WPI - Computer Science Department, MS Thesis Presentation, Joshua Malcarne , " Machine Learning for Optimizing Cognitive Radar Waveforms"

Joshua Malcarne 

MS student 

WPI – Computer Science Department 

Thursday, May 30th, 2024 

Time: 12:00 p.m. – 1:00 p.m.

Location: AK 218 

Thesis Advisor:
Professor: Alexander Wyglinski
Professor & Associate Dean of Graduate Studies

 

Thesis Reader:
Professor:  Emmanuel Agu, WPI – Computer Science Department 

Abstract:

This study proposes a novel machine learning model to optimize radar waveforms for coexistence with 5G signals in shared frequency bands, addressing the challenge of enabling 5G development while safeguarding radar systems. The approach involves optimizing radar waveforms for transmission through unallocated slots within the 5G signal space to mitigate interference—a method not previously explored. We evaluate two techniques: Stochastic Gradient Descent (SGD) and Deep Q-Learning (DQN). The SGD approach faced significant issues with waveform output, while the DQN method showed promise, successfully converging to optimal waveforms in initial tests. However, DQN produced lower-than-expected sidelobe-to-mainlobe ratios and rewards when applied to the 5G dataset due to problems with reward calculations and sidelobe-to-mainlobe ratio processing. Despite these challenges, the DQN approach established a foundation for developing machine learning models for spectrum sharing applications, including future development of varied window functions, exploration of alternative optimization metrics like signal-to-noise ratio (SNR), and creation of a labeled 5G dataset to improve training efficacy