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Code division multiple access (CDMA) is based on the spread-spectrum technology and is a dominant air interface for 2.5G, 3G, and future wireless networks. For the CDMA downlink, the transmitted CDMA signals from the base station (BS) propagate through a noisy multipath fading communication channel before arriving at the receiver of the user(More)
Blind Source Recovery (BSR) denotes recovery of original sources/signals from environments that may include convolution, temporal variation, and even nonlinearity. It also infers the recovery of sources even in the absence of precise environment identifiability. This paper describes, in a comprehensive fashion, a generalized BSR formulation achieved by the(More)
Blind Multi User Detection (BMUD) is the process to simultaneously estimate multiple symbol sequences associated with multiple users in the downlink of a Code Division Multiple Access (CDMA) communication system using only the received data. In this paper, we propose BMUD algorithms based on the Natural Gradient Blind Source Recovery (BSR) techniques in(More)
—We present a model of a basic recurrent neural network (or bRNN) that includes a separate linear term with a slightly " stable " fixed matrix to guarantee bounded solutions and fast dynamic response. We formulate a state space viewpoint and adapt the constrained optimization Lagrange Multiplier (CLM) technique and the vector Calculus of Variations (CoV) to(More)
Blind Source Recovery (BSR) denotes recovery of original sources or signals from environments, which may include convolution, temporal variation, and even non-linearity—without necessarily performing explicit environment identification. This paper presents two separate multiple-input multiple-output (MIMO) structures for dynamic linear Blind Source Recovery(More)
– The standard LSTM recurrent neural networks while very powerful in long-range dependency sequence applications have highly complex structure and relatively large (adaptive) parameters. In this work, we present empirical comparison between the standard LSTM recurrent neural network architecture and three new parameter-reduced variants obtained by(More)
We present two new hyperbolic source probability models to effectively represent sub-gaussian and super-gaussian families of sources for dynamic and convolutive Blind Source Recovery (BSR). Both models share a common boundary for the gaussian density function. The proposed hyperbolic probability model for the sub-gaussian densities is an extension of the(More)