Search WWW Search inass.org
»Journal Description
»Topics
»Call for Papers and Reviewers
»Author Guidelines
»Contents & Papers
»Call for Special Issues
»SCOPUS
 
»IEEE CIS
»INNS
»IEEE IS
DOI: http://dx.doi.org/10.22266/ijies2017.0430.15

Autonomous Distributed Power Control in Multi-Channel Cognitive Femtocell Network: Feasibility and Convergence

Author(s):

Anggun Fitrian Isnawati1,2*, Risanuri Hidayat1, Selo Sulistyo1, I Wayan Mustika1


Affiliations:

1Department of Electrical Engineering and Information Technology, Faculty of Engineering,Universitas Gadjah Mada, Yogyakarta, Indonesia
2Sekolah Tinggi Teknologi Telematika Telkom, Purwokerto, Indonesia







Abstract:

Dynamic user in mobile communication encourages the implementation of self-organized and non-cooperative distributed power control. To be implemented, the power control must meet the feasible and convergent conditions. Feasibility of power control must be qualified for non-negative power vectors and the limit of maximum power, whereas convergence is tested by the speed for achieving of convergent conditions on fixed point. If it is feasible, then the system will be convergent, but when it is infeasible then the power transmit of user will be negative and the system is never reach the convergence. While the semi feasible condition requires the implementation of a proposed method of HDCPC. When power transmit of user exceeded the maximum power, Pmax, HDCPC method choose the transmit power that equal with ½ Pmax compared to use Pmax that required on DCPC or to force the user turned off the power transmit on GDCPC. Results showed that it would be more efficient in power usage than DCPC and more implementable than GDCPC. Proposed HDCPC is done when there is no option of handover channel, while the results after user move to another channel is able to achieve the SINR target and spent less power. Related to the convergence analysis, it can be concluded that the larger the SINR target, the longer iterations required to achieve a convergent condition.


Keywords:

Feasibility, Convergence, Distributed power control, Non-negative power vector, HDCPC.


Full Text:




