NTIA Technical Report TR-10-470

Radio Spectrum Estimates Using 

Windowed Data and the Discrete 

Fourier Transform

Roger Dalke

NTIA Technical Report TR-10-470

Radio Spectrum Estimates Using 

Windowed Data and the Discrete 

Fourier Transform

Roger Dalke

U.S. DEPARTMENT OF COMMERCE

September 2010

CONTENTS

Page

1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. SPECTRAL ESTIMATES FOR PERIODIC SIGNALS . . . . . . . . . . . . 3

2.1 Fourier Coefficient Estimates . . . . . . . . . . . . . . . . . . . . . . . 4

3. SPECTRUM ESTIMATES FOR RANDOM PROCESSES . . . . . . . . . . 9

3.1 White Gaussian Noise . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.2 Stationary Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.3 Cyclostationary Processes . . . . . . . . . . . . . . . . . . . . . . . . 12

3.4 Statistical Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . 14

4. CONCLUDING REMARKS . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5. REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

iii

RADIO SPECTRUM ESTIMATES USING WINDOWED DATA AND THE

DISCRETE FOURIER TRANSFORM

Roger Dalke∗

Digital signal processing algorithms are commonly used to obtain radio spectrum estimates based on measurements. Such algorithms allow the user to

apply a variety of time-domain windows and the discrete Fourier transform to

RF signals and noise. The purpose of this report is to provide a description

of how signal processing options such as window type, duration, and sampling rate affect power spectrum estimates. Power spectrum estimates for

periodic RF signals and random processes (stationary and cyclostationary)

are analyzed. The results presented can be used to select signal processing

parameters and window types that minimize errors and uncertainties.

Key words: discrete Fourier transform; equivalent noise bandwidth; power spectrum; radio

noise; radio spectrum; spectrum measurement

1. INTRODUCTION

Spectrum measurements are an important tool for evaluating the characteristics of radio

signals and noise. Modern measurement devices employing digital signal processing technology are commonly used to obtain spectral estimates of radio signals. Implementation of

this technology requires engineers to determine various measurement and signal processing

parameters that meet practical requirements and at the same time do not induce deleterious

artifacts. The goal of this brief report is to describe how the application of a window, in

conjunction with the discrete Fourier transform (DFT), to measured data affects power spectrum estimates for periodic radio signals and radio noise. This is accomplished by comparing

the theoretical power spectrum to that obtained by calculating the power spectrum after

applying a window and DFT. The results presented can be used to minimize measurement

errors and uncertainties. In this report, we discuss spectrum estimates for some common

classes of radio signals and noise. The three classes covered are periodic signals (e.g., radar),

stationary noise processes (e.g., radio receiver noise, some types of environmental noise),

and cyclostationary processes (e.g., modulated communications signals, frequency hopped

or gated signals).

In Section 2, we obtain expressions for spectral estimates involving periodic signals. We show

that the obvious estimate of the signal line strength, i.e., the maximum value of the Fourier

transform of the window (near the spectral line), contains errors due to leakage from adjacent

lines and window scalloping error. Bounds on leakage errors for various window types are

obtained. It is shown that, in general, the leakage error can be reduced by increasing the

∗The author is with the Institute for Telecommunication Sciences, National Telecommunications and

Information Administration, U. S. Department of Commerce, Boulder, Colorado 80305.
number of signal periods within the measurement window. Scalloping errors depend on the

window type and can be significantly reduced by using a flat top window.

In Section 3 we calculate the power spectrum estimate for various random processes. First

we obtain the spectrum estimate for stationary white Gaussian noise passed through an ideal

anti-aliasing filter. The analysis is then extended to more general stationary and cyclostationary processes. The results presented show specifically how signal processing parameters

(particularly sample rate, window type, and window duration) affect the spectrum estimate.

