Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Abode, Daniel"'
Autor:
Abode, Daniel, Ana, Pedro Maia de Sant, Artemenko, Alexander, Adeogun, Ramoni, Berardinelli, Gilberto
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 (CICA) policy based on the logistic model and learn the parameters using
Externí odkaz:
http://arxiv.org/abs/2405.11355
Autor:
Bui, Van-Phuc, Abode, Daniel, Ana, Pedro M. de Sant, Muthineni, Karthik, Pandey, Shashi Raj, Popovski, Petar
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 Autonomous Guided Vehicle (AGV). The DT model, situated on a cloud platfor
Externí odkaz:
http://arxiv.org/abs/2404.14960
Autor:
Abode, Daniel, Adeogun, Ramoni, Salaün, Lou, Abreu, Renato, Jacobsen, Thomas, Berardinelli, Gilberto
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 in the factory environment with a limited number of sub-bands which mu
Externí odkaz:
http://arxiv.org/abs/2401.00950
6th Generation (6G) industrial wireless subnetworks are expected to replace wired connectivity for control operation in robots and production modules. Interference management techniques such as centralized power control can improve spectral efficienc
Externí odkaz:
http://arxiv.org/abs/2212.14051
Autor:
Freire, Pedro J., Spinnler, Bernhard, Abode, Daniel, Prilepsky, Jaroslaw E., Ali, Abdallah A. I., Costa, Nelson, Schairer, Wolfgang, Napoli, Antonio, Ellis, Andrew D., Turitsyn, Sergei K.
We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up to 99\% training process reduction, which we demonstrate in three expe
Externí odkaz:
http://arxiv.org/abs/2202.12689
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 up to 92% reduction in epochs or 90% in the training dataset.
Com
Com
Externí odkaz:
http://arxiv.org/abs/2106.13144
Autor:
Freire, Pedro J., Abode, Daniel, Prilepsky, Jaroslaw E., Costa, Nelson, Spinnler, Bernhard, Napoli, Antonio, Turitsyn, Sergei K.
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 by using well-developed techniques based on the concept of transfer lea
Externí odkaz:
http://arxiv.org/abs/2104.05081
Publikováno v:
Abode, D O, Berardinelli, G & Adeogun, R O 2023, ' Power Control for 6G In-factory Subnetworks with Partial Channel Information using Graph Neural Networks ', IEEE Transactions on Machine Learning in Communications and Networking .
Transmit power control (PC) will become increasingly crucial in alleviating interference as the densification of the wireless networks continues towards 6G. However, the practicality of most PC methods suffers from their complexity, including the sen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1266::35ff4f111a70c7cc7b83f789a1e7b08f
https://vbn.aau.dk/ws/files/542150302/Power_Control_for_6G_In_factory_Subnetworks_with_Partial_Channel_Information_using_Graph_Neural_Networks_1_.pdf
https://vbn.aau.dk/ws/files/542150302/Power_Control_for_6G_In_factory_Subnetworks_with_Partial_Channel_Information_using_Graph_Neural_Networks_1_.pdf
Publikováno v:
Abode, D O, Adeogun, R O & Berardinelli, G 2023, ' Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach ', I E E E Wireless Communications and Networking Conference. Proceedings .
6th Generation (6G) industrial wireless subnetworks are expected to replace wired connectivity for control operation in robots and production modules. Interference management techniques such as centralized power control can improve spectral efficienc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2cbe7cc9af2b84c0598d075b9235e05e
https://vbn.aau.dk/da/publications/febffaad-5aa8-4221-ad16-e97d67a90c55
https://vbn.aau.dk/da/publications/febffaad-5aa8-4221-ad16-e97d67a90c55
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