Spectrum Analyzer Fundamentals

8/13/2019 Spectrum Analyzer Fundamentals

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Spectrum AnalyzerFundamentalsChris Gillis

Application Engineer

Signal/Spectrum Analyzers & Signal Generators

[email protected]

+1.438.863.5760

October 29, 2013

University of British Columbia


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R&S at UBC: Spectrum Analyzer Fundamentals, October 2013 - 2

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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l FFT Analyzer


l Implementation








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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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Periodic

vs

Non-periodic


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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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Time Domain Frequency Domain

l For example, harmonics could easily be missed


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l FFT Analyzer


l Implementation








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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


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


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

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Date: 7.NOV.2006 12:17:44

Ref - 20 dBm Att 5 dB

CLRWR

A


*

1 PK

RBW 20 kHz

AQT 2.5 ms

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Date: 7.NOV.2006 12:17:11

Normal (Gaussian) FFT


CLRWR

A


1 AP

*RBW 20 kHz

VBW 50 kHz

SWT 50 ms

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Date: 7.NOV.2006 12:16:44

Channel


CLRWR

A


1 AP

*RBW 18 kHz

VBW 50 kHz

SWT 65 ms

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Date: 7.NOV.2006 12:16:17

RRC


CLRWR

A


1 AP

*RBW 20 kHz

VBW 50 kHz

SWT 2.5 ms

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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


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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*

*

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/

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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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RMS

detector

RMS detector

& trace ave

(Lin) or (Pwr)


l RMS detector measures true noise powerl We can apply linear or power trace averaging to an RMS detector.


*

*

1 RM

VIEW

2 RM

VIEW

*

A

3DB

RBW 200 kHz

VBW 2 MHz

SWT 2.5 ms


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-90


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*1 RM

VIEW

2 SA

AVG

*

A

3DB

RBW 200 kHz

VBW 500 kHz

SWT 2.5 ms


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SWP 1000 of 1000


RMS

detector

Sample

detector &

trace ave

(Lin) or (Pwr)


l RMS detector measures true noise powerl Sample detector & linear or power trace averaging yields the same results


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*

*

*

1 RM

VIEW

2 AV

VIEW

3 AV

VIEW

*

A

3DB

RBW 200 kHz

VBW 2 MHz

SWT 2 s*


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SWP 2 of 1000


RMS

detector

Average

detector &

Log trace

average



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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*

*

*

1 RM

VIEW

2 AV

VIEW

3 AV

VIEW

*

A

3DB

RBW 200 kHz

VBW 2 MHz

SWT 2.5 ms


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-95

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RMS

detector

Average

detector &

power trace

average



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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l FFT Analyzer


l Implementation


l Important Specifications





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 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




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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 ImplementationHigher Frequencies

l YIG filter allows for excellent selectivity

l Overcomes our problem with filters at high frequencies with

wide bandwidths



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Simplified Model vs Actual ImplementationHigher FrequenciesHarmonic Mixers

|mfLO nfRF| = fIF


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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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l FFT Analyzerl Superheterodyne Spectrum Analyzer

l Implementation







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 Settings


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Agenda


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l Implementation








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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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l Implementation







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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l Implementation







Vector Signal Analysis


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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



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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.

Documents

Spectrum Analyzer Fundamentals