Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Xavier Mestre"'
Publikováno v:
IEEE Access, Vol 8, Pp 211411-211421 (2020)
In this article, we propose a data-driven approach to group users in a Non-Orthogonal Multiple Access (NOMA) MIMO setting. Specifically, we formulate user clustering as a multi-label classification problem and solve it by coupling a Classifier Chain
Externí odkaz:
https://doaj.org/article/c7ab43040e344f30ba15dc84f1cc0942
Autor:
Carles Anton-Haro, Xavier Mestre
Publikováno v:
IEEE Access, Vol 7, Pp 20404-20415 (2019)
This paper investigates how angle-of-arrival (AoA) information can be exploited by deep-/machine-learning approaches to perform beam selection in the uplink of a mmWave communication system. Specifically, we consider a hybrid beamforming setup compri
Externí odkaz:
https://doaj.org/article/5e1d69512e3b4c718b1e7c22eb09e9cd
Publikováno v:
IEEE Access, Vol 5, Pp 13770-13786 (2017)
The definition of the next generation of wireless communications, so-called 5G networks, is currently underway. Among many technical decisions, one that is particularly fundamental is the choice of the physical layer modulation format and waveform, a
Externí odkaz:
https://doaj.org/article/55dc846e254b401792a40bc54fefb4fd
Publikováno v:
EURASIP Journal on Wireless Communications and Networking, Vol 2009 (2009)
Externí odkaz:
https://doaj.org/article/b13adfc3226f4be3bdde648a76f853de
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Publikováno v:
IEEE Transactions on Signal Processing. 70:3566-3581
In this article, the outlier production mechanism of the conventional Multiple Signal Classification (MUSIC) and the g-MUSIC Direction-of-Arrival (DoA) estimation technique is investigated using tools from Random Matrix Theory (RMT). A general Centra
Autor:
J. Gomez-Vilardebo, Xavier Mestre, Monica Navarro, Juan F. Sevillano, Ricard Abello, Jorge Quintanilla
Publikováno v:
2022 9th International Workshop on Tracking, Telemetry and Command Systems for Space Applications (TTC).
Autor:
Jesús Gómez-Viladebó, Xavier Mestre, Monica Navarro, J.F. Sevillano, R. Abello, Cgi Deutchland B.V. & Co.
This work presents a non-coherent receiver architecture for coded MFSK transmissions in deep-space scenarios. First, we review the statistical characterization of the optimal (uncoded) detector output for both waveforms under consideration: conventio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2659::0f3116665c6cc88654576d381a4bdff4
https://zenodo.org/record/7602542
https://zenodo.org/record/7602542
Autor:
J. Cristian Vaca-Rubio, Roberto Pereira, Xavier Mestre, David Gregoratti, Zheng-Hua Tan, Elisabeth de Carbalho, Petar Popovski
Publikováno v:
Vaca-Rubio, C J, Pereira, R, Mestre, X, Gregoratti, D, Tan, Z-H, Carvalho, E D & Popovski, P 2022, Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface . in 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022 ., 9943430, IEEE, Machine Learning for Signal Processing, 2022 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, Xi'an, China, 22/08/2022 . https://doi.org/10.1109/MLSP55214.2022.9943430
Environmental scene reconstruction is of great interest for autonomous robotic applications, since an accurate representation of the environment is necessary to ensure safe interaction with robots. Equally important, it is also vital to ensure reliab
Autor:
Roberto Pereira, Anay Ajit Deshpande, Cristian Jesús Vaca Rubio, Xavier Mestre, Andrea Zanella, David Gregoratti, Elisabeth De Carvalho, Petar Popovski
Publikováno v:
Aalborg University
Pereira, R, Deshpande, A A, Vaca Rubio, C J, Mestre, X, Zanella, A, Gregoratti, D, De Carvalho, E & Popovski, P 2022, User Clustering for Rate Splitting using Machine Learning . in 2022 30th European Signal Processing Conference (EUSIPCO) ., 9909639, IEEE Communications Society, Proceedings of the European Signal Processing Conference, 2022 30th European Signal Processing Conference (EUSIPCO), 29/08/2022 . https://doi.org/10.23919/EUSIPCO55093.2022.9909639
Pereira, R, Deshpande, A A, Vaca Rubio, C J, Mestre, X, Zanella, A, Gregoratti, D, De Carvalho, E & Popovski, P 2022, User Clustering for Rate Splitting using Machine Learning . in 2022 30th European Signal Processing Conference (EUSIPCO) ., 9909639, IEEE Communications Society, Proceedings of the European Signal Processing Conference, 2022 30th European Signal Processing Conference (EUSIPCO), 29/08/2022 . https://doi.org/10.23919/EUSIPCO55093.2022.9909639
Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shown to provide significant improvements in exploiting spatial diversity in wireless networks and provide high throughput for all users while minimising interference among them.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0ff654affeb7c05b43c8c513778838e
http://arxiv.org/abs/2205.11373
http://arxiv.org/abs/2205.11373