For the purposes of calculating power spectrum estimates, we will take the point of view that

sampling rates are selected so that aliasing errors can be ignored and only the periodicity

induced by sampling needs to be considered. Accordingly, analysis of spectrum estimates

will be based on calculating the strength of spectral lines of the periodic extension of the

windowed signal or noise. It is also assumed that the RF signals and noise are first converted

to baseband, which is the starting point of our analysis. It will at times be convenient to

omit limits on integrals of infinite extent. In what follows, integrals with unspecified limits

are of (doubly) infinite extent.

2
2. SPECTRAL ESTIMATES FOR PERIODIC SIGNALS

In this section we obtain an expression for the spectral estimate based on the application of

a window, w(t), and DFT to a periodic RF signal. We then compare the estimate to the

theoretical signal spectrum (i.e., the Fourier coefficients αj ) and describe how the choice of

window and related signal processing parameters affect the estimate.

Let ξ(t) be the baseband representation of a periodic radio signal with period Ts. The

window duration is T and the signal is sampled with a time increment of ∆t = T/N. We

require that w(t) be of bounded variation and that w(t) = 0 for t /∈ [0, T). It is assumed

that the spectrum of the window is symmetric and is maximum at zero Hertz. We will use

the following definition for the DFT of the windowed periodic signal

Xn =

N

X−1

k=0

w(k∆t)ξ(k∆t)e

−i2πnk/N n = 0, 1, . . . , N − 1 (1)

as the spectral estimate.

For the purposes of analysis, we need to write Xn in terms of the Fourier transforms of the

window and the signal. First we define the Fourier transform of the window as

W(f) = Zw(t)e

−i2πf tdt

and the Fourier transform of the periodic signal as

Ξ (f) = X

n∈ Z

αn δ(f − n/Ts) (2)

where Z is the set of integers and

αn =

1

Ts

Z

Ts

0

ξ(t)e

−i2πnt/Ts dt.

Returning to the DFT, we can write

Xn =

X

k∈ Z

φn(k) (3)

where φn(k) = w(k∆t)ξ(k∆t)e

−i2πkn∆t/T . The Poisson summation formula [1]

X

k∈ Z

φn(k) = X

m∈ Z

Z

e

i2πmkφn(k)dk (4)

is used to obtain

Xn =

N

T

X

m∈ Z

W ∗ Ξ



n

T

−

mN

T



3
or

Xn =

N

T







W ∗ Ξ



n

T



+

alias

z }| { X

m6=0

W ∗ Ξ



n

T

−

mN

T



n = 0, 1, . . . , N/2

W ∗ Ξ



n−N

T



+

X

m6=1

W ∗ Ξ



n

T

−

mN

T



| {z }

alias

n = N/2 + 1, . . . , N − 1

(5)

where the symbol ∗ denotes convolution and N is assumed to be even. The terms marked

“alias” are undesirable and result from inadequate sampling. Such errors can be minimized

by sampling at such a rate that aliasing terms are negligible. Broadband signals may require

the application of a filter prior to quantization to obtain, to the extent possible, a bandlimited

function. In this report, we will assume that the Nyquist frequency (N/(2T)) is sufficiently

greater than the highest significant frequency in the signal bandwidth so that aliasing errors

can be ignored, i.e., for “positive” frequencies

Xn =

N

T

W ∗ Ξ



n

T



n = 0, 1, . . . , N/2 (6)

and for “negative” frequencies

XN−n =

N

T

W ∗ Ξ



−

n

T



n = 1, . . . , N/2 − 1.

In what follows, we will explicitly treat only positive frequencies as the results are easily

extended to negative frequencies.

We will assume that, in general, T and Ts are not commensurate and set T = (M + ε)Ts

where 0 ≤ ε < 1 and M > 0 is an integer (note that M is the number of complete signal

periods in the measurement window). Using this combined with (2) and (6) yields

Xn =

N

T

X

k∈Z

αkW((n − (M + ε)k)/T). (7)

2.1 Fourier Coefficient Estimates

Referring to (7), we see that an obvious estimate for the Fourier coefficient αjis obtained by

choosing n so that |W((n−(M +ε)j)/T)| is maximum. We will therefore set n = jM +[jε],

where the square brackets denote the nearest integer function, i.e.,

[x] = (

max {n ∈ Z|n ≤ x + 1/2} x ≥ 0

min {n ∈ Z|n ≥ x − 1/2} x < 0

,

in (7) and obtain the estimate

XjM+[jε] =

N

T



αjW(([jε] − jε)/T) + X

k6=j

αkW((M(j − k) + [jε] − kε)/T)



 .

