Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
EURASIP Journal on Applied Signal Processing
Recurrent neural network based BER prediction for NLOS channels
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
Digital communication receivers using gaussian processes for machine learning
EURASIP Journal on Advances in Signal Processing
Equalisation of satellite mobile channels with neural network techniques
Space Communications
Recurrent neural network based bit error rate prediction for narrowband fading channel
CSN '07 Proceedings of the Sixth IASTED International Conference on Communication Systems and Networks
Joint nonlinear channel equalization and soft LDPC decoding with Gaussian processes
IEEE Transactions on Signal Processing
Equalisation of a wireless ATM channel using a pruned recurrent neural network
International Journal of Systems, Control and Communications
| Hi-index | 0.08 |
This paper introduces an adaptive derision feedback equalization using the multilayer perceptron structure of an M-ary PSK signal through a TDMA satellite radio channel. The transmission is disturbed not only by intersymbol interference (ISI) and additive white Gaussian noise, but also by the nonlinearity of transmitter amplifiers. The conventional decision feedback equalizer (DFE) is not well-suited to detect the transmitted sequence, whereas the neural-based DFE is able to take into account the nonlinearities and therefore to detect the signal much better. Nevertheless, the applications of the traditional multilayer neural networks have been limited to real-valued signals. To overcome this difficulty, a neural-based DFE is proposed to deal with the complex PSK signal over the complex-valued nonlinear MPSK satellite channel without performing time-consuming complex-valued back-propagation training algorithms, while maintaining almost the same computational complexity as the original real-valued training algorithm. Moreover, a modified back-propagation algorithm with better convergence properties is derived on the basis of delta-bar-delta rule. Simulation results for the equalization of QPSK satellite channels show that the neural-based DFE provides a superior bit error rate performance relative to the conventional mean square DFE, especially in poor signal-to-noise ratio conditions