Cognition as a Tool for Green Next Generation Networks

  • Fabrizio Granelli
  • Oliver Holland
  • Nelson L. S. da Fonseca
Part of the Signals and Communication Technology book series (SCT)


The chapter discusses issues related to the implementation of the different steps of the cognitive cycle, especially focusing on reasoning, and applies this to energy saving for green networking. The application of cognition to networking and communications can be readily implemented into current TCP/IP networks. Indeed, the use of the cognitive paradigm represents a way: (i) to address the multiple temporal and spatial fluctuations in the operation of a network, and (ii) to gain and take advantage of additional causal information related to the network configuration and its performance. Network performance is a multi-faceted concept, including simple measures such as throughput as well as far more complicated or subjective measures such as user-level QoS. Recently, an additional parameter has been added to this equation: energy consumption. The need for identifying suitable methodologies to optimize performance from the above viewpoints, also including the contradictory requirement to save energy, is driving research interests towards the emergence of “green networks”. Green networking represents an appropriate scenario where cognition and associated radio adaptation can immensely contribute to the given objectives. This chapter describes how cognitive networking can be implemented to support green network operation, proposing a test case demonstrating its potential in a 3G cellular context. Experimental results based on real traffic data demonstrate the capability of a 3G base station to implement cognition to the purpose to save energy without any a-priori information.



The authors wish to express their gratitude to Dr. Christian Facchini for his contribution in studying the problem of reasoning in cognitive networks. Some findings and examples presented in this chapter were studied in his PhD thesis [24].

The authors would also like to acknowledge the interactions and groundwork of the Mobile VCE Core 5 “Green Radio” research programme and the ICT-ACROPOLIS Network of Excellence,, which have in some aspects contributed to the realisation of this work as well as CNPq Science without Border project 402480/2012-0


  1. 1.
    Clark, D.D., Partridge, C., Ramming, J.C., Wroclawski, J.T.: A knowledge plane for the internet. In: Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, (Karlsruhe, Germany), pp. 3–10, ACM (2003)Google Scholar
  2. 2.
    Thomas, R.W., DaSilva, L.A., MacKenzie, A.B.: Cognitive networks. In: 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DyS-PAN), pp. 352–360 (2005)Google Scholar
  3. 3.
    Mahonen, P., Petrova, M., Riihijarvi, J., Wellens, M.: Cognitive wireless networks: Your network just became a teenager. In: 25th Conference on Computer Communications. Barcelona (2006)Google Scholar
  4. 4.
    Sutton, P., Doyle, L.E., Nolan, K.E.: A reconfigurable platform for cognitive networks. In: 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), pp. 1–5 (2006)Google Scholar
  5. 5.
    Fletcher, D., Nguyen, D., Cios, K.: Autonomous synthesis of fuzzy cognitive maps from observational data: preliminaries. In: IEEE Aerospace Conference, pp. 1–9 (2005)Google Scholar
  6. 6.
    Kosko, B.: Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 24(1), 65–75 (1986)CrossRefMATHGoogle Scholar
  7. 7.
    Kosko, B.: Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice-Hall, New Jersey (1992)Google Scholar
  8. 8.
    Friend, D.H., Thomas, R.W., MacKenzie, A.B., DaSilva, L.A.: Distributed learning and reasoning in cognitive networks: methods and design decisions, ch. 9, pp. 223–246. Wiley-Interscience, New York (2007)Google Scholar
  9. 9.
    Thomas, R.W.: Cognitive networks. Ph.D. dissertation, Virginia Tech, Virginia (2007)Google Scholar
  10. 10.
    Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Comput. 36(1), 41–50 (2003)Google Scholar
  11. 11.
    Mitola, J.I.: Cognitive radio for flexible mobile multimedia communications. IEEE International Workshop on Mobile Multimedia Communications (MoMuC-99), San Diego, pp. 3–10 (1999)Google Scholar
  12. 12.
    Fortuna, C., Mohorcic, M.: Trends in the development of communication networks: cognitive networks. Comput. Netw. 53(9), 1354–1376 (2009)CrossRefGoogle Scholar
  13. 13.
    Aguilar, J.: A survey about fuzzy cognitive maps papers. Int. J. Comput. Cogn. 3, 27–33 (2005)Google Scholar
  14. 14.
    Hu, L., Kovacs, I.Z., Mogensen, P., Klein, O., Stormer, W.: Optimal new site deployment algorithm for heterogeneous cellular networks. In: IEEE Vehicular Technology Conference (VTC), Fall 2011Google Scholar
  15. 15.
    Fehske, A.J., Ritcher, F., Fettweis, G.P.: Energy efficiency improvement through micro sites in cellular mobile radio networks. In: IEEE Global Communications Conference (GLOBECOM) Workshops, (2009)Google Scholar
  16. 16.
    Ritcher, F., Fettweis, G.: Cellular mobile network densification utilizing micro base stations. In: IEEE International Conference on Communications (ICC) (2010)Google Scholar
  17. 17.
    Bousia, A., Antonopoulos, A., Alonso, L., Verikoukis, C.: Green distance-aware base station sleeping algorithm in LTE-Advanced. In: IEEE International Conference on Communications (ICC) (2012)Google Scholar
  18. 18.
    Bousia, A., Kartsakli, E., Antonopoulos, A., Alonso, L., Verikoukis, C.: Game theoretic approach for switching off base stations in multi-operator environments. In: IEEE International Conference on Communications (ICC) (2013)Google Scholar
  19. 19.
    Badic, B. et al.: Energy efficient radio access architectures for green radio: large versus small cell size deployment. In: IEEE 70th Vehicular Technology Conference Fall (VTC 2009-Fall), pp. 1–5 (2009)Google Scholar
  20. 20.
    3GPP TR 36.814 V9.0.0, 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Further advancements for E-UTRA physical layer aspects (Release 9), March 2010Google Scholar
  21. 21.
    Wang, L., Aghvami, H., Nafisi, N., Sallent, O., Perez-Romero, J.: Voice capacity with coverage-based CRRM in a heterogeneous UMTS/GSM environment. In: Second International Conference on Communications and Networking in China, CHINACOM (2007)Google Scholar
  22. 22.
    Rodriguez, J., Marques, P., Radwan, A., Moessner, K., Tafazolli, R., Raspopoulos, M., Stavrou, S., Trapps, P., Noquet, D., Sithamparanathan, K., Gomes, A., Piesiewicz, R., Mokrani, H., Foglar, A., Verikoukis, Ch.: Cognitive radio and cooperative strategies for power saving in multi-standard wireless devices, Future Network and Mobile Summit (2010)Google Scholar
  23. 23.
    Facchini, C., Holland, O., Granelli, F., da Fonseca, N.L.S., Aghvami, H.: Dynamic green self-configuration of 3G base stations using fuzzy cognitive maps. Comput. Netw. (2013)Google Scholar
  24. 24.
    Facchini, C.: Bridging the gap between theory and implementation in cognitive networks: developing reasoning in today’s networks. Ph.D. Thesis, University of Trento, Italy, Dec 2011Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fabrizio Granelli
    • 1
  • Oliver Holland
    • 2
  • Nelson L. S. da Fonseca
    • 3
  1. 1.Dept. of Information Engineering and Computer Science (DISI)University of TrentoTrentoItaly
  2. 2.King’s College LondonLondonUK
  3. 3.Institute of ComputingState University of CampinasSão PauloBrazil

Personalised recommendations