Introduction to Communication, Control, and Signal Processing

An illustration of spectral shaping of a white-noise signal.

Spectral shaping of a white-noise signal. (Image by MIT OpenCourseWare. Courtesy of Prof. Alan Oppenheim and Prof. George Verghese.)


MIT Course Number


As Taught In

Spring 2010



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Course Features

Course Highlights

This course features a complete set of course notes, Signals, Systems and Inference.

Course Description

This course examines signals, systems and inference as unifying themes in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; group delay; state feedback and observers; probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization; least-mean square error estimation; Wiener filtering; hypothesis testing; detection; matched filters.

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Oppenheim, Alan, and George Verghese. 6.011 Introduction to Communication, Control, and Signal Processing, Spring 2010. (MIT OpenCourseWare: Massachusetts Institute of Technology), (Accessed). License: Creative Commons BY-NC-SA

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