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5g alto anrw award CCL conference Contact CSBC dash dash qoe demo disaggregation DriVE DRL edge computing godash has IBN ieee ieee #conference #award #NFVSDN20 #nfv #sdn #softwarization #bestpaper #phd ietf macsad mec ML necos Network Management nfv NFVSDN20 open source optical p4 padtec phd qoe quic sbrc sdn slicing tcp tip TNSM UAV video streaming virtualization Zero-touch Networks
  • New publication @ JNSM: Machine Learning-Assisted Closed-Control Loops for Beyond 5G Multi-Domain Zero-Touch Networks
  • New Publication @ SSP2021: Machine Learning Approach to Estimate Video QoE of Encrypted DASH Traffic in 5G Networks
  • Panel participation: SBFoton ROAD – Redes Ópticas Abertas e Desagregadas
  • 2021 NFV-SDN: Best Doctoral Symposium Paper
  • New publication @ TNSM: Harnessing UAVs for Fair 5G Bandwidth Allocation in Vehicular Communication via Deep Reinforcement Learning

June 2021

INTRIG :: Information & Networking Technologies Research and Innovation Group > 2021 > June
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2021 P4 Workshop: Most Novel Non-Networking Use of P4

Jun 24, 2021 Fabricio Rodriguez

The P4 Workshop is one of the biggest annual events organized by the P4 organization. The

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5g alto anrw award CCL conference Contact CSBC dash dash qoe demo disaggregation DriVE DRL edge computing godash has IBN ieee ieee #conference #award #NFVSDN20 #nfv #sdn #softwarization #bestpaper #phd ietf macsad mec ML necos Network Management nfv NFVSDN20 open source optical p4 padtec phd qoe quic sbrc sdn slicing tcp tip TNSM UAV video streaming virtualization Zero-touch Networks

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Recent Updates

  • New publication @ JNSM: Machine Learning-Assisted Closed-Control Loops for Beyond 5G Multi-Domain Zero-Touch Networks
  • New Publication @ SSP2021: Machine Learning Approach to Estimate Video QoE of Encrypted DASH Traffic in 5G Networks
  • Panel participation: SBFoton ROAD – Redes Ópticas Abertas e Desagregadas
  • 2021 NFV-SDN: Best Doctoral Symposium Paper
  • New publication @ TNSM: Harnessing UAVs for Fair 5G Bandwidth Allocation in Vehicular Communication via Deep Reinforcement Learning

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