2025
Conference article
Open Access
NB-IoT non-terrestrial network path loss estimation with precise 3D modeling of urban environment
Alibabaei Dermeni Najmeh, Calabrò A., Cassarà P., Gotta A., Marchetti E.
This study presents a novel simulation framework for Non-Terrestrial Networks (NTNs) that incorporates a realistic 3D ground model, including buildings and detailed environmental features, to enhance the accuracy of channel propagation modeling. Traditional approaches often rely on simplified terrain classifications, limiting their ability to capture the complexities of urban environments. To address this limitation, we integrate advanced ray-tracing techniques using Wireless InSite, MATLAB-based satellite constellation modeling, and high-resolution 3D urban mapping via Blender. This comprehensive framework allows for precise evaluations of NTN performance, including line-of-sight (LoS) and non-line-of-sight (N-LoS) conditions, by analyzing signal propagation characteristics with unprecedented granularity. The results demonstrate that detailed environmental modeling significantly impacts satellite visibility, signal attenuation, and multipath effects, highlighting the necessity of incorporating realistic urban structures in NTN simulations. The framework supports large-scale data analytics, enabling the application of machine learning techniques for network optimization, adaptive resource allocation, and enhanced connectivity planning. By bridging the gap between theoretical models and practical deployment scenarios, this work provides a powerful tool for advancing NTN research and improving global connectivity solutions.DOI: 10.1109/metroaerospace64938.2025.11114483
Project(s): RESTART “RESearch and innovation on future Telecommunications systems and networks, to make Italy more smart”
Metrics:
Alibabaei Dermeni Najmeh, Calabrò A., Cassarà P., Gotta A., Marchetti E.
This study presents a novel simulation framework for Non-Terrestrial Networks (NTNs) that incorporates a realistic 3D ground model, including buildings and detailed environmental features, to enhance the accuracy of channel propagation modeling. Traditional approaches often rely on simplified terrain classifications, limiting their ability to capture the complexities of urban environments. To address this limitation, we integrate advanced ray-tracing techniques using Wireless InSite, MATLAB-based satellite constellation modeling, and high-resolution 3D urban mapping via Blender. This comprehensive framework allows for precise evaluations of NTN performance, including line-of-sight (LoS) and non-line-of-sight (N-LoS) conditions, by analyzing signal propagation characteristics with unprecedented granularity. The results demonstrate that detailed environmental modeling significantly impacts satellite visibility, signal attenuation, and multipath effects, highlighting the necessity of incorporating realistic urban structures in NTN simulations. The framework supports large-scale data analytics, enabling the application of machine learning techniques for network optimization, adaptive resource allocation, and enhanced connectivity planning. By bridging the gap between theoretical models and practical deployment scenarios, this work provides a powerful tool for advancing NTN research and improving global connectivity solutions.DOI: 10.1109/metroaerospace64938.2025.11114483
Project(s): RESTART “RESearch and innovation on future Telecommunications systems and networks, to make Italy more smart”
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CNR IRIS 
| ieeexplore.ieee.org | CNR IRIS 
| CNR IRIS
2025
Journal article
Open Access
Leveraging explainable AI for 3-D geometry-based channel status prediction in UAV-assisted communication networks
Gholami L., Ducange P., Gotta A., Cassarà P.
Accurate prediction of receiver state is vital for optimizing network performance in urban settings, where rapid spatial variations in channel conditions pose significant challenges to communication quality. This paper presents a Machine Learning-based framework for predicting channel states in Unmanned Aerial Vehicle-assisted mmWave communication networks. Given that mmWave signals are susceptible to blockage by buildings and other urban structures, predicting receiver conditions at a specific location can be determined by directly deploying the geometric features describing the built-up environment surrounding the receiver. A set of geometrical features is extracted and used as input to train the adopted learning models, namely Decision Tree, Linear Decision Tree (LDT), Random Forest, Support Vector Machine, and Deep Neural Network (DNN), to estimate the probability of three distinct receiver states: Line-of-Sight, Non-Line-of-Sight, and Blocked. Experimental results indicate that the DNN-based model achieves the highest prediction accuracy and robustness, while the LDT provides computational efficiency and straightforward explainability. To improve the interpretability of the black-box DNN model, we employ the SHapley Additive exPlanations (SHAP) method, which identifies the most influential environmental features in state probability prediction. Furthermore, we enrich the standard 3GPP model by incorporating the top SHAP-ranked features, leading to notable performance improvements.Source: IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, vol. 6, pp. 7256-7269
DOI: 10.1109/ojcoms.2025.3603140
DOI: 10.36227/techrxiv.175624563.36832470/v1
Metrics:
Gholami L., Ducange P., Gotta A., Cassarà P.
Accurate prediction of receiver state is vital for optimizing network performance in urban settings, where rapid spatial variations in channel conditions pose significant challenges to communication quality. This paper presents a Machine Learning-based framework for predicting channel states in Unmanned Aerial Vehicle-assisted mmWave communication networks. Given that mmWave signals are susceptible to blockage by buildings and other urban structures, predicting receiver conditions at a specific location can be determined by directly deploying the geometric features describing the built-up environment surrounding the receiver. A set of geometrical features is extracted and used as input to train the adopted learning models, namely Decision Tree, Linear Decision Tree (LDT), Random Forest, Support Vector Machine, and Deep Neural Network (DNN), to estimate the probability of three distinct receiver states: Line-of-Sight, Non-Line-of-Sight, and Blocked. Experimental results indicate that the DNN-based model achieves the highest prediction accuracy and robustness, while the LDT provides computational efficiency and straightforward explainability. To improve the interpretability of the black-box DNN model, we employ the SHapley Additive exPlanations (SHAP) method, which identifies the most influential environmental features in state probability prediction. Furthermore, we enrich the standard 3GPP model by incorporating the top SHAP-ranked features, leading to notable performance improvements.Source: IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, vol. 6, pp. 7256-7269
DOI: 10.1109/ojcoms.2025.3603140
DOI: 10.36227/techrxiv.175624563.36832470/v1
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IEEE Open Journal of the Communications Society | doi.org | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Conference article
Open Access
Cross-modal distillation by additive importance measure in HITL autonomous driving
Bano S., Cassarà P., Gennaro C., Gotta A.
