title: Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization.
abstract: Stop-and-go decision-directed (S-and-G-DD) equalization is the most primitive blind equalization (BE) method for the cancelling of intersymbol-interference in data communication systems. Recently, this scheme has been applied to complex-valued multilayer feedforward neural network, giving robust results with a lower mean-square error at the expense of slow convergence. To overcome this problem, in this work, a fast converging recursive least squares (RLS)-based complex-valued backpropagation learning algorithm is derived for S-and-G-DD blind equalization. Simulation results show the effectiveness of the proposed algorithm in terms of initial convergence.
authors.affiliation: Dept. of Comput. Eng., King Fahd Univ. of Pet. and Miner., Dhahran, Saudi Arabia.
authors.forename: S
authors.initials: S
authors.lastname: Abrar
authors.forename: A
authors.initials: A
authors.lastname: Zerguine
authors.forename: M
authors.initials: M
authors.lastname: Bettayeb