design and simulation of communications and radar … · end-to-end modeling of radar systems radar...
TRANSCRIPT
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1 © 2013 The MathWorks, Inc.
Design and Simulation of
Communications and Radar Systems
Nick M. Zayed
Armerkom
Istanbul, Turkey
February 28, 2014
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2
Time Title
9:00 – 10:00
Building Communication System Simulations
Constructing a basic QPSK communications model in Simulink
802.11a Wireless LAN example
Adding RF design detail
10:00 – 10:45 4G LTE Design using the LTE System Toolbox
10:45 – 11:00 Break
11:00 – 11:30 Options for Accelerating MATLAB Simulations
11:30 – 12:15 Electronic Interference and Array Signal Processing
12:15 – 13:15 Lunch
13:15 – 14:15
End-to-End Modeling of Radar Systems
Radar Analysis with User-Interface Apps
Integrating the RF Subsystem
14:15 – 14:30 Break
14:30 – 16:30 Hands-on Workshop: Phased Array Systems
Agenda
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3 © 2013 The MathWorks, Inc.
Building Communication System
Simulations
9:00 – 10:00
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Challenges:
From specification to implementation
Elaborate specifications
into models
Assess system-level
performance of your design
Introduce innovative
proprietary algorithms
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Where do MATLAB and Simulink fit?
• Quickly proceed from MATLAB and
Simulink models to implementation
with…
C and HDL code generation
• MATLAB and Simulink for dynamic
and large scale system simulations
Uniting Baseband and RF
• MATLAB and Simulink for algorithm
development and analysis
Floating and Fixed-Point
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Core MathWorks Products
The leading environment for modeling,
simulating, and implementing dynamic
and embedded systems
– Linear & nonlinear, continuous & discrete-time,
hybrid, and multi-rate systems
– Foundation for model-based design, including
physical-domain modeling, automatic code
generation, and verification and validation
– Open architecture for integrating
models from other tools
– Applications in controls, signal processing,
communications, and other system
engineering areas
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Communications System Toolbox
Algorithm libraries in MATLAB Algorithm libraries in Simulink
Over 100 algorithms for
– Modulation, Interleaving, Channels, Source Coding
– Error Coding and Correction
– MIMO, Equalizers, Synchronization
– GPU-optimized components, SDR hardware
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Building a Physical Layer (PHY) model
of a QPSK communications system
Demo
>> QPSK
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IEEE 802.11a Wireless LAN System
Demo
>> wlan_eml_noRF
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Code Generation C and HDL code
Physical Layer (PHY) in MATLAB or Simulink Turbo / FEC Coding, Source Coding, Interleaving, Modulation, Synchronization, Channel Models, Equalizers, MIMO
Measurements BER, EVM, ACPR, CCDF, Eye Diagram
Examples LTE, 802.16, 802.11, Zigbee, Bluetooth, DVB-S.2, ATSC
GPU Acceleration Viterbi decoder, Turbo decoder, LDPC decoder, AWGN Hardware Connectivity
USRP Radios
Communications System Toolbox
Communications System Toolbox™ provides algorithms and tools for the design,
simulation, and analysis of communications systems
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Sharing information between digital
and RF engineers
BER
Amplitude@nominal 1
Eb/N0
Intersymbol interference
Delay spread
Here’s my digital
model. Can you add
some RF?
How much
RF would you like? Noise Figure,
IP3?
Measured
S-Parameters?
Circuit
Level Design?
Idealized Baseband Equivalent Baseband Circuit Envelope
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RF Modeling Technologies in Simulink
Faster Simulations
Higher Fidelity
Circuit envelope Multi-carrier systems and arbitrary architectures
Equivalent baseband Single carrier cascaded systems
Idealized baseband Mathematical analytical models
Demo
>> simRF libraries
>> wlan_eml_rf3
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Direct Conversion ISM Band Receiver:
Circuit Envelope Model
RF Design Detail
System Performance
Metrics Demo
>> simrfV2_direct_conv
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Summary
Simulink is the ideal platform for system-level simulation
– Works seamlessly with MATLAB
– Provides multi-domain modeling capability
(RF, Analog, and Digital)
Communications System Toolbox is ideal for getting
baseband models up and running quickly
SimRF provides an easy-to-use way to include RF
effects in system-level simulations
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Time Title
9:00 – 10:00
Building Communication System Simulations
Constructing a basic QPSK communications model in Simulink
802.11a Wireless LAN example
Adding RF design detail
10:00 – 10:45 4G LTE Design using the LTE System Toolbox
10:45 – 11:00 Break
11:00 – 11:30 Options for Accelerating MATLAB Simulations
11:30 – 12:15 Electronic Interference and Array Signal Processing
12:15 – 13:15 Lunch
13:15 – 14:15
End-to-End Modeling of Radar Systems
Radar Analysis with User-Interface Apps
Integrating the RF Subsystem
14:15 – 14:30 Break
14:30 – 16:30 Hands-on Workshop: Phased Array Systems
Agenda
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17 © 2013 The MathWorks, Inc.
4G LTE Design using the
LTE System Toolbox
10:00 – 10:45
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What is the LTE System Toolbox?
Release 8,9 and 10 (LTE-A)
~200 functions for physical layer (PHY) modeling
Link-level simulation
– No C or HDL code generation
Scope
– TDD & FDD,
– Uplink & Downlink
– Transmitter & Receiver
Conformance tests
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Standard-Compliant Solution
Tested against instrument-generated signals
– Rohde & Schwarz
– Agilent
Used in the industry since 2009
– Initially under Steepest Ascent
– MathWorks acquired Steepest Ascent
Successfully demodulates live LTE signals captured in
the field
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Typical Use Cases
1) Golden reference to verify in-house PHY models
2) Modular end-to-end link-level simulation
3) Signal generation and analysis
4) Signal information recovery
Transmitter
Test Waveform
Generation
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(1) Golden Reference for Verification
"We used LTE System Toolbox to validate our LTE baseband IP cores
and example design C models. This meant we could trust our own C
models matched the 3GPP specification and allowed us to verify our
RTL designs against the C models with confidence. Catching issues
early in simulation saved a lot of time later when it came to
hardware testing. The MATLAB environment allowed us to rapidly
assemble and run test cases."
Bill Wilkie, Director of Communications Signal Processing
(Europe), Xilinx.
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(1) Golden Reference for Verification
Objective:
– Need to validate internal models against an external reference
Features:
– Standard-compliant reference
– Varying levels of modeling detail:
low = scrambling, turbo coder, ….
medium = transport channels,
physical channels
Benefits:
– Reduce risk
– Detect errors early
– Independently confirm understanding of specifications
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(2) End-to-End Link-Level Simulation
"We rely on Steepest Ascent's LTE Toolbox for a variety of
project needs. One use is as a golden reference for our own
in-house simulator where the Toolbox's complete feature set
has allowed us to build industrial strength simulations rapidly
with guaranteed baseline requirements. Its extensible
design enables us to customise these models for our
own LTE research work. All this is made easier by the
great documentation and excellent product support."