With the advent of Advanced Driver Assistance Systems (ADAS) and intelligent transport system applications, recognizing driver emotions has become essential for a decision support system (DSS) with humans in the loop (HITL). Multimodal approaches using visual cues, speech, physiological signals, and driving patterns improve emotion recognition but are challenging in resource-constrained environments where only a subset of modalities is available. This work addresses these challenges by combining multi-modal benefits with single-modality inference for emotion recognition using unlabeled external road condition data. Unlike traditional methods that average teachers' contribution, the proposed cross-modal distillation (CMD) weights teachers thanks to the Shapley additive global explanation (SAGE) aid, which improves the student model's accuracy and provides an interpretation of it. Experimental evaluations of the PPBEmo dataset show that XA-CMD improves emotion recognition accuracy with other baselines and provides deeper insights into decision-making.Source: IEEE VTS ... VEHICULAR TECHNOLOGY CONFERENCE, pp. 1-5. Oslo, Norway, 17 - 20 june 2025
DOI: 10.1109/vtc2025-spring65109.2025.11174460
Metrics:
Bano S., Cassarà P., Gennaro C., Gotta A.
With the advent of Advanced Driver Assistance Systems (ADAS) and intelligent transport system applications, recognizing driver emotions has become essential for a decision support system (DSS) with humans in the loop (HITL). Multimodal approaches using visual cues, speech, physiological signals, and driving patterns improve emotion recognition but are challenging in resource-constrained environments where only a subset of modalities is available. This work addresses these challenges by combining multi-modal benefits with single-modality inference for emotion recognition using unlabeled external road condition data. Unlike traditional methods that average teachers' contribution, the proposed cross-modal distillation (CMD) weights teachers thanks to the Shapley additive global explanation (SAGE) aid, which improves the student model's accuracy and provides an interpretation of it. Experimental evaluations of the PPBEmo dataset show that XA-CMD improves emotion recognition accuracy with other baselines and provides deeper insights into decision-making.Source: IEEE VTS ... VEHICULAR TECHNOLOGY CONFERENCE, pp. 1-5. Oslo, Norway, 17 - 20 june 2025
DOI: 10.1109/vtc2025-spring65109.2025.11174460
Metrics:
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CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Towards efficient end-to-end connectivity in B5G-NTN: in orbit UPF and dual connectivity
Rahbarnodehi N., Gholami L., Cassarà P., Gotta A.
The growing integration of Non-Terrestrial Networks (NTNs) into beyond-5G (B5G) infrastructures opens up new opportunities for enhancing global mobile connectivity, particularly for customers and IoT devices in remote or underserved areas. However, the inherent latency and variability of satellite links and the topology dynamicity of low-orbit constellations introduce significant challenges in maintaining low-latency, high-throughput, and reliable end-to-end connections.This work investigates a distributed and hybrid B5G-NTN architecture built upon Dual Connectivity (DC) principle, where user equipments (UEs) can establish simultaneous connections via two satellite paths (e.g., LEO/GEO constellations or multi-LEOs) toward the 5G core (5GC).DOI: 10.1109/cscn67557.2025.11230721
Metrics:
Rahbarnodehi N., Gholami L., Cassarà P., Gotta A.
The growing integration of Non-Terrestrial Networks (NTNs) into beyond-5G (B5G) infrastructures opens up new opportunities for enhancing global mobile connectivity, particularly for customers and IoT devices in remote or underserved areas. However, the inherent latency and variability of satellite links and the topology dynamicity of low-orbit constellations introduce significant challenges in maintaining low-latency, high-throughput, and reliable end-to-end connections.This work investigates a distributed and hybrid B5G-NTN architecture built upon Dual Connectivity (DC) principle, where user equipments (UEs) can establish simultaneous connections via two satellite paths (e.g., LEO/GEO constellations or multi-LEOs) toward the 5G core (5GC).DOI: 10.1109/cscn67557.2025.11230721
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CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Handover and SINR-aware path optimization in 5G-UAV mmWave communication using DRL
Machumilane A., Gotta A., Cassarà P.
Path planning and optimization for unmanned aerial vehicles (UAVs)-assisted next-generation wireless networks is critical for mobility management and ensuring UAV safety and ubiquitous connectivity, especially in dense urban environments with street canyons and tall buildings. Traditional statistical and model-based techniques have been successfully used for path optimization in communication networks. However, when dynamic channel propagation characteristics such as line-of-sight (LOS), interference, handover, and signal-to-interference and noise ratio (SINR) are included in path optimization, statistical and model-based path planning solutions become obsolete since they cannot adapt to the dynamic and time-varying wireless channels, especially in the mmWave bands. In this paper, we propose a novel model-free actor-critic deep reinforcement learning (AC-DRL) framework for path optimization in UAV-assisted 5G mmWave wireless networks, which combines four important aspects of UAV communication: flight time, handover, connectivity and SINR. We train an AC-RL agent that enables a UAV connected to a gNB to determine the optimal path to a desired destination in the shortest possible time with minimal gNB handover, while maintaining connectivity and the highest possible SINR. We train our model with data from a powerful ray tracing tool called Wireless InSite, which uses 3D images of the propagation environment and provides data that closely resembles the real propagation environment. The simulation results show that our system has superior performance in tracking high SINR compared to other selected RL algorithms.Source: IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, pp. 2254-2259. Montreal, QC, Canada, 08-12 June 2025
DOI: 10.1109/icc52391.2025.11161054
DOI: 10.48550/arxiv.2504.02688
Project(s): TRANTOR
, RESTART
Metrics:
Machumilane A., Gotta A., Cassarà P.
