Digital Wireless Communication: Physical Layer Exploitation
Transcript of Digital Wireless Communication: Physical Layer Exploitation
Digital Wireless Communication:
Physical Layer Exploitation
Wireless Networking and Communications GroupDepartment of Electrical and Computer Engineering
The University of Texas at Austin, Austin TX USAhttp://www.profheath.org
Robert W. Heath Jr. Ph.D, P.E.KE5NCG
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Wireless is Everywhere
cellular networks local area networks
personal area networks emerging applications
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Where is Wireless Taught?
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Undergraduate
Graduate
Signals and Systems
Digital Signal Processing
Analog Communication
Digital Communication
Intro to Wireless
Advanced Wireless
time in EE
a typical curriculum (many exceptions of course)
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Why at the Graduate Level?It involves many different areas of expertise
Digital communication
Propagation
Antennas
Signal Processing
Probability
Etc.
Hot area of researchRequires some depth in areas of expertise
Curriculum is not widely available (vs e.g. signals and systems)
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Where Could it be Taught?
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Undergraduate
Graduate
Signals and Systems
Digital Signal Processing
Wireless Digital Comm.
time in EE
Lab-based approach to teach wireless to undergraduates
Digital Communication
Advanced Wireless
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Wireless Communications Lab @ UTPremises of the course
Wireless communication can be taught to undergraduates
Wireless communication can be taught without a communication background
Students can implement what they learn while they learn it
Key ideasTeach digital communication from a digital signal processing perspective
Incorporate modulation, channel estimation, equalization, synchronization
Use algorithmic design examples, not comprehensive theory
Leverage flexible software defined radio prototyping
Exploit LabVIEW & USRP
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Developed and tested over 7 years
EE 471C / EE 381V
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DSP Approach to Wireless
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Use systems approach for communication
Inputs System Outputs
0110110 h[n]
h(t)
0110110
time
time
time
time
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How this Fits with the Lab
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Source ChannelCoding
Modulation D/A RF Upconversion
Sink ChannelDecoding
Demodulation A/D RF Downconversion
channel
transmitter
receiver
Laptop with LabVIEW NI USRP 2921Real world
(all digital signal processing)
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How it Works at UTThe course is cross-listed for undergrads and grads
Pre-requisites: a course in digital signal processing and a course on probability
Undergraduates take in 3rd or 4th year as a 4 credit course
Graduate students take their 1st or 3rd semester
Structure of the course3 hours of lecture per week, covers the theory of the course
3 hours in the lab per week, demonstrate what has been learned
Homework assignments test the theory
Prelabs, labs, lab reports test what has been learned in the lab
Yes there are exams too......(why do the students complain of high workload??)
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Content of the CourseDigital comm overviewSignals, stochastic processesTransforms, sampling theormFrequency response, power spectrum, bandwidthUpconversion, downconversion, complex basebandQuadrature pulse amplitude modulationOptimal pulse shapesMaximum likelihood detection in AWGNSample timing offset, sample timing algorithmsFrequency selective channels, least squares channel estimationFrequency offset estimation and correction, frequency domain equalizationSingle carrier frequency domain equalization, OFDM, the cyclic prefixIEEE 802.11a, GSM standardIntroduction to propagation, large-scale fading, link budgets, path-lossSmall-scale fading, coherence time, coherence bandwidthProbability of error in fading channelsSources of diversity, Alalmouti space-time code, maximum ratio combiningIntroduction to MIMO communication, spatial multiplexingIntroduction to MIMO-OFDM, highlights of the IEEE 802.11n standard
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Mathematical preliminaries
Basic digital comm
Channel impairments
StandardsFading
MIMO
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Content of the CourseDigital comm overviewSignals, stochastic processesTransforms, sampling theormFrequency response, power spectrum, bandwidthUpconversion, downconversion, complex basebandQuadrature pulse amplitude modulationOptimal pulse shapesMaximum likelihood detection in AWGNSample timing offset, sample timing algorithmsFrequency selective channels, least squares channel estimationFrequency offset estimation and correction, frequency domain equalizationSingle carrier frequency domain equalization, OFDM, the cyclic prefixIEEE 802.11a, GSM standardIntroduction to propagation, large-scale fading, link budgets, path-lossSmall-scale fading, coherence time, coherence bandwidthProbability of error in fading channelsSources of diversity, Alalmouti space-time code, maximum ratio combiningIntroduction to MIMO communication, spatial multiplexingIntroduction to MIMO-OFDM, highlights of the IEEE 802.11n standard
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Donein the Lab
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Materials Developed for LectureTextbook that describes the theory
Introduction to Wireless Digital Communication: A Signal Processing Perspective by Robert W. Heath Jr.
