Tauseef Mushtaq

Active researcher in Telecommunication Engineering, working on cross-layer design of Physical and MAC layers to improve reliability and connectivity of dense networks. I have researched energy-constrained and low-maintenance sensor networks for massive IoT connectivity and designed optimal solutions using Reinforcement Learning-based algorithms such as Q-Learning, DQN, and PPO. I am also actively working on Quantum Internet, where the aim is to design Reinforcement Learning solutions for global quantum inte

• Developed 5 optimized UWB localization protocols (E80p, E80p+o, IPRF+o, I/CS+o, IPRF/CS+o) for AGV–TAG systems • Achieved ~25% increase in localization update frequency (up to ~8 Hz) vs baseline • Enabled scalable localization for 30–40 TAGs with >5 Hz performance • Implemented collision-aware and carrier-sense (CSMA/CA) mechanisms to reduce contention • Built MATLAB simulation framework for multi-agent wireless localization analysis

• Subjects: Digital Logic Designs, Computer Communication and Networks, Introduction to Computing, Assembly Language and Microprocessing. • Memeber of Departmental Exam Committee, Admission Committee and Sports Committee.

Designed physics-accurate simulators and developed Markov Decision Process models for optimal resource allocation problems for two distinct network domains. For Free-Space Quantum Networks assisted by LEO satellites. The goal was to develop a PPO-based entanglement scheduling framework focused on e