Research Article
Using Deep Learning and Radio Virtualisation for Efficient Spectrum Sharing Among Coexisting Networks
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@INPROCEEDINGS{10.1007/978-3-030-05490-8_16, author={Wei Liu and Joao Santos and Xianjun Jiao and Francisco Paisana and Luiz DaSilva and Ingrid Moerman}, title={Using Deep Learning and Radio Virtualisation for Efficient Spectrum Sharing Among Coexisting Networks}, proceedings={Cognitive Radio Oriented Wireless Networks. 13th EAI International Conference, CROWNCOM 2018, Ghent, Belgium, September 18--20, 2018, Proceedings}, proceedings_a={CROWNCOM}, year={2019}, month={1}, keywords={Machine learning Radio access technology classification Radio virtualisation Software-defined radio}, doi={10.1007/978-3-030-05490-8_16} }- Wei Liu
Joao Santos
Xianjun Jiao
Francisco Paisana
Luiz DaSilva
Ingrid Moerman
Year: 2019
Using Deep Learning and Radio Virtualisation for Efficient Spectrum Sharing Among Coexisting Networks
CROWNCOM
Springer
DOI: 10.1007/978-3-030-05490-8_16
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.
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