Adaptive Packet Equalization for Indoor Radio Channel Using Multilayer Neural Networks

This paper investigates the application of the multilayer perceptron structure to the packet-wise adaptive decision feedback equalization of a Mary QAM signal through a TDMA indoor radio channel in the presence of intersymbol interference (ISI) and additive Gaussian noise. Since the multilayer neural networks are capable of producing complex decision regions with arbitrarily nonlinear boundaries, this would greatly improve the performance of conventional decision feedback equalizer (DFE) where the decision boundaries of conventional DFE are linear. However, the applications of the traditional multilayer neural networks have been limited to realvalued signals. To tackle this difficulty, a neural-based DFE is proposed to deal with the complex QAM signal over the complex-valued fading multipath radio 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, this neural-based DFE trained by packet-wise backpropagation algorithm would approach an ideal equalizer after receiving a sufficient number of packets. In this paper, another fast packet-wise training algorithm with better convergence properties is derived on the basis of a recursive least-squares (RLS) routine. Results show that the neural-based DFE trained by both algorithms provides a superior bit-error-rate performance relative to the conventional least mean square (LMS) DFE, especially in poor signal to noise ratio conditions.