Iterative Channel Estimation for MIMO MC-CDMA€¦ · MC-CDMA and Iterative Channel Estimation...
Transcript of Iterative Channel Estimation for MIMO MC-CDMA€¦ · MC-CDMA and Iterative Channel Estimation...
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GermanAerospace Center
Iterative Channel Estimation for MIMO MC-CDMA
Stephan Sand, Ronald Raulefs, and Armin Dammann
German Aerospace Center (DLR)
2nd COST 289 Workshop, Antalya, Turkey, 6th July
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Outline
System model
Frame structure
Pilot aided channel estimation (PACE)
MC-CDMA and iterative channel estimation (ICE)
Extension of ICE to MIMO
Simulation results
Conclusions & outlook
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System Model: Downlink MC-CDMA Transmitter
π MODSource π
S/PCODMultiple Access Scheme
1
…
M
OFDM+ TG
PilotMUX
PilotSymb
OFDM+ TG
PilotMUX
PilotSymb
ST-CO
D
… …
1
NTX
…
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System Model: Receiver with Iterative Channel Estimation (ICE)
CSI
DMODπ-1SinkP/S
CSI
MIMO ChannelEstimator
DECOD
π MODCOD
DET
1
M
π
S/P
1
M
…π-1π-1
Multiple Access Scheme
ST-DEC
OD
ST-CO
D
NTX
PilotDEMUX
MIMO-TG
IOFDM
-TGIOFDM
… …
1
NRX
…
CSI
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Frame Structure
Burst transmission
Rectangular grid
Pilot distance in
frequency direction: Nl
Pilot distance between
OFDM symbols: Nk
2 × oversampling
channel transfer function
1
1
Nc
Ns
Nk
Nl
data symbolpilot symbol TX antenna 1
frequency
time
pilot symbol TX antenna 2
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Pilot Aided Channel Estimation (PACE)
PACE:Pilot symbols yield initial estimates for the channel transfer function at pilot symbol positions, i.e., the least-squares (LS) estimate:
where P denotes the set of pilot symbols.
Filtering pilot symbols yields final estimates for the complete channel transfer function:
where ωn’,k’,n,k is the shift-variant 2-D impulse response of the filter. Tn,k is the set of initial estimates that are actually used for filtering.
{ } ,
, ', ', , ', ' ,', '
ˆ , , 1, , , 1, , ,n l
n l n l n l n l n l c sn l
H H n N l Nω∈
= ∈ = =∑T
T P � �
{ }', ' ', '', ' ', '
', ' ', '
, ', 'n l n ln l n l
n l n l
R ZH H n l
S S= = + ∀ ∈P,
�
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Pilot Aided Channel Estimation (PACE)
Filter design:
knowledge of the Doppler and time delay power spectral densities
(PSDs)
⇒ optimal 2D FIR Wiener filter
separable Doppler and time delay PSDs
⇒ two cascaded 1-D FIR Wiener filters perform similar than 2D FIR
Wiener filter
in practice, Doppler and time delay PSDs are not perfectly known
⇒ robust design assuming rectangular Doppler and time delay PSDs
⇒ Set of filter coefficients can be pre-computed and stored in the receiver
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MC-CDMA and Iterative Channel Estimation (ICE)
MC-CDMA:Walsh-Hadamard spreading code: zero-valued subcarriers can occur during transmission
Example : Walsh-Hadamard spreading code, L=4, BPSK modulation, possible transmission points for one subcarrier (constellation) after spreading:
Zero-valued subcarriers occur with 37.5% probabilityHow to use estimated data in the LS-Estimate if zero-valued subcarrier occurs?
