MEASUREMENT AND MODELING OF INDOOR UWB CHANNEL AT … · MEASUREMENT AND MODELING OF INDOOR UWB CHANNEL AT 5 GHz WINLAB @ Rutgers University July 31, 2002 Saeed S. Ghassemzadeh...

MEASUREMENT AND MODELING OF INDOOR UWB CHANNEL AT 5 GHz

WINLAB @ Rutgers UniversityJuly 31, 2002

Saeed S. [email protected]

AT&T Labs - ResearchFlorham Park, New Jersey

This work is based on collaborations between the author and manypresent and former colleagues at both AT&T and Rutgers WINLAB. Special recognition and thanks go to: Larry J. Greenstein (WINLAB); Vahid Tarokh and Thorvardur Sveinsson (Harvard University); Rittwik Jana, Robert Miller, Christopher W. Rice, William Turin (AT&T); and Vinko Erceg (Zyray Wireless).

WINLAB-2AT&T Labs - Research

Outline

MotivationMeasurement technique and databasePath Loss (PL):− Data reduction and key findings− Model and simulation

Multipath Intensity Profile (MIP):− Data reduction and key findings on time domain

parameters− Average relative MIP and its variations− Model and simulation

ConclusionFuture work


In-Home Broadband Wireless Distribution

Wireless Data Network

Telephone / FaxDistribution

Ultra-WidebandGateway

Entertainment Distribution

Broadband ServicesBroadband ServicesCableXDSLFixed Wireless


Motivation

To create a channel model for UWB channel that:– Represents a realistic UWB channel without doing a

costly sounding experiments.

– Signifies a compact and simple method to simulate the multipath channel behavior.

– Is useable for range and performance evaluation of various PHYs in-home environment.

Most wireless channel models available, either:

– Do not represent UWB channel,

– Or are not in the environment and/or frequency spectrum of interest,

– Or have database that is small for statistical characterization of the channel parameters.


Swept Frequency Measurement TechniqueCenter frequency: 5 GHzFrequency bins: 401Bandwidth: 1.25 GHz fi Dt = 0.8 nsSweep rate: 400 ms

fi Df = 3.125 MHz, tmax = 320.8 ns

Complex Impulse Response of UWB ChannelComplex Frequency Response of UWB Channel

IDFT

[1] S.J. Howard, K. Pahlavan, “ Measurement and Analysis of the indoor radio channel in the frequency domain”, IEEE Trans. Instrum. Measure., 39:751-755, Oct. 1990.


Channel Sounder System Block Diagram

VectorNetworkAnalyzer

PAD

L = 17.325 dB

150’ cable

RF Out

AAgilent8753-ES LNABPF

PAD

Rx22

L = 17.325 dB

150’ cable

PA

LNABPFPAD

Rx21LNABPF

PAD

Rx11

LNABPFPAD

Rx12

B

SOFTWARE

Antenna1

Antenna2

HP-VEE Programs

Data Collection

MATLAB Programs

Post-Processing

HP-VEE ProgramsVNA / PC Controller

LABTOP

HPIBI/O

TxAntenna


Indoor UWB Channel Sounder


Data Base

Data base Includes:– Measurements at 5 GHz and 1.25 GHz ultra-wideband

channel– T-R separations ranging from 1m to ~15 m– Simultaneous measurements of 2 antennas separated

by 38 inches at each location over 2 minute intervals – From one wall to maximum of 4 walls penetration– 300,000 complex frequency responses at 712

locations in 23 different homes


Measurement Set-up

Transmit and receive antennas were separated such that T-R separations have uniform distribution.Measurements were performed in Line-of-Sight (LOS) and None Line-of- Sight (NLS).T-R separations in 1m to 15m in steps of ~ 0.9 m.


Path Loss (PL): Data Reduction

We define path loss as:

Typical representation of path loss vs. distance (d):

– do is a reference distance, e.g., do = 1 m.– Bracketed term is a least-squares fit to pathloss, PL(d).– PL0 ( intercept) and γ (path loss exponent) are chosen to

minimize .– S is the random scatter about the regression line, assumed to

be a zero-mean Gaussian variate with standard deviation σ dB.