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(2) End-to-End Link-Level Simulation
Objective:
– Want to design or optimize a subset of the LTE system
Features:
– Complete, verified environment available
– Simulations already set up to measure link-level performance
Throughput, BER, block error rate
Benefits:
– Time savings
– Easy maintenance (documentation, support & tracks standard)
– You can focus on added value
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(2) Demo: Equalizing the Downlink Grid
Receiver Transmitter Channel
Test Waveform
Generation
Synchronisation &
OFDM Demodulation
Channel Estimation
&
Equalisation
Fading Channel
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Transmitter
Test Waveform
Generation
(3) Signal Generation and Analysis
Objective:
– Need to generate standard-compliant signals, for example to test a
component such as an antenna or an amplifier
Features:
– High-level tools that generate customizable
standard-compliant signals (lteRMCULTool,lteRMCDLTool,lteTestModelTool)
– Powerful analysis capabilities (EVM, ACLR, …)
Benefits:
– No need for LTE expertise
– Flexibility due to parameters and MATLAB code
– Realistic signals to test components and model interferences
Rx Tx Ch
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(3) Signal Generation and Analysis
Reference Measurement Channels
>> lteRMCDLTool
Standard-compliant
signal available in the
MATLAB workspace
TS 36.101
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(4) Signal Information Recovery
Defense company
We have used Steepest Ascent software to successfully
process the standards-based aspects of data produced
from live exercises. Based on the excellent performance of
the toolkit, we feel that our decision was correct and justified
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(4) Signal Information Recovery
Objective:
– Want to extract information from a real-world LTE signal
Features:
– Synchronization and demodulation of actual signal
– Signal intelligence such as MIB and SIB1 extraction
– Positioning information
Benefits:
– Off-the-shelf demodulation capability
– Extensible, MATLAB-based solution
>> SIB1RecoveryExample
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Ease of Use and Flexibility
Granular transmit receive processing chains provide
access to
Ready-to-use conformance signals
Transport-channel level functions
Detail of processing chain
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High Level: Conformance Signals
GUI-based
MATLAB
Function
Signal Generators
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Mid-Level: Transport Channels
Downlink
overview
Uplink example
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Fine: Details of Processing Chain
Fine
Mid-level
PDSCH Example
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35 35
LTE Toolbox channel support
Downlink Uplink
Transport Channels & Control Information
Transport Channels & Control Information
DL-SCH HI UL-SCH UCI
BCH CFI
DCI
Physical Channels and Signals
Physical Channels and Signals
PDSCH PDCCH PUSCH SRS
PBCH Reference
Signals
PUCCH PRACH
PCFICH PSS - SSS DRS (PUCCH)
PHICH DRS (PUSCH)
Comprehensive set of functions covering:
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Documentation
Shipping Examples Functions
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Summary of Main Benefits
Comprehensive
– Comprehensive set of PHY models
– Numerous preset, extensible examples
Open environment
– MATLAB-based
– Link to test and measurement instruments
Standard-compliance
– Tested against hardware-based signal generators
– Trusted by numerous customers
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43
Time Title
9:00 – 10:00
Building Communication System Simulations
Constructing a basic QPSK communications model in Simulink
802.11a Wireless LAN example
Adding RF design detail
10:00 – 10:45 4G LTE Design using the LTE System Toolbox
10:45 – 11:00 Break
11:00 – 11:30 Options for Accelerating MATLAB Simulations
11:30 – 12:15 Electronic Interference and Array Signal Processing
12:15 – 13:15 Lunch
13:15 – 14:15
End-to-End Modeling of Radar Systems
Radar Analysis with User-Interface Apps
Integrating the RF Subsystem
14:15 – 14:30 Break
14:30 – 16:30 Hands-on Workshop: Phased Array Systems
Agenda
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44 © 2013 The MathWorks, Inc.
