Context-Aware Doubly-Robust Semi-Supervised Learning

Clement Ruah; Houssem Sifaou; Osvaldo Simeone; Bashir Al-Hashimi

Context-Aware Doubly-Robust Semi-Supervised Learning

Signal Processing 2025-06-27 v2 Machine Learning

Authors: Clement Ruah , Houssem Sifaou , Osvaldo Simeone , Bashir Al-Hashimi

Abstract

The widespread adoption of artificial intelligence (AI) in next-generation communication systems is challenged by the heterogeneity of traffic and network conditions, which call for the use of highly contextual, site-specific, data. A promising solution is to rely not only on real-world data, but also on synthetic pseudo-data generated by a network digital twin (NDT). However, the effectiveness of this approach hinges on the accuracy of the NDT, which can vary widely across different contexts. To address this problem, this paper introduces context-aware doubly-robust (CDR) learning, a novel semi-supervised scheme that adapts its reliance on the pseudo-data to the different levels of fidelity of the NDT across contexts. CDR is evaluated on the task of downlink beamforming where it outperforms previous state-of-the-art approaches, providing a 24% loss decrease when compared to doubly-robust (DR) semi-supervised learning in regimes with low labeled data availability.

Keywords

machine learning deep learning for wireless communications deep learning for detection and classification

Cite

@article{arxiv.2502.15577,
  title  = {Context-Aware Doubly-Robust Semi-Supervised Learning},
  author = {Clement Ruah and Houssem Sifaou and Osvaldo Simeone and Bashir Al-Hashimi},
  journal= {arXiv preprint arXiv:2502.15577},
  year   = {2025}
}

Comments

This work has been accepted for publication in IEEE Signal Processing Letters

Context-Aware Doubly-Robust Semi-Supervised Learning

Abstract

Keywords

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