4
It is useful to express the foregoing result as

XjM+[jε] =

N

T



αjW(εj/T) + X

k6=0

αj−kW(((M + ε)k − εj )/T)



 (8)

where εj = jε − [jε] and therefore 0 ≤ |εj| ≤ 1/2. The first term contains exactly what

we want, the Fourier coefficient αj corresponding to frequency j/Ts. The second term is

due to leakage from adjacent spectral lines. These undesirable contributions can be reduced

by selecting a window with a steep roll off and small sidelobes. Such spurious frequency

components are often referred to as spectral leakage. Before proceeding, some unnecessary

factors can be eliminated by redefining the estimate as

Xˆ

n =

T

NW(0)Xn. (9)

Referring to (8), we see that there are two types of errors. These will be called leakage error

and scalloping error [2] and are defined below. First, the leakage error for the j

th spectral

line is defined as follows

Ej (M) = 1

W(0)





X

k6=0

αj−kW(((M + ε)k − εj )/T)



 . (10)

Note that this error depends on the number of periods in the window (or window duration)

and the frequency of the spectral line (denoted by the subscript j). The estimate for the

j

thspectral line can now be written as

Xˆ

jM+[jε] = ejαj + Ej (M). (11)

Scalloping error is due to the factor that scales the j

th Fourier coefficient

ej =

W(εj/T)

W(0) . (12)

Note that, for commonly used windows, ejis independent of M and varies with the window

type and frequency of the spectral line. For typical windows, the scalloping error bounds

are

1 ≥ |ej| ≥ |W(1/(2T))/W(0)| .

2.1.1 Bounds on Spectral Leakage Errors

In this subsection we will obtain leakage error bounds for some common windows. We will

use the standard notation f(x) = O(g(x)) to mean that as x tends to a limit, f(x)/g(x)

remains bounded, i.e., f(x) is at most of the order g(x). We will also use the notation

f(x) = o(g(x)) to mean that f(x)/g(x) → 0 or f(x) is of smaller order than g(x). The

symbol O(1) will be used to signify a bounded function.

5
Referring to (10) we easily obtain a starting point for calculating the leakage error bound

|Ej (M)| ≤ Cj

|W(0)|

X

k6=0

χ(j − k)|W (((M + ε)k − εj )/T)| (13)

where Cj = maxk6=j|αk| is a constant and

χ(j − k) = (

1 |αj−k| 6= 0

0 else .

2.1.2 Uniform Window

The uniform window is defined as

w(t) = (

1 0 ≤ t < T

0 else

and hence, |W(f)| = |T sinc(π f T)|. Beginning with (13), we can simplify the error bound

as follows

|Ej (M)| ≤ Cj

X

k6=0

χ(j − k)











sin π((M + ε)k − εj )

πM k(1 + ε/M − εj/(M k))











≤

Cj

Mπ(1 − 1/(2M))

X

k6=0

χ(j − k)

|k|

. (14)

Note that Ej ≡ 0 when ε = 0 (i.e., T and Ts are commensurate).

Let L + 1 be the number of lines that define the nominal bandwidth of the periodic signal.

The bound can then be further simplified to

|Ej (M)| ≤ 2CjHL

Mπ(1 − 1/(2M)) (15)

where HL =

PL

k=1

1

k

are called harmonic numbers.

When L is small, HL is easy to calculate. More generally, to describe how this bound changes

with bandwidth, we can use an inequality from [3] to obtain

|Ej (M)| <

2Cj

Mπ(1 − 1/(2M)) 

γ + loge



L +

1

2



+

1

24L2



where γ = 0.57721 . . . is Euler’s constant. Note that this bound grows very slowly with the

number of lines (or equivalently, bandwidth). The important point is that, for large M, the

leakage error is inversely proportional to the number of signal periods in the uniform window.

