activity
20192022
most citedWi-Fi Rate Adaptation using a Simple Deep Reinforcement Learning Approach

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.NI20222 cited

Wi-Fi Rate Adaptation using a Simple Deep Reinforcement Learning Approach

Ruben Queiros, Eduardo Nuno Almeida, Helder Fontes +2

The increasing complexity of recent Wi-Fi amendments is making optimal Rate Adaptation (RA) a challenge. The use of classic algorithms or heuristic models to address RA is becoming…

cs.NI2020

On the Reproduction of Real Wireless Channel Occupancy in ns-3

Renato Cruz, Helder Fontes, José Ruela +2

In wireless networking R&D we typically depend on simulation and experimentation to evaluate and validate new networking solutions. While simulations allow full control over the sc…

cs.NI2019

A Routing Metric for Inter-flow Interference-aware Flying Multi-hop Networks

André Coelho, Eduardo Nuno Almeida, José Ruela +2

The growing demand for broadband communications anytime, anywhere has paved the way to the usage of Unmanned Aerial Vehicles (UAVs) for providing Internet access in areas without n…

cs.NI2019

A Token-Based MAC Solution for WiLD Point-To-Multipoint Links

Carlos Leocadio, Tiago Oliveira, Pedro Silva +2

The inefficiency of the fundamental access method of the IEEEE 802.11 standard is a well-known problem in scenarios where multiple long range and faulty links compete for the share…

cs.NI2019

Repeatable and Reproducible Wireless Networking Experimentation through Trace-based Simulation

Vitor Lamela, Helder Fontes, Tiago Oliveira +3

To properly validate wireless networking solutions we depend on experimentation. Simulation very often produces less accurate results due to the use of models that are simplificati…