Neural computation approach for the maximum-likelihood sequence estimation of communications signal

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.