Spurious Measurements: Optimizing for Speed and Accuracy · Optimizing for Speed and Accuracy Ben...

Copyright © 2012 Agilent Technologies

Spurious Measurements:

Optimizing for Speed and Accuracy

Ben ZarlingoProduct Manager, Signal Analysis

Microwave & Communications Division


© Agilent Technologies 2012

This Presentation

Spurious Measurements Context, Background

Measurement Overview, Principles

Specific Case Examples, Suggestions

Performance Optimization Techniques

Taking Advantage of Modern Analyzer Features

References, Additional Information



The Challenge: Acquiring Information

Optimize By Using Your Knowledge, Expertise, Priorities

Gather Needed Information; If Extra Then

Trade Extra For

– Faster measurement time

– Lower measurement/equipment cost

If More Information Needed, Consider

– Equipment availability

– Cost

– Time

Cost

Time

Information



Noise Converts ~ Deterministic Value to Statistical Distribution

GSM (MSK)

SNR 60+ dB

* CCDF varies significantly with span at low SNRs

CCDF

1 MHz Span*

1%

1 dB/div



Separating Signals from Noise

Signal/Noise Variation with RBW

Amplitude envelope vs. time

Best RBW is one matched to signal

Best ability to separate analyzer noise from signal

RBW (log)

SNR

(dB)

Noise-like

Real signals

can be one

type or

combination



Information:

What You Have vs. What You Need to Gather

Ask, Answer Questions to Bound the Task

Information Types

– Amplitude accuracy, variance

– Frequency accuracy, variance

– Time domain parameters, cause

– Other: bandwidth, modulation, isolation/crosstalk

What You Know vs. What You Need

– Know the spur frequency? If not, does it matter?

– Measure actual amplitude or only limit test?

– Accuracy, variance needs for amplitude, frequency

Presence or Absence of Other Signals

– Waveguide below cutoff will filter off lower frequencies

– Presence of lower frequencies can affect attenuation, preamp use



“Classic” Spectrum Analyzer Spurious

Measurement Setup: 2.51 dB SNR Benefit

Peak Detection

– Avoid missing signal that falls between display “bins” or “buckets” and

reduce effect of time-varying spurs

Video Averaging: Use Narrow Video Bandwidth Filter

– Trace averaging yields same benefit, but slower variance reduction

– Squaring the Average vs. Averaging the Square

– Under-measure noise 1.05 dB by squaring the average

– Log display processing (log amplification)

– Under-measure noise by 1.45 dB

Total “Noise Reduction” = 2.51 dB; Other Processing

Approaches Produce Same -2.51 dB Under-Response to

Noise or Noise-Like Signals



Detector Types, Averaging Scales Understanding Enough

Processing, Data Reduction Always Done

Signal Characteristics Affect Results

Basic Settings (RBW, Span, Sweep Time) Affect Results,

Even With Same Detector

Know Enough To:

– Use your knowledge of your signal

– Understand results, know what you are getting

– Make good measurements

– Identify signal problems, measurement errors



Traditional Spectrum Analyzer Block

Diagram (very simplified)

Analog signal processing, but principles apply to DSP

Order of envelope detector and log amp may be reversed

Digital technologies (ADC, DSP) moving to the left (input)

Input

Signal

Processor

and Display

Log AmpEnvelope

Detector

RBW

Local

Oscillator

Atten

VBW

ADCDetector

Mode

Log

Linlog



Display Detectors:

Peak, Negative Peak, Sample

Input is the IF envelope detector output (log or linear scaled)

Each output represents only one IF envelope value

Peak and neg peak detectors bias the output value, perform

some data reduction

Detector

Mode

Time

Volts

Peak

Neg Peak

Sample

Display points or buckets

Peak

Neg Peak

Sample



Averaging Detectors

Each output represents 2 or more IF envelope values

Reduces variance faster than single-value detectors;

reduce variance further by increasing sweep time

Good choice for noise, noise-like, or unknown statistics

Time

Volts

Average value

for this bucket

Samples of envelope detector output

Detector

Mode

Average



Averaging Processes in

Spectrum/Signal Analyzers

Reduce variance for noise, noise-like signals (noisy CW)

Critical to understand which parameter is being averaged

Consider all averaging processes which affect the meas.

