High-Speed WirelineHigh-Speed WirelineCommunication Systems: Communication Systems:

Semester Wrap-upSemester Wrap-upIan C. Wong, Daifeng Wang, and

Prof. Brian L. EvansDept. of Electrical and Comp. Eng.The University of Texas at Austin

http://signal.ece.utexas.edu

http://www.ece.utexas.edu/~bevans/projects/adsl

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OutlineOutline

• Asymmetric Digital Subscriber Line (ADSL) Standards– Overview of ADSL2 and ADSL2+

– Data rate vs. reach improvements

– ADSL2+

• Multichannel Discrete Multitone (DMT) Modulation– Dynamic spectrum management

– Channel identification

– Spectrum balancing

– Vectored DMT

• System Design Alternatives and Recommendations

3

11ADSL2 and ADSL2+ - the new standardsADSL2 and ADSL2+ - the new standards

• ADSL2 (G.992.3 or G.dmt.bis, and G.992.4 or G.lite.bis)– Completed in July 2002

– Minimum of 8 Mbps downstream and 800 kbps upstream

– Improvements on:

• Data rate vs. reach performance

• Loop diagnostics

• Deployment from remote cabinets

• Spectrum and power control

• Robustness against loop impairments

• Operations and Maintenance

• ADSL2+ (G.992.5)– Completed in January 2003

– Doubles bandwidth used for downstream data (~20 Mbps at 5000 ft)

1Figures and text are extensively referenced from [ADSL2] [ADSL2white]

4

Data rate vs. reach performance improvementsData rate vs. reach performance improvements

• Focus: long lines with narrowband interference

• Achieves 12 Mbps downstream and 1 Mbps upstream

• Accomplished through1. Improving modulation efficiency

2. Reducing framing overhead

3. Achieving higher coding gain

4. Employing loop bonding

5. Improving initialization state machine

6. Online reconfiguration

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1. Improved Modulation Efficiency1. Improved Modulation Efficiency

• Mandatory support of Trellis coding (G.992.3, §8.6.2)– Block processing of Wei's [Wei87] 16-state 4-dimensional trellis code

shall be supported to improve system performance

– Note: There was a proposal in 1998 by Vocal to use a Parallel concatenated convolutional code (PCCC), but it wasn’t included in the standard (http://www.vocal.com/white_paper/ab-120.pdf)

• Data modulated on pilot tone (optional, §8.8.1.2)– During initialization, the ATU-R receiver can set a bit to tell the ATU-

C transmitter that it wants to use the pilot-tone for data

– The pilot-tone will then be treated as any other data-carrying tone

• Mandatory support for one-bit constellations (§8.6.3.2)– Allows poor subchannels to still carry some data

6

2. Reduced framing overhead2. Reduced framing overhead

• Programmable number of overhead bits (§7.6)– Unlike ADSL where overhead bits are fixed and consume 32 kbps of

actual payload data

– In ADSL2, it is programmable between 4-32 kbps

– In long lines where data rate is low, e.g. 128 kbps,

• ADSL: 32/128 = 25% is overhead

• ADSL2: as low as 4/128 = 3.125% is overhead

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3. Achieved higher coding gain3. Achieved higher coding gain

• On long lines where data rates are low, higher coding gain from the Reed-Solomon (RS) code can be achieved

• Flexible framing allows RS code to have (§7.7.1.4)• 0, 2, 4, 6, 8, 10, 12, 14, or 16 redundancy octets

• 0 redundancy implies no coding at all (for very good channels)

• 16 would achieve the highest coding gain at the expense of higher overhead (for very poor channels)

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4. Loop Bonding4. Loop Bonding

• Supported through Inverse Multiplexing over ATM (IMA) standard (ftp://ftp.atmforum.com/pub/approved-specs/af-phy-0086.001.pdf)– Specifies a new sublayer (framing, protocols, management) between

Physical and ATM layer [IMA99]

