# Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations

arXiv: https://arxiv.org/abs/2105.01771 · HTML: https://mlsanity.com/paper/2105.01771/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 12 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 9 of 9)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Unsupervised Learning for Passive Beamforming](https://mlsanity.com/paper/2001.02348/) (2020)
- [Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2010.04376/) (2020)
- [Extreme URLLC: Vision, Challenges, and Key Enablers](https://mlsanity.com/paper/2001.09683/) (2020)
- [Deep Learning-based Phase Reconfiguration for Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2009.13988/) (2020)
- [Introduction to Judea Pearl's Do-Calculus](https://mlsanity.com/paper/1305.5506/) (2013)
- [Large Intelligent Surface for Positioning in Millimeter Wave MIMO Systems](https://mlsanity.com/paper/1910.00060/) (2019)
- [Indoor and Outdoor Physical Channel Modeling and Efficient Positioning for Reconfigurable Intelligent Surfaces in mmWave Bands](https://mlsanity.com/paper/2006.02240/) (2020)

## Built on by (most cited first, 1 of 1)

- [Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations](https://mlsanity.com/paper/2306.01306/) (2023)

## Abstract (arXiv)

In this paper, the problem of robust reconfigurable intelligent surface (RIS) system design under changes in data distributions is investigated. Using the notion of invariant risk minimization (IRM), an invariant causal representation across multiple environments is used such that the predictor is simultaneously optimal for each environment. A neural network-based solution is adopted to seek the predictor and its performance is validated via simulations against an empirical risk minimization-based design. Results show that leveraging invariance yields more robustness against unseen and out-of-distribution testing environments.