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

[email protected]

Robert W. Heath Jr. Ph.D, P.E.KE5NCG

Thursday, August 30, 12

http://www.profheath.org

http://www.profheath.org

mailto:[email protected]

mailto:[email protected]

Wireless is Everywhere

cellular networks local area networks

personal area networks emerging applications


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)


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)

4


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


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


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


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)


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


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


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


http://sine.ni.com/nips/cds/view/p/lang/en/nid/210385

http://sine.ni.com/nips/cds/view/p/lang/en/nid/210385

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

v


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.


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?


digital wireless communication: physical layer exploitation

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