Doc.: IEEE 802.15-02/240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time...

July 2002

Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC)

Slide 1

doc.: IEEE 802.15-02/240SG3a

Submission

Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs)Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs)

Submission Title: Empirically Based Statistical Ultra-Wideband Channel ModelDate Submitted: 24 June, 2002Source: Marcus Pendergrass, Time Domain Corporation 7057 Old Madison Pike, Huntsville, AL 35806Voice:256-428-6344 FAX: [256-922-0387], E-Mail: [email protected]

Re: Ultra-wideband Channel Models IEEE P802.15-02/208r0-SG3a, 17 April, 2002,

Abstract: An ultra-wideband (UWB) channel measurement and modeling effort, targeted towards the short-range, high data rate wireless personal area network (WPAN) application space, is described. Results of this project include a measurement database of 429 UWB channel soundings, including both line of sight and non line of sight channels, a statistical description of this database, and recommended models and modeling parameters for several UWB WPAN scenarios of interest.

Purpose: The information provided in this document is for consideration in the selection of a UWB channel model to be used for evaluating the performance of a high rate UWB PHY for WPANs.

Notice: This document has been prepared to assist the IEEE P802.15. It is offered as a basis for discussion and is not binding on the contributing individual(s) or organization(s). The material in this document is subject to change in form and content after further study. The contributor(s) reserve(s) the right to add, amend or withdraw material contained herein.Release: The contributor acknowledges and accepts that this contribution becomes the property of IEEE and may be made publicly available by P802.15.

July 2002


Slide 2

doc.: IEEE 802.15-02/240SG3a

Submission

Marcus Pendergrass and William C. Beeler

24 June 2002

with thanks to Laurie Foss, Joy Kelly, James Mann, Alan Petroff, Alex Petroff, Mitchell Williams, and Scott Yano for assistance and support.

Empirically Based Statistical Ultra-Wideband (UWB) Channel Model

July 2002


Slide 3

doc.: IEEE 802.15-02/240SG3a

Submission

Executive Summary• Important to characterize the Wireless Personal area

network (WPAN) environment.

• 429 channel soundings taken in residential and office environments.

• Statistical multipath models for 3 environments described: LOS 0-4 meters, NLOS 0-4 meters, NLOS 4 - 10 meters.

• Channel response modeled as a sum of scaled and delayed versions template waveform.

• Good fit to measurement data. Distortion <1dB.

• Recommendations offered

July 2002


Slide 4

doc.: IEEE 802.15-02/240SG3a

Submission

Outline

• Introduction

• Measurement Campaign

• Data Analysis

• Statistical Environmental Models

• Analytical Models

• Conclusions/Recommendations

July 2002


Slide 5

doc.: IEEE 802.15-02/240SG3a

Submission

• Introduction


• Data Analysis




July 2002


Slide 6

doc.: IEEE 802.15-02/240SG3a

Submission

Introduction

• Channel Impulse Response (CIR) modeling of radio-frequency channels necessary for system design, trades.

• Multipath channel effects will be a key determinant of system performance, reliability.

• Large literature on channel modeling available, including work on the UWB channel in particular.

• Important to characterize the wireless personal area network (WPAN) environment in both line of sight (LOS) and non line of sight (NLOS) cases.

• Models should be tuned to WPAN applications and environments.

July 2002


Slide 7

doc.: IEEE 802.15-02/240SG3a

Submission

Approach• Measurement Campaign

• Channel soundings taken in a variety of WPAN-type environments.

• Data Analysis• Deconvolution of channel impulse response (CIR) from

measurements. • Assessment of channel distortion. • Statistical analysis of UWB channel parameters as a

function of environment type.• Fit existing models to data

• IEEE 802.11 model.• The -K model.

• Assess goodness of fit• Recommend models, parameters

July 2002


Slide 8

doc.: IEEE 802.15-02/240SG3a

Submission

Overview of Results

• 429 channels soundings taken from 11 different home and office environments.– Data will be made available to SG3a.

