Fully Complex Multi-Layer Perceptron Network for Nonlinear Signal Processing
Journal of VLSI Signal Processing Systems
Approximation by fully complex multilayer perceptrons
Neural Computation
Fast Blind Equalization Using Complex-Valued MLP
Neural Processing Letters
Performance Comparison of Several Non-Linear Equalizers in the Context of Mobile Telecommunications
Information Systems Frontiers
Equalization of 8PSK Signals with a Recurrent Neural Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Complex-valued function approximation using a fully complex-valued RBF (FC-RBF) learning algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Low complexity blind equalization based on parzen window method
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
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
Equalization of 16 QAM signals with reduced bilinear recurrent neural network
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Equalisation of a wireless ATM channel using a pruned recurrent neural network
International Journal of Systems, Control and Communications
Advances in Artificial Neural Systems
A novel signal diagnosis technique using pseudo complex-valued autoregressive technique
Expert Systems with Applications: An International Journal
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Equalization of a wireless ATM channel with simplified complex bilinear recurrent neural network
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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Among the useful blind equalization algorithms, stochastic-gradient iterative equalization schemes are based on minimizing a nonconvex and nonlinear cost function. However, as they use a linear FIR filter with a convex decision region, their residual estimation error is high. In the paper, four nonlinear blind equalization schemes that employ a complex-valued multilayer perceptron instead of the linear filter are proposed and their learning algorithms are derived. After the important properties that a suitable complex-valued activation function must possess are discussed, a new complex-valued activation function is developed for the proposed schemes to deal with QAM signals of any constellation sizes. It has been further proven that by the nonlinear transformation of the proposed function, the correlation coefficient between the real and imaginary parts of input data decreases when they are jointly Gaussian random variables. Last, the effectiveness of the proposed schemes is verified in terms of initial convergence speed and MSE in the steady state. In particular, even without carrier phase tracking procedure, the proposed schemes correct an arbitrary phase rotation caused by channel distortion