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Cyclostationarity-Based Narrowband Interference Suppression for Heterogeneous Networks Using Neural Network

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Abstract

This paper proposes a narrowband interference (NBI) suppression algorithm for Direct Sequence-Code Division Multiple Access systems. The NBI is considered from heterogeneous networks, and predicted based on its cyclostationary characteristic using a nonlinear feed-forward neural network predictor which eliminates the nonlinearity of the spread spectrum (SS) signal in the NBI prediction. To further improve the suppression performance, this paper exploits the structure of the spreading code, and proposes an iterative code-aided algorithm to jointly estimate the NBI and the SS signal. Simulation results reveal that the proposed algorithm largely outperforms the conventional linear prediction filtering and linear-conjugate linear polyperiodically time-varying filtering methods in both the signal to interference plus noise ratio improvement and the bit error rates, when it operates in NBI-contaminated environments.

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References

  1. 1

    Mitola J. (2009) Cognitive radio architecture evolution. Proceedings of the IEEE 97(4): 626–641

  2. 2

    Nishimori, K., Yomo, H., Popovski, P., Takatori, Y., Prasad, R., & Kubota S. (2008). Interference cancellation and avoidance for secondary users co-existing with TDD-based primary systems. Springer Wireless Personal Communications (special issue on Cognitive Radio Technologies).

  3. 3

    Li T., Mow W. H., Lau V. K. N., Siu M., Cheng R. S., Murch R. D. (2007) Robust joint interference detection and decoding for OFDM-based cognitive radio systems with unknown interference. IEEE Journal on Selected Areas in Communications 25(3): 566–575

  4. 4

    Yang, Z., Zhao, T., & Zhao, Y. (2010). Narrowband interference suppression for OFDM systems with guard band. In Proceedings of IEEE Vehicular Technology Conference: VTC2010-Fall, Ottawa, Canada (pp. 1–5).

  5. 5

    Milstein L. B. (1988) Interference rejection techniques in spread spectrum communications. Proceedings of the IEEE 76(6): 657–671

  6. 6

    Rush L. A., Poor H. V. (1994) Narrowband interference suppression in CDMA spread spectrum communications. IEEE Transactions on Communications 42(234): 1969–1979

  7. 7

    Chang P. -R., Hu J. -T. (1999) Narrowband interference suppression in spread spectrum CDMA communications using pipelined recurrent neural network. IEEE Transactions on Vehicular Technology 48(2): 467–477

  8. 8

    Perez-Solano J. J., Felici-Castell S., Rodriguez-Hernandez M. A. (2008) Narrowband interference suppression in frequency- hopping spread spectrum using undecimated wavelet packet transform. IEEE Transactions on Vehicular Technology 57(3): 1620–1629

  9. 9

    Shayesteh M. G., Nasiri-Kenari M. (2009) Multiple-access performance analysis of combined time-hopping and spread-time cdma system in the presence of narrowband interference. IEEE Transactions on Vehicular Technology 58(3): 1315–1328

  10. 10

    Iltis R. A., Milstein L. B. (1985) An approximate statistical analysis of the Widrow LMS algorithm with application to narrow-band interference rejection. IEEE Transactions on Communications 33(2): 121–130

  11. 11

    Gardner W. A. (1990) Introduction to random processes with applications to signals and systems (2nd ed.). McGraw-Hill, New York, USA

  12. 12

    Gardner, W. A. (1994). Cyclostationarity in communications and signal processing. Piscataway, NJ: IEEE.

  13. 13

    Gelli G., Izzo L., Paura L. (1996) Cyclostationarity-based signal detection and source location in non-Gaussian noise. IEEE Transactions on Communications 44(3): 368–376

  14. 14

    Geli G., Paura L., Tulino A. M. (1998) Cyclostationarity-based filtering for narrow-band interference suppression in direct-sequence spread-spectrum systems. IEEE Journal on Selected Areas in Communications 16(9): 1747–1755

  15. 15

    Zhang, L., & Zhang, J. (2008). A suppressing NBI technique of DSSS based on the linear estimator in time-frequency domain). In Proceedings of IEEE International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, P. R. China (pp. 1–4).

  16. 16

    Lapedes, A., & Farber, R. (1987). Nonlinear signal processing using neural networks: Prediction and modeling. Technical Report, LA-UR87-2662. Los Alamos, New Mexico: Los Alamos National Laboratory.

  17. 17

    Connor J. T., Martin R. D., Atlas L. E. (1994) Recurrent neural networks and robust time series prediction. IEEE Transactions on Neural Networks 5(2): 240–254

  18. 18

    Xu, D., Zhao, P., Shen, F., & Zhao, H. (2008). Narrowband interference suppression using RKF-based recurrent neural network in spread spectrum system. In Proceedings of IEEE International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, P. R. China (pp. 1–5).

  19. 19

    Yang, Z., Zhao, T., Zhao, Y., & Yu, J. (2010). Iterative narrowband interference suppression for ds-cdma systems using feed-forward neural network. In Proceedings of IEEE Vehicular Technology Conference: VTC2010-Spring, Taipei, Taiwan (pp. 1–5).

  20. 20

    Vijayan R., Poor H. V. (1990) Nonlinear techniques for interference suppression in spread-spectrum systems. IEEE Transactions on Communications 38(7): 1060–1065

  21. 21

    Zeidler J. R., Satorius E. H., Chabries D. M., Wexler H. T. (1978) Adaptive enhancement of multiple sinusoids in uncorrelated noise. IEEE Transactions on Acoustics, Speech and Signal Processing 26(3): 240–254

  22. 22

    Widrow B., McCool J., Ball M. (1975) The complex LMS algorithm. IEEE Transactions on Signal Processing 63(4): 719–720

  23. 23

    Gardner W. A. (1993) Cyclic Wiener filtering: Theory and method. IEEE Transactions on Communications 41(1): 151–163

  24. 24

    Aazhang B., Paris B. P., Orsak G. C. (1992) Neural networks for multiuser detection in code-division multiple-access communications. IEEE Transactions on Neural Networks 40(7): 1212–1222

  25. 25

    Rumelhart D. E., Hinton G. E., Williams R. J. (1986) Learning internal representation by error propagation. MIT Press, Cambridge, MA

  26. 26

    Benvenuto N., Piazza F. (1992) On the complex backpropagation algorithm. IEEE Transactions on Signal Processing 40(4): 967–969

  27. 27

    Schell, S. V., & Gardner, W. A. (1990). Progress on signal-selective direction finding. In Proceedings of IEEE fifth ASSP Workshop Spectral Estimation and Modeling, Rochester, NY (pp. 144–148).

  28. 28

    Sahai, A., & Cabric, D. (2005). Cyclostationary feature detection. Tutorial presented at the IEEE DySPAN 2005 (Part II). http://www.eecs.berkeley.edu/sahai/Presentations/DySPAN05part2.ppt.

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Correspondence to Yuping Zhao.

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Yang, Z., Zhang, X. & Zhao, Y. Cyclostationarity-Based Narrowband Interference Suppression for Heterogeneous Networks Using Neural Network. Wireless Pers Commun 68, 993–1012 (2013). https://doi.org/10.1007/s11277-011-0495-0

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Keywords

  • Cyclostationarity
  • Neural network
  • Narrowband interference suppression
  • DS-CDMA