Adaptive non linear system identification and channel equalization usinf functional link artificial neural network

In system theory, characterization and identification are fundamental problems. When the plant behavior is completely unknown, it may be characterized using certain model and then, its identification may be carried out with some artificial neural networks(ANN) like multilayer perceptron(MLP) or functional link artificial neural network(FLANN) using some learning rules such as back propagation (BP) algorithm. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. The primary aim of the present thesis is to provide a framework for the systematic design of adaptation laws for nonlinear system identification and channel equalization. While constructing an artificial neural network the designer is often faced with the problem of choosing a network of the right size for the task. The advantages of using a smaller neural network are cheaper cost of computation and better generalization ability. However, a network which is too small may never solve the problem, while a larger network may even have the advantage of a faster learning rate. Thus it makes sense to start with a large network and then reduce its size. For this reason a Genetic Algorithm (GA) based pruning strategy is reported. GA is based upon the process of natural selection and does not require error gradient statistics. As a consequence, a GA is able to find a global error minimum. Transmission bandwidth is one of the most precious resources in digital communication systems. Communication channels are usually modeled as band-limited linear finite impulse response (FIR) filters with low pass frequency response. When the amplitude and the envelope delay response are not constant within the bandwidth of the filter, the channel distorts the transmitted signal causing intersymbol interference (ISI). The addition of noise during propagation also degrades the quality of the received signal. All the signal processing methods used at the receiver's end to compensate the introduced channel distortion and recover the transmitted symbols are referred as channel equalization techniques.When the nonlinearity associated with the system or the channel is more the number of branches in FLANN increases even some cases give poor performance. To decrease the number of branches and increase the performance a two stage FLANN called cascaded FLANN (CFLANN) is proposed.This thesis presents a comprehensive study covering artificial neural network (ANN) implementation for nonlinear system identification and channel equalization. Three ANN structures, MLP, FLANN, CFLANN and their conventional gradient-descent training methods are extensively studied. Simulation results demonstrate that FLANN and CFLANN methods are directly applicable for a large class of nonlinear control systems and communication problems

Adaptive Channel Equalization using Radial Basis Function Networks and MLP

One of the major practical problems in digital communication systems is channel distortion which causes errors due to intersymbol interference. Since the source signal is in general broadband, the various frequency components experience different steady state amplitude and phase changes as they pass through the channel, causing distortion in the received message. This distortion translates into errors in the received sequence. Our problem as communication engineers is to restore the transmitted sequence or, equivalently, to identify the inverse of the channel, given the observed sequence at the channel output. This task is accomplished by adaptive equalizers. Typically, adaptive equalizers used in digital communications require an initial training period, during which a known data sequence is transmitted. A replica of this sequence is made available at the receiver in proper synchronism with the transmitter, thereby making it possible for adjustments to be made to the equalizer coefficients in accordance with the adaptive filtering algorithm employed in the equalizer design. When the training is completed, the equalizer is switched to its decision directed mode. Decision feedback equalizers are used extensively in practical communication systems. They are more powerful than linear equalizers especially for severe inter-symbol interference (ISI) channels without as much noise enhancement as the linear equalizers. This thesis addresses the problem of adaptive channel equalization in environments where the interfering noise exhibits Gaussian behavior. In this thesis, radial basis function (RBF) network is used to implement DFE. Advantages and problems of this system are discussed and its results are then compared with DFE using multi layer perceptron net (MLP).Results indicate that the implemented system outperforms both the least-mean square(LMS) algorithm and MLP, given the same signal-to-noise ratio as it offers minimum mean square error. The learning rate of the implemented system is also faster than both LMS and the multilayered case

Development and applications of adaptive IIR and subband filters

Adaptive infinite impulse response (IIR) filter is a challenging research area. Identifiers and Equalizers are among the most essential digital signal processing devices for digital communication systems. In this study, we consider IIR channel both for system identification and channel equalization purposes. We focus on four different approaches: Least Mean Square (LMS), Recursive Least Square (RLS), Genetic Algorithm (GA) and Subband Adaptive Filter (SAF). ). The performance of conventional LMS and RLS based IIR system identification and channel equalization are found with the help of computer simulations. And also the convergence speed and the ability to locate the global optimum solution using a population based algorithm named Genetic Algorithm is given

Channel Equalization using GA Family

High speed data transmissions over communication channels distort the trans- mitted signals in both amplitude and phase due to presence of Inter Symbol Inter- ference (ISI). Other impairments like thermal noise, impulse noise and cross talk also cause further distortions to the received symbols. Adaptive equalization of the digital channels at the receiver removes/reduces the e®ects of such ISIs and attempts to recover the transmitted symbols. Basically an equalizer is an inverse ¯lter which is placed at the front end of the receiver. Its transfer function is inverse to the transfer function of the associated channel. The Least-Mean-Square (LMS), Recursive-Least-Square (RLS) and Multilayer perceptron (MLP) based adaptive equalizers aim to minimize the ISI present in the digital communication channel. These are gradient based learning algorithms and therefore there is possibility that during training of the equalizers, its weights do not reach to their optimum values due to ..

