1 citations · 1 across the 6 of their papers we have counts for
7 papers
Learning Compact Terrain-Context Representations for Feasibility-Aware Offline Reinforcement Learning in UAV Relaying Networks
Joseanne Viana, Viswak R Balaji, Boris Galkin +2
Offline reinforcement learning (RL) is an attractive tool for unmanned aerial vehicle (UAV) systems, where online exploration is costly and raises safety concerns. In terrain-aware…
Securing 5G and Beyond-Enabled UAV Networks: Resilience Through Multiagent Learning and Transformers Detection
Joseanne Viana, Hamed Farkhari, Victor P Gil Jimenez
Achieving resilience remains a significant challenge for Unmanned Aerial Vehicle (UAV) communications in 5G and 6G networks. Although UAVs benefit from superior positioning capabil…
Enhancing UAV Path Planning Efficiency Through Accelerated Learning
Joseanne Viana, Boris Galkin, Lester Ho +1
Unmanned Aerial Vehicles (UAVs) are increasingly essential in various fields such as surveillance, reconnaissance, and telecommunications. This study aims to develop a learning alg…
PCA-Featured Transformer for Jamming Detection in 5G UAV Networks
Joseanne Viana, Hamed Farkhari, Pedro Sebastiao +1
Unmanned Aerial Vehicles (UAVs) face significant security risks from jamming attacks, which can compromise network functionality. Traditional detection methods often fall short whe…
A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters
Joseanne Viana, Hamed Farkhari, Pedro Sebastiao +4
Synthetic datasets are beneficial for machine learning researchers due to the possibility of experimenting with new strategies and algorithms in the training and testing phases. Th…
Accurate and Reliable Methods for 5G UAV Jamming Identification With Calibrated Uncertainty
Hamed Farkhari, Joseanne Viana, Pedro Sebastiao +4
Only increasing accuracy without considering uncertainty may negatively impact Deep Neural Network (DNN) decision-making and decrease its reliability. This paper proposes five comb…