2

1 1

( )

1 = ( , ; )

;

= =

=

∑∑

rr t t

r tN M

i ji i

PG G PPl dP P

H f t dMN

Average received powerTransmit power

where ==

( )0 10 00

( ) 10 log ;dPL d PL S d dd

γ

= + + ≥

2S


Path Loss vs. Distance Scatter Plot

A fix path loss model over the population of data does not reproduce the variation due to individual homes.Intercept point, PLo, is 47 dB and 50.5 dB in LOS and NLOS. Path loss exponent, γ, is 1.7 and 3.1 for LOS and NLOS.


CDF of Shadow fading

Shadow-fading is log-normal as expected with zero mean and variance (over the population of data) of about 2.8 and 4.4 dB, in LOS and NLOS, respectively.


Path Loss: Key Findings

The intercept point depends on the materials blocking the signal within 1m of T-R separation and the home structure. The measured values of PLo for NLS were very close to that of LOS path loss plus a few dB more loss due to the obstacle(s) blocking the LOS path. We chose the intercept value to be the mean measured path loss at 1m in 23 homes.

Path loss exponent, γ, changes from one home to another. It is a Normal RV with NLOS[1.7, 0.3] and NNLOS[3.5, 0.97].

Shadow-fading, S, is zero mean Gaussian RV with variance that also changes from one home to another. This variance is also a Normal RV with NLOS[1.6, 0.5] and NNLOS[2.7, 0.98].

[2] V. Erceg, et.al., "An empirically based path loss model for wireless channels in suburban environments", IEEE JSAC, vol. 17, no. 7, pp. 1205-1211, July 1999.


CDF of Path Loss Exponents

Path loss exponent is a Normal random variable with NLOS[1.7, 0.3] and NNLOS[3.5, 0.97].


CDF of Variance of Shadow Fading

Variance of shadow fading is a Normal random variable with NLOS[1.6, 0.5] and NNLOS[2.7, 0.98].


The Path Loss Model

( ) ( )

1 2 3

d

1 1 10 2 2 3

B

1 10 3

0

2

,

0 10

,

( ) 10 log

10 log

1010 l loog go

o

n S n n

PL d P

PL d n d n n

L S

PL n d n n

n

dγ γ σ

γ γ

σ

γ γ σ

σ

σ

γ µ σ σ σ µ σ

γ

µ σ

µ µ

σ µ

σ

+

= + = = +

= + +

= + + + +

+= ++

Introducing three new RVs: and

; 15 m

od dσσ

≤ ≤

= Median path loss Random variation about median path loss+

n1, n2 and n3 are iid zero-mean, unit-variance Gaussian variates.n1 varies from one home to another while n2 and n3 vary from one location to another within each home.The variable part of above equation is not exactly Gaussian since n2¥n3 is not Gaussian. However, this product is small w.r.t. the other two Gaussian terms. Therefore, it can be approximated as azero mean random variate with standard deviation of:

( )22 2 2var 10100 logγ σ σσ σ µ σ= + +d


Model Simulation

For simulation purposes, it is practical to use truncated Gaussian distributions for n1, n2, n3 keeping g, s and S from taking on unrealistic values. One possible range for these values are:

1

2 3

[ 0.75,0.75], [ 2,2]∈ −

∈ −nn n


UWB Path Loss Model

10 1 10 2 2 3dB( ) ; 15 m

10 log

0 og

1 lγ γ σ σσ µ σµ

+

= + ≤+ + ≤

=

oo n dPL d PL dn dn nd

Media Random variation about median path ln path l oss+oss

1

2 3

[ 0.75,0.75], [ 2,2]∈ −

∈ −nn n


MIP: Data Reduction

The following steps are taken to get the MIPs :

− Calibration information is removed from the raw data.

− The response is then locally averaged over time (since the receiver was kept stationary and maximum Doppler measured was no more than a few tenths of Hz.).

− 401 point complex IFFT is taken to get the complex MIPs.

− The MIPs are then normalized to the total average power.

− Threshold (-30 dB) is set to +10 dB above the average noise floor (-40 dB).

− The noise is removed from the data and MIP is re-normalized so that the area under MIP is one.

− All MIPs are synchronized w.r.t. their delay at zero ns, representing the first return above the threshold.


