Transcript Equalizer Design to Maximize Bit Rate in ADSL Transceivers Prof. Brian L.

Equalizer Design to Maximize
Bit Rate in ADSL Transceivers
Prof. Brian L. Evans
Dept. of Electrical and Comp. Eng.
The University of Texas at Austin
http://signal.ece.utexas.edu
Last modified February 24, 2003
UT Ph.D. graduates: Dr. Güner Arslan (Silicon Labs), Dr. Biao Lu (Schlumberger)
UT Ph.D. students: Ming Ding, Kyungtae Han, Milos Milosevic (Schlumberger)
UT M.S. students: Zukang Shen, Ian Wong
UT senior design students: Wade Berglund, Jerel Canales, David J. Love,
Ketan Mandke, Scott Margo, Esther Resendiz, Jeff Wu
Other collaborators: Dr. Lloyd D. Clark (Schlumberger), Prof. C. Richard
Johnson, Jr. (Cornell), Prof. Sayfe Kiaei (ASU), Mr. Rick Martin (Cornell),
Dr. Lucio F. C. Pessoa (Motorola), Dr. Arthur J. Redfern (Texas Instruments)
Digital Subscriber Line (DSL)
Broadband Access
Interne
t
DSLAM
Central
Office
downstream
DSL
modem
DSL
modem
upstream
Voice
Switch
LPF
LPF
Customer Premises
Telephone
Network
DSLAM - Digital Subscriber Line Access Multiplexer
LPF – Low Pass Filter
2
Discrete Multitone (DMT) DSL Standards
• ADSL – Asymmetric DSL (G.DMT Standard)
– Maximum data rates supported (ideal case)
Echo cancelled: 14.94 Mbps downstream, 1.56 Mbps upstream
Frequency division multiplexing: 13.38 Mbps downstream, 1.56 Mbps up
– Widespread deployment in US, Canada, Western Europe, Hong Kong
Central office providers only installing frequency-division multiplexed ADSL
ADSL:cable modem market
G.DMT Asymmetric
1:2 in US & 5:1 worldwide
• VDSL – Very High Rate
Data band
DSL (Proposed Standard)
– Also has symmetric mode:
13, 9, or 6 Mbps
– Single carrier and DMT
– DMT VDSL
Higher speed G.DMT ADSL
Frequency division multiplex
2m subcarriers m  [8, 12]
Upstream
subcarriers
Downstream
subcarriers
Target upstream rate
Target downstream rate
ADSL DMT VDSL
25 kHz –
1 MHz –
1.1 MHz
12 MHz
32
256
256
2048/4096
1 Mbps
3 Mbps
8 Mbps
13/22 Mbps
3
Outline
• Multicarrier modulation
• Conventional equalizer
– Minimum Mean Squared Error design
[Stanford]
– Maximum Shortening Signal-to-Noise Ratio design
– Maximum Bit Rate design (optimal)
[Tellabs]
[UT Austin]
– Minimum Inter-symbol Interference design (near-optimal) [UT Austin]
• Per-tone equalizer
• Dual-path equalizer
• Conclusion
[Catholic University, Leuven, Belgium]
[UT Austin]
Received
bit stream
Message
bit stream
Transmitter
Channel
Equalizer
Receiver
4
Multicarrier Modulation
Single Carrier Modulation
• Ideal (non-distorting) channel over transmission band
– Flat magnitude response
– Linear phase response: delay is constant for all spectral components
– No intersymbol interference
• Impulse response for ideal channel over all frequencies
nk
– Continuous time: g d(t-T)
Channel
Equalizer
yk
xk
rk
ek
– Discrete time: g d[k-D]
+
w
+
h
+
• Equalizer
– Shortens channel
impulse response
(time domain)
– Compensates for
frequency distortion
(frequency domain)
Ideal Channel
z-D
g
Discretized Baseband System
5
Multicarrier Modulation
Multicarrier Modulation
• Divide channel into narrowband subchannels
– No inter-symbol interference (ISI) in subchannels if constant gain
within every subchannel and if ideal sampling
• Discrete multitone modulation
pulse
magnitude
– Based on fast Fourier transform (FFT)
– Standardized for ADSL
– Proposed for VDSL
DTFT-1
sinc
w
-wc
wc
channel
k
sin w c k )
k
carrier
Subchannels are 4.3 kHz wide in ADSL and DMT VDSL
subchannel
frequency
6
Multicarrier Modulation
Multicarrier Modulation by Inverse FFT Filter Bank
e
X1
g(t)
g(t)
X1
e
+
Discrete
time
X2
e
g(t) : pulse shaping filter
X N /2
1
k
N
j 2
2
k
N
x
j 2 f N / 2 t
x
j 2
x
j 2 f 2 t
x
g(t)
e
X N /2
e
x
e
X2
j 2 f1t
j 2
+
N /2
k
N
x
Xi : ith subsymbol from encoder
7
Multicarrier Modulation
Discrete Multitone Modulation Symbol
• Subsymbols are in general complex-valued
Xi
– ADSL uses 4-level Quadrature Amplitude
Modulation (QAM) during training
– ADSL uses QAM of 22, 23, 24, …, 215 levels
during data transmission
In-phase
• Mirror and conjugate subsymbols before
multicarrier modulation using inverse FFT
N/2 subsymbols
(one subsymbol
per carrier)
X0
X1
X2
XN/2
XN/21
N-point
Inverse
FFT
Quadrature
x1
x2
x3
QAM
one symbol of N
real-valued samples
*
X2*
X1*
xN
8
Multicarrier Modulation
Discrete Multitone Modulation Frame
• Frame is sent through D/A converter and transmitted
– Frame is the symbol with cyclic prefix prepended
– Cyclic prefix (CP) consists of last n samples of the symbol
copy
copy
CP
v samples
s y m b o l i
CP
s y m b o l i+1
N samples
N
16

