Abstract
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.
Similar content being viewed by others
References
Johnson, C. W. (2012). Long term evolution in bullets. Northampton, England: Chris Johnson.
Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.
Mitola, J, I. I. I., & Maguire, G. Q, Jr. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.
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.
Yücek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials, 11(1), 116–130.
Pesko, M., Javornik, T., Kosir, A., Stular, M., & Mohorcic, M. (2014). Radio environment maps: The survey of construction methods. TIIS, 8(11), 3789–3809.
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.
Kleinrock, L. (1975). Queueing systems, volume I: Theory. New York: Wiley Interscience.
Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge: MIT Press.
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.
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.
Kapetanakis, S., & Kudenko, D. (2002). Reinforcement learning of coordination in cooperative multi-agent systems. AAAI/IAAI, 2002, 326–331.
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.
ABSOLUTE. (2014). Aerial base stations with opportunistic links for unexpected and temporary events. [online]. http://www.absolute-project.eu/.
Baldini, G., Karanasios, S., Allen, D., & Vergari, F. (2014). Survey of wireless communication technologies for public safety. IEEE Communications Surveys Tutorials, 16, 619–641.
OPNET. OPNET web page. (2015). Available http://www.riverbed.com/products/performance-management-control/opnet.html.
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.
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).
Gomez, K., Goratti, L., Sithamparanathan, K., Zhao, Q., Grace, D., Svigelj, A., et al. (2015). System capacity assessments. http://www.absolute-project.eu/.
FARAMIR. Faramir web page. (2015). Available http://www.ict-faramir.eu/.
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).
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.
GRASS Development Team. (2012). Geographic resources analysis support system (GRASS) software. [online]. http://grass.osgeo.org/.
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.
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.
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).
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).
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).
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).
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.
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.
Ericsson Radio Systems, A. (2006). TEMS cellplanner universal common features, reference manual. Tech. Rep. Erricsson.
Acknowledgements
This work has been in part funded by the European Union from Social Fund and the FP7 Project ABSOLUTE (FP7-ICT-318632).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Javornik, T., Švigelj, A., Hrovat, A. et al. Distributed REM-Assisted Radio Resource Management in LTE-A Networks. Wireless Pers Commun 92, 107–126 (2017). https://doi.org/10.1007/s11277-016-3841-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-016-3841-4