Adaptive equalization using the complex backpropagation algorithm

For decreasing intersymbol interference (ISI) due to band-limited channels in digital communication, the uses of equalization techniques are necessary. Among adaptive equalization techniques, because of their ease of implementation and nonlinear capabilities, the neural networks have been used as an alternative for effectively dealing with the channel distortion, especially the nonlinear distortion. In the paper, the complex Backpropagation (BP) Neural Networks are proposed as nonlinear adaptive equalizers that can deal with both QAM and PSK signals of any constellation size (e. g. 32-QAM, 64-QAM and MPSK), and the complex BP algorithm for the new node activation functions having multi-output values and multi-saturation regions is presented. We also show that the proposed complex BPN provides, compared with the linear equalizer using the least mean squares (LMS) algorithm, an interesting improvement concerning Bit Error Rate (BER) when channel distortions are nonlinear.