Download pdf - Examples SystemVue 2010 - Keysightedadownload.software.keysight.com/eedl/systemvue/2010_07/pdf/examples.pdf · Warranty The material contained in this document is provided "as is",

SystemVue - Examples

1

SystemVue 2010.072010

Examples


2

© Agilent Technologies, Inc. 2000-2010395 Page Mill Road, Palo Alto, CA 94304 U.S.A.No part of this manual may be reproduced in any form or by any means (includingelectronic storage and retrieval or translation into a foreign language) without prioragreement and written consent from Agilent Technologies, Inc. as governed by UnitedStates and international copyright laws.

Acknowledgments Mentor Graphics is a trademark of Mentor Graphics Corporation inthe U.S. and other countries. Microsoft®, Windows®, MS Windows®, Windows NT®, andMS-DOS® are U.S. registered trademarks of Microsoft Corporation. Pentium® is a U.S.registered trademark of Intel Corporation. PostScript® and Acrobat® are trademarks ofAdobe Systems Incorporated. UNIX® is a registered trademark of the Open Group. Java™is a U.S. trademark of Sun Microsystems, Inc. SystemC® is a registered trademark ofOpen SystemC Initiative, Inc. in the United States and other countries and is used withpermission. MATLAB® is a U.S. registered trademark of The Math Works, Inc.. HiSIM2source code, and all copyrights, trade secrets or other intellectual property rights in and tothe source code in its entirety, is owned by Hiroshima University and STARC.

Errata The SystemVue product may contain references to "HP" or "HPEESOF" such as infile names and directory names. The business entity formerly known as "HP EEsof" is nowpart of Agilent Technologies and is known as "Agilent EEsof". To avoid broken functionalityand to maintain backward compatibility for our customers, we did not change all thenames and labels that contain "HP" or "HPEESOF" references.

Warranty The material contained in this document is provided "as is", and is subject tobeing changed, without notice, in future editions. Further, to the maximum extentpermitted by applicable law, Agilent disclaims all warranties, either express or implied,with regard to this manual and any information contained herein, including but not limitedto the implied warranties of merchantability and fitness for a particular purpose. Agilentshall not be liable for errors or for incidental or consequential damages in connection withthe furnishing, use, or performance of this document or of any information containedherein. Should Agilent and the user have a separate written agreement with warrantyterms covering the material in this document that conflict with these terms, the warrantyterms in the separate agreement shall control.

Technology Licenses The hardware and/or software described in this document arefurnished under a license and may be used or copied only in accordance with the terms ofsuch license.

Portions of this product is derivative work based on the University of California PtolemySoftware System.

In no event shall the University of California be liable to any party for direct, indirect,special, incidental, or consequential damages arising out of the use of this software and itsdocumentation, even if the University of California has been advised of the possibility ofsuch damage.

The University of California specifically disclaims any warranties, including, but not limitedto, the implied warranties of merchantability and fitness for a particular purpose. The


3

software provided hereunder is on an "as is" basis and the University of California has noobligation to provide maintenance, support, updates, enhancements, or modifications.

Portions of this product include code developed at the University of Maryland, for theseportions the following notice applies.

In no event shall the University of Maryland be liable to any party for direct, indirect,special, incidental, or consequential damages arising out of the use of this software and itsdocumentation, even if the University of Maryland has been advised of the possibility ofsuch damage.

The University of Maryland specifically disclaims any warranties, including, but not limitedto, the implied warranties of merchantability and fitness for a particular purpose. thesoftware provided hereunder is on an "as is" basis, and the University of Maryland has noobligation to provide maintenance, support, updates, enhancements, or modifications.

Portions of this product include the SystemC software licensed under Open Source terms,which are available for download at http://systemc.org/ . This software is redistributed byAgilent. The Contributors of the SystemC software provide this software "as is" and offerno warranty of any kind, express or implied, including without limitation warranties orconditions or title and non-infringement, and implied warranties or conditionsmerchantability and fitness for a particular purpose. Contributors shall not be liable forany damages of any kind including without limitation direct, indirect, special, incidentaland consequential damages, such as lost profits. Any provisions that differ from thisdisclaimer are offered by Agilent only.With respect to the portion of the Licensed Materials that describes the software andprovides instructions concerning its operation and related matters, "use" includes the rightto download and print such materials solely for the purpose described above.

Restricted Rights Legend If software is for use in the performance of a U.S.Government prime contract or subcontract, Software is delivered and licensed as"Commercial computer software" as defined in DFAR 252.227-7014 (June 1995), or as a"commercial item" as defined in FAR 2.101(a) or as "Restricted computer software" asdefined in FAR 52.227-19 (June 1987) or any equivalent agency regulation or contractclause. Use, duplication or disclosure of Software is subject to Agilent Technologies´standard commercial license terms, and non-DOD Departments and Agencies of the U.S.Government will receive no greater than Restricted Rights as defined in FAR 52.227-19(c)(1-2) (June 1987). U.S. Government users will receive no greater than LimitedRights as defined in FAR 52.227-14 (June 1987) or DFAR 252.227-7015 (b)(2) (November1995), as applicable in any technical data.

http://systemc.org/

http://systemc.org/


4

Adaptive Equalization Library Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Baseband Verification Library Design Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3GPP LTE Design Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Cognitive Radio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 DVB-2 Baseband Verification Design Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

DVB2 Baseband Verification Library - DVB-S2 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 DVB2 Baseband Verification Library - DVB-T2 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

WiMax Baseband Verification Design Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 WPAN Baseband Verification Design Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 WPAN_HRP_AWGN_BER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 WPAN_HRP_RawBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 WPAN_HRP_RxSensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 WPAN_HRP_TxEVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 WPAN_HRP_TxWaveform_Spec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 WPAN_LRP_TxWaveform_D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 ZigBee Baseband Verification Library Design Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

C++ Code Generation Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Comms Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 BER Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 OFDM Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Satellite Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Zigbee Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

SystemVue DPD Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

DPD for LTE with Hardware Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 DPD for user defined stimulus with Hardware Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 DPD for WCDMA Stimulus with Hardware Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 DPD and CFR Generic Examples for LTE and 3GPP-WCDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Hardware Design Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 FixedPoint Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Instrument Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 N5106A PXB Signal Generator Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Signal Studio Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Vector Signal Analyzer Sink Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Vector Signal Analyzer Source Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Math Language Scripting Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 MIMO Channel Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Model Building Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 PLL Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Radar Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 PD RADAR Performance Test Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 PDRADAR_DetectionProbability_AWGN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 PDRADAR_DetectionProbability_Cluttering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 PDRADAR_FalseAlarmRate_AWGN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44


5

PDRADAR_FalseAlarmRate_Cluttering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 PDRADAR_Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 PD RADAR Receiver Test Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 PDRADAR_Clutter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 PDRADAR_DynamicRange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 PDRADAR_Rx_Waveform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 PDRADAR_Selectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 PDRADAR_Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 PD RADAR Transmitter Test Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 PDRADAR_Waveform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

RF Architecture Design Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 RF Design Kit (Spectrasys) Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

X-Parameter Example Workspaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Signal Processing Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 VBScripting Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52


6

Adaptive Equalization Library ExamplesPath: Examples\Adaptive Equalization Library

Name Description Usage

BlindEqLms.wsv This example implements a blind equalizer. The adaptive filter LMScore block is used as the adaptive algorithm. Note that in this case thedesired signal is the output of the non linearity block. This means thatno training sequence is required.

EqualiserCoreRLS.wsv This example shows the use of the Complex RLS Adaptive Filter Coreblock in an inverse system identification scenario. An equivalentimplementation using the Complex RLS Adaptive Filter is alsoprovided. Note that the Complex RLS Adaptive Filter Core block onlyimplements the complex FIR filter with variable coefficients and theadaptive RLS algorithm controlling them. Therefore the subtractor anderror feedback loop need to be implemented externally when using theComplex RLS Adaptive Filter Core block.

EqualiserRealRLS.wsv This example shows the use of the Real RLS Adaptive Filter block in aninverse system identification scenario.

