Experimental Analysis of the Impact of Weather on Signal Strength in the CBRS Frequency Spectrum

Weather variations can significantly impact the performance of 5G and 6G networks. Although much research has been conducted on these impacts, there has been limited focus specifically on Citizens Broadband Radio Service (CBRS) networks, which operate at 3.5 GHz spectrum. Analyzing these effects is crucial for CBRS-based private networks, where reliability and high performance are critical. This paper explores how different weather parameters, such as temperature, relative humidity, absolute humidity, rainfall, and snowfall, affect the signal strength in the CBRS spectrum. Through an extensive experimental campaign within a CBRS-based private LTE network in Buffalo, NY, we collected signal strength data from 32 data collection points over 21 months. Using weather data from Oikolab, our data-driven analyses indicate that relative humidity is a more reliable predictor of signal strength fluctuations compared to temperature and absolute humidity. We also investigate how various types of precipitation affect signal strength, with rainfall causing immediate decreases and snowfall leading to more prolonged impacts. These insights are vital for enhancing understanding of the impact of weather on the CBRS spectrum, a topic not extensively covered in the existing literature. The datasets used in this study are publicly available at https://github.com/WN4SS/CBRSdata.

Authors

Institutions

Publication Details

Published
2025-06-08
DOI
https://doi.org/10.1109/iccworkshops67674.2025.11162390
Primary Topic
Power Line Communications and Noise
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Experimental Analysis of the Impact of Weather on Signal Strength in the CBRS Frequency Spectrum

Filippo Malandra, Biswajit Kumar Dash, Joseph A. Caezza
Power Line Communications and Noise
article

Experimental Analysis of the Impact of Weather on Signal Strength in the CBRS Frequency Spectrum

Filippo Malandra, Biswajit Kumar Dash, Joseph A. Caezza
article en

Abstract

Weather variations can significantly impact the performance of 5G and 6G networks. Although much research has been conducted on these impacts, there has been limited focus specifically on Citizens Broadband Radio Service (CBRS) networks, which operate at 3.5 GHz spectrum. Analyzing these effects is crucial for CBRS-based private networks, where reliability and high performance are critical. This paper explores how different weather parameters, such as temperature, relative humidity, absolute humidity, rainfall, and snowfall, affect the signal strength in the CBRS spectrum. Through an extensive experimental campaign within a CBRS-based private LTE network in Buffalo, NY, we collected signal strength data from 32 data collection points over 21 months. Using weather data from Oikolab, our data-driven analyses indicate that relative humidity is a more reliable predictor of signal strength fluctuations compared to temperature and absolute humidity. We also investigate how various types of precipitation affect signal strength, with rainfall causing immediate decreases and snowfall leading to more prolonged impacts. These insights are vital for enhancing understanding of the impact of weather on the CBRS spectrum, a topic not extensively covered in the existing literature. The datasets used in this study are publicly available at https://github.com/WN4SS/CBRSdata.

University at Buffalo, State University of New York (US)
Openalex Percentile: Top 75%
Power Line Communications and Noise
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.