– CP reduces throughput by factor of
N  v 17
• Linear convolution of frame with
channel impulse response
ADSL G.DMT Values
Down
Up
stream stream
4
n
32
64
N
512
– Is circular convolution if channel length is CP length plus one or shorter
– Circular convolution
frequency-domain equalization in FFT domain
– Time-domain equalization to reduce effective channel length and ISI
9
Multicarrier Modulation
Eliminating ISI in Discrete Multitone Modulation
• Time domain equalizer (TEQ)
n1
channel
impulse
response
– Finite impulse response (FIR) filter
– Effective channel impulse response:
convolution of TEQ impulse response
with channel impulse response
effective
channel
impulse
response
• Frequency domain equalizer (FEQ)
– Compensates magnitude/phase distortion
of equalized channel by dividing each FFT
coefficient by complex number
– Generally updated during data transmission
• ADSL G.DMT equalizer training
– Reverb: same symbol sent 1,024 to 1,536 times
– Medley: aperiodic sequence of 16,384 symbols
– At 0.25 s after medley, receiver returns number
of bits on each subcarrier that can be supported
D
D: transmission delay
n: cyclic prefix length
ADSL G.DMT Values
Down
Up
stream stream
4
n
32
64
N
512
10
Multicarrier Modulation
ADSL Transceiver: Data Transmission
N/2 subchannels N real samples
Bits
00110
S/P
quadrature
amplitude
modulation
(QAM)
encoder
mirror
data
and
N-IFFT
add
cyclic
prefix
D/A +
transmit
filter
P/S
TRANSMITTER
channel
RECEIVER
N/2 subchannels
P/S
QAM
demod
decoder
invert
channel
=
frequency
domain
equalizer
N real samples
N-FFT
and
remove
mirrored
data
remove
S/P cyclic
prefix
time
domain
equalizer
(FIR
filter)
receive
filter
+
A/D


conventional ADSL equalizer structure
11
Outline
• Multicarrier modulation
• Conventional equalizer
– Minimum Mean Squared Error design
[Stanford]
– Maximum Shortening Signal-to-Noise Ratio design
– Maximum Bit Rate design (optimal)
[Tellabs]
[UT Austin]
– Minimum Inter-symbol Interference design (near-optimal) [UT Austin]
• Per-tone equalizer
• Dual-path equalizer
• Conclusion
Received
bit stream
Message
bit stream
Transmitter
Channel
Equalizer
Receiver
12
Conventional Equalizer
Minimum Mean Squared Error (MMSE) TEQ Design
Channel
xk
h
nk
yk
+
z-D
TEQ
w
b
rk
ek
- +
bk-D
• Minimize E{ek2} [Chow & Cioffi, 1992]
– Chose length of b (e.g. n1 in ADSL) to shorten length of h * w
– b is eigenvector of minimum eigenvalue of channel-dependent matrix
T
T
-1
– Minimum MSE achieved when w  b R xy R yy where w  0
• Disadvantages
– Does not consider bit rate
– Deep notches in equalizer frequency response (zeros out low SNR bands)
– Infinite length TEQ: zeros of b lock onto unit circle (kills n subchannels)
13
Conventional Equalizer
Maximum Shortening SNR (MSSNR) TEQ Design
• Minimize energy in effective channel impulse response
outside of window of n+1 samples, which causes ISI
[Melsa, Younce & Rohrs, 1996]
• For each possible start position D of window of n+1 samples,
maxSSNR in dB)  max10log10
w
w
energy inside window after T EQ
energy outside window after T EQ
n1
h
• Disadvantages
w
channel
impulse
response
TEQ
effective
channel
impulse
response
– Does not consider channel noise
– Does not consider bit rate
– Requires Cholesky decomposition
D
– Equivalent to MMSE for additive white Gaussian channel noise
14
Conventional Equalizer
Maximum Shortening SNR (MSSNR) TEQ Design
•
Choose w to minimize energy outside window of desired length
Locate window to capture maximum channel impulse response energy
Channel
TEQ
hTwall h wall  wT HTwall H wall w  wT Aw
w
h
hTwinh win  wT HTwinH win w  wT Bw
hwin, hwall : effective channel within and outside window
• Objective function is shortening SNR (SSNR)
max SSNR in dB)  max 10log10
w
w
C
w opt 
B)q
T
 B)
wT Bw
subject to wT Bw  1
wT Aw
-1
A
B)
T
-1
-1
min
q min : eigenvector of minimumeigenvalueof C
15
Conventional Equalizer
Modeling Signal, ISI, and Noise at Receiver
Delay  y1  h~1a4
~ ~
 y  ~
y  xh  n
CP
 2   h~1a1
~
h  w h
 y3  h1a2
x is transmitted signal    ~
 y4   h1a3
Symbols a b
 y5  h~ a
   ~1 4
Symbol length
 y6   h1b4
N=4
 y    h~ b
CP
 7   ~1 1
Length of h~
 y8   h1b2
L=4
 y  ~
 9   h~1b3
Cyclic prefix
 y10   h1b4
v=1
  
 y11  
Delay
 y12  
D =1
Tail   
 y13  
• Receive
•
•
•
•
•
[Arslan, Evans & Kiaei, 2001]
ISI









