Search Results for author:
Found 6 papers, 2 papers with code
Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients
However, the location-dependent performance, in terms of transmission rates and susceptibility to transmission errors, poses major challenges for wireless FL's convergence speed and accuracy.
Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach
To this end, we propose and design a hybrid TL-aided approach that leverages the advantages of both policy reuse and distillation TL methods to provide safe and accelerated convergence in DRL-based O-RAN slicing.
How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing?
RAN slicing, a critical component of the O-RAN paradigm, enables network resources to be allocated based on the needs of immersive services, creating multiple virtual networks on a single physical infrastructure.
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks
Nevertheless, several challenges hinder the practical adoption of DRL in commercial networks.
QoS-SLA-Aware Adaptive Genetic Algorithm for Multi-Request Offloading in Integrated Edge-Cloud Computing in Internet of Vehicles
This paper proposes a novel Artificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA) to optimize the application's execution time for multi-request offloading in a heterogeneous edge-cloud computing system, which considers the impact of processing multi-requests overlapping and dynamic vehicle speed.
iDriveSense: Dynamic Route Planning Involving Roads Quality Information
Consequently, road segments assessments are conducted using fuzzy system models based on aspects such as the number of anomalies and their severity levels in each road segment.
