combined equalization and coding using precoding* ece 492 – term project betül arda selçuk köse...
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Combined Equalization and Coding Using Precoding*
ECE 492 – Term Project
Betül ArdaSelçuk Köse
Department of Electrical and Computer Engineering
University of Rochester
*“Combined equalization and coding using precoding” Forney, G.D., Jr.; Eyuboglu, M.V.
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Agenda
Introduction Capacity of Gaussian Channels Adaptive Modulation Brief History of Equalization Equalization Techniques Tomlinson-Harashima Precoding Combined Precoding and Coded Modulation Trellis Precoding Price’s Result & Attaining Capacity Conclusion
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Introduction What is the paper about?
Recently developed techniques to achieve capacity objectives
Tomlinson – Harashima precoding:Tomlinson – Harashima precoding: Precoding technique for uncoded modulation
C of bandlimited, high-SNR Gaussian channel C of ideal Gaussian channel
Precoding + coded modulation + shapingPrecoding + coded modulation + shaping Achieves nearly channel capacity of
bandlimited, high-SNR Gaussian channel Is precoding approachprecoding approach a practical route to
capacity on high-SNR+bandlimitedhigh-SNR+bandlimited channel? Decision feedback equalization structure
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Agenda IntroductionIntroduction Capacity of Gaussian Channels Adaptive ModulationAdaptive Modulation Brief History of EqualizationBrief History of Equalization Equalization TechniquesEqualization Techniques Tomlinson-Harashima PrecodingTomlinson-Harashima Precoding Combined Precoding and Coded Combined Precoding and Coded
ModulationModulation Trellis PrecodingTrellis Precoding Price’s Result & Attaining CapacityPrice’s Result & Attaining Capacity ConclusionConclusion
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with power constraint
C of Ideal Gaussian channels
Gaussian channel modelIdeal bandlimited Gaussian channel
Ex: Telephone channel SNR~28 to 36 dB & BW~2400 to 3200 Hz not ideal but C can be estimated by 9 to 12 bits/Hz or 20,000 b/s to 30,000 b/s
SNR=Sx/Sn=P/N0W
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of telephone channels ~ constant at the center drops at edges important to optimize B
If B is nearly optimal typically a flat transmit spectrum is almost as good as water-pouring spectrum
Capacity achieving band:
Determination of optimum water-pouring spectrum
C of Non-Ideal Gaussian channels
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Agenda IntroductionIntroduction Capacity of Gaussian ChannelsCapacity of Gaussian Channels Adaptive Modulation Brief History of EqualizationBrief History of Equalization Equalization TechniquesEqualization Techniques Tomlinson-Harashima PrecodingTomlinson-Harashima Precoding Combined Precoding and Coded Combined Precoding and Coded
ModulationModulation Trellis PrecodingTrellis Precoding Price’s Result & Attaining CapacityPrice’s Result & Attaining Capacity ConclusionConclusion
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Adaptive BW - Adaptive Rate Modulation
Coded modulation scheme with rate R bits/symbol (b/s/Hz), as close as possible to C
This scheme is suitable for point-to-point two-way applications: telephone-line modems To approach capacity: Tx needs to know the channel Not possible for one-way, broadcast, rapidly time-
varying channels unless ch. char.s are known a priori
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Adaptive BW - Adaptive Rate Modulation
Inherit delay due to long 1/Δf rules out some modem applications
Multicarrier modulation with few carriers and short 1/Δf ISI arises and must be equalized
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Agenda IntroductionIntroduction Capacity of Gaussian ChannelsCapacity of Gaussian Channels Adaptive ModulationAdaptive Modulation Brief History of Equalization Equalization TechniquesEqualization Techniques Tomlinson-Harashima PrecodingTomlinson-Harashima Precoding Combined Precoding and Coded Combined Precoding and Coded
ModulationModulation Trellis PrecodingTrellis Precoding Price’s Result & Attaining CapacityPrice’s Result & Attaining Capacity ConclusionConclusion
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History of Equalization
1967: Milgo4400 4800b/s W=1600Hz Manually adjustable equalizer knob on the front panel to zero a null meter
1960s: time of considerable research on adaptive modulation Focused on adaptation algorithms that did not require multiplications
1971: Codec9600C 9600b/s (V.29) Automatic adaptive digital LE for W=2400Hz and 16-QAM
1970s: modems more smaller, cheaper, reliable, versatile, but not faster Fractionally spaced equalizers:
fast-training algorithms for multipoint and half-duplex applications Even the first 14.4kb/s modem used uncoded modulation, fixed BW, LE 1983: Trellis coded modulation 9600b/s over dial lines 1985: adaptive rate-adaptive BW modem of the multicarrier type 1990: Combined equal., multidimensional TCM and shaping using trellis precoding
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Modem MilestonesYear Name Max.Rate Sym Modulation Eff.
