1109/VTCSpring.2016.7504435
  16. 16.
    Reference Radiation Patterns of Omnidirectional, Sectoral and Other Antennas for the Fixed and Mobile Services for Use in Sharing Studies in the Frequency Range from 400 MHz to about 70 GHz. Technical report, International Telecommunication Union (ITU), Recommendation ITU-R F.1336-4 (2014)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Engineering and Public PolicyCarnegie Mellon UniversityPittsburghUSA
  2. 2.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations