MU-MIMO scheme performance evaluations using measured channels in specific environments

Transcription

1 MU-MIMO scheme performance evaluations using measured channels in specific environments Christoph Mecklenbräuker with contributions from Giulio Coluccia, Giorgio Taricco, Christian Mehlführer, and Sebastian Caban

2 Performance evaluation principles Performance is representated as a functional T depending on the receiver input distribution F General linear MU-MIMO channel Y A = H A S A + I A + N F = joint distribution of (H A, S A, I A, N) A = random set of active links MASCOT

3 Role of the MU-MIMO scheduler The scheduler defines A depending in CQI feedback The scheduler shapes the statistics of H A and I A Given A, the PHY shapes the statistics of S A Evaluation of T(F ) must account for the dependencies among H A, S A, I A. MASCOT

4 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

5 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 rates do not match the simulated system - Non-realtime - It is questionable whether opportunistic MU-MIMO schemes based CSI feedback can be evaluated MASCOT

6 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

7 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

8 Example for Approach 1 Measure then simulate MEDAV s RUSK ATM channel sounder - 15 Tx elements (uniform circular array) - 8 Rx elements (uniform linear array) - Measurement band: MHz - Urban, Sub-urban, Rich-scattering environments - MASCOT

9 Mobile Tx, Static Rx MASCOT

10 Sub-urban environment Receiver s Weikendorf

11 Outdoor Channel impulse response MASCOT

12 Indoor rich scattering MASCOT

13 Spatial re-sampling (1) MASCOT

14 Spatial re-sampling (2) MASCOT

15 Optimum receiver vs. Mismatched ML [9] G. Taricco and E. Biglieri: Space-time decoding with imperfect channel estimation, IEEE Trans. Wireless Commun. 4(4): , July 2005 MASCOT

16 Urban environment MASCOT

17 Rich scattering environment MASCOT

18 Example Approach 2: Emulate with real front-ends 4 Tx Front-ends 4 Rx Front-ends 2.6 GHz band, 20 MHz bandwidth MASCOT

19 Experiment Set Up 3/42 Sebastian Caban Transmitter Roof or even higher

20 Experiment Set Up 5/42 Sebastian Caban Transmitter Roof or even higher Patch Antennas 20 dbm/antenna 2.5GHz 20dBm = 0.1 watt

21 Experiment Set Up 7/42 Sebastian Caban Channel real channel, urban environment TX 7 am

22 Experiment Set Up 8/42 Sebastian Caban Receiver Indoor

23 Experiment Set Up 9/42 Sebastian Caban Receiver Indoor 4 Monopoles

24 First Measurement 11/42 Sebastian Caban 13 E c /I or 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

25 Measured Impulse Responses 12/42 Sebastian Caban TX1 RX1 TX2 RX1 approx 160 m TX1 RX2 TX2 RX2

26 Measured Impulse Responses 13/42 Sebastian Caban

27 BLER Results 36/42 Sebastian Caban Parameters: 2x2 system Code rate of realizations 10 Codes 45 equalizer taps 15 channel taps 95% confidence intervals

28 BLER Results 37/42 Sebastian Caban Parameters: 2x2 system Code rate of realizations 10 Codes 45 equalizer taps 15 channel taps 95% confidence intervals

29 BLER Results 38/42 Sebastian Caban Parameters: 2x[4,3,2] system Code rate of realizations 10 Codes 45 equalizer taps 15 channel taps

30 BLER Results 39/42 Sebastian Caban Parameters: [4x4, 2x2] system Code rate of 0.7 [1788,6552] realizations 10 Codes 45 equalizer taps 15 channel taps equal E B Question: power normalization in measurements?

31 BLER Results 40/42 Sebastian Caban Parameters: 2x2 system Code rate of realizations [4,6,8,10] Codes 45 equalizer taps 15 channel taps

32 Conclusions 41/42 Sebastian Caban If the Matlab-code works, one extended week 1 needed to measure the influence of a certain parameter We 1 = 7 days, 16 hours a day Mobilkom One