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5. Improved initialization state machine5. Improved initialization state machine• Power cutback

– Reduction of transmit power spectral density level in any one direction– Reduce near-end echo and the overall crosstalk levels in the binder

• Receiver determined pilots– Avoid channel nulls from bridged taps or narrow band interference

from AM radio

• Initialization state length control – Allow optimum training of receiver and transmitter signal processing

functions

• Spectral shaping– Improve channel identification for training receiver time domain

equalizer during Channel Discovery and Transceiver Training phases

• Tone blackout (disabling tones) – Enable radio frequency interference (RFI) cancellation schemes

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6. Online reconfiguration (§10.2)6. Online reconfiguration (§10.2)

• Autonomously maintain operation within limits set by control parameters – Useful when line or environment conditions are changing

• Optimise ATU settings following initialization– Useful when employing fast initialization sequence that requires

making faster estimates during training

• Types of online reconfiguration– Bit swapping

• Reallocates data and power among the subcarriers

– Dynamic rate repartitioning (optional)

• Reconfigure the data rate allocation between multiple latency paths

– Seamless rate adaptation (optional)

• Reconfigure the total data rate

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ADSL2+ (G.992.5)ADSL2+ (G.992.5)

• Doubles the downstream bandwidth

• Significant increase in downstream data rates on shorter lines

12

OutlineOutline

• Asymmetric Digital Subscriber Line (ADSL) Standards– Overview of ADSL2 and ADSL2+

– Data rate vs. reach improvements

– ADSL2+

• Multichannel Discrete Multitone (DMT) Modulation– Dynamic spectrum management

– Channel identification

– Spectrum balancing

– Vectored DMT

• System Design Alternatives and Recommendations

13

Dynamic Spectrum ManagementDynamic Spectrum Management

• Allows adaptive allocation of spectrum to various users in a multiuser environment – Function of the physical-channel

– Used to meet certain performance metrics

– One can treat each DMT receiver as a separate user

• Better than static spectrum management – Adapts to environment rather than just designing for worst-case

– E.g. ADSL used static spectrum management (Power Spectral Density Masks) to control crosstalk

– Too conservative: limited rates vs. reach

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Dynamic Spectrum ManagementDynamic Spectrum Management

• Channel Identification Methods – Initialization and training

– Estimation of the channel transfer function

• Spectrum Balancing – Distributed power control (iterative waterfilling)

– Centralized power control (optimal spectrum management)

• Vectored Transmission Methods

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Training Sequences Training Sequences

• Training Sequence– Goal: estimate the channel impulse response before data transmission

– Type: periodic or aperiodic, time or frequency domain

– Power spectrum: approximately flat over the transmission bandwidth

– Design: optimize sequence autocorrelation functions

• Perfect Training Sequence– All of its out-of-phase periodic autocorrelation terms are 0 [1]

• Suggested training sequences for DMT– Pseudo-random binary sequence with N samples

– Periodic by repeating N samples or adding a cyclic prefix

[1] W. H. Mow, “A new unified construction of perfect root-of-unity sequences,” in Proc. Spread-Spectrum Techniques and Applications, vol. 3, 1996, pp. 955–959.

16

Training SequencesTraining Sequences

• y = S h + n– h: L-tap channel

– S: transmitted N x L Toeplitz matrix made up of N training symbols

– n: additive white Gaussian noise (AWGN)

Domain Method Minimum

MSE

Complexity Optimal Sequence*

Time Periodic (LS)[1] Yes High (2N) Yes

Aperiodic [2] No Medium (N2) YesL-Perfect (MIMO)

[3]

Almost Low (N log2N) Sometimes

Frequency Periodic [4] No Low (N log2N) Sometimes

[1] W. Chen and U. Mitra, "Frequency domain versus time domain based training sequence optimization," in Proc. IEEE Int. Conf. Comm., pp. 646-650, June 2000.