Path planning and optimization for unmanned aerial vehicles (UAVs)-assisted next-generation wireless networks is critical for mobility management and ensuring UAV safety and ubiquitous connectivity, especially in dense urban environments with street canyons and tall buildings. Traditional statistical and model-based techniques have been successfully used for path optimization in communication networks. However, when dynamic channel propagation characteristics such as line-of-sight (LOS), interference, handover, and signal-to-interference and noise ratio (SINR) are included in path optimization, statistical and model-based path planning solutions become obsolete since they cannot adapt to the dynamic and time-varying wireless channels, especially in the mmWave bands. In this paper, we propose a novel model-free actor-critic deep reinforcement learning (AC-DRL) framework for path optimization in UAV-assisted 5G mmWave wireless networks, which combines four important aspects of UAV communication: flight time, handover, connectivity and SINR. We train an AC-RL agent that enables a UAV connected to a gNB to determine the optimal path to a desired destination in the shortest possible time with minimal gNB handover, while maintaining connectivity and the highest possible SINR. We train our model with data from a powerful ray tracing tool called Wireless InSite, which uses 3D images of the propagation environment and provides data that closely resembles the real propagation environment. The simulation results show that our system has superior performance in tracking high SINR compared to other selected RL algorithms.Source: IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, pp. 2254-2259. Montreal, QC, Canada, 08-12 June 2025
DOI: 10.1109/icc52391.2025.11161054
DOI: 10.48550/arxiv.2504.02688
Project(s): TRANTOR
, RESTARTMetrics:
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arXiv.org e-Print Archive | CNR IRIS | ieeexplore.ieee.org | doi.org | doi.org | CNR IRIS | CNR IRIS
2025
Other
Restricted
Orbit-Routing Simulator: Advanced Routing in Multi-Orbit Satellite Networks
Abraham Gebrehiwot, Filippo Maria Lauria, Alberto Gotta
Traditional routing algorithms are not efficient when applied to multi-orbit satellite networks, encompassing LEO, MEO, and GEO satellites, as well as ground stations, since identifying optimal routing strategies jointly with a non-terrestrial network dynamic topology is a challenging topic. In response to that, this paper introduces the Orbit-Routing Simulator, a satellite simulation tool designed to tackle the intricacies of advanced routing algorithms. Orbit-Routing Simulator provides a robust environment for the design, testing, and evaluation of various routing strategies, tailored to meet the dynamic conditions of complex satellite constellations. It aims to enhance the efficiency and reliability of satellite communications by providing detailed insights into the operational effectiveness of the communication systems. This facilitates improved connectivity and network management by addressing the challenges of high mobility and variable communication channels in satellite networks. The paper concludes with several use cases and scenarios highlighting its practical applications and adaptability to real-world operational adjustments.DOI: 10.1109/asms/spsc64465.2025
Project(s): TRANTOR
Metrics:
Abraham Gebrehiwot, Filippo Maria Lauria, Alberto Gotta
Traditional routing algorithms are not efficient when applied to multi-orbit satellite networks, encompassing LEO, MEO, and GEO satellites, as well as ground stations, since identifying optimal routing strategies jointly with a non-terrestrial network dynamic topology is a challenging topic. In response to that, this paper introduces the Orbit-Routing Simulator, a satellite simulation tool designed to tackle the intricacies of advanced routing algorithms. Orbit-Routing Simulator provides a robust environment for the design, testing, and evaluation of various routing strategies, tailored to meet the dynamic conditions of complex satellite constellations. It aims to enhance the efficiency and reliability of satellite communications by providing detailed insights into the operational effectiveness of the communication systems. This facilitates improved connectivity and network management by addressing the challenges of high mobility and variable communication channels in satellite networks. The paper concludes with several use cases and scenarios highlighting its practical applications and adaptability to real-world operational adjustments.DOI: 10.1109/asms/spsc64465.2025
Project(s): TRANTOR
Metrics:
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doi.org | CNR IRIS | CNR IRIS | www.asmsconference.org
2025
Conference article
Open Access
Advancing the future of integrated 5G-satellite networks: a practical framework for performance evaluation, dataset generation, and AI-driven approaches
Alibabaie N., Calabrò A., Cassarà P., Gotta A., Marchetti E.
This paper introduces a framework for Satellite, Terrestrial Integrated Network (STIN), a modular and joint simulation tool for simulating and evaluating integrated terrestrial and non-terrestrial communication systems. The framework comprises various modules designed to model real-world environments, compute and analyze constellation features, and perform channel modeling. Through the seamless integration of these components, the STIN framework enables users to assess the performance of satellite constellations under diverse conditions and select optimal configurations for enhanced coverage and communication efficiency. The paper discusses the methodology and workflow of the framework and a preliminary implementation, suggesting avenues for obtaining communication dataseis to support AI-driven approaches.DOI: 10.5220/0013564400003970
Metrics:
Alibabaie N., Calabrò A., Cassarà P., Gotta A., Marchetti E.
This paper introduces a framework for Satellite, Terrestrial Integrated Network (STIN), a modular and joint simulation tool for simulating and evaluating integrated terrestrial and non-terrestrial communication systems. The framework comprises various modules designed to model real-world environments, compute and analyze constellation features, and perform channel modeling. Through the seamless integration of these components, the STIN framework enables users to assess the performance of satellite constellations under diverse conditions and select optimal configurations for enhanced coverage and communication efficiency. The paper discusses the methodology and workflow of the framework and a preliminary implementation, suggesting avenues for obtaining communication dataseis to support AI-driven approaches.DOI: 10.5220/0013564400003970
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CNR IRIS | www.scitepress.org | doi.org | CNR IRIS
2025
Conference article
Open Access
Explainable machine learning for environment-aware channel state prediction in UAV-based 6G networks
Gholami L., Ducange P., Rago A., Cassarà P., Gotta A.
The emergence of 6G networks demands environment-aware communication paradigms to ensure reliable and efficient connectivity, and Channel Knowledge Maps (CKMs) offer a promising solution by mapping spatial locations to detailed channel characteristics for proactive network optimization. In this context, this paper proposes an explainable Machine Learning (ML)-based framework that uses geometrical features to predict receiver state probabilities in UAV-based mmWave communication networks. Geometrical characteristics extracted from the environment surrounding each receiver are used to train ML models, namely Decision Tree (DT), K-Nearest Neighbors (KNN), and Deep Neural Network (DNN) models, to predict three receiver states probabilities: Line-of-Sight (LOS), No-Line-of-Sight (NLOS), and Blocked. Experimental results show that the DNN model outperforms DT and KNN, achieving higher accuracy across all states, albeit with no inherent explainability. To address this, the SHapley Additive exPlanations (SHAP) method is applied to indicate feature contributions to each state prediction of the black-box DNN model. This improves the interpretability and reliability of the proposed environment-aware framework for 6G UAV-based networks.DOI: 10.1109/wimob66857.2025.11257490
Project(s): TRANTOR
Metrics:
Gholami L., Ducange P., Rago A., Cassarà P., Gotta A.
The emergence of 6G networks demands environment-aware communication paradigms to ensure reliable and efficient connectivity, and Channel Knowledge Maps (CKMs) offer a promising solution by mapping spatial locations to detailed channel characteristics for proactive network optimization. In this context, this paper proposes an explainable Machine Learning (ML)-based framework that uses geometrical features to predict receiver state probabilities in UAV-based mmWave communication networks. Geometrical characteristics extracted from the environment surrounding each receiver are used to train ML models, namely Decision Tree (DT), K-Nearest Neighbors (KNN), and Deep Neural Network (DNN) models, to predict three receiver states probabilities: Line-of-Sight (LOS), No-Line-of-Sight (NLOS), and Blocked. Experimental results show that the DNN model outperforms DT and KNN, achieving higher accuracy across all states, albeit with no inherent explainability. To address this, the SHapley Additive exPlanations (SHAP) method is applied to indicate feature contributions to each state prediction of the black-box DNN model. This improves the interpretability and reliability of the proposed environment-aware framework for 6G UAV-based networks.DOI: 10.1109/wimob66857.2025.11257490
Project(s): TRANTOR
Metrics:
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CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Mobility-aware edge-assisted 5G communication framework analysis for driver emotion recognition
Cassarà P., Bano S., Gotta A.
