ABSTRACT
This chapter reviews the basic principles of adaptive signal processing. It provides a survey of the most popular adaptive signal processing techniques used in wireless communications. The chapter discusses channel identification and equalization, including satellite communication channels and Multiple-input multiple output channels. An artificial neural network is an adaptive, most often nonlinear system that learns to perform a function from data. The input–output training data are fundamental in neural network technology because they convey the necessary information to “discover” the optimal operating point. One fundamental issue is how to adapt the weights wi of the multilayer perceptrons (MLP) to achieve a given input–output map. The chapter addresses the following aspects: size of training set versus weights, search procedures, how to stop training, how to set the topology for maximum generalization. When the tap delay implements the short-term memory, straight backpropagation can be utilized since the only adaptive parameters are the MLP weights.
