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Using Inexpensive Hardware And Software Tools To Teach Software Defined Radio

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Conference

2010 Annual Conference & Exposition

Location

Louisville, Kentucky

Publication Date

June 20, 2010

Start Date

June 20, 2010

End Date

June 23, 2010

ISSN

2153-5965

Conference Session

Signal Processing Education

Tagged Division

Computers in Education

Page Count

10

Page Numbers

15.1328.1 - 15.1328.10

Permanent URL

https://peer.asee.org/15922

Download Count

62

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Paper Authors

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Cameron H. G. Wright, Ph.D, P.E., is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Wyoming, Laramie, WY. His research interests include signal and image processing, real-time embedded computer systems, biomedical instrumentation, and engineering education. He is a member of ASEE, IEEE, SPIE, NSPE, BMES, Tau Beta Pi, and Eta Kappa Nu. E-mail: c.h.g.wright@ieee.org

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Thad Welch

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Thad B. Welch, Ph.D, P.E., is Head of the Department of Electrical and Computer Engineering at Boise State University, Boise, ID. His research interests include the implementation of communication systems using DSP techniques, DSP education, multicarrier communication systems analysis, and RF signal propagation. He is a member of ASEE, IEEE, Tau Beta Pi, and Eta Kappa Nu. E-mail: t.b.welch@ieee.org

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Michael Morrow

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Michael G. Morrow, M.Eng.EE, P.E., is a Faculty Associate in the Department of Electrical and Computer Engineering at the University of Wisconsin, Madison, WI. His research interests include real-time digital systems, embedded system design, software engineering, curriculum design, and educational assessment techniques. He is a member of ASEE and IEEE. E-mail: morrow@ieee.org

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Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Using Inexpensive Hardware and Software Tools to Teach Software Defined Radio

Abstract

Signal processing topics such as software defined radio are more easily taught by using demonstra- tions and laboratory experiences that pique the students’ interest. This paper describes a new, inexpensive software defined radio educational platform based upon M ATLAB and the Texas Instruments C6713 dig- ital signal processing starter kit. We describe the various hardware and software issues and discuss how such a platform can be used in the classroom.

1 INTRODUCTION

Software defined radio (SDR) is a topic that is becoming increasingly necessary as part of either a digital signal processing (DSP) course or a digital communications course at most universities today, with the target audience being both undergraduate and graduate students. In the past, even when the theoretical aspects of SDR have been covered in such courses, actual demonstrations and lab exercises for students are rare, and even when given they are typically limited to a simple M ATLAB simulation. We believe that adding a hardware aspect to the presentation of SDR, with hands-on opportunities for students, greatly enhances their mastery of this important topic, as we have advocated for similar DSP topics in the past.1, 2 This paper describes a new, inexpensive SDR educational platform based upon custom M ATLAB data acquisition code capable of real-time operation with the Texas Instruments C6713 DSP starter kit (DSK).

2 TEACHING SOFTWARE DEFINED RADIO

2.1 How Much Detail?

One of the challenges in teaching a topic such as SDR to such a diverse student audience is to decide how much detail to include. One or more entire courses could be devoted to SDR and multirate digital communications,3, 4 but we are often limited to just a few lectures and perhaps one demonstration and/or lab exercise. In this situation, we consider the ideas of rate conversion and first-order bandpass sampling to be fundamental to getting students comfortable with the general ideas of SDR.5, 6 More detail can be added if time is available in the course, or can be included in a follow-on course for interested students.

With this in mind, students must be convinced of three things: 1) that the “gospel” of Fs ≥ 2fmax (i.e., sampling at least twice the highest signal frequency) that they learned regarding lowpass sampling is only a special case, 2) that for bandpass signals the selection of sampling frequency is more complicated, and that 3) aliasing is not always a bad thing. By choosing Fs properly, aliasing places the signal spectrum where we want it, yet avoids the overlapping of spectral replicas that would render the signal useless.7, 8

A bandpass signal is one where the energy is constrained to lie only between a lower frequency of fL and an upper frequency of fU . Thus the bandwidth of this signal is B = fU − fL . One useful form of the expression for predicting the range of acceptable sample frequencies for such a bandpass signal is Q Q−1 2B ≤ Fs ≤ 2B (1) n n−1 where Q = fU /B, and n is an integer such that 1 ≤ n ≤ ⌊Q⌋. In most real-world examples, the signal’s frequency content is already specified, leaving n as the first choice the students must learn to

Wright, C., & Welch, T., & Morrow, M. (2010, June), Using Inexpensive Hardware And Software Tools To Teach Software Defined Radio Paper presented at 2010 Annual Conference & Exposition, Louisville, Kentucky. https://peer.asee.org/15922

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