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TAMSA: Two-Stage Auction Mechanism for Spectrum Allocation in Cooperative Cognitive Radio Networks

  • Xinxiang Zhang
  • Jigang Wu
  • Long Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)

Abstract

Cooperative cognitive radio networks have been proposed to address spectrum starvation problem and enhance the transmission rate of mobile devices. Most works assume one user could afford the whole spectrum and neglect the selfishness nature, which is not practical. Based on group-buying, a two-stage auction mechanism named TAMSA is proposed to guarantee the quality of service and improve the utilization ratio of spectrum resources. TAMSA is an incentive mechanism involving the primary users (PUs) and relay nodes. TAMSA can also reduce the cost of the secondary users (SUs) and increase utilities for both PUs and relay nodes. In the first stage, SUs submit their budgets, valuations and demands for spectrum resources to relay nodes in group-buying, relay nodes calculate revenues and determine the winning SUs. In the second stage, we execute VCG auction between the relay nodes and PUs, with a maximum-weighted-matching algorithm. TAMSA can effectively allocate spectrum resources to meet the demands of SUs. We show that TAMSA is truthful, individual rational and computational efficient. Extensive simulation results show that TAMSA outperforms random algorithm by 256% in terms of average utility of PUs. TAMSA is able to improve the average utility of SUs and relay nodes significantly up to 213% and 10 times respectively. TAMSA is further improved by 28.33% and 78.65% in terms of average utility of PUs over TASG and TACC, respectively.

Keywords

Spectrum allocation VCG auction Incentive mechanism Cooperative cognitive radio networks 

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61702115 and 61672171, Natural Science Foundation of Guangdong, China under Grant No. 2018B030311007, and Major R&D Project of Educational Commission of Guangdong under Grant No. 2016KZDXM052. This work was also supported by China Postdoctoral Science Foundation Fund under Grant No. 2017M622632.

References

  1. 1.
    Zheng, Z., Wu, F., Tang, S., et al.: AEGIS: an unknown combinatorial auction mechanism framework for heterogeneous spectrum redistribution in noncooperative wireless networks. IEEE/ACM Trans. Netw. 24(3), 1919–1932 (2016)CrossRefGoogle Scholar
  2. 2.
    Zhu, Y., Li, B., Li, Z., et al.: Truthful spectrum auction design for secondary networks. In: INFOCOM, pp. 873–881. IEEE, Orlando, FL, USA (2012)Google Scholar
  3. 3.
    Chen, L., Huang, L., Xu, H., et al.: Optimal channel allocation for multi-PU and multi-SU pairs in underlay cognitive radio networks. Int. J. Ad Hoc Ubiquitous Comput. 27(1), 19–33 (2018)CrossRefGoogle Scholar
  4. 4.
    Wang, X., Huang, L., Xu, H., et al.: Truthful auction for resource allocation in cooperative cognitive radio networks. In: 24th International Conference on Computer Communication and Networks, pp. 1–8. IEEE, Las Vegas, NV, USA (2015)Google Scholar
  5. 5.
    Wang, X., Huang, L., Xu, H., et al.: Social welfare maximization auction for secondary spectrum markets: a long-term perspective. In: 13th IEEE International Conference on Sensing, Communication, and Networking, Communication, and Networking, pp. 1–9. IEEE, London, UK (2016)Google Scholar
  6. 6.
    Shen, F., Li, D., Lin, P.H., et al.: Auction based spectrum sharing for hybrid access in macro-femtocell networks under QoS requirements. In: IEEE International Conference on Communications, pp. 3335–3340. IEEE, London, UK (2015)Google Scholar
  7. 7.
    Wang, H., Liu, Z., Cheng, Z., et al.: Maximization of link capacity by joint power and spectrum allocation for smart satellite transponder. In: 23rd Asia-Pacific Conference on Communications, pp. 1–6. IEEE, Perth, WA, Australia (2017)Google Scholar
  8. 8.
    Jia, J., Zhang, Q., Zhang, Q., et al.: Revenue generation for truthful spectrum auction in dynamic spectrum access. In: 10th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 3–12. ACM, New Orleans, Louisiana, USA (2009)Google Scholar
  9. 9.
    Liu, Y., Tao, M., Huang, J.: An auction approach to distributed power allocation for multiuser cooperative networks. IEEE Trans. Wirel. Commun. 12(1), 237–247 (2012)CrossRefGoogle Scholar
  10. 10.
    Shi, W., Zhang, L., Wu, C., et al.: An online auction framework for dynamic resource provisioning in cloud computing. IEEE-ACM Trans. Netw. 24(4), 2060–2073 (2016)CrossRefGoogle Scholar
  11. 11.
    Feng, Z., Zhu, Y., Zhang, Q., et al.: TRAC: truthful auction for location-aware collaborative sensing in mobile crowdsourcing. In: INFOCOM, pp. 1231–1239. IEEE, Toronto, ON, Canada (2014)Google Scholar
  12. 12.
    Wu, F., Vaidya, N.: A strategy-proof radio spectrum auction mechanism in noncooperative wireless networks. IEEE Trans. Mob. Comput. 12(5), 885–894 (2013)CrossRefGoogle Scholar
  13. 13.
    Lee, C., Wang, P., Niyato, D.: A real-time group auction system for efficient allocation of cloud internet applications. IEEE Trans. Serv. Comput. 8(2), 251–268 (2015)CrossRefGoogle Scholar
  14. 14.
    Lin, P., et al.: Groupon in the Air: A three-stage auction framework for Spectrum Group-buying. In: INFOCOM, pp. 2013–2021. IEEE, Turin, Italy (2013)Google Scholar
  15. 15.
    Advaita, A., Gali, M.M., Chu, T.M.C., et al.: Outage probability of MIMO cognitive cooperative radio networks with multiple AF relays using orthogonal space-time block codes. In: Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 84–89. IEEE, Rome, Italy (2017)Google Scholar
  16. 16.
    Yang, D., Xue, G., Zhang, X.: Group buying spectrum auctions in cognitive radio networks. IEEE Trans. Veh. Technol. 66(1), 810–817 (2017)MathSciNetGoogle Scholar
  17. 17.
    Yang, D., Fang, X., Xue, G.: Truthful auction for cooperative communications. In: IEEE International Conference on Communications, pp. 1–10. IEEE, Ottawa, ON, Canada (2011)Google Scholar
  18. 18.
    Chen, L., Wu, J., Zhang, X.X., et al.: TARCO: two-stage auction for D2D relay aided computation resource allocation in HetNet. IEEE Trans. Serv. Comput. PP(99), 1 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Guangdong University of TechnologyGuangzhouChina

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