~
h2 a4
~
h2 a1
~
h2 a2
~
h2 a3
~
h2 a4
~
h2b4
~
h2b1
~
h2b2
~
h2b3
~
h2b4









signal
~
h3 a4
~
h3a1
~
h3 a2
~
h3 a3
~
h3 a4
~
h3b4
~
h3b1
~
h3b2
~
h3b3
~
h3b4









  n~1 
 ~ 
  n2 
  n~3 
~  ~ 
h4 a4   n4 
~  ~ 
n5
h4 a1

~ 
~
h4 a2   n6 
~ 
h4 a3    n~7 
~ 
~
h4 a4   n8 
~   
h4b4   n~9 
~  ~
h4b1 n10 
~  ~ 
h4b2   n11 
~
h4b3  n~12 
~  ~ 
h4b4   n13 
ISI
noise
16
Conventional Equalizer
Proposed Subchannel SNR Model
• Partition equalized channel
• Equalized channel impulse
~
into signal path, ISI path, noise
response hk  hk  wk
path [Arslan, Evans & Kiaei, 2001]
~
signal
hk
h k g k
nk
~
Channel
TEQ
ISI
rk
yk
hk h k 1 - g k )
xk
w
+
h
hknoise  wk
xk
w
x
h
Signal • Target window
gk
xk
h
w
x
ISI
1-gk
nk
1 D  k  D  

gk  
0 ot herwise
gk
1
w
...
Noise
D
D 
k
17
Conventional Equalizer
Proposed Subchannel SNR Definition
• SNR in ith subchannel (leads to maximum bit rate method)
[Arslan, Evans & Kiaei, 2001]
signal
2
S x ,i H i
signal power
SNR i 

noise power  ISI power S H noise 2  S H ISI
n ,i
x ,i
i
i
H isignal gain of hksignal in subchanneli
H iISI
gain of hkISI in subchanneli
H inoise gain of hknoise in subchanneli
• Divide SNRi numerator and
denominator by noise power
spectral density Sn,i (leads
to minimum ISI method)
2
S x ,i : t ransmited
t signal
power in subchanneli
S n ,i : channelnoise power
in subchanneli
S x ,i signal 2
Hi
S n ,i
SNR i 
2
S x ,i ISI
noise
Hi

Hi
S n ,i
Conventional subchannel SNR is Sx,i / Sn,i
2
18
Conventional Equalizer
Maximum Bit Rate (MBR) TEQ Design
• Subchannel SNR as nonlinear function of equalizer taps w
2
H
H isignal  q iH GHw
S x,i qi GHw
wT Ai w
SNR i 
2
2 
ISI
H
H
H
wT Bi w
H i  q i DHw
S n,i qi Fw  S x,i qi DHw
qi
S
T
H inoise  q iH Fw
x,i
H
G
G
qi is ith row of DFT matrix
q
FT Sn,i Hi
qi
Bi =
Ai =
qi
HT
DT
F
+
HT
qi
H
Sx,i
qi
H
D
H
• Maximize nonlinear function of bits/symbol with respect to w
N /2
1 wT Ai w
bDMT   log2 ( 1 
)
T
 w Bi w
i 1
– Good performance measure for comparison of TEQ design methods
– Not an efficient TEQ design method in computational sense
19
Conventional Equalizer
Minimum-ISI (Min-ISI) TEQ Design
• Rewrite proposed subchannel SNR [Arslan, Evans & Kiaei, 2001]
S x ,i signal 2
Hi
ISI power weighted in
S n ,i
frequency domain by
SNR i 
2
S x ,i ISI 2
inverse of noise spectrum
noise
Hi

Hi
S n ,i
• Generalize MSSNR method by weighting ISI in frequency
2
H
– ISI power in ith subchannel is ISI i  S x ,i q i DHw
– Minimize frequency weighted sum of subchannel ISI power
T
ISI

