[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Estimation of spreading sequences in LC-DS-CDMA signals based on sparse auto-encoder

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

A method based on a sparse auto-encoder (SAE) network for the estimation of spreading sequences in long-code direct-sequence code-division multiple access (LC-DS-CDMA) signals is proposed. First, a network classification model based on SAE and softmax classifier is established. Next, the effectiveness of the proposed method is verified by estimating Walsh sequences and m sequences. To estimate the spreading sequences, the LC-DS-CDMA signal is divided into fragments. Then, each user’s spreading sequence is separated by the fast independent component analysis (Fast-ICA) algorithm, and the amplitude fuzziness is eliminated by the delay-and-multiply method. Finally, the spreading sequences are estimated by the SAE model. Experimental results showed that the proposed algorithm could effectively estimate the spreading sequences of LC-DS-CDMA signals. Compared to the existing matching algorithm and Fast-ICA algorithm, the estimation time required by the proposed algorithm was shorter, and its estimation performance at low signal-to-noise ratios was superior.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Yao Y, Poor HV (2001) Eavesdropping in the synchronous CDMA channel: an EM-based approach. IEEE Trans Signal Process 49(8):1748–1756

    MathSciNet  MATH  Google Scholar 

  2. Haghighat A, Soleymani MR (2005) A MUSIC-based algorithm for blind user identification in multiuser DS-CDMA. EURASIP J Adv Signal Process 5:649–657

    MATH  Google Scholar 

  3. Qiu PY, Huang ZT, Jiang WL, Zhang C (2010) Blind multiuser spreading sequences estimation algorithm for the direct-sequence code division multiple access signals. IET Signal Process 4(5):465–478

    Google Scholar 

  4. Rouijel A, Minaoui K, Comon P, Aboutajdine D (2014) CP decomposition approach to blind separation for DS-CDMA system using a new performance index. EURASIP J Adv Signal Process 2014:128

    Google Scholar 

  5. Lu FB, Huang ZT, Jiang WL (2011) Blind estimation of spreading sequence of CDMA signals based on Fast-ICA and performance analysis. J Commun 32(88):136–142

    Google Scholar 

  6. Ren XT, Xu H, Huang ZT, Wang FH, Lu FB (2012) Fast-ICA based optimize blind estimation of spreading sequence of CDMA signals. Acta Electronica Sinica 40(8):1532–1538

    Google Scholar 

  7. Zhao ZJ, Qiang FF, Li M, Shen L, Wang HQ (2017) Blind estimation of pseudo-random noise codes in NPLSC-DSSS signals based on goodness of fit test. J Electron Inf Technol 39(3):749–753

    Google Scholar 

  8. Zhao ZJ, Gu XW, Qiang FF, Shen L (2017) Blind estimation of PN codes in multi-user LSC-DSSS signals. J Commun 12(1):55–61

    Google Scholar 

  9. Zhang HG, Wei P (2011) Blind despreading of synchronous multi-user long-code DS-SS signals. J Electron Inf Technol 33(7):1748–1751

    Google Scholar 

  10. Zhang TQ, Tian ZS, Chen QB, Lin XK, Zhou ZZ (2006) Use APEX neural networks to extract the PN sequence in lower SNR DS-SS signals. In: International conference on intelligent computing, Kunming, China, pp 1252–1257

    Google Scholar 

  11. Zhao DF, Zhang TQ, Jin X, Du XH (2010) DSSS signal spread spectrum codes blind identification based on BP neural network. Telecommun Eng 50(10):28–35

    Google Scholar 

  12. Zhao JT, Zhang TQ, Jiang XL, Ma BZ (2017) Blind estimate of PN sequence synchronous DS-CDMA based on LEAP neural network. Appl Res Comput 34(2):552–556

    Google Scholar 

  13. Zhang TQ, Zhao JT, Jiang XL (2016) PN code sequence blind estimate of synchronous DS-CDMA based on multi-principal component neural network. Syst Eng Electron 38(11):2638–2647

    MATH  Google Scholar 

  14. Dolz J, Betrouni N, Quidet M et al (2016) Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: a clinical study. Comput Med Imaging Graph 52:8–18

    Google Scholar 

  15. Kang K, Sun HB, Zhang CK, Carl B (2019) Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network. Evol Intel. https://doi.org/10.1007/s12065-018-00196-0

    Google Scholar 

  16. Sun W, Shao S, Zhao R, Yan R, Zhang X, Chen X (2016) A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 89:171–178

    Google Scholar 

  17. Rajaguru H, Prabhakar SK (2017) Logistic regression Gaussian mixture model and softmax discriminant classifier for epilepsy classification from EEG signals. In: IEEE international conference on computing methodologies and communication, Erode, India, pp 985–988

  18. Purohit G, Vyas D, Chaubey VK, Raju KS, Shekhar C (2019) A new XOR-Free approach to implement Walsh sequences. Wirel Pers Commun. https://doi.org/10.1007/s11277-019-06549-x

    Google Scholar 

  19. Li J, Liu N, Liu HM, Sun Y, He ZS (2013) A method of orthogonal polyphase code sequence design based on Walsh matrix. Chin J Radio Sci 28(3):577–583

    Google Scholar 

  20. Guo LL, Wu YH, Zhang XL (2002) A new method of generating Walsh functional sequence. J Harbin Eng Univ 23(1):99–102

    MATH  Google Scholar 

  21. Zhang TQ, Zhao L, Zhang T, Yang K (2018) A blind recognition method of binary pseudo-random sequence. J Electron Inf Technol 40(2):394–399

    Google Scholar 

  22. Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. In: Montavon G, Orr GB, Müller KR (eds) Neural network: tricks of the trade. Lecture notes in computer science, vol 7700. Springer, Berlin, Heidelberg,  pp 437–478

    Google Scholar 

Download references

Acknowledgements

We acknowledged our sincere thanks to all anonymous reviewers for their insightful comments and constructive suggestions to polish this paper in high quality. This research was supported by the National Natural Science Foundations of China (No. 61571172), Zhejiang Provincial Key Lab of Data Storage and Transmission Technology and the open project of Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking. We acknowledged our sincere thanks to them for their encouragement and valuable support.

Author information

Authors and Affiliations

  1. School of Electronic and Information, Hangzhou Dianzi University, Hangzhou, 310018, China

    Fangfang Qiang

  2. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Zhijin Zhao, Junna Shang & Lei Shen

  3. Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Hangzhou, 310027, China

    Lei Shen

Authors
  1. Fangfang Qiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhijin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junna Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fangfang Qiang.

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

Qiang, F., Zhao, Z., Shang, J. et al. Estimation of spreading sequences in LC-DS-CDMA signals based on sparse auto-encoder. Evol. Intel. 13, 235–246 (2020). https://doi.org/10.1007/s12065-019-00298-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-019-00298-3

Keywords

Search

Navigation