title: Decision feedback recurrent neural equalization with fast convergence rate.
abstract: Real-time recurrent learning (RTRL), commonly employed for training a fully connected recurrent neural network (RNN), has a drawback of slow convergence rate. In the light of this deficiency, a decision feedback recurrent neural equalizer (DFRNE) using the RTRL requires long training sequences to achieve good performance. In this paper, extended Kalman filter (EKF) algorithms based on the RTRL for the DFRNE are presented in state-space formulation of the system, in particular for complex-valued signal processing. The main features of global EKF and decoupled EKF algorithms are fast convergence and good tracking performance. Through nonlinear channel equalization, performance of the DFRNE with the EKF algorithms is evaluated and compared with that of the DFRNE with the RTRL.
authors.affiliation: School of Information Technology and Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada. jongsoo.choi@ieee.org
authors.forename: Jongsoo
authors.initials: J
authors.lastname: Choi
authors.forename: Martin
authors.initials: M
authors.lastname: Bouchard
authors.forename: Tet Hin
authors.initials: TH
authors.lastname: Yeap
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003661:Decision Support Techniques; D005246:Feedback; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes