messaoud ahmed ouameur, Université du Québec à Trois-Rivières

Research

Approximate computing Communication theory Deep learning Explainable AI (XAI)Hardware implementation Machine learning Meta learning Optimization methods

Latest papers

Channel modeling and capacity optimization for optical RIS aided NOMA in indoor multiuser visible light communication IoT systems.
Scientific reports · 2025 · senior author

All 1 publications

Latest funding

Méthodes et technologies émergentes de télécommunications 5G/6G pour les systèmes énergétiques
NSERC · 2024 · Co-investigator
$130,000
Méthodes et architectures alternatives pour la simulation temps réel des réseaux électriques modernes
NSERC · 2023 · Co-investigator
$130,000
Explainable Machine learning models for AI native radio access technologies (XAI-RAT)
NSERC · 2022 · Principal investigator
$99,000

All 5 grants and awards

1 publications.

01

Channel modeling and capacity optimization for optical RIS aided NOMA in indoor multiuser visible light communication IoT systems.

El Jbari M, Moussaoui M, Augusto Pereira de Figueiredo F, Ahmed Ouameur M

Scientific reports2025Senior author

DOI: 10.1038/s41598-025-26674-x PMID: 41310042

Méthodes et technologies émergentes de télécommunications 5G/6G pour les systèmes énergétiques

Co-investigatorNSERC2024Alliance Grants

Principal investigators: Massicotte, Daniel D

Keywords: 5g/6g; communication sémantique; intelligence artificielle; jumeau numérique temps réel; network function virtualization; réseau d'accès radio ouvert; réseau électrique intelligent; software define radio/network; surfaces intelligentes reconfig.; time sensitive network

$130,000

Méthodes et architectures alternatives pour la simulation temps réel des réseaux électriques modernes

Co-investigatorNSERC2023Alliance Grants

Principal investigators: Massicotte, Daniel D

Keywords: calculateurs quantiques (qpu); calcul haute performance; hardware in the loop; implémentation cpu/multicoeur; implémentation fpga; implémentation gpu; jumeaux numériques; outil de synthèse parallèle; réseaux électriques; simulateur temps réel

$130,000

Explainable Machine learning models for AI native radio access technologies (XAI-RAT)

Principal investigatorNSERC2022Discovery Grants Program - Individual

Principal investigators: Ahmed Ouameur, Messaoud

Keywords: communication theory; physical and medium access control layers; machine learning; deep learning; meta learning; explainable ai (xai); signal processing; optimization methods; approximate computing; hardware implementation

$99,000

Explainable Machine learning models for AI native radio access technologies (XAI-RAT)

Principal investigatorNSERC2022Discovery Launch Supplement

Principal investigators: ahmed ouameur, messaoud

$12,500

Méthodes d'amélioration de l'écoute sonore dans les systèmes audio haute performance

Co-investigatorNSERC2018Collaborative Research and Development Grants

Principal investigators: Massicotte, Daniel D

$520,000

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

Write to this professor

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Applying for

Mention their NSERC grant from 2022

Subject: Prospective PhD student: Approximate computing Dear Professor ahmed ouameur, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. I read your paper "Channel modeling and capacity optimization for optical RIS aided NOMA in indoor multiuser visible light communication IoT systems." (2025), and it connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your NSERC-funded project "Explainable Machine learning models for AI native radio access technologies (XAI-RAT)" (2022), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]