Software Defined Radio for Digital Signal Processing Related Courses
Author(s) -
Patrick Cutno,
Chi-Hao Cheng,
Zhiqiang Wu,
Bin Wang,
Deng Cao
Publication year - 2016
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/p.25831
Subject(s) - digital signal processing , software defined radio , signal processing , computer science , class (philosophy) , signal (programming language) , software , curriculum , multimedia , computer engineering , computer hardware , telecommunications , artificial intelligence , pedagogy , programming language , psychology
Software-defined radio (SDR) is widely used in undergraduate electrical and computer engineering curricula in the area of communication. It is commonly used as laboratory equipment for students to implement communication systems and verify communication theory learned in class. In this project, supported by a NSF TUES type II grant, Collaborative: TUES: Software Defined Radio Laboratory Platform for Enhancing Undergraduate Communication and Networking Curricula, we explore the possibility of applying the SDR as an education tool to teach fundamental signal processing concepts. To achieve this goal, we developed SDR based laboratory exercises. Although students are still required to develop analog/digital communication systems, the major focuses of these exercises are to illustrate fundamental signal processing concepts such as frequency-shift, spectra of real and complex valued signals, etc. The target students are junior level undergraduate students who have taken “Signals and Systems” but are not necessary enrolled in the class of “Digital Signal Processing (DSP).” Two undergraduate students who have taken the course of “Signals and Systems” but have not yet taken or finished “Digital Signal Processing” were invited to test laboratory exercises developed in this project. The goal of this project is to develop laboratory exercises to demonstrate theories covered in fundamental signal processing courses. Such courses are mathematically orientated and students often feel challenged in these classes. We believe that experimental exercises with real-life application examples can motivate students and help them to develop a better understanding of signal processing theories.
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