Important to ensure the consistency of all averaging processes involved

Processor

and Display

Log AmpEnvelope

Detector

RBW

VBW

ADCDetector

Mode

Log

Lin

Limited BW, but Other

BWs typically narrower

Video Bandwidth

Filtering or Smoothing

Averaging

or RMS

Detectors

Trace-to-Trace

Averaging,

Noise Marker

log



Averaging Processes & Scales

Four Processes in Most Analyzers– Trace Averaging

– Video Bandwidth Filtering

– Average Detector

– Noise or Band Power Markers (average across buckets, a subset of displayed trace)

In Agilent X-Series, PSA All are Locked to 1 of 3 Scales– Power (W)

– Voltage

– Log power (dB, dBm)

Locking Determined by selected “Average Type”

Locking Not Present on Some Analyzers



Averaging Scales—Log, Lin, Power

Logarithmic averaging scale—use for CW signals

– Traditional log scale measurement

– Narrow VBW, trace averaging

– Better accuracy, dynamic range for low-level CW signals

Voltage averaging scale—use for voltage envelopes

– Traditional linear scale measurement

– Measure rise/fall times of pulsed RF (TDMA) signals

Power averaging scale—use for time-varying signals

– Measure average power and report result on a dB scale



Potential Averaging Errors

Example: Averaging Power, dB vs. W

Average of the log is not equal to a log of the average

Narrow VBW or trace averaging performs an average of the

log, an error in measuring time-varying signals

Instead, average the power or report the RMS value of the

voltage of the signal when measuring time-varying signals

0 dBm

6 dBm

1 mW

4 mW Average of the log of the power = 3 dBm

(0 dBm + 6 dBm)/2

Log of the signal’s average power = 3.98 dBm

(1 mW + 4 mW)/2 = 2.5 mW



Log Scale Compression/Expansion

Assume: Average noise power -150 dBm/Hz

10-15 W/kHz ENBW, 1 fW/kHz

Measurement 1: Add 0.75 fW

Result: -147.6 dBm +2.4 dB

Measurement 2: Subtract 0.75 fW

Result: -156.0 dBm -6.0 dB

Log average: -151.8 dBm

Power average: -150.0 dBm



Log Scale Bias Benefit for CW Meas.Log scale better for single CW signal near noise

Video BW filtering

or averaging dB

See Agilent AN1303

Noise-free meas SNR 1 dB; pwr avg SNR 1 dB; log avg

1 dB SNR Example



Non-CW Spur, Close to Noise

Indicators

– Spur noisy

– Spur close to noise

– Spur appears wide

(compared to RBW)

– Multiple concerns

for accuracy



Narrower RBW Improves S/N

SNR Improved

Lower Measured Power

– Accurate result?

– Signal wider than RBW filter?



Band Power Marker, Averaging

Use Techniques Independentof Signal Statistics– Band power marker compensates

for signal spread

– Longer sweep, average detector to reduce variance

Spur With Residual FM– Noise-like signal



DANL vs. Other Measurement Priorities

Attenuation = 0 dB Optimal for Sensitivity

Attenuation = 10+ dB for Optimal Accuracy

Attenuation = 4-6 dB a Compromise

Problems with Impedance Match

– Use both preamplifier and attenuator?

Problems with Distortion, Compression

Chance of Analyzer Input Damage



Basic Spectrum Analyzer Cautions

Don’t Overdrive the Mixer/Front End

– A concern whenever the signal bandwidth is much larger than RBW

– Displayed power may understate actual signal power

• Understand total power of modulated or noise-like signals

Don’t Overdrive the Log Amplifier

Overload Exception

– If distortion products are known and not a problem for you

– Overload to improve DANL

– Add your information to improve information gathering

Use the Appropriate Display Detector

Avoid Inconsistent or Incompatible Averaging Methods

Video Averaging: Narrow VBW and/or dB Scale Trace Avg.



Techniques for Reducing DANL,

Improving Dynamic Range

Reduce Attenuation

Add Preamp

Reduce RBW

Add External Filtering

Better/Shorter Cables, Connectors, Move Analyzer

– 1 dB Gained/Lost = 1 dB Noise Figure

Time Averaging (where possible, not same as meas. avg.)

