COM-300: Stochastic models in communication

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Lectures in this course (27)

Stochastic Models for Communications

Covers stochastic models for communications and the estimation of correlation functions.

Linear Algebra: Matrices and Operations

Covers the basics of linear algebra, focusing on matrices and operations.

Application: Error Rate of PAM Signal

Explores the impact of noise on signal transmission and the calculation of error rates in communication systems.

Stochastic Processes: 2nd Order Analysis

Explores stochastic processes, stationarity, ergodicity, and Wiener filtering for image restoration.

Probability Theory: Random Variables and Covariance

Covers random variables, covariance, and joint probability distributions.

Stationarity in Stochastic Processes

Explores stationarity in stochastic processes, showcasing how statistical characteristics remain constant over time and the implications on random variables and Fourier transforms.

Stochastic Processes and Spectral Densities

Covers spectral densities, signal correlations, and white noise processes in stochastic systems.

Stochastic Models for Communications

Covers stochastic models for communications and the analysis of random processes in communication systems.

Communication Systems: Signal Transmission and Noise

Explores signal transmission, noise impact, and error probability calculations in communication systems.

Stochastic Models for Communications

Covers stochastic models for communications, including stationarity, ergodicity, power spectral density, and Wiener filter.

Signal Processing: Image Restoration and Linear Prediction

Explores image restoration and signal prediction using linear filters in signal processing.

Stochastic Models for Communications

Covers stochastic models for communications, including Poisson processes and counting processes.

Superposition and Decomposition

Explains superposition and decomposition of signals into simpler components.

Stochastic Models for Communications

Covers stochastic models for binary transmission in communications systems.

Stochastic Models for Communications

Covers stochastic models for communication systems, including concepts like stochastic processes and Markov chains.

Random Walks on Discrete Spaces

Explores random walks on discrete spaces and their properties, including multivariate random variables and Poisson distributions.

Markov Chains: Properties and Approximations

Explores Markov chains' properties, hitting time, and Stirling's formula approximation.

Markov Chains: Reversibility and Stationary Distribution

Explores reversibility in Markov chains and its impact on the stationary distribution, highlighting the complexity of non-reversible chains.

Stochastic Models for Communications

Covers the fundamentals of stochastic models for communications, focusing on Markov chains and Poisson processes.

Stochastic Models for Communications

Covers stochastic models for communication systems and their impact on system performance.

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