Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems

TL;DR

This paper introduces an unsupervised learning method for calibrating gain-phase impairments in ISAC systems, enabling accurate target localization without prior impairment knowledge.

Contribution

It presents a novel model-based unsupervised approach that jointly estimates gain-phase errors and localizes targets in 6G ISAC systems.

Findings

Accurately estimates gain-phase errors

Achieves target localization comparable to known impairments

Effective in single-target scenarios

Abstract

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.