Department of Electrical & Computer Engineering
Signal and Image Laboratory (SaIL)
The University of Arizona®
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Past Research |
Genetic Algorithms for Optimization of Wireless Devices
Student: Natalia Gaviria Gómez
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The evolution of telecommunication networks has been driven by a trend towards
unification, as illustrated in the figure below. Originally, separate networks
were built to provide communication for different services. The technology used
by each of these networks was especially designed for the kind of services
provided. However, over time, it became evident that the provision of a
single service over a whole network leads to the waste of very valuable resources.
Hence, a significant amount of effort was put into researching ways to provide
multiple services over a single network. As a result, we can now see data being
transmitted over traditional phone lines at a speed that was unimaginable
earlier. The use of a single network to provide multiple services not only
enhances the utilization of the available resources, but also helps to lower
the cost associated with providing such services. Today we have a number of
different networks using different technologies but providing the same services.
The current challenge being faced by the telecom community is integration of
these multiple networks into a single, seamless network. This evolution in network
technology calls for a simultaneous evolution in the network devices.
This project contributed to the evolution of wireless devices by developing a
flexible architecture with new functionalities in the management and control
planes. Specifically, we added adaptive and cognitive capabilities to the
network device, allowing it to monitor and assess the current situation,
and then introduce the necessary changes.
The mechanism was tested with the synthesis of an adaptive antenna array, as
illustrated in the figure below.
The focus of the project was optimizing different
objectives simultaneously, i.e. solving a multi-objective optimization problem.
We used an adaptive genetic algorithm to find the weights of the antenna
elements that maintain a high
efficiency while achieving nulls where needed (interference), and maintaining a
desired effective radiated voltage (ERV).
This work was a collaborative effort with Dr. Kevin McNeill, BAE Systems, Inc.
Publications:
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Natalia Gaviria, Kathleen L. Melde, Kevin M. McNeill, and Jeffrey J.
Rodriguez, "Development of Array Distributions for Smart Antennas with
Low Sidelobes, Interference-Nulling, and Effective Radiated Voltage
Constraints," 2006 IEEE Intl. Symp. on Antennas and Propagation
(AP-S), Albuquerque, NM, July 9-14, 2006. [ PDF ]
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Natalia Gaviria Gomez, Jeffrey J. Rodriguez, Kathleen L. Melde, and
Kevin M. McNeill, "Design of Low-Sidelobe Linear Arrays with High
Aperture Efficiency and Interference Nulls," IEEE Antennas and Propagation
Letters, vol. 8, 2009, pp. 607-610. [ PDF ]
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