5 citations · 7 across the 8 of their papers we have counts for
10 papers · 1 filter
Unsupervised End-to-End Array Calibration for Multi-Target Integrated Sensing and Communication
José Miguel Mateos-Ramos, Baptiste Chatelier, Luc Le Magoarou +3
In this work, we consider end-to-end calibration of an integrated sensing and communication (ISAC) base station (BS) under gain-phase and antenna displacement impairments without c…
Positioning via Digital-Twin-Aided Channel Charting with Large-Scale CSI Features
José Miguel Mateos-Ramos, Frederik Zumegen, Henk Wymeersch +2
Channel charting (CC) is a self-supervised positioning technique whose main limitation is that the estimated positions lie in an arbitrary coordinate system that is not aligned wit…
Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays
Baptiste Chatelier, José Miguel Mateos-Ramos, Vincent Corlay +4
Direction of arrival (DoA) estimation is a common sensing problem in radar, sonar, audio, and wireless communication systems. It has gained renewed importance with the advent of th…
Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin +2
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-outpu…
Joint Communication and Sensing for 6G -- A Cross-Layer Perspective
Henk Wymeersch, Sharief Saleh, Ahmad Nimr +14
As 6G emerges, cellular systems are envisioned to integrate sensing with communication capabilities, leading to multi-faceted communication and sensing (JCAS). This paper presents…
Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
José Miguel Mateos-Ramos, Baptiste Chatelier, Christian Häger +3
Integrated sensing and communications (ISAC) is envisioned as one of the key enablers of next-generation wireless systems, offering improved hardware, spectral, and energy efficien…
Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication under Hardware Impairments
José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin +2
We study model-based end-to-end learning in the context of integrated sensing and communication (ISAC) under hardware impairments. Hardware impairments are usually addressed by mea…
Model-Driven End-to-End Learning for Integrated Sensing and Communication
José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin +2
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 i…
Spatial Signal Design for Positioning via End-to-End Learning
Steven Rivetti, Josè Miguel Mateos-Ramos, Yibo Wu +5
This letter considers the problem of end-to-end learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a mult…
End-to-End Learning for Integrated Sensing and Communication
José Miguel Mateos-Ramos, Jinxiang Song, Yibo Wu +4
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 s…