6 citations · 10 across the 5 of their papers we have counts for
5 papers
Bridge to Real Environment with Hardware-in-the-loop for Wireless Artificial Intelligence Paradigms
Jeffrey Redondo, Nauman Aslam, Juan Zhang +1
Nowadays, many machine learning (ML) solutions to improve the wireless standard IEEE802.11p for Vehicular Adhoc Network (VANET) are commonly evaluated in the simulated world. At th…
Optimizing QoS in HD Map Updates: Cross-Layer Multi-Agent with Hierarchical and Independent Learning
Jeffrey Redondo, Nauman Aslam, Juan Zhang +1
The data collected by autonomous vehicle (AV) sensors such as LiDAR and cameras is crucial for creating high-definition (HD) maps to provide higher accuracy and enable a higher lev…
Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network
Jeffrey Redondo, Nauman Aslam, Juan Zhang +1
Reinforcement Learning (RL) algorithms have been used to address the challenging problems in the offloading process of vehicular ad hoc networks (VANET). More recently, they have b…
Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment
Jeffrey Redondo, Zhenhui Yuan, Nauman Aslam +1
One effective way to optimize the offloading process is by minimizing the transmission time. This is particularly true in a Vehicular Adhoc Network (VANET) where vehicles frequentl…
Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning
Jeffrey Redondo, Zhenhui Yuan, Nauman Aslam
High-definition (HD) Map systems will play a pivotal role in advancing autonomous driving to a higher level, thanks to the significant improvement over traditional two-dimensional…