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Digital Signal Processing (DSP) for Software Radio
October 12, 2023 @ 6:00 pm – 7:30 pm
First Video Release and Orientation, Thursday, September 21, 2023, 6:00PM – 6:30PM Additional videos released weekly in advance of that week’s live session!
Live Workshops: 6:00PM – 7:30PM; Thursdays, September 28, October 5, 12, 19, 26
Attendees will have access to the recorded session and exercises for two months (until January 26, 2024) after the live session ends!
New Format Combining Live Workshops with Pre-recorded Video
This is a hands-on course providing pre-recorded lectures that students can watch on their own schedule and an unlimited number of times prior to live Q&A/Workshop sessions with the instructor. Ten 1.5 hour videos released 2 per week while the course is in session will be available for up to two months after the conclusion of the course.
Course Summary
This course builds on the IEEE course “DSP for Wireless Communications” also taught by Dan Boschen, further detailing digital signal processing most applicable to practical real-world problems and applications in radio communication systems. Students need not have taken the prior course if they are familiar with fundamental DSP concepts such as the Laplace and Z transform and basic digital filter design principles.
This course brings together core DSP concepts to address signal processing challenges encountered in radios and modems for modern wireless communications. Specific areas covered include carrier and timing recovery, equalization, automatic gain control, and considerations to mitigate the effects of RF and channel distortions such as multipath, phase noise and amplitude/phase offsets.
Dan builds an intuitive understanding of the underlying mathematics through the use of graphics, visual demonstrations, and real-world applications for mixed signal (analog/digital) modern transceivers. This course is applicable to DSP algorithm development with a focus on meeting practical hardware development challenges, rather than a tutorial on implementations with DSP processors.
Now with Jupyter Notebooks!