8 research outputs found
Radiation pattern prediction for Metasurfaces: A Neural Network based approach
As the current standardization for the 5G networks nears completion, work
towards understanding the potential technologies for the 6G wireless networks
is already underway. One of these potential technologies for the 6G networks
are Reconfigurable Intelligent Surfaces (RISs). They offer unprecedented
degrees of freedom towards engineering the wireless channel, i.e., the ability
to modify the characteristics of the channel whenever and however required.
Nevertheless, such properties demand that the response of the associated
metasurface (MSF) is well understood under all possible operational conditions.
While an understanding of the radiation pattern characteristics can be obtained
through either analytical models or full wave simulations, they suffer from
inaccuracy under certain conditions and extremely high computational
complexity, respectively. Hence, in this paper we propose a novel neural
networks based approach that enables a fast and accurate characterization of
the MSF response. We analyze multiple scenarios and demonstrate the
capabilities and utility of the proposed methodology. Concretely, we show that
this method is able to learn and predict the parameters governing the reflected
wave radiation pattern with an accuracy of a full wave simulation (98.8%-99.8%)
and the time and computational complexity of an analytical model. The
aforementioned result and methodology will be of specific importance for the
design, fault tolerance and maintenance of the thousands of RISs that will be
deployed in the 6G network environment.Comment: Submitted to IEEE OJ-COM