[2] C. Tellambura, Y. J. Guo, and S. K. Barton, "Channel estimation using aperiodic binary sequence," IEEE Comm. Letters, vol. 2, pp. 140-142, May 1998.

[3] C. Fragouli, N. Al-Dhahir, W. Turin, “Training-Based Channel Estimation for Multiple-Antenna Broadband Transmissions," IEEE Trans. on Wireless Comm., vol.2, No.2, pp 384-391, March 2003

[4] C. Tellambura, M. G. Parker, Y. Guo, S . Shepherd, and S . K. Barton, “Optimal sequences for channel estimation using Discrete Fourier Transform techniques,” IEEE Trunsuctions on Communicutions, vol.47, no.2, pp. 230-238, Feb. 1999

* impulse-like autocorrelation and zero crosscorrelation

MIMO is multiple-input multiple-output

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Training-Based Channel Estimation for MIMOTraining-Based Channel Estimation for MIMO

• 2 x 2 MIMO ModelDuplex Channel

TX 1

RX 2

RX 1

TX 2

h11

h21 h12

h22

1 11 121 2

2 21 22

( ) ( )y Sh z [ ( , ) ( , )] z

( ) ( )

where y and z are of dimension 2( 1) 1

( ) (0) ( 1) , or 1, 2

( 1) (0)

( ) (1)( , )

( 1) (

t t

t

T

ij ij ij

i i

i ii t

i t i

y h L h LS L N S L N

y h L h L

N L

h L h h L i j

s L s

s L sS L N

s N s N

, 1, 2

)t

i

L

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Crosstalk Estimation Crosstalk Estimation

• Noises are “unknown” crosstalkers and thermal/radio– Power spectral density N(f)

– Frequency bandwidth of measurement

– Time interval for measurement

– Requisite accuracy

• Channel ID 1– Estimate gains at several frequencies

– Estimate noise variances at same frequencies

– SNR is then gain-squared/noise estimate

• Basic MIMO crosstalk ID– Near-end crosstalk (NEXT)

– Far-end crosstalk (FEXT)

Transmitter User i

Receiver User j

NEXT FEXT

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Spectrum BalancingSpectrum Balancing

• Decides the spectral assignment for each user– Allocation is based on channel line and signal spectra

– For single-user, ‘water-filling’ is optimal

– For the multiuser case, performance evaluation and/or optimization becomes much more complex

• Methods – Distributed power control

• No coordination at run-time required

• Set of data rates must be predetermined

– Centralized power control

• Coordination at central office (CO) transmitter is required

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Distributed Multiuser Power ControlDistributed Multiuser Power Control

• Iterative waterfilling approach[Yu, Ginis, & Cioffi, 2002]

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• Rate-adaptive problem with rate constraints

Centralized Optimal Spectrum ManagementCentralized Optimal Spectrum Management[Cendrillon, Yu, Moonen, Verlinden, & Bostoen, to appear]

22

Comparison among methodsComparison among methodsCO

RT

10K ft

7K ft10K ft

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Vectored Transmission MethodsVectored Transmission Methods

• Signal level coordination– Full knowledge of downstream transmitted signal and upstream

received signal at central office

– Block transmission at both ends fully synchronized

• Channel characterization– MIMO on a per-tone basis

Tx Rx

Rx Tx

CO RT

DS-Precoding

US-SuccessiveCrosstalk-Cancellation

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Upstream: Successive Crosstalk CancellationUpstream: Successive Crosstalk Cancellation

+=

uncorrelated components

K£K MIMO channel matrix for tone i

+=

K vector of received samples

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Downstream: MIMO Precoding Downstream: MIMO Precoding

Transmitted signal Original symbols

Channel

£

=Received signalcrosstalk-free

• We can also use Tomlinson-Harashima precoding(as used in High-speed DSL) to prevent energy increase

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CommentsComments

• Because of limited computational power at downstream Tx (reverse of that in typical DSL/Wireless systems)– Successive crosstalk cancellation at Rx makes more sense

