08-09-2010, 01:22 AM
VECTOR SIGNAL ANALYSER IMPLEMENTED AS A SYNTHETIC INSTRUMENT
Submitted by
Bibin Mohanan
S7 ECE
College Of Engineering, trivandrum
OUTLINE
Introduction
Synthetic instruments
Synthetic measurement system
Operation of synthetic instruments
Advantages of SIs
Block diagram of Vector signal analyser
Design consideration of VSA
Constellation &Error vector magnitude diagram
Conclusion
References
INTRODUCTION
Vector signal analyser is an instrument used for accurate measurement and analysis of digital baseband, IF and RF signals.
VSA can present many of the quality measures of a modulation process like modulation distortion, phase noise, intersymbol interference etc.
It is augmented with an embedded instrumentation package to quantify the observable parameters embedded in the signal.
SYNTHETIC INSTRUMENTS
All instruments perform the tasks of probing and monitoring the response of system under test.
An SI is a functional mode or personality component of a Synthetic measurement system.
An SI is a software that runs on a SMS to perform a specific synthesis, analysis or measurement function.
SI links a series of elemental hardware and software components.
SI utilize DSP algorithms for measurements.
SIs have many benefits over traditional test equipments.
SYNTHETIC MEASUREMENT SYSTEM
An SMS is a system that uses synthetic instruments implemented on a common,generic,physical hardware platform to perform a set of specific measurements.
SMS platform is duel cascade of three subsystems:
signal conditioning
A/D or D/A conversion
digital signal processing and control
BLOCK DIAGRAM OF SMS
BLOCK DIAGRAM OF SI
OPERATION OF SI
Short time analysis gives time domain attributes of the signal such as rise time, overshoot, settling time, duty cycle etc..
Long time analysis deals with spectral parameters.
Medium time analysis deals with modulation parameters of the input signal.
ADVANTAGES OF SIs
An SI provide test flexibility.
It provide ease of ATE upgrade via software modules.
It provides reduction in overall ATE size and footprint.
It provides reduction in ATE hardware costs.
It provides reduction in ATE development time.
It provides an increase in the measurement time.
It provides ATE interoperability.
It eliminates redundancy.
DESIGN CONSIDERATIONS OF VSA
VSAs combine super heterodyne technology with high speed ADCs and other DSP technologies
It offer fast, high-resolution spectrum measurements, demodulation, and advanced time-domain analysis.
A VSA is especially useful for characterizing complex signals such as burst, transient, or modulated signals used in communications, video, broadcast, sonar, and ultrasound imaging applications.
VSA is fundamentally a digital system that uses digital data and mathematical algorithms to perform data analysis.
VSA uses FFT algorithms for spectrum analysis and demodulator algorithm for vector analysis applications.
THEORY OF OPERATION OF VSA
The VSA spectrum analysis measurement process includes the following fundamental stages.
Signal conditioning with frequency translation
Analog-to-digital conversion
Quadrature detection
Digital filtering and resampling
Data windowing
FFT analysis
1.Signal conditioning with frequency translation
This stage includes several important functions that condition and optimize the signal for the analog-to-digital conversion and FFT analysis.
DC/AC coupling removes the unwanted DC biases in the measurement setup.
The signal is either amplified or attenuated for optimal signal level into the mixer.
The mixer stage provides frequency translation, or RF-to-IF down conversion, and mixes the signal down to the final IF.
The low pass filter prevent the aliasing.
2.Analog to Digital Converter
ADC sample the analog signal and convert it into digital signal.
The operating range of ADC is a limiting factor in the performance of the instrument in which it is embedded.
Bandwidth, dynamic range and signal to noise ratio are key specifications for these ADCs.
3.Quadrature detection
It down convert the input signal.
It consist of a pair of balanced mixers fed by the frequency and phase aligned quadrature oscillator.
The upper part extracts the cosine component of the input signal and rejects the sine component.
The lower part extracts the sine component and rejects the cosine component.
4.Digital filtering and resampling
Digital decimating filters and resampling perform the operations necessary to allow variable spans and resolution bandwidths.
The digital decimating filters simultaneously decrease the sample rate and limit the bandwidth of the signal (providing alias protection).
The sample rate into the digital filter is fs; the sample rate out of the filter is fs/n, where “n†is the decimation factor and is an integer value.
The decimating filters allow the sample rate and span to be changed by powers of two.
Resampling is done to obtain an arbitrary span.
The output of the digital decimating filters represents a band limited, digital version of the analog input signal in time-domain.
The sample memory captures the data stream from the decimating filter.
The sample memory is a circular FIFO (first in, first out) buffer.
It collects individual data samples into blocks of data called time records, to be used by the DSP for further data processing.
The time data collected in sample memory is the fundamental data used to produce all measurement results.
Time data correction capability provides more accurate data results.
It is implemented through an equalization filter.