References:
  1. S. A. Rabee, B. S. Sharif, and S. Sali, “An efficient algorithm for distributed power control in cellular radio systems,” 3G Mobile Communication Technologies, Third International Conference on (Conf. Publ. No. 489), pp. 123–127, 2002.
  2. J. Zander, “Performance of optimum transmitter power control in cellular radio systems,” Veh. Technol. IEEE Trans. On, Vol. 41, No. 1, pp. 57–62, 1992.
  3. A. F. Isnawati, R. Hidayat, S. Sulistyo, and I. W. Mustika, “A comparative study on centralized and distributed power control in cognitive femtocell network,” 8th International Conference on Information Technology and Electrical Engineering (ICITEE), 2016.
  4. N. Chakchouk and B. Hamdaoui, “QoS-aware autonomous distributed power control in co-channel femtocell networks,” Global Communications Conference (GLOBECOM), IEEE, pp. 567–571, 2012.
  5. J. Duan, J. Liu, S. Leng, and Q. Wang, “A game-based power control scheme for cognitive radio networks,” Computational Problem-Solving (ICCP), International Conference on, pp. 76–79, 2012.
  6. Z. Lu, Y. Sun, X. Wen, T. Su, and D. Ling, “An energy-efficient power control algorithm in femtocell networks,” Computer Science & Education (ICCSE), 7th International Conference on, pp. 395–400, 2012.
  7. A. J. Luah and C. K. Tan, “A Nash-based power control game for green communications via cognitive radio networks,” Sustainable Utilization and Development in Engineering and Technology (STUDENT), IEEE Conference on, pp. 164–169, 2012.
  8. J. Jiao, L. Jiang, and C. He, “A novel game theoretic utility function for power control in cognitive radio networks,” International Conference on Computational and Information Sciences, ICCIS, pp. 1553–1557, 2013.
  9. Z. Junhui, Y. Tao, G. Yi, W. Jiao, and F. Lei, “Power control algorithm of cognitive radio based on non-cooperative game theory,” Commun. China, Vol. 10, No. 11, pp. 143–154, 2013.
  10. A. F. Isnawati, R. Hidayat, S. Sulistyo, and I. W. Mustika, “Feasible solution of centralized power control for multi-channel cognitive femtocell network,” 7th International Conference on Information Technology and Electrical Engineering (ICITEE), 2015.
  11. M. Rasti, A. R. Sharafat, and J. Zander, “Pareto and energy-efficient distributed power control with feasibility check in wireless networks,” IEEE Trans. Inf. Theory, Vol. 57, No. 1, pp. 245–255, Jan. 2011.
  12. M. Rasti, M. Hasan, L. B. Le, and E. Hossain, “Distributed uplink power control for multi-cell cognitive radio networks,” IEEE Trans. Commun., Vol. 63, No. 3, pp. 628–642, Mar. 2015.
  13. L. Qian, X. Li, J. Attia, and Z. Gajic, “Power control for cognitive radio ad hoc networks,” Local & Metropolitan Area Networks, LANMAN, 15th IEEE Workshop on, pp. 7–12, 2007.
  14. N. Nie, C. Comaniciu, and P. Agrawal, “A game theoretic approach to interference management in cognitive networks,” Wireless Communications, Springer, pp. 199–219, 2007.
  15. G. J. Foschini and Z. Miljanic, “A simple distributed autonomous power control algorithm and its convergence,” IEEE Trans. Veh. Technol., Vol. 42, No. 4, pp. 641–646, Nov. 1993.
  16. S. Koskie and Z. Gajic, “A Nash game algorithm for SIR-based power control in 3G wireless CDMA networks,” IEEEACM Trans. Netw., Vol. 13, No. 5, pp. 1017–1026, Oct. 2005.
  17. S. A. Grandhi and J. Zander, “Constrained power control in cellular radio systems,” Vehicular Technology Conference, IEEE 44th, pp. 824–828, 1994.
  18. F. Berggren, R. Jäntti, and S.-L. Kim, “A generalized algorithm for constrained power control with capability of temporary removal,” Veh. Technol. IEEE Trans. On, Vol. 50, No. 6, pp. 1604–1612, 2001.
  19. Y. A. Al-Gumaei, K. A. Noordin, A. W. Reza, and K. Dimyati, “A new power control game in two-tier femtocell networks,” Telematics and Future Generation Networks (TAFGEN), 1st International Conference on, pp. 131–135, 2015.
  20. X. Li, L. Qian, and D. Kataria, “Downlink power control in co-channel macrocell femtocell overlay,” Information Sciences and Systems, CISS. 43rd Annual Conference on, pp. 383–388, 2009.
  21. S. Im, H. Jeon, and H. Lee, “Autonomous distributed power control for cognitive radio networks,” Vehicular Technology Conference, VTC, IEEE 68th, pp. 1–5, 2008.
  22. H. Koivo and M. Elmusrati, Systems engineering in wireless communication, Hoboken, NJ: John Wiley & Sons, 2009.
  23. V. Chandrasekhar, J. G. Andrews, T. Muharemovic, Z. Shen, and A. Gatherer, “Power control in two-tier femtocell networks,” IEEE Trans. Wirel. Commun., Vol. 8, No. 8, pp. 4316–4328, Aug. 2009.
  24. O. Durowoju, K. Arshad, and K. Moessner, “Distributed power control algorithm for cognitive radios with primary protection via spectrum sensing under user mobility,” Ad Hoc Netw., Vol. 10, No. 5, pp. 740–751, Jul. 2012.
  25. R. D. Yates, “A framework for uplink power control in cellular radio systems,” Sel. Areas Commun. IEEE J. On, Vol. 13, No. 7, pp. 1341–1347, 1995.
  26. A. Al Talabani, A. Nallanathan, and H. X. Nguyen, “A novel chaos based cost function for power control of cognitive radio networks,” IEEE Commun. Lett., Vol. 19, No. 4, pp. 657–660, Apr. 2015.
  27. T. Holliday, N. Bambos, P. Glynn, and A. Goldsmith, “Distributed power control for time varying wireless networks optimality and convergence,” Proceedings of the annual ALLERTON Conference on Communication Control and Computing, 2012.
  28. L. I. Dong, D. A. I. Xianhua, and H. Zhang, “Game theoretic analysis of joint rate and power allocation in cognitive radio networks,” Int. J. Commun. Netw. Syst. Sci., Vol. 2, No. 01, 2009.

INASS Home | Copyright@2008 The Intelligent Networks and Systems Society