ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Implementation of platform for long-term evolution cell perspective resource utilization analysis

  • Um, Jungsun (Radio Resource Research Team, Electronics and Telecommunications Research Institute) ;
  • Kim, Igor (Radio Resource Research Team, Electronics and Telecommunications Research Institute) ;
  • Park, Seungkeun (Radio Resource Research Team, Electronics and Telecommunications Research Institute)
  • Received : 2019.10.14
  • Accepted : 2020.09.23
  • Published : 2021.04.15
Download PDF
⟨ Previous Next ⟩

Abstract

As wireless communication continues to develop in limited frequency resource environments, it is becoming important to identify the state of spectrum utilization and predict the amount needed in future. It is essential to collect reliable information for data analysis. This paper introduces a platform that enables the gathering of the scheduling information of a long-term evolution (LTE) cellular system without connecting to the network. A typical LTE terminal can confirm its assigned resource information using the configuration parameters delivered from a network. However, our platform receives and captures only the LTE signal over the air and then enables the estimation of the data relevant to scheduling for all terminals within an LTE cell. After extracting the control channel signal without loss from all LTE subframes, it detects valid downlink control information using the proposed algorithm, which is based on the error vector magnitude of depatterned symbols. We verify the reliability of the developed platform by comparing it with real data from mobile phones and service operators. The average difference in resource block utilization is only 0.28%.

