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8/13/2019 Spectrum Analyzer Fundamentals
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Spectrum AnalyzerFundamentalsChris Gillis
Application Engineer
Signal/Spectrum Analyzers & Signal Generators
+1.438.863.5760
October 29, 2013
University of British Columbia
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Agendal Frequency vs Time Domain
l Spectrum Analyzers
l FFT Analyzer
l Superheterodyne Spectrum Analyzer
l Implementation
l Important Settings
lImportant Specifications
l Common Measurements
l Additional Functionality
l Vector Signal Analysis
l Real-time Spectrum Analysis
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Agendal Frequency vs Time Domain
l Spectrum Analyzers
l FFT Analyzer
l Superheterodyne Spectrum Analyzer
l Implementation
l Important Settings
lImportant Specifications
l Common Measurements
l Additional Functionality
l Vector Signal Analysis
l Real-time Spectrum Analysis
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Frequency vs Time Domain
l Fourier Transform linkstime and frequency domain
l For periodic signals, this is a Fourier Series
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Frequency vs Time Domain
Periodic
vs
Non-periodic
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Frequency vs Time Domain
Looking at the time or frequency domain can reveal differentinformation about the signal
Oscilloscope: look at amplitude vs time
Spectrum Analyzer: look at power vs frequency
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Frequency vs Time Domain
Time Domain Frequency Domain
l For example, harmonics could easily be missed
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Agendal Frequency vs Time Domain
l Spectrum Analyzers
l FFT Analyzer
l Superheterodyne Spectrum Analyzer
l Implementation
l Important Settings
lImportant Specifications
l Common Measurements
l Additional Functionality
l Vector Signal Analysis
l Real-time Spectrum Analysis
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FFT Analyzer
l As time and frequency are linked by the Fourier transform,we could just capture time data and compute the Fourier
transform
l Instead of capturing infinite time, we can compute the
Discrete Fourier Transform, which transforms discrete time
data into discrete spectrum data
l Use Fast Fourier Transform algorithms
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FFT Analyzer
l According to Nyquist, you need a sampling frequency atleast twice the highest frequency component to properly
recreate a signal
l Encounter problems with bandwidth, range
>10 samplesAlias
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Filter Bank Spectrum Analyzer
l Problem: not very practical
f1
f2
f3
f4
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Tunable Filter Spectrum Analyzer
l Problem: bandpass filter changes bandwidth depending on center
frequency
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Simplified Swept Tuned Block Diagram
InputAtten
MixerEnvelopeDetector
Log Amp
LPFBPF
Display
Sawtooth
Local
Oscillator
IFAmplifier
ResolutionBW Filter
y
x
VideoBW Filter
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Input Mixer
InputAtten
MixerEnvelopeDetector
Log Amp
LPFBPF
Display
Sawtooth
Local
Oscillator
IFAmplifier
ResolutionBW Filter
y
x
VideoBW Filter
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Types of Mixing
l Fixed RF, Swept LO and IF
l Fixed LO, Swept RF and IF
l Fixed IF, Swept LO and RF (used in spectrum analyzers)
l Upconversion
l IF frequency is higher than RF and LO frequency
