Machine Learning for Enabling 5G and Satellite Network Coexistence in FR3 Spectrum

WINLAB Summer Internship 2025

Group Members: Audrey Wang, Tulika Punia, Srishti Hazra

Project Overview

• write about what is RFI (specific to our 5g and satellite problem)
• goal is to design a ML pipeline that could:
  1. detect RFI from transmitting signals
  2. then mitigate the interferences by optimizing beam forming

Week 1 (5/27 - 5/29):

Slides: ​Week 1 Presentation

Progress:

• Conducted literature review on relevant research papers
  1. ​Modeling the Impact of 5G Leakage on Weather Prediction
  2. ​Will Emerging Millimeter-Wave Cellular Networks Cause Harmful Interference to Weather Satellites?
  3. ​How Does the Growth of 5G mmWave Deployment Affect the Accuracy of Numerical Weather Forecasting?
• Understood the high level idea of what Radio Frequency Interference(RFI) and frequency allocations are
• Explored how ML can be implemented to minimize the interference between satellite and 5G in different spectrums

Week 2 (6/2 - 6/5):

Slides: ​Week 2 Presentation

Progress:

• Familiar with the pros and cons of the different approaches to beam-forming, especially the benefits of ML application
• Read research papers related to a physical-testbed-generated RFI dataset, and got familiar with the data generation process:
  1. ​A Physical Testbed and Open Dataset for Passive Sensing and Wireless Communication Spectrum Coexistence
  2. ​Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features
  3. ​Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks

Week 3 (6/9 - 6/12):

Slides: ​Week 3 Presentation

Progress:

• Began to process data with Numpy and h5py, generating Power Spectral Density (PSD) graphs and spectrograms from L1A data
• Utilized the radiometer's ability to interpret all received power as thermal radiation to quantify RFI by detecting abnormal temperature increases
• Saved plotted graphs into a 2D Numpy array for future model training use (4 columns: RFI Scenario | PSD | Spectrogram | Temp Difference)

Week 4 (6/16 - 6/19):

Slides: ​Week 4 Presentation

Progress:

• Continued generating PSDs and spectrograms using Jupyter Notebook (link to ​code)

• Sample PSD and spectrogram for RFI scenario "tr_fc0_4RB_Gain-20_sn2" (transition band; central frequency 0; 4 resource blocks; gain -20; sample number 2)

Week 5 (6/23 - 6/26):

Slides: ​Week 5 Presentation

• In progress to generate all graphs for fc1, 2, 3 for both a) transition-band and b) out-of-band scenarios

Week 6 (6/30 - 7/3):

Slides: ​Week 6 Presentation

• Finished generating 348 sets of graphical data for the following inference scenarios:
  1. Center frequencies 1423.5mHz(fc1), 1433.5mHz(fc2), 1443.5mHz(fc3) for transition band
  2. Center frequencies 1440.5mHz(fc1), 1442.5mHz(fc2), 1444.5mHz(fc3) for out-of-band
• Identified clean vs. RFI-contaminated signal shapes in Power Spectral Density (PSD) plots:
  1. Uniform (Clean): flat-top spectrum, power emitted uniformly across bandwidth
  2. Spikes (RFI): abrupt, sharp spikes in power at random frequency levels

Week 7 (7/7 - 7/10):

Slides: ​Week 7 Presentation

Progress:

• Machine learning models:
  • Developed a CNN for RFI detection using pre-trained model (ResNet-18) with the last classification layer modified and retrained
  • Developed SVM as a baseline to assess the efficiency of the CNN approach (underutilization of the neural network)
    • Performed cross-validation to ensure the models were not overfitting
    • Both the CNN and the SVM reached >95% accuracy
• Continued obtaining data from transition band (100 additional rows: 1413.5mHz(fc0), 1453.5mHz(fc4)
• Identified Friis Transmission Formula & Stefan-Boltzmann Law which will be used to find beam efficiency

Week 8 (7/14 - 7/17):

Slides: ​Week 8 Presentation