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.
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