• Do the QR decomposition also at Rx

• Don’t need to feedback channel information, since it is used at the receiver only

• Transmit optimization procedures can also be done at Rx– It is actually simpler since we can assume that the cross-talk is

cancelled out

• Just do single-user waterfilling for each separate user (loop)

– Optimal power allocation settings fed back to transmitter

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OutlineOutline

• Asymmetric Digital Subscriber Line (ADSL) Standards– Overview of ADSL2 and ADSL2+

– Data rate vs. reach improvements

– ADSL2+

• Multichannel Discrete Multitone (DMT) Modulation– Dynamic spectrum management

– Channel identification

– Spectrum balancing

– Vectored DMT

• System Design Alternatives and Recommendations

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Training-Based Channel Estimation for MIMOTraining-Based Channel Estimation for MIMO• Linear Least Squares

– Low complexity but enhances noise. Assumes S has full column rank

• MMSE– zero-mean and white Gaussian noise:

– Sequences satisfy above are optimal sequences

– Optimal sequences: impulse-like autocorrelation and zero crosscorrelation

11 12 -1

21 22

ˆ ˆh= =(S S) S y

ˆ ˆH Hh h

h h

2z - 1R 2 I

t

HN LE zz

2 -1ˆ ˆMSE h h h h 2 ((S S) )H

HE Tr 21 1 2 1

2

1 2 2 2

S S S S2MMSE = , iff S S= ( 1)I

1 S S S S

H H

Ht LH H

t

LN L

N L

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Simple Channel Estimation for MIMOSimple Channel Estimation for MIMO

• How to design s1(L,Nt) and s2(L,Nt) ?

• Simple and intuitive method ( 2 X 2 )– Sending the training data at only one TX( turn off another TX) during

one training time slot, i.e.

– Very Low Complexity and even No Need to Design Training Sequences

– But Time Consuming

• Design training sequences to estimate the channel during one training time slot

0 0

1 1

,1 ,21 2 11 12

,1 ,21 2 21 22

0 : 0 ,

1: 0 ,

t t

t t

y ytime s s s h h

s sy y

time s s s h hs s

Method Computational Complexity

Time

Simple Low HighDesign TS High Low

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Design Training Sequences for MIMODesign Training Sequences for MIMO

• Recommendation Design Method I– Design instead a single training sequence s (2L, Nt+L+1)

– s1=[s(0)…s(Nt)], s2=[s(L)…s(Nt+L)]

– MMSE but High searching complexity

• Recommendation Design Method II– A sequence s produces s1 and s2 with 0 cross correlation by encoding

– Lower MSE and Only s with good auto-correlation properties

– Trellis Code:

– Block Code: ~ time-reversing

* complex conjugation

* *1 1 12 1

2 2 21 2

S

y h zS S

y h zS S

11 2( ) ( ), ( ) ( 1) ( 1)kps k s k s k s k

* *1 2 1 2 1 2

1 1 2 1

1 1 2 1

[ ] [ ]

1) S =S , S =S

2) S =S , S =S

s s s s s s

2S S ( 1)IHt LN L

Method Computational Complexity

MMSE

I High Yes

II Low Almost

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Choice of Multichannel MethodChoice of Multichannel Method• Choice of methods is a performance-complexity tradeoff

• Loop bonding simplest to implement, but poor performance

• Spectrum balancing methods– Iterative waterfilling at the receiver can be implemented pretty easily

• Pre-determine target rates through offline analysis

• No coordination needed among the loops

• Just feedback the power allocation settings to corresponding Tx

– Optimal spectrum management

• We can simply maximize rate-sum (all weights=1)

• Coordination at Rx is needed (jointly optimize across loops)

• Vectored transmission– Coordination on both sides are required

– Run-time complexity is not too bad: O(K3) QR-Decomposition only need to be done at training

