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Title: Genetic algorithms for word length optimization of FFT processors
Author: Sulaiman, Nasri
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2007
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Genetic algorithms (GAs) are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover to find best solutions to optimization and search problems. GAs are used in wide variety of applications in fields ranging from computer science, engineering, evolvable hardware, economics, mathematics, physics and biogenetics to name a few. A fast Fourier transform (FFT) is an efficient algorithm to compute the discrete Fourier transform (DFT) and it’s inverse. An FFT processor is used in applications such as signal processing and telecommunications, etc. It is one of the most power consuming block in wireless receivers such as Multi-Carrier Code Division Multiple Access (MC-CDMA). The portability requirement of these receiver systems imposes the need of low power architectures. Thus, designing an FFT processor with low power consumption is of crucial importance for overall system power. Power consumption of an FFT processor depends on the size of word length of the FFT coefficients. One way to reduce the power consumption in this processor is by reducing the switching activity in the FFT coefficients. This can be achieved using smaller word length for the FFT coefficients. This in turn reduces the SNR in the output signals of the FFT. This thesis investigates the impact of word length optimization of FFT coefficients on switching activity and SNR using GAs. The quality of GAs solutions are compared with non-GA solutions in order to determine the feasibility of using GAs to achieve optimum performance in terms of switching activity and SNR. Results show that GAs can find solutions with smaller word length and have significant reductions in switching compared to the non-GA solutions. This thesis also investigates some of the varying parameter settings, such as mutation domain, population size, crossover rate and mutation probability in the GAs, which affects the quality of search performance towards convergence and the speed of convergence.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available