The asymptotic behavior of the leakage error for the uniform window can be summarized as

follows:

6
|Ej (M)| ∼ (

O(1/M) ε 6= 0

0 ε = 0 .

These results show that the leakage error can be made as small as desired (within practical

limits) by increasing M. This is not true for the scalloping error. Referring to (12), the

scalloping error can be as much as 4 dB. This problem can be mitigated by using a modified

version of the uniform window known as the flat top window which is discussed in the

following subsection.

2.1.3 Flat Top and Related Windows

The flat top window is defined as

w(t) = ( P4

`=0(−1)`a` cos 2π`t/T 0 < t < T

0 else .

Typical values for the coefficients a` are given in the following table.

Table 1: Flat Top Window Coefficients

a0 0.215578948

a1 0.416631580

a2 0.277263158

a3 0.083578947

a4 0.006947368

The magnitude of the Fourier transform of the flat top window is

|W(f)| = |Tsinc(πfT)Y(f)|

where

Y (f) = 1

2

X

4

`=0

(−1)`a`

 

1

1 − `/fT +

1

1 + `/fT !

.

The spectrum of this window is the product of the uniform window spectrum, treated in the

previous section, and |Y (f)|. Therefore, we need to determine how this function modifies

the previous result.

Referring to (13), when k = 1, fT = M + ε − εj, so that for M ≤ 4 and when |ε − εj| << 1,

|Y ((M + ε − εj )/T)| = O(1/|ε − εj|).

7
In this case,

|sinc(π(M + ε − εj))Y((M + ε − εj)/T)| = O(1).

Unlike the uniform window, the leakage error does not vanish when ε = 0. Also, in general,

the magnitude of the leakage error does not decrease with M until M > 4. When M >> 4,

|Y (((M + ε)k − εj )/T)| = O(1) and the behavior is essentially the same as the uniform

window, i.e.,

|Ej (M)| ∼ O(1/M).

The main advantage of the flat top window is the small scalloping error. Referring to (12),

the worst case scalloping error is about 0.01 dB. This is significantly less than the worst case

scalloping error for the uniform window.

The Hanning and Hamming windows are of the same form with a2 = a3 = a4 = 0. Hence,

the leakage error is somewhat better for smaller values of M; however, the scalloping errors

in both cases are greater than the flattop window (about 1.5 dB).

2.1.4 Ideal Gaussian Window

For this window, |W(f)| =

√

2πσT e−2(πσfT)

2

. We call this window ideal because the effects

of time domain truncation are not included. When T is large, such effects will be small. For

this window, (13) reduces to

|Ej (M)| ≤ Cj

X

k6=0

χ(j − k)e

−2(πσ(k(M+ε)−εj ))2

.

As before let L + 1 lines define the nominal bandwidth. Since maxj εj = 1/2 and min ε = 0

we have

|Ej (M)| ≤ 2Cj

X

L

k=1

e

−2k

2

(πσ(M−1/(2k)))2

.

The first term of the series is O(e

−2(πσ(M−1/2))2

) and successive terms are o(e

−2k

2

(πσ(M−1/2))2

).

Due to the exponential character, when πσ is not too small, terms involving |k| > 1 and

M > 1 are negligible and it is reasonable to estimate leakage error using the adjacent lines.

Hence, in general, we can say that the leakage error behaves as follows

|Ej (M)| ∼ O(e

−2(πσ(M−1/2))2

).

The obvious advantage of the Gaussian window is that the leakage error essentially decreases

exponentially with M2.

A typical value for the noise equivalent bandwidth (see Section 3.1) of a Gaussian top window

is 2.215/T which corresponds to πσ = 0.4. For this case, the maximum scalloping error is

about 0.7 dB. The exponentially decreasing leakage error and small scalloping error makes

the Gaussian window quite attractive.

8
3. SPECTRUM ESTIMATES FOR RANDOM PROCESSES

In this section, we obtain estimates for the mean power at the DFT frequencies (i.e. n/T).

