MU-MIMO scheme performance evaluations using measured ... · MU-MIMO scheme performance evaluations...

MU-MIMO scheme performance evaluationsusing measured channels in specificenvironments

Christoph Mecklenbräuker

with contributions from Giulio Coluccia, Giorgio Taricco,Christian Mehlführer, and Sebastian Caban

www.ist-mascot.org

© MASCOT 2007 2

Performance evaluation principles

Performance is representated as a functional Tdepending on the receiver input distribution F

General linear MU-MIMO channel YA = HASA + IA + N

F = joint distribution of (HA , SA , IA , N)

A = random set of active links

© MASCOT 2007 3

Role of the MU-MIMO scheduler

The scheduler defines A depending in CQIfeedback

The scheduler shapes the statistics of HA and IA

Given A, the PHY shapes the statistics of SA

Evaluation of T(F ) must account for thedependencies among HA , SA , IA .

© MASCOT 2006 2

Three approaches considered

Approach 1: Measure then simulate- Measure the MIMO channels with a channel sounder- Store the MIMO channels in a database- Run a Matlab simulation based on the stored channels

Approach 2: Emulate using real frontends- Take two PCs running Matlab- Transmitter PC

- Use Matlab to format a MIMO transmission block- Transmit it via a real MIMO transmitter frontend

- Receiver PC- Receive it from a real MIMO receiver frontend- Implement the Detector/Decoder in Matlab

- Evaluate performance using a side channel

Approach 3: Real-time testbed

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Approach 1: Measure then Simul.

Advantages- Perfect repeatability of the performance evaluation- Very suitable for MU-MIMO performance evaluation- You can simulate with perfect channel state information (if

you wish so)- Many environments can be handled- Great freedom in defining ensemble averages

Disadvantages- The MIMO channel‘s behaviour must be

- simulated in Matlab (convolution), and- idealised (analog Tx/Rx front-end behaviour!)

- Channel measurement time, duration, and sampling ratesdo not match the simulated system

- Non-realtime- It is questionable whether opportunistic MU-MIMO schemes

based CSI feedback can be evaluated

© MASCOT 2006 4

Approach 2: Emulate

Advantages:- Hic et nunc- MIMO channel is used rather than simulated- Real front-end behaviour is included in performance

Disadvantages:- Performance evaluations are not perfectly repeatable- Great care must be taken when comparing two

competing MU-MIMO schemes- MU-MIMO performance evaluation is significantly

more costly than Single User case.- Non-realtime- It is questionable whether opportunistic schemes with

CSI feedback can be evaluated

© MASCOT 2006 5

Approach 3: Real-time Testbed

Advantages- Front-end effects included- Real MIMO channel behaviour- Opportunistic schemes with limited CSI feedback can

be evaluated

Disadvantages- Costly- Little flexibility- Performance evaluation not perfectly repeatable- It is difficult to emulate perfect CSI.- It is difficult to analyse a „single effect“

© MASCOT 2006 6

Example for Approach 1Measure then simulate

MEDAV‘s RUSK ATM channel sounder- 15 Tx elements (uniform circular array)- 8 Rx elements (uniform linear array)

- Measurement band: 1940-2060 MHz

- Urban, Sub-urban, Rich-scattering environments

- http://www.ftw.at/Measurements

© MASCOT 2006 7

Mobile Tx, Static Rx

Sub-urban environmentReceiver‘s view @ Weikendorf

© MASCOT 2006 9

Outdoor Channelimpulse response

© MASCOT 2006 10

Indoor rich scattering

© MASCOT 2006 11

Spatial re-sampling (1)

© MASCOT 2006 12

Spatial re-sampling (2)

© MASCOT 2006 13

Optimum receiver vs.Mismatched ML

[9] G. Taricco and E. Biglieri:Space-time decoding with imperfect channel estimation,IEEE Trans. Wireless Commun.4(4):1874-1888, July 2005

© MASCOT 2006 14

Urban environment

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Rich scattering environment

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Example Approach 2:Emulate with real front-ends

