Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal
Representations
- URL: http://arxiv.org/abs/2105.01771v1
- Date: Tue, 4 May 2021 21:36:31 GMT
- Title: Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal
Representations
- Authors: Sumudu Samarakoon and Jihong Park and Mehdi Bennis
- Abstract summary: 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.
- Score: 55.50218493466906
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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