Communication Dans Un Congrès Année : 2024

Semi-Supervised End-to-End Learning for Integrated Sensing and Communications

Résumé

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.

Dates et versions

hal-04785446 , version 1 (15-11-2024)

Identifiants

Citer

José Miguel Mateos-Ramos, Baptiste Chatelier, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, et al.. Semi-Supervised End-to-End Learning for Integrated Sensing and Communications. 1st IEEE International Conference on Machine Learning for Communication and Networking (IEEE ICMLCN), May 2024, Stockholm, Sweden. ⟨10.1109/ICMLCN59089.2024.10624785⟩. ⟨hal-04785446⟩
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