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

Auto-encoder

joint radar and communications

model-driven machine learning.

integrated sensing and communication

Författare

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

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Publicerad i

IEEE International Conference on Communications

15503607 (ISSN)

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

Konferens

2023 IEEE International Conference on Communications, ICC 2023
Rome, Italy, 2023-05-27 - 2023-05-31

Forskningsprojekt

A New Waveform for Joint Radar and Communications Beyond 5G

Europeiska kommissionen (EU) (EC/H2020/888913), 2020-09-01 -- 2022-08-31.

A flagship for B5G/6G vision and intelligent fabric of technology enablers connecting human, physical, and digital worlds (Hexa-X )

Europeiska kommissionen (EU) (EC/HE/101120332), 2023-10-01 -- 2027-09-30.

Europeiska kommissionen (EU) (EC/2020/101015956), 2021-01-01 -- 2023-06-30.

Fysikbaserad djupinlärning för optisk dataöverföring och distribuerad avkänning

Vetenskapsrådet (VR) (2020-04718), 2021-01-01 -- 2024-12-31.

6G Artificial Intelligence Radar

Chalmers AI-forskningscentrum (CHAIR), 2021-05-01 -- 2023-04-30.

Kategorisering

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier (SSIF 2011)

Signalbehandling

Identifikatorer

DOI

10.1109/ICC45041.2023.10278889

Mer information

Senast uppdaterat

2025-03-19