Arooj Fatima

PROFESSIONAL PROFILE Electrical Engineering graduate with hands-on experience in Python, C/C++, machine learning, and data analysis, seeking an industry role in advanced wireless and 6G technologies. My FYP focuses on deep reinforcement learning for 6G-enabled ISAC with Age of Information optimization, and I aim to apply ML, signal processing, and system modeling skills to real-world intelligent communication systems. EDUCATION BE Electrical Engineering School of Electrical Engineering and Computer Science (SEECS) , 3.93 (2026) INTERNSHIP EXPERIENCE Information Processing and Transmission (IPT) Lab 01-Mar-2025 - 24-Jan-2026 • Conducting research aimed at tackling the evolving challenges in next-generation wireless networks. • Exploring and evaluating the feasibility of machine learning, particularly deep reinforcement learning, for optimizing future mobile networks. • Published research papers in IEEE IoT Journal and IEEE Wireless Communication Letters. Adept Tech Solutions 01-Jul-2025 - 31-Aug-2025 • Developed and tested AI-driven solutions for wireless communication applications. • Applied machine learning models to optimize system performance and enhance network efficiency. • Collaborated with cross-functional teams to integrate AI modules into practical communication workflows. Descon Engineering Limited, DEST Division 01-Jul-2025 - 15-Aug-2025 Worked with the Engineering Management team to support planning, coordination, and performance analysis of engineering projects. Performed data analysis and reporting using Microsoft Excel, including data cleaning, trend analysis, and summary dashboards. Developed interactive Power BI dashboards to visualize project metrics, resource utilization, and progress tracking for decision support. Gained practical exposure to engineering workflows, data-driven management, and industrial project environments. FINAL YEAR PROJECT Age-Aware Deep Reinforcement Learning for Resource Allocation in 6G- Enabled IoT networks The emergence of 6G networks is expected to revolutionize the Internet of Things (IoT) landscape by enabling ultra-reliable, low- latency, and intelligent connectivity for massive device deployments. As real-time IoT applications—such as industrial automation, autonomous systems, and remote monitoring—demand timely and energy-efficient data delivery, conventional resource allocation strategies fall short in meeting the stringent performance requirements. In this work, we propose an intelligent, age-aware scheduling framework powered by deep reinforcement learning (DRL) to enhance the freshness of information and optimize resource allocation in 6G- enabled IoT networks. Our approach integrates key enablers such as cognitive radio and non-orthogonal multiple access (CR- NOMA), along with realistic considerations like energy harvesting, queue dynamics, and interference constraints. By leveraging advanced DRL algorithms, we demonstrate significant improvements in system performance with respect to Age of Information (AoI), energy sustainability, and throughput. This research highlights the potential of AI-driven decision-making to unlock scalable, context- aware communication in future-generation IoT infrastructures.

Electrical Engineering graduate with hands-on experience in Python, C/C++, machine learning, and data analysis, seeking an industry role in advanced wireless and 6G technologies. My FYP focuses on deep reinforcement learning for 6G-enabled ISAC with Age of Information optimization, and I aim to apply ML, signal processing, and system modeling skills to real-world intelligent communication systems.