Abstract
This work leverages recent advances in machine learning for radio environment monitoring with context awareness, and uses the obtained information for creating radio slices that can optimally coexist with ongoing traffic in a given spectrum band. We instantiate radio slices as virtualised radios built on a software-defined radio platform. Then, we describe a proof-of-concept experiment that validates and demonstrates our proposed solution.
The project leading to this publication has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 732174 (ORCA project).
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The physical layer receiver at the UE SDR decodes frames and inserts them into a queue, which is referred to as the RX queue.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Liu, W., Santos, J.F., Jiao, X., Paisana, F., DaSilva, L.A., Moerman, I. (2019). Using Deep Learning and Radio Virtualisation for Efficient Spectrum Sharing Among Coexisting Networks. In: Moerman, I., Marquez-Barja, J., Shahid, A., Liu, W., Giannoulis, S., Jiao, X. (eds) Cognitive Radio Oriented Wireless Networks. CROWNCOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 261. Springer, Cham. https://doi.org/10.1007/978-3-030-05490-8_16
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DOI: https://doi.org/10.1007/978-3-030-05490-8_16
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