EqualiserRealRLSCore.wsv This example shows the use of the Real RLS Adaptive Filter Core blockin an inverse system identification scenario. An equivalentimplementation using the Real RLS Adaptive Filter is also provided.Note that the Real RLS Adaptive Filter Core block only implements thereal FIR filter with variable coefficients and the adaptive RLS algorithmcontrolling them. Therefore the subtractor and error feedback loopneed to be implemented externally when using the Real RLS AdaptiveFilter Core block.

InverseSysIdAPACore.wsv This model implements a complex arithmetic inverse systemidentification setup using the APA algorithm. The unknown system is acomplex FIR filter. The error signal is plotted once it has gone throughthe error filter. The identified filter weights are also plotted. Note thatthey represent the inverse of the impulse response of the unknownfilter. The frequency response of both unknown channel and equalizerare also plotted.

InverseSysIdRealAPACore.wsv This model implements a real arithmetic inverse system identificationsetup using the APA algorithm. The unknown system is a real FIRfilter. The error signal is plotted once it has gone through the errorfilter. The identified filter weights are also plotted. Note that theyrepresent the inverse of the impulse response of the unknown filter.The frequency response of both unknown channel and equalizer arealso plotted.

SysIdAPA.wsv In this example a system identification configuration has been setup,where the adaptive filter attempts to identify the impulse response ofa complex arithmetic unknown system. Both the unknown system andthe adaptive filter are excited with white noise. The unknown system isrepresented here by a complex FIR filter. Its output is used as thedesired signal input of the adaptive filter as shown.

SysIdLMS.wsv In this example a system identification configuration has been setup,where the adaptive filter attempts to identify the impulse response ofa complex arithmetic unknown system. Both the unknown system andthe adaptive filter are excited with white noise. The unknown system isrepresented here by a complex FIR filter. Its output is used as thedesired signal input of the adaptive filter as shown.


7

SysIdLMSCore.wsv This example shows the functionality of the LMS_AdaptFltCore part.This block contains the core functionality of the adaptive filter, i.e. theFIR filter with variable weights and the adaptive algorithm whichcontrols them. Therefore it does not contain the calculation of the errorsignal and the feedback loop required to pass this signal back into theadaptive filter.

SysIdQR.wsv In this example a system identification configuration has been setup,where the adaptive filter attempts to identify the impulse response ofa complex arithmetic unknown system. Both the unknown system andthe adaptive filter are excited with white noise. The unknown system isrepresented here by a complex FIR filter. Its output is used as thedesired signal input of the adaptive filter as shown.

SysIdRealAPA.wsv This model implements a real arithmetic system identification setupusing the APA algorithm. The unknown system is a real FIR filter. Theerror signal is plotted once it has gone through the error filter. Theidentified filter weights are also plotted.

SysIdRealLMS.wsv This example implements an unknown system identification setupusing the real LMS adaptive filter. The unknown filter is represented bya real FIR filter. A real error filter is used to smooth out the variationsof the error signal.

SysIdRealLMSCore.wsv This example shows two implementations of an adaptive IIR filterusing the real LMS core parts. The first implementation shows how touse these parts in feedforward and feedback configurations. Thesecond implementation shows how to use just one part and setting theparameters to include internally feedback and feedforward parts.

SysIdRealQR.wsv This example implements an unknown system identification setupusing the real QR adaptive filter. The unknown filter is represented bya real FIR filter. A real error filter is used to smooth out the variationsof the error signal.

SysIdRLS.wsv In this example a system identification configuration has been setup,where the adaptive filter attempts to identify the impulse response ofa complex arithmetic unknown system. Both the unknown system andthe adaptive filter are excited with white noise. The unknown system isrepresented here by a complex FIR filter. Its output is used as thedesired signal input of the adaptive filter as shown.


8

Baseband Verification Library DesignExamplesAgilent SystemVue has a number of add-on libraries to support design and verificaiton ofbaseband algorithm and system architectures. As part of these add-on products AgilentSystemVue provides a number of examples to aid in the usage of the libraries and to aidin understanding of the specific standards they were created for along with theperformance requirements of those standards

ContentsPath: Examples\Baseband Verification\sub-folder

DVB-2 (examples)LTE (examples)WiMAX (examples)WPAN (examples)ZigBee (examples)W-CDMA (examples)

3GPP LTE Design Examples This 3GPP LTE Wireless Design Library includes 18 design examples for FDD/TDD LTEdownlink/uplink transmitter measurements, downlink/uplink BER and throughputperformance measurements.Path: Examples\Baseband Verification\LTE


3GPP_LTE_CFR_EVM.wsv This example workspace explores Crest factor reduction (CFR)for a LTE OFDMA System. CFR is a technique for reducing thepeak-to average ratio (PAR) of an orthogonal frequencydivision multiplexing (OFDM) waveform. The algoithm used inthis example is based on a modified version of the algorithm in"Constrained Clipping for Crest Factor Reduction in Multiple-user OFDM", In Proc. IEEE Radio and Wireless Symposium,pp.341-344, Jan. 2007. This CFR algorithm consists of oneIFFT, time domain polar clipping, in-band and out-of-bandpost-processing and one FFT.

3GPP_LTE_DL_ChannelCoding.wsv This example workspace demonstrates the 3GPP LTE downlinkchannel coding, channel decoding and swept Throughput vsSNR measurements.

3GPP_LTE_DL_ETM.wsv This example workspace demonstrates E-UTRA Test Modelsfollowing 3GPP TS 36.141 V8.5.0(2009-12) for both LTEDownlink FDD and TDD mode.

3GPP_LTE_DL_FDD_TestCase.wsv This example workspace demonstrates swept Thoughput vsSNR measurements for LTE FDD downlink in a fadingenvironment for the configurations that are defined in 8 ofTS36.101 V8.6.0.


9

3GPP_LTE_DL_MIMO_Throughput.wsv This example workspace demonstrates swept Thoughput vsSNR measurements for LTE downlink in a fading environment.

3GPP_LTE_DL_SISO_BER.wsv This example workspace demonstrates swept BER and BLER vsSNR measurements for a LTE downlink SISO system. Twoenvironments are tested: AWGN (Additive White GaussianNoise) and fading.

3GPP_LTE_DL_Tx.wsv This example workspace demonstrates spectrum and CCDFmeasurements for a LTE downlink transmitter with oneantenna, two antennas and four antennas.

3GPP_LTE_DL_TxEVM.wsv This example workspace demonstrates EVM measurements forLTE downlink transmitter in FDD and TDD modes.

3GPP_LTE_SignalDownload.wsv This example workspace demonstrates how to download LTEsignal generated by LTE sources in SystemVue LTE library toAgilent signal generators such as E4438C. The phasediscontinuity between two consecutive frames is eliminated.

3GPP_LTE_UL_BER.wsv This example workspace demonstrates swept BER and BLER vsSNR measurements for an LTE uplink system in AWGN(Additive White Gaussian Noise) and fading.

3GPP_LTE_UL_ChannelCoding.wsv This example workspace demonstrates the 3GPP LTE UplinkFDD Channel coding, channel decoding and swept BER andBLER vs SNR measurements.

3GPP_LTE_UL_PRACH_Detection.wsv This example workspace demonstrates PRACH detectionmeasurements for a LTE Uplink in Fading and AGWNenvironment, following 8.4.2 of 36.104.

3GPP_LTE_UL_SIMO_Throughput.wsv This example workspace demonstrates Throughput vs SNRmeasurements for a LTE Uplink system in Fading channel with2 and 4 recceiver antennas, following the configuration in 8.2of 36.104.

3GPP_LTE_UL_SISO_Throughput.wsv This example workspace demonstrates Throughput vs SNRmeasurements for a LTE Uplink system in AWGN (AdditiveWhite Gaussian Noise) channel with SISO.

3GPP_LTE_UL_TX.wsv This example workspace demonstrates a spectrum and a CCDFmeasurement of a LTE Uplink transmitter.

3GPP_LTE_UL_TxEVM.wsv This example workspace demonstrates EVM measurements forLTE Uplink transmitter in FDD and TDD modes.

Cognitive RadioExamples in this directory implement cognitive radio spectrum sensing and signalgeneration that utilize the SystemVue LTE and WiMAX Baseband Verification examples.

Path: Examples\Baseband Verification\Cognitive Radio


Cognitive_Radio_Example.wsv This example implements Spectrum Sensing algorithm usingSystemVue Math Language to detect space available in acaptured spectrum and will generate either an LTE or WiMAXsignal to fit the available spectrum

MathLanguage,LTE, WiMAX

DVB-2 Baseband Verification Design ExamplesPath: Examples\Baseband Verification\DVB2


10

ContentsDVBS2 (examples)DVBT2 (examples)


11

DVB2 Baseband Verification Library -DVB-S2 ModelsName Description Usage

DVBS2_Tx.wsv This example workspace demonstrates a spectrum and a CCDF measurementof an DVB-S2 transmitter. The spectrum measurement is achieved using theSpectrumAnalyzer part. The resulting spectrum is shown in theDVBS2_Tx_Spectrum Measurements graph. The CCDF measurement isachieved usign the CCDF part. The resulting CCDF curve is shown in theDVBS2_Tx_CCDF_Measurements graph. A reference curve (CCDF of whitegaussian noise) is also plotted on this graph.

DVBS2_AWGN_BER.wsv This example workspace demonstrates the BER and PER measurements ofthe DVB-S2 receiver on AWGN channel. Different FecFrame, CodeRate andModType can be changed to get BER and PER results. The PER referencecurve is plotted on this graph.


12

DVB2 Baseband Verification Library -DVB-T2 ModelsName Description Usage

DVBT2_Spectrum_CCDF.wsv This example illustrates the generation of DVB-T2 un-coded source byusing Agilent SystemVue. This DVB-T2 source follows section 8 (FrameBuilder, except 8.3,8.4 and 8.5) and section 9 (OFDM Generation) inETSI EN 302 755 v1.1.1 "Digital Video Broadcasting (DVB); Framestructure channel coding and modulation for a second generation digitalterrestrial tellevision broadcasting system (DVB-T2)".

WiMax Baseband Verification Design ExamplesPath: Examples\Baseband Verification\WiMax


WiMAX_DL_Source_Spectrum_CCDF.wsv This example workspace demonstrates a spectrum and aCCDF measurement of a WiMAX downlink source.

WiMAX_UL_AWGN_BER.wsv This example workspace demonstrates a swept BER vsEb/No measurement for a WiMAX uplink in AWGN (AdditiveWhite Gaussian Noise).

WPAN Baseband Verification Design ExamplesThis WPAN Wireless Design Library includes several design examples for WPAN HRP andDirectional LRP transmitter measurement, HRP BER and receiver sensitivity measurement.Six example workspaces are provided in the WPAN Wireless Design Library.

Path: Examples\Baseband Verification\WPAN


13


WPAN_HRP_TxEVM.wsv This example measures the EVM of WPAN HRP transmitter. Foradditional documentation see WPAN_HRP_TxEVM (examples)

WPAN_HRP_TxWaveform_Spec.wsv This example measures the Waveform, Spectrum and CCDF ofWPAN HRP transmitter. For additional documentation seeWPAN_HRP_TxWaveform_Spec (examples)

WPAN_LRP_TxWaveform_D.wsv This example measures the Waveform, Spectrum and CCDF ofWPAN Directional LRP transmitter. For additional documentationsee WPAN_LRP_TxWaveform_D (examples)

WPAN_HRP_AWGN_BER.wsv This example measures the WPAN HRP BER and FER on AWGNchannel. Users can change HRPModeIdx from 0 to 2 inSignal_Generation_VARs and get BER and FER results fordifferent modulations and code rates. For additionaldocumentation see WPAN_HRP_AWGN_BER (examples)

WPAN_HRP_RawBER.wsv This example measures the WPAN Raw BER and FER on AWGNchannel. For additional documentation see WPAN_HRP_RawBER(examples)

WPAN_HRP_RxSensitivity.wsv This example measures the WPAN HRP receiver sensitivity. Theminimum power input to a single receiver is defined such that theerror criterion of BER less than 1e-7 is met. A compliant HRPreceiver shall have a sensitivity that is less than -50 dBm for HRPmode index 0. For additional documentation seeWPAN_HRP_RxSensitivity (examples)

WPAN_HRP_AWGN_BERThe design for HRP BER measurement under AWGN channel is shown below:

Users can change HRPModeIdx from 0 to 2 in Equations and get BER results for differentmodulations. Please note the HRPModeIdx for each sub-packets should be the same. InEquations, the Eb/N0 and corresponding SNR is calculated. The number of frames forsimulating BER is defined which may be varied for different Eb/N0.BitsPerOFDMSymbol is calculated and output in the Simulation Log window. The designshould be simulated twice to get the value of BitsPerOFDMSymbol. For the first simulation,after reading the BitsPerOFDMSymbol from the Simulation Log window, the simulation canbe stopped. Then the value of BitsPerOFDMSymbol should be filled into Equation. With thecorrect value of BitsPerOFDMSymbol, starting the simulation again.The performances of BER under AWGN for HRP Mode index 0, 1 and 2 are given in Graphand DataSet below:


14

References

1. IEEE P802.15.3c/D08, "Part 15.3: Wireless Medium Access Control (MAC) and PhysicalLayer (PHY) Specifications for High Rate Wireless Personal Area Networks (WPANs):Amendment 2: Millimeter-wave based Alternative Physical Layer Extension", 2009.

WPAN_HRP_RawBERThe design for HRP Raw BER (Uncoded BER) measurement under AWGN channel is shownbelow:

Users can change HRPModeIdx from 0 to 2 in Equations and get BER results for differentmodulations. Please note the HRPModeIdx for each sub-packets should be the same. InEquations, the Eb/N0 and corresponding SNR is calculated. The number of frames forsimulating BER is defined which may be varied for different Eb/N0.


15

The performances of RawBER under AWGN for QPSK and 16QAM are given in Graph andDataSet below:

References


WPAN_HRP_RxSensitivityThe design for HRP receiver minimum input level sensitivity measurement is shown below:

The minimum power input to a single receiver is defined such that the error criterion ofBER less than 1e-7 is met. An HRP receiver shall have a sensitivity that is less than -50dBm for HRP mode index 0.


16

References


WPAN_HRP_TxEVMBelow is the transmitter EVM measurement design:

The transmitter evm of each frame, average evm, average EVM for each subcarrierandand constellation are shown in Graph and DataSet.Below is the constellation and average EVM for each subcarrierand.


17


18

References


WPAN_HRP_TxWaveform_SpecThis design measures the HRP transmitter Waveform, Spectrum and CCDF. The design isshown below:

The transmitter Waveform, Spectrum and CCDF are shown in Graph and DataSet.


19


20

References


WPAN_LRP_TxWaveform_DThis design measures the directional LRP transmitter Waveform, Spectrum and CCDF. Thedesign is shown below:

The transmitter Waveform, Spectrum and CCDF are shown in Graph and DataSet below:


21


22

References


ZigBee Baseband Verification Library Design ExampleThis ZigBee Wireless Design Library includes several design examples for ZigBeetransmitter spectrum, BER under AWGN channel, receiver sensitivity and adjacent andalternate jamming resistance measurements. Five example workspaces are provided inthe ZigBee Wireless Design Library.

Path: Examples\Baseband Verification\ZigBee


23


ZigBee_Adjacent_Jamming_Resistace.wsv This example workspace demonstrates Adjacent Jammingresistance for ZigBee system as defined in 6.5.3.4 and6.6.3.5 of IEEE Std 802.15.4-2006.

ZigBee_Alternate_Jamming_Resistace.wsv This example workspace demonstrates Alternate Jammingresistance for ZigBee system as defined in 6.5.3.4 and6.6.3.5 of IEEE Std 802.15.4-2006.

ZigBee_AWGN_BER.wsv This example workspace demostrates swept BER vs SNRmeasurements for ZigBee system under AWGN channel.

ZigBee_Sensitivity.wsv This example workspace demostrates swept FER vsTransmit signal power for ZigBee sensitivity measurementas defined in 6.1.7 of IEEE Std 802.15.4-2006.

ZigBee_TxWaveform_Spec.wsv This example workspace demonstrates the spectrummeasurement and VSA 89601 connection of ZigBeetransmitter.