l Downconversion
l IF frequency is lower that RF and LO frequency
RF
LO
IF
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RF1 GHz
LO
1.1 GHz
Possible frequencies on IF
portto name a few:
LO-RF=100MHz
LO+RF= 2.1GHz
LO=1.1 GHz
RF=1 GHz
2LO-RF=1.2 GHz
2RF-LO= 900 MHz
IF
Mixer Example
{|mfLO nfRF| = fIF
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Resolution Bandwidth
InputAtten
MixerEnvelopeDetector
Log Amp
LPFBPF
Display
Sawtooth
Local
Oscillator
IFAmplifier
ResolutionBW Filter
y
x
VideoBW Filter
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Ref -20 dBm Att 5 dB
CLRWR
A
Center 1 GHz Span 100 kHz10 kHz/
*
1 AP
RBW 20 kHz
SWT 2.5 ms
VBW 50 kHz
-120
-110
-100
-90
-80
-70
-60
-50
-40
-30
-20
Date: 7.NOV.2006 12:17:44
Ref - 20 dBm Att 5 dB
CLRWR
A
Center 1 GHz Span 100 kHz10 kHz/
*
1 PK
RBW 20 kHz
AQT 2.5 ms
-120
-110
-100
-90
-80
-70
-60
-50
-40
-30
-20
Date: 7.NOV.2006 12:17:11
Normal (Gaussian) FFT
Ref -20 dBm Att 5 dB
CLRWR
A
Center 1 GHz Span 100 kHz10 kHz/
1 AP
*RBW 20 kHz
VBW 50 kHz
SWT 50 ms
-120
-110
-100
-90
-80
-70
-60
-50
-40
-30
-20
Date: 7.NOV.2006 12:16:44
Channel
Ref -20 dBm Att 5 dB
CLRWR
A
Center 1 GHz Span 100 kHz10 kHz/
1 AP
*RBW 18 kHz
VBW 50 kHz
SWT 65 ms
-120
-110
-100
-90
-80
-70
-60
-50
-40
-30
-20
Date: 7.NOV.2006 12:16:17
RRC
Ref -20 dBm Att 5 dB
CLRWR
A
Center 1 GHz Span 100 kHz10 kHz/
1 AP
*RBW 20 kHz
VBW 50 kHz
SWT 2.5 ms
-120
-110
-100
-90
-80
-70
-60
-50
-40
-30
-20
Date: 7.NOV.2006 12:15:43
5 Pole
Default Setting for standard spectrum analyzing tasks
IF Filter Types
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Resolution Bandwidth
2 kHz
200 Hz
Signals separated by
1kHz cant be resolved
by 2kHz RBW
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Resolution Bandwidth and DANL*
100 kHz
300 kHz
1 MHz
RBW
*DANL: Displayed Average Noise Level
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Envelope Detector
InputAtten
MixerEnvelopeDetector
Log Amp
LPFBPF
Display
Sawtooth
Local
Oscillator
IFAmplifier
ResolutionBW Filter
y
x
VideoBW Filter
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Envelope DetectorRMS detector (power average)
RMS detector reports the true noise power. (The
RMS value)
Ave detector (voltage average)
Averages the noise voltage, then converts to power.
This is lower by 1.05 dB.
(squaring the ave is not equal to averaging the square)
Sample detector
Takes the first sample
Randomly located between peaks
Sample detector & trace averaging
Noise averaging is done on a log scale, introducing
a new error of 2.51 dB
Total error is now 2.51 dB
pixel n
(8 sampl es)
pixel n+1
(8 samples)
displayedpixels
positivepeak
sample
rms
negativepeak
A/Dsamples
(linearrange)
s1 s2 s3 s4 s5 s6 s6 s 8 s 1 s2 s3 s4 s5 s6 s6 s8
ave
N
i
irms sNV 1
21
N
i
iave sNV 1
1
Samples / pixel is determined by sweep time and sample rate
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Ref -90 dBm Att 5 dB
*
*
1 RM
VIEW
2 AV
VIEW
3 SA
VIEW
*
A
3DB
RBW 200 kHz
VBW 500 kHz
SWT 2.5 ms
Center 1 GHz Span 10 MHz1 MHz/
-100
-99
-98
-97
-96
-95
-94
-93
-92
-91
-90
Spectrum AnalyzersHow to measure noise
Delta: 1.05 dB
Delta: 2.51 dB
RMS
detector
Average
detector
Sampledetector
& trace
ave (Log)
l Measure Noise with different detectors
l RMS detector measures true noise power
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Spectrum AnalyzersHow to measure noise
RMS
detector
RMS detector
& trace ave
(Lin) or (Pwr)
l Measure Noise with different detectors
l RMS detector measures true noise powerl We can apply linear or power trace averaging to an RMS detector.
