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Modified Infomax Algorithm for Smaller Data Block Lengths

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Abstract

Independent component analysis (ICA) is a signal processing technique used for blind source separation of the mixed received data. In general, performance of the ICA algorithms degrade as data block lengths become smaller. The existing work for smaller data blocks lengths inserts extra bits in the transmitted data blocks that increases the data blocks lengths at the transmitter side and removes them after processing through the ICA algorithm at receiver side. Insertion of the extra bits in the transmitted data blocks decreases the bandwidth efficiency and increases the transmission power. In this paper, firstly we analyze the infomax (IF) algorithm of the ICA for different blocks lengths, step sizes and number of iterations. Performance degradation of the IF algorithm observed for reduced lengths of the processing data blocks due to the expectation operator used in the update equation of the algorithm that can’t be avoided through the learning rate parameter and the number of iterations. Secondly, we propose a modified Infomax (MIF) algorithm for smaller block lengths. The proposed MIF algorithm performs well for smaller block lengths with negligible increase in the computational complexity as compared to the existing IF algorithm. The proposed MIF algorithm processes the received data blocks without changing their lengths at the transmitter side. Simulation results show that the proposed algorithm outperforms the existing IF algorithm for smaller blocks lengths.

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Notes

  1. Super-Gaussian means that the pdf is more spiky as compared to Gaussian distribution.

  2. Sub-Gaussian means that the pdf is flat as compared to Gaussian distribution.

  3. Negentropy means negative entropy. This concept is introduced to get positive value of entropy.

References

  1. Ameri, M. R., Shokripour, M., Mohammadpour, A., & Nassiri, V. (2013). Parametric independent component analysis for stable distributions. Artificial Intelligence Research, 2, 27–34.

    Article  Google Scholar 

  2. Baiyan, L., & Jinhua, T. (2011). Overcomplete ICA algorithm of speech signal extraction in underdetermined mixtures. In 2011 international conference on electric information and control engineering (ICEICE) (pp. 1520–1522). doi:10.1109/ICEICE.2011.5777453.

  3. Doebelin, E. O. (2004). Measurement systems: Application and design. New Delhi: Tata McGraw-Hill Publishing Company Limited.

    Google Scholar 

  4. Gao, J., Zhu, X., & Nandi, A. (2009). Non-redundant precoding and PAPR reduction in MIMO OFDM systems with ICA based blind equalization. IEEE Transactions on Wireless Communications, 8(6), 3038–3049. doi:10.1109/TWC.2009.080541.

    Article  Google Scholar 

  5. Gonzalez, E., Gorriz, J., Ramirez, J., & Puntonet, C. (2010). Elitist genetic algorithm guided by higher order statistic for blind separation of digital signals. In 36th annual conference on IEEE industrial electronics society IECON 2010 (pp. 1123–1128). doi:10.1109/IECON.2010.5675526.

  6. Gu, F., Zhang, H., & Zhu, D. (2010). Maximum likelihood blind equalization via blind separation using fractional sampling. In 12th IEEE international conference on communication technology (ICCT) (pp. 195–198). doi:10.1109/ICCT.2010.5689286.

  7. Guidara, R., Hosseini, S., & Deville, Y. (2009). Blind separation of nonstationary Markovian sources using an equivariant Newton–Raphson algorithm. Signal Processing Letters, IEEE, 16(5), 426–429. doi:10.1109/LSP.2009.2016448.

    Article  Google Scholar 

  8. Gupta, M., & Santhanam, B. (2004). ICA based blind adaptive MAI suppression in DS-CDMA systems. In Digital signal processing workshop, 2004 and the 3rd IEEE signal processing education workshop. 2004 IEEE 11th (pp. 201–205). doi:10.1109/DSPWS.2004.1437942.

  9. Hyvaarinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. New York: Wiley.

    Book  Google Scholar 

  10. Jin, Q., & Liang, G. (2010). A new algorithm of infomax for small numbers of sound signal separation. In International conference on artificial intelligence and education (ICAIE) (pp. 159–162). doi:10.1109/ICAIE.2010.5641410.

