Interference Mitigation Based on Intelligent Location Selection in a Canonical Communication Network

Junyue Qu; Yueming Cai; Jianchao Zheng; Wendong Yang; Weiwei Yang; Yajie Hu

doi:10.1515/freq-2015-0164

Article

Interference Mitigation Based on Intelligent Location Selection in a Canonical Communication Network

Junyue Qu , Yueming Cai , Jianchao Zheng , Wendong Yang , Weiwei Yang and Yajie Hu

Published/Copyright: December 8, 2015

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From the journal Frequenz Volume 70 Issue 1-2

Abstract

In this letter, the interference mitigation in a canonical communication network is discussed from the perspective of intelligent location selection. A potential game model is constructed and a location-selection algorithm is designed combining no-regret procedure. With the proposed algorithm, all nodes can update their strategies with limited information exchange. Specifically, our proposed algorithm can converge to a set of correlated equilibria which are the globally or locally optimal solution to the problem of interference minimization. Moreover, our proposed algorithm can achieve distributed implementation without a central node. Simulation results demonstrate that the total interference can be mitigated efficiently with our proposed algorithm. And the proposed algorithm can converge fast.

Keywords: interference mitigation; potential game; no regret learning; best response; intelligent location selection

Acknowledgement

This work is supported by the Project of Natural Science Foundations of China (No. 61301162 and 61301163) and the Jiangsu Provincial Natural Science Foundation of China (No. BK20130067).

Appendix

The proof that the proposed game G is a standard potential game is shown here.

(7)2Φln,l−n=−∑n=1NIn=−∑n=1N∑m=1,m≠nNpHmn=−∑m=1,m≠nNpHmn+∑q=1,q≠nN∑m=1,m≠qNpHmq=−∑m=1,m≠nNpHmn−∑m=1,m≠nNpHnm−∑q=1,q≠nN∑m=1,m≠q,m≠nNpHmq=−∑m=1,m≠nNp0.097βlmx−lnx2+lmy−lny2−α−∑m=1,m≠nNp0.097βlnx−lmx2+lny−lmy2−α−∑q=1,q≠nN∑m=1,m≠q,m≠nNp0.097βlmx−lqx2+lmy−lqy2−α

And then,

(8)Φ(ln,l−n)−Φ(l′n,l−n)=−∑m=1,m≠nNp0.097β[(lmx−lnx)2+(lmy−lny)2]−α−12∑q=1,q≠nN∑m=1,m≠q,m≠nNp0.097β[(lmx−lqx)2+(lmy−lqy)2]−α+∑m=1,m≠nNp0.097β[(lmx−l′nx)2+(lmy−l′ny)2]−α+12∑q=1,q≠nN∑m=1,m≠q,m≠nNp0.097β[(lmx−lqx)2+(lmy−lqy)2]−α=∑m=1,m≠nNp0.097β[(lmx−l′nx)2+(lmy−l′ny)2]−α−∑m=1,m≠nNp0.097β[(lmx−lnx)2+(lmy−lny)2]−α=un(ln,l−n)−un(l′n,l−n).

The proof is over.

References

[1] W. Nam, D. Bai, J. Lee, and I. Kang, “Advanced interference management for 5G cellular networks,” IEEE Commun. Mag., vol. 52, pp. 52–60, May 2014.10.1109/MCOM.2014.6815893Search in Google Scholar

[2] X. Kang, R. Zhang, and M. Motani, “Price-based resource allocation for spectrum-sharing femtocell networks: a stackelberg game approach,” IEEE J. Select. Areas Commun., vol. 30, pp. 538–549, Dec. 2012.10.1109/JSAC.2012.120404Search in Google Scholar

[3] J. Zheng, Y. Cai, W. Yang, Y. Wei, and W. Yang, “A fully distributed algorithm for dynamic channel adaptation in canonical communication networks,” IEEE Wireless Commun. Lett., vol. 2, pp. 491–494, Jun. 2013.10.1109/WCL.2013.061213.130319Search in Google Scholar

[4] J. Zheng, Y. Cai, Y. Xu, and A. Anpalagan, “Distributed channel selection for interference mitigation in dynamic environment: a game-theoretic stochastic learning solution,” IEEE Trans. Vehicular Technol., vol. 63, pp. 4757–4762, Mar. 2014.10.1109/TVT.2014.2311496Search in Google Scholar

[5] K. Lee, S. Jha, and N. Bulusu, “Adaptive, distributed location management in mobile, wireless networks,” in IEEE International Conference on Communications, Paris, France, pp. 4077–4081, Jun. 2004.10.1109/ICC.2004.1313316Search in Google Scholar

[6] S. Ali, M. Ismail, and K. Mat, “Development of a mobility management simulator for 3G cellular network,” in IEEE International Conference on Telecommunications and Malaysia International Conference on Communications, Penang, pp. 741–746, May 2007.Search in Google Scholar

[7] X. Chen and J. Huang, “Distributed spectrum access with spatial reuse,” IEEE J. Select. Areas Commun., vol. 31, pp. 593–603, Mar. 2013.10.1109/JSAC.2013.130323Search in Google Scholar

[8] S. Hart and A. Mas-Colell, “A simple adaptive procedure leading to correlated equilibrium,” Econometrica, vol. 68, no. 5, pp. 1127–1150, Sep. 2000.10.1142/9789814390705_0002Search in Google Scholar

[9] B. Babadi and V. Tarokh, “GADIA: a greedy asynchronous distributed interference avoidance algorithm,” IEEE Trans. Inform. Theory, vol. 56, pp. 6228–6252, Dec. 2010.10.1109/TIT.2010.2081090Search in Google Scholar

[10] N. Bambos, “Toward power-sensitive network architectures in wireless communications: concepts, issues, and design aspects,” IEEE Pers. Commun., vol. 5, pp. 50–59, Jun. 1998.10.1109/98.683739Search in Google Scholar

[11] S. Hart and D. Schmeidler, “Existence of correlated equilibria,” Math. Operations Res., vol. 14, no. 1, pp. 18–25, 1989.10.1142/9789814390705_0001Search in Google Scholar

Received: 2015-7-21

Published Online: 2015-12-8

Published in Print: 2016-1-1

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Articles in the same Issue

https://doi.org/10.1515/freq-2015-0164

Keywords for this article

interference mitigation; potential game; no regret learning; best response; intelligent location selection