<p>Reconfigurable Intelligent Surfaces (RISs), which can be implemented using metasurface technology or reflect/transmit antenna array technology, have garnered significant attention in research studies focused on both their technological aspects and potential applications. While various modeling approaches have been proposed - ranging from electromagnetic simulations and analytical integral formulations to simplified approaches based on scattering matrix theory - there remains a great need for efficient and electromagnetically-consistent macroscopic models that can accurately simulate scattering from RISs, particularly for realistic simulations of RIS-based wireless networks. Building on previous work based on the characterization of the RIS through a surface impedance (or ”spatial modulation”) function and a few parameters, in the present paper we propose a fully ray-based approach for the computation of the re-radiated field that can be easily embedded in efficient, forward ray tracing (also known as ”ray launching”) models. We validate the proposed model by comparison to well established methods available in the literature. Results show that, although the considered method is based on a completely different formulation and is much more efficient than integral formulation methods, results are almost indistinguishable in some benchmark cases.</p>
This paper considers the problem of predicting whether or not a transmitter and a receiver are in Line-of-Sight (LOS) condition. While this problem can be easily solved using a digital urban database and applying ray tracing, we consider the scenario in which only few high-level features descriptive of the propagation environment and of the radio link are available. LOS prediction is modelled as a binary classification Machine Learning problem, and a baseline classifier based on Gradient Boosting Decision Trees (GBDT) is proposed. A synthetic raytracing dataset of Manhattan-like topologies is generated for training and testing a GBDT classifier, and its generalization capabilities to both locations and environments unseen at training time are assessed. Results show that the GBDT model achieves good classification performance and provides accurate LOS probability modelling. By estimating feature importance, it can be concluded that the model learned simple decision rules that align with common sense.
<p>Reconfigurable Intelligent Surfaces (RISs), which can be implemented using metasurface technology or reflect/transmit antenna array technology, have garnered significant attention in research studies focused on both their technological aspects and potential applications. While various modeling approaches have been proposed - ranging from electromagnetic simulations and analytical integral formulations to simplified approaches based on scattering matrix theory - there remains a great need for efficient and electromagnetically-consistent macroscopic models that can accurately simulate scattering from RISs, particularly for realistic simulations of RIS-based wireless networks. Building on previous work based on the characterization of the RIS through a surface impedance (or ”spatial modulation”) function and a few parameters, in the present paper we propose a fully ray-based approach for the computation of the re-radiated field that can be easily embedded in efficient, forward ray tracing (also known as ”ray launching”) models. We validate the proposed model by comparison to well established methods available in the literature. Results show that, although the considered method is based on a completely different formulation and is much more efficient than integral formulation methods, results are almost indistinguishable in some benchmark cases.</p>
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