Muhammad Sufyan Haider

PROFESSIONAL PROFILE An accomplished Electrical Engineering student with a top-ranking academic record and hands-on experience in developing and optimizing next-generation telecom systems. Skilled in implementing cutting-edge solutions for 5G/6G networks, IoT, and wireless communication systems, with a strong focus on practical, real-world applications. Experienced in using deep reinforcement learning (DRL) to enhance network efficiency, optimize resource allocation, and improve security in dynamic communication environments. Proficient in Python, C/C++, and MATLAB for building and deploying scalable solutions that improve network performance, throughput, and reliability. Adept at using simulation tools to model and analyze network architectures, ensuring seamless integration of new technologies like AI-driven optimization and CR-NOMA. Proven track record in collaborating on large-scale, NATO-funded projects and developing high-impact solutions for real-world telecom challenges. Ready to bring technical expertise and innovative solutions to accelerate the success of telecom networks. EDUCATION Electrical Engineering SEECS , Islamabad , 3.94 (2026) INTERNSHIP EXPERIENCE SHAILLA Research Project 16-Aug-2025 - 16-Dec-2025 Engaged in a three-year NATO-funded collaboration between Gebze Technnical University, Istanbul Medipol University, Polytechnique Montreal and NUST. • Investigating vulnerabilities of HAPS links to eavesdropping and other physical-layer security threats. • Developing adaptive attack models and AI/ML-based countermeasures for enhancing secrecy performance. Information processing and Transmission Lab 15-Mar-2025 - 19-Jan-2026 Pioneering novel network architectures and analyzing their performance using simulations and analytical techniques. • 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. Optical Networks and Transmission Lab 01-Jun-2025 - 30-Aug-2025 Investigated the O-RAN architecture to understand network design for next-generation wireless systems. • Developed synthetic RF datasets for training RAN Intelligent Controllers (RICs) using GANs. • Enhanced training data diversity, improving robustness of AI- driven RAN optimization tasks. Digital Empowerment Network 01-Jul-2024 - 01-Oct-2024 Built ML models to accurately predict customer purchasing behavior and improve sales forecasting. • Analyzed customer segmentation datasets to effectively uncover trends and support data-driven decisions. • Developed a targeted marketing strategy using insights from data analysis to significantly boost customer engagement. 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-

An accomplished Electrical Engineering student with a top-ranking academic record and hands-on experience in developing and optimizing next-generation telecom systems. Skilled in implementing cutting-edge solutions for 5G/6G networks, IoT, and wireless communication systems, with a strong focus on practical, real-world applications. Experienced in using deep reinforcement learning (DRL) to enhance network efficiency, optimize resource allocation, and improve security in dynamic communication environments. Proficient in Python, C/C++, and MATLAB for building and deploying scalable solutions that improve network performance, throughput, and reliability. Adept at using simulation tools to model and analyze network architectures, ensuring seamless integration of new technologies like AI-driven optimization and CR-NOMA. Proven track record in collaborating on large-scale, NATO-funded projects and developing high-impact solutions for real-world telecom challenges. Ready to bring technical expertise and innovative solutions to accelerate the success of telecom networks.