24

C++ Code Generation ExamplesStarting with Agilent SystemVue 2010.01, C++ Code Generation from block levelschematic models is now supported. All SystemVue product configurations are enabled togenerate licensed and compiled dynamically linked libraries for use in Agilent ADS, AgilentSystemVue, or any Win32 progra. Using W1718 C++ Code Generator will allowSystemVue to generate C++ source code along with compiled .dlls. There is a new librarycategory under the "Algorithm Library" that contains all supported code gen-able models.

Path: Examples\C-Code Generation


CodeGen_CIC_Filter.wsv This example demonstrates how you can use built-in SystemVue parts, toimplement a Cascaded integrator-comb (CIC) filter that can be targeted tothe SystemVue C++ Code Generator.

On the Web

ArticlesForumKnowledge CenterTechnical SupportTrainingVideos

http://www.agilent.com/find/eesof-library


http://www.agilent.com/find/eesof-systemvue-forum


http://www.agilent.com/find/eesof-knowledgecenter


http://www.agilent.com/find/eesof-support


http://www.agilent.com/find/eesof-class


http://www.agilent.com/find/eesof-systemvue-videos



25

Comms Examples ContentsPath: Examples\sub-folder

BER (examples)OFDM (examples)Satellite (examples)Zigbee (examples)

Path: Examples\Comms


Bluetooth.wsv This example workspace demonstrates simple hopping and demdulation of aBluetooth-like signal, but not modulated to the actual RF frequency, using a lowerfrequency instead to speed simulation.

Costas.wsv This example workspace demonstrates a second-order Costas Loop fordemodulation of a BPSK waveform. The Costas Loop is a specialized form of PLLthat regenerates the carrier from a bi-phase modulated signal using quadraturemixer.

DQPSKModem.wsv

This example illustrates a model of a DQPSK transceiver with detailed algorithmicmodeling of DQPSK encoder, DQPSK decoder, bit slicer, and Grey decoder. An RFmodel is added with overal BER analysis at the reciver.

DQPSK EncoderModeling.wsv

This example illustrates SystemVue modeling polymorphism with multipleimplemenations of a DQPSK encoder in floating point, fixed point, and MathLanguage. Verification of encoder performance is analyzed using Agilent VectorSignal Analyzer.

QAM16.wsv This is a simple 16 symbol Quadrature Amplitude Modulation example

BER ExamplesPath: Examples\Comms\BER


26


PAM-QAM_BER_Importance_Sampling.wsv

This workspace shows simulated Bit Error Ratio (BER)performance vs. theory for several PAM and QAM modulationformats using the "BER_IS" measurement sink. Thismeasurement alogirthm uses the "Improved ImportanceSampling" method to provide a highly-efficient analyticalestimate of the true BER.

QPSK_BER_CODED_Viterbi.wsv This example workspace demonstrates setup of BERsimulation for a QPSK modulated system including FEC withconvolutional coding and Viterbi decoding. Improvements toBER with soft decision detection are shown

QPSK_BER_Importance_Sampling.wsv This example workspace demonstrates setup of BERsimulation for modulated signals using QPSK as an examplewith swept control over EbNo using Math language equationsand sweep controller. BER is facilitated using both MonteCarlo and Importance Sampling with comparison againtheoretical BER curves.

Transceiver_BER_with_Scripting.wsv This example combines simulation scripting using SystemVueMath language with a typical setup for BER simulation usingswept EbNo simulation. User has control over multiplemodulation formats with swept control over EbNo for acurateBER simulation

OFDM ExamplesPath: Examples\Comms\OFDM


Cognitive_Radio_Example.wsv This is a Cognitive Radio whitespace algorithmexample that demonstrates spectrum sensing andadaptive OFDMA spectrum usage with a 3GPP LTE(Long Term Evolution) commercial downlink waveform.Math Language algorithms are used for spectral "whitespace" sensing and adaptive LTE DL waveform creationto fill the white space.

Cognitive_Radio_Generic_OFDM_Example.wsv This example demonstrates Cognitive Radio adaptionto varying channel conditions and capacityrequirements using Orthogonal frequency-divisionmultiplexing (OFDM) techniques. By selectivelydisabling subcarriers, wideband signals can avoidinterfering with narrow-band signals, even if they arelocated in the same main channel. This exampleillustrates this concept with the new generic OFDMmodels available in the base SystemVue algorithmlibrary.

OFDM_Custom_Signal.wsv This example workspace demonstrates the use ofgeneric OFDM building blocks found in SystemVue tocreate a arbitrary OFDM signal. The signal created isvery similar to 802.11a, with 52 sub-carriers, 48 datasub-carriers with use settable modulation, and 4 pilotcarriers. Long and short pre-emble is also created andmultiplexed into the final OFDM spectrum.

On the Web


27


Satellite Examples

Contents

Path: Examples\sub-folder

Galilleo (examples)GPS (examples)

Galileo Examples

Path: Examples\Comms\Satellite\Galileo


Galileo_E1_Src.wsv This example illustrates the usage of generic building blocks for creatingcustom satcom signaling to the Galileo specification with SystemVue. Theblocks used in this example are all from the standard "Algorithm" library withinSystemVue. Where a standard algorithmic model was not sufficient,SystemVue's Math language model interface was used. To see the underlyingMath Language algorithm for these blocks simply double click on the partsymbol to see the source code.

GNSS_BOC_n_m.wsv This example illustrates the usage of generic building blocks for creatinggeneric satcom signaling with Binary Offset Carrier (BOC) using AgilentSystemVue. The blocks used in this example are all from the standard"Algorithm" library within SystemVue.

GNSS_BOC1.wsv This example illustrates the usage of generic building blocks for creatinggeneric satcom signaling with Binary Offset Carrier (BOC) and Binary PhaseShift Keying (BPSK) using Agilent SystemVue.

GPS Examples

Path: Examples\Comms\Satellite\GPS














28


GPS_L1_Source_and_RF_TX.wsv This example generates GPS ranging code of P-Code and C/A-Codes in GPS satellite navigation. The GPS L1 source is alsogenerated by using P-Code and C/A Codes. The GPS navigation(NAV) applications have data coding requirements defined in thetechnical specification IS-GPS-200D (Navstar GPS Space Segment/ Navigation User Interfaces). For GPS NAV applications, two basicranging codes are generated (among several others). Theprecision (P) code is the principal NAV ranging code. Thecoarse/acquisition (C/A) code is used primarily for acquisition ofthe P code.

GPS_L1C_Source_and_RF_TX.wsv This example generates GPS PRN ranging codes L1CPi(t) andL1CDi(t) and overlay codes L1COi(t) based on IS-GPS-800specification. The GPS L1C source is also generated by usingranging codes L1CPi(t) and L1CDi(t) and overlay codes L1COi(t).In this L1C source, BCH coder, LDPC coder and interleaver are alsoincluded.

GPS_L2_Source_and_RF_TX.wsv This example generates GPS ranging code of P-Code and C/A-Codes in GPS satellite navigation. The GPS L2 source is alsogenerated by using P-Code and C/A Codes. The GPS navigation(NAV) applications have data coding requirements defined in thetechnical specification IS-GPS-200D (Navstar GPS Space Segment/ Navigation User Interfaces). For GPS NAV applications, two basicranging codes are generated (among several others). Theprecision (P) code is the principal NAV ranging code. Thecoarse/acquisition (C/A) code is used primarily for acquisition ofthe P code.

GPS_L2C_Source_and_RF_TX.wsv This example generates GPS PRN ranging codes P-Code, L2 CM-Code and L2 CL-Code based on IS-GPS-200D specification. TheGPS L2C source is also generated by using ranging codes P-Code,L2 CM-Code and L2 CL-Code. The FEC (convolutional coder) is alsoimplemented.

GPS_L5_Source_and_RF_TX.wsv This example generates GPS PRN ranging codes L5-Codes (I5i(t)and Q5i(t)) based on IS-GPS-705 specification. The GPS L5sourceis also generated by using ranging codes L5-Codes. The FEC(convolutional coder) is also implemented.

Zigbee ExamplesPath: Examples\Comms\Zigbee


29


Zigbee_Adjacent_Jamming_Resistance.wsv This example workspace demonstrates Adjacent channeljamming resistance for ZigBee systems in the 868.0 -868.6 MHz band.

Zigbee_Alternate_Jamming_Resistance.wsv This example workspace demonstrates Alternate jammingresistance for ZigBee systems in the 868.0 - 868.6 MHzband.

Zigbee_AWGN_BER.wsv This example workspace demostrates swept BER vs SNRmeasurements for ZigBee system under AWGN channelwith example designs for 868MHz, 915 MHz, and 2450MHz bands

Zigbee_Sensitivity.wsv This example workspace demostrates swept FER vsTransmit signal power for ZigBee sensitivitymeasurements with example designs for 868MHz, 915MHz, and 2450 MHz bands

Zigbee_TxWaveform_Spec.wsv This example workspace demonstrates spectralmeasurements and vector demodulation of ZigBeetransmitter waverforms with example designs for868MHz, 915 MHz, and 2450 MHz bands. AgilentE89601A VSA SW is utilized for the dynamicdemodulation and analysis.

Zigbee868_915Mhz.wsv This workspace includes two examples to generateZigbee signal source based on IEEE 802.15.4 868 MHzPHY specification and 915 MHz PHY specification.

Zigbee_2450MHz.wsv This workspace includes two examples to generateZigbee signal source based on IEEE 802.15.4 2450 MHzPHY specification.


30

SystemVue DPD ExamplesExamples for Agilent's SystemVue Digital Pre-Distortion applications kit. This kit allowscharacterization of DPD pre-distorter and non-linear amplifier characteristics using timedomain techniques. The provided extraction and modeling technologies allows for full non-linear characterization including memory effects. The supplied examples can be used assimple demonstrators as well as for complete modeling and pre-distortion of actual HWdevices by utilizing Agilent measurement HW with the DPD kit.

ContentsPath: Examples\DPD

DPD and CFR Simulation (examples)DPD LTE Hardware Verificaiton (examples)DPD UserDefined Hardware Verificaiton (examples)DPD WCDMA Hardware Verificaiton (examples)


31

DPD for LTE with Hardware ExtractionThe workspaces in this directory rely on SystemVue being connected to Agilentmeasurement HW, i.e. MXG Complex Signal Generators and MXA Signal Analyzers, forproper operation. These workspaces also function very tightly with an integrated GraphicalUser Interface within SystemVue.

It is recommended that these workspaces only be used after reading the SystemVue usersguide on Digital Predistortion applications kit:

SystemVue <version_no> > DPD Baseband Verification Library


32

DPD for user defined stimulus withHardware ExtractionThe workspaces in this directory rely on SystemVue being connected to Agilentmeasurement HW, i.e. MXG Complex Signal Generators and MXA Signal Analyzers, forproper operation. These workspaces also function very tightly with an integrated GraphicalUser Interface within SystemVue.

It is recommend that these workspaces only be used after reading the SystemVue usersguide on Digital Predistortion applications kit



33

DPD for WCDMA Stimulus withHardware ExtractionThe workspaces in this directory rely on SystemVue being connected to Agilentmeasurement HW, i.e. MXG Complex Signal Generators and MXA Signal Analyzers, forproper operation. These workspaces also function very tightly with an integrated GraphicalUser Interface within SystemVue.

It is recommended that these workspaces only be used after reading the SystemVue usersguide on Digital Predistortion applications kit



34

DPD and CFR Generic Examples for LTEand 3GPP-WCDMA*Path: Examples\DPD\DPD and CFR Simulation


DPD_LTE_DL_PANonlinearityMemory.wsv This example demonstrates how you can use SystemVue tosimulate the DPD with the LTE downlink waveform as thestimulus. Stimulus waveforms are stored in supplied filesfor an LTE DL signal. The workspace is organized in folderswith each folder corresponding to a step of the simulation.These steps are in accord with the hardware verificationflow, except for the missing steps to capture the waveformfrom the instruments since we are using a DUT simulationmodel, "AmplifierWithMemory". These steps are in accordwith the hardware verification flow, except for the missingof Step 2 and Step 5 since there is no need to capture thewaveform from the instruments.

CFR_3GPPWCDMA.wsv This example illustrates the implementation of crest factorreduction for 3GPP WCDMA (4 Carriers) signal using AgilentSystemVue. CCDF, PAPR, and Spectrum are analyzed.

CFR_LTE_DL.wsv This example illustrates the usage of crest factor reductionfor 3GPP LTE Downlink OFDMA signal using AgilentSystemVue. CCDF, PAPR, and EVM are analyzed.

DPD_LTE_DL_Simulation.wsv This example demonstrates how you can use SystemVue tosimulate the DPD with the LTE downlink waveform as thestimulus. The workspace is organized in folders with eachfolder corresponding to a step of the simulation. Thesesteps are in accord with the hardware verification flow,except for the missing of Step 2 and Step 5 since there isno need to capture the waveform from the instruments.


35

Hardware Design Examples ContentsPath: Examples\sub-folder


CORDIC_NCO.wsv Numerically controlled oscillator implemented using theCORDIC algorithm in rotation mode

CORDIC_Vectoring.wsv Rectangular to polar conversion implemented using theCORDIC algorithm in vectoring mode

DualNCO.wsv LUT based numerically controlled oscillator with sineand cosine outputs

FixedPoint Please refer FixedPoint (examples) Examples for moredetails

FMMod.wsv Simple FM modulation using Fixed point integrator andmath

HDLCodeGeneration\GFSKMod\GFSKMod.wsv Gaussian Frequency Shift Keying modulator

HDLCodeGeneration\IIR_DDS\IIR_DDS.wsv Sinusoid generation using an IIR filter

LMS.wsv Channel/System identification LMS filter example

MACFIR.wsv MAC FIR implemention with time shared hardware

NCO.wsv Direct Digital Synthesis (DDS) / LUT based numericallycontrolled oscillator

WiMAX_SVue_SDR_IQ_Modulator.wsv A collection of designs to implement a DUC, digitalupconverter, with lookup tables (LUTS) allowingcreation of WiMAX and LTE arbitrary waveforms. Thisdesign can be implemented on a Nallatech FPGA forwaveform prototyping

TCM.wsv This is a Trellis Coder-Decoder example completlydesigned in Fixed Point. The design can beimplemented on a Nallatech FPGA system forprototyping

FixedPoint ExamplesPath: Examples\Hardware Design


FixedPoint\BPSK\BPSK.wsv Binary Phase Shift Keying fixed point implementation example

FixedPoint\CIC\CIC.wsv Cascade Integrated Comb filter fixed point implementationexample

FixedPoint\M-ary_PSK\M-ary_PSK.wsv M-ary Phase Shift Keying fixed point implementation example

FixedPoint\M-ary_QAM\M-ary_QAM.wsv

M-ary Quadrature Amplitude Modulation fixed pointimplementation example

FixedPoint\pi-by-4 DQPSK\pi-by-4DQPSK.wsv

Pi/4 rotated Differential Quadrature Phase Shift Keying fixedpoint implementation example

FixedPoint\QPSK\QPSK.wsv Quadrature Phase Shift Keying fixed point implementationexample


36

Instrument ExamplesSee Examples (examples) page on how to access and run these examples

ContentsPath: Examples\sub-folder

N5106A SignalDownloader (examples)VSA89600ASink (examples)VSA89600ASource (examples)SignalStudio (examples)

Simple Examples to Exercise Instrument Control parts

Path: Examples\Instruments and its sub-folders


ESG SignalDownloaderExample.wsv

An example on how to use the SignalDownloader_E4438C (algorithm)to download and play out waveform in Agilent ESG or MXG series RFSignal Synthesizer such as E4438C or N5182A.)

