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The techniques of applied probability and statistical signal processing and apply to communication and signal processing.
Random Signals for Communications and Signal Processing Pham Van Tuan Electronic & Telecommunication Engineering Danang University of Technology Course Administration ! The prerequisite of this course: " Linear Systems Theory in Discrete and Continuous Time " Basic Signals in Discrete and Continuous Time " Differential and Integral Calculus " Principles of Engineering Statistics " Principles of Probability " Facility with MATLAB ! Goals: " To learn the techniques of applied probability and statistical signal processing and apply to communication and signal processing. ! Credits: 5 ! Grading: hw (20%); lab (20%); midterm (20%); final exam (30%); final project (10%) ! Contents: " Discrete-Time Random Process " Signal Modeling " Wiener Filters " Spectrum Estimation " Adaptive Filters " Applications in Communications and Signal Processing ! Textbook: " M. H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley, 1996. " John A. Gubner, Probability and Random Processes for Electrical and Computer Engineering, Cambridge Uni. Press, 2006 " Peter Vary, Digital Speech Transmission, Wiley, 2006. ! Course reference: " Alle-Jan van der Veen and Geert Leus, ET4235: DIGITAL SIGNAL PROCESSING, 2011. Course Materials ! At the end of this course, students will be able to: " Calculate the probability of combinations of events using hand and computer analysis. " Write computer (MATLAB) programs to compute many probability distributions. " Solve for the distributions of random variable arising from certain functions of random variables. " Model datasets arising in communications using common probabilistic models. " Analyze the effect of randomness on communication signals. " Model and analyze the linear systems using multivariate Gaussian distributions. " Design statistical signal processing systems for communications applications. Learning Objectives