Digital Communications: Module 1 Introduction to Digital Communications and Information Theory.pdf
These free pdf files are part of ‘Digital Communications‘ web course that is primarily intended for use by undergraduate students who may be preparing for graduate level studies in the area of electrical communications engineering. Only 1 basic modules, consists of 4 Lesson of Digital communications. You may also be interested in reading the Module 2 of the Digital Communications ebook.
- Lesson 1 Introduction to Digital Communications, you will learn about Lesson-wise organization of this course, Schematic description of a representative digital communication system, Milestones in the history of electronic communications, Names and usage of electromagnetic bands, Typical transmission loss for several physical media, Block Schematic Description of a Digital Communication System.
- Lesson 2 Signals and Sampling Theory, you will learn about: Need for careful representation of signals, Types of signals, Nyquist’s sampling theorem and its practical implications, Band pass representation of narrow band signals, Nyquist’s Uniform Sampling Theorem for Lowpass Signals, Flat Top Sampling, Frequency response of conveniently realizable analog lowpass filter, Sampling of narrow bandpass signals: – an inefficient approach, Sampling of narrow bandpass signals: – a better approach.
- Lesson 3 Information Theoretic Approach to Digital Communications, you will learn about: Scope of Information Theory, Measures of self and mutual information, Entropy and average mutual information, Examples of random experiments with multiple outcomes, Joint Experiment, Conditional Probability, Mutual Information, Self-Information, Entropy (average self-information), Average Mutual Information, Example of a Binary Symmetric Channel (BSC)
- Lesson 4 Coding for discrete sources, you will learn about: Need for coding source letters, Variable length coding, Prefix condition code, Kraft Inequality Theorem, Huffman Coding, Source Coding Theorem, Variable length Coding, Kraft Inequality Theorem, Binary Huffman Coding (an optimum variable-length source coding scheme), Source Coding Theorem (for coding source letters with variable length codes)
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