Muddassir Sadiq

PROFESSIONAL PROFILE Keen to learn and grow as a wireless communications researcher with strong expertise in deep learning–enabled next-generation networks, including 6G, ISAC, CR-NOMA, MIMO, and intelligent reflecting surfaces. Experienced in applying deep reinforcement learning to resource management, scheduling, and security-aware optimization, with hands-on research contributions published in leading IEEE journals. Demonstrated ability to conduct analytical modeling, simulations, and system-level performance evaluation for IoT, HAPS, and RIS-assisted networks. Motivated to contribute to cutting-edge research and collaborative teams addressing latency, security, and efficiency challenges in future wireless communication systems. EDUCATION BE Electrical Engineering School of Electrical Engineering and Computer Science , 3.7 (2026) INTERNSHIP EXPERIENCE Information Processing and Transmission (IPT) Lab 01-Mar-2025 - 23-Jan-2026 Conducting research on next-generation wireless network architectures through analytical modeling and simulations. Actively exploring deep reinforcement learning techniques for AI-native network optimization, with multiple works submitted to top-tier wireless communication journals. Adept Tech Solutions 30-Apr-2025 - 31-Jul-2025 Developed and evaluated AI-driven solutions for wireless communication applications. Applied machine learning models to optimize system performance and collaborated with multidisciplinary teams to integrate AI modules into practical communication workflows. Water and Power Development Authority (WAPDA) 01-Jul-2023 - 31-Aug-2023 Gained hands-on exposure to power generation, transmission systems, and national grid operations. Assisted in testing electrical equipment and observed grid stability and safety practices. 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.

Keen to learn and grow as a wireless communications researcher with strong expertise in deep learning–enabled next-generation networks, including 6G, ISAC, CR-NOMA, MIMO, and intelligent reflecting surfaces. Experienced in applying deep reinforcement learning to resource management, scheduling, and security-aware optimization, with hands-on research contributions published in leading IEEE journals. Demonstrated ability to conduct analytical modeling, simulations, and system-level performance evaluation for IoT, HAPS, and RIS-assisted networks. Motivated to contribute to cutting-edge research and collaborative teams addressing latency, security, and efficiency challenges in future wireless communication systems.