• Environmental signal distortion estimated.

• Multipath channel parameters described statistically:• RMS delay• Distribution of multipath arrival times.• Average power decay profile.

• Ability of existing models to capture the phenomenology of the data assessed.

• Recommendations made for models and parameters.

July 2002


Slide 9

doc.: IEEE 802.15-02/240SG3a

Submission

• Introduction


• Data Analysis




July 2002


Slide 10

doc.: IEEE 802.15-02/240SG3a

Submission

Purpose• Support statistical analysis WPAN propagation

environments by obtaining a well-documented set of diverse measurements of the UWB channel.

– Short range (0-4 meters), and medium range (4 - 10 meters)

– LOS and NLOS channels

– office and residential environments

July 2002


Slide 11

doc.: IEEE 802.15-02/240SG3a

Submission

Measurement Plan

• NLOS and LOS measurements for WPAN multipath channel characterization.

• Metal stud and wooden stud environments.

– Metal studs typical of office environments; wooden studs more typical of residential environments.

– 11 different office and home locations

• Detailed documentation for each channel sounding

– X,Y,Z coordinates of transmit/receive antenna locations.

– Channel categorized as LOS or NLOS

July 2002


Slide 12

doc.: IEEE 802.15-02/240SG3a

Submission

Test Setup Details

• Summary:

– Approximately omni-directional transmit/receive antennas (roughly 3 dBi gain)

– PCS and ISM band pass rejection filter

– Effective noise figure: 4.8 dB at receive antenna terminals

– Gain: 19.8 dB

– Radiated power at approximately -10 dBm in the 3 to 5 GHz spectrum (close to FCC UWB limit)

July 2002


Slide 13

doc.: IEEE 802.15-02/240SG3a

Submission

Test Setup Details

• Data recorded:

– 100 ns channel record.

– 4096 data points per record.

– Effective sampling time is 24.14 ps (20 GHz Nyquist frequency).

– 350 averages per data point per channel record (for high SNR).

– Triggered sampling for accurate determination of effective LOS arrival time.

– Channel stimulus is UWB signal with 3 to 5 GHz 3 dB bandwidth, approximately 1.7 ns pulse duration.

July 2002


Slide 14

doc.: IEEE 802.15-02/240SG3a

Submission

Channel Measurement Test Setup

Miteq30 dB @ 4 GHz0.4 nsec delay

PreampNF = 2.2

+15 VdcP.S.

Calex CM1.15.400-115

Rcv Ant & Mount

BC1 50 ft.RG-223/U

21 dB @ 4 GHz78 nsec delay

22 nsec Delay LineHP54008A

2 dB @ 4 GHz

HP8449B1.5 nsec delay37 dB @ 4 GHz

PreampNF = 9

BC6 2 ft.RG-223/U

-1.3 dB @ 4 GHz3 nsec delay

HP8494B0-11 dB, 1 dB Step Variable Attenuator

HP8495B0-70 dB, 10 dB Step Variable Attenuator

4.5" of Semi Rigid

1.4 nsec delay-0.8 dB @ 4 GHz

at 0 dB step

High Pass Filter,ISM, & PCS Notch

f (GHz)

dB

3

2.44

1.88 -1.6 dB

Male to MaleSMA

1.0 nsec delay

Floppy

HP54750A

Ch.1

Ch.2

Trig

DSO

5.75

Transmit Ant

Channel

350 Averages10 ns/div

BC2 3 ft.RG-223/U

-2.0 dB @ 4 GHz nsec delay

Trigger Cable 50 ft.

RG-223/U21 dB @ 4 GHz78 nsec delay

TDC SG

BC5 2 ft.RG-223/U

-1.3 dB @ 4 GHz3 nsec delay

July 2002


Slide 15

doc.: IEEE 802.15-02/240SG3a

Submission

Measurement Issues• Received pulse distortion

– Need accurate received pulse templates for deconvolution analysis.– Resolution: assessment of waveform distortion due to the angle

of arrival of the incoming signal.