Options for Accelerating MATLAB
Simulations
11:00 – 11:30
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Simulation acceleration options in MATLAB
Best
Practices
MATLAB to C
(MEX) User’s Code
GPU
processing
Parallel
Computing
GPU System objects
comm.gpu.TurboDecoder
comm.gpu.ViterbiDecoder
comm.gpu.LDPCDecoder
comm.gpu.PSKDemodulator
comm.gpu.AWGNChannel
phased.gpu.ConstantGammaClutter
Parallel Computing Toolbox
MATLAB Coder
Pre-allocation / Vectorization
System Objects
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Task 1 Task 2 Task 3 Task 4 Task 1 Task 2 Task 3 Task 4
Parallel Simulation Runs
Time Time
TOOLBOXES
BLOCKSETS
Worker
Worker
Worker
Worker
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parfor construct
matlabpool available workers
No modification of algorithm
Use parfor loop instead of for loop
Parallel computation leads to further acceleration
More cores = more speed
Demos
>> DemoScript_SimAccel
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MATLAB Acceleration Strategies
Option
Product
1. Best Practices in Programming • Vectorize & Pre-allocate Arrays & Matrices • Use System Objects instead of functions
MATLAB, Toolboxes, System Toolboxes
2. Compiled Code (MEX)
MATLAB, MATLAB Coder
3. More Processors or Cores
• Parallel constructs (e.g., parfor, matlabpool)
• GPUs (GPU system objects)
Parallel Computing Toolbox
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49
Time Title
9:00 – 10:00
Building Communication System Simulations
Constructing a basic QPSK communications model in Simulink
802.11a Wireless LAN example
Adding RF design detail
10:00 – 10:45 4G LTE Design using the LTE System Toolbox
10:45 – 11:00 Break
11:00 – 11:30 Options for Accelerating MATLAB Simulations
11:30 – 12:15 Electronic Interference and Array Signal Processing
12:15 – 13:15 Lunch
13:15 – 14:15
End-to-End Modeling of Radar Systems
Radar Analysis with User-Interface Apps
Integrating the RF Subsystem
14:15 – 14:30 Break
14:30 – 16:30 Hands-on Workshop: Phased Array Systems
Agenda
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50 © 2013 The MathWorks, Inc.
Electronic Interference and
Array Signal Processing
11:30 – 12:15
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51
Modeling Electronic Interference Scenarios
Partition model into Transmitter, Jammer,
and RF subsystems
Introduce Jamming, Co-channel Interference,
and Smart Antenna Configurations
Simulate to determine expected performance
and operational limits
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52 © 2013 The MathWorks, Inc.
Array Signal Processing Algorithms
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53
Receiving Signals with an Omnidirectional
Antenna
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54
Receiving Signals with a Sensor Array
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55
Receiving a Broadside Signal (azimuth = 0)
with a Sensor Array
∆t = 0
S
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56
Aligned or Coherent Signals
Signal
Wavefronts
S
Enhanced Signal
(coherent sum)
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57
Receiving a Signal from an Arbitrary Azimuth (𝜃)
d
𝜃
S
∆𝑡 =𝑑
𝑐sin 𝜃
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58
Non-aligned or Non-coherent Signals
S
t1
t2
Signal
Wavefronts
Non-coherent Sum
t1 = ∆𝑡
t2 = 2∆𝑡 ∆𝑡 =
𝑑
𝑐sin 𝜃
d
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59
Array Response Pattern
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60
Alignment via time delays
S
t1
t2
t2
t1
0
Signal
Wavefronts
delays Aligned
Signals
Enhanced Signal
(coherent sum)
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61
Converting Time Delays to Carrier Phase Shifts
d
𝜃
∆∅ = 2𝜋𝑓 𝑑
𝑐sin 𝜃
∆∅ = 𝜋 sin 𝜃
S
∆𝑡 =𝑑
𝑐sin 𝜃
∆∅ = 2𝜋𝑓∆𝑡
𝑑 = 𝞴/2
𝝺 = 𝑐/𝑓
(non-aligned)
Non-coherent Sum
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62
Electronic Steering
S
t1
t2
t2
t1
0
Signal
Wavefronts
Phase Shift
Aligned
Signals
Enhanced Signal
(coherent sum)
fn = A + i B
f2
f1
0
∆∅ = 𝜋 sin 𝜃 Demos
>> Array Processing
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66
Time Title
9:00 – 10:00
Building Communication System Simulations
Constructing a basic QPSK communications model in Simulink
802.11a Wireless LAN example
Adding RF design detail
10:00 – 10:45 4G LTE Design using the LTE System Toolbox
10:45 – 11:00 Break
11:00 – 11:30 Options for Accelerating MATLAB Simulations
11:30 – 12:15 Electronic Interference and Array Signal Processing
12:15 – 13:15 Lunch
13:15 – 14:15
End-to-End Modeling of Radar Systems
Radar Analysis with User-Interface Apps
Integrating the RF Subsystem
14:15 – 14:30 Break
14:30 – 16:30 Hands-on Workshop: Phased Array Systems
Agenda
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67 © 2013 The MathWorks, Inc.
