A new cross-correlation and constant modulus type algorithm for PAM-PSK signals

Abstract

Citation: LUO, Y. and CHAMBERS, J., 2003

MIMO OFDM Blind Channel Equalization using Multilayer Neural Network in Impulsive Noise Environment

“…(10) (11) Where weight of input to hidden layer at th node is and is the number of hidden layer nodes. The following equation (12) shows anyone of the neural estimator output.…”

“…Hence signals can be effectively processed through nonlinear channels using neural networks. The natural structure of neural network which has multiple inputs and multiple outputs is more suitable for MIMO systems [10][11][12][13][14]. Conventional feed forward neural networks viz., radial basis function(RBF), back propagation (BP), multilayer perception(MLP) have been employed for MIMO-OFDM system channel equalization [14][15][16][17].…”

Multilayer Neural Network based on MIMO and Channel Estimation for Impulsive Noise Environment in Mobile Wireless Networks

“…Though multi path transmit and receive antennas which are used in MIMO increase the capacity it results in increased complexity of channel equalization at the receiver [6][7].High bandwidth efficiency, simple and efficient implementation and reduction of ISI are offered by OFDM [2,8]. Many algorithms were proposed to remove the ISI, to separate different signals and to improve the convergence properties [9][10][11][12][13].…”

“…Hence signals can be effectively processed through nonlinear channels using neural networks. The natural structure of neural network which has multiple inputs and multiple outputs is more suitable for MIMO systems [10][11][12][13][14]. Conventional feed forward neural networks viz., radial basis function(RBF), back propagation (BP), multilayer perception(MLP) have been employed…”