The estimate will be compared to the theoretical power spectrum to quantify the effects

of the window and related signal processing parameters. First we will evaluate the spectral

estimate for stationary Gaussian noise. Following this, we will obtain results for more general

stationary and cyclostationary processes.

As in the previous section, we begin the analysis by using the Poisson summation formula

((3) and (4)). Now, however, ξ(t) is assumed to be a zero-mean random process. The Fourier

transform estimates obtained by applying the DFT to noise are zero-mean random variables,

therefore we need to talk about statistics. For the purposes of this report, we will be content

to calculate the variance.

From (3) and (4) we have

var (Xn) = N

T

X

m∈Z

var





Z

T

0

w(k)ξ(k)e

−i2πk(n−mN)/T dk



 . (16)

Before proceeding, we note that the stochastic integral exists in the sense of the quadratic

mean and

var





Z

T

0

w(k)ξ(k)e

−i2πk(n−mN)/T dk



 =

Z

T

0

Z

T

0

w(k)w

∗

(`)e

−i2π(k−`)(n−mN)/T γ(k, `)dkd`

if the covariance function γ(t, s) = E {ξ(t)ξ

∗

(s)} is continuous in [0, T] × [0, T] and the

Riemann integral on the right hand side exists [4]. This result extends to Riemann-Stieltjes

integrals in which case γ(t, s) needs only to be of bounded variation over the rectangle.

As in the previous section, we explicitly treat only positive frequencies and assume that

effects of aliasing can be ignored (see (5)). Since ξ(t) is zero mean (i.e., var (Xn) = E {|Xn|

2}),

(16) can be written in terms of Fourier transforms of the window and noise as follows

E

n

|Xˆ

n|

2

o

= E

(

1

|W(0)|

2

ZZ W(f)W∗

(g) Ξ(n/T − f) Ξ∗(n/T − g)df dg

)

n = 0, . . . , N/2−1.

(17)

where for convenience we have used Xˆ

n (see (9)). Here, we have assumed that the the Fourier

transform of noise, Ξ(f), exists in the sense that it can be treated as a generalized random

process.

Since we will be considering broadband noise, an anti-aliasing filter needs to be used prior

to quantization and the application of the DFT. We will use r(t) (or r(t, s) if the process is

not stationary) to denote the covariance of the process prior to filtering and

R(f) = Zr(t)e

−i2πf tdt

9
to denote the power spectrum. The notation ξ(t) (and the Fourier transform Ξ(f)) will be

used to describe the process after application of the anti-alias filter.

3.1 White Gaussian Noise

In this subsection, we consider white Gaussian noise passed through an ideal anti-alias filter

h(t) with Fourier transform

H(f) = (

1 −N/(2T) ≤ f ≤ N/(2T)

0 else . (18)

The noise has a constant power spectral density R(f) = N and γ(t) = N

R

h(x)h

∗

(t + x)dx.

The Fourier transform of the filtered noise, Ξ(f), is a generalized random process with

covariance E {Ξ(f) Ξ∗(g)} = N|H(f)|

2

δ(f − g) and from (17) we obtain

E

n

|Xˆ

n|

2

o

=

N

|W(0)|

2

Z

|W(f)|

2

|H(n/T − f)|

2

df. (19)

The estimate should be proportional to N for all frequencies (i.e., n/T) of interest for measurement purposes. This is accomplished by choosing a window with a reasonably steep roll

off (i.e., W(f) has negligible support outside of the bandpass range of the anti-alias filter in

(18)), small sidelobes, and a sufficiently fast sample rate so that

Z

|W(f)|

2

|H(n/T − f)|

2

df ≈ |H(n/T)|

2

Z

|W(f)|

2

df. (20)

It is important to note that this approximation breaks down for frequencies near Nyquist

due to end effects of the filter. Hence, the Nyquist frequency should be selected so that (20)

is good approximation over the frequency range of interest.

