Context-Aware Doubly-Robust Semi-Supervised Learning

TL;DR

This paper introduces a context-aware semi-supervised learning method that adaptively leverages synthetic data from a network digital twin, improving downlink beamforming performance especially with limited labeled data.

Contribution

The paper proposes a novel context-aware doubly-robust learning scheme that adjusts reliance on pseudo-data based on the fidelity of the network digital twin across different contexts.

Findings

Achieves 24% loss decrease over previous methods in low labeled data regimes.

Outperforms state-of-the-art semi-supervised approaches in downlink beamforming.

Demonstrates effectiveness of adaptive pseudo-data reliance in heterogeneous network conditions.

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.