E4438C andN5182A only

ESG MIMOConfigurationExample.wsv

An example on how to use two SignalDownloader_E4438C (algorithm)to configure 2 E4438C's to provide synchronized signals for MIMOapplications

E4438C only

MXG MIMOConfigurationExample.wsv

An example on how to use two SignalDownloader_E4438C (algorithm)to configure 2 N5182A 's to provide synchronized signals for MIMOapplications

N5182A only

Practical Applications

Name Description

Generate LTE RFSignals

Generate LTE modulated RF signals with RF Vector Synthesizers from AgilentTechnologies

N5106A PXB Signal Generator ExamplesSimple Examples to Exercise N5106A Signal Downloader Part are as follows:Path: Examples\Instruments\N5106A SignalDownloader


Bento N5106ASignalDownloader.wsv

An example on how to use the SignalDownloader_N5106A (algorithm)to download and play out waveform in Agilent Bento N5106A

N5106A

Signal Studio ExamplesSimple Examples to read Signal Studio wfm files:Path: Examples\Instruments\SignalStudio

http://edocs.soco.agilent.com/display/eesofkc/SystemVue+2009+generate+LTE+December+2008+UL+QPSK+5MHz+RB+encoded+download+ESG+MXG+ARB+Source+save+IQ+file+ASCII+binary+SignalStudio+wfm+N5106A+PXB



http://www.home.agilent.com/agilent/product.jspx?cc=US&lc=eng&nid=-536902260.0

http://www.home.agilent.com/agilent/product.jspx?cc=US&lc=eng&nid=-536902260.0


37

SignalStudio_WFM_BuiltIn.wsv An example on how to use the ReadSignalStudioFile(algorithm) model in ReadFile Part (algorithm) to read AgilentSignal Studio waveforms installed with SystemVue

SignalStudioWFM_LTE_DL_5MHz_25RB.wsv An example on how to use the ReadSignalStudioFile(algorithm) model in ReadFile Part (algorithm) to read an LTEwaveform file of Agilent Signal Studio waveform format.

Vector Signal Analyzer Sink ExamplesSimple Examples to Exercise VSA Sink Part are as follows:Path: Examples\Instruments\VSA89600Sink

VSA89600 DemodQPSK.wsv

An example on how to use the VSA_89600_Sink (algorithm) to control Glacier 89600to process waveforms generated by the simulation

Vector Signal Analyzer Source ExamplesSimple Examples to Exercise VSA Source Part are as follows:Path: Examples\Instruments\VSA89600Source

VSA89600 RecallQAM512 Data.wsv

An example on how to use the VSA_89600_Source (algorithm) part to control Glacier89600 for bringing measurement data captured by Glacier 89600 into the simulation

On the Web















38

Math Language Scripting ExamplesPath: Examples\Math Language Scripting


ParameterSweeping.wsv This example uses a simple SineGen source and Sink to create aplatform for multi-dimensional parametric sweeps. The amplitude,frequency and phase of the sinusoid generator are set to betunable and hence can be selected as sweep parameters fromanalyses mounted on the basic data analysis process.

Scripting_SinkFile_Output.wsv This tutorial workspace shows how to use a Math Language scriptto generate output files for each value in a sequence.

Mathlangugage


39

MIMO Channel Modeling*Path: Examples\MIMO Channel Model


ChannelCapacity_Measurements.wsv This example workspace demonstrates the channel capacity forWinnerII channel and the correlation based channel. Thechannels are configured to be 2X2 MIMO mode. The calculationof channel capacity is focused on one path for the multi-pathchannel.

ChannelImR_Measurements.wsv This example workspace demonstrates the impulse response,power spectrum and the fading waveform for WinnerII channelor the correlation based channel. The channels are configured tobe 2X2 MIMO mode. You can run the measurements of the onechannel by opening it and closing the other one in theschematic.

ChannelThroughput_LTE.wsv This example workspace demonstrates swept Throughput vs.SNR measurements for LTE downlink in winnerII channel andcorrelation based channel environments. The configuration istested: a 2x2 (MIMO Transmit Diversity)

On the Web















40

Model Building ExamplesPath: Examples\Model Building Examples


C Modeling\Simple Model BuilderExample.wsv

This example demonstrates how you can use the C++ ModelBuilder to build custom models.

Math Language Modeling\MathLangDemo.wsv

This example demonstrates the use of the MathLang model.The 'Network' design has a sinusoid source, a MathLangblock, and two sinks to record the input and output of theMathLang block.

Math Language Modeling\MathLangDemo with MATLAB.wsv

This example introduces the MATLAB integration, as well ashow to work with framed data (matrix datatype), and usingthe "TimeSynchronizer" part to adjust for algorithmiclatency, caused by array processing.

Math Language Modeling\MathLangMultirate Demo.wsv

This example demonstrates the use of the MathLang model.The two branches of the design are identical in functionality -what is achieved by using built-in system level parts in onebranch is performed via equations in a single Math Languagepart.

Math Language Modeling\MathLangSymbol Demo.wsv

This example demonstrates the use of the MathLang modelwith an arbitrary symbol.

Math LanguageModeling\Dynamic_paths.wsv

This example shows how MathLang blocks can be used inrun-time tuning to select between signal processing paths foreither baseband or for RF Envelopes. A few S-parameterresponses are used to simulate the bandlimited response ofa channelized receiver front-end, chosen interactively with aslider.

Math LanguageModeling\Math_AnalogDistortion.wsv

This example shows a more analog application for the mathlanguage modeling, generating transistor-like distortion withinteractive sliders. A run-time tuning example is also shown.

CIC Filter.wsv This example demonstrates how you can use built-inSystemVue parts, MathLang, and the C++ Model Builder toimplement a Cascaded integrator-comb (CIC) filter.

On the Web















41

PLL ExamplesPath: Examples\PLL


PLL_test.wsv This example workspace demonstrates a simple PLL subnetwork model. Passedparameters on the model allow setting the loop characteristics.

On the Web















42

Radar Examples ContentsPath: Examples\Radar

PD RADAR Transmitter Test (examples)PD RADAR Receiver Test (examples)PD RADAR Performance Test (examples)

Path: Examples\Radar


Radar_Chirp.wsv This example workspace demonstrates a some simple Radar source models withvarious impairements. Example 1 shows a pulsed radar source with RF TX and RXmodels. Example 2 shows a chirp radar source with Radar channel for modelingdistance and doppler frequency. Example 3 builds an interference signal on top ofthe chirp radar source. Example 4 is a model of a Constant False Alarm Rate (CFAR)detector using a Cell Averaging structure.

PD_Radar.wsv This workspace provides a Pulse-Doppler (PD) radar system design with signalgenerator, RF transmitter, antenna, clutters, RF receiver, moving target detection(MTD), constant false alarm rate (CFAR) processor and signal detector for simulationpurpose. The simulation designs can be used as templates for different PDapplications.

PD RADAR Performance Test Examples


PDRADAR_DetectionProbability_AWGN This example measures a PD RADAR's detection probabilityunder AWGN1. The PD RADAR is detecting a target in a distance of 60kmand a velocity of 60m/s2. The received Signal to Noise Ratio (SNR) is swept from -18dB to -10dB3. The PD RADAR using a Hamming window based pulsecompressor with a BT product of 504. The Pulse Doppler (PD) processing utilize a moving targetdetection (MTD) with a CPI(Coherent Processing Interval) of32 pulses5. The CFAR (Constant False Alarm Rate) algorithm is CellAverage (CA)6. On an Intel Core 2 E6850/3.0G 4GB PC powered by MSWindows XP and SystemVue2010.07, Simulation time is about45 minutes7. Users can set up the system parameters or replace acomponent according to their particular requirements.

PDRADAR_DetectionProbability.wsv This example measures a PD RADAR's detection probabilitywith Clutter1. The PD RADAR is detecting a target in a distance of 60kmand a velocity of 60m/s2. The received Signal to Noise Ratio (SNR) is swept from -14dB to -6dB3. The clutter is with a Rayleigh distribution in amplitude and a


43

Gaussian distribution in spectrum and stored in data files forsimulation speed up purpose.4. The Clutter to Noise Ratio (CNR) is set to 10dB5. The PD RADAR using a Hamming window based pulsecompressor with a BT product of 506. The Pulse Doppler (PD) processing utilize a moving targetdetection (MTD) with a CPI(Coherent Processing Interval) of32 pulses7. The CFAR (Constant False Alarm Rate) algorithm is CellAverage (CA)8. On a Intel Core 2 E6850/3.0G 4GB PC powered by MSWindows XP and SystemVue2010.07, Simulation time is about90 minutes9. Users can set up the system parameters or replace acomponent according to their particular requirements.

