ICCK

Mohammed Anis OUKEBDANE

Yildiz Technical University

Section 01

Academic Profile

Working as a TÜBİTAK fellow and a PhD researcher in Telecommunications at Yıldız Technical University, I am actively contributing to create the future of wireless systems, artificial intelligence-powered UAV networks, and mobile invention. Strong foundation in computer networking, embedded systems, and Android programming, I concentrate in developing end-to-end solutions linking, empowering, and transforming. From national sales leadership in the IT sector to practical engineering of AI-driven systems for disaster response, smart health, and 6G network optimisation, my career covers Along with publishing peer-reviewed research on deep learning, terabit processors, and mobile health in top-tier publications including IEEE and Springer, I have produced over thirty digital products including award-nominated platforms for education, logistics, fitness, and crisis intervention. Leading developer for several TEKNOFEST and academic projects, I reconstruct connection in disaster zones using UAV-based ad hoc networks (FANETs), therefore tying innovation with useful applications. Although I still speak five languages and participate regularly in cross-cultural initiatives, my technology stack consists of Python, Kotlin, Flutter, Flask, MySQL, and CCNA systems. In essence, I see technology as way of building systems that matter, scale, and serve. Currently focused on pioneering AI-integrated 6G designs, edge-intelligent UAV systems, and mobile-first tools for emerging markets, I have an eye on driving the next wave of worldwide connectivity and digital resilience.

Section 02

Editorial Roles

This user currently does not serve as an editor for any ICCK journals.

Section 03

ICCK Publications

Free Access | Research Article | 21 March 2026
A Decentralised Multi-Agent DRL-based Approach for Pedestrian and Vehicle Traffic Signals Controlling Systems Optimisation
ICCK Transactions on Mobile and Wireless Intelligence | Volume 2, Issue 1: 31-43, 2026 | DOI: 10.62762/TMWI.2025.878487
Abstract
Urban traffic congestion is a major issue that negatively affects mobility efficiency, environmental sustainability and road safety. Many recent methods for controlling traffic signals have used methods based on deep reinforcement learning (DRL) and provided positive results. However, it focused primarily on vehicle flow and have not taken into account pedestrian dynamics due to inherent difficulty related to accurately sensing all pedestrians. As a result of these limitations, recent advances in sixth-generation (6G) localisation technology will provide new opportunities to provide precise, low-latency tracking of pedestrians at signalized intersections, allowing for improved control of ped... More >

Graphical Abstract
A Decentralised Multi-Agent DRL-based Approach for Pedestrian and Vehicle Traffic Signals Controlling Systems Optimisation
Free Access | Review Article | 28 July 2025
Computer Vision-Powered 6G Networks: Technologies, Applications, and Challenges
ICCK Transactions on Mobile and Wireless Intelligence | Volume 1, Issue 1: 19-31, 2025 | DOI: 10.62762/TMWI.2025.159776
Abstract
Aiming to move from conventional throughput-centric paradigms to intelligent, context-aware systems able of perception and autonomous decision-making, sixth-generation (6G) wireless networks is seeking. Driven by recent developments in deep learning and edge artificial intelligence, computer vision (CV) proves to be a key enabler for such perceptive 6G systems. This paper offers a thorough overview bringing together the scattered terrain of CV-enabled 6G technologies. It benchmarks current models against major 6G performance criteria, evaluates architectural paradigms including federated and split learning, and presents a disciplined taxonomy of use cases. This study also notes the possibili... More >

Graphical Abstract
Computer Vision-Powered 6G Networks: Technologies, Applications, and Challenges