We then have the desired result

E

n

|Xˆ

n|

2

o

≈ N Beq n = 0, 1, · · · < N/2

where

Beq =

R

|W(f)|

2df

|W(0)|

2

is the equivalent noise bandwidth of the window [2].

The total noise power in the measurement bandwidth BM < N/T is estimated by adding

up the power contribution of each discrete frequency. If we let L be the number of discrete

frequencies in BM, then the power in BM is

P ≈ N LBeq.

10
At this point it is useful to introduce normalized equivalent noise bandwidth B(n)

eq = T Beq

which characterizes the window but is independent of the window duration T. The estimated

power in the measurement bandwidth can now be written as

P ≈ N

L

T

B

(n)

eq

≈ NBMB

(n)

eq .

Hence, the estimated power in the measurement bandwidth is the actual noise power in that

bandwidth scaled by the normalized equivalent noise bandwidth of the window.

Generally, the noise power density is not flat. This state of affairs is analyzed in the next

subsection.

3.2 Stationary Processes

Again, the covariance is a function of a single variable and it follows that E {Ξ(f) Ξ∗(g)} =

R(f)|H(f)|

2

δ(f−g). Here, we do not assume that R(f) is constant and the noise is Gaussian.

To allow for broadband noise we include an anti-alias filter as in the previous subsection (see

(18)).

Equation (17) reduces to

E

n

|Xˆ

n|

2

o

=

1

|W(0)|

2

Z

|W(f)|

2R(n/T − f)|H(n/T − f)|2

df.

Since the actual noise power spectral density at frequency n/T is R(n/T) we want

Z

|W(f)|

2R(n/T − f)|H(n/T − f)|2

df ≈ R(n/T)|H(n/T)|

2

Z

|W(f)|

2

df.

To accomplish this, we need to select a window that is narrow with a steep roll off and small

sidelobes so that R(f) is approximately constant over the bandwidth of the window. We

also assume that the maximum frequency of interest is sufficiently below Nyquist (as in the

previous subsection) so that

E

n

|Xˆ

n|

2

o

≈ R(n/T)Beq n = 0, 1, · · · < N/2.

As before, the total power in BM < N/T is obtained by adding the contribution of each

discrete frequency

P ≈

B(n)

eq

T

X

n∈I

R(n/T)

where I is the interval [−T BM/2, T BM/2].

The actual noise power for the process is r(0) = RR(f)df. So, if the measurement bandwidth

contains the noise power spectrum and if N and T are chosen so that

r(0) ≈

1

T

X

n∈I

R(n/T),

11
the total power estimate is (approximately) proportional to the actual power as required,

i.e.,

P ≈ r(0)B

(n)

eq .

Not surprisingly, the total power estimate contains a discrete approximation for the integral

over the power spectrum. To obtain a reasonable estimate of the power, the frequency

spacing (1/T) should be small enough to adequately sample the power spectrum. Sampling

errors can be minimized as desired by increasing T (and also N since increasing the time

increment alone could lead to aliasing errors).

3.3 Cyclostationary Processes

In this subsection we extend the previous analysis to include cyclostationary processes. As

before, we will allow for broadband processes and include the anti-alias filter of (18). By

definition, the covariance function r(t, s) is periodic in both t and s. We will assume that

the Fourier series exists so that we can write

r(t, s) = X

m∈Z

rm(s − t)e

i2πmt/T0

where T0 is the period of the covariance function and rm(s − t) are the Fourier coefficients.

Here ξ(t) represents the filtered process, which is cyclostationary, with covariance γ(t, s) and

Fourier transform Ξ(f). We then can write

E {Ξ(f) Ξ∗(g)} =

ZZ γ(t, s)e

−i2π(f t−gs)

dt ds

=

X

m∈Z

ZZZZ rm(z)e

i2πmx/T0 h(t − x)h∗

(s − z − x)e

−i2π(f t−gs)

dt ds dz dx

and hence,

E {Ξ(f) Ξ∗(g)} = H(f)H

∗

(g)

X

m∈Z

Rm(−g)δ(f − g − m/T0)

where Rm(f) is the Fourier transform of rm(t). Application of this last result to (17) gives

E

n

|Xn|

2

o

=

1

|W(0)|

2

X

m∈Z

Z

Rm(n/T−f−m/T0)W∗(f+m/T0)W(f)H(n/T−f)H

∗

(n/T−f−m/T0)df.

