Blind adaptive stochastic neural network for multiuser detection

In this paper some blind adaptive methods are introduced for multiuser detection (MUD). The detector architecture contains a channel identifier followed by a stochastic Hopfield (1985) net. Blind channel identification is proposed to be carried out by either the Kohonen (see Self-Organizing Maps, Sp...

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Bibliographic Details
Main Authors: Jeney, G., Levendovszky, J., Kovacs, L.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Decorrelation
Detectors
Hopfield neural networks
Multiaccess communication
Multiuser detection
Neural networks
Parameter estimation
Recurrent neural networks
Stochastic processes
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Summary:In this paper some blind adaptive methods are introduced for multiuser detection (MUD). The detector architecture contains a channel identifier followed by a stochastic Hopfield (1985) net. Blind channel identification is proposed to be carried out by either the Kohonen (see Self-Organizing Maps, Springer, 2000) algorithm or by a novel adaptive decorrelation technique. Based on the estimated channel parameters the stochastic Hopfield net implements a near optimal decision. Besides describing the related algorithms, the paper contains extensive simulations to evaluate the performance of the proposed detector structures.
ISSN:1090-3038
2577-2465
DOI:10.1109/VETECS.2001.945018