Book is 200 pages, unpublished but available for use for free (has been used as is for several years)
Target completion date is end of 2012
Lecture notesIn LaTeX form approximately 84 pages
Slides are forthcoming
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(0)[ ]TXg n[ ]s n L
L
1z1z
D/C xE ( )x t
( 1)[ ]LTXg n
xT
Figure 20: An implementation of transmit pulse shaping using upsampling.
2 Implementing pulse-shaping at the receiver
• Objective: Implement discrete-time pulse shaping at the receiver using oversampling combinedwith downsampling.
• Objective: Apply downsampling identities to implement and simplify receive pulse shaping• Thus far we have considered the following QAM receiver
• This structure does not map to a flexible implementation� Requires analog matched filtering� Take advantage of flexible digital processing� Does not lend itself to various synchronization tasks
• How can we implement pulse-shaping in discrete-time?• Let Tz = T/M for some integer M . If 1/Tz > Nyquist rate then this is called oversampling• Suppose that we implement the analog filter using discrete-time processing• We have already solved this problem using discrete-time processing of a bandlimited continuous-
time signal
D/C( )z t [ ]RXg nC/D C/D
zT zT zT MT
[ ]y n
Figure 21: An implementation of receive matched filtering in continuous-time using discrete-time process-ing.
g[n] = Tzgrx(nTz)
For simplicity we neglect the Tz term since receiver scaling does not matter
grx[n] := grx(nTz)
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Materials Developed for LabLaboratory manual
DIGITAL COMMUNICATIONS: Physical Layer Exploration Using the NI USRP
141 pages
8 Laboratory experiments
Lab experimentsBackground information
Include prelab to be completed prior to lab
Laboratory experiments
Postlab
Complete software framework
TA guide with solutions
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Dr. Robert W. Heath, University of Texas at Austin
DIGITAL COMMUNICATIONS
PHYSICAL LAYER EXPLORATION LAB USING THE NI USRP™ PLATFORM
front.pdf 1 9/12/11 4:46 PM
Included with the Digital Communications Teaching Bundlehttp://sine.ni.com/nips/cds/view/p/lang/en/nid/210385
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Outline of Lab Manual
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Contents
Preface vii
About the Author xi
Lab 1: Part 1 Introduction to NI LabVIEW 1
Lab 1: Part 2 Introduction to NI RF Hardware 10
Lab 2: Part 1 Modulation and Detection 22
Lab 2: Part 2 Pulse Shaping and Matched Filtering 35
Lab 3: Synchronization 51
Lab 4: Channel Estimation & Equalization 63
Lab 5: Frame Detection & Frequency Offset Correction 82
Lab 6: OFDM Modulation & Frequency Domain Equalization 99
Lab 7: Synchronization in OFDM Systems 115
Lab 8: Channel Coding in OFDM Systems 130
Appendix A: Reference for Common LabVIEW VIs 139
Bibliography 141
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Sample Pages
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Lab 3: Synchronization:Symbol Timing Recovery in Narrowband
Channels
Summary
In this lab you will consider the problem of symbol timing recovery alsoknown as symbol synchronization. Timing recovery is one of several syn-chronization tasks; others will be considered in future labs.