Modern vehicles are equipped with sophisticated systems that continuously monitor both their mechanical condition and the well-being of passengers. In the vehicular network scenario, this availability of a vast amount of data has encouraged ever more the development of ML-based systems to enable highly reliable functionalities for supporting autonomous driving. However, a major challenge is to enable vehicles to process ML-based complex models quickly and efficiently. This problem can be solved by utilizing edge-based computing solutions where computing and storage resources available in network infrastructures are located near the vehicles. By offloading some of the processing tasks to these local resources, vehicles can achieve faster response times and enhanced efficiency. In this paper, we analyze the computing and communication performance of a federated multimodal distillation approach for driver emotion detection for a vehicular communication semi-urban scenario, which will be modeled by implementing stochastic geometry models. The aim of our analysis is to evaluate the impact of a complex ML-based approach on the communication infrastructure, where locally distilled models suitable for constrained devices are federated into a global model. We also investigate the impact on the network of the load due to the learning procedure when IID data and non-IID are considered. The numerical results highlight the strengths and weaknesses of the communication infrastructure when heterogeneous wireless technologies such as 5G and WiFi are involved in handling this type of approach for vehicles with limited computational resources.Source: IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, pp. 1-6. Milano, Italy, 24-27 March 2025
DOI: 10.1109/wcnc61545.2025.10978719
Metrics:
Cassarà P., Bano S., Gotta A.
Modern vehicles are equipped with sophisticated systems that continuously monitor both their mechanical condition and the well-being of passengers. In the vehicular network scenario, this availability of a vast amount of data has encouraged ever more the development of ML-based systems to enable highly reliable functionalities for supporting autonomous driving. However, a major challenge is to enable vehicles to process ML-based complex models quickly and efficiently. This problem can be solved by utilizing edge-based computing solutions where computing and storage resources available in network infrastructures are located near the vehicles. By offloading some of the processing tasks to these local resources, vehicles can achieve faster response times and enhanced efficiency. In this paper, we analyze the computing and communication performance of a federated multimodal distillation approach for driver emotion detection for a vehicular communication semi-urban scenario, which will be modeled by implementing stochastic geometry models. The aim of our analysis is to evaluate the impact of a complex ML-based approach on the communication infrastructure, where locally distilled models suitable for constrained devices are federated into a global model. We also investigate the impact on the network of the load due to the learning procedure when IID data and non-IID are considered. The numerical results highlight the strengths and weaknesses of the communication infrastructure when heterogeneous wireless technologies such as 5G and WiFi are involved in handling this type of approach for vehicles with limited computational resources.Source: IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, pp. 1-6. Milano, Italy, 24-27 March 2025
DOI: 10.1109/wcnc61545.2025.10978719
Metrics:
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CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Resource orchestration in a terrestrial and non-terrestrial framework
Borsatti D., Rago A., Piro G., Grieco L. A., Araniti G., Pizzi S., Rinaldi F., Morosi S., Tarchi D., Matera F., Settembre M., Salvo P., Gotta A., Sambo N., Sacchi C., Patrone F.
This paper shows how the ITA-NTN project aims to develop a comprehensive orchestration framework that bridges the gap between Terrestrial Network (TN) and Non-Terrestrial Network (NTN), ensuring seamless connectivity, efficient resource utilization, and enhanced network resilience. Novel approaches for management and orchestration are described, taking into account NTN element movements, handover, energy consumption, and power supply. AI-driven mechanisms are shown in the framework of a proposed TNNTN architecture with an evolution towards a cloud-native approach, also considering the role of IPv6.DOI: 10.23919/aeit67669.2025.11218120
Project(s): RESTART
Metrics:
Borsatti D., Rago A., Piro G., Grieco L. A., Araniti G., Pizzi S., Rinaldi F., Morosi S., Tarchi D., Matera F., Settembre M., Salvo P., Gotta A., Sambo N., Sacchi C., Patrone F.
This paper shows how the ITA-NTN project aims to develop a comprehensive orchestration framework that bridges the gap between Terrestrial Network (TN) and Non-Terrestrial Network (NTN), ensuring seamless connectivity, efficient resource utilization, and enhanced network resilience. Novel approaches for management and orchestration are described, taking into account NTN element movements, handover, energy consumption, and power supply. AI-driven mechanisms are shown in the framework of a proposed TNNTN architecture with an evolution towards a cloud-native approach, also considering the role of IPv6.DOI: 10.23919/aeit67669.2025.11218120
Project(s): RESTART
Metrics:
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CNR IRIS | ieeexplore.ieee.org | doi.org | Flore (Florence Research Repository) | Flore (Florence Research Repository) | CNR IRIS
2024
Conference article
Open Access
Channel modeling for millimeter-wave UAV communication based on explainable generative neural network
Gholami L., Ducange P., Cassarà P., Gotta A.
This paper proposes an enhanced method for channel modeling in millimeter-wave wireless UAV-assisted communication networks. It addresses the need for accurate, data-efficient, and interpretable channel models for user-centric networks, obtained by integrating the Generative Adversarial Network (GAN) framework with eXplainable AI (XAI) systems. The methodology incorporates Deep SHAP to optimize the generator’s gradient descent process, improving model accuracy. By comparing metrics such as Kullback-Leibler divergence and Wasserstein Distance, the model demonstrates superiority in capturing real parameter distributions. Moreover, it indicates robust performance with significantly fewer training samples, making it a promising solution for real-world deployment.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3793, pp. 177-184. Valletta, Malta, 17-19 july 2024
Gholami L., Ducange P., Cassarà P., Gotta A.
This paper proposes an enhanced method for channel modeling in millimeter-wave wireless UAV-assisted communication networks. It addresses the need for accurate, data-efficient, and interpretable channel models for user-centric networks, obtained by integrating the Generative Adversarial Network (GAN) framework with eXplainable AI (XAI) systems. The methodology incorporates Deep SHAP to optimize the generator’s gradient descent process, improving model accuracy. By comparing metrics such as Kullback-Leibler divergence and Wasserstein Distance, the model demonstrates superiority in capturing real parameter distributions. Moreover, it indicates robust performance with significantly fewer training samples, making it a promising solution for real-world deployment.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3793, pp. 177-184. Valletta, Malta, 17-19 july 2024
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ceur-ws.org | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
On dual connectivity in 6G Leo constellation
Machumilane A., Gotta A.
