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Metric meta-learning optimization towards Intent-Based Networking implementation

Zero-touch network management is essential for beyond 5G and 6G systems, requiring full automation through a closed loop of data...

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DEEXP: Revealing Model Vulnerabilities for Spatio-Temporal Mobile Traffic Forecasting with Explainable AI

The ability to perform mobile traffic forecasting effectively with Deep Neural Networks (DNN) is instrumental to optimise resource management in...

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PhD Thesis defense: On Green Edge Computing with Machine Learning Applications

This thesis finds efficient solutions to dynamically support verticals at the edge while at the same time keeping an eye...

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PhD Thesis defense: Towards practical LiFi networks with hybrid radio-frequency and optical wireless communication technologies

This thesis focuses on identifying, investigating, and addressing three main limitations of LiFi systems in a typical indoor scenario. First,...

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PhD Thesis defense: Securing the Electromagnetic Spectrum: Large-Scale Detection, Analysis, and Management of Wireless Threats

This thesis investigates the roles of Software Defined-Radio and 5G technologies in shaping the future of wireless networks, with a...

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Advancing mmWave Communications for Mobile Devices

MmWave communication has garnered attention over the last decade due to its potential to provide large bandwidth capable of meeting...

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The Dawn of AI-Native Networks and Transforming Connectivity with AI-as-a-Service

With the journey towards 6G, we envision the network evolving into an AI-native platform that not only utilizes AI internally...

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Towards Ultra-Low Latency User-Plane Cyberattack Detection in SDN-based Smart Grids

Machine Learning (ML) models are widely used for cyberattack detection in Smart Grids (SG) based on Software-Defined Networks (SDN). However,...

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Summer School on advanced topics in 5G-6G Communications

As part of the “Teleco Renta” telecommunications studies promotion plan, a summer school on 5G/6G has been organized with international...

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Jewel: Resource-Efficient Joint Packet and Flow Level Inference in Programmable Switches

Embedding machine learning (ML) models in programmable switches realizes the vision of high-throughput and low-latency inference at line rate. Recent...

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