# Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems

arXiv: https://arxiv.org/abs/2410.04176 · HTML: https://mlsanity.com/paper/2410.04176/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [Model-based Implicit Neural Representation for sub-wavelength Radio Localization](https://mlsanity.com/paper/2506.06387/) (2025)

## Abstract (arXiv)

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.