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Adaptive thu phát không dây P8

Neural Network Based Equalization In this chapter, we will give an overview of neural network based equalization. Channel equalization can be viewed as a classification problem. The optimal solution to this classification problem is inherentlynonlinear. Hence we will discuss, how the nonlinear structureof the artificial neural network can enhancethe performance of conventional channel equalizers as and examinevarious neural network designs amenable channel equalization, such the soto called multilayer perceptron network [236-2401, polynomial perceptron network 1241-2441 and radial basis function network 185,245-2471 | Adaptive Wireless Tranceivers L. Hanzo C.H. Wong M.S. Yee Copyright 2002 John Wiley Sons Ltd ISBNs 0-470-84689-5 Hardback 0-470-84776-X Electronic Chapter Neural Network Based Equalization In this chapter we will give an overview of neural network based equalization. Channel equalization can be viewed as a classification problem. The optimal solution to this classification problem is inherently nonlinear. Hence we will discuss how the nonlinear structure of the artificial neural network can enhance the performance of conventional channel equalizers and examine various neural network designs amenable to channel equalization such as the so-called multilayer perceptron network 236-240 polynomial perceptron network 241-244 and radial basis function network 85 245-247 . We will examine a neural network structure referred to as the Radial Basis Function RBF network in detail in the context of equalization. As further reading the contribution by Mulgrew 248 provides an insightful briefing on applying RBF network for both channel equalization and interference rejection problems. Originally RBF networks were developed for the generic problem of data interpolation in a multi-dimensional space 249 250 . We will describe the RBF network in general and motivate its application. Before we proceed our forthcoming section will describe the discrete time channel model inflicting intersymbol interference that will be used throughout this thesis. 8.1 Discrete Time Model for Channels Exhibiting Intersymbol Interference A band-limited channel that results in intersymbol interference ISI can be represented by a discrete-time transversal filter having a transfer function of L 8.1 n 0 where fn is the nth impulse response tap of the channel and L 1 is the length of the channel impulse response CIR . In this context the channel represents the convolution of 299 300 CHAPTER 8. NEURAL NETWORK BASED EQUALIZATION Figure 8.1 Equivalent discrete-time model of a channel exhibiting intersymbol .