doc.: ieee 802.11-03/161r0a submission march 2003 vinko erceg, zyray wireless; et al.slide 1 indoor...

March 2003

Vinko Erceg, Zyray Wireless; et al.Slide 1

doc.: IEEE 802.11-03/161r0a

Submission

Indoor MIMO WLAN Channel Models

Vinko Erceg, Laurent Schumacher, Persefoni Kyritsi, Daniel S. Baum, Andreas Molisch,

and Alexei Y. Gorokhov

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

List of Participants• Vinko Erceg (Zyray Wireless)• Laurent Schumacher (Namur University)• Persefoni Kyritsi (Aalborg University)• Daniel Baum (ETH University)• Andreas Molisch (Mitsubishi Electric)• Alexei Gorokhov (Philips Research)• Srinath Hosur (Texas Instruments)• Srikanth Gummadi (Texas Instruments)• Eilts Henry (Texas Instruments)• Eric Jacobsen (Intel)• Sumeet Sandhu (Intel)• David Cheung (Intel)• Qinghua Li (Intel)• Clifford Prettie (Intel)• Heejung Yu (ETRI)• Yeong-Chang Maa (InProComm)• Richard van Nee (Airgo)• Jonas Medbo (Erricsson)• Eldad Perahia (Cisco Systems)• Helmut Boelcskei (ETH Univ.)

• Hemanth Sampath (Marvell)• H. Lou (Marvell)• Pieter van Rooyen (Zyray Wireless)• Pieter Roux (Zyray Wireless)• Majid Malek (HP)• Timothy Wakeley (HP)• Dongjun Lee (Samsung)• Tomer Bentzion (Metalink)• Nir Tal (Metalink)• Amir Leshem (Metalink, Bar IIan University)• Guy Shochet (Metalink)• Patric Kelly (Bandspeed)• Vafa Ghazi (Cadence)• Mehul Mehta - Mickey (Synad Technologies)• Bobby Jose (Mabuhay Networks)• Charles Farlow (California Amplifier)• Claude Oestges (Louvain University)• Robert W. Heath (University of Texas at

Austin)

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

WLAN MIMO Channel Modeling Goals

• To develop a set of multiple-input multiple-output (MIMO) channel models backwards compatible with existing 802.11 channel models developed by Medbo and Schramm [1].

• Channel models can be used to evaluate new WLAN proposals based on multiple antenna technologies.

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Existing Models Overview

• Five delay profile models for single-input single-output (SISO) systems were proposed in [1] for different environments (A-E):

1. Model A for a typical office environment, non-line-of-sight (NLOS) conditions, and 50 ns rms delay spread.

2. Model B for a typical large open space and office environments, NLOS conditions, and 100 ns rms delay spread.

3. Model C for a large open space (indoor and outdoor), NLOS conditions, and 150 ns rms delay spread.

4. Model D, same as model C, line-of-sight (LOS) conditions, and 140 ns rms delay spread (10 dB Ricean K-factor at the first delay).

5. Model E for a typical large open space (indoor and outdoor), NLOS conditions, and 250 ns rms delay spread.

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

We Follow Cluster Modeling Approach

• In each of the A-E models distinct clusters can be clearly identified.

• Clustering approach is a common approach used for indoor channel modeling, verified by numerous experimental data.

• Pioneer work done by Saleh and Valenzuela [2] and further elaborated and extended upon by many researchers [3-8].

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Channel Model-A Example

-50 0 50 100 150 200 250 300 350 4000

5

10

15

20

25

30

Delay in Nanoseconds

Rel

ativ

e dB

• Three clusters can be clearly identified. Remaining models B-E have 4,6,6, and 5 clusters, respectively.

Cluster 1

Cluster 2

Cluster 3

dB

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Spatial Representation of 3 Clusters

LOS

Cluster 2

Tx Antennas

Rx Antennas

R2

Cluster 3

R3

Cluster 1

R1

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Modeling Approach• Only time domain information from A-E SISO models can be

determined (delay of each delay within each cluster and corresponding power using extrapolation methods).

• In addition, for the MIMO clustering approach the following parameters have to be determined:

– Power azimuth spectrum (PAS) shape of each cluster and tap– Cluster angle-of-arrival (AoA), mean – Cluster angular spread (AS) at the receiver– Cluster Angle-of-departure (AoD), mean – Cluster AS at the transmitter – Tap AS (we assume 5o for all)– Tap AoA– Tap AoD

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Cluster and Tap PAS Shape

• Cluster and tap PAS follow Laplacian distribution.

