1 citations · 1 across the 13 of their papers we have counts for
13 papers
A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation
Fin Gentzen, Marla Grunewald, Iulisloi Zacarias +2
Large Language Models (LLMs) are increasingly deployed as autonomous agents, transitioning from static conversational interfaces to dynamic systems capable of complex reasoning, to…
Maximum Achievable Burst Size in All-Optical Satellite Networks
Thomas Roethig, Sleman Mouammar, Ítalo Brasileiro +2
We analyze the maximum burst size achievable in all-optical satellite networks across different constellations. With a 100 Gbps uplink capacity, a WDM-based optical burst switching…
Enhancing Secure Intent-Based Networking with an Agentic AI: The EU Project MARE Approach
Iulisloi Zacarias, Marla Grunewald, Fin Gentzen +2
In the EU project MARE, a novel plane was proposed and used in combination with intent-based networking (IBN), allowing the operator to focus on what, rather than on how. Recently,…
Efficient Self-Learning and Model Versioning for AI-native O-RAN Edge
Mounir Bensalem, Fin Gentzen, Tuck-Wai Choong +3
The AI-native vision of 6G requires Radio Access Networks to train, deploy, and continuously refine thousands of machine learning (ML) models that drive real-time radio network opt…
A Secured Intent-Based Networking (sIBN) with Data-Driven Time-Aware Intrusion Detection
Urslla Uchechi Izuazu, Mounir Bensalem, Admela Jukan
While Intent-Based Networking (IBN) promises operational efficiency through autonomous and abstraction-driven network management, a critical unaddressed issue lies in IBN's implici…
On Effectiveness of Graph Neural Network Architectures for Network Digital Twins (NDTs)
Iulisloi Zacarias, Oussama Ben Taarit, Admela Jukan
Future networks, such as 6G, will need to support a vast and diverse range of interconnected devices and applications, each with its own set of requirements. While traditional netw…
Optimizing Split Learning Latency in TinyML-Based IoT Systems
Zied Jenhani, Mounir Bensalem, Jasenka Dizdarević +1
Split learning (SL) addresses the limitation of running deep learning inference directly on low-power edge/IoT nodes, in which it executes part of the inference process on the sens…
A Cross-Layer Analysis of Network Antifragility with RIS-assisted Links under Jamming Attacks
Mounir Bensalem, Thomas Röthig, Admela Jukan
Antifragility is an economics term defined as measure of (monetary) benefits gained from the adverse events and variability of the markets. This paper integrates for the first time…
An Optimization Driven Link SINR Assurance in RIS-assisted Indoor Networks
Cao Vien Phung, Max Franke, Ehsan Tohidi +5
Future smart factories are expected to deploy applications over high-performance indoor wireless channels in the millimeter-wave (mmWave) bands, which on the other hand are suscept…
Optimizing LoRa for Edge Computing with TinyML Pipeline for Channel Hopping
Marla Grunewald, Mounir Bensalem, Admela Jukan
We propose to integrate long-distance LongRange (LoRa) communication solution for sending the data from IoT to the edge computing system, by taking advantage of its unlicensed natu…
Effective ML Model Versioning in Edge Networks
Fin Gentzen, Mounir Bensalem, Admela Jukan
Machine learning (ML) models, data and software need to be regularly updated whenever essential version updates are released and feasible for integration. This is a basic but most…
On Efficient Topology Management in Service-Oriented 6G Networks: An Edge Video Distribution Case Study
Zied Ennaceur, Mounir Bensalem, Admela Jukan +3
An efficient topology management in future 6G networks is one of the fundamental challenges for a dynamic network creation based on location services, whereby each autonomous netwo…
Enhancing Path Selections with Interference Graphs in Multihop Relay Wireless Networks
Cao Vien Phung, Andre Drummond, Admela Jukan
The multihop relay wireless networks have gained traction due to the emergence of Reconfigurable Intelligent Surfaces (RISs) which can be used as relays in high frequency range wir…