Predicting Wireless Network Signal Strength Based on Weather Conditions

Architecture of the project.

Use machine learning models to predict the signal strength of wireless networks under diverse weather conditions! 

Project description

Weather conditions can significantly affect the performance of wireless networks. Weather factors like temperature, humidity, rain, and snowfall impact network signal strength and network reliability. Understanding these impacts is essential for maintaining reliable network operations. This project uses machine learning models, such as deep learning to predict the signal strength of wireless networks operating on the 3.5 GHz Citizens Broadband Radio Service (CBRS)spectrum—a 150 MHz band within the 3.5 GHz range. By using real weather data combined with network signal measurements, the goal to accurately predict changes in signal strength under different weather conditions. 

Project outcome

  • Explore how weather factors (temperature, humidity, rain, and snowfall) affect signal strength in wireless networks.
  • Develop and implement a predictive model using machine learning techniques, such as deep learning, to predict signal strength in CBRS)-based wireless networks.

Project details

Timing, eligibility and other details
Length of commitment Less than a semester; 0-2 months
Start time Anytime 
In-person, remote, or hybrid? Hybrid Project 
Level of collaboration Individual student project 
Benefits Academic credit 
Who is eligible All undergraduate students who are comfortable with programming in Python. Understanding of basic machine learning concepts. Nice to Have: Working knowledge of machine learning models, such as deep learning. Familiarity with the basics of wireless network.

Core partners

  • Faculty: Dr. Malandra
  • Wireless Networks for Smart Systems (WN4SS) Lab

Project mentor

Filippo Malandra

Assistant Professor of Research

Electrical Engineering

Phone: (716) 645-1151


Start the project

  1. Email the project mentor using the contact information above to express your interest and get approval to work on the project. (Here are helpful tips on how to contact a project mentor.)
  2. After you receive approval from the mentor to start this project, click the button to start the digital badge. (Learn more about ELN's digital badge options.) 

Preparation activities

Once you begin the digital badge series, you will have access to all the necessary activities and instructions. Your mentor has indicated they would like you to also complete the specific preparation activities below. Please reference this when you get to Step 2 of the Preparation Phase. 

Send an email to the faculty to express your interest and apply for this opportunity. Please share your resume and course transcripts. You will then be contacted and an initial meeting will be set up. 


Electrical Engineering, Research Experience