Title :
Domains of attraction of Shalvi-Weinstein receivers
Author :
Gu, Ming ; Tong, Lang
Author_Institution :
Voyan Technol., Santa Clara, CA, USA
fDate :
7/1/2001 12:00:00 AM
Abstract :
Domains of attraction (DoA) of Shalvi-Weinstein (1990) receivers are analyzed. It is shown that there is a one-to-one correspondence between DoA in the receiver parameter space and those in the global (or combined channel-receiver) parameter space. For general noiseless channels, DoA of SW receivers in the global response space are the minimum distance decision regions on a unit sphere. In the presence of noise and for the class of orthogonal channels, DoA of SW receivers for independent and identically distributed (i.i.d.) input signals are the minimum distance decision regions on an ellipsoid determined by the channel coefficients and the noise variance. The DoA in the receiver parameter space are also characterized for the general nonuniformly distributed sources. The size of the DoA is shown to be affected by the signal power, the signal constellation, the noise level, and the channel condition. It is also demonstrated that although the optima of the Shalvi-Weinstein algorithm and those of the constant modulus algorithm are one-to-one correspondent, their DoA are different in general
Keywords :
adaptive equalisers; adaptive estimation; deconvolution; receivers; Shalvi-Weinstein algorithm; Shalvi-Weinstein receivers; adaptive filters; blind deconvolution; blind equalizer; blind signal estimation; channel coefficients; channel condition; combined channel-receiver; constant modulus algorithm; domains of attraction; general nonuniformly distributed sources; global parameter space; global response space; i.i.d. input signals; minimum distance decision regions; noise level; noise variance; noiseless channels; orthogonal channels; receiver parameter space; signal constellation; signal power; unit sphere; Blind equalizers; Constellation diagram; Convergence; Cost function; Direction of arrival estimation; Ellipsoids; Filters; Noise level; Signal processing algorithms; Stochastic processes;
Journal_Title :
Signal Processing, IEEE Transactions on