  11. Jin, Q., Wang, G., & Liu, Y. (2010). Blind signal separation by entropy maximization (INFOMAX). In 6th international conference on wireless communications networking and mobile computing (WiCOM) (pp. 1–5). Chaina.

  12. Jun, B., Xiao-hong, S., Hai-yan, W., & Xue, Z. (2009). Natural gradient improvement methods in blind source separation. In CISP ’09. 2nd international congress on image and signal processing (pp. 1–5). doi:10.1109/CISP.2009.5301512.

  13. Jun-Mei, Y. (2007). A new adaptive filter algorithm for system identification using independent component analysis. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (pp. 1341–1344). Honolulu, Hawaii, USA.

  14. Kattepur, A., Sattar, F., & See, C. M. S. (2010). Doppler aided blind source separation of communication signals. In 10th international conference on information sciences signal processing and their applications (ISSPA) (pp. 526–529). doi:10.1109/ISSPA.2010.5605598.

  15. Khosravy, M., Alsharif, M. R., & Yamashita, K. (2010). An optimum ICA based multiuser data separation for short message service. In Advances in Computer Science and Information Technology Lecture Notes in Computer Science, 6059, 279–286.

  16. Lee, Z. H., & Lim, W. G. (2009). Multi-user multimodulus algorithm in blind source separation and equalization for MIMO systems. In IEEE 9th Malaysia international conference on communications (MICC) (pp. 234–237). doi:10.1109/MICC.2009.5431502.

  17. Lee, T. W., & Sejnowski, T. J. (1998). Independent component analysis for mixed subgaussian and super-gaussian sources. In Technical Report on Computational Neurobiology Lab. La Jolla: The Salk Institute.

  18. Li, H., & Adali, T. (2008). A class of complex ICA algorithms based on the kurtosis cost function. IEEE Transactions on Neural Networks, 19(3), 408–420. doi:10.1109/TNN.2007.908636.

    Article  Google Scholar 

  19. Li, X., Li, W., Sun, Y., & Zhao, H. (2010). Blind source separation of vibration signal of electric traction experiment system. In International conference on intelligent control and information processing (ICICIP) (pp. 93–96). doi:10.1109/ICICIP.2010.5564224.

  20. Liu, H., & Sun, J. (2009). Blind MIMO-OFDM channel estimation based on ICA and KRLS algorithm. In WiCom ’09. 5th international conference on wireless communications, networking and mobile computing (pp. 1–5). doi:10.1109/WICOM.2009.5304098.

  21. Lou, W., Shi, G., & Zhang, J. (2009). Research and application of ICA technique in fault diagnosis for equipments. In IEEE international conference on intelligent computing and intelligent systems, 4, 310–313. doi:10.1109/ICICISYS.2009.5357659.

  22. Mehrabian, H., Chopra, R., & Martel, A. (2013). Calculation of intravascular signal in dynamic contrast enhanced-MRI using adaptive complex independent component analysis. IEEE Transactions on Medical Imaging, 32(4), 699–710. doi:10.1109/TMI.2012.2233747.

    Article  Google Scholar 

  23. Mikhael, W. B., & Yang, T. (2006). A gradient-based optimum block adaptation ICA technique for interference suppression in highly dynamic communication channels. EURASIP Journal on Applied Signal Processing, 2006, 1–10.

    Article  Google Scholar 

  24. Obradovic, D., Madhu, N., Szabo, A., & Wong, C. S. (2004). Independent component analysis for semi-blind signal separation in MIMO mobile frequency selective communication channels. In Proceedings of 2004 IEEE international joint conference on neural networks (vol. 1, p. 58). doi:10.1109/IJCNN.2004.1379869.

  25. Park, H. M., Oh, S. H., & Lee, S. Y. (2006). A modified infomax algorithm for blind signal separation. Neurocomputing, 70, 229–240.

    Article  Google Scholar 

  26. Parmar, S.D., & Unhelkar, B. (2009). Separation performance of ICA algorithms in communication systems. In IMPACT ’09. International IEEE on multimedia, signal processing and communication technologies (pp. 142–145). doi:10.1109/MSPCT.2009.5164195.

