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Autonomous Spectrum Assignment of White Space Devices

  • Chaitali DiwanEmail author
  • Srinath Srinivasa
  • Bala Murali Krishna
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 228)

Abstract

White-space spectrum has temporal and spatial variations, and fragmentation, making the spectrum assignment for devices in this space challenging. In this paper, we propose an autonomous agent model for spectrum assignment of white space devices at a given location. Each white space device (WSD) acts autonomously out of self-interest, choosing a strategy from its bag of strategies. It obtains a payoff based on its choice and choices made by all other WSDs. Based on the payoffs received by different strategies, WSDs evolve their strategic profile over time. This has the effect of demographic changes in the population which is published as demographic profile by the Master. WSDs are expected to choose a strategy with a probability distribution based on this, for optimising network utilisation. In evaluation runs, network utilisation levels in such an approach are found to be high, and approaching optimal values computed in a centralised fashion.

Keywords

White spaces Dynamic spectrum access Multi-agent systems Evolutionary game theory White space database Optimising spectrum utilisation 

Notes

Acknowledgements

This work is partially supported by EU-India REACH Project under Grant ICI+/2014/342-896. The project aims to develop advanced technical solutions for providing high-speed broadband internet access in rural India in the unlicensed white space spectrum.

References

  1. 1.
    Zhu, L., Chen, V., Malyar, J., Das, S., McCann, P.: Protocol to access white-space (PAWS) databases (2015)Google Scholar
  2. 2.
    Murty, R., Chandra, R., Moscibroda, T., Bahl, P.: Senseless: a database-driven white spaces network. IEEE Trans. Mob. Comput. 11(2), 189–203 (2012)CrossRefGoogle Scholar
  3. 3.
    Li, C., Liu, W., Li, J., Liu, Q., Li, C.: Aggregation based spectrum allocation in cognitive radio networks. In: 2013 IEEE/CIC International Conference on Communications in China-Workshops (CIC/ICCC), pp. 50–54. IEEE (2013)Google Scholar
  4. 4.
    Halldórsson, M.M., Halpern, J.Y., Li, L.E., Mirrokni, V.S.: On spectrum sharing games. In: Proceedings of the Twenty-Third Annual ACM Symposium on Principles of Distributed Computing, pp. 107–114. ACM (2004)Google Scholar
  5. 5.
    Cao, L., Zheng, H.: Distributed spectrum allocation via local bargaining. In: SECON, pp. 475–486 (2005)Google Scholar
  6. 6.
    Chen, D., Zhang, Q., Jia, W.: Aggregation aware spectrum assignment in cognitive ad-hoc networks. In: 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom 2008, pp. 1–6. IEEE (2008)Google Scholar
  7. 7.
    Cao, L., Zheng, H.: Distributed rule-regulated spectrum sharing. IEEE J. Sel. Areas Commun. 26(1), 130–145 (2008)CrossRefGoogle Scholar
  8. 8.
    Nie, N., Comaniciu, C.: Adaptive channel allocation spectrum etiquette for cognitive radio networks. Mob. Netw. Appl. 11(6), 779–797 (2006)CrossRefGoogle Scholar
  9. 9.
    Suris, J.E., DaSilva, L.A., Han, Z., MacKenzie, A.B.: Cooperative game theory for distributed spectrum sharing. In: IEEE International Conference on Communications, ICC 2007, pp. 5282–5287. IEEE (2007)Google Scholar
  10. 10.
    Bourdena, A., Kormentzas, G., Pallis, E., Mastorakis, G.: A centralised broker-based CR network architecture for TVWS exploitation under the rtssm policy. In: 2012 IEEE International Conference on Communications (ICC), pp. 5685–5689. IEEE (2012)Google Scholar
  11. 11.
    Bourdena, A., Kormentzas, G., Skianis, C., Pallis, E., Mastorakis, G.: Real-time TVWS trading based on a centralized CR network architecture. In: 2011 IEEE GLOBECOM Workshops (GC Workshop), pp. 964–969. IEEE (2011)Google Scholar
  12. 12.
    Pei, Y., Ma, Y., Peh, E.C.Y., Oh, S.W., Tao, M.-H.: Dynamic spectrum assignment for white space devices with dynamic and heterogeneous bandwidth requirements. In: 2015 IEEE Wireless Communications and Networking Conference (WCNC), pp. 36–40. IEEE (2015)Google Scholar
  13. 13.
    Kash, I.A., Murty, R., Parkes, D.C.: Enabling spectrum sharing in secondary market auctions. IEEE Trans. Mob. Comput. 13(3), 556–568 (2014)CrossRefGoogle Scholar
  14. 14.
    Chen, X., Huang, J.: Evolutionarily stable open spectrum access in a many-users regime. In: 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011), pp. 1–5. IEEE (2011)Google Scholar
  15. 15.
    Anandkumar, A., Michael, N., Tang, A.: Opportunistic spectrum access with multiple users: learning under competition. In: 2010 Proceedings IEEE INFOCOM, pp. 1–9. IEEE (2010)Google Scholar
  16. 16.
    Liu, K., Zhao, Q.: Decentralized multi-armed bandit with multiple distributed players. In: Information Theory and Applications Workshop (ITA), pp. 1–10. IEEE (2010)Google Scholar
  17. 17.
    Li, H.: Multi-agent q-learning of channel selection in multi-user cognitive radio systems: a two by two case. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, pp. 1893–1898. IEEE (2009)Google Scholar
  18. 18.
    Felegyhazi, M., Čagalj, M., Hubaux, J.-P.: Efficient MAC in cognitive radio systems: a game-theoretic approach. IEEE Trans. Wireless Commun. 8(4), 1984–1995 (2009)CrossRefGoogle Scholar
  19. 19.
    Niyato, D., Hossain, E.: Competitive spectrum sharing in cognitive radio networks: a dynamic game approach. IEEE Trans. Wireless Commun. 7(7), 2651–2660 (2008)CrossRefGoogle Scholar
  20. 20.
    Han, Z., Pandana, C., Liu, K.: Distributive opportunistic spectrum access for cognitive radio using correlated equilibrium and no-regret learning. In: IEEE Wireless Communications and Networking Conference, WCNC 2007, pp. 11–15. IEEE (2007)Google Scholar
  21. 21.
    Chen, X., Huang, J.: Spatial spectrum access game: nash equilibria and distributed learning. In: Proceedings of the Thirteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 205–214. ACM (2012)Google Scholar
  22. 22.
    Xu, Y., Wang, J., Wu, Q., Anpalagan, A., Yao, Y.-D.: Opportunistic spectrum access in cognitive radio networks: global optimization using local interaction games. IEEE J. Sel. Top. Sig. Process. 6(2), 180–194 (2012)CrossRefGoogle Scholar
  23. 23.
    Wicke, D., Freelan, D., Luke, S.: Bounty hunters and multiagent task allocation. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 387–394. International Foundation for Autonomous Agents and Multiagent Systems (2015)Google Scholar
  24. 24.
    Jain, R.: REACH Internal Technical Report - Internet usage data in rural India. IIM Ahmedabad, India (2017)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Chaitali Diwan
    • 1
    Email author
  • Srinath Srinivasa
    • 1
  • Bala Murali Krishna
    • 1
  1. 1.International Institute of Information TechnologyBangaloreIndia

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