Skip to main content
SpringerLink
Log in

An Enhanced ICA Based on Minimum BER Criterion and Nesterov-Accelerated Adaptive Moment Estimation

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this paper, an enhanced independent component analysis (ICA) method is proposed for blind separation of noisy mixture signals. Considering the conventional ICA methods always have an inadequate capacity of resisting noisy effect. This phenomenon is usually encountered in wireless receiving processing. To obtain the improved separation quality, two mechanisms are conducted to formulate the modified cost function and the powerful optimization learning for investigating an enhanced ICA method.The fundamental of the proposed method is derived from the minimum bit error rate (BER) criterion and the Nesterov-accelerated adaptive moment estimation (Nadam) optimization approach. The main work of this paper includes the following facets. Firstly, a maximum likelihood (ML) principle cost function with minimum BER constraint is derived. Secondly, the modified cost function is by utilizing Nadam learning processing. Lastly, theoretical analysis and experiment results verify the improved performance quality of the proposed enhanced ICA method compared with popular representative ICA methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Rosângela, F. C., Vítor, H. N., Ricarde, L. D. Q., et al. (2016). Signal and images: Advances and results in speech, estimation, compression (pp. 3–23). Boca Raton: CRC.

    Google Scholar 

  2. Yu, X. C., Hu, D., & Xu, J. D. (2014). Blind source separation theory and applications (pp. 19–166). Tower: Wiley.

    Book  Google Scholar 

  3. Comon, P., & Jutten, C. (2010). Handbook of blind source separation. Oxford: Elsevier.

    Google Scholar 

  4. Luo, Z. Q., Li, C. J., & Zhu, L. D. (2018). A comprehensive survey on blind source separation for wireless adaptive processing: Principles, perspectives, challenges and new research directions. IEEE Access, 6, 66685–66708.

    Article  Google Scholar 

  5. Li, C. J., Zhu, L. D., & Luo, Z. Q. (2017). Underdetermined blind source separation of adjacent satellite interference based on sparseness. China Communications, 14(4), 140–149.

    Article  Google Scholar 

  6. Chen, X., Wang, Z. J., & Mckeown, M. J. (2016). Joint blind source separation for neurophysiological data analysis. IEEE Signal Processing Magazine, 33(3), 86–107.

    Article  Google Scholar 

  7. Luo, Z. Q., & Zhu, L. D. (2015). A charrelation matrix-based blind adaptive detector for DS-CDMA systems. Sensors, 15(8), 20152–20168.

    Article  Google Scholar 

  8. Albataineh, Z., & Salem, F. (2015). Robust blind multiuser detection algorithm using fourth-order cumulant matrices. Circuits, Systems, and Signal Processing, 34(8), 2577–2595.

    Article  MathSciNet  Google Scholar 

  9. Luo, Z. Q., Zhu, L. D., & Li, C. J. (2014). Employing ICA for inter-carrier interference cancellation and symbol recovery in OFDM systems. In IEEE global communications conference (pp. 3501–3505).

  10. Luo, Z. Q., Zhu, L. D., & Li, C. J. (2016). Independent component analysis based blind adaptive interference reduction and symbol recovery for OFDM systems. China Communications, 13(2), 41–54.

    Google Scholar 

  11. Luo, Z. Q., Zhu, L. D., & Li, C. J. (2017). Vandermonde constrained tensor decomposition based blind carrier frequency synchronization for OFDM transmissions. Wireless Personal Communications, 95(3), 3459–3475.

    Article  Google Scholar 

  12. Luo, Z. Q., Li, C. J., & Zhu, L. D. (2017). Robust blind separation for MIMO systems against channel mismatch using second-order cone programming. China Communications, 14(6), 168–177.

    Article  Google Scholar 

  13. Luo, Z. Q., & Li, C. J. (2018). Robust wireless statistic division multiplexing and its performance analysis. International Journal of Distributed Sensor Networks, 14(12), 1–14.

    Article  Google Scholar 

  14. Li, C. J., Zhu, L. D., Xie, A. H., et al. (2017). Blind separation of weak object signal against the unknown strong jamming in communication systems. Wireless Personal Communications, 97(3), 4265–4283.

