Wireless Personal Communications

, Volume 92, Issue 1, pp 107–126 | Cite as

Distributed REM-Assisted Radio Resource Management in LTE-A Networks

  • Tomaž Javornik
  • Aleš Švigelj
  • Andrej Hrovat
  • Mihael Mohorčič
  • Kemal Alič


The successful actions of public-safety personnel during disaster recovery depend heavily on rapidly deployable and reliable mission-critical communication networks. As part of the Aerial Base Stations with Opportunistic Links for Unexpected Temporary Events project we focused on designing, prototyping and demonstrating a high-capacity, IP, mobile-data network with a low latency and large coverage, suitable for many forms of multi-media delivery, including public-safety and temporary-event use cases. In this paper we focus on a rapidly deployable wireless network based on the LTE-A-enabled, low-altitude Platforms and portable land mobile units to support disaster-relief activities. In order to minimize the inter- and intra-network interference during the radio networks operating phase, we have proposed and evaluated a novel, central-based, dynamic radio resource management algorithm for downlink communications that applies radio-interference maps from the radio environment map and traffic demands at a particular eNB. Using this we are able to efficiently allocate radio resources based on quality-of-service demands. The radio environmental maps are used to calculate the radio coverage and signal strength. In addition, we present the developed framework, which can be applied as a tool for the design, modelling, simulation and evaluation of an LTE-A network for emergency use cases and for estimating the system capacity in a dynamic (roll-in, roll-out phase) network deployment. The proposed algorithm is evaluated with the simulation model using possible real use cases (i.e., forest fire, and earthquake in an urban area) in real remote and urban regions of Slovenia.


LTE Radio resource management Performance evaluation Simulations Radio environmental maps 



This work has been in part funded by the European Union from Social Fund and the FP7 Project ABSOLUTE (FP7-ICT-318632).


