Journal of Communications and Networks

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

DOI QR Code

Composite Differential Evolution Aided Channel Allocation in OFDMA Systems with Proportional Rate Constraints

  • Received : 2013.04.26
  • Accepted : 2014.04.11
  • Published : 2014.10.31
⟨ Previous Next ⟩

Abstract

Orthogonal frequency division multiple access (OFDMA) is a promising technique, which can provide high downlink capacity for the future wireless systems. The total capacity of OFDMA can be maximized by adaptively assigning subchannels to the user with the best gain for that subchannel, with power subsequently distributed by water-filling. In this paper, we propose the use of composite differential evolution (CoDE) algorithm to allocate the subchannels. The CoDE algorithm is population-based where a set of potential solutions evolves to approach a near-optimal solution for the problem under study. CoDE uses three trial vector generation strategies and three control parameter settings. It randomly combines them to generate trial vectors. In CoDE, three trial vectors are generated for each target vector unlike other differential evolution (DE) techniques where only a single trial vector is generated. Then the best one enters the next generation if it is better than its target vector. It is shown that the proposed method obtains higher sum capacities as compared to that obtained by previous works, with comparable computational complexity.

Keywords

References

  1. E. Lawrey, "Multiuser OFDM," in Proc. ISSPA, Brisbane, Australia, 1999, pp. 761-764.
  2. D. Tse and P. Viswanath, "Fundamentals of wireless communication," Cambridge University Press, Cambridge, 2005.
  3. S. Gheitanchi, F. H. Ali, and E. Stipidis, "Particle swarm optimization for resource allocation in OFDMA," in Proc. IEEE DSP, July 2007, pp. 383-386.
  4. J. Jang and K. B. Lee, "Transmit power adaptation for multiuser OFDM system," IEEE J. Sel. Areas Commun., vol. 21, pp. 171-178, 2003.
  5. I. Kim, I. S. Park, and Y. H. Lee, "Use of linear programming for dynamic subchannel and bit allocation in multiuser OFDM," IEEE Trans. Veh. Tech., vol. 55, pp. 1195-1207, 2006.
  6. Y. B. Reddy, "Genetic Algorithm approach for adaptive subchannel bit and power allocation," in Proc. IEEE ICNSC, UK, 2007, pp. 15-17.
  7. N. Sharma et al., "Rate adaptive resource allocation for multiuser OFDM using NSGA-II," in Proc. IEEE WCSN, Indore, 2008, pp. 161-166.
  8. Z. Shen, J. G. Andrews, and B. L. Evans, "Optimal power allocation in multiuser OFDM systems," in Proc. IEEE GCC, CA, USA, Dec. 2003, pp. 337-341.
  9. Z. Shen, J. G. Andrews, and B. L. Evan, "Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints," IEEE Trans. Wireless Commun. vol. 46, pp. 2726-2737, 2005.
  10. Z. Tang et al., "Cross-layer resource allocation for multiuser OFDM systems based on ESGA," in Proc. IEEE VTC , MD, USA, 2007, pp. 1573-1577.
  11. C. Y.Wong et al., "Multiuser OFDM system with adaptive subchannel, bit and power allocation," IEEE J. Sel. Areas Commun., vol. 17, pp. 1747-1758, 1999.
  12. I. C. Wong et al., "A low complexity algorithm for proportional resource allocation in OFDMA systems," in Proc. IEEE IWSPS, Texas, USA, 2004, pp. 1-6.
  13. N. Sharma and K. R. Anupama, "A novel genetic algorithm for adaptive resource allocation in multiuser OFDM systems with proportional rate constraint," International Journal of Recent Trends in Engineering, Academy Publishers, vol. 2, no. 5, pp. 135-139, 2009.
  14. H. Zhu and J. Wang, "Chunk-based resource allocation in OFDMA systems-Part I: Chunk allocation," IEEE Trans. Commun., vol. 57, no. 9, pp. 2734-2744, Sept. 2009.
  15. Y. Huang and B. Rao, "An analytical framework for heterogeneous partial feedback design in heterogeneous multicell OFDMA networks," IEEE Trans. Signal Process., vol. 61, no. 3, pp. 753-769, Feb. 2013.
  16. A. Kuhne and A. Klein, "Throughput analysis of multi-user OFDMAsystems using imperfect CQI feedback and diversity techniques," IEEE J. Sel. Areas Commun., vol. 26, no. 8, pp. 1440-1450, Oct. 2008.