Keywords

References

  1. M. Shafi et al., 5G: A tutorial overview of standards, trails, challenges, deployment and practice, IEEE J. Sel. Areas Commun. 35 (2017), no. 6, 1201-1221. https://doi.org/10.1109/JSAC.2017.2692307
  2. Cisco, Cisco Visual Networking Index: forecast and trends 2017-2022, Cisco White Paper, 2019.
  3. J. Guey et al., On 5G radio access architecture and technology [Industry Perspectives], IEEE. Wirel. Commun. 22 (2015), no. 5, 2-5. https://doi.org/10.1109/MWC.2015.7306369
  4. O. Yilmax, O. Teyeb, and A. Orsino, Overview of LTE-NR dual connectivity, IEEE Commun. Mag. 57 (2019), no. 6, 138-144. https://doi.org/10.1109/mcom.2019.1800431
  5. Evolved universal Terrestrial Radio Access (E-UTRA); Physical layer procedures; (Release 12), 3GPP, TR36.213 v12.1.0, 2013.
  6. J. Jeon, NR wide bandwidth operations, IEEE Commun. Mag. 56 (2018), no. 3, 42-46. https://doi.org/10.1109/mcom.2018.1700736
  7. E. Pateromickelakis et al., LAA as a key enabler in slice-aware 5G RAN: Challenges and opportunities, IEEE Commun. Stand. Mag. 2 (2018), no. 1, 29-35. https://doi.org/10.1109/mcomstd.2018.1700061
  8. A. Astaneh and S. Gheisari, Review and comparison of routing metrics in cognitive radio networks, Emerg. Sci. J. 2 (2018), no. 4, 191-201.
  9. M. Bichlera, V. Gretschkob, and M. Janssen, Bargaining in spectrum auctions: A review of the german auction in 2015, Telecommun. Policy 41 (2017), no. 5-6, 325-340. https://doi.org/10.1016/j.telpol.2017.01.005
  10. Y. Chen, L. Duan, and Q. Zhang, Financial analysis of 4G network deployment, in Proc. IEEE Conf. Comput. Commun. (Hong Kong) 2015, pp. 1607-1615.
  11. S. Yang and L. Hanzo, Fifty years of MIMO detection: The road to large-scale MIMOs, IEEE Commun. Surv. Tutor. 17 (2015), no. 4, 1941-1988. https://doi.org/10.1109/COMST.2015.2475242
  12. G. Y. Li et al., Multi-cell coordinated scheduling and MIMO in LTE, IEEE Commun. Surv. Tutor. 16 (2014), no. 2, 761-775. https://doi.org/10.1109/SURV.2014.022614.00186
  13. R. Liao et al., MU-MIMO MAC protocols for wireless local area networks: A survey, IEEE Commun. Surv. Tutor. 18 (2016), no. 1, 162-183. https://doi.org/10.1109/COMST.2014.2377373
  14. R. Mostafa et al., Closed-loop transmit diversity techniques for small wireless terminals and their performance assessment in a flat fading channel, ETRI J. 34 (2012), no. 3, 319-329. https://doi.org/10.4218/etrij.12.0111.0413
  15. C. Xu et al., Two decades of MIMO design tradeoffs and reduced-complexity MIMO detection in near-capacity systems, IEEE Access 5 (2017), 18564-18632. https://doi.org/10.1109/ACCESS.2017.2707182
  16. T. Nakamura et al., Trends in small cell enhancements in LTE advanced, IEEE Commun. Mag. 51 (2013), no. 2, 98-105. https://doi.org/10.1109/MCOM.2013.6461192
  17. RCRWireless reports, Small cells and network densification: Policy, spectrum, fiber and mobile networks, July 2019, available at https://www.rcrwireless.com/.
  18. RCRWireless Reports, Small cell testing update: Getting small cell networks right, Feb. 2016, available at https://www.rcrwireless.com/.
  19. Q. Ye et al., User association for load balancing in heterogeneous cellular networks, IEEE Trans. Wireless Commun. 12 (2013), no. 6, 2706-2716. https://doi.org/10.1109/TWC.2013.040413.120676
  20. S. Park et al., An evaluation methodology for spectrum usage in LTE-A networks: Traffic volume and resource utilization perspective, IEEE Access 7 (2019), 67863-67873. https://doi.org/10.1109/access.2019.2918646
  21. A. Mavrogiorgou et al., Internet of Medical Things (IoMT): Acquiring and transforming data into HL7 FHIR through 5G network slicing, Emerg. Sci. J. 3 (2019), no. 2, 64-77. https://doi.org/10.28991/esj-2019-01170
  22. D. S. Kalistratov, Wireless video monitoring of the megacities transport infrastructure, Civil Emerg. J. 5 (2019), no. 5, 1033-1040. https://doi.org/10.28991/cej-2019-03091309
  23. P. Si et al., Optimal cooperative internetwork spectrum sharing for cognitive radio systems with spectrum pooling, IEEE Trans. Veh. Technol. 59 (2010), no. 4, 1760-1768. https://doi.org/10.1109/TVT.2010.2041941
  24. A. Cokl et al., Stink bug communication with multimodal signals transmitted through air and substrate, Emerg. Sci. J. 3 (2019), no. 6, 407-424. https://doi.org/10.28991/esj-2019-01203
  25. K. Javadi and N. Komjani, Investigation into low SAR PIFA antenna and design a very low SAR U-slot antenna using frequency selective surface for cell-phones and wearable applications, Ital. J. Sci. Eng. 1 (2017), no. 6, 145-157.
  26. J. Um, I. Kim, and S. Park, Implementation of platform for measurement and analysis on LTE traffic and radio resource utilization, in Proc. IEEE Int. Conf. Consumer Electron. (Las Vegas, NV, USA), Jan. 2019, pp.1-2.
  27. S. Kumar et al., LTE radio analytics made easy and accessible, in Proc. ACM Conf. SIGCOMM (Chicago, IL, USA), Aug. 2014, pp. 211-222.
  28. R. Falkenberg, C. Ide, and C. Wietfeld, Client-based control channel analysis for connectivity estimation in LTE networks, in Proc. IEEE Veh. Technol. Conf. (Montreal, Canada), Sept. 2016, 2016, pp. 1-6.
  29. B. Nicola and W. Joerg, OWL a reliable online watcher for LTE control channel measurements, in Proc. Annu. Int. Conf. Mobile Comput. Netw. (New York, NY, USA), Oct. 2016, pp. 25-30
  30. Keysight Brochure, 89600 WLA software, available at https://www.literature.cdn.keysight.com/.
  31. Rohde & Schwarz Brochure, TSME Ultracompact Drive Test Scanner, available at https://www.rohde-schwarz.com/brochuredatasheet/tsme/.
  32. S. Sesia, I. Toufik, and M. Baker, LTE-the UMTS long term evolution: From theory to practice, 2nd ed, John Wiley & Sons Ltd., UK, 2011.
  33. Evolved Universal Terrestrial Radio Access (E-UTRA); Multiplexing and channel coding; (Release 12), TR36.212 v12.1.0, 2013.
  34. F. C. Robey et al., A CFAR adaptive matched filter detector, IEEE Trans. Aerosp. Electron. Syst. 28 (1992), no. 1, 208-216. https://doi.org/10.1109/7.135446
  35. A. Mashhour and A. Borjak, A method for computing error vector magnitude in GSM EDGE systems-simulation results, IEEE Commun. Lett. 5 (2001), no. 3, 88-91. https://doi.org/10.1109/4234.913149