7.2.1 Introduction
By now you should be familiar with the derivation of the Fourier series for continuous-time, periodic functions. This derivation leads us to the following equations that you should be quite familiar with:
f(t) =X
n
cnejω0nt
(7.1)
cn = T1R
n
f(t)e−(jω0nt)dt
= T1f ã ejω0nt
(7.2) wherecn tells us the amount of frequencyω0nin f(t).
In this module, we will derive a similar expansion for discrete-time, periodic functions.
In doing so, we will derive theDiscrete Time Fourier Series(DTFS), or theDiscrete Fourier Transform(DFT).
7.2.2 Derivation of DTFS
Much like a periodic, continuous-time function can be thought of as a function on the interval [0, T]
A periodic, discrete-time signal (with period N) can be thought of as a finite set of numbers. For example, say we have the following set of numbers that describe a periodic, discrete-time signal, whereN = 4:
{. . . ,3,2,−2,1,3, . . .}
We can represent this signal as either a periodic signal or as just a single interval as follows:
note: The set of discrete time signals with periodN equalCN.
Just like the continuous case, we are going to form a basis using harmonic sinusoids . Before we look into this, it will be worth our time to look at the discrete-time, complex sinusoids in a little more detail.
T f(t)
(a)
T
(b)
Figure 7.1: We will just consider one interval of the periodic function throughout this section. (a) Periodic Function (b) Function on the interval [0, T]
f[n]
n
(a)
n
(b)
Figure 7.2: Here we can look at just one period of the signal that has a vector length of four and is contained inC4. (a) Periodic Function (b) Function on the interval [0, T]
Figure 7.3: Complex sinusoid with frequencyω= 0
Figure 7.4: Complex sinusoid with frequencyω=π4
7.2.2.1 Complex Sinusoids
If you are familiar with the basic sinusoid signal and with complex exponentials then you should not have any problem understanding this section. In most texts, you will see the the discrete-time, complex sinusoid noted as:
ejωn
Example 7.1:
Example 7.2:
7.2.2.1.1 In the Complex Plane
The complex sinusoid can be directly mapped onto our complex plane, which allows us to easily visualize changes to the complex sinusoid and extract certain properties. The absolute value of our complex sinusoid has the following characteristic:
∀n, n∈R:|ejωn|= 1 (7.3) which tells that our complex sinusoid only takes values on the unit circle. As for the angle, the following statement holds true:
∠ejωn=wn (7.4)
(a) (b) (c)
Figure 7.5: These images show that as nincreases, the value of ejωn moves around the unit circle counterclockwise. (a)n= 0 (b)n= 1 (c)n= 2
(a) (b)
Figure 7.6: (a) N=7 (b) Here we have a plot of Re ej2π7n
.
Asnincreases, we can pictureejωnequaling the values we get moving counterclockwise around the unit circle. See the images below for an illustration:
note: Forejωn to be periodic, we needejωN = 1 for someN. Example 7.3:
For our first example let us look at a periodic signal whereω=2π7 andN = 7.
Example 7.4:
Now let us look at the results of plotting a non-periodic siganl where ω = 1 and N = 7.
7.2.2.1.2 Aliasing
Our complex sinusoids have the following property:
ejωn=ej(ω+2π)n (7.5)
(a) (b) Figure 7.7: (a) N=7 (b) Here we have a plot of Re ejn
.
(a) (b)
Figure 7.8: Plot of our complex sinusoid with a frequency greater than pi.
Given this property, if we have a sinusoid with frequencyω+ 2π, then this signal ”aliases”
to a sinusoid with frequencyw.
note: Eachejωnis unique for ω∈[0,2π) 7.2.2.1.3 ”Negative” Frequencies
If we are given a signal with frequency π < ω <2π, then this signal will be represented on our complex plane as:
From the above images, the value of our complex sinusoid on the complex plane may be more easily interpreted as cycling ”backwards” (clockwise) around the unit circle with frequency 2π−ω. Rotating counterclockwise by w is the same as rotating clockwise by 2π−ω.
