TY - JOUR
T1 - Blind adaptive equalization of MIMO systems
T2 - New recursive algorithms and convergence analysis
AU - Radenkovic, Miloje S.
AU - Bose, Tamal
AU - Ramkumar, Barathram
N1 - Funding Information:
Manuscript received August 20, 2009; revised January 20, 2010; accepted April 27, 2010. Date of current version July 16, 2010. This work was supported in part by the National Science Foundation under Grant 0809036 and in part by the Institute for Critical Technology and Applied Science. This paper was recommended by Associate Editor J. Manton.
PY - 2010
Y1 - 2010
N2 - An adaptive (recursive in time) filtering method is proposed for blind deconvolution of multiple-input multiple-output (MIMO) channels modeled by an autoregressive moving average (ARMA) process. This method consists of two recursive schemes. The adaptive blind identification algorithm estimates the MIMO system impulse response. These estimates are then used in an adaptive Wiener-type filter to extract the instantaneous mixture of input sources. Such a mixture is further processed by a blind source separation algorithm to obtain the individual sources. Only second-order (SOS) statistics are used, and precise knowledge of the system order is not required as long as it is overmodeled. We also present an algorithm for the case of time-varying parameters. It is proved that the developed algorithms are globally convergent with probability one.
AB - An adaptive (recursive in time) filtering method is proposed for blind deconvolution of multiple-input multiple-output (MIMO) channels modeled by an autoregressive moving average (ARMA) process. This method consists of two recursive schemes. The adaptive blind identification algorithm estimates the MIMO system impulse response. These estimates are then used in an adaptive Wiener-type filter to extract the instantaneous mixture of input sources. Such a mixture is further processed by a blind source separation algorithm to obtain the individual sources. Only second-order (SOS) statistics are used, and precise knowledge of the system order is not required as long as it is overmodeled. We also present an algorithm for the case of time-varying parameters. It is proved that the developed algorithms are globally convergent with probability one.
KW - Adaptive blind equalization
KW - blind equalization of IIR MIMO systems
KW - blind equalization of time-varying channels
KW - convergence analysis of blind equalizers
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U2 - 10.1109/TCSI.2010.2052486
DO - 10.1109/TCSI.2010.2052486
M3 - Article
AN - SCOPUS:77954888228
SN - 1549-8328
VL - 57
SP - 1475
EP - 1488
JO - IEEE Transactions on Circuits and Systems I: Regular Papers
JF - IEEE Transactions on Circuits and Systems I: Regular Papers
IS - 7
M1 - 5508376
ER -