  17. Y. Wang, Z. Cai, and Q. Zhang, "Differential evolution with composite trial vector generation strategies and control parameters," IEEE Trans. Evol. Comput., vol. 15, no. 1, pp. 55-66, 2011.
  18. R. Storn and K. Price, "Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces," J. Global Optimization, vol. 11, no. 4, pp. 341-359, 1997.
  19. S. Das, A. Konar, and U. K. Chakraborty, "Two improved differential evolution schemes for faster global search," GECCO, 2005, pp. 991-998.
  20. S. Das et al., "Differential evolution using a neighborhood-based mutation operator," IEEE Trans. Evol. Comput. vol. 13, no. 3, pp. 526-553, 2009.
  21. S. Das et al, "Particle swarm optimization and differential evolution algorithms: Technical analysis, applications and hybridization perspectives," Ying Liu, Ed, Advances of Computational Intelligence in Industrial Systems, Studies in Computational Intelligence, vol. 116, pp. 1-38, 2008.
  22. S. Das, A. Konar, and U. K. Chakraborty, "Improving particle swarm optimization by differentially perturbed velocity", in Proc. GECCO, NY, USA, 2005, pp. 177-184.
  23. A. Ghosh et al., "An improved differential evolution algorithm with fitness-based adaptation of the control parameters," Information Science, vol. 181, no. 15, pp. 3749-3765, 2011.
  24. S. Das and P. N. Suganthan, "Differential evolution: a survey of the stateof- the-art," IEEE Trans. Evol. Compt., vol. 15, no. 1, pp. 4-31, 2011.
  25. R. Angira and B. V. Babu, "Optimization of non-linear chemical processes using modified differential evolution," in Proc. IICAI, 2005, pp. 911-923.
  26. S. Gheitanchi, F. H. Ali, and E. Stipidis, "Particle swarm optimization for resource allocation in OFDMA," in Proc. IEEE DSP, July 2007, pp. 383- 386.
  27. V. Feoktistov and S. Janaqi, "Generalization of the strategies in differential evolution," in Proc. IPDPS, 2004, pp. 165-170.
  28. F. Xue, A. Sanderson, and R. Graves, "Pareto-based multi-objective differential evolution," in Proc. IEEE CEC, vol. 2, 2003, pp. 862-869.
  29. S. Paterlini and T. Krink, "High performance clustering with differential evolution," in Proc. IEEE CEC, vol. 2, 2004, pp. 2004-2011.
  30. M. Omran, A. Engelbrecht, and A. Salman, "Differential evolution methods for unsupervised image classification," in Proc. IEEE CEC, vol. 2, 2005, pp. 966-973.
  31. R. Storn, "Differential evolution design for an iir-filter with requirements for magnitude and group delay," Technical report TR 95-026, ICSI, Berkeley, CA, 1995.
  32. B. Babu and R. Angira, "Optimization of non-linear functions using evolutionary computation," in Proc. ISME, India, 2001, pp. 153-157.
  33. R. Angira and B. Babu, "Evolutionary computation for global optimization of non-linear chemical engineering processes," in Proc. ISPSEC,Mumbai, 2003, pp. 87-91.
  34. H. Abbass, "A memetic pareto evolutionary approach to artificial neural networks," Lecture notes in artificial intelligence, vol. 2256. Berlin, Germany, Springer-Verlag, 2002.
  35. B. Babu and M. Jehan, "Differential evolution for multi-objective optimization" in Proc. IEEE CEC, vol. 4, 2003, pp. 2696-2703.
  36. R. Storn, "System design by constraint adaptation and differential evolution" IEEE Trans. Evol. Compt., vol .3, no. 1, pp.22-34, 1999.
  37. C. A. Coello, "Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art," Comput. Methods Appl. Mechanics Eng., vol. 191, no. 11-12, pp. 1245-1287, 2002.
  38. E. Mezura-Montes editor, "Constraint-handling in evolutionary optimization," Heidelberg: Springer-Verlag, Berlin, 2009.
  39. H. Lu and W. Chen, "Dynamic-objective particle swarm optimization for constrained optimization problems," J. Global Optimization, vol. 12, pp. 409-419, 2006.
  40. K. Zielinski and R. Laur, "Stopping criteria for a constrained singleobjective particle swarm optimization algorithm," Informatica, vol. 31, pp. 51-59, 2007.
  41. Z. Chai et al., "On the use of immune clonal optimization for joint subcarrier and power allocation in OFDMA with proportional fairness rate," International J. Commun. Syst., vol. 26, no. 10, pp.1273-1287, 2013.