Channel-Adaptive Machine Learning-Based mmWave Beamforming
Project Details
Program
Electrical and Computer Engineering
Field of Study
Wireless Communications, Machine Learning, mmWave Beamforming
Division
Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link
Project Description
This project focuses on integrating machine learning algorithms into mmWave beamforming to dynamically adapt to changing channel conditions. The intern will develop and test adaptive beam selection techniques using real-time channel state information, enhancing the efficiency of mmWave communication systems in mobile and non-line-of-sight environments.
About the Researcher
Abdulrahman Alhamed
Assistant Professor of Electrical & Computer Engineering Principal Investigator, ITASS Lab
Desired Project Deliverables
- A machine learning model for adaptive beamforming
- Simulation results demonstrating improved beam selection efficiency
- Implementation on a software-defined radio (SDR) or mmWave testbed
Recommended Student Background
Background in wireless communications and signal processing
Experience with machine learning (Python, TensorFlow, PyTorch)
Familiarity with MATLAB, mmWave channel modeling, and beamforming techniques
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Be part of the journey with VSRP
3-6 months
Internship period
100+
Research Projects
3.5/4
Cumulative GPA
310
Interns a Year