In recent years, multi-antenna techniques are being considered as a potential solution to increase the flow of future wireless communication systems. The objective of this article is to study the emission and reception system MIMO (Multiple Input Multiple Output), and present the different reception decoding techniques. First we will present the least complex technical, linear receivers such as the zero forcing equalizer (ZF) and minimum mean squared error (MMSE). Then a nonlinear technique called ordered successive cancellation of interferences (OSIC) and the optimal detector based on the maximum likelihood criterion (ML), finally, we simulate the associated decoding algorithms for MIMO system such as ZF, MMSE, OSIC and ML, thus a comparison of performance of these algorithms in MIMO context.
[1] A. Ikhlef, K. Abed Meraim, and D. Le Guennec. “A new blind adaptive
signal separation and equalization algorithm for MIMO convolutive
systems.” In IEEE Statistical Signal Processing Workshop, August 2007.
[2] E. Moulines, J. F. Cardoso, and E. Gassiat. “Maximum likelihood for
blind separation and deconvolution of noisy signals using mixture
models.” Proceedings of ICASSP, 4:3617–3620, April 1997.
[3] S. Daumont and D. Le Guennec. “Adaptive Analytical Simplified
Constant Modulus Algorithm: A BSS algorithm for MIMO systems with
time-varying environments.” European Association for Signal
Processing, August 2009.
[4] M. Enescu, M. Sirbu, and V. Koivunen. “Adaptive equalization of timevarying
MIMO channels.” Signal Processing, 85:81–93, January 2002.
[5] G.D. Golden, C.J. Foschini, R.A. Valenzuela, and P.W. Wolniansky.
“Detection algorithm and initial laboratory results using V-BLAST.”
Electronics Letters, 35(1):14–15, January 1999.
[6] D. T. Pham, Ph. Garat, and C. Jutten. “Separation of a mixture of
independent sources through a maximum likekihood approach.” In
Signal Processing VI pages 771–774, 1992.
[7] D. Le Ruyet and B. Ozbek. Systèmes MIMO et codage spatio-temporel.
Revue de l’électricité et de l’électronique, (4), 2005.
[8] J. Yang and S. Roy. “On joint transmitter and receiver optimization for
Multiple-InputMultiple-Onput (MIMO) transmission systems”. Neural
Computation, 42(12):3221–3231, 1994.
[9] V. Zarzoso and P. Comon. “Modulation recognition for MIMO
systems”. IEEE Transactions on Communications, 56:10–13, January
2008.
[10] G. J. Foschini. “Layered space-time architecture for wireless
communication in a fading environment when using multiple antennas.”
Bell Labs Technical Journal, 1(2):41–59, September 1996.
[11] C.J. Foschini, G. D. Golden, R.A. Valenzuela, and P.W. “Wolniansky.
Simpli-fied processing for wireless communication at high spectral
efficiency”. IEEE Journal on Selected Areas in Communications,
17(11):1841–1852, November 1999.
[12] S. Daumont. «Techniques de demodulation aveugle en interception de
signaux MIMO ». Signal and Image processing. Université Rennes 1,
2009. French.
[13] Greenstein, L.J. (1978) A multipath fading channel model for terrestrial
digital radio systems. IEEE Trans. Commun., 26(8), 1247–1250.
[14] Uysal, M. and Georghiades, C.N. (2000) Error performance analysis of
space-time codes over Rayleigh fading channels. Journal of
Communication and Networks, 2(4), 351–355.
[15] Larsson, E.G., Ganesan, G., Stoica, P., and Wong, W.H. (2002) On the
performance of orthogonal space-time block coding with quantized
feedback. IEEE Commun. Letters, 12(6), 487–489.
[16] Love, D.J., Heath, R.W. Jr, Santipach, W., and Honig, M.L. (2004)
What is the value of limited feedback for MIMO channels? IEEE
Commun. Mag., 42(10), 54–59.
[17] Rappaport, T.S. (2002). Wireless Communications. New Jersey,
Prentice-Hall.
[18] Ryu, J.H., Lee, Y.H., “Combined Equalization and Nonlinear Distortion
Cancellation for Transmission of QAM Signal in Fixed Wireless
Channel,” IEEE Transactions on Communications, ICASSP, pp.489-492,
2003.
[19] Tang, X., Alouini, M. S., Goldsmith, A. J., “Effect of Channel
Estimation Error on MQAM BER Performance in Rayleigh Fading,”
IEEE Transactions on Communications, Vol.47, No.12, December 1999.
[20] Glover, I.A., Grant, P.M. “Digital Communications”, 2nd edition. Essex,
England. Pearson Prentice Hall, 2004.
[21] S. Mahanta, A. Rajauria, “Analysis of MIMO System through ZF &
MMSE Detection Scheme”, IJECT Vol. 4, Issue Spl - 4, April - June
2013.
[22] S. J. Young, “Cepstral mean compensation for HMM recognition in
noise”, in Proc. Workshop on Speech Processing in Adverse
Environments, (Cannes, France), Nov. 1992.
[23] A. E. Rosenberg, C. H. Lee and F. K. Soong, “Cepstral channel
normalization techniques for HMM-based speaker recognition”, in Proc.
Int. Conf. Spoken Language Processing, 1994, pp. 1835-1838.
[24] B. H. Juang and K. K. Paliwal, “Hidden Markov models with first-order
equalization for noisy speech recognition”, IEEE Trans. Signal
Processing, Vol. 40, pp. 2136-2143, Sept. 1992.
[1] A. Ikhlef, K. Abed Meraim, and D. Le Guennec. “A new blind adaptive
signal separation and equalization algorithm for MIMO convolutive
systems.” In IEEE Statistical Signal Processing Workshop, August 2007.
[2] E. Moulines, J. F. Cardoso, and E. Gassiat. “Maximum likelihood for
blind separation and deconvolution of noisy signals using mixture
models.” Proceedings of ICASSP, 4:3617–3620, April 1997.
[3] S. Daumont and D. Le Guennec. “Adaptive Analytical Simplified
Constant Modulus Algorithm: A BSS algorithm for MIMO systems with
time-varying environments.” European Association for Signal
Processing, August 2009.
[4] M. Enescu, M. Sirbu, and V. Koivunen. “Adaptive equalization of timevarying
MIMO channels.” Signal Processing, 85:81–93, January 2002.
[5] G.D. Golden, C.J. Foschini, R.A. Valenzuela, and P.W. Wolniansky.
“Detection algorithm and initial laboratory results using V-BLAST.”
Electronics Letters, 35(1):14–15, January 1999.
[6] D. T. Pham, Ph. Garat, and C. Jutten. “Separation of a mixture of
independent sources through a maximum likekihood approach.” In
Signal Processing VI pages 771–774, 1992.
[7] D. Le Ruyet and B. Ozbek. Systèmes MIMO et codage spatio-temporel.
Revue de l’électricité et de l’électronique, (4), 2005.
[8] J. Yang and S. Roy. “On joint transmitter and receiver optimization for
Multiple-InputMultiple-Onput (MIMO) transmission systems”. Neural
Computation, 42(12):3221–3231, 1994.
[9] V. Zarzoso and P. Comon. “Modulation recognition for MIMO
systems”. IEEE Transactions on Communications, 56:10–13, January
2008.
[10] G. J. Foschini. “Layered space-time architecture for wireless
communication in a fading environment when using multiple antennas.”
Bell Labs Technical Journal, 1(2):41–59, September 1996.
[11] C.J. Foschini, G. D. Golden, R.A. Valenzuela, and P.W. “Wolniansky.
Simpli-fied processing for wireless communication at high spectral
efficiency”. IEEE Journal on Selected Areas in Communications,
17(11):1841–1852, November 1999.
[12] S. Daumont. «Techniques de demodulation aveugle en interception de
signaux MIMO ». Signal and Image processing. Université Rennes 1,
2009. French.
[13] Greenstein, L.J. (1978) A multipath fading channel model for terrestrial
digital radio systems. IEEE Trans. Commun., 26(8), 1247–1250.
[14] Uysal, M. and Georghiades, C.N. (2000) Error performance analysis of
space-time codes over Rayleigh fading channels. Journal of
Communication and Networks, 2(4), 351–355.
[15] Larsson, E.G., Ganesan, G., Stoica, P., and Wong, W.H. (2002) On the
performance of orthogonal space-time block coding with quantized
feedback. IEEE Commun. Letters, 12(6), 487–489.
[16] Love, D.J., Heath, R.W. Jr, Santipach, W., and Honig, M.L. (2004)
What is the value of limited feedback for MIMO channels? IEEE
Commun. Mag., 42(10), 54–59.
[17] Rappaport, T.S. (2002). Wireless Communications. New Jersey,
Prentice-Hall.
[18] Ryu, J.H., Lee, Y.H., “Combined Equalization and Nonlinear Distortion
Cancellation for Transmission of QAM Signal in Fixed Wireless
Channel,” IEEE Transactions on Communications, ICASSP, pp.489-492,
2003.
[19] Tang, X., Alouini, M. S., Goldsmith, A. J., “Effect of Channel
Estimation Error on MQAM BER Performance in Rayleigh Fading,”
IEEE Transactions on Communications, Vol.47, No.12, December 1999.
[20] Glover, I.A., Grant, P.M. “Digital Communications”, 2nd edition. Essex,
England. Pearson Prentice Hall, 2004.
[21] S. Mahanta, A. Rajauria, “Analysis of MIMO System through ZF &
MMSE Detection Scheme”, IJECT Vol. 4, Issue Spl - 4, April - June
2013.
[22] S. J. Young, “Cepstral mean compensation for HMM recognition in
noise”, in Proc. Workshop on Speech Processing in Adverse
Environments, (Cannes, France), Nov. 1992.
[23] A. E. Rosenberg, C. H. Lee and F. K. Soong, “Cepstral channel
normalization techniques for HMM-based speaker recognition”, in Proc.
Int. Conf. Spoken Language Processing, 1994, pp. 1835-1838.
[24] B. H. Juang and K. K. Paliwal, “Hidden Markov models with first-order
equalization for noisy speech recognition”, IEEE Trans. Signal
Processing, Vol. 40, pp. 2136-2143, Sept. 1992.
@article{"International Journal of Information, Control and Computer Sciences:70390", author = "Said Elkassimi and Said Safi and B. Manaut", title = "Equalization Algorithms for MIMO System", abstract = "In recent years, multi-antenna techniques are being considered as a potential solution to increase the flow of future wireless communication systems. The objective of this article is to study the emission and reception system MIMO (Multiple Input Multiple Output), and present the different reception decoding techniques. First we will present the least complex technical, linear receivers such as the zero forcing equalizer (ZF) and minimum mean squared error (MMSE). Then a nonlinear technique called ordered successive cancellation of interferences (OSIC) and the optimal detector based on the maximum likelihood criterion (ML), finally, we simulate the associated decoding algorithms for MIMO system such as ZF, MMSE, OSIC and ML, thus a comparison of performance of these algorithms in MIMO context.
", keywords = "Multiple Input Multiple Outputs (MIMO), ZF,
MMSE, Ordered Interference Successive Cancellation (OSIC), ML,
Interference Successive Cancellation (SIC).", volume = "9", number = "5", pages = "1361-8", }
