Robust reconfigurable intelligent surfaces via invariant risk and causal representations

Samarakoon, Sumudu; Park, Jihong; Bennis, Mehdi (2021-09-12)
 
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https://doi.org/10.1109/SPAWC51858.2021.9593252

Samarakoon, Sumudu
Park, Jihong
Bennis, Mehdi
Institute of Electrical and Electronics Engineers
12.09.2021

S. Samarakoon, J. Park and M. Bennis, "Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations," 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021, pp. 301-305, doi: 10.1109/SPAWC51858.2021.9593252.

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doi:https://doi.org/10.1109/SPAWC51858.2021.9593252
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https://urn.fi/URN:NBN:fi-fe202301031248
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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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