• Determination of line of sight delay time in NLOS channels.– Accurate determination of multipath intensity profiles for NLOS

channels requires knowing where the line of sight path would have arrived, had it not been obstructed.

– Resolution: careful design and characterization of test setup and parameters (group delays, NF, antenna pattern, etc.), along with periodic excitation of the environment. Utilize known delays of test equipment, known transmit/receive locations, and periodic triggering to estimate what the direct path arrival time would have been for a NLOS channel.

July 2002


Slide 16

doc.: IEEE 802.15-02/240SG3a

Submission

Measurement Issue:Received Pulse Distortion

• Accurate received waveform template needed for effective deconvolution of channel impulse response.

• Sources of waveform distortion:– environment (non-linear group delay, frequency-selective

attenuation, etc.)

– interference (intermittent and steady state)

– antenna pattern

• Environmental distortion to be estimated in data analysis.

• Interference in minimized with appropriate filtering (PCS, ISM bands).

• Distortion due to non-ideal antenna pattern was assessed empirically.– distortion as a function of elevation angle.

July 2002


Slide 17

doc.: IEEE 802.15-02/240SG3a

Submission

Typical Normalized Antenna Azimuth and Elevation Patterns (omni-

directional antennas)0

TDC SG

July 2002


Slide 18

doc.: IEEE 802.15-02/240SG3a

Submission

Received Pulse Distortion Test Setup

July 2002


Slide 19

doc.: IEEE 802.15-02/240SG3a

Submission

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0.0E+00 5.0E-10 1.0E-09 1.5E-09 2.0E-09 2.5E-09

TX 90 Deg Above Boresight Facing TX 60 Deg Above TX 30 Deg Below

– For angles of elevation between -70 degrees and +70 degrees, waveform distortion was found to be minimal.

– Significant distortion near 90 degrees elevation; however, signal is severely attenuated in this region.

– Use of a single received pulse template was judged acceptable for deconvolution analysis.

Pulse Distortion Test Results

Normalized amplitudesNormalized amplitudes

July 2002


Slide 20

doc.: IEEE 802.15-02/240SG3a

Submission

• In our test set-up, periodic excitation of the environment (non time-hopped) allowed for more accurate calculation of LOS delays.

• With periodic excitation the channel ring-down from previous pulse can add to the recorded response data if the record length is shorter than the ring-down time of the channel.

• Random excitation decorrelates the previous pulse’s ring-down from the recorded response through the DSO averaging process.

• Effect is most pronounced in channels with high RMS delay spread.

Measurement Issue:Determination of LOS Delay

July 2002


Slide 21

doc.: IEEE 802.15-02/240SG3a

Submission

Periodic Channel Stimulus Example

0 5 10 15 20 25 30 35 40 45 50-4

-3

-2

-1

0

1

2

3

4x 10

-3 6m, Periodic, NLOS

Am

plitu

de

Time (ns)

July 2002


Slide 22

doc.: IEEE 802.15-02/240SG3a

Submission

Random Channel Stimulus Example

0 5 10 15 20 25 30 35 40 45 50-4

-3

-2

-1

0

1

2

3

4x 10

-3 6m, Psuedo Random, NLOS

Am

plitu

de

Time (ns)

July 2002


Slide 23

doc.: IEEE 802.15-02/240SG3a

Submission

Minimal Effect on RMS Delay

– Ability to accurately determine LOS delay was judged important enough to utilize periodic (non time-hopped) pulse trains.

0 5 10 15 20 25 30 35-4

-3

-2

-1

0

1

2

3

4x 10

-3 6m, NLOS, Periodic (Black) and Psuedo Random (Red)

Am

plitu

de

Time (ns)

CIR RMS Delay=16.3

CIR RMS Delay=15.5

July 2002


Slide 24

doc.: IEEE 802.15-02/240SG3a

Submission

Channel Measurement Environments

• 11 different office and home environments

• Metal and wood stud constructions

• Distances less than or equal to 10 meters.

