Anti-Interference Performance Improvement Using Probability Control in Cognitive CDMA Communication System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

Cognitive code division multiple access (CCDMA) systems commonly use power control to reduce the interference between the users. But the power control is not always optimal. Probability control is a recently introduced method that allows better mitigation of multiple access interference in CCDMA networks. In this paper, we apply the probability control to CCDMA system. It was found that the performance reflected by the signal to noise plus interference ratio (SINR) resulting from probability control is better than the one resulting from power control by some simulation results.

Keywords

Probability control CCDMA Communication system SINR 

Notes

Acknowledgments

This project is financially supported by the National Natural Science Foundation of China (NSFC 61171070) and Fundamental Research Funds for the Central Universities of china (YWF-11-03-Q-063).

References

  1. 1.
    Bergel I, Messer H, Messer M (2008) Optimization of CDMA systems with respect to transmission probability, part I: mutual information rate optimization. IEEE Trans Wirel Commun 7(6):2075–2083CrossRefGoogle Scholar
  2. 2.
    Bergel I, Messer H (2008) Optimization of CDMA systems with respect to transmission probability, part II: signal to noise plus interference ratio optimization. IEEE Trans Wirel Commun 7(6):2084–2093CrossRefGoogle Scholar
  3. 3.
    Babaei A, Jabbari B (2010) Transmission probability control game for coexisting random aloha wireless networks in unlicensed bands. In: The 71st IEEE vehicular technology conference, pp 1–5Google Scholar
  4. 4.
    Bergel I, Dorfand Y, Shtessman E (2008) Implementation of probability control over convolutional codes. In: Proceedings of IEEE 19th international symposium on indoor and mobile radio communications, pp 1–5Google Scholar
  5. 5.
    Smolyar L, Bergel I, Messer H (2007) Joint downlink power allocation, beamforming weights and base assignment. In: IEEE 8th workshop on signal processing advances in wireless communications, pp 1–5Google Scholar
  6. 6.
    Mitola J III, Maguire, GQ Jr (1999) Conitive radio: making software radios more personal. Pers Commun IEEE 6(4):13–18Google Scholar
  7. 7.
    Haykin S (2005) Cognitive radio: Brain-empowered wireless communications. IEEE J Sel Areas Commun 23(2):201–220CrossRefGoogle Scholar
  8. 8.
    Sendonaris A, Erkip E, Aazhang B (2003) User cooperation diversity. Part II. implementation aspects and performance analysis. IEEE Trans Commun 51(11):1939–1948CrossRefGoogle Scholar
  9. 9.
    Ganesan G, Li Y (2007) Cooperative spectrum sensing in cognitive radio, part I: two user networks. IEEE Trans Wireless Commun 6(6):2204–2213CrossRefGoogle Scholar
  10. 10.
    Farrokh R-F, Ray Liu KJ, Tassiulas L (1997) Downlink power control and base station assignment. IEEE Commun Lett 1(4):102–104Google Scholar
  11. 11.
    Liu S, Chang Y, Wang G, Yang D (2012) Distributed resource allocation in two-hierarchy networks. ETRI J 34(2):159–167CrossRefGoogle Scholar
  12. 12.
    Buzzi S, Saturnino D (2011) A game-theoretic approach to energy-efficient power control and receiver design in cognitive CDMA wireless networks. IEEE J Sel Top Signal Process 5(1):137–150CrossRefGoogle Scholar
  13. 13.
    Mahyari MM, Shikh-Bahaei MR (2012) Joint optimization of rate and outer loop power control for CDMA-based cognitive radio networks. In: International conference on computing, networking and communications, pp 392–396Google Scholar
  14. 14.
    Choi K, Kim S (2003) Optimum uplink power/rate control for minimum delay in CDMA networks. ETRI J 25(6):437–444CrossRefGoogle Scholar
  15. 15.
    Kim N-M, Kim M-R, Kim E-J, Shin S-J, Yu H-I, Yun S-B (2008) Robust cognitive-radio-based OFDM architecture with adaptive traffic allocation in time and frequency. ETRI J 30(1):21–32CrossRefMATHGoogle Scholar
  16. 16.
    Gariby M, Gariby T, Zamir R 92008) Managing the degree of impulsiveness of other cell interference. In: IEEE international conference on communications, pp 1398–1403Google Scholar
  17. 17.
    Gariby M, Gariby T, Zamir R (2006) The most favorable impulsive interference for ternary CDMA. In: IEEE international symposium on information theory, pp 942–946Google Scholar
  18. 18.
    Gariby M, Gariby T, Zamir R (2008) Capacity of impulsive modulation over multipath interference channels. In: 4th IEEE international conference on circuits and systems for communications, pp 191–195Google Scholar
  19. 19.
    Verdu S (1998) Multiuser dection. Cambridge University Press, New YorkGoogle Scholar
  20. 20.
    Mueller-Gritschneder D, Graeb H, Schlichtmann U (2009) A successive approach to compute the bounded Pareto front of practical multiobjective optimization problems. SIAM J Optim 20:915–934CrossRefMATHMathSciNetGoogle Scholar
  21. 21.
    Petko JS, Werner DH (2011) Pareto optimization of thinned planar arrays with elliptical mainbeams and low sidelobe levels. IEEE Trans Antennas Propag 99:1748–1751CrossRefGoogle Scholar
  22. 22.
    Boche H, Naik S, Schubert M (2011) Pareto boundary of utility sets for multiuser wireless systems. IEEE/ACM Trans Netw (TON) 19(2):589–601CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Reliability and System EngineeringBeihang UniversityBeijingChina
  2. 2.School of Electronics and Information EngineeringBeihang UniversityBeijingChina

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