  27. Radenkovic, M., Bose, T., & Ramkumar, B. (2010). Blind adaptive equalization of MIMO systems: New recursive algorithms and convergence analysis. IEEE Transactions on Circuits and Systems I: Regular Papers, 57(7), 1475–1488. doi:10.1109/TCSI.2010.2052486.

    Article  MathSciNet  Google Scholar 

  28. Razaghi, H., Saatchi, R., Offiah, A., Burke, D., Bishop, N., & Gautam, S. (2013). Assessing material densities by vibration analysis and independent component analysis. Malaysian Journal of Fundamental and Applied Sciences, 9, 123–128.

    Google Scholar 

  29. Sandmair, A., Zaib, A., & Leon, F. P. (2010). Adaptive underdetermined ICA for handling an unknown number of sources. Latent Variable Analysis and Signal Separation, Lecture Notes in Computer Science, 6365, 181–188.

  30. Saruwatari, H., Mori, Y., Takatani, T., Ukai, S., Shikano, K., Hiekata, T., & Morita, T. (2005). Two-stage blind source separation based on ICA and binary masking for real-time robot audition system. In IEEE/RSJ international conference on intelligent robots and systems (pp. 2303–2308). doi:10.1109/IROS.2005.1544983.

  31. Sawada, H., Araki, S., & Makino, S. (2011). Underdetermined convolutive blind source separation via frequency bin-wise clustering and permutation alignment. IEEE Transactions on Audio, Speech, and Language Processing, 19(3), 516–527. doi:10.1109/TASL.2010.2051355.

    Article  Google Scholar 

  32. Shi, Z., Tan, X., Jiang, Z., Zhang, H., & Guo, C. (2010). Noisy blind source separation by nonlinear autocorrelation. In 2010 3rd international congress on image and signal processing (CISP) (vol. 7, pp. 3152–3156). doi:10.1109/CISP.2010.5647807.

  33. Stone, J. V. (2004). Independent component analysis, a tutorial introduction. Cambridge: The MIT Press.

    Google Scholar 

  34. Uddin, Z., & Alam, F. (2010). Hardware implementation of blind source separation of speech signals using independent component analysis. International Journal of Electrical and Computer Sciences, 10, 85–86.

    Google Scholar 

  35. Xiang, Y., Ng, S. K., & Nguyen, V. (2010). Blind separation of mutually correlated sources using precoders. IEEE Transactions on Neural Networks, 21(1), 82–90. doi:10.1109/TNN.2009.2034518.

    Article  Google Scholar 

  36. Zarzoso, V., & Comon, P. (2010). Robust independent component analysis by iterative maximization of the kurtosis contrast with algebraic optimal step size. IEEE Transactions on Neural Networks, 21(2), 248–261. doi:10.1109/TNN.2009.2035920.

    Article  Google Scholar 

  37. Zhang, N., & Liu, T. (2009). The application of ant colony optimization algorithm in linear-combination blind source separation problem. In CISP ’09. 2nd international congress on image and signal processing (pp. 1–4). doi:10.1109/CISP.2009.5304441.

  38. Zhu, Y., & Vikram, A. (2008). Tracking multiple targets using blind source separation algorithms. In Technical Report CSU-ECE-TR-08-02. Electrical and Computer Engineering Department, Cleveland State University.

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Authors and Affiliations

  1. COMSATS Institute of Information Technology, Wah Campus, Wah Cantt, Pakistan

    Zahoor Uddin, Ayaz Ahmad, Muhammad Iqbal & Muhammad Naeem

Authors
  1. Zahoor Uddin
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  2. Ayaz Ahmad
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  3. Muhammad Iqbal
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  4. Muhammad Naeem
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Correspondence to Zahoor Uddin.

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Uddin, Z., Ahmad, A., Iqbal, M. et al. Modified Infomax Algorithm for Smaller Data Block Lengths. Wireless Pers Commun 87, 245–267 (2016). https://doi.org/10.1007/s11277-015-3041-7

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