Complex bilinear recurrent neural network for equalization of a digital satellite channel

Equalization of satellite communication using complex-bilinear recurrent neural network (C-BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it has been more effectively used in modeling highly nonlinear systems with time-series characteristics than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C-BLRNN) for dealing with the complex input values. C-BLRNN is then applied to equalization of a digital satellite communication channel for M-PSK, which has severe nonlinearity with memory due to the traveling wave tube amplifier. The proposed C-BLRNN based equalizer for a channel model is compared with currently used Volterra filter equalizer and conventional complex MLPNN equalizer. The results show that the proposed C-BLRNN based equalizer gives very favorable results in both of MSE and BER criteria over Volterra filter equalizer and complex MLPNN equalizer.