Future wireless networks will use high-frequency millimeter-wave (mmWave) links for transmitting and receiving information with high throughput. A key difference between mmWave links and conventional sub-6GHz links is that mmWave links are severely affected by weather conditions. Students working on this project will use a state-of-the-art mmWave radar to assess the impact of wind speed, temperature, humidity, and other factors on the high-frequency link. The end goal of the project is to develop a classifier that can infer weather conditions based on the signal received from the mmWave radar. In this project, students are expected to learn how the mmWave radar works, design experiments to obtain labeled data, perform measurements, and develop the classifier.

This project is eligible for a matching fund stipend from the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Gil Zussman
  • Center/Lab: Electrical Engineering and Computer Science
  • Location: CEPSR 8th floor
  • he Wireless and Mobile Networking lab focuses on next generation (beyond-5G) wireless networks and cover the entire spectrum between the physical layer and the application layer. Specifically, projects focus on applying learning tools to networks and on using networks to support learning and inference (for example, in smart city environments).

Project Timeline

  • Earliest starting date: 3/1/2022
  • End date: 8/25/2022
  • Number of hours per week of research expected during Spring/Summer 2022: ~10
  • Number of hours per week of research expected during Summer 2022: ~35

Candidate requirements

  • Skill sets: Some background in Digital Signal Processing such as FFT is required. Some experience with Python is required. Some experience with classification predictive modeling is preferred. Some understanding of wireless networks, or interest to learn is preferred. Must be able to access the Columbia Morningside Heights campus
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes