MSML621

Machine Learning for Wireless Communications

This course provides an introduction to the fundamental concepts of wireless communications and how machine learning can be applied to the domain. Example applications include signal detection, modulation classification, spectrum sensing, cellular network optimization, and cognitive radio. In addition, students will get hands-on experience using software-defined radios to explore the RF spectrum around them and detect/classify different types of signals. Topics include sampling, filtering, frequency domain/FFTs, digital modulation, cellular/IoT technologies, and how supervised, unsupervised, and reinforcement learning can be applied to these topics.

Past Semesters

Marc Lichtman

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Marc Lichtman

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Average rating: N/A

Include P/F semesters During the Spring 2020 and Spring 2021 semesters, students could choose to take some of their courses pass-fail mid-semester which skews grade data aggregated across multiple semesters.

Average GPA of 3.01 between 8 students*

A-: 37.5%

A: 12.5%

B-: 12.5%

B+: 12.5%

W: 12.5%

other: 12.5%

* "W"s are considered to be 0.0 quality points. "Other" grades are not factored into GPA calculation. Grade data not guaranteed to be correct.