– Transmit optimization is also simpler than spectrum balancing methods

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ComparisonComparison

Loop Bonding

Iterative Waterfilling

Optimal Spectrum Balancing

Vectored-DMT

Design

Complexity

Low Medium Medium High

Computational Complexity

Low Medium Very high High

Coordination Required

Low Medium High Very high

Data-rate performance

Low Medium High Very High

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Backup SlidesBackup Slides

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ADSL2 improvements over ADSLADSL2 improvements over ADSL

• Application-related features– Improved application support for an all digital mode of operation and

voice over ADSL operation;

– Packet TPS-TC1 function, in addition to the existing Synchronous Transfer Mode (STM) and Asynchronous TM (ATM)

– Mandatory support of 8 Mbit/s downstream and 800 kbit/s upstream for TPS-TC function #0 and frame bearer #0;

– Support for Inverse Multiplexing for ATM (IMA) in the ATM TPS-TC;

– Improved configuration capability for each TPS-TC with configuration of latency, BER and minimum, maximum and reserved data rate.

1Transport Protocol Specific-Transmission Convergence

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ADSL2 improvements over ADSL (cont.)ADSL2 improvements over ADSL (cont.)

• PMS-TC1 related features– A more flexible framing, including support for up to 4 frame bearers, 4

latency paths;

– Parameters allowing enhanced configuration of the overhead channel;

– Frame structure with

• Receiver selected coding parameters;

• Optimized use of RS coding gain;

• Configurable latency and bit error ratio;

– OAM2 protocol to retrieve more detailed performance monitoring information;

– Enhanced on-line reconfiguration capabilities including dynamic rate repartitioning.

1 Physical Media Specific-Transmission Convergence2 Operations, Administration, and Maintenance

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ADSL2 improvements over ADSL (cont.)ADSL2 improvements over ADSL (cont.)• Physical Media Dependent (PMD) related features

– New line diagnostics procedures for both successful and unsuccessful initialization scenarios, loop characterization and troubleshooting;

– Enhanced on-line reconfiguration capabilities including bitswaps and seamless rate adaptation;

– Optional short initialization sequence for recovery from errors or fast resumption of operation;

– Optional seamless rate adaptation with line rate changes during showtime;

– Improved robustness against bridged taps with RX determined pilot;– Improved transceiver training with exchange of detailed transmit signal

characteristics;– Improved SNR measurement during channel analysis;– Subcarrier blackout to allow RFI measurement during initialization and

SHOWTIME;– Improved performance with mandatory support of trellis coding, one-bit

constellations, and optional data modulated on the pilot-tone

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ADSL2 improvements over ADSL (cont.)ADSL2 improvements over ADSL (cont.)

• PMD related features (cont.)– Improved RFI robustness with receiver determined tone ordering;

– Improved transmit power cutback possibilities

– Improved Initialization with RX/TX controlled duration of init. states;

– Improved Initialization with RX-determined carriers for modulation of messages;

– Improved channel identification capability with spectral shaping during Channel Discovery and Transceiver Training;

– Mandatory transmit power reduction to minimize excess margin under management layer control;

– Power saving feature with new L2 low power state and L3 idle state;

– Spectrum control with individual tone masking under operator control through CO-Management Information Base;

– Improved conformance testing including increase in data rates for many existing tests.

38

BibliographyBibliography[ADSL2] ITU-T Standard G.992.3, Asymmetric digital subscriber line transceivers 2

(ADSL2), Feb. 2004

[ADSL2white] ADSL2 and ADSL2plus-The new ADSL standards. Online: http://www.dslforum.org/aboutdsl/ADSL2_wp.pdf, Mar. 2003

[Wei87] L.-F.Wei, “Trellis-coded modulation with multidimensional constellations,” IEEE Trans. Inform. Theory, vol. IT-33, pp. 483-501, July 1987.

[IMA99] ATM Forum Specification af.phy-0086.001, Inverse Multiplexing for ATM (IMA), Version 1.1., Mar. 1999