End-to-End Modeling of Radar Systems
13:15 – 14:15
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68
Radar Block Diagram
Waveform
Generator
Transmit
Amplifier
Radar
Target
Receive
Array RF Front
End RX Signal
Processor
Rx Power = Tx Power - 10*log10(AircraftDistance^4) +
10*log10(AircraftCrossSection*(lambda^2)/(4*pi)^3)
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69
A Multi-Disciplined Challenge
Waveform
Generator
Transmit
Amplifier
Radar
Target
Receive
Array RF Front
End RX Signal
Processor
Digital Analog / RF Mathematical
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70
Radar Analysis with User-Interface Apps
Waveform
Generator
Transmit
Amplifier
Phased Array System Toolbox Apps
Radar Waveform Analyzer
– Rectangular
– FM – Modulated
– Pulse-Coded
Radar Equation Calculator
– Range
– Peak Transmit Power
– SNR
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71
Simulating Radar Systems with
MATLAB and Simulink
Demo
>>Simulink Radar Model
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72
Radar Component Specification
Redesign system to meet changing requirements
– Increase detection range from 5 km to 10 km
– Maintain 50 m range resolution
– Reuse existing RF hardware (i.e., retain peak transmit power)
Demo (Apps)
>>Simulink Radar Model
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73
Radar Array Processing Algorithms
Beamforming
– phased.PhaseShiftBeamformer
Direction Of Arrival Estimation
– phased.BeamscanEstimator
Constant False Alarm Rate (CFAR)
– phased.CFARDetector
Space-Time Adaptive Processing
– phased.STAPSMIBeamformer
RX Signal
Processor
Angle (Spatial)
RG
(K
) Chan (M)
Range
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74
Radar Targets and Environment
Radar
Target Radar Targets
– phased.RadarTarget
Jammers
– phased.BarrageJammer
Clutter
– phased.gpu.ConstantGammaClutter
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75 © 2013 The MathWorks, Inc.
Modeling the RF Subsystem
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76
How do we bridge the gap between engineers
who speak different languages?
Huh? Range
Radar Cross Section
Doppler
PD, PFA
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77
How do we bridge the gap between engineers
who speak different languages?
Noise Figure
Power @ 50
S-Parameters
IP3 Huh?
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78
Solution: Multi-domain Modeling
“Here’s the
RF model”
“Here are
some initial
RF specs”
End-to-end model serves as:
• Executable Specification
• Test Environment
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79
RF Subsystem Design using SimRF
SimRF allows us to model RF architectures and RF
components using:
– Standard RF parameters (e.g., IP3, noise figure)
– Measured component data (e.g., measured s-parameters)
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80
SimRF Simulation Technologies / Libraries
Equivalent Baseband (faster)
Circuit Envelope (more accurate)
Demo (RF)
>>Simulink Radar Model
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81
Workflow with MATLAB and Simulink
Engineering Tasks Features of MATLAB and Simulink
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82
Workflow with MATLAB and Simulink
Engineering Tasks MATLAB User-Interface Apps
Specifications and
System Tradeoffs
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83
Workflow with MATLAB and Simulink
Engineering Tasks Signal Processing & SimRF Libraries
Specifications and
System Tradeoffs
Explore Digital
Algorithms and RF
Architectures
RF Front
End
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84
Workflow with MATLAB and Simulink
Engineering Tasks Simulink plus SimRF
Specifications and
System Tradeoffs
Explore Digital
Algorithms and RF
Architectures
Multi-domain system
simulation combining
digital and analog
subsystems
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85
Summary - What We Used Today
Simulink & MATLAB
Communications
System Toolbox
LTE System Toolbox
MATLAB Coder
Parallel Computing
Toolbox
Phased Array
System Toolbox
SimRF
ScramblingModulation
mapper
Layer
mapperPrecoding
Resource element
mapper
OFDM signal
generation
Resource element
mapper
OFDM signal
generationScrambling
Modulation
mapper
layers antenna portscodewords
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86
Key Take-Aways
MATLAB® and Simulink® provide:
Single platform that facilitates collaboration between multiple
engineering teams
– System engineers
– Signal processing engineers
– RF engineers
Robust and user-friendly environment to explore design
tradeoffs between multiple domains
– Digital: Communications and Radar algorithms
– Analog: RF receiver architecture
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87 © 2013 The MathWorks, Inc.