PDRADAR_FalseAlarmRate_AWGN.wsv This example measures a PD RADAR's false alarm rate underAWGN1. The input to the PD radar receiver is Noise only.2. The PD RADAR using a Hamming window based pulsecompressor with a BT product of 503. The Pulse Doppler (PD) processing utilize a moving targetdetection (MTD) with a CPI(Coherent Processing Interval) of32 pulses4. The CFAR (Constant False Alarm Rate) algorithm is CellAverage (CA)5. On an Intel Core 2 E6850/3.0G 4GB PC powered by MSWindows XP and SystemVue2010.07, simulation time is about90 minutes.6. Users can set up the system parameters or replace acomponent according to their particular requirements.

PDRADAR_FalseAlarmRate.wsv This example measures a PD RADAR's false alarm rate withClutter1. The inputs to the PD radar receiver are Clutter and Noise.2. The clutter is with a Rayleigh distribution in amplitude and aGaussian distribution in spectrum and stored in data files forsimulation speed up purpose.3. The Clutter to Noise Ratio (CNR) is set to 10dB4. The PD RADAR using a Hamming window based pulsecompressor with a BT product of 505. The Pulse Doppler (PD) processing utilize a moving targetdetection (MTD) with a CPI(Coherent Processing Interval) of32 pulses6. The CFAR (Constant False Alarm Rate) algorithm is CellAverage (CA)7. On an Intel Core 2 E6850/3.0G 4GB PC powered by MSWindows XP and SystemVue2010.07, simulation time is about180 minutes8. Users can set up the system parameters or replace acomponent according to their particular requirements.

PDRADAR_Measurement.wsv This example measures a target's range and velocity in clutterand noise1. The PD RADAR is detecting a target in a distance of 100kmand a velocity of 60m/s2. The received Signal to Noise Ratio (SNR) is -10dB3. The clutter is with a Rayleigh distribution in amplitude and aGaussian distribution in spectrum and stored in data files forsimulation speed up purpose.4. The Clutter to Noise Ratio (CNR) is set to 10dB


44

5. The PD RADAR using a Hamming window based pulsecompressor with a BT product of 506. The Pulse Doppler (PD) processing utilize a moving targetdetection (MTD) with a CPI(Coherent Processing Interval) of32 pulses7. The CFAR (Constant False Alarm Rate) algorithm is CellAverage (CA)8. On an Intel Core 2 E6850/3.0G 4GB PC powered by MSWindows XP and SystemVue2010.07, simulation time is about90 minutes9. Users can set up the system parameters or replace acomponent according to their particular requirements.

PDRADAR_DetectionProbability_AWGN PDRADAR_DetectionProbability_Cluttering PDRADAR_FalseAlarmRate_AWGN PDRADAR_FalseAlarmRate_Cluttering PDRADAR_Measurement PD RADAR Receiver Test Examples

Contents


45


PDRADAR_Rx_Waveform.wsv This example measures the waveform of a PD RADAR receiver withclutter and noise1. The waveform includes the components from target echo, clutterand noise2. Both the RF waveform and the RF spectrum of the received signalare measured.3. On an Intel Core 2 E6850/3.0G 4GB PC powered by MS Windows XPand SystemVue2010.07, Simulation time is about 1 minute

PDRADAR_Clutter.wsv This example measures the clutter signal of the radar environment.The clutter refers to radio frequency (RF) echoes returned from targetswhich are uninteresting to the radar operators.1. A statistical model is set up to simulate the real world clutter.2. The clutter has certain magnitude probability density functions andpower spectrum densities.3. Results are shown in associated graphs.4. Simulation time is about 11.93 seconds on an Intel Core 2E6850/3.0G 4GB PC powered by MS Windows XP andSystemVue2010.07.

PDRADAR_DynamicRange.wsv This example measures dynamic range of the radar receiver. Thedynamic range is the input signal power range to be amplified withoutdistortion.1. Measured the 1 dB gain compression power of the low noiseamplifier in RF.2. Measured the output IF signal power.3. Measured the baseband signal power after ADC and digital downconverter.4. Results are shown in associated graphs5. Simulation time is about 10.26 seconds on an Intel Core 2E6850/3.0G 4GB PC powered by MS Windows XP andSystemVue2010.07.

PDRADAR_Selectivity.wsv This example measures the adjacent band selectivity of the radarreceiver. Radar frequency sensitivity is a very important characteristicas it determines radar's interference immunity.1. A filtered noise is constructed to simulation the interference in theadjacent band of the radar signal.2. The simulation is carried out with digital IF and analog RF front end,where, both DUC/DDC and front end circuit effects are considered.3. Results are shown in associated graphs.4. Simulation time is about 19585.87 seconds on an Intel Xeon CPU5130 2.00GHz, 2 processors, 3G RAM PC powered by MS Windows XPand SystemVue2010.07.

PDRADAR_Sensitivity.wsv This example measures sensitivity of the radar receiver. Radarsensitivity is determined by the ability to reliably detect weak signalsin the presence of noise.1. A noise model is the simulated thermal noise (The noise spectrumdensity is -173.975 dBm/Hz).2. The simulation is carried out with digital IF and analog RF front end,where, both DUC/DDC and front end circuit effects are considered.3. Results are shown in associated graphs.4. Simulation time is about 21975.57 seconds on an Intel Core 2E6850/3.0G 4GB PC powered by MS Windows XP andSystemVue2010.07.

PDRADAR_Clutter PDRADAR_DynamicRange


46

PDRADAR_Rx_Waveform PDRADAR_Selectivity PDRADAR_Sensitivity PD RADAR Transmitter Test Examples

Contents


PDRADAR_Tx_Waveform.wsv This example measures the radar Transmitter RF signal and IF signalwaveforms and spectrums1. The radar signal is a linear frequency modulation pulse in thisexample.2. The simulation is carried out with digital IF and analog RF front end,where, both DUC/DDC and front end circuit effects are considered.3. Results are shown in associated graphs.4. Simulation time is about 12.95 seconds on an Intel Core 2E6850/3.0G 4GB PC powered by MS Windows XP andSystemVue2010.07.

PDRADAR_WaveformOn the Web















47

RF Architecture Design Examples ContentsPath: Examples\sub-folder

RF Design Kit (examples)X Parameters (examples)

Path: Examples\RF Architecture


3GPP_LTE_DL_SISO_BER_RF_Link.wsv This example workspace demonstrates swept BER andBLER for an LTE downlink SISO system using a realistic LTERF downconverter sub-system. To run this example you willneed a license for both W1719 RF Design Kit (inlcuded inW1464 RF Architect Product bundle) AND the W1910 3GPPLTE Baseband Verification Library. The RF sub-system ismodeled using SystemVue's "RF Architect" frequencydomain analysis capability with the resulting sub-systemmodel embedded into a time domain simulation for LTE DLBER.

Oscillator_Phase_Noise.wsv A phase noise modeling example is demonstrated in Design"Random PN". An Oscillator source is used to generate a 1GHz tone at a power level of 10 dBm into 50 Ohms. Thetone is colored with phase noise, whose frequencyspecification is defined in the PhaseNoiseData parameter.For this example, f offsetMin is 1 kHz and f offsetMax is 400

kHz.

QPSK_RF_Link_Demo.wsv A combined DSP-RF design flow is described. TX and RX RFdesigns are evaluated using RF System analyses(Spectrasys) to determine the RF design broad bandfrequency domain response. These RF designs are includedin DSP design and use Data Flow analysis to determine thecombined bandpass time domain response.

RF_LinkDSP-RFAnalysis

QPSK_with_Interferer_VSA.wsv This example workspace illustrates basic capability inSystemVue for creating custom RF architectures andmodulation structures that can be quickly and easilyanalyzed, including incorporation of RF impairments andchannel interferrence.