It determine the final pass band characteristic of the signal path.
It compensate the pass band imperfections comes from the preceding stages.
5.Data Windowing
Data windowing uses a window function to modify the time-domain data by forcing it to become periodic in the time record.
The signal should be periodic to compute the spectrum accurately by FFT.
Windowing is done by multiplying the time record by a weighted window function.
Windowing distorts the data in the time domain to improve accuracy in the frequency domain.
Commonly used Windows & uses:
Window Uses
Uniform : Transient and self- windowing
data
Hanning : General purpose
Gaussian top : High dynamic range
Flat top : High amplitude accuracy
6.FFT Analysis
The FFT is a record oriented algorithm and operates on sampled data in a special way.
FFT waits until a number of samples (N) have been obtained (called a time record), then transforms the complete block.
If a time record N samples long, is the input to the FFT, then frequency spectrum N samples long, is the output.
A typical record length for FFT analysis is 1024 sample points.
The frequency spectrum produced by the FFT is symmetrical about the sample frequency fs/2.
the output of the FFT algorithm is (N/2) +1 frequency points, extending from 0 Hz to fs/2.
MEASUREMENTS USING VSA
VSA can give the following measurements
Spectra and histogram
Eye pattern
I-Q imbalances
ISI levels
Jilter characteristics
signal constellation
Error vector magnitude
SIGNAL CONSTELLATION
A constellation diagram is a representation of a signal modulated by a digital modulation scheme such as QAM or QPSK.
It represents the possible symbols that may be selected by a given modulation scheme as points in the complex plane.
Measured constellation diagrams can be used to recognize the type of interference and distortion in a signal.
ERROR VECTOR MAGNITUDE
Error vector magnitude (EVM) is a common figure of merit for assessing the quality of digitally modulated telecommunication signals.
EVM expresses the difference between the expected complex voltage value of a demodulated symbol and the value of the actual received symbol.
Constellation space
Showing error vector
Magnitude &other related
Quantities.
REFERENCES
W. Lowdermilk and F. Harris, “Vector signal analyzer implemented as a synthetic instrument,†IEEE transaction on Instrumentation and Measurement,VOL.58,NO.2 February 2009, pp. 157–166.
W. Lowdermilk and F. Harris, “Signal conditioning and data collection in synthetic instruments,†in Proc. Autotestcon, 2003, pp. 426–431.
W. Lowdermilk and F. Harris, “Extracting masked signal parameters with a synthetic instrument,†in Proc. Autotestcon, 2004, pp. 140–146.
Submitted by
Bibin Mohanan
S7 ECE
College Of Engineering, trivandrum
OUTLINE
Introduction
Synthetic instruments
Synthetic measurement system
Operation of synthetic instruments
Advantages of SIs
Block diagram of Vector signal analyser
Design consideration of VSA
Constellation &Error vector magnitude diagram
Conclusion
References
INTRODUCTION
Vector signal analyser is an instrument used for accurate measurement and analysis of digital baseband, IF and RF signals.
VSA can present many of the quality measures of a modulation process like modulation distortion, phase noise, intersymbol interference etc.
It is augmented with an embedded instrumentation package to quantify the observable parameters embedded in the signal.
SYNTHETIC INSTRUMENTS
All instruments perform the tasks of probing and monitoring the response of system under test.
An SI is a functional mode or personality component of a Synthetic measurement system.
An SI is a software that runs on a SMS to perform a specific synthesis, analysis or measurement function.
SI links a series of elemental hardware and software components.
SI utilize DSP algorithms for measurements.
SIs have many benefits over traditional test equipments.
SYNTHETIC MEASUREMENT SYSTEM
An SMS is a system that uses synthetic instruments implemented on a common,generic,physical hardware platform to perform a set of specific measurements.
SMS platform is duel cascade of three subsystems:
signal conditioning
A/D or D/A conversion
digital signal processing and control
BLOCK DIAGRAM OF SMS
BLOCK DIAGRAM OF SI
OPERATION OF SI
Short time analysis gives time domain attributes of the signal such as rise time, overshoot, settling time, duty cycle etc..
Long time analysis deals with spectral parameters.
Medium time analysis deals with modulation parameters of the input signal.
ADVANTAGES OF SIs
An SI provide test flexibility.
It provide ease of ATE upgrade via software modules.
It provides reduction in overall ATE size and footprint.
It provides reduction in ATE hardware costs.
It provides reduction in ATE development time.
It provides an increase in the measurement time.
It provides ATE interoperability.
It eliminates redundancy.
DESIGN CONSIDERATIONS OF VSA
VSAs combine super heterodyne technology with high speed ADCs and other DSP technologies
It offer fast, high-resolution spectrum measurements, demodulation, and advanced time-domain analysis.
A VSA is especially useful for characterizing complex signals such as burst, transient, or modulated signals used in communications, video, broadcast, sonar, and ultrasound imaging applications.