1962 Bell 201 2.4 1.2 4PSK 2
1967 Milgo4400 4.8 1.6 8PSK 3
1971 Codex 9600C 9.6 2.4 16-QAM 4
1980 Paradyne 14.4 2.4 64-QAM 6
1984 Codex 2600 16.8 2.4 Trellis 256-QAM 7
1985 Codex 2680 19.2 2.74 8-D(state) Trellis 160-QAM
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1984 V.32 9.6 2.4 2D TC 4
1991 V.32 bis 14.4 2.4 2D TC 128-QAM 6
1994 V.34 28.8 2.4-3.4 4D TC 960-QAM ~9
1998 V.90 56 same same same
TCM has made possible the development of very high speed modems.
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Agenda IntroductionIntroduction Capacity of Gaussian ChannelsCapacity of Gaussian Channels Adaptive ModulationAdaptive Modulation Brief History of EqualizationBrief History of Equalization Equalization Techniques Tomlinson-Harashima PrecodingTomlinson-Harashima Precoding Combined Precoding and Coded Combined Precoding and Coded
ModulationModulation Trellis PrecodingTrellis Precoding Price’s Result & Attaining CapacityPrice’s Result & Attaining Capacity ConclusionConclusion
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Classical Equalization Techniques
Channel is ideal iff:
D transform
&
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Equalization Tech. – ZF-LE
LE can be satisfactory in a QAM modem if the channel has no nulls or near-nulls If H(θ) ~ const. over {-π < θ ≤ π} noise enhancement not very serious |H(θ)|2 has a near-null noise enhancement becomes very large |H(θ)|2 has a null h(D) not invertible, ZF-LE not well-defined
To approach capacity, transmission band must be expanded to entire usable BW of the channel
Leads to severe attenuation at band edges LE no longer suffices
Zero-forcing linear equalization
r(D) is filtered by 1/h(D) to produce an equalized
response
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Equalization Tech. – ZF-DFE
ISI removed and noise is white
||1/h||2 ≥1 SNRZF-DFE ≥ SNRZF-LE
& iff h(D)=1 SNRZF-DFE=SNRZF-LE
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Equalization Tech. – MLSE Optimum equalization structure if ISI exists
xk drawn from M-pt signal set, h(D) has length v Channel can be modeled as Mv-state machine
Mv-state Viterbi algorithm can be used to implement MLSE
M and/or v is too large complex to implement
If no severe SNR SNR of matched filter bound Matched filter bound: bound on the best SNR
achievable with h(D) If SNR is severe
MLSE fails to achieve this SNR, performance analysis become difficult
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Agenda IntroductionIntroduction Capacity of Gaussian ChannelsCapacity of Gaussian Channels Adaptive ModulationAdaptive Modulation Brief History of EqualizationBrief History of Equalization Equalization TechniquesEqualization Techniques Tomlinson-Harashima Precoding Combined Precoding and Coded Combined Precoding and Coded
ModulationModulation Trellis PrecodingTrellis Precoding Price’s Result & Attaining CapacityPrice’s Result & Attaining Capacity ConclusionConclusion
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Tomlinson-Harashima Precoding
Precoding works even if h(D) is not invertible i.e. ||1/h||2 is infinite.
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Tomlinson-Harashima Precoding
Tx knows h(D) y(D) = d(D)+2Mz(D) is chosen
x(D) = y(D)/h(D) is in (-M,M] Large M, x(D) PAM seq.
Values continuous in (-M,M] Rx symbol-by-symbol
Ordinary PAM on ideal channel Pe same as with ideal ZF-DFE
Same as on an ideal ch. with SNRZF-DFE=Sx/Sn
Key PointsKey Points
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Tomlinson-Harashima Precoding
At first, TH appeared to be an attractive alternative to ZF-DFE
Its performance is no better than ZF-DFE under the ideal ZF-DFE assumption For uncoded systems ideal ZF-DFE
assumption works well Therefore, DFE is preferred to TH
DFE does not require CSI at tx
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Agenda IntroductionIntroduction Capacity of Gaussian ChannelsCapacity of Gaussian Channels Adaptive ModulationAdaptive Modulation Brief History of EqualizationBrief History of Equalization Equalization TechniquesEqualization Techniques Tomlinson-Harashima PrecodingTomlinson-Harashima Precoding Combined Precoding and Coded
Modulation Using an Interleaver Combining Trellis Encoder and Channel Combined Precoding and Coded Modulation
Trellis PrecodingTrellis Precoding Price’s Result & Attaining CapacityPrice’s Result & Attaining Capacity ConclusionConclusion
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Interleaver
M.V. Eyüboğlu, “Detection of coded modulation signals on linear severely distorted channels using decision-feedback noise prediction with interleaving,” IEEE Trans. Commun., Vol. 36, No. 4, pp.401-09, April 1988.