K
q
DHw

w
Xw
 i  i
H
i
i
2
i
– Penalize ISI power in high conventional SNR subchannels: K i 
– Constrain signal path gain to one to prevent all-zero solution for w
S x ,i
S n ,i
| h signal |2 | GHw|2  wT Yw  1
– Solution is generalized eigenvector of X and Y
20
Conventional Equalizer
Simulation Results for 17-Tap TEQ
Achievable percentage of upper bound on bit rate
ADSL
Maximum Maximum
CSA Minimum Geometric Shortening Minimum Maximum
Loop
MSE
SNR
SNR
ISI Bit Rate
43%
84%
62%
99%
99%
1
70%
73%
75%
98%
99%
2
64%
94%
82%
99%
99%
3
70%
68%
61%
98%
99%
4
61%
84%
72%
98%
99%
5
62%
93%
80%
99%
99%
6
57%
78%
74%
99%
99%
7
66%
90%
71%
99%
100%
8
Cyclic prefix length
FFT size (N)
Coding gain
Margin
32
512
4.2 dB
6 dB
Input power
Noise power
Crosstalk noise
POTS splitter
Upper
Bound
(Mbps)
9.059
10.344
8.698
8.695
9.184
8.407
8.362
7.394
23 dBm
-140 dBm/Hz
8 ADSL disturbers
5th order Chebyshev
21
Conventional Equalizer
Simulation Results for Three-Tap TEQ
Achievable percentage of matched filter bound on bit rate
ADSL
Maximum Maximum
CSA Minimum Geometric Shortening Minimum Maximum
Loop
MSE
SNR
SNR
ISI Bit Rate
54%
70%
96%
97%
98%
1
47%
71%
96%
96%
97%
2
57%
69%
92%
98%
99%
3
46%
66%
97%
97%
98%
4
52%
65%
96%
97%
98%
5
60%
71%
95%
98%
99%
6
46%
63%
93%
96%
97%
7
55%
61%
94%
98%
99%
8
Cyclic prefix length
FFT size (N)
Coding gain
Margin
32
512
4.2 dB
6 dB
Input power
Noise power
Crosstalk noise
POTS splitter
Upper
Bound
(Mbps)
9.059
10.344
8.698
8.695
9.184
8.407
8.362
7.394
23 dBm
-140 dBm/Hz
8 ADSL disturbers
5th order Chebyshev
22
Conventional Equalizer
Bit Rate vs. Number of TEQ Taps
• Min-ISI and MBR
give similar bit rate
• Three-tap Min-ISI,
MBR, and MSSNR
achieve matched
filter bound (MFB)
• Beyond three taps,
MSSNR bit rate falls
• 3-tap Min-ISI beats
21-tap MMSE
• Maximum Geometric
SNR close to MMSE
cyclic prefix (n)
FFT size (N)
coding gain
margin
32
512
4.2 dB
6 dB
Lw
input power
23 dBm
noise power
-140 dBm/Hz
crosstalk noise 8 ADSL disturbers
23
Conventional Equalizer
Drawbacks to Minimum ISI Method
• High complexity to
compute X and Y matrices
• Recursively calculate
diagonal elements of X and
Y from first column
[Wu, Arslan, Evans, 2000]
• Sensitivity to transmission
delay parameter D
• Reformulate Minimum ISI
objective function
– Requires computationally
intensive search
• Does not work for all TEQ
lengths
• Develop iterative method
for reformulated objective
– Formulation does not work for
TEQ lengths longer than n
– Also sensitivity to fixed-point
implementation due to
Cholesky decomposition
– Works for any TEQ length
– Does not require a Cholesky
decomposition
– Works well under fixed-point
arithmetic
24
Outline
• Multicarrier modulation
• Conventional equalizer
– Minimum Mean Squared Error design
– Maximum Shortening Signal-to-Noise Ratio design
– Maximum Bit Rate design (optimal)
– Minimum Inter-symbol Interference design (near-optimal)
• Per-tone equalizer
• Dual-path equalizer
• Conclusion
[Catholic University, Leuven, Belgium]
Received
bit stream
Message
bit stream
Transmitter
Channel
Equalizer
Receiver
25
Per-Tone Equalizer
Drawbacks to Using Single FIR Filter for TEQ
• Conventional
equalizer
N real
samples
time
domain
equalizer
(FIR
filter)
remove
cyclic S/P
prefix
N/2 complex
samples
N-FFT
and
remove
mirrored
data
invert
channel
=
frequency
domain
equalizer
• Equalizes all tones in combined fashion: may limit bit rate
• Output of conventional equalizer for tone i computed using
sequence of linear operations
Zi = Di rowi(QN ) Y w
Di is the complex scalar value of one-tap FEQ for tone i
QN is the N  N complex-valued FFT matrix
Y is an N  Lw real-valued Toeplitz matrix of received samples
w is a Lw  1 column vector of real-valued TEQ taps
Yw
represents
convolution
26
Per-Tone Equalizer
Frequency-Domain Per Tone Equalizer
• Rewrite equalized FFT coefficient for each of N/2 tones
[Van Acker, Leus, Moonen, van de Wiel, Pollet, 2001]
Zi = Di rowi(QN ) Y w = rowi(QN Y) ( w Di ) = rowi(QN Y) wi
– Take sliding FFT to produce N  Lw matrix product QN Y
– Design wi for each tone
N+n
z-1
N + Lw – 1
channels
Sliding
N-Point
FFT
z-1
N+n
z-1
y
N+n
(Lw-frame)
W1,0
W1,1
W1,2
W1,Lw1
WN/2,0
WN/2,1
WN/2,2
WN/2,Lw-1
FEQ is a linear combiner
of up to N/2 Lw-tap FEQs
27
Per-Tone Equalizer
Simulation Results
CSA
Loop
1
2
3
4
5
6
7
8
Average
MMSE
UEC
86.3%
87.2%
83.9%
81.9%
88.6%
82.7%
75.8%
82.6%
83.6%
Maximum
SSNR
95.0%
96.5%
97.0%
95.4%
97.1%
96.4%
96.7%
97.5%
96.4%
Cyclic prefix length
FFT size (N)
Coding gain
Margin
32
512
0 dB
0 dB
Minimum
ISI
97.5%
97.3%
97.3%
98.2%
97.2%
98.3%
96.3%
97.5%
97.5%
Data Rate
Maximum
99.6%
99.6%
99.5%
99.3%
99.6%
99.5%
98.8%
98.7%
99.3%
Input power
Noise power
Crosstalk noise
Tx/Rx filters
Least Sq.
