Browsing by Author "Madyastha, Raghavendra K."

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    A globally convergent algorithm for training multilayer perceptrons for data classification and interpolation
    (1991) Madyastha, Raghavendra K.; Aazhang, Behnaam
    This thesis addresses the issue of applying a "globally" convergent optimization scheme to the training of multi-layer perceptrons, a class of Artificial Neural Networks, for the detection and classification of signals in single- and multi-user communication systems. The research is motivated by the fact that a multi-layer perceptron is theoretically capable of approximating any nonlinear function to within any specified accuracy. The object function to which we apply the optimization algorithm is the error function of the multilayer perceptron, i.e., the average of the sum of the squares of the differences between the actual and the desired outputs to specified inputs. Until recently, the most widely used training algorithm has been the Backward Error Propagation algorithm, which is based on the algorithm for "steepest descent" and hence, is at best linearly convergent. The algorithm discussed here combines the merits of two well known "global" algorithms--the Conjugate Gradients and the Trust Region algorithms. A further technique known as preconditioning is used to speed up the convergence by clustering the eigenvalues of the "effective Hessian". The Preconditioned Conjugate Gradients--Trust Regions algorithm is found to be superlinearly convergent and hence, outperforms the standard backpropagation routine.
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    Antenna arrays for wireless CDMA communication systems
    (1997) Madyastha, Raghavendra K.; Aazhang, Behnaam
    The estimation of code delays along with amplitudes and phases of different users constitutes the first stage in the demodulation process in a CDMA communication system. The delay estimation stage is termed the acquisition stage and forms the bottleneck for the detection of users' bitstreams; accurate detection necessitates accurate acquisition. Most existing schemes incorporate a single sensor at the receiver, which leads to an inherent limit in the acquisition based capacity, which is the number of users that can be simultaneously acquired. In this thesis we combine the benefits of spatial processing in the form of an antenna array at the receiver along with code diversity to gain an increase in the capacity of the system. An additional parameter to be estimated now is the direction of arrival (DOA) of each user. We demonstrate the gains in parameter estimation with the incorporation of spatial diversity. We propose two classes of delay-DOA estimation algorithms--a maximum likelihood algorithm and a subspace based algorithm (MUSIC). With reasonable assumptions on the system we are able to derive computationally efficient estimation algorithms and demonstrate the gains achieved in exploiting multiple sensors at the receiver. In addition, we also investigate the benefits of spatial diversity in linear multiuser detection. We consider two linear multiuser detectors, the decorrelating detector and the linear MMSE detector (chosen for their near-far properties) and characterize the performance increase in the multisensor case. We observe that in many cases, the gain can be directly captured in terms of the number of sensors in the array.