Advertisement

Wireless Personal Communications

, Volume 109, Issue 4, pp 2627–2635 | Cite as

A PSO-Based Hybrid Adaptive Equalization Algorithm for Asynchronous Cooperative Communications

  • Jie-Ling WangEmail author
  • Kaiyu Zhi
  • Rui Zhang
Article
  • 17 Downloads

Abstract

This paper proposes an adaptive equalization algorithm for asynchronous cooperative communications in ad hoc networks, where amplify-and-forward relays are adopted, each of which is equipped with single antenna. Adaptive equalization technique is carried out at the destination node in this paper to remove inter-symbol interference, which is caused by the retransmissions of the asynchronous relays. Least mean squares (LMS) has been regarded as an effective adaptive method, but it has difficulty in obtaining the optimal solution. In this paper, we present a hybrid adaptive scheme by combining particle swarm optimization (PSO) with conventional LMS algorithm, where PSO is utilized to search the optimal solution during the iterative process, and LMS is employed to avoid the local convergence, which is usually caused in PSO. Numerical simulation results show that, the proposed scheme outperforms conventional LMS algorithm in convergence performance over Rayleigh flat fading channel, and meanwhile, a signal–noise-ratio gain of 6 dB or so is obtained when BER is 10−3.

Keywords

Equalization algorithm Particle swarm optimization Least mean square Rayleigh flat fading channel Asynchronous cooperative communications 

Notes

Acknowledgements

This work was supported by the China Postdoctoral Science Foundation under Grant 2017M623129. The work was also supported in part by Natural Science Foundation of China and in part by the Fundamental Research Funds for the Central Universities under Grant JB180112, and also in part by the Program of Introducing Talents of Discipline to Universities under Grant B08038.

References

  1. 1.
    Aguilar, T., Syue, S. J., Gauthier, V., Afifi, H., & Wang, C. L. (2011). CoopGeo: A beaconless geographic cross-layer protocol for cooperative wireless ad hoc networks. IEEE Transactions on Wireless Communications,10(8), 2554–2565.CrossRefGoogle Scholar
  2. 2.
    Wang, H., Xia, X. G., & Yin, Q. (2009). Computationally efficient equalization for asynchronous cooperative communications with multiple frequency offsets. IEEE Transactions on Wireless Communications,8(2), 648–655.CrossRefGoogle Scholar
  3. 3.
    Wang, H. M. (2017). Full-diversity uncoordinated cooperative transmission for asynchronous relay networks. IEEE Transactions on Vehicular Technology,66(1), 468–480.Google Scholar
  4. 4.
    Guo, X., & Xia, X. G. (2008). Distributed linear space-time convolutional codes achieving asynchronous full cooperative diversity with MMSE-DFE receivers (special paper). In IEEE Wireless Communications and Networking Conference, Las Vegas, NV, 2008 (pp. 459–464).Google Scholar
  5. 5.
    Vahidnia, R., & Shahbazpanahi, S. (2013). Single-carrier equalization and distributed beamforming for asynchronous two-way relay networks. In 21st European Signal Processing Conference (EUSIPCO 2013), Marrakech, 2013 (pp. 1–5).Google Scholar
  6. 6.
    Xiaohua, L., Fan, N., Jui-Te, H., & Mo, C. (2005). Channel equalization for stbc-encoded cooperative transmissions with asynchronous transmitters. In Conference Record of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers (pp. 457–461). Pacific Grove, CA.Google Scholar
  7. 7.
    Jiang, Y., Xiao, J., & You, X. (2012). A simplified equalization method for asynchronous cooperative relay systems. IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Paris,2012, 258–262.Google Scholar
  8. 8.
    Gupta, A., & Joshi, S. (2008). Variable step-size LMS algorithm for fractal signals. IEEE Transactions on Signal Processing,56(4), 1411–1420.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kwong, R. H., & Johnston, E. W. (1992). A variable step size LMS algorithm. IEEE Transactions on Signal Processing,40(7), 1633–1642.CrossRefGoogle Scholar
  10. 10.
    Slock, D. T. M. (1993). On the convergence behavior of the LMS and the normalized LMS algorithms. IEEE Transactions on Signal Processing,41(9), 2811–2825.MathSciNetCrossRefGoogle Scholar
  11. 11.
    Duttweiler, D. L. (2000). Proportionate normalized least-mean-squares adaptation in echo cancelers. IEEE Transactions on Speech and Audio Processing,8(5), 508–518.CrossRefGoogle Scholar
  12. 12.
    Iqbal, N., Zerguine, A., & Al-Dhahir, N. (2014). Adaptive equalisation using particle swarm optimisation for uplink SC-FDMA. Electronics Letters,50(6), 469–471.CrossRefGoogle Scholar
  13. 13.
    Lakshmikanth, S., Natraj, K. R. & Rekha, K. R. (2014). Performance analysis of industrial noise cancellation with pso based wiener filter using adaptive LMS & NLMS. In 2014 International Conference on Communication and Signal Processing (pp. 363–368). Melmaruvathur.Google Scholar
  14. 14.
    Krusienski, D. J., & Jenkins, W. K. (2004). A particle swarm optimization-least mean squares algorithm for adaptive filtering. In Conference record of the 38th Asilomar conference on signals, systems and computers (Vol. 1, pp. 241–245).Google Scholar
  15. 15.
    Wang, J., Yang, H., Xiaolin, H., & Wang, X. (2011). An adaline neural network-based multi-user detector improved by particle swarm optimization in CDMA Systems. Journal of Wireless Personal Communication,59(2), 191–203.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Labs of Integrated Services Networks, Collaborative Innovation Center of Information Sensing and UnderstandingXidian UniversityXi’anChina
  2. 2.Telecommunication Engineering DepartmentXidian UniversityXi’anChina

Personalised recommendations