Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations

TL;DR

This paper proposes a robust design method for reconfigurable intelligent surfaces using invariant risk minimization and causal representations, enhancing performance across diverse and unseen environments.

Contribution

It introduces an invariant risk minimization framework for RIS system design, improving robustness to distributional shifts compared to traditional methods.

Findings

Invariant risk minimization improves robustness against out-of-distribution data.

Neural network-based predictor outperforms empirical risk minimization in simulations.

The approach maintains optimality across multiple environments.

Abstract

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.