Title :
Effects of input data correlation on the convergence of blind adaptive equalizers
Author :
J.P. LeBlanc;K. Dogancay;R.A. Kennedy;C.R. Johnson
Author_Institution :
Sch. of Electr. Eng., Cornell Univ., Ithaca, NY, USA
Abstract :
A variety of blind equalization algorithms exist. These algorithms, which draw on some theoretical justification for the demonstration or analysis of their purportedly ideal convergence properties, almost invariably rely on the input data being independent and identically distributed (i.i.d.). In contrast, in this paper we show that input correlation can have a marked effect on the character of algorithm convergence. We demonstrate that under suitable input data correlation and channels: (i) undesirable local minima present in the i.i.d. case are absent for certain correlated sources implying ideal global convergence for some situations and, (ii) the most commonly employed practical algorithm can exhibit ill-convergence to closed-eye minima even under the popular single spike initialization when an eye-opening equalizer parameterization is possible.
Keywords :
"Convergence","Blind equalizers","Adaptive equalizers","Displays","Systems engineering and theory","Australia","Algorithm design and analysis","Modulation coding","Decoding","Dispersion"
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.390035