The opening of the unlicensed radio spectrum creates new opportunities and new challenges for communication technology that can be faced by Machine Learning techniques. In this work, we discuss the potential benefits and the challenges with reference to the recent research developments in this area. Applications go from channel estimation to Signal quality control, and from signal classification to action control. We survey Machine learning and Deep Learning algorithms with possible radio applications and highlight the corresponding challenges.

Almazrouei, E., Gianini, G., Almoosa, N., Damiani, E. (2020). What can Machine Learning do for Radio Spectrum Management?. In Q2SWinet 2020 - Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks (pp.15-21). ACM [10.1145/3416013.3426443].

What can Machine Learning do for Radio Spectrum Management?

Gianini, G;
2020

Abstract

The opening of the unlicensed radio spectrum creates new opportunities and new challenges for communication technology that can be faced by Machine Learning techniques. In this work, we discuss the potential benefits and the challenges with reference to the recent research developments in this area. Applications go from channel estimation to Signal quality control, and from signal classification to action control. We survey Machine learning and Deep Learning algorithms with possible radio applications and highlight the corresponding challenges.
paper
machine learning; radio signals; wireless communication;
English
19th ACM symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet 2020 - 16 November 2020 through 20 November 2020
2020
Li, C; Mostefaoui, A
Q2SWinet 2020 - Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks
9781450381208
2020
15
21
reserved
Almazrouei, E., Gianini, G., Almoosa, N., Damiani, E. (2020). What can Machine Learning do for Radio Spectrum Management?. In Q2SWinet 2020 - Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks (pp.15-21). ACM [10.1145/3416013.3426443].
File in questo prodotto:
File Dimensione Formato  
Almazrouei-2020-Q2SWinet 2020-VoR.pdf

Solo gestori archivio

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Licenza: Tutti i diritti riservati
Dimensione 902.82 kB
Formato Adobe PDF
902.82 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/454836
Citazioni
  • Scopus 4
  • ???jsp.display-item.citation.isi??? ND
Social impact