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

  • View
    218

  • Download
    0

Embed Size (px)

Text of MU-MIMO scheme performance evaluations using measured ... MU-MIMO scheme performance evaluations

  • MU-MIMO scheme performance evaluationsusing measured channels in specificenvironments

    Christoph Mecklenbruker

    with contributions from Giulio Coluccia, Giorgio Taricco,Christian Mehlfhrer, 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

  • MASCOT 2006 3

    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 channels 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

    MEDAVs 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 environmentReceivers 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

  • MASCOT 2006 15

    Rich scattering environment

  • MASCOT 2006 16

    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 week1 needed to measure the influence of a certain parameter

    If the Matlab-code works,one extended week1 needed to measure the influence of a certain parameter

    1 = 7 days, 16 hours a day

Related documents