Feedforward neural network for blind equalization with PSK signals
Neural Computing and Applications
Complex-valued Neural Networks: Utilizing High-dimensional Parameters
Complex-valued Neural Networks: Utilizing High-dimensional Parameters
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Complex-valued neurons with phase-dependent activation functions
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Nonlinear blind equalization schemes using complex-valued multilayer feedforward neural networks
IEEE Transactions on Neural Networks
Decision feedback recurrent neural equalization with fast convergence rate
IEEE Transactions on Neural Networks
Proceedings of the CUBE International Information Technology Conference
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This paper presents a novel blind sequence estimation of multiple phase shift keying (MPSK) signals approach using dynamically driven recurrent neural networks (DDRNN) with the continuous multi-threshold phase activation function (CMTPAF). With the consideration of the characteristics of MPSK signals, a CMTPAF is designed, the parameters of the CMTPAF are illustrated, and the two new concepts of accumulation points and repulsion points are proposed. The weight matrix of DDRNN-CMTPAF is constructed by utilizing the unitary signal space matrix obtained from singular value decomposition for the receiving signal matrix. It is important that the energy functions of synchronous and asynchronous modes in the designed DDRNN-CMTPAF are proposed and proved.