Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
Preprint, 2024
Gain-phase impairments (GPIs) affect both communication and sensing in 6G integrated sensing and communication (ISAC). We study the effect of GPIs in a single-input, multiple-output orthogonal frequency-division multiplexing ISAC system and develop a model-based unsupervised learning approach to simultaneously (i) estimate the gain-phase errors and (ii) localize sensing targets. The proposed method is based on the optimal maximum a-posteriori ratio test for a single target. Results show that the proposed approach can effectively estimate the gain-phase errors and yield similar position estimation performance as the case when the impairments are fully known.
Unsupervised learning.
Orthogonal frequency-division multiplexing
Gain-phase errors
Model-based learning
Author
José Miguel Mateos Ramos
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Christian Häger
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Musa Furkan Keskin
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Luc Le Magoarou
INSA Rennes
Henk Wymeersch
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
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Swedish Research Council (VR) (2022-03007), 2023-01-01 -- 2026-12-31.
A holistic flagship towards the 6G network platform and system, to inspire digital transformation, for the world to act together in meeting needs in society and ecosystems with novel 6G services
European Commission (EC) (101095759-Hexa-X-II), 2022-12-01 -- 2025-06-30.
SAICOM
Swedish Foundation for Strategic Research (SSF) (FUS21-0004), 2022-06-01 -- 2027-05-31.
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Areas of Advance
Information and Communication Technology
Subject Categories (SSIF 2011)
Electrical Engineering, Electronic Engineering, Information Engineering