Dual connectivity (DC) has garnered significant attention in 5G evolution, allowing for enhancing throughput and reliability by leveraging the channel conditions of two paths. However, when the paths exhibit different delays, such as in terrestrial and non-terrestrial integrated networks with multi-orbit topologies or in networks characterized by frequent topology changes, like Low Earth Orbit (LEO) satellite constellations with different elevation angles, traffic delivery may experience packet reordering or triggering congestion control mechanisms. Additionally, real-time traffic may experience packet drops if their arrival exceeds a play-out threshold. Different techniques have been proposed to address these issues, such as packet duplication, packet switching, and network coding for traffic scheduling in DC. However, if not accurately designed, these techniques can lead to resource waste, encoding/decoding delays, and computational overhead, undermining DC’s intended benefits. This paper provides a mathematical framework for calculating the average end-to-end packet loss in case of a loss process modeled with a Discrete Markov Chain - typical of a wireless channel - when combining packet duplication and packet switching or when network coding is employed in DC. Such metrics help derive optimal policies with full knowledge of the underlying loss process to be compared to empirical models learned through Machine Learning algorithms.DOI: 10.1109/meditcom61057.2024.10621383
Metrics:
Machumilane A., Gotta A.
Dual connectivity (DC) has garnered significant attention in 5G evolution, allowing for enhancing throughput and reliability by leveraging the channel conditions of two paths. However, when the paths exhibit different delays, such as in terrestrial and non-terrestrial integrated networks with multi-orbit topologies or in networks characterized by frequent topology changes, like Low Earth Orbit (LEO) satellite constellations with different elevation angles, traffic delivery may experience packet reordering or triggering congestion control mechanisms. Additionally, real-time traffic may experience packet drops if their arrival exceeds a play-out threshold. Different techniques have been proposed to address these issues, such as packet duplication, packet switching, and network coding for traffic scheduling in DC. However, if not accurately designed, these techniques can lead to resource waste, encoding/decoding delays, and computational overhead, undermining DC’s intended benefits. This paper provides a mathematical framework for calculating the average end-to-end packet loss in case of a loss process modeled with a Discrete Markov Chain - typical of a wireless channel - when combining packet duplication and packet switching or when network coding is employed in DC. Such metrics help derive optimal policies with full knowledge of the underlying loss process to be compared to empirical models learned through Machine Learning algorithms.DOI: 10.1109/meditcom61057.2024.10621383
Metrics:
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CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
FedCMD: a federated cross-modal knowledge distillation for drivers’ emotion recognition
Bano S., Tonellotto N., Cassarà P., Gotta A.
Emotion recognition has attracted a lot of interest in recent years in various application areas such as healthcare and autonomous driving. Existing approaches to emotion recognition are based on visual, speech, or psychophysiological signals. However, recent studies are looking at multimodal techniques that combine different modalities for emotion recognition. In this work, we address the problem of recognizing the user’s emotion as a driver from unlabeled videos using multimodal techniques. We propose a collaborative training method based on cross-modal distillation, i.e., “FedCMD” (Federated Cross-Modal Distillation). Federated Learning (FL) is an emerging collaborative decentralized learning technique that allows each participant to train their model locally to build a better generalized global model without sharing their data. The main advantage of FL is that only local data is used for training, thus maintaining privacy and providing a secure and efficient emotion recognition system. The local model in FL is trained for each vehicle device with unlabeled video data by using sensor data as a proxy. Specifically, for each local model, we show how driver emotional annotations can be transferred from the sensor domain to the visual domain by using cross-modal distillation. The key idea is based on the observation that a driver’s emotional state indicated by a sensor correlates with facial expressions shown in videos. The proposed “FedCMD” approach is tested on the multimodal dataset “BioVid Emo DB” and achieves state-of-the-art performance. Experimental results show that our approach is robust to non-identically distributed data, achieving 96.67\% and 90.83\% accuracy in classifying five different emotions with IID (independently and identically distributed) and non-IID data, respectively. Moreover, our model is much more robust to overfitting, resulting in better generalization than the other existing methods.Source: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, vol. 15 (issue 3), pp. 1-27
DOI: 10.1145/3650040
Metrics:
Bano S., Tonellotto N., Cassarà P., Gotta A.
Emotion recognition has attracted a lot of interest in recent years in various application areas such as healthcare and autonomous driving. Existing approaches to emotion recognition are based on visual, speech, or psychophysiological signals. However, recent studies are looking at multimodal techniques that combine different modalities for emotion recognition. In this work, we address the problem of recognizing the user’s emotion as a driver from unlabeled videos using multimodal techniques. We propose a collaborative training method based on cross-modal distillation, i.e., “FedCMD” (Federated Cross-Modal Distillation). Federated Learning (FL) is an emerging collaborative decentralized learning technique that allows each participant to train their model locally to build a better generalized global model without sharing their data. The main advantage of FL is that only local data is used for training, thus maintaining privacy and providing a secure and efficient emotion recognition system. The local model in FL is trained for each vehicle device with unlabeled video data by using sensor data as a proxy. Specifically, for each local model, we show how driver emotional annotations can be transferred from the sensor domain to the visual domain by using cross-modal distillation. The key idea is based on the observation that a driver’s emotional state indicated by a sensor correlates with facial expressions shown in videos. The proposed “FedCMD” approach is tested on the multimodal dataset “BioVid Emo DB” and achieves state-of-the-art performance. Experimental results show that our approach is robust to non-identically distributed data, achieving 96.67\% and 90.83\% accuracy in classifying five different emotions with IID (independently and identically distributed) and non-IID data, respectively. Moreover, our model is much more robust to overfitting, resulting in better generalization than the other existing methods.Source: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, vol. 15 (issue 3), pp. 1-27
DOI: 10.1145/3650040
Metrics:
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IRIS Cnr | ACM Transactions on Intelligent Systems and Technology | IRIS Cnr | ACM Transactions on Intelligent Systems and Technology | Archivio della Ricerca - Università di Pisa | CNR IRIS | CNR IRIS
2024
Conference article
Restricted
Orchestration and control of non-terrestrial networks employing free space optics
Settembre M., Matera F., Sambo N., Cossu G., Ciaramella E., Gotta A.
