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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
2
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
5
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
7
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)
8
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]
9
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
10
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
11
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
14
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
15
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
17
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
18
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
19
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
20
Distributed Multiuser Power ControlDistributed Multiuser Power Control
• Iterative waterfilling approach[Yu, Ginis, & Cioffi, 2002]
21
• 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
23
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
24
Upstream: Successive Crosstalk CancellationUpstream: Successive Crosstalk Cancellation
+=
uncorrelated components
K£K MIMO channel matrix for tone i
+=
K vector of received samples
25
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
26
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
27
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
28
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
29
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
30
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
31
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
32
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
33
Backup SlidesBackup Slides
34
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
35
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
36
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
37
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
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