Filter Bank
Per Tone Bound (Mbps)
99.5%
11.417
99.5%
12.680
99.6%
10.995
99.1%
11.288
99.5%
11.470
99.4%
10.861
99.6%
10.752
99.2%
9.615
99.4%
11.135
23.93 dBm
–140 dBm/Hz
49 ADSL disturbers
2nd order Chebyshev
Bit rates averaged over 2-32 tap equalizers
1,000 symbols transmitted (accuracy of ± 60 kbps or ± 0.5%)
28
Per-Tone Equalizer
Implementation Complexity Comparison
• Data transmission
– Modified per tone equalizer has similar arithmetic complexity as a
conventional equalizer but much higher memory usage and memory I/O
– Memory I/O is larger bottleneck on programmable DSP
Equalizer
Per Tone
(sliding FFT)
Modified
Per Tone
Conventional
Million
Real MACS
(8 taps)
98
Word of
Memory
(8 taps)
7,232
Million Real
MACs
(32 taps)
295
Words of
Memory
(32 taps)
19,520
55
6,151
105
18,463
59
3,188
112
3,236
• Training
– Conventional: design/adapt Lw real FIR filter coefficients. O(Lw3)
– Per-tone equalizer: design/adapt ½ N Lw complex taps. O(N Lw3)
– Per-tone equalizer can train for groups of tones to reduce complexity
29
Outline
• Multicarrier modulation
• Conventional equalizer
– Minimum Mean Squared Error design
– Maximum Shortening Signal-to-Noise Ratio design
– Maximum Bit Rate design (optimal)
– Minimum Inter-symbol Interference design (near-optimal)
• Per-tone equalizer
• Dual-path equalizer
• Conclusion
[UT Austin]
Received
bit stream
Message
bit stream
Transmitter
Channel
Equalizer
Receiver
30
Dual-Path Equalizer
Dual-Path Time Domain Equalizer
• Per tone equalizer
– Achieves higher bit rate than single-FIR TEQ
– Has significantly more implementation complexity to train equalizer
than MMSE, MSSNR, and Min-ISI single-FIR TEQs
• Dual-path TEQ [Ding, Redfern & Evans, 2002]
–
–
–
–
First FIR TEQ equalizes entire available bandwidth
Second FIR TEQ tailored for subchannels with higher SNR
Path selection for each subchannel is fixed during training
Enables reuse of previous ASIC designs of conventional equalizers
TEQ 1
TEQ 2
FFT
FFT
Path
Selection
for each
Subchannel
FEQ
31
Dual-Path Equalizer
Simulation Results
• ANSI-13 Loop
– Crosstalk: 24 DSL disturbers
– Additive white Gaussian noise
• Dual-Path TEQ
– Both paths use tones 33-255
– Second path only optimizes
tones 55-85
• Achieved Bit Rate
– Path 1:
2.5080 Mbps
– Dual Path: 2.6020 Mbps
– 4% improvement in bit rate
Results for ANSI-13 channel
[Ding, Redfern & Evans, 2002]
32
Conclusion
Contributions by Research Group
• New methods for single-path time-domain equalizer design
– Maximum Bit Rate method maximizes bit rate (upper bound)
– Minimum Inter-Symbol Interference method (real-time, fixed-point)
• Minimum Inter-Symbol Interference TEQ design method
– Generalizes Maximum Shortening SNR by frequency weighting ISI
– Improve bit rate in an ADSL transceiver by change of software only
– Implemented in real-time on three fixed-point digital signal processors:
Motorola 56000, TI TMS320C6200 and TI TMS320C5000
http://www.ece.utexas.edu/~bevans/projects/adsl
• New dual-path time-domain equalizer
• Comparison to frequency-domain per-tone equalizer
– Competitive bit rates
– Lower implementation complexity in training and data transmission
33
Conclusion
Matlab DMTTEQ Toolbox 3.1
• Single-path, dual-path, per-tone & TEQ filter bank equalizers
Available at http://www.ece.utexas.edu/~bevans/projects/adsl/dmtteq/
default
parameters
from
G.DMT
ADSL
standard
23
-140
various
performance
measures
different
graphical
views
34
Backup Slides
Introduction
Applications of Broadband Access
Residential
Application
Downstream Upstream Willing to pay
rate (kb/s)
rate (kb/s)
384
9
High
Database Access
384
9
Low
On-line directory; yellow pages
1,500
1,500
High
Video Phone
1,500
64
Low
Home Shopping
1,500
1,500
Medium
Video Games
3,000
384
High
Internet
6,000
0
Low
Broadcast Video
24,000
0
High
High definition TV
Demand
Potential
Medium
High
Medium
Medium
Medium
Medium
High
Medium
Business
Application
Downstream Upstream Willing to pay
rate (kb/s)
rate (kb/s)
384
9
Medium
On-line directory; yellow pages
1,500
9
Medium
Financial news
1,500
1,500
High
Video phone
3,000
384
High
Internet
3,000
3,000
High
Video conference
6,000
1,500
High
Remote office
10,000
10,000
Medium
LAN interconnection
45,000
45,000
High
Supercomputing, CAD
Demand
Potential
High
Low
Low
High
Low
Medium
Medium
Low
36
Introduction
Selected DSL Standards
Standard Meaning
Integrated Services
ISDN
Digital Network
T-Carrier One
T1
(requires two pairs)
High-Speed Digital
HDSL
Subscriber Line
(requires two pairs)
HDSL2 Single Line HDSL
G.Lite
ADSL
G.DMT
ADSL
VDSL
Splitterless
Asymmetric Digital
Subscriber Line
Asymmetric Digital
Subscriber Line
Very High-Speed
Digital Subscriber
Line (proposed)
Data Rate
144 kbps
Mode
Symmetric
Applications
