Transmission of information over noisy channels. Measures of information and transmission channel capacity. Use of codes to improve the reliability of such transmission. Mathematical theory of information. Transmission at rates above channel capacity. Includes linear codes, error detecting and correcting codes, Hamming codes.
Why take this course?
Electrical engineering graduate students who wish to pursue research or careers in the data communications field require the course to understand the performance limits of this field. Communications research also requires a basic knowledge of data transmission through noise channels.
Prerequisites: 4634, 5605, STAT 4714
This course sequence requires a basic understanding of probability theory, stochastic processes and digital communication theory. The probability theory may be obtained in STAT 4714, the stochastic processes may be obtained in 5605, and the digital communication theory may be obtained in 4634.
Major Measurable Learning Objectives
measure information quantitatively.
Understand how data sequences can be compressed.
Determine limits on maximum compression.
understand how information can be reliably transmitted over a noisy channel.
Analyze the capacity of noisy communication channels.
Percentage of Course
Probability of Error and Information Measurement
Channel Capacity, Kraft Inequality
Data Compaction, Entropy, Markov Sources, Shannon’s Theorem
Error and performance Channel Bounds
Noisy Channels, Mutual Information, Capacity of Discrete Noisy Channels