End-to-End Learning for Integrated Sensing and Communication

Author:

Mateos-Ramos Jose Miguel1,Song Jinxiang1,Wu Yibo1,Hager Christian1,Keskin Musa Furkan1,Yajnanarayana Vijaya2,Wymeersch Henk1

Affiliation:

1. Chalmers University of Technology,Department of Electrical Engineering,Sweden

2. Ericsson Research,India

Funder

European Commission

Publisher

IEEE

Reference47 articles.

1. Automotive Radar Interference Mitigation using a Convolutional Autoencoder

2. Joint design of radar waveform and detector via end-to-end learning with waveform constraints;jiang;IEEE Trans Aerosp Electron Syst,2021

3. Waveform learning for next-generation wireless communication systems;aoudia,2021

4. An Introduction to Deep Learning for the Physical Layer

5. A comprehensive survey of machine learning applied to radar signal processing;lang,2020

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1. A set of Models for Device Positioning in Sixth Generation Networks. Part 1. Methods Survey and Problem Statement;Proceedings of Telecommunication Universities;2024-09-04

2. Joint Sensing and Semantic Communications with Multi-Task Deep Learning;IEEE Communications Magazine;2024-09

3. Incorporating Sensing into MIMO-NOMA Communications: Theoretical Joint Rates Bound and Learning-Based Optimal Design;ICC 2024 - IEEE International Conference on Communications;2024-06-09

4. Semi-Supervised End-to-End Learning for Integrated Sensing and Communications;2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN);2024-05-05

5. End-to-End Learning for SLP-based ISAC Systems;2024 IEEE Wireless Communications and Networking Conference (WCNC);2024-04-21

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