Internet Access, Voice, Pair
Gain (2 channels)
1.544 Mbps Symmetric Enterprise, Expansion,
Internet Service
1.544 Mbps Symmetric Pair Gain (12 channels),
Internet Access, T1/E1
replacement
1.544 Mbps Symmetric Same as HDSL except pair
gain is 24 channels
up to 1.5 Mbps Downstream Internet Access, Digital
up to 512 kbps Upstream Video
up to 10 Mbps Downstream Internet Access, Digital
up to 1 Mbps Upstream Video
up to 22 Mbps Downstream Internet Access, Digital
up to 3 Mbps Upstream Video, Broadcast Video
up to 13 Mbps Symmetric
Courtesy of Shawn McCaslin (Cicada Semiconductor, Austin, TX)
37
Introduction
Discrete Multitone DSL Standards
• Discrete multitone (DMT) modulation uses multiple carriers
• ADSL – Asymmetric DSL (G.DMT)
– Asymmetric: 8 Mbps downstream and 1 Mbps upstream
– Data band: 25 kHz – 1.1 MHz
– Maximum data rates possible in standard (ideal case)
• Echo cancelled: 14.94 Mbps downstream, 1.56 Mbps upstream
• Frequency division multiplexing: 13.38 Mbps downstream, 1.56 Mbps up
– Widespread deployment in US, Canada, Western Europe, Hong Kong
• Central office providers only installing frequency-division ADSL
• ADSL modems have about 1/3 of market, and cable modems have 2/3
• VDSL – Very High Rate DSL
– Asymmetric: either 22/3 or 13/3 Mbps downstream/upstream
– Symmetric: 13, 9, or 6 Mbps each direction
– Data band: 1 – 12 MHz
– DMT and single carrier modulation supported
– DMT VDSL essentially higher speed version of G.DMT ADSL
38
Introduction
A Digital Communications System
Message
Source
Noise
Modulator
Transmitter
•
•
•
•
Decoder
Encoder
Channel
Message
Sink
Demodulator
Receiver
Encoder maps a group of message bits to data symbols
Modulator maps these symbols to analog waveforms
Demodulator maps received waveforms back to symbols
Decoder maps the symbols back to binary message bits
39
Introduction
Intersymbol Interference (ISI)
2.1
• Ideal channel
– Impulse response is impulse
– Flat frequency response
1.7
111 1
• Non-ideal channel
– Causes ISI
– Channel memory
– Magnitude and phase
variation
1
*
1
.7
.4
.7
.1
=
Received
Channel
signal
impulse
-1
Transmitted response
signal
Threshold
at zero
• Received symbol is weighted
sum of neighboring symbols
11 1 1 1
– Weights are determined by channel
impulse response
Detected
signal
40
Introduction
Combat ISI with Equalization
• Equalization because channel response is not flat
• Zero-forcing equalizer
– Inverts channel
– Flattens freq. response
– Amplifies noise
• MMSE equalizer
– Optimizes trade-off
between noise
amplification and ISI
Zero-forcing
equalizer
frequency
response
MMSE
equalizer
frequency
response
Channel
frequency
response
• Decision-feedback
equalizer
– Increases complexity
– Propagates error
41
Introduction
Cyclic Prefix
Repeated
symbol
cyclic
prefix
*
to be
removed
=
equal
42
Multicarrier Modulation
Open Issues for Multicarrier Modulation
• Advantages
– Efficient use of bandwidth without full channel equalization
– Robust against impulsive noise and narrowband interference
– Dynamic rate adaptation
• Disadvantages
– Transmitter: High signal peak-to-average power ratio
– Receiver: Sensitive to frequency and phase offset in carriers
• Open issues
–
–
–
–
–
Pulse shapes of subchannels (orthogonal, efficient realization)
Channel equalizer design (increase bit rate, reduce complexity)
Synchronization (timing recovery, symbol synchronization)
Bit loading (allocation of bits in each subchannel)
Echo cancellation
43
Conventional Equalizer
TEQ Algorithm
• ADSL standards
– Set aside 1024 frames (~.25s) for TEQ estimation
– Reserved ~16,000 frames for channel and noise estimation for the
purpose of SNR calculation
• TEQ is estimated before the SNR calculations
• Noise power and channel impulse response can be
estimated before time slot reserved for TEQ if the TEQ
algorithm needs that information
44
Conventional Equalizer
Single-FIR Time-Domain Equalizer Design Methods
• All methods below perform optimization at TEQ output
• Minimizing the mean squared error
– Minimize mean squared error (MMSE) method [Chow & Cioffi, 1992]
– Geometric SNR method [Al-Dhahir & Cioffi, 1996]
• Minimizing energy outside of shortened (equalized)
channel impulse response
– Maximum Shortening SNR method [Melsa, Younce & Rohrs, 1996]
– Divide-and-conquer methods [Lu, Evans, Clark, 2000]
– Minimum ISI method [Arslan, Evans & Kiaei, 2000]
• Maximizing bit rate [Arslan, Evans & Kiaei, 2000]
• Implementation
– Geometric SNR is difficult to automate (requires human intervention)
– Maximum bit rate method needs nonlinear optimization solver
– Other methods implemented on fixed-point digital signal processors
45
Conventional Equalizer
Minimum Mean Squared Error (MMSE) TEQ
nk
xk
yk
h
rk
w
+
b
z-D
ek

