| Version 19 (modified by , 5 months ago) ( diff ) |
|---|
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
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
Summary
- Read Papers on PAAMs, 5G data leakage onto satellites
- Getting Familiar with devices such as PAAMs and Spectrum Analyzer
Week 2
Summary
- followed GNURadio OFDM Tutorial on Orbit Sandbox 1 and Sandbox 2
Week 3
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
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
Attachments (15)
-
OFDM image.png
(119.8 KB
) - added by 5 months ago.
OFDM week 2
- week 3 image OFDM Signal.gif (453.5 KB ) - added by 5 months ago.
- waterfall week 4.gif (3.6 MB ) - added by 5 months ago.
- wifi week 4.gif (1.6 MB ) - added by 5 months ago.
- fosphorProg2.png (2.4 MB ) - added by 5 months ago.
- fosphorProgress1.png (2.3 MB ) - added by 5 months ago.
- fr2maskgraph.png (204.7 KB ) - added by 5 months ago.
- PAAMMoving.gif (5.4 MB ) - added by 5 months ago.
- week7Setup.png (262.1 KB ) - added by 4 months ago.
- week7Graph.png (79.6 KB ) - added by 4 months ago.
- visualization4outsideexperiements.jpg (811.2 KB ) - added by 4 months ago.
- rxspectrumgraph week 8.png (43.8 KB ) - added by 4 months ago.
- gnuradio week 9 actually.png (237.8 KB ) - added by 4 months ago.
- week9 converting binary to legible.png (1.6 MB ) - added by 4 months ago.
-
classification_example.ipynb
(1.2 MB
) - added by 4 months ago.
This is a jupyter notebook that contains a simple pipeline of ML processing for the data we extracted. The processing is by no means perfect, nor we expected it to be - it is a proof of concept.
Note:
See TracWiki
for help on using the wiki.