  1. 1.
    Johnson, C. W. (2012). Long term evolution in bullets. Northampton, England: Chris Johnson.Google Scholar
  2. 2.
    Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.CrossRefGoogle Scholar
  3. 3.
    Mitola, J, I. I. I., & Maguire, G. Q, Jr. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.CrossRefGoogle Scholar
  4. 4.
    Song, M., Xin, C., Zhao, Y., & Cheng, X. (2012). Dynamic spectrum access: From cognitive radio to network radio. IEEE Wireless Communications, 19(1), 23–29.CrossRefGoogle Scholar
  5. 5.
    Yücek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials, 11(1), 116–130.CrossRefGoogle Scholar
  6. 6.
    Pesko, M., Javornik, T., Kosir, A., Stular, M., & Mohorcic, M. (2014). Radio environment maps: The survey of construction methods. TIIS, 8(11), 3789–3809.Google Scholar
  7. 7.
    Clancy, C., Hecker, J., Stuntebeck, E., & Shea, T. O. (2007). Applications of machine learning to cognitive radio networks. IEEE Wireless Communications, 14(4), 47–52.CrossRefGoogle Scholar
  8. 8.
    Kleinrock, L. (1975). Queueing systems, volume I: Theory. New York: Wiley Interscience.zbMATHGoogle Scholar
  9. 9.
    Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge: MIT Press.Google Scholar
  10. 10.
    Nie, J., & Haykin, S. (1999). A Q-learning-based dynamic channel assignment technique for mobile communication systems. IEEE Transactions on Vehicular Technology, 48(5), 1676–1687.CrossRefGoogle Scholar
  11. 11.
    Galindo-Serrano, A., & Giupponi, L. (2010). Distributed Q-learning for aggregated interference control in cognitive radio networks. IEEE Transactions on Vehicular Technology, 59(4), 1823–1834.CrossRefGoogle Scholar
  12. 12.
    Kapetanakis, S., & Kudenko, D. (2002). Reinforcement learning of coordination in cooperative multi-agent systems. AAAI/IAAI, 2002, 326–331.zbMATHGoogle Scholar
  13. 13.
    Morozs, N., Clarke, T., Grace, D., & Zhao, Q. (2014). Distributed Q-learning based dynamic spectrum management in cognitive cellular systems: Choosing the right learning rate. In 2014 IEEE symposium on computers and communication (ISCC) (pp. 1–6). IEEE.Google Scholar
  14. 14.
    ABSOLUTE. (2014). Aerial base stations with opportunistic links for unexpected and temporary events. [online].
  15. 15.
    Baldini, G., Karanasios, S., Allen, D., & Vergari, F. (2014). Survey of wireless communication technologies for public safety. IEEE Communications Surveys Tutorials, 16, 619–641.CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Javornik, T., Hrovat, A., Vilhar, A., Vucnik, M., Ozimek, I., & Pesko, M. (2014). Radio environment map (REM): An approach for provision wireless communications in disaster areas. In 2014 1st International workshop on cognitive cellular systems (CCS) (pp. 1–5). IEEE.Google Scholar
  18. 18.
    Denkovski, D., Atanasovski, V., Gavrilovska, L., Riihijärvi, J., & Mähönen, P. (2012). Reliability of a radio environment map: Case of spatial interpolation techniques. In 2012 7th International ICST conference on cognitive radio oriented wireless networks and communications (CROWNCOM) (pp. 248–253).Google Scholar
  19. 19.
    Gomez, K., Goratti, L., Sithamparanathan, K., Zhao, Q., Grace, D., Svigelj, A., et al. (2015). System capacity assessments.
  20. 20.
    FARAMIR. Faramir web page. (2015). Available
  21. 21.
    Cai, T., van de Beek, J., Sayrac, B., Grimoud, S., Nasreddine, J., Riihijärvi, J., et al. (2011). Design of layered radio environment maps for ran optimization in heterogeneous LTE systems. In 2011 IEEE 22nd international symposium on personal communications: indoor and mobile radio (pp. 172–176).Google Scholar
  22. 22.
    Pesko, M., Javornik, T., Vidmar, L., Košir, A., Štular, M., & Mohorčič, M. (2015). The indirect self-tuning method for constructing radio environment map using omnidirectional or directional transmitter antenna. EURASIP Journal on Wireless Communications and Networking, 2015(1), 1–12.CrossRefGoogle Scholar
  23. 23.
    GRASS Development Team. (2012). Geographic resources analysis support system (GRASS) software. [online].
  24. 24.
    Hrovat, A., Ozimek, I., Vilhar, A., Celcer, T., Saje, I., & Javornik, T. (2010). Radio coverage calculations of terrestrial wireless networks using an open-source grass system. WSEAS Transactions on Communications, 9(10), 646–657.Google Scholar
  25. 25.
    Sundaresan, K., Arslan, M. Y., Singh, S., Rangarajan, S., & Krishnamurthy, S. V. (2016). FluidNet: A flexible cloud-based radio access network for small cells. IEEE/ACM Transactions on Networking, 24, 915–928.CrossRefGoogle Scholar
  26. 26.
    Atanasovski, V., van de Beek, J., Dejonghe, A., Denkovski, D.,Gavrilovska, L., Grimoud, S., et al. (2011). Constructingradio environment maps with heterogeneous spectrum sensors. In 2011 IEEE symposium on new frontiers in dynamic spectrum access networks (DySPAN) (pp. 660–661).Google Scholar
  27. 27.
    Denkovski, D., Rakovic, V., Pavloski, M., Chomu, K., Atanasovski, V., & Gavrilovska, L. (2012). Integration of heterogeneous spectrum sensing devices towards accurate REM construction. In2012 IEEE wireless communications and networking conference (WCNC) (pp. 798–802).Google Scholar
  28. 28.
    van de Beek, J., LidstrÃm, E., Cai, T., Xie, Y., Rakovic, V., Atanasovski, V., et al. (2012). Rem-enabled opportunistic LTE in the tv band. In 2012 IEEE international symposium on dynamic spectrum access networks (DYSPAN) (pp. 272–273).Google Scholar
  29. 29.
    Iacobelli, L., Fouillot, P., & Martret, C. J. L. (2012). Radio environment map based architecture and protocols for mobile ad hoc networks. In 2012 The 11th annual mediterranean Ad Hoc networking workshop (Med-Hoc-Net) (pp. 32–38).Google Scholar
  30. 30.
    Wang, S., Wang, Y., Coon, J. P., & Doufexi, A. (2012). Energy-efficient spectrum sensing and access for cognitive radio networks. IEEE Transactions on Vehicular Technology, 61, 906–912.CrossRefGoogle Scholar
  31. 31.
    Libnik, R., Svigelj, A., & Kandus, G. (2008). Performance evaluation of sip based handover in heterogeneous access networks. WSEAS Transactions on Communications, 7(5), 448–458.Google Scholar
  32. 32.
    Ericsson Radio Systems, A. (2006). TEMS cellplanner universal common features, reference manual. Tech. Rep. Erricsson.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Tomaž Javornik
    • 1
  • Aleš Švigelj
    • 1
  • Andrej Hrovat
    • 1
  • Mihael Mohorčič
    • 1
  • Kemal Alič
    • 1
  1. 1.Department of Communication SystemsJožef Stefan InstituteLjubljanaSlovenia

Personalised recommendations