This lecture discusses the use of graph models to represent reconfigurable antennas. The rise
of antennas that adapt to their environment and change their operation based on the user's
request hasn't been met with clear design guidelines. There is a need to propose some rules for
the optimization of any reconfigurable antenna design and performance. Since reconfigurable
antennas are seen as a collection of self-organizing parts graph models can be introduced to
relate each possible topology to a corresponding electromagnetic performance in terms of
achieving a characteristic frequency of operation impedance and polarization. These models
help designers understand reconfigurable antenna structures and enhance their functionality
since they transform antennas from bulky devices into mathematical and software accessible
models. The use of graphs facilitates the software control and cognition ability of
reconfigurable antennas while optimizing their performance.This lecture also discusses the
reduction of redundancy complexity and reliability of reconfigurable antennas and
reconfigurable antenna arrays. The full analysis of these parameters allows a better
reconfigurable antenna implementation in wireless and space communications platforms. The use
of graph models to reduce the complexity while preserving the reliability of reconfigurable
antennas allow a better incorporation in applications such as cognitive radio MIMO satellite
communications and personal communication systems. A swifter response time is achieved with
less cost and losses. This lecture is written for individuals who wish to venture into the
field of reconfigurable antennas with a little prior experience in this area and learn how
graph rules and theory mainly used in the field of computer science networking and control
systems can be applied to electromagnetic structures. This lecture will walk the reader through
a design and analysis process of reconfigurable antennas using graph models with a practical
and theoretical outlook.
