Causal Wiener filter banks for periodically correlated time series
Signal Processing
An augmented CRTRL for complex-valued recurrent neural networks
Neural Networks
Waveform diversity via mutual information
Digital Signal Processing
Complex ICA using generalized uncorrelating transform
Signal Processing
On Wiener filtering of certain locally stationary stochastic processes
Signal Processing
On the equivalence of time and frequency domain maximum likelihood estimation
Automatica (Journal of IFAC)
On widely linear Wiener and tradeoff filters for noise reduction
Speech Communication
MIMO OFDM systems with digital RF impairment compensation
Signal Processing
Augmented second-order statistics of quaternion random signals
Signal Processing
Complex Gaussian scale mixtures of complex wavelet coefficients
IEEE Transactions on Signal Processing
An adaptive approach for the identification of improper complex signals
Signal Processing
Informed source separation through spectrogram coding and data embedding
Signal Processing
Information-theoretic analysis of underwater acoustic OFDM systems in highly dispersive channels
Journal of Electrical and Computer Engineering - Special issue on Underwater Communications and Networking
Wireless Personal Communications: An International Journal
Testing blind separability of complex Gaussian mixtures
Signal Processing
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The covariance of complex random variables and processes, when defined consistently with the corresponding notion for real random variables, is shown to be determined by the usual complex covariance together with a quantity called the pseudo-covariance. A characterization of uncorrelatedness and wide-sense stationarity in terms of covariance and pseudo-covariance is given. Complex random variables and processes with a vanishing pseudo-covariance are called proper. It is shown that properness is preserved under affine transformations and that the complex-multivariate Gaussian density assumes a natural form only for proper random variables. The maximum-entropy theorem is generalized to the complex-multivariate case. The differential entropy of a complex random vector with a fixed correlation matrix is shown to be maximum if and only if the random vector is proper, Gaussian, and zero-mean. The notion of circular stationarity is introduced. For the class of proper complex random processes, a discrete Fourier transform correspondence is derived relating circular stationarity in the time domain to uncorrelatedness in the frequency domain. An application of the theory is presented