Digital Communications I

Introduction. The model of a digital communications system. Source Encoder, Discrete Channel Encoder, Modulator. The model of the Channel. Fundamentals of Information Theory. The measure information. Entropy. The source coding. The theorems of Shannon. The Optimal codes. Codes Fixed Variable Input Output. The Code of Huffman. The Block Coding. Codes Variable Input Output Fixed. The Code Lempel-Ziv. The Convolutional Codes. Trellis Diagram. Signals Modulation of a Digital Communications System. The channel Additive White Gaussian Noise. The vector spaces Signals. The orthogonalization of signals. The Gram-Schmit Process. Representation of signals in the vector space. The design of the optimum receiver. The cross-correlation receiver. The custom filter. Receiver Design Custom filters.

Objectives

Measure Information quantity; design source code; knowledge of modulation methods; construct a signal vector space; design a modulator; evaluate the error probability

Prerequisites

Signals and systems; probability and statistics; linear algebra.

Syllabus

The model of a digital communications system. Source Encoder, Discrete Channel Encoder, Modulator. The model of the Channel. Fundamentals of Information Theory. The measure information. Entropy. The source coding. The theorems of Shannon. The Optimal codes. Codes Fixed Variable Input Output. The Code of Huffman. The Block Coding. Codes Variable Input Output Fixed. The Code Lempel-Ziv. The Convolutional Codes. Trellis Diagram. Signals Modulation of a Digital Communications System. The channel Additive White Gaussian Noise. The vector spaces Signals. The orthogonalization of signals. The Gram-Schmit Process. Representation of signals in the vector space. The design of the optimum receiver. The cross-correlation receiver. The custom filter. Receiver Design Custom filters.