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Títol:
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Deep learning for experimental hybrid terrestrial and satellite interference management
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Autor/a:
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Henarejos, Pol; Vázquez Oliver, Miguel Ángel; Pérez Neira, Ana Isabel
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Altres autors:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
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Abstract:
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Interference Management is a vast topic present in many disciplines. The majority of wireless standards suffer the drawback of interference intrusion and the network efficiency drop due to that. Traditionally, interference management has been addressed by proposing signal processing techniques that minimize their effects locally. However, the fast evolution of future communications makes difficult to adapt to new era. In this paper we propose the use of Deep Learning techniques to present a compact system for interference management. In particular, we describe two subsystems capable to detect the presence of interference, even in high Signal to Interference Ratio (SIR), and interference classification in several radio standards. Finally, we present results based on real signals captured from terrestrial and satellite networks and the conclusions unveil the courageous future of AI and wireless communications. |
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Abstract:
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Peer Reviewed |
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Matèries:
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-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Satèl·lits i ràdioenllaços
-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
-Artificial satellites in navigation
-Wireless communication systems
-Artificial intelligence
-Satellite communication systems
-Satellites
-Signal processing
-Interference management
-Learning techniques
-Network efficiency
-Satellite communications
-Signal processing technique
-Terrestrial networks
-Wireless communications
-Wireless standards
-Satèl·lits artificials en navegació
-Comunicació sense fil, Sistemes de |
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Drets:
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Tipus de document:
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Article - Versió publicada
Objecte de conferència |
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Publicat per:
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Institute of Electrical and Electronics Engineers (IEEE)
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Compartir:
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