Gaussian Random Vectors White Gaussian Noise (WGN)
By Robert Gallager Lizhong Zheng - MIT
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798
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Lecture Description
Jointly Gaussian random vectors and processes and white Gaussian noise (WGN)
Course Description
This course serves as an introduction to the theory and practice behind many of today's communications systems. 6.450 forms the first of a two-course sequence on digital communication. The second class, 6.451, is offered in the spring. Topics covered include: digital communications at the block diagram level, data compression, Lempel-Ziv algorithm, scalar and vector quantization, sampling and aliasing, the Nyquist criterion, PAM and QAM modulation, signal constellations, finite-energy waveform spaces, detection, and modeling and system design for wireless communication.
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