The evolution of communication technology in recent years has been substantial, yet many challenges lay ahead as communication systems evolve into 5G and the Internet of Things. A strong knowledge of digital communication provides a foundational platform for exploring advanced topics in wireless and optical communication, as well as the design and development of future communication technologies.
Explore sophisticated ways to apply powerful mathematical tools and recent developments in deep neural networks towards the analysis and design of practical communication systems.
- Ayfer Ozgur Aydin Assistant Professor, Electrical Engineering
- Detection and probability of error for binary and M-ary signals (PAM, QAM, PSK)
- Receiver design and sufficient statistics
- Design trade-offs: rate, bandwidth, power and error probability
- Coding and decoding (block codes, convolutional coding and Viterbi decoding)
- Recurrent Neural Networks and their use for robust and adaptive receiver design
- Controlling the Spectrum
Note on Course Availability
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.
Basic knowledge of Fourier transforms (Stanford Course EE179 OR EE261) AND probabilistic systems analysis (Stanford Course EE178 OR EE278). Some programming experience and basic familiarity with neural networks will be useful but not required.