Measurement Processing (take advantage of Moore’s Law)

Noise Power Subtraction/Noise Correction

Noise Floor Extension (NFE)

– Leverages knowledge of analyzer/circuit behavior (adds information)



Understanding Signal Analyzer Dyn. Range

Key Factors

In the presence of a carrier:

Want high dynamic range

Formula to Estimate:

(TOI – DANL {in dB}) x 2/3rds

Example: (at 1 GHz, PXA)

+20 – (-155) = 175 x 0.667

= 116.7 dB

PXA Specs for TOI and DANL

(From Agilent PXA Specs Guide)PXA Specifications Reference:

PXA Specifications Guide

Dynamic Range section.

Part Number N9020-90017



Preamplifiers

NF of Preamp Should be Less than NF of Analyzer

Benefits

– Improved sensitivity

– May be built-in, full frequency range

– Internal preamps are switchable

– If internal, gain can be included in analyzer calibration

– External preamps can be optimized for your

specific needs

Drawbacks

– Best when input is small signals only

– May limit TOI, increase distortion

– May have limited bandwidth

– Calibration less convenient if external



IF Gain Setting

Set to High for Small Signals, Lowest Analyzer Noise

Set to Low for Large Signals, Lowest Distortion

You Know More

About Your

Signal than the

Analyzer Does



Digital Filter ShapeBetter shape factor, biggest selectivity benefit for different signal

levels

Equivalent selectivity at a wider, faster-sweeping RBW

digital filters swept an additional 3-4x faster

30 kHz Digital Filter



28

Digital IF Ref Level and Amp Scaling

Logging and scaling processes now DSP

No need to adjust Ref Level

faster, simpler programming



Sweep Speed Tradeoffs

Analog Filter Default: RBW2/2

Analog Filter, Accuracy mode: RBW2/4 to RBW2/8

Digital Filter Default: RBW2 (with accuracy)

Sweep Speed Problem

– Narrow sweeps with slow sweep times from narrow autocoupled RBWs

– Wide sweeps with narrow RBWs to reduce noise level

Solution: Switch to FFT Mode with “Best Speed” Rules

– Small tradeoff in dynamic range for large improvement in sweep speed

Sweep Rate (Hz) RBW2



Swept & FFT Measurements

FFT– Faster for narrow RBWs, typically narrower

spans

– Modern analyzers can provide FFT results directly or step/concatenate FFTs and process to provide swept-equivalent results

– Always used for captured signals/recordings

Swept– No span limit

– Flexible bandwidths, detectors, averaging

– Benefits of classic setup apply

– Segmented or zone sweeps

Choosing– Analyzers, applications can choose automatically

– Manual choices for speed, dynamic range, optimize for specific situation



Take Advantage of Instrument Processing

Thousands or Millions of Measurements/Second,

Processed Locally

Reduce Data Bus Traffic

– Ideally a table of results or simple go/no-go

Offload System CPU, Local Processing Instead

– Measurement processing

– Measurement setup changes (frequency ranges, RBWs, limits)

Available Local Processing

– List sweep

– Zone sweep, zone span

– Dedicated spurious emissions mode



Harmonics and Spur Search Approaches

Use traditional swept measurement and Marker Peak

Search function

Use “1-button” Harmonic Distortion and Spurious

Measurements (Power Suite)

Use “List Sweep”, with pre-defined list of frequencies

that correspond to known harmonic frequencies

Use traditional swept measurement with Peak Table

readout



Example: Spurious Emissions Mode



List Sweep (SCPI Only)

Eliminate Unnecessary Overhead associated with setting up individual measurements

Multiple Zero Span measurements at multiple frequencies

Different RBW, VBW, Sweep times (etc) at different points

Peak, Mean average power results

Up to 2000 Points

PXA Early Engagement Visits

Agilent Confidential

March 13, 2009

X-Series Signal Analyzer Measurement Speed Reference:

App Note 1583: Maximizing Measurement Speed with the Agilent’s X-series Signal

Analyzers Lit Number 5989-4947EN



Peak Table

Read out up to 2000

peaks above a threshold



Processing Improvements for Sweep Speed

2 to 3 times speed improvement for FFT sweeps with

large spans and narrow RBWs (common use case for

spur searches).

Changes implemented that sped up FFT sweeps:

Halved number of points in FFTs while maintaining same

amplitude accuracy.

Enabled more parallel processing in FFT sweep

algorithms.



