Feedforward neural network for blind equalization with PSK signals

Authors:
Rajoo Pandey
Affiliations:
Department of Electronics and Communication Engineering, National Institute of Technology, 136119, Kurukshetra, India
Venue:
Neural Computing and Applications
Year:
2005

Citing 0
Cited 2

Generalized derivation of neural network constant modulus algorithm for blind equalization

WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Letters: Blind sequence estimation of MPSK signals using dynamically driven recurrent neural networks

Neurocomputing

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Abstract

Most of the cost functions used for blind equalization are nonconvex and nonlinear functions of tap weights, when implemented using linear transversal filter structures. Therefore, a blind equalization scheme with a nonlinear structure that can form nonconvex decision regions is desirable. The efficacy of complex-valued feedforward neural networks for blind equalization of linear and nonlinear communication channels has been confirmed by many studies. In this paper we present a complex valued neural network for blind equalization with M-ary phase shift keying (PSK) signals. The complex nonlinear activation functions used in the neural network are especially defined for handling the M-ary PSK signals. The training algorithm based on constant modulus algorithm (CMA) cost function is derived. The improved performance of the proposed neural network in both, stationary and nonstationary environments, is confirmed through computer simulations.