Thank You for Attending!
© 2013 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks,
Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand
names may be trademarks or registered trademarks of their respective holders.
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88
Time Title
9:00 – 10:00
Building Communication System Simulations
Constructing a basic QPSK communications model in Simulink
802.11a Wireless LAN example
Adding RF design detail
10:00 – 10:45 4G LTE Design using the LTE System Toolbox
10:45 – 11:00 Break
11:00 – 11:30 Options for Accelerating MATLAB Simulations
11:30 – 12:15 Electronic Interference and Array Signal Processing
12:15 – 13:15 Lunch
13:15 – 14:15
End-to-End Modeling of Radar Systems
Radar Analysis with User-Interface Apps
Integrating the RF Subsystem
14:15 – 14:30 Break
14:30 – 16:30 Hands-on Workshop: Phased Array Systems
Agenda
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89 © 2013 The MathWorks, Inc.
Phased Array Systems Workshop
14:30 – 16:30
Nick M. Zayed
Armerkom
Ankara, Turkey
February 28, 2014
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90
The Pre-requisites
Basic familiarity of the MATLAB language and
environment
Attended seminars/webinars/presentations on the
Phased Array System Toolbox
Have engaged in the phased array related
projects/programs, such as radar system design
Eagerness to learn and use the product
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91
Agenda
Introduction (5 minutes)
Exercise # 1: Array Design and Analysis (40 minutes)
Exercise # 2: Digital Beamforming (40 minutes)
Exercise # 3: Modeling a Mono-Static Radar System (20 minutes)
Q & A (15 minutes)
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92
Phased Array System Toolbox
A rich set of features with an open and extensible framework
Phased array design and analysis • Linear, rectangular, conformal geometries
• Shading, tapering
• Element position and orientation
• Gain, delay, steering vector
• Subarrays
Temporal processing • Time varying gain, pulse compression
• Coherent, non-coherent integration
• Signal detection and ROC curves
• CFAR processing, range/Doppler estimation
Waveform design and analysis • Pulsed CW, FMCW
• LFM and stepped FM
• Phase coded
• Staggered PRF
• Ambiguity function
• Matched filter
Spatial processing • Digital beamforming: narrowband & broadband,
Conventional, MVDR, LCMV, Frost, time delay,
time delay LCMV, subband phase shift
• DOA processing: Monopulse, MVDR,
beamscan, ESPRIT, Root-MUSIC
Signal modeling framework • Monostatic and multistatic scenarios
• Transmitter and receiver preamp models
• Point target and Swerling target models
• Narrowband and broadband modeling
• Platform motion
• Clutter and jammer models
Space-time adaptive processing • Displaced phase center array (DPCA)
• Adaptive DPCA
• Sample matrix inversion (SMI)
• Angle-Doppler response
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93
Exercises #1: Array Design and Analysis
a. Creating/defining a 10 element Uniform Linear Array
b. Analyzing and visualizing the array response
c. Examining the use of steering vector on an ULA
d. (Optional) observing the ULA responses for azimuth
cut with different elevations
