DÜXAR researches 6G communications, AI-driven networking, and autonomous UAV swarms — turning advanced theory into systems that fly, connect, and save lives. Directed by Mohammed Anis Oukebdane.
Lab Director · Ph.D. Researcher · Lecturer at Biruni University
Mohammed Anis Oukebdane is a Ph.D. researcher and lecturer bridging the gap between advanced academic theory and high-impact commercial solutions. He directs the lab at Biruni University, where he lectures in the Electrical & Electronics Engineering department, while advancing 6G and UAV research with Yıldız Technical University and TÜBİTAK. His work sits at a rare convergence of deep technical expertise — AI, native Android, and network engineering — and proven business acumen gained as a national sales manager. He has led finalist teams at TEKNOFEST, filed two national patents, and shipped freelance applications in healthcare and logistics, driven to engineer systems that are technically innovative, commercially viable, and user-centric.
Ph.D. in Electronics & Communication Engineering (in progress)
Yıldız Technical University · Istanbul, Türkiye
2023 – Present · GPA 3.64 / 4
M.Sc. in Networks & Telecommunication Engineering
University of Mustapha Stambouli · Mascara, Algeria
2018 – 2020 · GPA 3.52 / 4
B.Sc. in Telecommunications
University of Mustapha Stambouli · Mascara, Algeria
2015 – 2018 · GPA 3.39 / 4
IEEE Access
Proposes a hybrid deep-learning model that forecasts 6G network traffic and drives proactive wireless resource optimization, improving spectral efficiency and QoS under highly dynamic loads.
ICCK Transactions on Mobile and Wireless Intelligence
Surveys how computer vision can augment 6G networks — from vision-aided beam management to sensing-communication convergence — and maps the open technical challenges.
IEEE Access
A comprehensive study of machine learning across the 6G stack, identifying where ML delivers measurable gains, where it struggles, and the research directions that matter most.
9th International Symposium on Innovative Approaches in Smart Technologies
Presents a cluster-based UAV swarm network architecture that restores connectivity in disaster zones, with a working implementation and field-oriented evaluation.
TÜRKPATENT — Patent Application 2025/008721
Patent: a MAC-layer channel access method that dynamically optimizes the contention window for flying ad-hoc networks (FANETs), reducing collisions in dense UAV swarms. (Original title: "FANET'ler için Dinamik Çekişme Penceresi Optimizasyonuna Dayalı Bir Kanal Erişim Yöntemi ve Sistemi".)
TÜRKPATENT — Patent Application 2025/010437
Patent: an AI-driven system in which unmanned aerial vehicles establish emergency communication links and coordinate rescue operations during disasters. (Original title: "Yapay Zeka Kullanılarak Afetlerde İnsansız Hava Araçlarıyla Acil Durum Haberleşme Sistemi ve Kurtarma Operasyonu".)
Algerian Journal of Health Sciences
Validates the basketball throw test as an objective, reliable field measure of upper-body muscular strength for health-related fitness assessment.
Computational Intelligence Techniques for Combating COVID-19
Details the COVIDz system: convolutional neural networks trained on chest X-ray imagery to detect COVID-19, with an analysis of accuracy, robustness, and deployment constraints.
Journal of Networks & Telecommunications
Evaluates candidate architectures for a terabit-class networking chip, benchmarking configuration trade-offs in throughput, latency, and power.
International Journal of Mobile Computing
Describes an Android e-blood-bank platform that matches donors with recipients in real time, covering system design, matching logic, and adoption results.
Medical Imaging and AI Journal
A web-deployed deep-learning predictor that screens chest X-rays for COVID-19, making the COVIDz model accessible to clinicians through the browser.
Journal of Healthcare Informatics
Introduces iMED, a ubiquitous healthcare platform supporting chronic patients with continuous monitoring, prediction, and diagnosis-support features.
IEEE Healthcom Proceedings
Early results on applying deep learning to coronavirus disease detection from medical imaging, presented at IEEE Healthcom 2020.
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The lab's flagship program: cooperative communication software that automates UAV swarms for coordinated task execution. Produced multiple Q1 journal papers, two national patent filings, and back-to-back TEKNOFEST finalist runs (2024, 2025 — Top 1%).
A patented MAC-layer channel access method that adapts the contention window in real time for flying ad-hoc networks, cutting collision rates in dense drone swarms. Filed with TÜRKPATENT (2025/008721).
Hybrid deep-learning models that forecast 6G traffic and proactively allocate wireless resources — published in IEEE Access and feeding directly into the lab's 6G research agenda.
AI-coordinated UAV network that restores communications and supports rescue operations in disaster zones. Winner of the Special Jury Prize at the 2025 Disaster Technologies Summit; patented (TÜRKPATENT 2025/010437).
Web-based predictor that screens chest X-rays for coronavirus disease with convolutional neural networks. Published across three venues, including IEEE Healthcom.
A healthcare platform for chronic patients combining continuous monitoring with prediction and diagnosis-support features, published in the Journal of Healthcare Informatics.
An Android application connecting blood donors with recipients and hospitals in real time — published in the International Journal of Mobile Computing.
A full food-delivery ecosystem: three user-facing Android apps plus an admin panel, delivered end-to-end for a commercial client.
No projects with this status.
Fluid antenna multiple access (FAMA) and reconfigurable intelligent surfaces for 6G
Deep reinforcement learning for UAV swarm channel access and FANET MAC optimization
Semantic communications and 6G network traffic prediction
Computer-vision-aided beam management in mmWave and THz bands
Cluster-based UAV networks for emergency and disaster communications
Embedded systems and ROS/Gazebo simulation tooling for the swarm testbed