Ref -90 dBm Att 5 dB
*
*
1 RM
VIEW
2 RM
VIEW
*
A
3DB
RBW 200 kHz
VBW 2 MHz
SWT 2.5 ms
Center 1 GHz Span 10 MHz1 MHz/
-100
-99
-98
-97
-96
-95
-94
-93
-92
-91
-90
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Ref -90 dBm Att 5 dB
*1 RM
VIEW
2 SA
AVG
*
A
3DB
RBW 200 kHz
VBW 500 kHz
SWT 2.5 ms
Center 1 GHz Span 10 MHz1 MHz/
-100
-99
-98
-97
-96
-95
-94
-93
-92
-91
-90
SWP 1000 of 1000
Spectrum AnalyzersHow to measure noise
RMS
detector
Sample
detector &
trace ave
(Lin) or (Pwr)
l Measure Noise with different detectors
l RMS detector measures true noise powerl Sample detector & linear or power trace averaging yields the same results
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Ref -90 dBm Att 5 dB
*
*
*
1 RM
VIEW
2 AV
VIEW
3 AV
VIEW
*
A
3DB
RBW 200 kHz
VBW 2 MHz
SWT 2 s*
Center 1 GHz Span 10 MHz1 MHz/
-100
-99
-98
-97
-96
-95
-94
-93
-92
-91
-90
SWP 2 of 1000
Spectrum AnalyzersHow to measure noise
RMS
detector
Average
detector &
Log trace
average
l Measure Noise with different detectors
l RMS detector measures true noise power
l Ave detector plus any trace averaging does not yield the same result
Do not use trace averaging with the average detector
Delta: 1.05 dBAverage
detector
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Ref -90 dBm Att 5 dB
*
*
*
1 RM
VIEW
2 AV
VIEW
3 AV
VIEW
*
A
3DB
RBW 200 kHz
VBW 2 MHz
SWT 2.5 ms
Center 1 GHz Span 10 MHz1 MHz/
-100
-99
-98
-97
-96
-95
-94
-93
-92
-91
-90
Spectrum AnalyzersHow to measure noise
RMS
detector
Average
detector &
power trace
average
l Measure Noise with different detectors
l RMS detector measures true noise power
l Ave detector plus any trace averaging does not yield the same result
Do not use trace averaging with the average detector
Delta: 1.05 dBAverage
detector
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Detector and Trace Usage
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Video Filter
InputAtten
MixerEnvelope
Detector
Log Amp
LPFBPF
Display
Sawtooth
Local
Oscillator
IFAmplifier
ResolutionBW Filter
y
x
VideoBW Filter
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Video Filter
500kHz
500Hz
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l Tunablel Sweeps across measurement Span
l Linear sawtooth drives LO and X-position on Display
l Repetition rate (sweep time) determined by RBW
l Sweep time can be manually adjusted
(for certain measurements)
l Not perfect, has phase noise
Local Oscillator
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What is Phase Noise?
Ideal Signal (noiseless)
V(t) = A sin(2t)
where
A = nominal amplitude
= nominal frequency
Real SignalV(t) = [A + E(t)] sin(2t + (t))
where
E(t) = amplitude fluctuations
(t)= phase fluctuations
Key Point: Phase Noise is unintentional phase modulation on a carrier that
spreads its spectrum
Level
f
Level
f
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Phase NoiseUnit of Measure
Phase Noise is expressed as (f)
(f) is defined as single sideband power due to phase
fluctuations in a rectangular 1Hz bandwidth at a
specified offset, f, from the carrier
(f) has units of dBc/Hz
FREQUENCY
AMPLITUDE
1 Hz
V0 V0+f
O(f)
LOG A
LOG f
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Phase NoiseWho cares?
Modulator designers
Phase noise degrades EVM
Transmitter designers
Phase noise degrades adjacent channel power (ACPR)
Receiver designers
Phase noise degrades receiver sensitivity and selectivity
Radar designers Phase noise degrades sensitivity to small return signals in
the presence of clutter
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Why do we measure Phase Noise?
l Especially relevant: phase noise
impacts the ability to detect smallsignals near larger interfering signals
IF
Wanted signal mixed
to IF by the LO
IF
But an interferer can mix
with phase noise of the
LO to the same IF
Wh t h if t f t?
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What happens if you sweep too fast?
l
Frequency errorl Amplitude error
A d
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Agendal Frequency vs Time Domain
l Spectrum Analyzers
l FFT Analyzer
l Superheterodyne Spectrum Analyzer
l Implementation
l Important Settings
l Important Specifications
l Common Measurements
l Additional Functionality
l Vector Signal Analysis
l Real-time Spectrum Analysis
Di f S h t d S t A l
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Diagram of Superheterodyne Spectrum Analyzer
Si lifi d M d l A t l I l t ti
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Simplified Model vs Actual ImplementationWhy we have multiple IF stages
l
If we do straight downconversion, our input, LO and imagefrequencies overlap. This would require complex filtering to
eliminate
Simplified Model s Act al Implementation
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l
If we use a high IF, filtering becomes much easier!