4 Tx Front-ends 4 Rx Front-ends 2.6 GHz band, 20 MHz bandwidth

3/42 Sebastian Caban

scaban@nt.tuwien.ac.at

TransmitterTransmitter

Experiment Set Up

• Roofor even higher

• Roofor even higher

5/42 Sebastian Caban

scaban@nt.tuwien.ac.at

TransmitterTransmitter

Experiment Set Up

• Roofor even higher

• Patch Antennas• 20 dBm/antenna• 2.5 GHz

• Roofor even higher

• Patch Antennas• 20 dBm/antenna• 2.5 GHz

20dBm = 0.1 watt

7/42 Sebastian Caban

scaban@nt.tuwien.ac.at

ChannelChannel

Experiment Set Up

• real channel, urban environment• real channel, urban environment

7 amTX

8/42 Sebastian Caban

scaban@nt.tuwien.ac.at

ReceiverReceiver

Experiment Set Up

• Indoor• Indoor

9/42 Sebastian Caban

scaban@nt.tuwien.ac.at

ReceiverReceiver

Experiment Set Up

• Indoor• 4 Monopoles• Indoor• 4 Monopoles

11/42 Sebastian Caban

scaban@nt.tuwien.ac.atFirst Measurement

• 13 Ec/Ior values• 2 and 4 transmit antennas• 2 CQI values• 4, 6, 8, and 10 spreading-codes active• 6552 realizations

• 13 Ec/Ior values• 2 and 4 transmit antennas• 2 CQI values• 4, 6, 8, and 10 spreading-codes active• 6552 realizations

• 4 hours net measurement time• 600 GB of data received• 4 days of work for 23 PCs

• 4 hours net measurement time• 600 GB of data received• 4 days of work for 23 PCs

12/42 Sebastian Caban

scaban@nt.tuwien.ac.atMeasured Impulse Responses

approx 160 m

TX1 → RX1TX1 → RX1 TX2 → RX1TX2 → RX1

TX2 → RX2TX2 → RX2TX1 → RX2TX1 → RX2

13/42 Sebastian Caban

scaban@nt.tuwien.ac.atMeasured Impulse Responses

36/42 Sebastian Caban

scaban@nt.tuwien.ac.atBLER Results

Parameters:• 2x2 system• Code rate of 0.7• 1000 realizations• 10 Codes• 45 equalizer taps• 15 channel taps

95% confidence intervals

Parameters:• 2x2 system• Code rate of 0.7• 1000 realizations• 10 Codes• 45 equalizer taps• 15 channel taps

95% confidence intervals

37/42 Sebastian Caban

scaban@nt.tuwien.ac.atBLER Results

Parameters:• 2x2 system• Code rate of 0.7• 6552 realizations• 10 Codes• 45 equalizer taps• 15 channel taps

95% confidence intervals

Parameters:• 2x2 system• Code rate of 0.7• 6552 realizations• 10 Codes• 45 equalizer taps• 15 channel taps

95% confidence intervals

38/42 Sebastian Caban

scaban@nt.tuwien.ac.atBLER Results

Parameters:• 2x[4,3,2] system• Code rate of 0.7• 6552 realizations• 10 Codes• 45 equalizer taps• 15 channel taps

Parameters:• 2x[4,3,2] system• Code rate of 0.7• 6552 realizations• 10 Codes• 45 equalizer taps• 15 channel taps

39/42 Sebastian Caban

scaban@nt.tuwien.ac.atBLER Results

Parameters:• [4x4, 2x2] system• Code rate of 0.7• [1788,6552]

realizations• 10 Codes• 45 equalizer taps• 15 channel taps

• equal EB

Parameters:• [4x4, 2x2] system• Code rate of 0.7• [1788,6552]

realizations• 10 Codes• 45 equalizer taps• 15 channel taps

• equal EB

Question: power normalization in measurements?Question: power normalization in measurements?

40/42 Sebastian Caban

scaban@nt.tuwien.ac.atBLER Results

Parameters:• 2x2 system• Code rate of 0.7• 6552 realizations• [4,6,8,10] Codes• 45 equalizer taps• 15 channel taps

Parameters:• 2x2 system• Code rate of 0.7• 6552 realizations• [4,6,8,10] Codes• 45 equalizer taps• 15 channel taps

41/42 Sebastian Caban

scaban@nt.tuwien.ac.atConclusions

WeWe

MobilkomMobilkomOneOne

• If the Matlab-code works,one “extended week”1 needed to measure the influence of a certain parameter

• If the Matlab-code works,one “extended week”1 needed to measure the influence of a certain parameter

1 = 7 days, 16 hours a day