End-to-End Learning for Integrated Sensing and Communication
Paper in proceeding, 2022

Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments.

Machine learning

Integrated sensing and communication

Auto-encoder

Joint radar and communications

Author

José Miguel Mateos Ramos

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Jinxiang Song

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Yibo Wu

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Ericsson

Christian Häger

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Furkan Keskin

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Vijaya Yajnanarayana

Ericsson

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

IEEE International Conference on Communications

15503607 (ISSN)

Vol. 2022-May 1942-1947
9781538683477 (ISBN)

2022 IEEE International Conference on Communications, ICC 2022
Seoul, South Korea,

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Subject Categories

Embedded Systems

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/ICC45855.2022.9838308

More information

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1/3/2024 9