    Article  Google Scholar 

  15. Cai, X., Wang, X., Huang, Z. T., et al. (2016). Performance analysis of ICA in sensor array. Sensors, 16(5), 1–24.

    Article  Google Scholar 

  16. Tsai, T. H., Chiou, Y. H., & Liu, P. Y. (2016). Design and implementation of blind source separation for noise reduction. In International conference on consumer electronics (pp. 1–2).

  17. Bell, A. J., & Sejnowski, T. J. (1995). An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159.

    Article  Google Scholar 

  18. Liu, J. Q., Feng, D. Z., & Zhang, W. W. (2009). Adaptive improved natural gradient algorithm for blind source separation. Neural Computation, 21(3), 872–889.

    Article  MathSciNet  Google Scholar 

  19. Scarpiniti, M., Scardapane, S., Comminiello, D., et al. (2016). Effective blind source separation based on the adam algorithm. In 26th Italian workshop on neural networks (WIRN 2016) (pp. 57–v66).

  20. Moazzen, I. (2013). Array signal processing for beamforming and blind source separation. Doctoral Dissertation, University of Victoria, Canada

  21. Ding, Y. W., Davidson, T. D., Luo, Z. Q., et al. (2013). Minimum BER block for zero-forcing equalization. IEEE Transaction on Signal Processing, 51(9), 2410–2423.

    Article  Google Scholar 

  22. Kingma, D. P., & Ba, J. L. (2016). Adam: a method for stochastic optimization. In The 3rd international conference for learning representation (pp. 1–15).

  23. Luo, Z. Q., Jing, C. F., Chen, Y., & Xiong, X. Z. (2021). A new underdetermined NMF based anti-collision algorithm for RFID systems. In ISA transactions (pp. 1–10).

  24. Chen, H. B., Tse, C. K., & Feng, J. C. (2009). Impact of topology on performance and energy efficiency in wireless sensor networks for source extraction. IEEE Transactions on Parallel and Distributed Systems, 20(6), 886–897.

    Article  Google Scholar 

  25. Deng, C. L., Wei, Y. M., Shen, Y. H., et al. (2018). RSNT-cFastICA for complex-valued noncircular signals in wireless sensor networks. KSII Transactions on Internet and Information Systems, 12(10), 4814–4834.

    Google Scholar 

  26. Luo, Z. Q., Li, C. J., & Zhu, L. D. (2019). Full-duplex cognitive radio using guided independent component analysis and cumulant criterion. IEEE Access, 7, 27065–27074.

    Article  Google Scholar 

  27. Luo, Z. Q., Chen, Y., Li, C. J., Xiong, X. Z., & Zhu, L. D. (2020). Minimum BER criterion and adaptive moment estimation based enhanced ICA for wireless communications. IEEE Access, 8, 152071–152080.

    Article  Google Scholar 

  28. Dozat, T. (2016). Incorporating Neterov Momentum into Adam, report (pp. 1–6).

  29. Ruder, S. (2017). An overview of gradient descent optimization algorithms (pp. 1–14). arXiv.org.

Download references

Acknowledgements

The paper is supported in part by National Natural Science Foundation of China under Grant 61801319, in part by Sichuan Science and Technology Program under Grant 2020JDJQ0061, 2021YFG0099, in part by Sichuan University of Science and Engineering talent introduction project under Grant 2020RC33 and 2018RC17, in part by Innovation Fund of Chinese Universities under Grant 2020HYA04001, in part by Applied Basic Research Programs of Science and Technology Department of Zigong under Grant 2019YYJC29, in part by Artificial Intelligence Key Laboratory of Sichuan Province Project under Grant 2021RZJ03.

Author information

Authors and Affiliations

  1. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, China

    Zhongqiang Luo, Yan Chen & Chaofu Jing

  2. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, China

    Zhongqiang Luo

Authors
  1. Zhongqiang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaofu Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhongqiang Luo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, Z., Chen, Y. & Jing, C. An Enhanced ICA Based on Minimum BER Criterion and Nesterov-Accelerated Adaptive Moment Estimation. Wireless Pers Commun 122, 3913–3929 (2022). https://doi.org/10.1007/s11277-021-09117-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09117-4

Keywords

Search

Navigation