COM-302: Principles of digital communications

Lectures in this course (23)

Explores random variables, expectation, and independence in probability theory.

Covers binary testing setups, observations, and proposals for next week's readings.

By Emre Telatar explores binary hypothesis testing and decision rule design.

Covers Gaussian noise and decision-making in communication systems, emphasizing the importance of understanding Gaussian noise in signal processing.

Covers hypothesis testing with Gaussian noise and the standard Gaussian distribution.

Covers the concept of sufficient statistics, focusing on bounds and error probability.

Covers the concept of sufficient statistics and hypothesis testing in reading assignments by Emre Telatar.

Covers projection, rotation, white Gaussian noise, and waveform observation in linear algebra.

Covers the concept of spanning and combining vectors in a product space.

Covers the theory behind Gaussian processes and the design of receivers using MAP calculations.

Covers the alternate formulation of signal processing and the sufficiency of the Waliland ratio in signal sets.

Covers volume calculation in decoding regions for signal transmission trade-offs.

Discusses trade-offs in communication systems, focusing on bits sent vs. energy per bit.

Covers the principles of digital communication, focusing on the Nyquist Sampling Theorem and signal space dimension.

Covers the computation of power spectral density and the design of communication systems.

Covers the concept of sphere packing and its significance in time shifts and Nyquist criterion.

Covers the design and analysis of signal constellations in communication systems.

Covers the decoding process of convolutional codes and the analysis of eye diagrams.

Covers Convolutional Codes, focusing on mapping symbols and paths.

Delves into the analysis and applications of convolutional codes, highlighting the significance of correct implementation for optimal results.

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