Enabling Spectrum Coexistence of 5G mmWave and Passive Weather Sensing

WINLAB Summer Internship 2024

Advisors: Narayan Mandayam, Ivan Seskar

Graduates: Hariharan Venkat

Group Members: Despoina Kosmopoulou, John Allen Manego, Mark Moroney, Prakshab Adhikari, Archisa Arora

Project Objective

The 5G data frequency is close to the weather band, which can result in spectral leakage affecting weather satellites and affecting weather prediction patterns. Our task is to develop an experimental pipeline to measure spectral leakage on weather satellites. We will use Phased Array Antenna Modules (PAAMs) to mimic the 5G satellite and a spectrum analyzer to mimic the weather satellite.

Week 1

​Week 1 Presentation

Summary

• Read Papers on PAAMs, 5G data leakage onto satellites

• Getting Familiar with devices such as PAAMs and Spectrum Analyzer

Week 2

​Week 2 Presentation

Summary

• Followed GNURadio OFDM Tutorial on Orbit Sandbox 1 and Sandbox 2

Week 3

​Week 3 Presentation

Summary

• Used the sb1 PAAMs to transmit orthogonal frequency-division multiplexing (OFDM)signal to transmit our signal

• Getting familiar with GNURadio and Sandbox 1(Cosmos)

Week 4

​Week 4 Presentation

Summary

• Measured the Oscillator error and the noise floor for practice

• Followed the Spectrum visualization with Fosphor tutorial

• Able to see the wifi in Winlab (big purple area)

• Create a waterfall diagram with the OFDM signal tutorial

Week 5

​Week 5 Presentation

Summary

• Practiced Transmitting a OFDM Signal

• Worked on using Foshphor with the different group

• Found the Spectrum Mask on the FR2 Band

Week 6

​Week 6 Presentation

Summary

• Practiced Transmitting a OFDM Signal

• Started Testing the x,y movement controls of the PAAM device

• Worked on the θ Theta and φ Fi directional beam forming with the PAAM device

• Set up and used the spectrum analyzer to listen to the PAAM conversation

Week 7

​Week 7 Presentation

Summary

• Created a Ruby code to get raw data from a graph and send it onto an Excel sheet

• Set up our station outside to mimic 5G transmission and weather satellites

• Transmitted a simple sine wave and measured the relative power of the signal using spectrum analyzer

• Used matlab to create a 2D and 3D graph of the data we received from the Spectrum Analyzer

Week 8

​Week 8 Presentation

Summary

• Processed and visualized measurements taken in the prior week

• Planned our next experiment: Sensing Water Interference - Collect transmission data for various topologies (no jug obstructing, empty jug, full water jug) and apply simple ML algorithms to classify into Water Present/No Water Present

• Set up and played with GNU Radio

Week 9

​Week 9 Presentation

Summary

• Executed the water interference experiments with 3 states aforementioned

• Obtained output in binary data, which was converted to understandable data and visualized

• Engaged in “Synchronization” and Pilot Carrier Data Extraction

• Laid out the groundwork for ML

Week 10

Summary

• Did some data processing based on previous week's measurements
  1. Fixed some initial frequency shift that was not accounted for during measurements
  2. Used file sinks after the FFT blocks for headers and payload data to collect fft vectors
  3. Extracted pilot and sync word information from these blocks
  4. Samples used in the dataset are groupings of the above information

• Fed the data into various ML models

• Created Error Estimation metric for previous week's measurements using autocorrelation

• An F1-score of around 80% was achieved, while we tried to make sure the models generalized as much as possible

Some example code for the simple ML model is included in the classification_example.ipynb attached file or at this link: ​Colab for ML Model.