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# lab1 - ECE 146A Communications I Lab 1 Introduction to...

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ECE 146A: Communications I Lab 1: Introduction to Matlab, Simulink, and the Communication Toolbox Lab Report Due: At the next lab session on Thursday, January 13, 2011 1 Objective The objective of this lab is to learn how to access Matlab, Simulink, and the Communications Toolbox, and to become familiar with the basic operations of these applications. 1.1 Matlab Using Matlab, you will review numerical calculations with complex numbers, and learn about vector operations such as vector import and export procedures. You will also familiarize yourself with Matlab’s graphical plotting capabilities. 1.2 Simulink Using Simulink, you will simulate a simple dynamic system that includes a signal generator and a scope. You will also demonstrate how a periodic waveform can be generated by summing together many sinusoids with different frequencies. 1.3 Communication Toolbox Using the Communication Toolbox, you will become familiar with some applications that will be needed later in the other software labs. 2 Equipment Matlab, Simulink, and the Communication Toolbox software are available on the workstations in the lab. 3 Matlab 3.1 Introduction Matlab is a computing environment specially designed for matrix computations. It is widely used for the study of a variety of applications, including circuits, signal processing, control systems, 1

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communications, image processing, symbolic mathematics, statistics, neural networks, wavelets, and system identification. Its large library of built-in functions and toolboxes, as well as its graphical capabilities, make it a valuable tool for electrical engineering education and research. Matlab has an interactive mode in which user commands are interpreted immediately as they are typed. Alternatively, a program (called a script) can be written in advance using a text editor, saved as a file, and then executed in Matlab. 3.2 Matrix Manipulation The basic objects manipulated by Matlab are two-dimensional matrices (though recent versions can process multidimensional matrices). Recall that a vector is a special case of a matrix that has only one row or one column. In this course, we will define a column vector, which corresponds to a single column of a matrix, e.g., an N × 1 matrix with N rows and one column. A row vector is obtained from a column vector by using the transpose operator. You will find that Matlab is extremely powerful when performing matrix manipulations because many scalar operations operate in parallel for all elements of a matrix. This almost eliminates the need for iterative loops employed in most conventional programming languages. For example, in order to generate s ( n ) = sin(2 πn/ 1000) for n = 1 , . . . , 1000, we can write the following program in C: for ( n = 1; n < = 1000; n + +) { s ( n ) = sin(2 π n/ 1000) } In Matlab, we could use a for loop as follows: for n = 1 : 1000, s ( n ) = sin(2 π n/ 1000); end However, it is much simpler to write: s = sin(2 π (1 : 1000) / 1000); Since Matlab programs are interpreted (not compiled), for loops and while loops are inefficient.
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