iterative channel estimation for mimo mc-cdma€¦ · mc-cdma and iterative channel estimation...

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!