The wireless communication channel is not well modeled by simple ad-ditive white Gaussian noise. A more realistic channel model also includesattenuation, phase shifts, and propagation delays. Perhaps the simplest chan-nel model is known as the frequency flat channel. The frequency flat channelcreates the received signal
z(t) = !ej"x(t ! #d) + v(t), (1)
where ! is an attenuation, " is a phase shift, and #d is the delay.The objective of this lab is to correct for the delay caused by #d in discrete-
time. The approach will be to determine an amount of delay k̂ and then todelay the filtered received signal by k̂ prior to downsampling. This willmodified the receiver processing as illustrated in Figure 1.
Two algorithms will be implemented for symbol synchronization in thislab: the maximum energy method and the Early Late gate algorithm. Themaximum energy method attempts to find the sample point that maximizesthe average received energy. The early–late gate algorithm implements adiscrete-time version of a continuous-time optimization to maximize a certain
C/D
SymbolSync
M
TM
( )z t [ ]RXg n
k̂
k̂z
T =z
Figure 1: Receiver with symbol synchronization after the digital matched filtering.
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Lab 2: Part 2 Pulse Shaping and Matched Filtering 43
Figure 3: Hierarchy of code framework for new simulator.
top rx.vi and provides each with the appropriate inputs. The parts of thesimulator you will be modifying are located in transmitter.vi and receiver.vishown in Figures 4 and 5 respectively.
You will be putting your VIs into transmitter.vi and receiver.vi, replacingthe locked versions that are already there. After doing this, you will thenopen up simulator.vi, that you will use to confirm your VIs operate correctlybefore implementing them on the NI-USRP.
Notice that pulse shaping.vi and matched filtering.vi do not take any pa-rameters as inputs. All of the pulse shaping and oversampling parameters youneed to use for these VIs can be accessed from the modulation parameters incluster. After building these VIs, replace the existing code in the simulatorwith your code. Replace a subVI in the transmitter or receiver with your code
Figure 4: Block diagram of transmitter.vi.
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Methodology for the LabAll labs have already been implemented, functions are locked
Both over-the-air and simulation capable functions
Students work in groups of 2 or 3
Each week students implement a new blockFor example modulation, or demodulation, or some synchronization
During the pre-labStudent implements software, verifies it is correct, answers prelab questions
During the labStudent demonstrates correction function to TA, answers inlab questions
After the labStudents write a short lab report documenting what they learned
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Lab Size and EquipmentCourse has 30-40 students enrolled every year
Half undergraduates, half graduates
Lab has ten workstations for transmit / receiveStudents work in teams of 2 or 3
Accommodate 20 students in the lab for 3 hours
One TA services two lab sessions, grades homework, holds office hours
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Operational ChallengesCovering material required before the lab
Only an issue the first few weeks of the class
Keeping PCs up-to-dateSolved by requiring students to use their own laptops
Finding high quality TAs with enough experienceNot an issue after a couple of years
Avoiding copying of code, plagiarismNeed better tools for detecting plagiarism in graphical code
Helping students that fall behindThe labs build on each other every week
Falling behind can be a big problem
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Possible Evolution of the LabLab equipment can be checked out, experiments performed
How to avoid all students doing the same thing?
Do they really do the experiment themselves?
Lab equipment can be networked and accessed remotelyHow to manage access to the equipment?
How to configure equipment for experiments?
What is the difference between this and simulation?
Lab equipment in the cloudPerhaps not owned by the university, equipment pooled together
Some examples of this already for research applications
Is it still “real”??
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ConclusionWireless communication can be taught to undergraduates
Laboratory approach makes wireless more concreteAvoids simply drowning in mathematics
Useful for graduate students as well
Students build a practical foundation for further studyGood preparation for industry
Practical insights make for more relevant research
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Questions?
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