Free Space Optics (FSO) offers enormous advantages in terms of transmission capacity also for Non-Terrestrial Networks (NTNs). However, the adoption of FSO in hybrid terrestrial and NTN requires sophisticated orchestration mechanisms to manage and optimize the interplay between fiber, FSO, and radio links. In this work, after having analyzed the current Management and Orchestration proposals for 5G towards 6G networks, with main reference to the optical communication layer, we propose to adopt the NETCONF protocol for configuration and monitoring in FSO systems. This choice allows us, in the integration between terrestrial and NTN, to have a crossdomain approach valid for all the optical layer, from the core to the NTN access.DOI: 10.1109/icop62013.2024.10803660
Project(s): RESearch and innovation on future Telecommunications systems and networks, to make Italy more smart
Metrics:
Settembre M., Matera F., Sambo N., Cossu G., Ciaramella E., Gotta A.
Free Space Optics (FSO) offers enormous advantages in terms of transmission capacity also for Non-Terrestrial Networks (NTNs). However, the adoption of FSO in hybrid terrestrial and NTN requires sophisticated orchestration mechanisms to manage and optimize the interplay between fiber, FSO, and radio links. In this work, after having analyzed the current Management and Orchestration proposals for 5G towards 6G networks, with main reference to the optical communication layer, we propose to adopt the NETCONF protocol for configuration and monitoring in FSO systems. This choice allows us, in the integration between terrestrial and NTN, to have a crossdomain approach valid for all the optical layer, from the core to the NTN access.DOI: 10.1109/icop62013.2024.10803660
Project(s): RESearch and innovation on future Telecommunications systems and networks, to make Italy more smart
Metrics:
See at:
doi.org | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2023
Journal article
Open Access
Towards a fully-observable Markov decision process with generative models for integrated 6G-non-terrestrial networks
Machumilane A., Cassarà P., Gotta A.
The upcoming sixth generation (6G) mobile networks require integration between terrestrial mobile networks and non-terrestrial networks (NTN) such as satellites and high altitude platforms (HAPs) to ensure wide and ubiquitous coverage, high connection density, reliable communications and high data rates. The main challenge in this integration is the requirement for line-of-sight (LOS) communication between the user equipment (UE) and the satellite. In this paper, we propose a framework based on actorcritic reinforcement learning and generative models for LOS estimation and traffic scheduling on multiple links connecting a user equipment to multiple satellites in 6G-NTN integrated networks. The agent learns to estimate the LOS probabilities of the available channels and schedules traffic on appropriate links to minimise end-to-end losses with minimal bandwidth. The learning process is modelled as a partially observable Markov decision process (POMDP), since the agent can only observe the state of the channels it has just accessed. As a result, the learning agent requires a longer convergence time compared to the satellite visibility period at a given satellite elevation angle. To counteract this slow convergence, we use generative models to transform a POMDP into a fully observable Markov decision process (FOMDP). We use generative adversarial networks (GANs) and variational autoencoders (VAEs) to generate synthetic channel states of the channels that are not selected by the agent during the learning process, allowing the agent to have complete knowledge of all channels, including those that are not accessed, thus speeding up the learning process. The simulation results show that our framework enables the agent to converge in a short time and transmit with an optimal policy for most of the satellite visibility period, which significantly reduces end-to-end losses and saves bandwidth. We also show that it is possible to train generative models in real time without requiring prior knowledge of the channel models and without slowing down the learning process or affecting the accuracy of the models.Source: IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, vol. 4, pp. 1913-1930
DOI: 10.1109/ojcoms.2023.3307209
Project(s): TRANTOR, RESTART, Sustainable Mobility National Research Center
Metrics:
Machumilane A., Cassarà P., Gotta A.
The upcoming sixth generation (6G) mobile networks require integration between terrestrial mobile networks and non-terrestrial networks (NTN) such as satellites and high altitude platforms (HAPs) to ensure wide and ubiquitous coverage, high connection density, reliable communications and high data rates. The main challenge in this integration is the requirement for line-of-sight (LOS) communication between the user equipment (UE) and the satellite. In this paper, we propose a framework based on actorcritic reinforcement learning and generative models for LOS estimation and traffic scheduling on multiple links connecting a user equipment to multiple satellites in 6G-NTN integrated networks. The agent learns to estimate the LOS probabilities of the available channels and schedules traffic on appropriate links to minimise end-to-end losses with minimal bandwidth. The learning process is modelled as a partially observable Markov decision process (POMDP), since the agent can only observe the state of the channels it has just accessed. As a result, the learning agent requires a longer convergence time compared to the satellite visibility period at a given satellite elevation angle. To counteract this slow convergence, we use generative models to transform a POMDP into a fully observable Markov decision process (FOMDP). We use generative adversarial networks (GANs) and variational autoencoders (VAEs) to generate synthetic channel states of the channels that are not selected by the agent during the learning process, allowing the agent to have complete knowledge of all channels, including those that are not accessed, thus speeding up the learning process. The simulation results show that our framework enables the agent to converge in a short time and transmit with an optimal policy for most of the satellite visibility period, which significantly reduces end-to-end losses and saves bandwidth. We also show that it is possible to train generative models in real time without requiring prior knowledge of the channel models and without slowing down the learning process or affecting the accuracy of the models.Source: IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, vol. 4, pp. 1913-1930
DOI: 10.1109/ojcoms.2023.3307209
Project(s): TRANTOR
, RESTART, Sustainable Mobility National Research CenterMetrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS
2023
Conference article
Open Access
A federated channel modeling system using generative neural networks
Bano S., Cassarà P., Tonellotto N., Gotta A.
The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike traditional centralized learning methods that are specific to certain geographical areas and inappropriate for others, we propose a generalized model that uses Federated Learning (FL) for channel estimation and can predict the air-to-ground path loss between a low-altitude platform and a terrestrial terminal. To this end, our proposed FL-based Generative Adversarial Network (FL-GAN) is designed to function as a generative data model that can learn different types of data distributions and generate realistic patterns from the same distributions without requiring prior data analysis before the training phase. To evaluate the effectiveness of the proposed model, we evaluate its performance using Kullback-Leibler divergence (KL), and Wasserstein distance between the synthetic data distribution generated by the model and the actual data distribution. We also compare the proposed technique with other generative models, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GAN models. The results of the study show that the synthetic data generated by FL-GAN has the highest similarity in distribution with the real data. This shows the effectiveness of the proposed approach in generating data-driven channel models that can be used in different regions.Source: IEEE VTS ... VEHICULAR TECHNOLOGY CONFERENCE. Florence, Italy, 20-23/06/2023
DOI: 10.1109/vtc2023-spring57618.2023.10199491
Project(s): TRANTOR, RESTART
Metrics:
Bano S., Cassarà P., Tonellotto N., Gotta A.
