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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

http://signal.ece.utexas.edu

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


Outline l.jpg
Outline

  • 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, §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


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 (§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)


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
Outline

  • 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

h11

h12

h21

TX 2

RX 2

h22


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

CO

10K ft

RT

7K ft

Comparison among methods


Vectored transmission methods l.jpg

Tx

Rx

Rx

Tx

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

DS-Precoding

RT

CO

US-Successive

Crosstalk-Cancellation


Upstream successive crosstalk cancellation l.jpg

=

+

K vector of

received samples

K£K MIMO

channel matrix for tone i

=

+

uncorrelated components

Upstream: Successive Crosstalk Cancellation


Downstream mimo precoding l.jpg

Transmitted signal

Original symbols

£

Channel

=

Received signal

crosstalk-free

Downstream: MIMO Precoding

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


Comments l.jpg
Comments

  • 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
Outline

  • 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
Bibliography

[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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