RMS Delay Spread and Coherence Bandwidth

1

1

2

222

( , )

( , )

ττ τ

ττ τ

τ=

=

⋅≡ − ≡

∑

∑

Lni

n iL

i

i

i

h t

rmsh t

The rms delay spread is defined as:

where

12

1

*

1 (0,0) ( , )

( , )

1( ,0) ( , ) ( , ), 0

−

=

=

−

= ≡ =∑−

= ≥− ∑

N k

i

N

i

i i kHh

Hh

Hh

H f t PGiN k

k t

R k H f t H f t kN k

R

RBc

Note : Mean Path Gain

Frequency Correlation Function:

Defining as 3-dB width of and using inverse relationship

3 dB

10 log 10 log 10 log 10 log10 10 10 10

τατ

α τ−

−= ⋅ ⋅

= ⋅ = ⋅ − ⋅ ⋅ + ⋅RMS

Bc rmsB K Sc

B B K Sc c rms

and , then:between


Time/Frequency Domain Channel Parameters

Excess delay and rms delay spread:Maximum excess delay observed was 70 ns.rms delay spread has a normal distribution over all locations and homes.RMS delay spread increases with T-R separation and therefore with path loss.Min. and Max. of rms delay spread:− LOS: 1.1ns and 16.6 ns− NLS: 0.75 ns and 21 ns

Mean and Standard deviation of RMS delay spread:− LOS: 4.7 and 2.2 ns− NLS: 8.4 ns and 3.8 ns


Time/Frequency Domain Channel Parameters

Coherence Bandwidth:

Average coherence bandwidth is about 90 MHz and 29 MHz in LOS and NLS, respectively.

Maximum Doppler frequency observed was 0.1Hz.


Distribution of RMS Delay Spread, tRMS


RMS Delay Spread vs. T-R Separation


RMS Delay Spread vs. Path Loss


UWB Frequency Correlation Function

0 50 100 150 200 250 300 350 400 450 5000

0.2

0.4

0.6

0.8

1

3-dB width Correlation Functio n (MHz)

Pro

babi

lity

Cb le

ss th

an a

bsci

ssa

LOS DataNLS Data

-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1

Frequency Separation (MHz)

Fre

quen

cy C

orre

lati

on

LOS DataNLS Data


Relationship Between Cb and τrms

1 10 1001

10

100

1000

σR MS (nsec)

Bc (M

Hz)

S lope ~ 562 .1 MHz per 10 nsec, σBc = 61.49, MHz

NLS DataLS Fit, Bc |dB =27.7 -1.32 × 10 × log10(σRMS)


Relationship Between Cb and τrms

1 10 1001

10

100

1000

σR MS (nsec)

Bc (M

Hz)

S lope ~ 38.71 MHz per 10 nsec, σBc = 103.8, MHz

LOS DataLS F it, Bc |dB =20.5 -0.186 × 10 × log10(σRMS)


Doppler-Power Spectrum

Fd = 0.1 Hz @ 3 dB Bandwidth


The Relative MIP Model

Tapped-delay line model with randomly selected relative MIP power, random amplitude and phase variation.

Relative MIP Model

S

Z-1

Z-L

Path 1Pm1

Path LPmL

_1

_ _

Lrelative i

i

Path Loss relative imi

T

T P

P PP P

P=

×=

=∑

a1 +jb1

aL +jbL


Average Relative MIP

Relative MIPs are MIPs that are averaged over all locations in homes prior to normalization to their maximum power.


Multipath Amplitude and Phase Distribution

The multipath amplitudes undergo small variation which can be best characterized by Rician distribution with a K-factor greater than 40 dB.

The phases of the multipath components are uniformly distributed between 0 and 2p (Note: We can assume also same distribution for carrier-less transmissions with equal probability and uniform distribution, phases taking on values of 1 or –1).

The decibel-variation of multipath components are correlated with correlation coefficient r:

0 0.25ρ≤ ≤


The Relative MIP Model Concept– NLSTypical representation of the multipath delay profile shape has been reported as a decaying exponential.

Following this intuition and observing the randomness of the shape of profile over the population of our data, we formed the following function:

where a is decibel-decay constant and S is the decibel-variation about the median relative MIP.

The model assumes that the power of the first return for median relative MIP is the strongest one. This simplified the model considerably with insignificant increase in the slope.

dB( )τ ατ= +relP S


The Relative MIP Model Concept – NLS

at term is a least square fit to the decibel-power of each multipath component. a is then found such that the MSE of S is minimized.We then characterize a and S over the population of homes.

We observed the following:- Value of a [dB/ns] are normally distributed RVs, N[-0.50, 0.13].

- Values of S [dB] are normally distributed RVs N[-0.41, 7.80].- The mean of S was constant in each home; however, we

observed that the standard deviation of S, sS, changes from one home to another. This variation was normally distributed over all homes with N[7.20, 0.88].


