Roberto Rigolin F. Lopes
YOU?
Author Swipe
View article: GNN-based Deep Reinforcement Learning with Adversarial Training for Robust Optimization of Modern Tactical Communication Systems
GNN-based Deep Reinforcement Learning with Adversarial Training for Robust Optimization of Modern Tactical Communication Systems Open
This paper investigates the feasibility of a Graph Neural Network (GNN)-based Deep Reinforcement Learning (DRL) for tackling complex optimization problems in modern communication systems deployed to tactical networks. Our methodology consi…
View article: GNN-based Deep Reinforcement Learning with Adversarial Training for Robust Optimization of Modern Tactical Communication Systems
GNN-based Deep Reinforcement Learning with Adversarial Training for Robust Optimization of Modern Tactical Communication Systems Open
This paper investigates the feasibility of a Graph Neural Network (GNN)-based Deep Reinforcement Learning (DRL) for tackling complex optimization problems in modern communication systems deployed to tactical networks. Our methodology consi…
View article: DataFITS: A Heterogeneous Data Fusion Framework for Traffic and Incident Prediction
DataFITS: A Heterogeneous Data Fusion Framework for Traffic and Incident Prediction Open
11466
View article: Adversarial Attacks Against Reinforcement Learning Based Tactical Networks: A Case Study
Adversarial Attacks Against Reinforcement Learning Based Tactical Networks: A Case Study Open
986
View article: A Bayesian Inference Model for Dynamic Neighbor Discovery in Tactical Networks
A Bayesian Inference Model for Dynamic Neighbor Discovery in Tactical Networks Open
28
View article: Maximizing the Probability of Message Delivery Over Ever-Changing Communication Scenarios in Tactical Networks
Maximizing the Probability of Message Delivery Over Ever-Changing Communication Scenarios in Tactical Networks Open
This letter introduces a stochastic model to maximize the probability of message delivery over ever-changing communication scenarios in tactical networks. Our model improves modern tactical systems implementing store-and-forward mechanisms…
View article: TNT: A Tactical Network Test platform to evaluate military systems over ever-changing scenarios
TNT: A Tactical Network Test platform to evaluate military systems over ever-changing scenarios Open
This paper addresses the challenge of testing military systems and applications over different communication scenarios with both network conditions and user data flows changing independently. We assume that systems developed to handle ever…
View article: Maximizing the Probability of Message Delivery over Ever-changing Communication Scenarios in Tactical Networks
Maximizing the Probability of Message Delivery over Ever-changing Communication Scenarios in Tactical Networks Open
This letter introduces a stochastic model to maximize the probability of message delivery over ever-changing communication scenarios in tactical networks. Our model improves modern tactical systems implementing store-and-forward mechanisms…
View article: Maximizing the Probability of Message Delivery over Ever-changing Communication Scenarios in Tactical Networks
Maximizing the Probability of Message Delivery over Ever-changing Communication Scenarios in Tactical Networks Open
This letter introduces a stochastic model to maximize the probability of message delivery over ever-changing communication scenarios in tactical networks. Our model improves modern tactical systems implementing store-and-forward mechanisms…
View article: Queuing over Ever-changing Communication Scenarios in Tactical Networks
Queuing over Ever-changing Communication Scenarios in Tactical Networks Open
This paper introduces a hierarchy of queues complementing each other to handle ever-changing communication scenarios in tactical networks. The first queue stores the QoS-constrained messages from command and control systems. These messages…
View article: Queuing over Ever-changing Communication Scenarios in Tactical Networks
Queuing over Ever-changing Communication Scenarios in Tactical Networks Open
This paper introduces a hierarchy of queues complementing each other to handle ever-changing communication scenarios in tactical networks. The first queue stores the QoS-constrained messages from command and control systems. These messages…
View article: Quantizing Radio Link Data Rates to Create Ever-changing Network Conditions in Tactical Networks
Quantizing Radio Link Data Rates to Create Ever-changing Network Conditions in Tactical Networks Open
Several sources of randomness can change the radio link data rate at the edge of tactical networks. Simulations and field experiments define these sources of randomness indirectly by choosing the mobility pattern, communication technology,…
View article: Quantizing Radio Link Data Rates to Create Ever-changing Network Conditions in Tactical Networks
Quantizing Radio Link Data Rates to Create Ever-changing Network Conditions in Tactical Networks Open
Several sources of randomness can change the radio link data rate at the edge of tactical networks. Simulations and field experiments define these sources of randomness indirectly by choosing the mobility pattern, communication technology,…
View article: Queuing Over Ever-Changing Communication Scenarios in Tactical Networks
Queuing Over Ever-Changing Communication Scenarios in Tactical Networks Open
This paper introduces a hierarchy of queues complementing each other to handle ever-changing communication scenarios in tactical networks. The first queue stores the QoS-constrained messages from command and control systems. These messages…
View article: Road Data Enrichment Framework Based on Heterogeneous Data Fusion for ITS
Road Data Enrichment Framework Based on Heterogeneous Data Fusion for ITS Open
In this work, we propose the Road Data Enrichment (RoDE), a framework that fuses data from heterogeneous data sources to enhance Intelligent Transportation System (ITS) services, such as vehicle routing and traffic event detection. We desc…
View article: Quantizing Radio Link Data Rates to Create Ever-Changing Network Conditions in Tactical Networks
Quantizing Radio Link Data Rates to Create Ever-Changing Network Conditions in Tactical Networks Open
S.188015-188035