Department of Electrical & Computer Engineering Signal and Image Laboratory (SaIL) The University of Arizona®

Past Research

Genetic Algorithms for Optimization of Wireless Devices

Student: Natalia Gaviria Gómez

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:

  1. 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 ]

  2. 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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