e. (Optional) creating a comformal array of 8 elements (a
circular array)
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94
Exercises #1: Array design and Analysis
Screenshots
0
0.5
1
-2-1
01
2
0
0.2
0.4
0.6
0.8
1
z
yx
-40
-30
-20
-10
0
-30
150
-60
120
-90
90
-120
60
-150
30
180 0
Azimuth Cut (elevation angle = 0.0
)
Normalized Power (dB), Broadside at 0.00 degrees
Norm
alize
d P
ow
er
(dB
)
-80 -60 -40 -20 0 20 40 60 80-80
-60
-40
-20
0
Azimuth Angle (degrees)
Norm
alize
d P
ow
er
(dB
)
Before steering
-80 -60 -40 -20 0 20 40 60 80-80
-60
-40
-20
0
Azimuth Angle (degrees)
Norm
alize
d P
ow
er
(dB
)
After steering
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95
Exercises #2: Conventional and Adaptive
Beamformers
a. Simulating the Radar return Signal
b. Create a beamforming array
c. Perform beamforming with a Phase Shift Beamformer
d. Modeling the Interference Signals
e. Use MVDR Beamformer when interferers are present
f. (Optional) Apply and observe LCMV beamformer
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96
Exercises #2: Conventional and Adaptive
Beamformers
Screenshots
0 0.2 0.4 0.6 0.8 1
x 10-4
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s)
Mag
nitu
de (
V)
0 2 4 6 8
x 10-5
-20
-10
0
10Pulse at Antenna 1
Time (s)
Mag
nitu
de (
dB
)
0 2 4 6 8
x 10-5
-20
-10
0
10Pulse at Antenna 2
Time (s)
Mag
nitu
de (
dB
)
-50 0 50-80
-70
-60
-50
-40
-30
-20
-10
0
10
20
Azimuth Angle (degrees)
Po
wer
(dB
)
Azimuth Cut (elevation angle = 0.0)
MVDR
PhaseShift
0 2 4 6 8
x 10-5
-100
-80
-60
-40
-20
0Output of MVDR Beamformer With Presence of Interference
Time (s)
Mag
nitu
de (
dB)
0 20 40 60 80-40
-30
-20
-10
0
10
20
30
Azimuth Angle (degrees)
Po
wer
(dB
)
Azimuth Cut (elevation angle = 0.0)
LCMV
MVDR
0 2 4 6 8
x 10-5
-25
-20
-15
-10
-5
0
5Output of Phase Shift Beamformer
Time (s)
Mag
nitu
de
(dB
)
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97
Exercises #3: Modeling Mono-static Radar
System
Waveform design/generation
Receiver design
– Thermal noise, no clutter
Transmitter design
– Determine required SNR and transmit peak power
– Design Radiator and Collector
Targets and environment
Signal processing and detection
– Perform beamforming & matched filtering
– Perform range normalization
– Range detection & Doppler estimation
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98
Exercises #3: Modeling Mono-static Radar
System
Screenshots
10-10
10-5
100
0
0.2
0.4
0.6
0.8
1
SNR=0dB
SNR=3dB
SNR=5dB
Nonfluctuating Noncoherent Receiver Operating Characteristic (ROC) Curves
Pfa
Pd
-40
-30
-20
-10
0
-30
150
-60
120
-90
90
-120
60
-150
30
180 0
Azimuth Cut (elevation angle = 0.0
)
Normalized Power (dB), Broadside at 0.00 degrees
Norm
alize
d P
ow
er
(dB
)
0 0.5 1 1.5 2 2.5 3
x 10-5
-350
-300
-250
-200
-150
Time (s)
Pow
er
(dB
w)
0 500 1000 1500 2000 2500 3000 3500 4000 4500
-350
-300
-250
-200
-150
Distance (m)
0 0.5 1 1.5 2 2.5 3
x 10-5
-350
-300
-250
-200
-150
Time (s)
Pow
er
(dB
w)
0 500 1000 1500 2000 2500 3000 3500 4000 4500
-350
-300
-250
-200
-150
Distance (m)
0 0.5 1 1.5 2 2.5 3
x 10-5
-350
-300
-250
-200
-150
Time (s)
Pow
er
(dB
w)
0 500 1000 1500 2000 2500 3000 3500 4000 4500
-350
-300
-250
-200
-150
Distance (m)
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