Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations
This work addresses robustness in RIS systems for wireless communications, presenting an incremental improvement by applying existing invariance methods to a specific domain.
The paper tackles the problem of designing robust reconfigurable intelligent surface (RIS) systems under distribution shifts by using invariant risk minimization to achieve optimal performance across multiple environments, with simulations showing improved robustness against unseen and out-of-distribution testing environments.
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.