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Interleaver (Cont’d)
Transmitted message aaaabbbbccccddddeeeeffffggg
g
Received message aaaabbbbccc____deeeeffffgggg
Transmitted message aaaabbbbccccddddeeeeffffgggg
Interleaved abcdefgabcdefgabcdefgabcdefg
Received message abcdefgabcd____bcdefgabcdefg
De-interleaved aa_abbbbccccdddde_eef_ffg_gg
Without interleaver
With interleaver
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Combining Trellis Encoder and Channel
Finite state machine representation of trellis encoder and channel
MLSE Algorithm
Reduced state-sequence estimation algorithms are used to make the computation faster.
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Combined Precoding and Coded Modulation
y(D)=d(D)+2Mz(D) where M is a multiple of 4.
r(D)=y(D)+n(D)
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Agenda IntroductionIntroduction Capacity of Gaussian ChannelsCapacity of Gaussian Channels Adaptive ModulationAdaptive Modulation Brief History of EqualizationBrief History of Equalization Equalization TechniquesEqualization Techniques Tomlinson-Harashima PrecodingTomlinson-Harashima Precoding Combined Precoding and Coded Combined Precoding and Coded
ModulationModulation Trellis Precoding Price’s Result & Attaining CapacityPrice’s Result & Attaining Capacity ConclusionConclusion
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Trellis Precoding = Shaping+Precoding+Coding
(N ) then shaping gain1.53dB(1.53dB is the difference between average energies of
Gaussian and uniform distribution) Shaping on regions Trellis Shaping Shell Mapping
Distribution approaches truncated Gaussian
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Trellis Precoding = Coding+Precoding+Shaping Coding gains of 3 to 6
dB for 4 to 512 states.
Binary codes Sequential decoding of
convolution codes Turbo codes Low-density parity
check codes. Non-binary codes
Sequential decoding of trellis codes
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Trellis Precoding = Precoding+Coding+Shaping
“DFE in transmitter”
It combines nicely with coded modulation with “no glue”
It has Asymptotically optimal performance
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Agenda IntroductionIntroduction Capacity of Gaussian ChannelsCapacity of Gaussian Channels Adaptive ModulationAdaptive Modulation Brief History of EqualizationBrief History of Equalization Equalization TechniquesEqualization Techniques Tomlinson-Harashima PrecodingTomlinson-Harashima Precoding Combined Precoding and Coded Combined Precoding and Coded
ModulationModulation Trellis PrecodingTrellis Precoding Price’s Result & Attaining Capacity ConclusionConclusion
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Price’s Result
“As SNR on any linear Gaussian Channel the gap between capacity and QAM performance with ideal ZF-DFE is independent of channel noise and spectra.”
Improved result can be achieved using MSSE type equalization
Ideal MSSE-optimized tail canceling equalization +Capacity-approaching ideal AWGN channel coding=Approach to the capacity of any linear Gaussian channel
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Attaining Capacity
•Coding: can achieve 6dB, max 7.5 dB•Shaping: can achieve 1 dB, max 1.53 dB•Total: can achieve 7 dB, max 9 dB
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Agenda IntroductionIntroduction Capacity of Gaussian ChannelsCapacity of Gaussian Channels Adaptive ModulationAdaptive Modulation Brief History of EqualizationBrief History of Equalization Equalization TechniquesEqualization Techniques Tomlinson-Harashima PrecodingTomlinson-Harashima Precoding Combined Precoding and Coded Combined Precoding and Coded
ModulationModulation Trellis PrecodingTrellis Precoding Price’s Result & Attaining CapacityPrice’s Result & Attaining Capacity Conclusion
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Conclusion
We can approach channel capacity by combining known codes for coding gain with simple shaping techniques for shaping gain.
Can approach channel capacity for ideal and non-ideal channels.
In principle, on any band-limited linear Gaussian channel one can approach capacity as closely as desired.*
* R. deBuda, “some optimal codes have structure”, IEEE Journal of Selected Areas of Communication, Vol. SAC-7, 893-899, August 1989.
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References
D.Forney and V.Eyuboglu, “Combined Equalization and Coding Using Precoding,” IEEE Communication Magazine, Vol. 29, pp.24-34, December 1991
R. Price, “Nonlinearly Feedback Equalized PAM versus Capacity for Noisy Filter Channels,” Proceedings of ICC '72, June 1972
M. V. Eyuboglu and G. D.Forney, Jr., “Trellis Precoding: Combined Coding, Precoding and Shaping for Intersymbol Interference Channels,” IEEE Transactions on Information Theory, Vol. 38, pp. 301-314, March 1992.
R. deBuda, “Some Optimal Codes Have Structure”, IEEE Journal of Selected Areas of Communication, Vol. SAC-7, 893-899, August 1989.
M.V. Eyüboğlu, “Detection of Coded Modulation Signals on Linear Severely Distorted Channels Using Decision-Feedback Noise Prediction with Interleaving,” IEEE Transactions on Communications, Vol. 36, No. 4, pp.401-09, April 1988.
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