Example 7.5:
Let us plot our complex sinusoid,ejωn, where we haveω= 5π4 andn= 1.
This plot is the same as a sinusoid with ”negative” frequency− 3π4 .
Figure 7.9: The above plot of our given frequency is identical to that of one where ω=− 3π4
.
point: It makes more physical sense to chose [−π, π) as the interval for ω.
Remember thatejωnande−(jωn)areconjugates. This gives us the following notation and property:
ejωn∗=e−(jωn) (7.6) The real parts of of both exponentials in the above equation are the same; the imaginary parts are negative of one another. This idea is the basic definition of a conjugate.
Now that we have looked over the concepts of complex sinusoids, let us turn our attention back to finding a basis for discrete-time, periodic signals. After looking at all the complex sinusoids, we must answer the question of which discrete-time sinusoids do we need to represent periodic sequences with a perioidN.
Equivalent Question: Find a set of vectors ∀n, n = {0, . . . , N−1} : bk = ejωkn such that{bk}are a basisforCn
In answer to the above question, let us try the ”harmonic” sinusoids with a fundamental frequencyω0=2πN:
Harmonic Sinusoid
ej2πNkn (7.7)
ej2πNkn is periodic with periodN and hask”cycles” betweenn= 0 andn=N−1.
Theorem 7.1:
If we let
∀n, n={0, . . . , N−1}:bk[n] = 1
√
Nej2πNkn
where the exponential term is a vector in CN, then {bk} |k={0,...,N−1} is an or- thonormal basis (Section 6.7.3)forCN.
Proof:
First of all, we must show{bk}is orthonormal, i.e. bk ã bl=δkl bk ã bl=
N−1
X
n=0
bk[n]bl[n]∗
= 1 N
N−1
X
n=0
ej2πNkne−(j2πNln)
bk ã bl= 1 N
N−1
X
n=0
ej2πN(l−k)n
(7.8)
(a) (b)
(c)
Figure 7.10: Examples of our Harmonic Sinusoids (a) Harmonic sinusoid withk= 0 (b) Imaginary part of sinusoid, Im
ej2πN1n
, withk= 1 (c) Imaginary part of sinusoid, Im
ej2πN2n
, withk= 2
Ifl=k, then
bk ã bl = N1 PN−1 n=0 (1)
= 1 (7.9)
Ifl6=k, then we must use the ”partial summation formula” shown below:
N−1
X
n=0
(αn) =
∞
X
n=0
(αn)−
∞
X
n=N
(αn) = 1
1−α− αN
1−α =1−αN 1−α bk ã bl= 1
N
N−1
X
n=0
ej2πN(l−k)n
where in the above equation we can say thatα=ej2πN(l−k), and thus we can see how this is in the form needed to utilize our partial summation formula.
bk ã bl= 1 N
1−ej2πN(l−k)N 1−ej2πN(l−k)
!
= 1 N
1−1 1−ej2πN(l−k)
= 0 So,
bk ã bl=
1 ifk=l
0 ifk6=l (7.10)
Therefore: {bk} is an orthonormal set. {bk} is also a basis (Section 4.1.3), since there are N vectors which are linearly independent (Section 4.1.1) (orthoganality implies linear independence).
And finally, we have shown that the harmonic sinusoids n
√1
Nej2πNkno
form an orthonormal basis forCn
7.2.2.2 Discrete-Time Fourier Series (DTFS)
Using the steps shown above in the derivation and our previous understanding of Hilbert Spaces and Orthogonal Expansions, the rest of the derivation is automatic. Given a discrete- time, periodic signal (vector inCn)f[n], we can write:
f[n] = 1
√ N
N−1
X
k=0
ckej2πNkn
(7.11)
ck = 1
√ N
N−1
X
n=0
f[n]e−(j2πNkn)
(7.12) Note: Most people collect both the √1
N terms into the expression forck.
Discrete Time Fourier Series: Here is the common form of the DTFS with the above note taken into account:
f[n] =
N−1
X
k=0
ckej2πNkn
ck = 1 N
N−1
X
n=0
f[n]e−(j2πNkn) This what thefftcommand in MATLAB does.