Example of Laplacian AoA (AoD) distribution, cluster, AS = 30o

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Cluster AoA and AoD

• It was found in [3,4] that the relative cluster mean AoAs have a random uniform distribution over all angles.

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Cluster AS

• We use the following findings to determine cluster AS:

• In [3] the mean cluster AS values were found to be 21o and 25o for two buildings measured. In [4] the mean AS value was found to be 37o. To be consistent with these findings, we select the mean cluster AS values for models A-E in the 20o to 40o range.

• For outdoor environments, it was found that the cluster rms delay spread (DS) is highly correlated (0.7 correlation coefficient) with the AS [9]. It was also found that the cluster rms delay spread and AS can be modeled as correlated log-normal random variables. We apply this finding to our modeling approach.

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Cluster AS: Cont’

• The mean AS per model was determined using the formula (per model mean AS values in the 20o – 40o range)

where DS is cluster delay spread.

• Cluster AS variation within each model was determined using 0.7 correlation with cluster DS and assuming log-normal distributions.

65.357.0 DSAS

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Cluster AS: Cont’• Resulting cluster AS (at the receiver) and DS for all five models (A-E) is shown in the figure below

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Tap AS, AoA, and AoD• We assume that each tap PAS shape is Laplacian with AS = 5o.• Following constraints that satisfy cluster AS and AoA (AoD), tap AoA and

AoD can be determined using numerical methods.

where li is a zero-mean, unit-variance Laplacian random variable, i is a scaling parameter related to the power roll-off coefficient of the cluster, is a parameter that is determined using numerical global search method to satisfy the required AS and mean AoA of each cluster; o is the mean cluster AoA; 2

tot is cluster AS, and 2,i is tap AS.

N

iili

1)0(0

20

2,

2)0(1

2

iilN

iitot

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Tap AS, AoA, and AoD: Cont’

Tap 1

Tap 2

Tap 3 Tap

4 Tap

5

Example: Distribution of taps within a cluster.

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

MIMO Channel Model A: Table of Parameters

Tap index

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Excess delay [ns]

0 10 20 30 40 50 60 70 80 90 110 140 170 200 240 290 340 390

Cluster 1 Power [dB]

0 -0.9 -1.7 -2.6 -3.5 -4.3 -5.2 -6.1 -6.9 -7.8 -

9.0712

-11.19

91

-13.79

54

-16.39

18

-19.37

10

-23.20

17

Mean AoA = 154.4757°

AoA [°]

166.646

172.6334

156.1558

150.0347

148.4607

109.7505

121.8078

152.7446

171.0199

155.2886

156.0954

159.6128

154.2316

149.2625

154.386

153.8697

Composite AS =

18.4380°

AS [°]

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

Mean AoD = 333.7349°

AoD [°]

346.8507

320.9888

345.5986

292.1785

342.3962

350.6262

311.292

349.5338

336.1867

326.1379

350.5996

353.77

349.6664

19.2542

359.4043

325.516

Composite AS = 19.7471

AS [°]

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5


-

6.6756

-9.572

9

-12.17

54

-14.77

79

-17.43

58

-21.99

28

-25.58

07

Mean AoA = 320.5062°

AoA [°]

315.8 315.8042

2.6764

273.5867

347.9928

323.2924

328.8476

Composite AS =

21.8956°

AS [°]

5 5 5 5 5 5 5

Mean AoD = 50.3512°

AoD [°]

68.67

51 27.43

82 47.14

56 15.64

74 50.40

85 39.56

65 52.11

88


AS [°]

5 5 5 5 5 5 5


-

18.8433

-23.23

81

-25.24

63 -26.7

Mean AoA = 248.58°

AoA [°]

239.5305

238.1292

251.7416

322.5986

Composite AS =

24.6891°

AS [°]

5 5 5 5

Mean AoD = 266.6654°

AoD [°]

249.4722

264.9037

318.4249

303.1991


AS [°]

5 5 5 5

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Next Steps

• So far we have completely defined PAS of each tap (AS and Laplacian AoA distribution) and AoA of each tap. These parameters were determined so that the cluster AS and mean cluster AoA requirements are met (experimentally determined published results).

• Next, we show how we use tap AoA and AS information to calculate per tap transmit and receive antenna correlation matrices and from that finally the MIMO channel matrices H.

