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

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,
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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.