w  w0 w1  wLw -1
]
b  b0 b1  bn ] T
- +

bˆ  0D | bT | 0 Lh -D-n -1
bk-D
T
]
T
MSE  {ek2 }  bˆ T R xxbˆ - 2bˆ T R xy w  wT R yy w
minimumMSE is achievedonlyif bT Rxy  wT Ryy

]
MSE  bˆ T R xx - R xy R -yy1 R yx bˆ  bˆ T R x|y bˆ
T
Define RD  OT Rx|yO then MSE  b RΔb
O selects the proper part out of Rx|y corresponding to the delay D
46
Conventional Equalizer
Near-optimal Minimum-ISI (Min-ISI) TEQ Design
• Generalizes MSSNR method by frequency weighting ISI
2
– ISI power in ith subchannel is ISI i  S x ,i q iH DHw
– Minimize ISI power as a frequency weighted sum of subchannel ISI
T
ISI

K
q
DHw

w
Xw
 i  i
H
i
i
2
i
– Constrain signal path gain to one to prevent all-zero solution
| h signal |2 | GHw|2  wT Yw  1
– Solution is a generalized eigenvector of X and Y
• Possible weightings
– Amplify ISI objective function in subchannels with low
noise power (high SNR) to put ISI in low SNR bins:
Ki 
S x ,i
S n ,i
Ki  S x,i
– Set weighting to be constant in all subchannels (MSSNR): Ki  1
– Set weighting equal to input power spectrum:
• Performance virtually equal to MBR (optimal) method
47
Conventional Equalizer
Efficient Implementations of Min-ISI Method
• Generalized eigenvalue problem can solved with
generalized power iteration:
Xw k 1  Ywk
• Recursively calculate diagonal elements of X and Y from
first column [Wu, Arslan, Evans, 2000] Method
Bit Rate MACs
Original
99.6%
132,896
Recursive
99.5%
44,432
Row-rotation
99.5%
25,872
No-weighting
97.8%
10,064
48
Conventional Equalizer
Motivation for Divide-and-Conquer Methods
• Fast methods for implementing Maximum SSNR method
• Maximum SSNR Method
– For each D, maximum SSNR method requires
• Multiplications: ( L  7 ) L  5 L2  25 L3
h
w
w
w
6
2
3
• Additions:
5
3
25
( Lh - ) Lw - L2w  L3w
6
2
3
• Divisions:
L2w
– Exhaustive search for the optimal delay D
0  D  Lh  Lw -n - 2  0  D  499
• Divide Lw TEQ taps into (Lw - 1) two-tap filters in cascade
– Design first two-tap filter then second and so forth (greedy approach)
• Develop heuristic to estimate the optimal delay
49
Conventional Equalizer
Divide-and-Conquer Approach
• The ith two-tap filter is initialized as either
– Unit tap constraint (UTC)
1
wi   
 gi 
– Unit norm constraint (UNC)
 sin  i 
wi  

cos

i

• Calculate best gi or i by using a greedy approach either by
– Minimizing
1
(Divide-and-conquer TEQ minimization)
SSNR
– Minimizing energy in hwall (Divide-and conquer TEQ cancellation)
• Convolve two-tap filters to obtain TEQ
50
Conventional Equalizer
Divide-and-Conquer TEQ Minimization (UTC)
• At ith iteration, minimize Ji over gi
 a1,i
1 gi ] 
T
w Awi
 a 2 ,i
J i  iT

w i Bwi
 b1,i
1 gi ] 
b2,i
a 2 ,i   1 
a3,i   g i  a1,i  2a2,i g i  a3,i g i2

b2,i   1  b1,i  2b2,i g i  b3,i g i2
b3,i   g i 
• Closed-form solution
g i 1, 2 ) 
- a3,i b1,i - a1,i b3,i )
2a3,i b2,i - a2,i b3,i )

D
2a3,i b2,i - a2,i b3,i )
D  a3,i b1,i - a1,i b3,i ) - 4a3,i b2,i - a2,i b3,i )a2,i b1,i - a1,i b2,i )
2
51
Conventional Equalizer
Divide-and-Conquer TEQ Minimization (UNC)
• At ith iteration, minimize Ji over i
 a1,i
sin  i 1 i ]) 
T
a 2 ,i
w i Awi

Ji  T

w i Bwi
 b1,i
sin  i 1 i ]) 
b2,i
 a1,i
1 i ] 
a 2 ,i


 b1,i
1 i ] 
b2,i
• where
a 2 ,i  
 1 
 sin  i   


a3,i  
i  
b2,i  
 1 


sin


i  

b3,i  
i  
a 2 ,i   1 
a3,i  i 
b2,i   1 
b3,i  i 
Calculate i in
the same way
as gi for UTC
version of this
method
1
 sin  i 


1
wi  
 sin  i 
 sin  i  


cos i 
cos i sin  i 
i 
52
Conventional Equalizer
Divide-and-Conquer TEQ Cancellation (UTC)
• At ith iteration, minimize Ji over gi

)
2
~
~
~T ~
J i  h wallh wall   hi -1 k )  gi hi -1 k - 1) ,

kS
S  1, 2,, D, D  n  2,, Lh~
i -1

• Closed-form solution for the ith two-tap FIR filter
~
~
 hi -1 (k - 1)hi -1 (k )
gi  - kS
~2
h
 i -1 (k - 1)
kS
53
Conventional Equalizer
Divide-and-Conquer TEQ Cancellation (UNC)
• At ith iteration, minimize Ji over I

)
2
~
~
~T ~
J i  h wallh wall   hi -1 k )sin  i  hi -1 k - 1) cos i ,

kS
S  1, 2,, D, D  n  2,, Lh~
i -1

• Closed-form solution
2
2




a
a
 , cos   0.51 

sin  i   0.51  2
i
2
2
2


a  4b 
a  4b 


~2
~2
~
~
a   hi -1 k ) - hi -1 k - 1) , b   hi -1 (k - 1) hi -1 (k )
kS

)
kS
54
Conventional Equalizer
Computational Complexity
• Computational complexity for each candidate D
Method



Memory
(words)
Maximum
SSNR
DC-TEQ-minimization (UTC)
DC-TEQ-cancellation (UNC)
DC-TEQ-cancellation (UTC)
120379 118552
441
1899
53240
52980
60
563
42280
42160
20
555
41000
40880
20
554
G.DMT
ADSL
Lh = 512
n = 32
Lw = 21
• Divide-and-conquer methods vs. maximum SSNR method
– Reduces multiplications, additions, divisions, and memory
– No matrix calculations (saves on memory accesses)
– Avoids matrix inversion, and eigenvalue and Cholesky decompositions
55
Conventional Equalizer
Heuristic Search for the Optimal Delay
• Estimate optimal delay D before computing TEQ taps
D ratio  arg max
D
energyinside a window of originalh
energyoutside a window of originalh
• Total computational cost
– Multiplications:
Lh
– Additions:
3Lh - 3
– Divisions:
Lh
• Performance of heuristic vs. exhaustive search
– Reduce computational complexity by factor of 500
– 2% loss in SSNR for TEQ with four taps or more
– 8% loss in SSNR for two-tap TEQ
56
Conventional Equalizer
Comparison of Earlier Methods
Method
MMSE
MSSNR
Geometric
Advantages

