Projects

Main Research Projects

Role: Work Package Leader (WP2: XAI-based long-term evolution of the V2X PHY layer Design).
Abstract: Future networks are expected to be a platform of “connected intelligence “solving human and societal challenges. This concept, referred to as “native artificial intelligence (AI)”, is regarded as one of the pillars of 6G. In this paradigm, intelligence will be integrated at various levels of the communication network infrastructure to enhance performance and meet the requirements, particularly those related to vehicular communications (V2X) such as enhanced reliability and responsiveness. However, the use of AI as black-box models poses significant risks and challenges. It is therefore crucial to understand and be able to trust the decisions made by these models. To address this issue, we plan to develop an explainable AI (XAI) framework that aims to explain the logic behind the black-box model behaviors for applications related to V2X networks. This could be accomplished by leveraging the insights gained from advanced XAI techniques to further refine and optimize the AI-driven solution, such as through enhanced optimization of both data and model components. This strategy will improve the network at various levels PHY layer, Software Defined Networking and Network Function Virtualization, thus ensuring a safe and efficient deployment.
Keywords: Wireless Communications, PHY Layer, AI, XAI, Optimization.
Duration: October 2024 - March 2028.

Role: Research and Development (R&D) Project Manager.
Abstract: This project focuses on leveraging AI to enable SDN network automation, specifically targeting two critical applications: traffic matrix prediction and KPI modeling. The study evaluates the transition from traditional statistical methods to advanced DL models and Graph Neural Networks, demonstrating how these AI-based solutions effectively capture complex, nonlinear behaviors. By optimizing these architectures for real-time responsiveness and structural accuracy, the project provides a robust framework for intelligent resource management and automated decision-making for SDN networks.
Keywords: Software-defined Network (SDN), Network Automation, Traffic Prediction, Network Modelling.
Duration: January 2024 - August 2025.

Role: Postdoctoral Resracher.
Abstract: The ECOTRAIN project aims to develop a system of autonomous, lightweight, battery-powered rail vehicles. The primary objective of this role is to propose and develop Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication solutions. This involves studying potential V2X technologies, modeling Key Performance Indicators (KPIs) tailored to the ECOTRAIN context, and investigating physical-layer enhancements to ensure robust system performance.
Keywords: Autonomus Train, V2X Communivations, Mission-Critical Applications.
Duration: January 2023 - December 2024.

Role: PhD (Jan. 2019 - Dec. 2021), Postdoctoral Researcher (Jan. 2022 - Dec. 2022).
Abstract: The rapid evolution of wireless communications is essential for intelligent transportation, yet the high mobility of vehicular environments creates significant reliability challenges due to time-varying channels and the Doppler effect. Traditional channel estimation methods often struggle in these scenarios, as they are either too computationally complex for real-time use or too simplistic to remain robust. This thesis proposes leveraging Deep Learning (DL) to bridge this gap, offering a data-driven approach that identifies complex patterns without relying on rigid statistical models. By integrating optimized DL architectures, the research demonstrates that these systems can achieve high performance and superior robustness in dynamic environments while maintaining a low-complexity profile suitable for real-time implementation.
Keywords: Artificial Intelligence (AI), Vehicular Communications, Channel Estimation.
Duration: January 2019 - December 2022.

Role: Master’s Thesis (Oct. 2015 - Nov. 2017), Research Assistant (Dec. 2017 - Nov. 2018).
Abstract: The exponential growth of Big Earth Observation Data, driven by advanced satellite instrumentation and real-time streaming, has become a cornerstone for developing sustainable smart cities. Building detection is a fundamental component of this progress, as it allows for the precise updating of Geographic Information Systems (GIS) and more effective urban planning. This project introduces a novel detection approach that utilizes high-resolution satellite imagery and multi-feature analysis—including color, shape, and shadow detection—to accurately identify building units.
Keywords: Remote Sensing, Image Processing, Object Detection, Building Detection.
Duration: October 2015 - November 2018.

Role: Engineering Senior Project (FYP).
Abstract: This project introduces an integrated Home Automation system leveraging NFC (Near Field Communication) and Raspberry Pi architecture to streamline domestic management. By centralizing the control of household appliances and access points—such as lighting and electronic door locks into a singular mobile interface, the system eliminates manual operation in favor of digital oversight. The core of the research focuses on the synergy between the Raspberry Pi’s processing capabilities and NFC’s secure, short-range triggers to create a responsive and user-friendly smart home environment.
Keywords: Home Automation, Control System, Near Field Communications (NFC), Raspberry Pi.
Duration: March 2015 - August 2015.