Integrated sensing and communications (ISAC) is envisioned as one of the key enablers of next-generation wireless systems, offering improved hardware, spectral, and energy efficiencies. In this paper, we consider an ISAC transceiver with an impaired uniform linear array that performs single-target detection and position estimation, and multiple-input single-output communications. A differentiable model-based learning approach is considered, which optimizes both the transmitter and the sensing receiver in an end-to-end manner. An unsupervised loss function that enables impairment compensation without the need for labeled data is proposed. Semi-supervised learning strategies are also proposed, which use a combination of small amounts of labeled data and unlabeled data. Our results show that semi-supervised learning can achieve similar performance to supervised learning with 98.8% less required labeled data.
@article{arxiv.2310.09940,
title = {Semi-Supervised End-to-End Learning for Integrated Sensing and Communications},
author = {José Miguel Mateos-Ramos and Baptiste Chatelier and Christian Häger and Musa Furkan Keskin and Luc Le Magoarou and Henk Wymeersch},
journal= {arXiv preprint arXiv:2310.09940},
year = {2024}
}