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

MIMO Channel Matrix Formulation

• Example 4 x 4 MIMO matrix H for each tap is as follows

where Xij (i-th receiving and j-th transmitting antenna) are correlated zero-mean, unit variance, complex Gaussian random variables as coefficients of the Rayleigh matrix HV, exp(jij) are the elements of the fixed matrix HF, K is the Ricean K-factor, and P is the power of each tap.

44434241

34333231

24232221

14131211

1

1

1

1

1

1

44434241

34333231

24232221

14131211

XXXX

XXXX

XXXX

XXXX

K

jejejeje

jejejeje

jejejeje

jejejeje

K

KP

vHKFH

K

KPH

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

MIMO Channel Matrix Formulation: Cont’

To correlate the Xij elements of the matrix X, the following method can be used

4x4 MIMO channel transmit and receive correlation matrices are

2/12/1txRiidHrxRX

1434241

*3413231

*24*23121

*14*13*121

txtxtx

txtxtx

txtxtx

txtxtx

txR

1434241

*3413231

*24*23121

*14*13*121

rxrxrx

rxrxrx

rxrxrx

rxrxrx

rxR

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

MIMO Channel Matrix Formulation: Cont’

• Correlation coefficients for each tap can be determined using tap PAS (represented by Laplacian distribution and corresponding AS) and tap AoA (AoD)

• where D = 2d/ (for linear antenna array) and RXX and RXY are the cross-correlation functions between the real parts (equal to the cross-correlation function between the imaginary parts) and between the real part and imaginary part, respectively.

)()( DXYjRDXXR

dPASDDXXR )()sincos()(

dPASDDXYR )()sinsin()(

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

MIMO Channel Matrix H Generation

• Use power, AS, AoA and AoD tap parameters from tables A-D.

• Per tap, calculate transmit and receive correlation matrices.

• Using correlation matrices and Hiid generate instantiations of channel matrices H, as many as required by simulation.

Matlab program that can be used to generate per tap H matrices will be provided by Laurent Schumacher [10] (most likely by May 2003).

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

Conclusion

• WLAN MIMO channel models were developed based on extensive published experimental data and models.

• The models are based on per tap correlation matrices determined from tap AS and AoA.

• Matlab program will be provided that generates instantiations of channel matrices H for each tap and antenna combination.

March 2003


doc.: IEEE 802.11-03/161r0a

Submission

References

[1] J. Medbo and P. Schramm, “Channel models for HIPERLAN/2,” ETSI/BRAN document no. 3ERI085B.[2] A.A.M. Saleh and R.A. Valenzuela, “A statistical model for indoor multipath propagation,” IEEE J. Select. Areas Commun., vol. 5, 1987,

pp. 128-137. [3] Q.H. Spencer, et. al., “Modeling the statistical time and angle of arrival characteristics of an indoor environment,” IEEE J. Select. Areas

Commun., vol. 18, no. 3, March 2000, pp. 347-360. [4] R.J-M. Cramer, R.A. Scholtz, and M.Z. Win, “Evaluation of an ultra-wide-band propagation channel,” IEEE Trans. Antennas Propagat.,

vol. 50, no.5, May 2002, pp. 561-570. [5] A.S.Y. Poon and M. Ho, “Indoor multiple-antenna channel characterization from 2 to 8 GHz,” submitted to ICC 2003 Conference.[6] G. German, Q. Spencer, L. Swindlehurst, and R. Valenzuela, “Wireless indoor channel modeling: Statistical agreement of ray tracing

simulations and channel sounding measurements,” in proc. IEEE Acoustics, Speech, and Signal Proc. Conf., vol. 4, 2001, pp. 2501-2504.[7] J-G. Wang, A.S. Mohan, and T.A. Aubrey,” Angles-of-arrival of multipath signals in indoor environments,” in proc. IEEE Veh. Technol.

Conf., 1996, pp. 155-159.[8] Chia-Chin Chong, David I. Laurenson and Stephen McLaughlin, “Statistical Characterization of the 5.2 GHz Wideband Directional Indoor

Propagation Channels with Clustering and Correlation Properties,” in proc. IEEE Veh. Technol. Conf., vol. 1, Sept. 2002, pp. 629-633. [9] K.I. Pedersen, P.E. Mogensen, and B.H. Fleury, “A stochastic model of the temporal and azimuthal dispersion seen at the base station in

outdoor propagation environments,” IEEE Trans. Veh. Technol., vol. 49, no. 2, March 2000, pp. 437-447.[10] L. Schumacher, Namur University, Belgium, ([email protected]).

doc.: ieee 802.11-03/161r0a submission march 2003 vinko erceg, zyray wireless; et al.slide 1 indoor...

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