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Sobre

Sobre

Rúben Queirós concluiu em 2020 o Mestrado em Engenharia Electrotécnica e de Computadores na Faculdade de Engenharia da Universidade do Porto, Portugal. Atualmente é doutorando no Programa Doutoral de Engenharia Eletrotécnica e de Computadores, na mesma instituição. É Investigador Auxiliar no INESC TEC desde 2020, na área de Redes Sem Fios (WiN). Participou no projeto SMART open call e no projeto de investigação da UE InterConnect. Os seus interesses de investigação incluem Wi-Fi, Adaptação de Débito, Reinforcement Learning e Redes Voadoras.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Rúben Miguel Queirós
  • Cargo

    Assistente de Investigação
  • Desde

    21 fevereiro 2020
001
Publicações

2023

On the Analysis of Computational Delays in Reinforcement Learning-Based Rate Adaptation Algorithms

Autores
Trancoso, R; Pinto, J; Queirós, R; Fontes, H; Campos, R;

Publicação
Simulation Tools and Techniques - 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings

Abstract
Several research works have applied Reinforcement Learning (RL) algorithms to solve the Rate Adaptation (RA) problem in Wi-Fi networks. The dynamic nature of the radio link requires the algorithms to be responsive to changes in link quality. Delays in the execution of the algorithm due to implementional details may be detrimental to its performance, which in turn may decrease network performance. These delays can be avoided to a certain extent. However, this aspect has been overlooked in the state of the art when using simulated environments, since the computational delays are not considered. In this paper, we present an analysis of computational delays and their impact on the performance of RL-based RA algorithms, and propose a methodology to incorporate the experimental computational delays of the algorithms from running in a specific target hardware, in a simulation environment. Our simulation results considering the real computational delays showed that these delays do, in fact, degrade the algorithm’s execution and training capabilities which, in the end, has a negative impact on network performance. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

2023

Rate Adaptation Aware Positioning for Flying Gateways Using Reinforcement Learning

Autores
Pantaleão, G; Queirós, R; Fontes, H; Campos, R;

Publicação
Simulation Tools and Techniques - 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings

Abstract
With the growing connectivity demands, Unmanned Aerial Vehicles (UAVs) have emerged as a prominent component in the deployment of Next Generation On-demand Wireless Networks. However, current UAV positioning solutions typically neglect the impact of Rate Adaptation (RA) algorithms or simplify its effect by considering ideal and non-implementable RA algorithms. This work proposes the Rate Adaptation aware RL-based Flying Gateway Positioning (RARL) algorithm, a positioning method for Flying Gateways that applies Deep Q-Learning, accounting for the dynamic data rate imposed by the underlying RA algorithm. The RARL algorithm aims to maximize the throughput of the flying wireless links serving one or more Flying Access Points, which in turn serve ground terminals. The performance evaluation of the RARL algorithm demonstrates that it is capable of taking into account the effect of the underlying RA algorithm and achieve the maximum throughput in all analysed static and mobile scenarios. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

2023

RateRL: A Framework for Developing RL-Based Rate Adaptation Algorithms in ns-3

Autores
Queirós, R; Ferreira, L; Fontes, H; Campos, R;

Publicação
Simulation Tools and Techniques - 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings

Abstract

2023

Trajectory-Aware Rate Adaptation for Flying Networks

Autores
Queirós, R; Ruela, J; Fontes, H; Campos, R;

Publicação
Simulation Tools and Techniques - 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings

Abstract

2022

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

Autores
Queiros, R; Almeida, EN; Fontes, H; Ruela, J; Campos, R;

Publicação
2022 27TH IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (IEEE ISCC 2022)

Abstract
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 unfeasible due to the large combination of configuration parameters along with the variability of the wireless channel. We propose a simple Deep Reinforcement Learning approach for the automatic RA in Wi-Fi networks, named Data-driven Algorithm for Rate Adaptation (DARA). DARA is standard-compliant. It dynamically adjusts the Wi-Fi Modulation and Coding Scheme (MCS) solely based on the observation of the Signal-to-Noise Ratio (SNR) of the received frames at the transmitter. Our simulation results show that DARA achieves higher throughput when compared with Minstrel High Throughput (HT)

Teses
supervisionadas

2022

On the Performance Impact of Computational Delays of RL-Based Networking Algorithms through Improved ns-3 Digital Twins

Autor
João Paulo Ferreira Pinto

Instituição
UP-FEUP

2018

Metodologias e técnicas de avaliação das NE de acessibilidade em procedimentos de compras públicas de produtos e serviços de TIC

Autor
Márcio Ricardo Alves Martins

Instituição
UTAD

2018

Applications to dynamical systems to immunology and to random exchange economies

Autor
Aliyu Yusuf Ahmad

Instituição
UP-FCUP

2018

A Decision Support System for Investments in Public Transport Infrastructure

Autor
Marcos Paulo Schlickmann

Instituição
UP-FEUP

2017

Software Engineering for Healthcare IoT Ecosystems

Autor
Pedro Martins Pontes

Instituição
UP-FEUP