# Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning

arXiv: https://arxiv.org/abs/2410.16760 · HTML: https://mlsanity.com/paper/2410.16760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [mpNet: variable depth unfolded neural network for massive MIMO channel estimation](https://mlsanity.com/paper/2008.04088/) (2020)
- [Learn to Rapidly and Robustly Optimize Hybrid Precoding](https://mlsanity.com/paper/2301.00369/) (2023)
- [Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication under Hardware Impairments](https://mlsanity.com/paper/2307.04111/) (2023)
- [Model-Driven End-to-End Learning for Integrated Sensing and Communication](https://mlsanity.com/paper/2212.10211/) (2022)
- [Model-based Deep Learning for High-Dimensional Periodic Structures](https://mlsanity.com/paper/2309.12223/) (2023)

## Abstract (arXiv)

This paper introduces an innovative end-to-end model-based deep learning approach for efficient electromagnetic analysis of high-dimensional frequency selective surfaces (FSS). Unlike traditional data-driven methods that require large datasets, this approach combines physical insights from equivalent circuit models with deep learning techniques to significantly reduce model complexity and enhance prediction accuracy. Compared to previously introduced model-based learning approaches, the proposed method is trained end-to-end from the physical structure of the FSS (geometric parameters) to its electromagnetic response (S-parameters). Additionally, an improvement in phase prediction accuracy through a modified loss function is presented. Comparisons with direct models, including deep neural networks (DNN) and radial basis function networks (RBFN), demonstrate the superiority of the model-based approach in terms of computational efficiency, model size, and generalization capability.