The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike traditional centralized learning methods that are specific to certain geographical areas and inappropriate for others, we propose a generalized model that uses Federated Learning (FL) for channel estimation and can predict the air-to-ground path loss between a low-altitude platform and a terrestrial terminal. To this end, our proposed FL-based Generative Adversarial Network (FL-GAN) is designed to function as a generative data model that can learn different types of data distributions and generate realistic patterns from the same distributions without requiring prior data analysis before the training phase. To evaluate the effectiveness of the proposed model, we evaluate its performance using Kullback-Leibler divergence (KL), and Wasserstein distance between the synthetic data distribution generated by the model and the actual data distribution. We also compare the proposed technique with other generative models, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GAN models. The results of the study show that the synthetic data generated by FL-GAN has the highest similarity in distribution with the real data. This shows the effectiveness of the proposed approach in generating data-driven channel models that can be used in different regions.Source: IEEE VTS ... VEHICULAR TECHNOLOGY CONFERENCE. Florence, Italy, 20-23/06/2023
DOI: 10.1109/vtc2023-spring57618.2023.10199491
Project(s): TRANTOR
, RESTARTMetrics:
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CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Learning-based traffic scheduling in non-stationary multipath 5G non-terrestrial networks
Machumilane A., Gotta A., Cassarà P., Amato G., Gennaro C.
In non-terrestrial networks, where low Earth orbit satellites and user equipment move relative to each other, line-of-sight tracking and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous line-of-sight estimation and channel impairment compensation are crucial for user equipment to access a satellite and maintain connectivity. In this paper, we propose a framework based on actor-critic reinforcement learning for traffic scheduling in non-terrestrial networks scenario where the channel state is non-stationary due to the variability of the line of sight, which depends on the current satellite elevation. We deploy the framework as an agent in a multipath routing scheme where the user equipment can access more than one satellite simultaneously to improve link reliability and throughput. We investigate how the agent schedules traffic in multiple satellite links by adopting policies that are evaluated by an actor-critic reinforcement learning approach. The agent continuously trains its model based on variations in satellite elevation angles, handovers, and relative line-of-sight probabilities. We compare the agent's retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis while considering the dense urban area of Paris, where high-rise buildings significantly affect the line of sight. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1times the satellite visibility time at given elevations, which guarantees efficient use of satellite visibility.Source: REMOTE SENSING (BASEL), vol. 15 (issue 7)
DOI: 10.3390/rs15071842
Project(s): TRANTOR
Metrics:
Machumilane A., Gotta A., Cassarà P., Amato G., Gennaro C.
In non-terrestrial networks, where low Earth orbit satellites and user equipment move relative to each other, line-of-sight tracking and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous line-of-sight estimation and channel impairment compensation are crucial for user equipment to access a satellite and maintain connectivity. In this paper, we propose a framework based on actor-critic reinforcement learning for traffic scheduling in non-terrestrial networks scenario where the channel state is non-stationary due to the variability of the line of sight, which depends on the current satellite elevation. We deploy the framework as an agent in a multipath routing scheme where the user equipment can access more than one satellite simultaneously to improve link reliability and throughput. We investigate how the agent schedules traffic in multiple satellite links by adopting policies that are evaluated by an actor-critic reinforcement learning approach. The agent continuously trains its model based on variations in satellite elevation angles, handovers, and relative line-of-sight probabilities. We compare the agent's retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis while considering the dense urban area of Paris, where high-rise buildings significantly affect the line of sight. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1times the satellite visibility time at given elevations, which guarantees efficient use of satellite visibility.Source: REMOTE SENSING (BASEL), vol. 15 (issue 7)
DOI: 10.3390/rs15071842
Project(s): TRANTOR
Metrics:
See at:
Remote Sensing | CNR IRIS | ISTI Repository | www.mdpi.com | ZENODO | CNR IRIS
2023
Journal article
Open Access
Artificial intelligence of things at the edge: scalable and efficient distributed learning for massive scenarios
Bano S., Tonellotto N., Cassarà P., Gotta A.
Federated Learning (FL) is a distributed optimization method in which multiple client nodes collaborate to train a machine learning model without sharing data with a central server. However, communication between numerous clients and the central aggregation server to share model parameters can cause several problems, including latency and network congestion. To address these issues, we propose a scalable communication infrastructure based on Information-Centric Networking built and tested on Apache Kafka®. The proposed architecture consists of a two-tier communication model. In the first layer, client updates are cached at the edge between clients and the server, while in the second layer, the server computes global model updates by aggregating the cached models. The data stored in the intermediate nodes at the edge enables reliable and effective data transmission and solves the problem of intermittent connectivity of mobile nodes. While many local model updates provided by clients can result in a more accurate global model in FL, they can also result in massive data traffic that negatively impacts congestion at the edge. For this reason, we couple a client selection procedure based on a congestion control mechanism at the edge for the given architecture of FL. The proposed algorithm selects a subset of clients based on their resources through a time-based backoff system to account for the time-averaged accuracy of FL while limiting the traffic load. Experiments show that our proposed architecture has an improvement of over 40% over the network-centric based FL architecture, i.e., Flower. The architecture also provides scalability and reliability in the case of mobile nodes. It also improves client resource utilization, avoids overflow, and ensures fairness in client selection. The experiments show that the proposed algorithm leads to the desired client selection patterns and is adaptable to changing network environments.Source: COMPUTER COMMUNICATIONS, vol. 205, pp. 45-57
DOI: 10.1016/j.comcom.2023.04.010
DOI: https://doi.org/10.1016/j.comcom.2023.04.010
Project(s): TEACHING
Metrics:
Bano S., Tonellotto N., Cassarà P., Gotta A.
Federated Learning (FL) is a distributed optimization method in which multiple client nodes collaborate to train a machine learning model without sharing data with a central server. However, communication between numerous clients and the central aggregation server to share model parameters can cause several problems, including latency and network congestion. To address these issues, we propose a scalable communication infrastructure based on Information-Centric Networking built and tested on Apache Kafka®. The proposed architecture consists of a two-tier communication model. In the first layer, client updates are cached at the edge between clients and the server, while in the second layer, the server computes global model updates by aggregating the cached models. The data stored in the intermediate nodes at the edge enables reliable and effective data transmission and solves the problem of intermittent connectivity of mobile nodes. While many local model updates provided by clients can result in a more accurate global model in FL, they can also result in massive data traffic that negatively impacts congestion at the edge. For this reason, we couple a client selection procedure based on a congestion control mechanism at the edge for the given architecture of FL. The proposed algorithm selects a subset of clients based on their resources through a time-based backoff system to account for the time-averaged accuracy of FL while limiting the traffic load. Experiments show that our proposed architecture has an improvement of over 40% over the network-centric based FL architecture, i.e., Flower. The architecture also provides scalability and reliability in the case of mobile nodes. It also improves client resource utilization, avoids overflow, and ensures fairness in client selection. The experiments show that the proposed algorithm leads to the desired client selection patterns and is adaptable to changing network environments.Source: COMPUTER COMMUNICATIONS, vol. 205, pp. 45-57
DOI: 10.1016/j.comcom.2023.04.010
DOI: https://doi.org/10.1016/j.comcom.2023.04.010
Project(s): TEACHING
Metrics:
See at:
CNR IRIS | ISTI Repository | Computer Communications | CNR IRIS | CNR IRIS | CNR IRIS
2023
Patent
Restricted
System and method for supporting an operator for navigation
Sebastiani L., Di Summa Maria, Viganò G. P., Sacco M., Cassarà P, Gotta A., Figari M., Martelli M., Zaccone R.
