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High-Speed Wireline Communication Systems: Semester Wrap-up. Ian C. Wong, Daifeng Wang, and Prof. Brian L. Evans Dept. of Electrical and Comp. Eng. The University of Texas at Austin http://signal.ece.utexas.edu. http://www.ece.utexas.edu/~bevans/projects/adsl. Outline.

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High speed wireline communication systems semester wrap up l.jpg

High-Speed WirelineCommunication Systems: Semester Wrap-up

Ian C. Wong, Daifeng Wang, and

Prof. Brian L. Evans

Dept. of Electrical and Comp. Eng.The University of Texas at Austin



Outline l.jpg

  • 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

1 adsl2 and adsl2 the new standards l.jpg
1ADSL2 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]

Data rate vs reach performance improvements l.jpg
Data rate vs. reach performance improvements

  • Focus: long lines with narrowband interference

  • Achieves 12 Mbps downstream and 1 Mbps upstream

  • Accomplished through

    • Improving modulation efficiency

    • Reducing framing overhead

    • Achieving higher coding gain

    • Employing loop bonding

    • Improving initialization state machine

    • Online reconfiguration

1 improved modulation efficiency l.jpg
1. 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, §

    • 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 (§

    • Allows poor subchannels to still carry some data

2 reduced framing overhead l.jpg
2. 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

3 achieved higher coding gain l.jpg
3. 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 (§

    • 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)

4 loop bonding l.jpg
4. 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]

5 improved initialization state machine l.jpg
5. 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

6 online reconfiguration 10 2 l.jpg
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

Adsl2 g 992 5 l.jpg
ADSL2+ (G.992.5)

  • Doubles the downstream bandwidth

  • Significant increase in downstream data rates on shorter lines

Outline12 l.jpg

  • 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

Dynamic spectrum management l.jpg
Dynamic 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

Dynamic spectrum management14 l.jpg
Dynamic 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

Training sequences l.jpg
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.

Training sequences16 l.jpg
Training 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)

MIMO is multiple-input multiple-output

* impulse-like autocorrelation and zero crosscorrelation

[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

Training based channel estimation for mimo l.jpg
Training-Based Channel Estimation for MIMO

  • 2 x 2 MIMO Model

Duplex Channel

TX 1

RX 1




TX 2

RX 2


Crosstalk estimation l.jpg
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)

Spectrum balancing l.jpg
Spectrum 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

Distributed multiuser power control l.jpg
Distributed Multiuser Power Control

[Yu, Ginis, & Cioffi, 2002]

  • Iterative waterfilling approach

Centralized optimal spectrum management l.jpg
Centralized Optimal Spectrum Management

[Cendrillon, Yu, Moonen, Verlinden, & Bostoen, to appear]

  • Rate-adaptive problem with rate constraints

Comparison among methods l.jpg

10K ft


10K ft


7K ft

Comparison among methods

Vectored transmission methods l.jpg





Vectored 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






Upstream successive crosstalk cancellation l.jpg



K vector of

received samples


channel matrix for tone i



uncorrelated components

Upstream: Successive Crosstalk Cancellation

Downstream mimo precoding l.jpg

Transmitted signal

Original symbols




Received signal


Downstream: MIMO Precoding

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

Comments l.jpg

  • 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

Outline27 l.jpg

  • 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

Training based channel estimation for mimo28 l.jpg
Training-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

Simple channel estimation for mimo l.jpg
Simple 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

Design training sequences for mimo l.jpg
Design 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

Choice of multichannel method l.jpg
Choice 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

Adsl2 improvements over adsl l.jpg
ADSL2 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

Adsl2 improvements over adsl cont l.jpg
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 Convergence

2 Operations, Administration, and Maintenance

Adsl2 improvements over adsl cont36 l.jpg
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

Adsl2 improvements over adsl cont37 l.jpg
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.

Bibliography l.jpg

[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