Journal of Communications and Networks

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지
DOI QR코드

DOI QR Code

Stochastic MAC-layer Interference Model for Opportunistic Spectrum Access: A Weighted Graphical Game Approach

  • Zhao, Qian (College of Communications Engineering, PLA University of Science and Technology) ;
  • Shen, Liang (College of Communications Engineering, PLA University of Science and Technology) ;
  • Ding, Cheng (College of Communications Engineering, PLA University of Science and Technology)
  • Received : 2014.11.19
  • Accepted : 2015.07.01
  • Published : 2016.06.30
⟨ Previous Next ⟩

Abstract

This article investigates the problem of distributed channel selection in opportunistic spectrum access networks from a perspective of interference minimization. The traditional physical (PHY)-layer interference model is for information theoretic analysis. When practical multiple access mechanisms are considered, the recently developed binary medium access control (MAC)-layer interference model in the previous work is more useful, in which the experienced interference of a user is defined as the number of competing users. However, the binary model is not accurate in mathematics analysis with poor achievable performance. Therefore, we propose a real-valued one called stochastic MAC-layer interference model, where the utility of a player is defined as a function of the aggregate weight of the stochastic interference of competing neighbors. Then, the distributed channel selection problem in the stochastic MAC-layer interference model is formulated as a weighted stochastic MAC-layer interference minimization game and we proved that the game is an exact potential game which exists one pure strategy Nash equilibrium point at least. By using the proposed stochastic learning-automata based uncoupled algorithm with heterogeneous learning parameter (SLA-H), we can achieve suboptimal convergence averagely and this result can be verified in the simulation. Moreover, the simulated results also prove that the proposed stochastic model can achieve higher throughput performance and faster convergence behavior than the binary one.

Keywords

Acknowledgement

Supported by : National Science Foundation of China

References

  1. Q. Zhao and B. Sadler, "A survey of dynamic spectrum access," IEEE Signal Process. Mag., vol. 24, no. 3, pp. 79-89, May 2007. https://doi.org/10.1109/MSP.2007.361604
  2. Y. Xu et al., "Decision-theoretic distributed channel selection for opportunistic spectrum access: Strategies, challenges and solutions," IEEE Commun. Surv. Tut., vol. 15, no. 4, pp. 1689-1713, 2013. https://doi.org/10.1109/SURV.2013.030713.00189
  3. M. Liu et al., "Congestion games with resource reuse and applications in spectrum sharing," GameNets, pp. 171-179, 2009.
  4. Y. Xu et al., "Distributed channel selection in CRAHNs with heterogeneous spectrum opportunities: A local congestion game approach," IEICE Trans. Commun., vol. E95-B, no. 3, pp. 991-994, 2012. https://doi.org/10.1587/transcom.E95.B.991
  5. P. Gupta and P. R. Kumar, "The capacity of wireless networks," IEEE Trans. Inf. Theory, vol. 46, no. 2, pp. 388-404, 2000. https://doi.org/10.1109/18.825799
  6. C. Lacatus and D. Popescu, "Adaptive interference avoidance for dynamic wireless systems: A game-theoretic approach," IEEE J. Sel. Topics Signal Process., vol. 1, no. 1, pp. 189-202, 2007. https://doi.org/10.1109/JSTSP.2007.897060
  7. Y. Ding, Y. Huang, G. Zeng, and L. Xiao, "Using partially overlapping channels to improve throughput in wireless mesh networks," IEEE Trans. Mobile Comput., vol. 11, no. 11, pp. 1720-1733, 2012. https://doi.org/10.1109/TMC.2011.215
  8. Y. Xu, Q. Wu, L. Shen, J. Wang, and A. Anpalagan, "Opportunistic spectrum access with spatial reuse: Graphical game and uncoupled learning solutions," IEEE Trans. Wireless Commun., vol. 12, no. 10, pp. 4814-4826, 2013. https://doi.org/10.1109/TWC.2013.092013.120862
  9. Y. Xu et al., "Opportunistic spectrum access in cognitive radio networks: Global optimization using local interaction games," IEEE J. Sel. Signal Process., vol. 6, no. 2, pp. 180-194, 2012. https://doi.org/10.1109/JSTSP.2011.2176916
  10. N. Nie and C. Comaniciu, "Adaptive channel allocation spectrum etiquette for cognitive radio networks," Mobile Netw. Applications, vol. 11, no. 6, pp. 779-797, 2006. https://doi.org/10.1007/s11036-006-0049-y
  11. M. Maskery et al., "Decentralized dynamic spectrum access for cognitive radios: Cooperative design of a non-cooperative game," IEEE Trans. Commun., vol. 57, no. 2, pp. 459-469, 2009. https://doi.org/10.1109/TCOMM.2009.02.070158
  12. M. Felegyhazi, M. Cagalj, and J. P. Hubaux, "Efficient MAC in cognitive radio systems: A game-theoretic approach," IEEE Trans. Wireless Commun., vol. 8, no. 4, pp. 1984-1995, 2009. https://doi.org/10.1109/TWC.2009.080284
  13. C. Peng, H. Zheng, and B. Zhao, "Utilization and fairness in spectrum assignemnt for opportunistic spectrum access," Mobile Netw. App, vol. 11, no. 4, pp. 555-576, 2006. https://doi.org/10.1007/s11036-006-7322-y
  14. H. Li and Z. Han, "Competitive spectrum access in cognitive radio networks: Graphical game and learning," in Proc. IEEE WCNC, 2010.
  15. M. Azarafrooz and R. Chandramouli, "Distributed learning in secondary spectrum sharing graphical game," in Proc. IEEE GLOBECOM, pp. 1-6, 2011.
  16. Q. D. La, Y. H. Chew, and B. H. Soong, "An interference-minimization potential game for OFDMA-based distributed spectrum sharing systems," IEEE Trans. Veh. Technol., vol. 60, no. 7, pp. 3374-3385, Sept. 2011. https://doi.org/10.1109/TVT.2011.2158596
  17. G. Stuber, Principles of Mobile Communications, 2nd ed. Kluwer Academic Publishers, 2001.
  18. D. Niyato, E. Hossain, and Z. Han, "Dynamic spectrum access in IEEE 802.22-based cognitive wireless networks: A game theoretic model for competitive spectrum bidding and pricing," IEEE Wireless Commun., vol. 16, no. 2, pp. 16-23, 2009. https://doi.org/10.1109/MWC.2009.4907555
  19. J. Jia, Q. Zhang, and X. Shen, "HC-MAC: A hardware-constrained cognitive MAC for efficient spectrum management," IEEE J. Sel. Areas Commun., vol. 26, no. 1, pp. 466-479, 2008.
  20. J. M. Koljonen et al., "Distributed generalized graph coloring," in Proc. IEEE SASO, 2010.
  21. D. Monderer and L. S. Shapley, "Potential games," Games Economic Behavior, vol. 14, pp. 124-143, 1996. https://doi.org/10.1006/game.1996.0044
  22. H. Kameda and E. Altman, "Inefficient noncooperation in networking games of common-pool resources," Games Economic Behavior, vol. 26, no. 7, pp. 1260-1268, 2008.
  23. Y. Xu, Q. Wu, J. Wang, L. Shen, and A. Anpalagan, "Opportunistic spectrum access using partially overlapping channels: Graphical game and uncoupled learning," IEEE Trans. Commun., vol. 61, no. 9, pp. 3906-3918, 2013. https://doi.org/10.1109/TCOMM.2013.072913.120881