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Network Selection Algorithm for Heterogeneous Wireless Networks Based on Multi-Objective Discrete Particle Swarm Optimization

  • Zhang, Wenzhu (State Key Lab. of Integrated Service Networks, Xidian University) ;
  • Kwak, Kyung-Sup (School of Information and Communication Engineering, Inha University) ;
  • Feng, Chengxiao (State Key Lab. of Integrated Service Networks, Xidian University)
  • Received : 2012.05.04
  • Accepted : 2012.07.02
  • Published : 2012.07.31

Abstract

In order to guide users to select the most optimal access network in heterogeneous wireless networks, a network selection algorithm is proposed which is designed based on multi-objective discrete particle swarm optimization (Multi-Objective Discrete Particle Swarm Optimization, MODPSO). The proposed algorithm keeps fast convergence speed and strong adaptability features of the particle swarm optimization. In addition, it updates an elite set to achieve multi-objective decision-making. Meanwhile, a mutation operator is adopted to make the algorithm converge to the global optimal. Simulation results show that compared to the single-objective algorithm, the proposed algorithm can obtain the optimal combination performance and take into account both the network state and the user preferences.

Keywords

References

  1. L. Zhou, H. Wang, S. Lian, Y. Zhang and A. Vasilakos, "Availability-aware multimedia scheduling in heterogeneous wireless networks," IEEE Transactions on Vehicular Technology, vol.60, no.3, pp.1161-1170, Mar. 2011. https://doi.org/10.1109/TVT.2011.2104420
  2. X. Gelabert, J. Perez-Romero, O. Sallent and R. Agusti, "A Markovian Approach to Radio Access Technology Selection in Heterogeneous Multiaccess/Multiservice Wireless Networks," IEEE Transactions on Mobile Computing, vol.7, no.10, pp.1257-1270, Oct. 2008. https://doi.org/10.1109/TMC.2008.50
  3. Y. Zhang, R. Yu, S. Xie, W. Yao, Y. Xiao and M. Guizani, "Home M2M Networks: Architectures, Standards, and QoS Improvement," IEEE Communications Magazine, special issue on "Recent Progress in Machine-to-Machine Communications", vol.49, no.4, pp.44-52, Apr. 2011.
  4. Y. J. Kwon and D. H. Cho , "Load Based Cell Selection Algorithm for Faulted Handover in Indoor Femtocell Network," in Proc. of 2011 IEEE 73rd Vehicular Technology Conference, pp.1-5, May 15-18, 2011.
  5. N. Li and J. Lin, "A two phase RAT selection strategy based on network load and SIR for CDMA-OFDMA heterogeneous networks," in Proc. of 2010 2nd IEEE International Conference on Network Infrastructure and Digital Content, pp.90-94, Sept. 24-26, 2010.
  6. N. Funabiki, J. Shimizu, T. Nakanishi, K. Watanabe and S. Tomisato, "An Extension of Active Access-Point Selection Algorithm for Throughput Maximization in Wireless Mesh Networks," in Proc. of 2011 Third International Conference on Intelligent Networking and Collaborative Systems, pp. 367-372, Nov. 30-Dec. 2, 2011.
  7. D. Niyato and E. Hossain, "Dynamics of Network Selection in Heterogeneous Wireless Networks: An Evolutionary Game Approach," IEEE Transactions on Vehicular Technology, vol.58, no.4, pp.2008-2017, May, 2009. https://doi.org/10.1109/TVT.2008.2004588
  8. H. Tabrizi, G. Farhadi and J. Cioffi, "A Learning-Based Network Selection Method in Heterogeneous Wireless Systems," in Proc. of 2011 IEEE Global Telecommunications Conference, pp.1-5, Dec. 5-9, 2011.
  9. T. Al-Gizawi, K. Peppas, D. I. Axiotis, E. N. Protonotarios and F. Lazarakis, "Interoperability criteria, mechanisms, and evaluation of system performance for transparently interoperating WLAN and UMTS-HSDPA networks," IEEE Network, vol.19, no.4, pp.66-72, July/August, 2005. https://doi.org/10.1109/MNET.2005.1470685
  10. F. Bari, and V. Leung, "Application of ELECTRE to Network Selection in A Heterogeneous Wireless Network Environment," in Proc. of IEEE Wireless Communications and Networking Conference, pp.3810-3815, Mar. 11-15, 2007.
  11. O. E. Falowo and O. Olowole, "Effect of RAT selection based on service symmetry and network duplex mode in heterogeneous wireless networks," in Proc. of IEEE Africon 2011 Conference, pp.1-6, Sept. 13-15, 2011.
  12. W. Sun and Y. Yuan, Optimization Theory and Methods: Nonlinear Programming, Springer, New York, 2006.
  13. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. of IEEE International Conference on Neural Networks, 1995 Proceedings, pp.1942-1948 vol.4, Nov. 27-Dec.1, 1995.
  14. J. Kennedy and R. Mendes, "Neighborhood topologies in fully informed and best-of-neighborhood particle swarms," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol.36, no.4, pp.515-519, July, 2006. https://doi.org/10.1109/TSMCC.2006.875410
  15. A. P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, Wiley, Hoboken, NJ, 2005.
  16. C. A. C. Coello, G. T. Pulido and M. S. Lechuga, "Handling multiple objectives with particle swarm optimization," IEEE Transactions on Evolutionary Computation, vol.8, no.3, pp.256-279, Jun., 2004. https://doi.org/10.1109/TEVC.2004.826067
  17. X. L. Long, "Network-based Multi-robot System Research," PhD dissertation, Zhejiang University, 2007.
  18. IEEE Std 802.16TM-2009, "Part 16: Air Interface for Broadband Wireless Access Systems," May, 2009.
  19. M. Hempel, H. Sharif, W. Wang, T. Zhou and P. Mahasukhon, "A Study of Critical Baseline Performance Characteristics for IEEE 802.16e-2005," in Proc. of International Wireless Communications and Mobile Computing Conference, 2008, pp.837-842, Aug. 6-8, 2008.
  20. G. Bianchi and I. Tinnirello, "Improving load balancing mechanisms in wireless packet networks," in Proc. of IEEE International Conference on Communications, 2002, pp.891-895 vol.2, Apr. 28-May 2, 2002.
  21. C. Y. Wong and R. S. Cheng, "Multiuser OFDM with adaptive subcarrier, bit and power allocation," IEEE Journal on Selected Areas in Communications, vol.17, no.10, pp.1747-1758, Oct. 1999. https://doi.org/10.1109/49.793310

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