Simplified Model vs Actual ImplementationWhy we have multiple IF stages
Simplified Model vs Actual Implementation
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l
However we cant simply downconvert to DC as we stillhave filtering issues
l Creating a very narrowband filter at a high frequency is
difficult
Simplified Model vs Actual ImplementationWhy we have multiple IF stages
Diagram of Superheterodyne Spectrum Analyzer
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Diagram of Superheterodyne Spectrum Analyzer
Simplified Model vs Actual Implementation
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Simplified Model vs Actual ImplementationLocal Oscillator
l
Use a synthesized signal for the LOl Locked to reference signal (internal or external)
l Use multiplication and division factors
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Simplified Model vs Actual Implementation
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Simplified Model vs Actual ImplementationHigher Frequencies
l YIG filter allows for excellent selectivity
l Overcomes our problem with filters at high frequencies with
wide bandwidths
Simplified Model vs Actual Implementation
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Simplified Model vs Actual ImplementationHigher FrequenciesHarmonic Mixers
|mfLO nfRF| = fIF
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Simplified Model vs Actual Implementation
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Simplified Model vs Actual ImplementationDiagram of FSW
l Different paths for different frequency ranges and
bandwidths
l Pre-amplifier option for looking at weaker signals
l Signals are digitized higher and higher up the chain
l FFTs are used in combination with heterodyne principle
Agenda
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Agendal Frequency vs Time Domain
l Spectrum Analyzers
l FFT Analyzerl Superheterodyne Spectrum Analyzer
l Implementation
l Important Settings
l Important Specifications
l Common Measurements
l Additional Functionality
l Vector Signal Analysis
l Real-time Spectrum Analysis
Important Settings
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Important Settings
l Center frequency and span
l Number of points
l Resolution Bandwidth
l Video Bandwidth
l Sweep Time
l Detector
l Trigger
l Reference level
l Attenuation
Important Settings
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Important SettingsReference Level + Attenuation
Important Settings
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Important SettingsReference Level + Attenuation
Important Settings
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Important SettingsReference Level + Attenuation
Agenda
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Agendal Frequency vs Time Domain
l Spectrum Analyzers
l FFT Analyzerl Superheterodyne Spectrum Analyzer
l Implementation
l Important Settings
l Important Specifications
l Common Measurements
l Additional Functionality
l Vector Signal Analysis
l Real-time Spectrum Analysis
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Important Specifications
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Important Specifications
l Displayed Average Noise Level (DANL)
l There are typically processing techniques to lower the noise floor
With preamp.
With preamp. + noise correction
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Dynamic Range: Internal Distortion
The difference (in dB) between the Input Level that produces
distortion products equal to the noise floor and the noise floor level
(DANL)
But, what type of distortion?
Compression Point
Second Order
Third order
Dynamic range:
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f1 12f 3f1
harmonics
2nd order 3rd order
frequency
level
f 3f2f2 2 2f -f 2f - f 2f - f2 1 1 2 12 f +f12
Intermod.intermod.3rd orderintermod.2nd order
Dynamic range:
Intermodulation and Harmonics
D namic Range
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Dynamic Range:
WCDMA ACLR
Other Important Specifications
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Other Important Specifications
l Speed
l Sweep speed and processing speed
l Measurement uncertainty
Agenda
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Agendal Frequency vs Time Domain
l Spectrum Analyzers
l FFT Analyzerl Superheterodyne Spectrum Analyzer
l Implementation
l Important Settings
l Important Specifications
l Common Measurements
l Additional Functionality
l Vector Signal Analysis
l Real-time Spectrum Analysis
Measurement functions
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Measurement functions
l Time domain power
l CP / ACP (Single and Multi-Carrier)
l Spectrum Emission Mask
l Occupied bandwidth
l Spurious search
l Noise
l Statistics (CCDF)
l TOI
l Harmonics
Agenda
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Agendal Frequency vs Time Domain
l Spectrum Analyzers
l FFT Analyzerl Superheterodyne Spectrum Analyzer
l Implementation
l Important Settings
l Important Specifications
l Common Measurements
l Additional Functionality
l Vector Signal Analysis
l Real-time Spectrum Analysis
Vector Signal Analysis
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Vector Signal Analysis
l Digitize RF signal
l Bandwidths as high as 320MHz are possible
l Phase information is obtained (which is discarded in
spectrum analysis)
l I and Q data: signals can be demodulated
Vector Signal Analysis
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Vector Signal Analysis
BPSK GMSK
QPSK
What is real-time
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What is real time
l A Real-Time spectrum analyzer shows the spectrum without
any loss of data:
FFT
Time
FFT FFT FFT
No Blind Time !
How is it implemented?
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How is it implemented?
Diagram of FSVR Real-Time implementation
Real-time Spectrum Analysis
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ea t e Spect u a ys s
References
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Christoph Rauscher, Roland Minihold, Volker Janssen.
Fundamentals of Spectrum Analysis (2008). Rohde & Schwarz.