Muddassir Sadiq

Academic

Career

Verbatim text

Muddassir Sadiq
Cell: 923027777373 |  Email: mudassirsadiq56@gmail.com
LinkedIn: https://www.linkedin.com/in/muddassir-sadiq/
Address: SHAMAS COLONY HOUSE NO.1 STREET NO.1 NEAR TOWNSATALLITE AHMAD PUR EAST DISTRICT BHAWALPUR , Ahmad pur east ,
Pakistan
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.

AI enrichment