Software defined radio-based mixed signal detection laboratories for enhancing undergraduate communication and networking curricula

Communication and networking courses, especially wireless communication and networking courses, have become more and more important in many disciplines such as Electrical Engineering, Computer Science, and Computer Engineering. Due to costly hardware needed for communication and networking teaching laboratories, many of these courses are taught without a laboratory. In the rare cases of existing labs, such hardware based teaching labs lack the flexibility to evolve over time and adapt to different environments. Supported by a NSF TUES type II project, we have developed a series of software defined radio (SDR) based mixed signal detection laboratories for enhancing undergraduate communication and networking 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, in this paper, we report the development of a SDR based mixed radio frequency signal detection platform with a graphical user interface (GUI). This user-friendly GUI will allow students to adjust RF parameters such as carrier frequency, symbol rate, pulse shaping filter, etc., and mix multiple RF signals together. Additionally, students are able to observe the transmitted signal in both time and frequency at both transmitter and receiver. At receiver side, the SDR based platform also provides students the functionality of performing RF signal detection via different detection methods including energy based detection, waveform based detection, and cyclostationary analysis based detection. It is shown that by exploiting sophisticated signal processing techniques such as cyclostationary analysis, mixed RF signal components can be detected and identified. Through collaboration among the three participating institutions (Wright State University, Miami University (a mostly undergraduate serving institution), and Central State University (an HBCU)), based platform will be integrated in undergraduate curricula of all three institutions.