DataFlow RFModels

SData Demo.wsv This examples highlights the setup and usage of the SDatapart/model which allows importation of S21 S-parameterdata into the SystemVue time domain data flow simulation

ZIF_3GPP_test.wsv This workspace demonstrates SystemVue's capability forsimulating RF Subsystems

DataFlow RFmodels

ZIF_two_tone_test.wsv This workspace demonstrates SystemVue's capability forsimulating RF Subsystems

DataFlow RFmodels

RF Design Kit (Spectrasys) Examples


48

Path: Examples\RF Architecture\RF Design Kit


5GHZ VSWR Detector.wsv This is a simple example of a 3 sector 5.8 GHz receiver that can beused as a TX powermeter or VSWR tester. This example will show the importance or RFarchitecture workand how many design parameters can be confidently selected usingan RF architecturedesign tool.

AppCAD 1.9 GHZ CDMAHandset Receiver.wsv

This is a simple illustration of how a dumbed down Spectrasyssimulation will give the same answers as the AppCAD NoiseCalcexample '1.9 GHz Handset Receiver'. Furthermore, a more realisticdesign is created showing the value of Spectrasys abovespreadsheets.

Diversity TX and HybridAmp.wsv

This is a simple illustration of a diversity transmitter with a hybridamplifier. Two IS95 carriers are created at 1955 and 1965 MHz.

Dual Band FrequencyPlan.wsv

This example illustrates the IF performance of a dual band CDMAreceiver.

What IF,Spectrasys

Freq Dependent Attn.wsv This example shows how a frequency dependent attenuator can beused to create a filter mask.

Mixer Model Noise.wsv Shows how mixer fundamental and image noise is calculated.Simulated and manually calculated results are compared.

SystemAnalysisEquationsTuningVariables

Resistor NoiseAnalysis.wsv

This workspace illustrates how noise is calculated and simulated inSpectrasys

NoiseEquations

Simple Table Mixer.wsv This is example will help the user understand table mixerconfiguration and operation.

Simple Transceiver.wsv This is a illustration of how sub-network models. Transmitter andreceiver schematics are created are re-used at a top leveltransceiver schematic that incorporates a diversity receiver. Customsymbols were also created for the transmitter and receiver.

Transceiver_RFLink.wsv This tutorial example shows how an RF air interface defined inSpectrasys can be dropped into a DSP link-level simulation using theRFLink component. The effect of RF Transmit IP3 and filter rolloff caneasily be seen on the received baseband constellation.

Tx RX Chain.wsv This is a illustration an entire transmit and receive chain includingthe path loss between the transmitter and receiver. Receiver noise iscalculated along with TX output Spectrum.

Spectrasys

WhatIF Dual AnalysisOnly.wsv

In this particular example we have an RF input spectrum from 275 to325 MHz. The desired IF is a difference IF at 800 MHz derived from ahigh side LO. Since we have chosen the IF frequency to be 800 MHzand the IF bandwidth is 1 MHz then the LO will range from 1075.5 to1124.5 MHz. In this given example we are looking at the 'SingleIntermediate Frequency' behavior only.


49

X-Parameter Example WorkspacesPath: Examples\RF Architecture


QPSK_Xparam_RFLink.wsv This example contains 2 Data Flow simulation schematics, namely, QPSKand QPSK_VSA. Both of them demonstrate how to bring an actual RFdevice (in this example, an RF Amplifier) into a system level simulation inSystemVue through the X-parameters characterization of the RF device("AMP_XParams.mdf" file in this example).

Xparams_CircuitLink.wsv This example contains Spectrasys simulations to show how CIrcuit_linkshould be used when cascading non-linear parts such as X-parameterspart. The RF_Circuit design uses a Circuit_link part to bring theCascSubNet into top level Spectrasys design for simulation. TheRF_CascSubnet design contains a top level design that is equivalent tothe CascSubNet. Note the warning messages about cascading non-linearparts in top Spectrasys design when simulating the RF_CascSubnet.

Xparams_FilePreviewer.wsv This example plots the gain compression of an X-parameter file so thatyou can verify the response of the part before applying it in a systemsimulation. This tutorial also shows how to set up a simple simulation.

On the Web















50

Signal Processing ExamplesPath: Examples\Signal Processing


CrossCorr.wsv This example workspace demonstrates theCrossCorr block and use of cross correlationto calculate signal delay.

Spectrum Analysis.wsv This example workspace demonstrates theuse of the Spectrum Analyzer sink.

DSP\Aliasing.wsv By increasing the frequency of the sine wavesource and comparing with the 1000Hz sinewave, we can observe that for frequenciesabove fs/2 (5000Hz in this example), thediscrete version of the signal is seen to aliasto have a frequency below 5000Hz.

DSP\Dithering Quant Errors.wsv This shows the effect of quantization onspurs in the frequency domain. By adding abit of AWGN to dither quantization errors,energy in the spurs is spread out, increasingthe Spur Free Dynamic Range. SFDR isobserved in the Spectrum graph window.

DSP\NonLinear_Quantizer.wsv This tutorial illustrates how linearsuperposition of two sequences does notapply depending on where signals arequantized.

The following list describes Digital Signal Processing(DSP) tutorial examples from Steepest Ascent, LTD.DSPedia

DSPedia-Chap6 Frequency Domain\6.01_FourierSeries.wsv

DSPedia-Chap6 Frequency Domain\6.02_Square.wsv

DSPedia-Chap6 Frequency Domain\6.04_Square2.wsv

DSPedia-Chap6 Frequency Domain\6.05_triangle.wsv

DSPedia-Chap6 Frequency Domain\6.06_quantize2.wsv

DSPedia-Chap6 Frequency Domain\6.07_fft_scaling.wsv

DSPedia-Chap6 Frequency Domain\6.08_zero_pad.wsv

DSPedia-Chap6 FrequencyDomain\6.10_Windowed_Sine.wsv

DSPedia-Chap6 Frequency Domain\6.11_Window.wsv

DSPedia-Chap6 FrequencyDomain\6.12_Frequency_Discriminationwsv.wsv

DSPedia-Chap6 FrequencyDomain\6.13_Sine_in_Noise.wsv

DSPedia-Chap6 FrequencyDomain\6.14_Waterfall_Chirp.wsv

DSPedia-Chap6 Frequency Domain\6.16_FFT_Part.wsv

DSPedia-Chap6 Frequency Domain\6.17_IFFT.wsv

DSPedia-Chap6 Frequency


51

Domain\6.18_IFFT_Quantized.wsv

DSPedia-Chap7Digital_Filtering\7.01_low_pass_three_sines.wsv

DSPedia-Chap7Digital_Filtering\7.02_low_pass_sweep.wsv

DSPedia-Chap7 Digital_Filtering\7.03_sum_of_sines.wsv

DSPedia-Chap7Digital_Filtering\7.04_low_pass_noise.wsv

DSPedia-Chap7 Digital_Filtering\7.05_bandpass.wsv

DSPedia-Chap7 Digital_Filtering\7.06_cascade.wsv

DSPedia-Chap7 Digital_Filtering\7.10_Zdomain.wsv

DSPedia-Chap7 Digital_Filtering\7.11_Zdomain.wsv

DSPedia-Chap7Digital_Filtering\7.12_MovingAverage.wsv

DSPedia-Chap7 Digital_Filtering\7.13_Differentiator.wsv

DSPedia-Chap7 Digital_Filtering\7.14_Comb.wsv

DSPedia-Chap7 Digital_Filtering\7.15_LinearPhase.wsv

DSPedia-Chap7 Digital_Filtering\7.16_IIR.wsv

DSPedia-Chap7Digital_Filtering\7.17_IIR_Butterworth.wsv

DSPedia-Chap7 Digital_Filtering\7.18_IIR_AllPass.wsv

DSPedia-Chap7Digital_Filtering\7.22_FIR_Wordlength.wsv

This example designs a FIR filter, fromspecification to implementable fixed-pointmodel.

On the Web















52

VBScripting ExamplesPath: Examples\VBScripting


Buttons.wsv Click buttons on a schematic to launch a VBScript to runsimulations.

Parameter Script.wsv VBScript to set part parameters and run simulations.