  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.
