2 citations · 4 across the 14 of their papers we have counts for
4 papers · 1 filter
ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing
Fatemeh Lotfi, Hossein Rajoli, Fatemeh Afghah
Advanced wireless networks must support highly dynamic and heterogeneous service demands. Open Radio Access Network (O-RAN) architecture enables this flexibility by adopting modula…
Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing
Fatemeh Lotfi, Hossein Rajoli, Fatemeh Afghah
Modern wireless networks must adapt to dynamic conditions while efficiently managing diverse service demands. Traditional deep reinforcement learning (DRL) struggles in these envir…
Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning
Fatemeh Lotfi, Fatemeh Afghah
With emerging applications such as autonomous driving, smart cities, and smart factories, network slicing has become an essential component of 5G and beyond networks as a means of…
Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning
Alireza Shamsoshoara, Fatemeh Lotfi, Sajad Mousavi +2
This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environ…