Daniel Abode
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View article: PosGNN: A Graph Neural Network Based Multimodal Data Fusion for Indoor Positioning in Industrial Non-Line-of-Sight Scenarios
PosGNN: A Graph Neural Network Based Multimodal Data Fusion for Indoor Positioning in Industrial Non-Line-of-Sight Scenarios Open
In industrial environments, the wireless infrastructure is functional for offering services such as communication and positioning of industrial assets. However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in industrial sce…
View article: Goal-Oriented Interference Coordination in 6G In-Factory Subnetworks
Goal-Oriented Interference Coordination in 6G In-Factory Subnetworks Open
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 density. This paper proposes a goal-oriented joint power a…
View article: Goal-Oriented Interference Coordination in 6G In-Factory Subnetworks
Goal-Oriented Interference Coordination in 6G In-Factory Subnetworks Open
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 density. This paper proposes a goal-oriented joint power a…
View article: Communication-Aware Dynamic Speed Control for Interference Mitigation in 6G Mobile Subnetworks
Communication-Aware Dynamic Speed Control for Interference Mitigation in 6G Mobile Subnetworks Open
6G subnetworks may be installed in mobile entities such as robots navigating the factory floor in proximity. The potential high density of the subnetworks may result in interference limitation. Conventional interference mitigation approach…
View article: Control-Aware Transmit Power Allocation for 6G In-Factory Subnetwork Control Systems
Control-Aware Transmit Power Allocation for 6G In-Factory Subnetwork Control Systems Open
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 parame…
View article: Power Control for 6G In-Factory Subnetworks With Partial Channel Information Using Graph Neural Networks
Power Control for 6G In-Factory Subnetworks With Partial Channel Information Using Graph Neural Networks Open
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 high complexity, includi…
View article: Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks
Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks Open
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-ban…
View article: Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach
Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach Open
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 spectra…
View article: Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems
Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems Open
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…
View article: Transfer Learning for Neural Networks-Based Equalizers in Coherent Optical Systems
Transfer Learning for Neural Networks-Based Equalizers in Coherent Optical Systems Open
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 t…
View article: Power and Modulation Format Transfer Learning for Neural Network\n Equalizers in Coherent Optical Transmission Systems
Power and Modulation Format Transfer Learning for Neural Network\n Equalizers in Coherent Optical Transmission Systems Open
Transfer learning is proposed to adapt an NN-based nonlinear equalizer across\ndifferent launch powers and modulation formats using a 450km TWC-fiber\ntransmission. The result shows up to 92% reduction in epochs or 90% in the\ntraining dat…
View article: Transfer Learning for Neural Networks-based Equalizers in Coherent\n Optical Systems
Transfer Learning for Neural Networks-based Equalizers in Coherent\n Optical Systems Open
In this work, we address the question of the adaptability of artificial\nneural networks (NNs) used for impairments mitigation in optical transmission\nsystems. We demonstrate that by using well-developed techniques based on the\nconcept o…
View article: Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems
Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems Open
Transfer learning is proposed to adapt 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.
View article: Adapting Internet of Things and Neural Network in Modelling Demand Side Energy Consumption and Management
Adapting Internet of Things and Neural Network in Modelling Demand Side Energy Consumption and Management Open
With the recent application of micro-grid system and off-grid renewable energy power system using internet of things (IoT) for the efficacy in demand side consumption management. The study employed usage of IoT supported with statistical i…
View article: An Integrated Machine Learning Algorithm for Energy Management and Predictions
An Integrated Machine Learning Algorithm for Energy Management and Predictions Open
Management of energy consumption from the demand side has been inefficient and this has inadvertently affected the efforts of energy management on the supply side. This paper describes how machine learning integrated with Internet of thing…