TITLE:

BLIND SOURCE RECOVERY: THEORETICAL FORMULATIONS, IMPLEMENTATIONS AND APPLICATION TO CDMA COMMUNICATION SYSTEMS (No. 48)

DATE:

Friday, April 30th, 2004

TIME:

3:30 PM

LOCATION:

GMCS 214

SPEAKER:

Khurram Waheed, Department of Electrical & Computer Engineering, San Diego State University

ABSTRACT:

Blind Source Recovery (BSR) is an autonomous (or unsupervised) stochastic adaptation approach that denotes recovering signals from measurements in environments that may include convolution, transients, and even possible nonlinearity. The primary goal of BSR is to recover original source signals, as best as possible, even in the absence of precise environment identifiability. A discrete-time optimization framework for BSR has been adopted based on minimization of the Kullback-Lieblar Divergence, subject to the constraints of a state-space representation, using the Riemannian contra-variant gradient adaptation.

The modeling of the environment and its representation is vital in the proposed framework. The adoption of the state space framework allows for the derivation of more general update laws capable of catering to most known filtering paradigms. For the interesting case of multi-variable linear time-invariant dynamic BSR, parametric update laws for discrete-time canonical feedforward and feedback state space configurations have been derived. Higher Order Statistics have been explored to obtain adaptation algorithms that are purely blind of actual source distributions.

The BSR framework has been successfully applied to multi-user detection problems in modern CDMA wireless communication networks. The proposed schemes provide superior performance compared to the conventional detection schemes especially under imposing conditions of congestion and un-modeled multiple access interference (MAI). These promising new results clearly demonstrate the effectiveness and practicality of the formulated approaches as applied to the domain of communications.

HOST:

Michael O’Sullivan

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