Machine Learning, Signal Processing, and Digital Twins for Massive MIMO, 5G, and 6G networks
View My ResearchI am Ryan M. Dreifuerst. My core focus lies at the intersection of signal processing and machine learning for next-generation wireless communication networks. I am passionate about solving physical layer challenges for 5G and 6G systems through domain-specific optimization.
I successfully defended my PhD at North Carolina State University on March 20th 2024. This marked the culmination of five years of research working alongside Prof. Robert W. Heath Jr. across both the University of Texas at Austin and NC State.
I am beginning my post graduate professional journey at MITRE.
Advisor: Prof. Robert W. Heath Jr.
Thesis: Machine-learning based codebook design for beam management
Advisor: Prof. Robert W. Heath Jr.
Thesis: Low resolution sinusoid decomposition and estimation
GPA: 4.0
GPA: 4.0
Innovating beamforming and codebook design to enable larger array sizes and optimize extreme MIMO for site-specific sub-6 GHz and mmWave environments.
Leveraging deep learning, reinforcement learning, and neural networks to optimize coverage, capacity, resource scheduling, and signal analysis.
Developing low-resolution signal decomposition and advanced CSI (Channel State Information) feedback mechanisms to reduce overhead and improve network efficiency.