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contributor.authorMohanty, Debiddutta-
contributor.authorPanda, G (Guide)-
date.accessioned2008-04-24T04:14:45Z-
date.available2008-04-24T04:14:45Z-
date.issued2008-
identifier.citationChannel Equalization using GA Family, Doctoral Thesis submitted to National Institute of Technolgy Rourkelaen
identifier.urihttp://hdl.handle.net/2080/661-
descriptionCopyright for the thesis belongs to National Institute of Technology, Rourkelaen
description.abstractHigh 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 the mean square error (MSE) being trapped to local minimum. In other words true Weiner solution is not achieved because of gradient based training. The bit-error-rate (BER) performance of the equalizer further degrades when data transmission takes place through nonlinear channels. The standard derivative based algorithms su®er from local minima problem while obtaining the solution of the weights. To prevent the premature settling of the weights, evolutionary computing based update algorithm is proposed which is essentially a derivative free technique. Equalization is basically an iterative process of minimization of mean square error. Thus equalization can be viewed as opti- mization problem. The minimization of squared error is achieved iteratively using GA. Thus GA based approach is an e±cient method to achieve adaptive channel equalization. In the present thesis classes of new adaptive channel equalizers are proposed using derivative free evolutionary computing tools such as Genetic Algo- rithm (GA) and Particle swarm optimization (PSO). These algorithms are suitably used to update the weights of the proposed equalizers. The performance of these equalizers is evaluated in terms of speed of convergence, computational time and bit-error-rate (BER) and is compared with its LMS based counter part. It is ob- served that the new set of adaptive equalizers o®er improved performance so far as the accuracy of reception is concerned. However, in order of increasing training time the equalizers may be arranged as the adaptive Genetic Algorithm (AGA), Particle Swarm Optimization (PSO), Real coded Genetic Algorithm (RCGA), Bi- nary coded Genetic Algorithm (BGA) based equalizer. However being a population based algorithm, standard Genetic Algorithm (SGA) su®ers from slower convergence rate. To minimize the training time three di®erent adaptive GAs (AGAs) are proposed in the thesis and their convergence times have been compared. The thesis also investigates on the new equalizers us- ing Real coded Genetic Algorithm (RCGA) and Binary coded Genetic Algorithm (BGA). Their performances are also evaluated. In the conventional FLANN (Functional Link Arti¯cial Neural Network) [1] structure the complexity increases due to incorporation of more number of paths after functional expansions. To reduce the structural complexity some pruning of structure is essential. Keeping this in mind the GA based pruning strategy is used in the FLANN identi¯er. It is observed that about 50% of the total signal paths can be pruned keeping the performance identical to that of original FLANN structure.en
format.extent5169924 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherNIT Rourkelaen
titleChannel Equalization using GA Familyen
typeThesisen
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