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Nonconvex utility-based power allocation for cognitive radio MIMO system over fading channels

  • Meiqin TangEmail author
  • Xinjiang Wei
  • Wuquan Li
Methodologies and Application
  • 9 Downloads

Abstract

In this paper, we present a power control algorithm in cognitive radio multiple-input–multiple-output (MIMO) system over time-varying fading channels based on the utility-based framework. In particular, a more general objective function framework is proposed under a long- or short-term transmitting and interference power constraints for secondary users, which results the optimization problem is nonconvex. A stochastic optimization algorithm is proposed based on an improved quantum-behaved particle swarm optimization (IQPSO), which can solve the nonconvex optimization problem efficiently. The acceleration coefficients are adjusted with time-varying equations which can help the particles jump out the local optimum. Due to the characteristics of wave function of quantum Delta potential well model, IQPSO can get better optimal solutions in search space for the stochastic optimization problem. To show the efficiency of the proposed algorithm, the utilities of the MIMO CR system got by IQPSO are compared with other approaches in the literature in different case studies, which show that the proposed algorithm can solve the nonconvex problem efficiently and achieve significant throughput.

Keywords

Cognitive radio (CR) Nonconvex optimization Particle swarm optimization (PSO) Power allocation 

Notes

Acknowledgements

This work is supported by Natural Science Foundation of Shandong Grants 2015ZRB01121, National Natural Science Foundation of China under Grant 61374108, 61573179, and the Young Taishan Scholars Program of Shandong Province of China under Grant tsqn20161043.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

References

  1. Chiang M, Zhang S, Hande P (2005) Distributed rate allocation for inelastic flows: optimization frameworks, optimality conditions, and optimzal algorithms. In: Proceedings of IEEE INFOCOM’2005, vol 4, pp 2679–2690Google Scholar
  2. Deep K, Thakur M (2007) A new mutation operator for real coded genetic algorithms. Appl Math Comput 193:211–230MathSciNetzbMATHGoogle Scholar
  3. Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceeding of the IEEE international conference on evolutionary computation, vol 1, pp 81–86Google Scholar
  4. Elalem M, Zhao L (2013) Effective capacity optimization for cognitive radio network based on underlay scheme in gamma fading channels. In: Proceedings of CNCCCNS’2013, pp 714–718Google Scholar
  5. Fan RF, Jiang H (2011) Average rate maximization in relay networks over slow fading channels. IEEE Trans Veh Technol 60(8):3865–3881CrossRefGoogle Scholar
  6. Goldsmith A (2005) Wireless communications. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  7. Gong XW, Vorobyov SA, Tellambura C (2011) Optimal bandwidth and power allocation for sum ergodic capacity under fading channels in cognitive radio networks. IEEE Trans Signal Process 59(4):1814–1826MathSciNetCrossRefzbMATHGoogle Scholar
  8. Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 23(2):201–220CrossRefGoogle Scholar
  9. Kang X, Liang Y-C, Nallanathan A, Garg HK, Zhang R (2009) Optimal power allocation for fading channels in cognitive radio networks: ergodic capacity and outage capacity. IEEE Trans Wirel Commun 8:940–950CrossRefGoogle Scholar
  10. Kelly FP, Maulloo A, Tan D (1998) Rate control for communication networks: shadow prices, proportional fairness and stability. Int J Oper Res Soc 49(3):237–252CrossRefzbMATHGoogle Scholar
  11. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neutral networks’95, pp 1942–1948Google Scholar
  12. Kuo RJ, Zulvia FE, Suryadi K (2012) Hybrid particle swarm optimization with genetic algorithm for solving capacitated vehicle routing problem with fuzzy demand—a case study on garbage collection system. Appl Math Comput 219:2574–2588MathSciNetzbMATHGoogle Scholar
  13. Le L, Hossain E (2008) Resource allocation for spectrum underlay in cognitive radio networks. IEEE Trans Wirel Commun 7(12):53065315CrossRefGoogle Scholar
  14. Mitola J, Maguire GQ (1999) Cognitive radios: making software radios more personal. IEEE Pers Commun 6(4):13–18CrossRefGoogle Scholar
  15. Phan KT, Vorobyov SA, Sidiropoulos ND, Tellambura C (2009) Spectrum sharing in wireless networks via QoS-aware secondary multicast beamforming. IEEE Trans Signal Process 57:2323–2335MathSciNetCrossRefzbMATHGoogle Scholar
  16. Price K, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global opitimization. Springer, BerlinzbMATHGoogle Scholar
  17. Ratnaweera A, Halgamuge SK, Wastson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficents. IEEE Trans Evolut Comput 8(3):240–255CrossRefGoogle Scholar
  18. Rosen SL, Harmonosky CM (2005) An improved simulated annealing simulation optimization method for discrete parameter stochastic systems. Comput Oper Res 32:343–358MathSciNetCrossRefzbMATHGoogle Scholar
  19. Spectrum Policy Task Force (2002) Rep. ET Docket no. 02-135Google Scholar
  20. Sun J, Feng B, Xu WB (2004) Particle swarm optimization with particles having quantum behavior. In: IEEE proceedings of congress on evolutionary computer, pp 325–331Google Scholar
  21. Sun J, Xu WB, Feng B (2004) A global search strategy of quantum-behaved particle swarm optimization. In: Proceedings of IEEE conference on cybernetics and intelligent systems, pp 111–116Google Scholar
  22. Sun J, Xu WB, Feng B (2005) Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In: Proceedings of IEEE conference on systems, man and cybernetics, pp 3049–3054Google Scholar
  23. Wu Q, Law R, Wu E, Lin JX (2013) A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization. Inf Sci 238:96–110MathSciNetCrossRefzbMATHGoogle Scholar
  24. Xiao M, Shroff NB, Chong EKP (2003) A utility-based power control scheme in wireless cellular systems. IEEE/ACM Trans Netw 11(2):210–221CrossRefGoogle Scholar
  25. Xing Y, Mathur CN, Haleem MA, Chandramouli R, Subbalakshmi KP (2007) Dynamic spectrum access with QoS and interference temperature constraints. IEEE Trans Mobile Comput 6(4):423–433CrossRefGoogle Scholar
  26. Zhang Y, Leung C (2010) An efficient power loading scheme for OFDM-based cognitive radio systems. IEEE Trans Veh Technol 59(4):1858–1864CrossRefGoogle Scholar
  27. Zhang R, Cui S, Liang Y-C (2009) On ergodic sum capacity of fading cognitive multiple-access and broadcast channels. IEEE Trans Inf Theory 55:5161–5178MathSciNetCrossRefzbMATHGoogle Scholar
  28. Zhang R, Liang Y-C, Cui S (2010) Dynamic resource allocation in cognitive radio networks: a convex optimization perspective. IEEE Signal Process Mag 27(3):102C114CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsLudong UniversityYantaiPeople’s Republic of China

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