Abstract:
From the introduction of wireless communication to the development and deployment
of forth generation of wireless telecommunication systems, the speed of reliability of
communication between human users was prioritized. As other technologies grew and
became widespread, more and more devices needed to be connected to the internet to
be used efficiently. Automation and data driven systems started to contribute to a no ticeable portion of data services utilization. These devices now constitute a number of
application scenarios, for which certain network metrics have to be met for the proper
functioning of the systems. 6G systems aim to fully enable internet of things supporting
machine to machine communication, along with high data rate and a number of other
features necessary for several service classes. Intelligent Reflecting Surfaces (IRSs) have
the potential to be one of the key enabling technologies in 6G with the ability to manip ulate the propagation environment of the signals. In this study we explore and develop
potential methods for optimal IRS placement in a single base station SISO downlink
OFDMA communication system. The problem of distributed IRS placement is ad dressed using smart clustering methods. The performance of these clustering techniques
is enhanced using geometrical techniques and gradient descent. The results are then
compared with classical grid-search optimized grid-search and the genetic algorithm.
The results show that the intersection enhanced smart clustering algorithms, with the
least computational complexity, perform close to the grid search algorithms. This makes
them useful for energy constrained scenarios. The gradient descent optimized clustering
technique provides the best performance in terms of outage but is more computationally
complex than the intersection-based algorithm. Finally, the interference performance of
the system is heavily dependant on the number and type of clustering. The increase
in the total number of IRS elements in the system decreases the performance difference
between systems with different number of clusters.
