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

2GermanAerospace Center

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


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

…


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


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


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,

�


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


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=

�


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

⎧⎪⎪⎪⎪= >⎨⎪⎪ ≤⎪⎪⎩

��

�


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−

=≠

= −∑�


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


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%


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



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



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!

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

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