Abstract
A novel detection approach for signals in digital communications is proposed in this paper by using the NNTCTG (neural network with transient chaos and time-varying gain) developed by the author (1997, 1998). The maximum-likelihood signal detection problem can be always described as a complex optimization problem with so many local optima that conventional Hopfield-type neural networks cannot be applied. To amend the drawbacks of Hopfield-type networks, the NNTCTG is used to search for globally optimal or near-optimal solutions of the optimization problems with lots of local optima, since it has richer and more flexible dynamics than conventional networks with only point attractors. We established a neuro-based detection model for digital communication signals and analyzed its working procedure in detail. Two simulation experiments were conducted to illustrate the validity and effectiveness of the proposed approach.
Published Version
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