8 papers
Goal-Oriented Interference Coordination in 6G In-Factory Subnetworks
Daniel Abode, Pedro Maia de Sant Ana, Ramoni Adeogun +2
Subnetworks are expected to enhance wireless pervasiveness for critical applications such as wireless control of plants, however, they are interference-limited due to their extreme…
Control-Aware Transmit Power Allocation for 6G In-Factory Subnetwork Control Systems
Daniel Abode, Pedro Maia de Sant Ana, Alexander Artemenko +2
In this paper, we develop a novel power control solution for subnetworks-enabled distributed control systems in factory settings. We propose a channel-independent control-aware (CI…
Digital Twin of Industrial Networked Control System based on Value of Information
Van-Phuc Bui, Daniel Abode, Pedro M. de Sant Ana +3
The paper examines a scenario wherein sensors are deployed within an Industrial Networked Control System, aiming to construct a digital twin (DT) model for a remotely operated Auto…
Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks
Daniel Abode, Ramoni Adeogun, Lou Salaün +3
In this paper, we present an unsupervised approach for frequency sub-band allocation in wireless networks using graph-based learning. We consider a dense deployment of subnetworks…
Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach
Daniel Abode, Ramoni Adeogun, Gilberto Berardinelli
6th Generation (6G) industrial wireless subnetworks are expected to replace wired connectivity for control operation in robots and production modules. Interference management techn…
Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems
Pedro J. Freire, Bernhard Spinnler, Daniel Abode +7
We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up…
Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems
Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky +1
Transfer learning is proposed to adapt an NN-based nonlinear equalizer across different launch powers and modulation formats using a 450km TWC-fiber transmission. The result shows…
Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems
Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky +4
In this work, we address the question of the adaptability of artificial neural networks (NNs) used for impairments mitigation in optical transmission systems. We demonstrate that b…