Radio Resource Management based on Genetic Algorithms for OFDMA Networks.
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OFDMA will be the multiple access scheme for next generation networks, including LTE and LTE-A. These networks will provide higher data rates than now, up to several hundred Mbps. These new networks, using a higher carrier frequency and offering flexible bandwidth for different application types, require advanced techniques for radio resource management. One approach that has been suggested to improve the radio resource management is to use smart and semi-smart antennas, so that the coverage of a certain cell can be divided into several adjustable sectors by using different antenna patterns. However, for a multi-cell environment, there is a need to prevent there being a gap between adjacent cells as the antenna patterns change. In this work, Genetic Algorithms are used to optimize the antenna patterns to get better coverage together with better cell throughput, at the same time making sure there are no gaps. This thesis not only considers the overall problem, but also investigates the suitability of the Genetic Algorithm itself, with it being optimized to improve the performance of the radio resource management in LTE networks. The influence of selection rate and mutation rate on GA is investigated and tested by simulation. These Genetic Algorithms are used in a model of multi-cell LTE networks to optimise subchannel allocation combined with dynamic sectorisation. Different types of scenarios are considered and the Genetic Algorithm is used to solve the problem of combining subchannel allocation with dynamic sectorisation to give the best overall performance of the LTE network.
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