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Software Defined Radio-based Signal Detection and RF Parameter Estimation Platform for Enhancing Electrical and Computer Engineering Curricula

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2016 ASEE Annual Conference & Exposition


New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016





Conference Session

SDR & Programming in ECE Education

Tagged Division

Electrical and Computer

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Paper Authors


Zhiping Zhang Wright State University

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Zhiping Zhang received his B.S. degree in electrical engineering from Nankai University, Tianjin, China, in 2001, M.S. and Ph.D. degrees in intelligence science from Peking University, Beijing, China, in 2004 and 2011 respectively. From 2011 to 2013, he was a Postdoctoral Research Fellow at the Department of Computer Science and Technology, Peking University. Since 2013, he has served as a research faculty member and co-director of Broadband, Mobile and Wireless Networking Laboratory at the Department of Electrical Engineering of Wright State University.

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Zhiqiang Wu Wright State University

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Dr. Zhiqiang Wu received his BS from Beijing University of Posts and Telecommunications in 1993, MS from Peking University in 1996, and PhD from Colorado State University in 2002, all in electrical engineering. He has worked at West Virginia University Institute of Technology as assistant professor from 2003 to 2005. He joined Wright State University in 2005 and currently serves as full professor. Dr. Wu is the author of national CDMA network management standard of China. He also co-authored one of the first books on multi-carrier transmission for wireless communication. He has published more than 100 papers in journals and conferences. He has served as Chair of Acoustic Communication Interest Group of IEEE Technical Committee on Multimedia Communications. His research has been supported by the National Science Foundation, Air Force Office of Scientific Research, Air Force Research Laboratory, Office of Naval Research, and NASA. His work on software defined radio implementation of cognitive radio won the Best Demo Award at IEEE Globecom 2010.

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Bin Wang Wright State University

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Prof. Bin Wang earned his Ph.D. from the Ohio State University in 2000. He joined the Wright State University in September 2000, where he is currently full professor of computer science and engineering. His research interests include optical networks, real-time computing, mobile and wireless networks, cognitive radio networks, trust and information security, and semantic web. He is a recipient of the US Department of Energy Career Award. His research has been supported by US Department of Energy, National Science Foundation, Air Force Office of Scientific Research, Air Force Research Laboratories, Ohio Supercomputer Center, and the State of Ohio.

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The advances of communication and networking have changed the world fundamentally. Communication and networking courses, especially wireless communication and networking courses, have become an integral part of the Electrical Engineering, Computer Science, and Computer Engineering curricula. However, most of these courses are taught at many institutions without a laboratory. For those courses associated with labs, often special hardware based experiment systems are used. These experiment systems are expensive so most schools cannot afford them. More importantly, such systems lack the flexibility to evolve over time and adapt to different environments. Supported by a NSF TUES type II project, we have developed a software defined radio (SDR) based signal detection and radio frequency (RF) parameter estimation platform for enhancing electrical and computer engineering curricula. In our previous NSF funded CCLI project “Evolvable wireless laboratory design and implementation for enhancing undergraduate wireless engineering education”, we have developed and demonstrated the first nationwide example of evolvable SDR based laboratories for three existing undergraduate courses. In this project, we are developing new lab components that can be adopted by multiple courses ranging from freshman year introductory course to senior year capstone design projects. Specifically, we have developed a SDR based signal detection and RF parameter estimation platform which can be adopted by many courses of electrical and computer engineering curricula. This SDR based platform is equipped with a user-friendly graphical user interface (GUI). Students can easily adjust the center frequency and bandwidth of the RF spectrum and choose different signal detection/RF parameter estimation algorithms. The GUI also displays the RF signal captured by the SDR, the instantaneous spectrum, statistical analysis of the signal such as cumulative distribution function (CDF). We have integrated a suite of signal detection and RF parameter estimation algorithms in the platform, including energy based detection, matched filter based detection, cyclostationary analysis based detection, fourth order cumulants based detection, etc. The platform is capable of detecting unknown RF signals, estimating their RF parameters such as carrier frequency, symbol rate, and modulation type. This platform can be adopted by many undergraduate and graduate level courses of electrical and computer engineering. Such courses include digital communication and wireless communication, microwave technology, radar, antenna design, communication network, cyber security, etc. The user-friendly GUI of this platform makes it a useful tool to attract high school students and freshmen year students into STEM fields. The light weight and portable nature of the SDRs make this platform easily transferable from one place to another, making it an attractive tool for capstone senior design teams as well. Through collaboration among the three participating institutions (including an Historically Black College)), the developed SDR based general modulation/demodulation platform will be integrated in undergraduate curricula of all three institutions.

Zhang, Z., & Wu, Z., & Wang, B. (2016, June), Software Defined Radio-based Signal Detection and RF Parameter Estimation Platform for Enhancing Electrical and Computer Engineering Curricula Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.25836

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