A system (100) for supporting an operator for navigation, comprising: a main shipboard control unit (101); a plurality of sources of information (102) representative of a ship, the plurality of sources of information (102) being operatively connected to the main shipboard control unit (101); at least one user interface (102) operatively connected to the main shipboard control unit (101). The main shipboard control unit (101) comprises: a navigation simulation module (104) configured to determine, based on the information provided by the plurality of sources of information (102), information representative of a simulated anti-collision route of the ship, the navigation simulation module (104) further being configured to determine, based on the information provided by the plurality of sources of information (102), information representative of the status of the ship, the navigation simulation 15 module (104) further being configured to generate a message comprising the information representative of a simulated anti-collision route of the ship and information representative of the status of the ship, said navigation simulation module (104) being configured to provide such message on a data 20 25 30 communication channel; a message routing module (107) operatively connected to the navigation simulation module (104), the message routing module (107) being configured to send such message provided on the data communication channel from the navigation simulation module (104) to a set plurality of recipients interested in receiving messages provided on the data communication channel. The at least one user interface (103) being configured as a recipient interested in receiving a message provided on the data communication channel by the navigation simulation module (104), the at least one user interface (103) being further configured to display a processing of such message overlapping or substituting the real navigation scenario visible by a shipboard bridge operator (OP).
Sebastiani L., Di Summa Maria, Viganò G. P., Sacco M., Cassarà P, Gotta A., Figari M., Martelli M., Zaccone R.
A system (100) for supporting an operator for navigation, comprising: a main shipboard control unit (101); a plurality of sources of information (102) representative of a ship, the plurality of sources of information (102) being operatively connected to the main shipboard control unit (101); at least one user interface (102) operatively connected to the main shipboard control unit (101). The main shipboard control unit (101) comprises: a navigation simulation module (104) configured to determine, based on the information provided by the plurality of sources of information (102), information representative of a simulated anti-collision route of the ship, the navigation simulation module (104) further being configured to determine, based on the information provided by the plurality of sources of information (102), information representative of the status of the ship, the navigation simulation 15 module (104) further being configured to generate a message comprising the information representative of a simulated anti-collision route of the ship and information representative of the status of the ship, said navigation simulation module (104) being configured to provide such message on a data 20 25 30 communication channel; a message routing module (107) operatively connected to the navigation simulation module (104), the message routing module (107) being configured to send such message provided on the data communication channel from the navigation simulation module (104) to a set plurality of recipients interested in receiving messages provided on the data communication channel. The at least one user interface (103) being configured as a recipient interested in receiving a message provided on the data communication channel by the navigation simulation module (104), the at least one user interface (103) being further configured to display a processing of such message overlapping or substituting the real navigation scenario visible by a shipboard bridge operator (OP).
2023
Conference article
Open Access
Traffic scheduling in non-stationary multipath non-terrestrial networks: a reinforcement learning approach
Machumilane A., Gotta A., Cassarà P., Gennaro C., Amato G.
In Non-Terrestrial Networks (NTNs), where LEO satellites and User Equipment (UE) move relative to each other, Line-of-Sight (LOS) tracking, and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous LOS estimation and channel impairment compensation are crucial for a UE to access a satellite and maintain connectivity. In this paper, we propose a Actor-Critic (AC)-Reinforcement Learning (RL) framework for traffic scheduling in NTN scenarios where the channel state is non-stationary due to the variability of LOS, which depends on the current satellite elevation. We deploy the framework as an agent in a Multi-Path Routing (MPR) scheme where the UE can access more than one satellite simultaneously to improve link reliability and throughput. We study how the agent schedules traffic on multiple satellite links by adopting the AC version of RL. The agent continuously trains based on variations in satellite elevation angles, handoffs, and relative LOS probabilities. We compare the agent retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis considering the dense urban area of Chicago, where high-rise buildings significantly affect the LOS. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1 times the satellite visibility time at certain elevations, which guarantees efficient use of satellite visibility.Source: IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, pp. 4094-4099. Rome, Italy, 28/05-01/06/2023
DOI: 10.1109/icc45041.2023.10279788
DOI: 10.5281/zenodo.8430896
DOI: 10.5281/zenodo.8430897
Project(s): TRANTOR, RESTART
Metrics:
Machumilane A., Gotta A., Cassarà P., Gennaro C., Amato G.
In Non-Terrestrial Networks (NTNs), where LEO satellites and User Equipment (UE) move relative to each other, Line-of-Sight (LOS) tracking, and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous LOS estimation and channel impairment compensation are crucial for a UE to access a satellite and maintain connectivity. In this paper, we propose a Actor-Critic (AC)-Reinforcement Learning (RL) framework for traffic scheduling in NTN scenarios where the channel state is non-stationary due to the variability of LOS, which depends on the current satellite elevation. We deploy the framework as an agent in a Multi-Path Routing (MPR) scheme where the UE can access more than one satellite simultaneously to improve link reliability and throughput. We study how the agent schedules traffic on multiple satellite links by adopting the AC version of RL. The agent continuously trains based on variations in satellite elevation angles, handoffs, and relative LOS probabilities. We compare the agent retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis considering the dense urban area of Chicago, where high-rise buildings significantly affect the LOS. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1 times the satellite visibility time at certain elevations, which guarantees efficient use of satellite visibility.Source: IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, pp. 4094-4099. Rome, Italy, 28/05-01/06/2023
DOI: 10.1109/icc45041.2023.10279788
DOI: 10.5281/zenodo.8430896
DOI: 10.5281/zenodo.8430897
Project(s): TRANTOR
, RESTARTMetrics:
See at:
ZENODO | ZENODO | CNR IRIS | ieeexplore.ieee.org | ISTI Repository | doi.org | CNR IRIS | CNR IRIS