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Ultra Low-Cost Software-Defined Radio: A Mobile Studio for Teaching Digital Signal Processing
Author(s) -
Cory J. Prust,
Steven S. Holland,
Richard Kelnhofer
Publication year - 2020
Publication title -
papers on engineering education repository (american society for engineering education)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--23216
Subject(s) - software defined radio , computer science , studio , digital signal processing , software , signal processing , digital radio , embedded system , multimedia , computer hardware , computer architecture , telecommunications , operating system
Software-defined radio (SDR) is being used by many institutions as a teaching tool to illustrate and explore concepts presented in signal processing and communication courses. The inherent flexibility of SDR coupled with the ability to capture, visualize, and process real-world signals provides numerous benefits in classroom and laboratory settings. Furthermore, exposure to SDR is increasingly important for students wishing to pursue careers in the telecommunication, networking, and radar fields. An undergraduate laboratory can be outfitted with relatively highperformance SDRs at a reasonable cost. It was recently discovered that USB digital television tuners can be used as SDR receivers. Since this discovery, the tuners have been successfully used in a wide variety of applications. At a cost less than $20 (USD), these so-called “RTL-SDR” devices set a new price point for SDR technology that is particularly attractive within an educational context. This paper presents the use of these low-cost SDRs and supporting software for teaching digital signal processing (DSP) concepts to undergraduate electrical and computer engineering students. The proposed approach creates an interactive learning environment based on mobile studio pedagogy. A series of studio projects have been developed, each of which requires implementation and testing of DSP algorithms on data received by student-owned SDRs. Data sources include signals of opportunity as well as instructor-generated test signals. The result is a mobile learning environment in which students can visualize and apply abstract theoretical concepts, implement real-time algorithms, and rapidly test their designs using real-world data.

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