Starting a Low‑Cost SDR Beacon Receiver Project for Studying Extended Tropospheric Propagation on 432/902/1296 MHz

TitleStarting a Low‑Cost SDR Beacon Receiver Project for Studying Extended Tropospheric Propagation on 432/902/1296 MHz
Publication TypeConference Proceedings
Year of Conference2026
AuthorsShahrouzi, N, Frissell, N, Beattie, Jr, G
Conference NameHamSCI Workshop 2026
Date Published03/2026
PublisherHamSCI
Conference LocationNewington, CT
Abstract

In this project, we are beginning to build and test a low cost SDR based receive system to study extended tropospheric propagation on 432 MHz, 902 MHz, and 1296 MHz, with the main goal of understanding when and how radio signals travel far beyond normal line of sight due to atmospheric or other tropospheric conditions such as refraction, inversion layer ducting, troposcatter, and aircraft scatter. These extended propagation events are important not only for amateur radio operators who monitor weak signals, but also for synchronized wireless systems where late arriving signals can create interference or affect timing. Our first step at the University of Scranton is to deploy a receiving station using an RTL SDR class device, a low noise preamplifier, and band appropriate antennas, so we can start monitoring known beacons and signals of opportunity. We will collect time stamped RSSI values, CW and WSJT mode decode results (including WSPR, FT8, and Q65). In parallel, we will gather atmospheric data at 15–60 minute intervals along or near the propagation path so we can compare signal behavior against weather patterns such as temperature inversions, humidity changes, fog, or pressure gradients. Since this is the first year and the project is just starting, our main focus is on building a stable receive chain, confirming consistent beacon reception, and creating a data pipeline that supports long term propagation studies. When we have stable data, we plan to analyze the signatures of extended propagation events and explore which atmospheric features are most responsible for them, and later we will attempt an initial machine learning model to predict “event vs. no event.” The project is a new collaboration between the University of Scranton, HamSCI, VIAVI Solutions, and the Texas A&M MTT S student group, and we also plan to create simple documentation and deployment guides so students, clubs, and under resourced schools can reproduce this low cost system and join the research effort over time.

Refereed DesignationNon-Refereed