By choosing a window with a steep roll off and small sidelobes, so that the window spectrum

is negligible when |f| ≥ 1/(2T0), we obtain the m = 0 term of the sum which is the timeaverage of the expected value, i.e.,

D

E

n

|Xn|

2

oE =

1

|W(0)|

2

Z

R0(n/T − f)|W(f)|

2

|H(n/T − f)|

2

df.

12
Here, the notation h·i is used to emphasize the fact that this is a time average.

As in the previous subsections, the window (and window duration) should be selected so

that

Z

R0(n/T − f)|W(f)|

2

|H(n/T − f)|

2

df ≈ R0(n/T)|H(n/T)|

2

Z

|W(f)|

2

df.

We then have the desired spectral estimate,

D

E

n

|Xn|

2

oE ≈ R0(n/T)Beq.

Note that R0(f) is the time average power density.

The total power in BM < N/T is obtained by adding the contribution of each discrete

frequency

hPi ≈ B(n)

eq

T

X

n∈I

R0(n/T). (21)

Assuming that the noise power spectrum is contained in the measurement bandwidth, we

can compare this result with the actual power for the process. For this, we will use the

following expression to calculate the time average of the average power of the zero-mean

cyclostationary process n(t):

hPi = lim

Z→∞

1

Z

Z/2

Z

−Z/2

E

n

|n(t)|

2

o

dt.

Applying the Fourier series representation of the covariance function, we have

hPi = lim

Z→∞

X

n

rn(0)

Z/2

Z

−Z/2

e

i2πnt/T0 dt = lim

Z→∞

X

n

rn(0)sinc(πnZ/T0) = r0(0)

(recall that r0(0) = RR0(f)df). If the measurement bandwidth contains the power spectrum

and if N and T are chosen so that

r0(0) ≈

1

T

X

n∈I

R0(n/T),

we find that the total power estimate (21) and the average power are related as follows

hPi ≈ r0(0)B

(n)

eq .

Note that this result is similar to what we found for stationary processes. Here, the total

power estimate contains a discrete approximation for the integral over the time average

power spectrum. As before, the estimate is scaled by the normalized equivalent bandwidth

of the window. Hence, a frequency spacing that adequately samples the (time average) power

spectrum should be selected. Such sampling errors can be reduced as desired by increasing

T (and also N since increasing the time increment alone could lead to aliasing errors).

13
3.4 Statistical Uncertainties

We have shown that if the window is narrow with a steep roll off, small sidelobes, and T

is large enough, var (Xn) is (approximately) proportional to the power spectral density (or

average power spectral density in the case of a cyclostationary process). In practice, we only

obtain an estimate of var (Xn) using for example an arithmetic mean of several realizations

(e.g., the Welsh method [5]). Therefore statistical uncertainties need to be considered.

The random variables |Xn|

2 are asymptotically (with N) independent chi-square random

variables with 2 degrees of freedom and (e.g., [6])

var |Xˆ

n|

2



∼ |R(n/T)Beq|

2

for stationary processes and

D

var |Xˆ

n|

2

E ∼ |R0(n/T)Beq|

2

for cyclostationary processes.

If we denote the i

th realization of Xn as X(i)

n

and arithmetic mean as

CM =

1

M

X

M

i=1

|X

(i)

n

|

2

,

the CM are asymptotically chi-square with 2M degrees of freedom. The estimate is biased

and, to the extent that aliasing can be ignored and N is large,

E {CM} ≈ R(n/T)Beq

var (CM) ≈

|R(n/T)Beq|

2

M

(22)

for stationary processes and

E {CM} ≈ R0(n/T)Beq

var (CM) ≈

|R0(n/T)Beq|

2

M

(23)

for cyclostationary processes.

