# Machine Learning for Metasurfaces Design and Their Applications

arXiv: https://arxiv.org/abs/2211.01296 · HTML: https://mlsanity.com/paper/2211.01296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 67 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Global optimization of dielectric metasurfaces using a physics-driven neural network](https://mlsanity.com/paper/1906.04157/) (2019)
- [Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO](https://mlsanity.com/paper/2008.10846/) (2020)
- [Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications](https://mlsanity.com/paper/2002.12271/) (2020)
- [Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning](https://mlsanity.com/paper/1904.10136/) (2019)
- [Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm](https://mlsanity.com/paper/2007.05952/) (2020)
- [Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems](https://mlsanity.com/paper/2001.11085/) (2020)
- [Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1905.07726/) (2019)
- [Unsupervised Learning for Passive Beamforming](https://mlsanity.com/paper/2001.02348/) (2020)
- [Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operation](https://mlsanity.com/paper/2002.11101/) (2020)
- [Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2006.02201/) (2020)
- [A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2009.02540/) (2020)
- [The Rise of Intelligent Reflecting Surfaces in Integrated Sensing and Communications Paradigms](https://mlsanity.com/paper/2204.07265/) (2022)
- [Intelligent Time-Varying Metasurface Transceiver for Index Modulation in 6G Wireless Networks](https://mlsanity.com/paper/2006.04889/) (2020)
- [Shaping mmWave Wireless Channel via Multi-Beam Design using Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2110.00101/) (2021)
- [IRS-Aided Radar: Enhanced Target Parameter Estimation via Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2110.13251/) (2021)
- [OptM3Sec: Optimizing Multicast IRS-Aided Multiantenna DFRC Secrecy Channel with Multiple Eavesdroppers](https://mlsanity.com/paper/2201.09436/) (2022)
- [Cramer-Rao Lower Bound Optimization for Hidden Moving Target Sensing via Multi-IRS-Aided Radar](https://mlsanity.com/paper/2210.05812/) (2022)

## Abstract (arXiv)

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.