The Relative MIP Model – NLS


Distribution of S Over All Homes – NLS


Distribution of sS Over All Homes – NLS


Distribution of a Over All Homes – NLS


The Relative MIP Model – NLS

( ) ( ) ( )

1 2 3

dB

1 2

1 2

1

2 3

2 3

,

( )

( ) 15 m

s s

s s

s s

s s s

re

s

l

s s s

o

n S n n

P S

n n n n ndn n n dn

α α σ σ

α

α α

α

σ σ

α α σ σ

α µ σ µ σ σ µ σ

τ ατ

µ σ τ µ σσµ τ µ τ σ

σ τ µ µ σµ

µ

=

+ +

= + = + = +

= +

= + + + + + + +

= ≤++ ≤

Introducing 3 RVs:and

= Random variation about median dMedian delay p elay profil+r f eo ile

n1, n2 and n3 are iid zero-mean, unit-variance Gaussian variates.

n2 is a fast-varying RV and varies from one delay to another. n1and n3 are slow varying RVs and vary from one home to another.

The variable part of above equation is not exactly Gaussian since n2¥n3 is not Gaussian. However, this product is small w.r.t. the other two Gaussian terms.


Flowchart for the Channel SimulatorStart

• Generate RVs {a and S and s} from the model equation

• Generate ti ; i = 0:89

• Plug constants into the model equation• Normalize to maximum• Assign –30 £ TH (Threshold) £ 0 dB• Set i = 0

P(ti)|dB £ TH dB • Keep the multipath component• Record its delay and relative power

Drop the multipath component

P(ti)|dB £ TH dB

Generate n1, n2 and n3

i =i + 1 Done?

• Sum the relative power of all multipaths (i.e. Total channel power)

• Multiply the linear power of each multipath by path loss an divide by total channel power (i.e. multipath component power).

• Scale Rician coefficients by this path power and rotate its phase by a uniformly selected phase

• Sum all paths.

Complex Channel Impulse response


Live Channel Simulation Show……


Channel Simulator Results

We simulated the model to compare its statistical behavior with that of measured data. Specifically, we looked at:

− CDF of tRMS : Simulated vs. measured.

− Average simulated profile vs. measured.

− Standard deviation of the model variation about the median: Simulated vs. measured.


CDF of tRMS: Simulated vs. Measured


Average MIP: Simulated vs. Measured


Model Error: Simulated vs. Measured


The Relative MIP Model – LOS


The Relative MIP Model- LOS

1 2 3 4, , ,c c s s sCn n S n nα α σ σα µ σ µ σ µ σ σ µ σ= + = + = + = +Introducing four RVs:

and

( ) ( )

( ) ( ) ( )( )( )

( )2 1

dB

2 1 3 4

3 3 4 )

( ) 0.8

( 0.8 )

15 m & 00.

8

0 8

.

α σ

α α σ

σ

α

σ

µ τµ

τ ατ τ

µ σ µ σ τ µ µ σ

σ σ τ µ

τ

τ τσµ τ

+

+

= + + −

= + + + + + −

=+ ≤

+ ++ + −

−≤ ≥

=

rel

c c s

oc

c s

P C S u n

n n n n n

s

n n

uu n

n n u n

s dn

ss

dMedian delay p

+ Random variation about median delay profile

rofile


Distribution of C and a


Distribution of S and sS


Conclusion

We reported on the statistics and dependencies of channel parameters such as path loss, shadowing, delay spread, Doppler spectrum and average MIP for UWB indoor channels.

We presented simple statistical model for multipath that is easily integrated with the path loss model.

The models are based on over 300,000 UWB frequency responses at 712 locations in 23 homes.

The models statistically regenerates the properties of the indoor channel with small error.

The model can be used for simulation and performance evaluation of the UWB systems and can be upgraded with further measurements.


Work in Progress

UWB Coexistence: Analysis, Simulation and Measurements.

UWB Propagation in Commercial Buildings with 4GHz bandwidth.

MIMO Measurements (2x2 or 4x4 ?)

MEASUREMENT AND MODELING OF INDOOR UWB CHANNEL AT … · MEASUREMENT AND MODELING OF INDOOR UWB CHANNEL AT 5 GHz WINLAB @ Rutgers University July 31, 2002 Saeed S. Ghassemzadeh...

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