14
4. CONCLUDING REMARKS

In this report, we have described how the application of a window in conjunction with the

DFT to periodic radio signals and radio noise affect power spectrum estimates. The results

are used to describe how window characteristics and related signal processing parameters

affect measurement errors and uncertainties.

In the case of periodic radio signals, we show that there are errors due to spectral leakage and

window scalloping. These errors depend on the window type and related signal processing

parameters. In particular, error bounds for various windows are presented. In all cases, the

leakage error can be reduced by increasing the number of signal periods in the window (i.e.,

the window duration). By far, the leakage error decreases most rapidly (as a function of the

window duration) for the Gaussian window. The scalloping error is independent of window

duration and is smallest for the flat top window.

In the case of stationary noise, we describe how the window and related signal processing

parameters affect both the estimated power spectral density and the total power in the

measurement bandwidth. It is shown that the window should be selected so that the noise

power spectral density is essentially constant over the bandwidth of the window. Also, the

window duration should be long enough so that the noise power spectrum is adequately

sampled.

Cyclostationary random processes were also considered. In this case, we examined estimates

of the time average of the power spectrum. It was found that in addition to the considerations

described for stationary processes, the window bandwidth should be less than (one-half) the

repetition rate of the covariance function.

15
5. REFERENCES

[1] J. J. Benedetto and G. Zimmerman, “Sampling multipliers and the Poisson summation

formula,” J. Fourier Ana. App., vol. 3, no. 5, pp. 505-523, 1997.

[2] F. J. Harris, “On the use of windows for harmonic analysis for the discrete Fourier

transform,” Proc. of the IEEE, vol. 66, no. 1, pp. 51-83, Jan. 1978.

[3] R.M. Young,“Euler’s constant,” Math. Gaz. 75, pp. 187-190, 1991.

[4] H. Cramer and M. R. Leadbetter, Stationary and Related Stochastic Processes, New

York, NY: John Wiley and Sons, Inc., 1967.

[5] A. V. Oppenheim and R. W. Schafer, Digital Signal Processing, Englewood Cliffs, NJ:

Prentice-Hall, 1975, p.553.

[6] R. Dalke, “Statistical considerations for noise and interference measurements,” NTIA

Technical Report TR-09-458, Nov. 2008.

16
FORM NTIA-29 U.S. DEPARTMENT OF COMMERCE

(4-80) NAT’L. TELECOMMUNICATIONS AND INFORMATION ADMINISTRATION

BIBLIOGRAPHIC DATA SHEET

1. PUBLICATION NO.

TR-10-470

2. Government Accession No. 3. Recipient’s Accession No.

4. TITLE AND SUBTITLE

Radio Spectrum Estimates Using Windowed Data and the Discrete Fourier Transform

5. Publication Date

September 2010

6. Performing Organization Code

NTIA/ITS.T

7. AUTHOR(S)

Roger Dalke

9. Project/Task/Work Unit No.

8. PERFORMING ORGANIZATION NAME AND ADDRESS 3105012-300

Institute for Telecommunication Sciences

National Telecommunications & Information Administration

U.S. Department of Commerce

325 Broadway

Boulder, CO 80305

10. Contract/Grant No.

11. Sponsoring Organization Name and Address

National Telecommunications & Information Administration

Herbert C. Hoover Building

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NTIA Technical Report

14. SUPPLEMENTARY NOTES

15. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant bibliography or 

literature survey, mention it here.)

Digital signal processing algorithms are commonly used to obtain radio spectrum estimates based on measurements. Such 

algorithms allow the user to apply a variety of time-domain windows and the discrete Fourier transform to RF signals and 

noise. The purpose of this report is to provide a description of how signal processing options such as window type, duration, 

and sampling rate affect power spectrum estimates. Power spectrum estimates for periodic RF signals and random processes 

(stationary and cyclostationary) are analyzed. The results presented can be used to select signal processing parameters and 

window types that minimize errors and uncertainties.

16. Key Words (Alphabetical order, separated by semicolons)

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