Abstract
Independent component analysis (ICA) is a signal processing technique used for blind source separation of the mixed received signals. In wireless communication, it is widely used in multiple input multiple output (MIMO) systems. Generally, the ICA algorithms assume mixing in static or quasi static wireless channels. Performance of these algorithms degrade for time varying scenario, where the quasi stationarity does not hold. Furthermore, if data blocks lengths are reduced for achieving quasi stationarity, the performance of these algorithms further degrade. In this paper, we propose an ICA based transceiver called the MIMO UD-transceiver system that performs well for time varying mixing scenario in the wireless channels. The proposed system also performs well for smaller blocks lengths. We use the well known ICA algorithm, the FastICA algorithm as a bench mark to evaluate the performance of the proposed system. Simulation is performed over 16 QAM signals. We compare results of the MIMO UD-ransceiver with the conventional MIMO transceiver, when both employ FastICA algorithm for separation. We also compare performance of the proposed system with the only available ICA algorithm for time varying wireless channels, the OBAICA algorithm. Results show that the proposed MIMO UD-transceiver outperforms the conventional MIMO transceiver and OBAICA algorithm for time varying mixing scenario in terms of signal to interference ratio and bit error rate.
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Stone, J. V. (2004). Independent component analysis: A tutorial introduction. Cambridge: MIT Press.
Khorshidia, G. S., Douauda, G., Beckmannb, C. F., Glasserd, M. F., Griffantia, L., & Smitha, S. M. (2014). Automatic denoising of functional MRI data combining independent component analysis and hierarchical fusion of classifiers. NeuroImage, 90, 449468.
Ge, Z., & Song, Z. (2014). Ensemble independent component regression models and soft sensing application. Chemometrics and Intelligent Laboratory Systems, 130, 115122.
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.
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).
Saruwatari, H., Mori, Y., Takatani, T., Ukai, S., Shikano, K., Hiekata, T., et al. (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).
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 (vol. 4, pp. 310–313).
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.
Boiret, M., Rutledge, D. N., Gorretta, N., Ginot, Y., & Roger, J. (2014). Application of independent component analysis on raman images of a pharmaceutical drug product: Pure spectra determination and spatial distribution of constituents. Journal of Pharmaceutical and Biomedical Analysis, 90, 7884.
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.
Zhang, Y., & Kassam, S. A. (2011). Blind equalization using coarse quantizer BSS nonlinearity. In 45th annual conference on information sciences and systems (CISS) (pp. 1–6).
Wangn, X., Huang, Z., Zhou, Y., & Ren, X. (2013). Approaches and applications of semi-blind signal extraction for communication signals based on constrained independent component analysis: The complex case. Neurocomputing, 101, 204–216.
Jiang, Y., Zhu, X., Lim, E., Dong, L., & Huang, Y. (2011). Low-complexity independent component analysis based semi-blind receiver for wireless multiple-input multiple-output systems. International Journal of Design, Analysis and Tools for Circuits and Systems, 2, 91–98.
Gao, J., Zhu, X., & Nandi, A. K. (2008). Linear precoding aided blind equalization with independent component analysis in Mimo OFDM systems. In 16th European signal processing conference (EUSIPCO 2008), Lausanne, Switzerland, August 25–29, 2008.
Liu, H., & Sun, J. (2009). Blind MIMO-OFDM channel estimation based on ICA and krls algorithm. In 5th international conference on wireless communications, networking and mobile computing, 2009. WiCom ’09 (pp. 1–5).
Curnew, S., & Ilow, J. (2007). Blind signal separation in mimo ofdm systems using ica and fractional sampling. In International symposium on signals, systems and electronics, 2007. ISSSE ’07. (pp. 67–70).
Sarperi, L., Zhu, X., & Nandi, A. (2007). Blind OFDM receiver based on independent component analysis for multiple-input multiple-output systems. IEEE Transactions on Wireless Communications, 6(11), 4079–4089.
Sarperi, L., Zhu, X., & Nandi, A. (2008). Semi blind layered space-frequency equalization for single carrier MIMO systems with block transmission. IEEE Transactions on Wireless Communications, 7(4), 1203–1207.
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.
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.
Zhao, X., & Davies, M. (2008). ’A feasible blind equalization scheme in large constellation MIMO systems. In IEEE international conference on acoustics, speech and signal processing, ICASSP 2008 (vol. 2008, pp. 1845–1848).
Zarzoso, V. (2003). Exploiting independence for co-channel interference cancellation and symbol detection in multiuser digital communications. In Proceedings of seventh international symposium on signal processing and its applications, 2003 (vol. 2, pp. 303–306).
Weikert, O., & Zolzer, U. (2006).New approach for resolving ambiguities for semi-blind equalization of mimo frequency selective channels. In IEEE 17th international symposium on personal, indoor and mobile radio communications, 2006 (pp. 1–5).
Haghighat, A. (2006) ICA-based signal equalization for digital receivers. In IEEE 64th vehicular technology conference, 2006. VTC-2006 Fall. 2006 (pp. 1–5).
Homayounzadeh, A., & Shirazi, M. (2009). ICA-based equalization of wireless channels in block transmission communication systems. In International conference on signal processing systems (Vol. 2009, pp. 201–204).
Gu, F., Zhang, H., & Zhu, D. (2010) Maximum likelihood blind equalization via blind separation using fractional sampling. In 2010 12th IEEE international conference on communication technology (ICCT) (pp. 195–198).
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.
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), 2009 (pp. 234–237).
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 (vol. 6059, 279–286).
Agirman-Tosun, H., Liu, Y., Haimovich, A., Simeone, O., Su, W., Dabin, J., et al. (2011). Modulation classification of mimo-ofdm signals by independent component analysis and support vector machines. In Conference record of the forty fifth Asilomar conference on signals, Systems and computers (ASILOMAR) (vol. 2011, pp. 1903–1907).
Muhlhaus, M., Oner, M., Dobre, O., Jakel, H., & Jondral, F. (2013). A novel algorithm for MIMO signal classification using higher-order cumulants. In IEEE radio and wireless symposium (RWS) (vol. 2013, pp. 7–9).
Parmar, S. D., & Unhelkar, B. (2009). Separation performance of ICA algorithms in communication systems. In International multimedia, signal processing and communication technologies, IMPACT ’09 (vol. 2009, pp. 142–145).
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), 2010 (pp. 526–529).
Zhao, X., & Davies, M. (2010). Coding-assisted blind MIMO separation and decoding. IEEE Transactions on Vehicular Technology, 59(9), 4408–4417.
Bingham, E., & Hyvarinen, A. (2000). A fast fixed point algorithm for independent component analysis of complex valued signals. International Journal of Neural Systems, 10, 1–8.
Openheim, A. V., Schafer, R. W., & Buck, J. R. (2006). Discrete time signal processing, 2nd edn. Pearson education signal processing series.
Ma, X., hui Zhao, C., & Qiao, G. (2009). The underwater acoustic MIMO OFDM system channel equalizer basing on independent component analysis. In WRI international conference on communications and mobile computing, 2009 (vol. 2, pp. 568–572).
Stone, J. V. (2004). Independent component analysis: A tutorial introducton. Cambridge, MA: The MIT Press.
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Uddin, Z., Ahmad, A. & Iqbal, M. ICA Based MIMO Transceiver For Time Varying Wireless Channels Utilizing Smaller Data Blocks Lengths. Wireless Pers Commun 94, 3147–3161 (2017). https://doi.org/10.1007/s11277-016-3769-8
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DOI: https://doi.org/10.1007/s11277-016-3769-8