Model-Driven End-to-End Learning for Integrated Sensing and Communication
Paper i proceeding, 2023

Integrated sensing and communication (ISAC) is envisioned to be one of the pillars of 6G. However, 6G is also expected to be severely affected by hardware impairments. Under such impairments, standard model-based approaches might fail if they do not capture the underlying reality. To this end, data-driven methods are an alternative to deal with cases where imperfections cannot be easily modeled. In this paper, we propose a model-driven learning architecture for joint single- target multi-input multi-output (MIMO) sensing and multi-input single-output (MISO) communication. We compare it with a standard neural network approach under complexity constraints. Results show that under hardware impairments, both learning methods yield better results than the model-based standard baseline. If complexity constraints are further introduced, model- driven learning outperforms the neural-network-based approach. Model-driven learning also shows better generalization performance for new unseen testing scenarios

model-driven machine learning.

integrated sensing and communication


joint radar and communications


José Miguel Mateos Ramos

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Christian Häger

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Musa Furkan Keskin

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Luc Le Magoarou

INSA Rennes

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

IEEE International Conference on Communications

15503607 (ISSN)

Vol. 2023-May 5695-5700
9781538674628 (ISBN)

2023 IEEE International Conference on Communications, ICC 2023
Rome, Italy,

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