Maximize bit rate

Minimize ISI
Bit Rate
Low-medium
High
Low-medium
Disadvantages

Nonlinear optimization
Computational complexity
Artificial constraints
Low

Ad-hoc parameters
Lowpass frequency response
Unrealistic assumptions

Medium
High




57
Conventional Equalizer
MBR TEQ vs. Geometric TEQ
Method
MBR
Geometric
Advantages
Maximize channel capacity
Minimize ISI
Bit rate



optimal
Low-medium
Disadvantages
Low-pass frequency response
Computationally complex
Artificial constraints
Ad-hoc parameters
Nonlinear optimization
Unrealistic assumptions








58
Conventional Equalizer
Min-ISI TEQ vs. MSSNR TEQ
Method
Min-ISI
MSSNR
Advantages
Maximize channel capacity
Minimize ISI
Frequency domain weighting
Bit rate


high
Disadvantages
Computationally complex
very high

high
high
• Min-ISI weights ISI power with the SNR
– Residual ISI power should be placed in high noise frequency bands
1
1
 0.09
 0.1 SNR 50 
signal power
10  1
10
SNR i 
noise power  ISI power
1
1
SNR 2 
 10 SNR 2 
 0.9
0.1
0 .1  1
59
SNR 50 
Conventional Equalizer
Bit Rate vs. Cyclic Prefix (CP) Size
• Matched filter
bound decreases
because CP has no
new information
• Min-ISI and MBR
achieve bound with
16-sample CP
• Other design
methods are erratic
• MGSNR better for
15-28 sample CPs
TEQ taps (Lw)
FFT size (N)
coding gain
margin
17
512
4.2 dB
6 dB
input power
23 dBm
noise power
-140 dBm/Hz
crosstalk noise 8 ADSL disturbers
60
Conventional Equalizer
Simulation Results
• Min-ISI, MBR, and
MSSNR achieve
matched filter
bound owith CP of
27 samples
• Min-ISI with 13sample CP beats
MMSE with 32sample CP
• MMSE is worst
TEQ taps (Lw)
FFT size (N)
coding gain
margin
3
512
4.2 dB
6 dB
input power
23 dBm
noise power
-140 dBm/Hz
crosstalk noise 8 ADSL disturbers
61
Per-Tone Equalizer
Bit Allocation Comparison
• AWG 26 Loop:
12000 ft + AWGN
Equalizer
Per Tone
Bit Rate
5.7134 Mbps
MBR
5.4666 Mbps
MSSNR
5.2903 Mbps
Min ISI
5.2586 Mbps
ARMA
4.5479 Mbps
MMSE
4.4052 Mbps
• Simulation
– NEXT from 24 DSL disturbers
– 32-tap equalizers: least squares training used for per-tone equalizer
62
Per-Tone Equalizer
Subchannel SNR
63
Per-Tone Equalizer
Frequency-Domain Per-Tone Equalizer
• Rearrange computation of FFT coefficient for tone i
[Van Acker, Leus, Moonen, van de Wiel, Pollet, 2001]
Zi = Di rowi(QN ) Y w = rowi(QN Y) ( w Di )
QN Y produces N  Lw complex-valued matrix produced by sliding FFT
Zi is inner product of ith row of QN Y (complex) and w Di (complex)
TEQ has been moved into FEQ to create multi-tap FEQ as linear combiner
• After FFT demodulation, each tone equalized separately
Equalize each carrier independently of other carriers (N/2 carriers)
Maximize bit rate at output of FEQ by maximizing subchannel SNR
• Sliding FFT to produce N  Lw matrix product QN Y
Receive one ADSL frame (symbol + cyclic prefix) of N + n samples
Take FFT of first N samples to form the first column
Advance one sample
Take FFT of N samples to form the second column, etc.
64
Per-Tone Equalizer
Per-Tone Equalizer: Implementation Complexity
Real MACs
Words
TEQ
Lw fs
2 Lw
Sampling rate
FFT
2 N log2(N) fsym
4N
FEQ
4 Nu fsym
4 Nu
Conventional
Per Tone
FFT
Real MACs
Words
2 N log2(N) fsym
4N+2n
Sliding FFT 2 (Lw – 1) N fsym
Combiner
Modified.
Per Tone
FFT
4 Lw Nu fsym
Real MACs
N
Adds
Value
fs
2.208 MHz
Symbol rate
fsym
4 kHz
TEQ length
Lw
3-32
Symbol length
N
512
Subchannels used
Nu
256
Cyclic prefix
length
n
32
Words
4N
(Lw – 1) fsym
2 (Lw + 1) Nu fsym
Symbol
2 (Lw + 1) Nu
2 N log2(N) fsym
Differencing
Combiner
Parameter
Lw – 1
2 Lw Nu
65
Dual-Path Equalizer
Dual-Path TEQ (Simulated Channel)
Optimized for subchannel 2-250
Optimized for subchannel 2-30
66
Motorola CopperGold ADSL Chip
•
•
•
•
Announced in March 1998
5 million transistors, 144 pins, clocked at 55 MHz
1.5 W power consumption
DMT processor consists
– Motorola MC56300 DSP core
– Several application specific ICs
• 512-point FFT
• 17-tap FIR filter for time-domain channel equalization based on MMSE
method (20 bits precision per tap)
• DSP core and memory occupies about 1/3 of chip area
67