Machine Learning for Metasurfaces Design and Their Applications
- URL: http://arxiv.org/abs/2211.01296v1
- Date: Wed, 2 Nov 2022 17:19:37 GMT
- Title: Machine Learning for Metasurfaces Design and Their Applications
- Authors: Kumar Vijay Mishra, Ahmet M. Elbir and Amir I. Zaghloul
- Abstract summary: Machine/deep learning (ML/DL) techniques are proving critical in reducing the computational cost and time of RIS inverse design.
This chapter provides a synopsis of DL techniques for both inverse RIS design and RIS-assisted wireless systems.
- Score: 20.350142630673197
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Metasurfaces (MTSs) are increasingly emerging as enabling technologies to
meet the demands for multi-functional, small form-factor, efficient,
reconfigurable, tunable, and low-cost radio-frequency (RF) components because
of their ability to manipulate waves in a sub-wavelength thickness through
modified boundary conditions. They enable the design of reconfigurable
intelligent surfaces (RISs) for adaptable wireless channels and smart radio
environments, wherein the inherently stochastic nature of the wireless
environment is transformed into a programmable propagation channel. In
particular, space-limited RF applications, such as communications and radar,
that have strict radiation requirements are currently being investigated for
potential RIS deployment. The RIS comprises sub-wavelength units or meta-atoms,
which are independently controlled and whose geometry and material determine
the spectral response of the RIS. Conventionally, designing RIS to yield the
desired EM response requires trial and error by iteratively investigating a
large possibility of various geometries and materials through thousands of
full-wave EM simulations. In this context, machine/deep learning (ML/DL)
techniques are proving critical in reducing the computational cost and time of
RIS inverse design. Instead of explicitly solving Maxwell's equations, DL
models learn physics-based relationships through supervised training data. The
ML/DL techniques also aid in RIS deployment for numerous wireless applications,
which requires dealing with multiple channel links between the base station
(BS) and the users. As a result, the BS and RIS beamformers require a joint
design, wherein the RIS elements must be rapidly reconfigured. This chapter
provides a synopsis of DL techniques for both inverse RIS design and
RIS-assisted wireless systems.
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