VSA is fundamentally a digital system that uses digital data and mathematical algorithms to perform data analysis.
VSA uses FFT algorithms for spectrum analysis and demodulator algorithm for vector analysis applications.
THEORY OF OPERATION OF VSA
The VSA spectrum analysis measurement process includes the following fundamental stages.
Signal conditioning with frequency translation
Analog-to-digital conversion
Quadrature detection
Digital filtering and resampling
Data windowing
FFT analysis
1.Signal conditioning with frequency translation
This stage includes several important functions that condition and optimize the signal for the analog-to-digital conversion and FFT analysis.
DC/AC coupling removes the unwanted DC biases in the measurement setup.
The signal is either amplified or attenuated for optimal signal level into the mixer.
The mixer stage provides frequency translation, or RF-to-IF down conversion, and mixes the signal down to the final IF.
The low pass filter prevent the aliasing.
2.Analog to Digital Converter
ADC sample the analog signal and convert it into digital signal.
The operating range of ADC is a limiting factor in the performance of the instrument in which it is embedded.
Bandwidth, dynamic range and signal to noise ratio are key specifications for these ADCs.
3.Quadrature detection
It down convert the input signal.
It consist of a pair of balanced mixers fed by the frequency and phase aligned quadrature oscillator.
The upper part extracts the cosine component of the input signal and rejects the sine component.
The lower part extracts the sine component and rejects the cosine component.
4.Digital filtering and resampling
Digital decimating filters and resampling perform the operations necessary to allow variable spans and resolution bandwidths.
The digital decimating filters simultaneously decrease the sample rate and limit the bandwidth of the signal (providing alias protection).
The sample rate into the digital filter is fs; the sample rate out of the filter is fs/n, where “n†is the decimation factor and is an integer value.
The decimating filters allow the sample rate and span to be changed by powers of two.
Resampling is done to obtain an arbitrary span.
The output of the digital decimating filters represents a band limited, digital version of the analog input signal in time-domain.
The sample memory captures the data stream from the decimating filter.
The sample memory is a circular FIFO (first in, first out) buffer.
It collects individual data samples into blocks of data called time records, to be used by the DSP for further data processing.
The time data collected in sample memory is the fundamental data used to produce all measurement results.
Time data correction capability provides more accurate data results.
It is implemented through an equalization filter.
It determine the final pass band characteristic of the signal path.
It compensate the pass band imperfections comes from the preceding stages.
5.Data Windowing
Data windowing uses a window function to modify the time-domain data by forcing it to become periodic in the time record.
The signal should be periodic to compute the spectrum accurately by FFT.
Windowing is done by multiplying the time record by a weighted window function.
Windowing distorts the data in the time domain to improve accuracy in the frequency domain.
Commonly used Windows & uses:
Window Uses
Uniform : Transient and self- windowing
data
Hanning : General purpose
Gaussian top : High dynamic range
Flat top : High amplitude accuracy
6.FFT Analysis
The FFT is a record oriented algorithm and operates on sampled data in a special way.
FFT waits until a number of samples (N) have been obtained (called a time record), then transforms the complete block.
If a time record N samples long, is the input to the FFT, then frequency spectrum N samples long, is the output.
A typical record length for FFT analysis is 1024 sample points.
The frequency spectrum produced by the FFT is symmetrical about the sample frequency fs/2.
the output of the FFT algorithm is (N/2) +1 frequency points, extending from 0 Hz to fs/2.
MEASUREMENTS USING VSA
VSA can give the following measurements
Spectra and histogram
Eye pattern
I-Q imbalances
ISI levels
Jilter characteristics
signal constellation
Error vector magnitude
SIGNAL CONSTELLATION
A constellation diagram is a representation of a signal modulated by a digital modulation scheme such as QAM or QPSK.
It represents the possible symbols that may be selected by a given modulation scheme as points in the complex plane.
Measured constellation diagrams can be used to recognize the type of interference and distortion in a signal.
ERROR VECTOR MAGNITUDE
Error vector magnitude (EVM) is a common figure of merit for assessing the quality of digitally modulated telecommunication signals.
EVM expresses the difference between the expected complex voltage value of a demodulated symbol and the value of the actual received symbol.
Constellation space
Showing error vector
Magnitude &other related
Quantities.
REFERENCES
W. Lowdermilk and F. Harris, “Vector signal analyzer implemented as a synthetic instrument,†IEEE transaction on Instrumentation and Measurement,VOL.58,NO.2 February 2009, pp. 157–166.
W. Lowdermilk and F. Harris, “Signal conditioning and data collection in synthetic instruments,†in Proc. Autotestcon, 2003, pp. 426–431.
W. Lowdermilk and F. Harris, “Extracting masked signal parameters with a synthetic instrument,†in Proc. Autotestcon, 2004, pp. 140–146.