Hamza Irshad Bhatti

PROFESSIONAL PROFILE Motivated Electrical Engineering undergraduate at the National University of Sciences and Technology (NUST) with strong academic performance and hands-on experience in wireless communication, computer networks, and next-generation 5G/6G technologies. Experienced in research-driven environments, with practical exposure to non-orthogonal multiple access (NOMA), signal interference cancellation, deep reinforcement learning for wireless systems, and software-defined networking. Skilled in C/C++, Python, MATLAB, NS2, Mininet, and network simulation and modeling tools. Demonstrated ability to design and implement innovative engineering solutions through academic and research projects, complemented by proven leadership, teamwork, and communication skills. Aspires to contribute to advanced research and industry development in wireless communications and intelligent networking systems. EDUCATION Bachelors in Electrical Engineering School of Electrical Engineering & Computer Science (SEECS) , Islamabad , 3.43/4.00 (2026) INTERNSHIP EXPERIENCE Information Processing & Transmission (IPT) Lab, SEECS 20-Jun-2025 - 19-Apr-2026 I conducted research on NOMA-based Backscatter Communication (BackCom) to enhance network performance. In addition I worked on improving Signal Interference Cancellation (SIC) and Channel State Information (CSI) through Deep Reinforcement Learning (DRL) algorithms for robust and efficient wireless systems and publised a research paper in IEEE WCNC conference 2026. Optical Networks and Technologies (ONT) Lab, SEECS 19-Jul-2025 - 18-Sep-2025 I worked on designing and testing Layer-3 network solutions using Mininet. I participated in hands-on projects and on advanced networking protocols and topologies. I explored emerging networking technologies and gained practical insights into the academic research process. XFlow Research Inc. 21-Jun-2025 - 01-Jan-2026 I contributed to next-generation 5G/6G technology development, focusing on network optimization and innovation. I actively assisted in projects involving SONiC Network Operating System (NOS) and advanced software-defined networking (SDN) concepts. I acquired deep understanding of future mobile network architectures and industry research practices. FINAL YEAR PROJECT Reinforcement Learning-based Performance Optimization of BackScatter Systems with Practical Constraints This project focuses on optimizing the uplink performance of a two-user NOMA-assisted backscatter communication (BackCom) system for IoT networks. It studies how practical system impairments, specifically imperfect successive interference cancellation (SIC) and inaccurate channel state information (CSI), affect system performance. The project formulates a sum-rate maximization problem under quality-of-service and fairness constraints and uses a Deep Deterministic Policy Gradient (DDPG)–based deep reinforcement learning algorithm to learn an adaptive power-allocation policy. The performance of the proposed learning-based approach is compared with exhaustive search methods under both perfect and imperfect SIC and CSI conditions to evaluate robustness, achievable sum rate, and computational complexity.

Motivated Electrical Engineering undergraduate at the National University of Sciences and Technology (NUST) with strong academic performance and hands-on experience in wireless communication, computer networks, and next-generation 5G/6G technologies. Experienced in research-driven environments, with practical exposure to non-orthogonal multiple access (NOMA), signal interference cancellation, deep reinforcement learning for wireless systems, and software-defined networking. Skilled in C/C++, Python, MATLAB, NS2, Mininet, and network simulation and modeling tools. Demonstrated ability to design and implement innovative engineering solutions through academic and research projects, complemented by proven leadership, teamwork, and communication skills. Aspires to contribute to advanced research and industry development in wireless communications and intelligent networking systems.