Time Averaging: An Overlooked Technique

Vector (Time) Average to Remove Noise– Coherent (time-triggered) time domain averaging

– Requires repeated signal, triggering

– FFT, not swept mode operation

Coherent vector average of sets of time samples creates a time-averaged time record

For each sample the average of synchronized measurements converges to the noiseless value

Q

IPhase



Time Averaging Example

Trace average (power)

reduces variance only Time average

reduces noise

Coherent averaging

calculates spectrum

from averaged time

samples



CW Signal Measured Near Analyzer

Noise Floor

Actual S/N

Displayed

S/N

CW Signal

Apparent

Signal

Ampl & Freq

Axes Expanded

Example: No Noise Subtraction or Noise Correction



Noise Subtraction, “Noise Floor Extension”NFE Calculations in Agilent PXA Improve DANL

Analyzer Noise Power is Calculated/Subtracted in Real Time

3 dB Error

without NFE

“No” Error

Improved Noise Floor or

Displayed Avg Noise Lvl.



Known vs. Unknown Signals &

Noise Floor Extension or Noise Subtraction

Spur Location

Known

Spur Location

Unknown

No NFE or

Noise Subtraction

Using NFE or

Noise Subtraction

“Classic

Case”

Zero Span

Avg Detector

Pwr Avg Scale

Peak Detector

VBW Filtering

Log Avg Scale

Zero Span

Avg Detector

Log Avg Scale

Peak Detector

VBW Filtering

Pwr Avg Scale



Gated Sweep for

Time-Selective Spurious Measurements

Gate lengthDelay

Gated Sweep Applicable to Wide, Narrow Spans

Easier Setup with Modern Analyzers

Flexibility Similar to

Non-Gated Meas

– RBW, VBW

– Detectors

– Sweep times



Time-Varying Spurs:Time Capture, Recording, Post-Processing

Gapless, Real-Time 250-500 Msamples

– Change CF & span

after capture

– External, internal triggers,

pre/post trigger delay

– Capture data is

continuous, real-time

– Maximum span limited

by IF/ADC BW

43

Spectrum

IF Time

Spectrum

RF Envelope

IF Time



Gap-Free Analysis of Freq, Amplitude, Time

Band-Selective, Level-Selective Triggers, Holdoff

Pre-Trigger Delay Captures Events Before they Happen

Relate Spurs to:

– LO Switching

– Transients (pwr, etc.)

44

Spectrum

IF Time



Low Noise Path DANL Improvement

3 dB

@3.6 GHz

8-10 dB

@26 GHz

Example:

Spur Search

20-50x faster

at 18 GHz



Trade Low Noise Path DANL for Speed

Noise FloorImprovement

7-8 dB

SpeedImprovement

~32x



References, Additional Information“Spectrum Analyzer Measurements and Noise” Joe Gorin, Agilent Technologies

Application Note 1303, lit. number 5966-4008E

“Spectrum Analyzer Detectors & Averaging for Wireless Measurements” Joe Gorin &

Ben Zarlingo http://www.agilent.com/cm/wireless/pdf/detector_averaging.pdf

“Using Noise Floor Extension in the PXA Signal Analyzer” Agilent application note, lit.

number 5990-5340EN

Webcast “Use capture, playback & triggering to completely analyze a signal” http://www.home.agilent.com/agilent/eventDetail.jspx?cc=US&lc=eng&ckey=1976536&nid=-

11143.0.00&id=1976536&pselect=SR.GENERAL

“Spectrum Analysis Basics” Agilent application note AN-150 lit. number 5952-0292EN

“Optimizing RF and Microwave Spectrum Analyzer Dynamic Range” Agilent

application note AN-1315 lit. number 5968-4545E

“Achieving Amplitude Accuracy in Modern Spectrum Analyzers“ Joe Gorin,

Microwaves & RF Magazine Sept. 2008 http://www.mwrf.com/Article/ArticleID/19724/19724.html

“Swept and FFT Analysis” Agilent PSA product note, lit. number 5980-3081EN

“Bringing New Power and Precision to Gated Spectrum Measurements” Wright, Gorin,

Zarlingo, High Frequency Electronics, August 2007 http://www.highfrequencyelectronics.com/Archives/Aug07/HFE0807_Zarlingo.pdf



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