⎟⎠⎞
⎜⎝⎛04
-3 -2 -1 1 2 3 4-4
⎟⎠⎞
⎜⎝⎛04⎟
⎠⎞
⎜⎝⎛14
⎟⎠⎞
⎜⎝⎛14
⎟⎠⎞
⎜⎝⎛24
', '', '
', 'ˆ
n ln l
n l
RH
S=
�
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MC-CDMA and Iterative Channel Estimation (ICE)
Modified LS Method:
If the reconstructed subcarrier is zero or below a certain threshold, set the LS channel estimates to zeroThe filtered channel estimates are independent of subcarriers that would only cause noise enhancement and degrade the channel estimates.
,', '
', '', '
( ), ,', '( ), , ( ),
', ' ', '( ),', '
( ),', '
if pilot symbol
if estimated data symbol
0 if estimated data symbol
m pn l m
n lmn l
i m pn li m p i m
n l n l thi mn l
i mn l th
RS
S
RH S
S
S
ρ
ρ
⎧⎪⎪⎪⎪= >⎨⎪⎪ ≤⎪⎪⎩
���
�
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Extension of ICE to MIMO
Data symbols from different transmit antennas superimpose non-orthogonal:
Interference reduced receive signal:
Initially estimate the CSI between transmit antenna m and receive antenna p by first canceling the current estimates of the received signals from the other transmit antennas
,, , , ,
1
TXNp r p r pn l n l n l n l
rR H S Z
=
= +∑
( ), , ( 1), , ( ),, , , ,
1
ˆTXN
i m p p i r p i rn l n l n l n l
rr m
R R H S−
=≠
= −∑�
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Simulation Results: ScenarioBandwidth 101.5 MHzSubcarriers 768FFT length 1024Sampling duration Tspl 7.4 ns
Detection MMSE, PIC
Max Doppler channel estimator 0.01 ΔfMax delay channel estimator TGI =226 Tspl
Guard interval TGI 226 Tspl
Subcarrier spacing Δf 131.836 kHzOFDM symbols / Frame 64Modulation 4-QAMCoding Conv. code,
R=1/2, (133,171)
Pilot spacing frequency 3Pilot spacing time 9
fD,max 0.01Δf ≈ 1500 Hzτmax 176 Tspl
Np 12
ΔP 1dB
Δτ 16 Tspl
Δτ: tap spacing
time
ΔP: decay between adjacent taps
…Np: number of non-zero taps
Channel model
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Simulation Results: fD=1500Hz (300km/h @ 5GHz)
Default Values:Spreading Length: L=8Users: K=8Rob. Wiener filter: 15x4 filter coefficients both for PACE, ICEICE: Probability of subcarrier ignored:ρth=0.8: 50%ρth=0.5: 30%ρth<0.5: 8%
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Default Values:Spreading Length: L=8Users: K=8Rob. Wiener filter: 15x4 filter coefficients both for PACE, ICEICE: mod. LS-CE: ρth=0, hard feedback, 1 Iteration, all users detected PIC: soft feedback, 1 Iteration
Simulation Results: fD=1500Hz (300km/h @ 5GHz)
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Default Values:Spreading Length: L=8Users: K=8STBC: AlamoutiPilot symbol power:SISO 1Alamouti 1/2SNR loss (pilot overhead):SISO 0.18 dBAlamouti 0.38 dB
Simulation Results: fD=1500Hz (300km/h @ 5GHz)
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Default Values:Spreading Length: L=8Users: K=8STBC: AlamoutiPilot symbol power:SISO 1Alamouti 1/2SNR loss (pilot overhead):SISO 0.18 dBAlamouti 0.38 dB
Simulation Results: fD=1500Hz (300km/h @ 5GHz)
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Conclusions & Outlook
ICE for MIMO MC-CDMA with Walsh-Hadamard spreading: zero-valued subcarriers and non-orthogonal data symbols
Modified LS channel estimation method and interference reduced received signal
Simulation results indicate:Small threshold for modified LSRobust ICE improves robust PACEPerformance gains with robust ICE even for scenarios optimized for robust PACEMIMO gain reduced by pilot overhead and energy constraint
Outlook:Soft feedback in ICE to improve convergenceReduce pilot overhead for MIMO MC-CDMA
Thank you!