Nguyen, T.
, Caromi, R.
and Kallas, K.
(2022),
Deep Learning for Path Loss Prediction in the 3.5 GHz CBRS Spectrum Band, IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, US, [online], https://doi.org/10.1109/WCNC51071.2022.9771737, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933148
(Accessed October 31, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Deep Learning for Path Loss Prediction in the 3.5 GHz CBRS Spectrum Band
Published
Author(s)
, ,
Abstract
In the 3.5 GHz citizen broadband radio service (CBRS) band, accurate path loss prediction is very important to protect the incumbent from harmful interference caused by the lower tier users. Within the current CBRS standards developed by the Wireless Innovation Forum (WInnForum), the irregular terrain model (ITM), also known as the Longley Rice model, is used for path loss calculation. However, the model does not include clutter data, and thus, it underestimates the path loss. This paper utilizes a model-aided deep learning technique with satellite images to improve path loss prediction. Numerical study shows that the proposed approach can outperform the Longley Rice model and some tuned or fitted propagation models.Proceedings Title
IEEE Wireless Communications and Networking Conference (WCNC)
Conference Dates
April 10-13, 2022
Conference Location
Austin, TX, US
Conference Title
2022 IEEE Wireless Communications and Networking Conference (WCNC)
Pub Type
Conferences
Download Paper
Keywords
3.5 GHz, CBRS, deep learning, machine learning, path loss prediction
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created May 19, 2022, Updated November 29, 2022