Spontaneous and explicit estimation of time delays in the absence/presence of multipath propagation.

by Hing-cheung So.Thesis (Ph.D.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 133-141).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Time Delay Estimation (TDE) and its Applications --- p.1Chapter 1.2 --- Goal of the Work --- p.6Chapter 1.3 --- Thesis Outline --- p.9Chapter 2 --- Adaptive Methods for TDE --- p.10Chapter 2.1 --- Problem Description --- p.11Chapter 2.2 --- The Least Mean Square Time Delay Estimator (LMSTDE) --- p.11Chapter 2.2.1 --- Bias and Variance --- p.14Chapter 2.2.2 --- Probability of Occurrence of False Peak Weight --- p.16Chapter 2.2.3 --- Some Modifications of the LMSTDE --- p.17Chapter 2.3 --- The Adaptive Digital Delay-Lock Discriminator (ADDLD) --- p.18Chapter 2.4 --- Summary --- p.20Chapter 3 --- The Explicit Time Delay Estimator (ETDE) --- p.22Chapter 3.1 --- Derivation and Analysis of the ETDE --- p.23Chapter 3.1.1 --- The ETDE system --- p.23Chapter 3.1.2 --- Performance Surface --- p.26Chapter 3.1.3 --- Static Behaviour --- p.28Chapter 3.1.4 --- Dynamic Behaviour --- p.30Chapter 3.2 --- Performance Comparisons --- p.32Chapter 3.2.1 --- With the LMSTDE --- p.32Chapter 3.2.2 --- With the CATDE --- p.34Chapter 3.2.3 --- With the CRLB --- p.35Chapter 3.3 --- Simulation Results --- p.38Chapter 3.3.1 --- Corroboration of the ETDE Performance --- p.38Chapter 3.3.2 --- Comparative Studies --- p.44Chapter 3.4 --- Summary --- p.48Chapter 4 --- An Improvement to the ETDE --- p.49Chapter 4.1 --- Delay Modeling Error of the ETDE --- p.49Chapter 4.2 --- The Explicit Time Delay and Gain Estimator (ETDGE) --- p.52Chapter 4.3 --- Performance Analysis --- p.55Chapter 4.4 --- Simulation Results --- p.57Chapter 4.5 --- Summary --- p.61Chapter 5 --- TDE in the Presence of Multipath Propagation --- p.62Chapter 5.1 --- The Multipath TDE problem --- p.63Chapter 5.2 --- TDE with Multipath Cancellation (MCTDE) --- p.64Chapter 5.2.1 --- Structure and Algorithm --- p.64Chapter 5.2.2 --- Convergence Dynamics --- p.67Chapter 5.2.3 --- The Generalized Multipath Cancellator --- p.70Chapter 5.2.4 --- Effects of Additive Noises --- p.73Chapter 5.2.5 --- Simulation Results --- p.74Chapter 5.3 --- TDE with Multipath Equalization (METDE) --- p.86Chapter 5.3.1 --- The Two-Step Algorithm --- p.86Chapter 5.3.2 --- Performance of the METDE --- p.89Chapter 5.3.3 --- Simulation Results --- p.93Chapter 5.4 --- Summary --- p.101Chapter 6 --- Conclusions and Suggestions for Future Research --- p.102Chapter 6.1 --- Conclusions --- p.102Chapter 6.2 --- Suggestions for Future Research --- p.104Appendices --- p.106Chapter A --- Derivation of (3.20) --- p.106Chapter B --- Derivation of (3.29) --- p.110Chapter C --- Derivation of (4.14) --- p.111Chapter D --- Derivation of (4.15) --- p.113Chapter E --- Derivation of (5.21) --- p.115Chapter F --- Proof of unstablity of A°(z) --- p.116Chapter G --- Derivation of (5.34)-(5.35) --- p.118Chapter H --- Derivation of variance of αs11(k) and Δs11(k) --- p.120Chapter I --- Derivation of (5.40) --- p.123Chapter J --- Derivation of time constant of αΔ11(k) --- p.124Chapter K --- Derivation of (5.63)-(5.66) --- p.125Chapter L --- Derivation of (5.68)-(5.72) --- p.129References --- p.13