• 471 channel soundings taken in total.

• Complete documentation of measurement locations and environments.

July 2002


Slide 25

doc.: IEEE 802.15-02/240SG3a

Submission

Example Measurement Locations

A Typical Office Environment13'-9"

10'-3

"

6'-9

"17

.7

36" door

file cabinet16.5"x28"

desk27'x47.5"

bookcase12"x30"

bookcase12"x30"

desk

27.5

"x47

"

desk27.5"x47"

shelf40"x12"

lampz =

185.4

z = 74.9

z = 125.7

Chairz= 48

Chairz 19

Chairz = 48

z =81.3

49

z =124.4

z =71

z =71

trash

tras

h

10"x15"z = 38

10"x

15"

z =

38

chair

z = 52

z=47

z =

48

14"x

35"

z = 166.4

z = 74.9

z = 74.9z =

125.7

1

3

17

11

19

15

13

7

9

5

23

74.9

z = 74.9

80.9

z = 166.4199.6

z = 74.9

z = 166.4

292.

8

103.4

z = 74.9

268.

3

z = 86.3

269

z = 74.9

252.9

z = 52

250.8

z = 52

114126

z = 86

67.87"

31

2.5

419.2

205.

7

Lampz=185

Lampz=185

Receiverz = 0cm

July 2002


Slide 26

doc.: IEEE 802.15-02/240SG3a

Submission

13'-9.5"

21'-4.5"

1'-0

"

5'-1"

13'-8.5"

14'-4

"

1'-8

"

conference table4' x 12'

69.5" 54.5"

59.5

"

57.5

"

table

15" x 45"

36"

14x1450"

14.5"

trash

18" 49" 70"28" 93"

18"

21"

24"

Conference Room 245

Example Measurement Locations

Conference Room

July 2002


Slide 27

doc.: IEEE 802.15-02/240SG3a

Submission

Bill Beeler Living Room5/22/02 1:50p

1/4"=1'0"

11'-3"

3'-9"

10'-0"

1'-0"

1'-8 9/16"

10'-2"6'-3"

4'-6 5

/8"

7'-3"

2'-3 1/8"

6'-8"

19

19

19

15

34

34

3

4

34

20 20

28

28 28

44

25

frontdoor

endtable17x23

endtable17x23

coffee table19x47.5

tv/s

tere

o

wall

unit

24x 5

4

36"

34"

38" x 88"

38"

x 6

5"

38" x 65"

metalbarbells

meta

l

bucket

12"

x

17"

cart

metal fan12" x 24"

33.

5" hallway

kitchenarea

diningarea

endtable17x2320

metalstairstepexcerisemachine

under table

firepla

ce

openning in wall tokitchen 72"

hallw

ay

books

12" 1

2"

window98"12" 12"

sto

ne surfa

ce

Example Measurement LocationsResidential Living Room

July 2002


Slide 28

doc.: IEEE 802.15-02/240SG3a

Submission

Measurement Database• 471 channel soundings taken in total.• Database consists of a subset of 429 of these

channels:– All measurements vertically polarized.– Includes received waveform scans and extracted channel

impulse responses.– Includes calculated channel parameters, including RMS

delay and path loss.– Also includes various measurement meta-data, including

• locations of transmitter and receiver• channel categorized as LOS or NLOS.• calculated line of sight delay time• environment type (wood stud, metal stud)• polarization• number of intervening walls between transmitter and receiver.

July 2002


Slide 29

doc.: IEEE 802.15-02/240SG3a

Submission

• Introduction


• Data Analysis




July 2002


Slide 30

doc.: IEEE 802.15-02/240SG3a

Submission

Analysis Goals

• Extract a description of the channel that is independent of the channel stimulus.

• Estimate “distortion” caused by the propagation environments.

• Produce a statistical description of channel parameters as a function of environment type.

July 2002


Slide 31

doc.: IEEE 802.15-02/240SG3a

Submission

Major Analysis Assumptions

• Channel modeled as a linear time-invariant (LTI) filter.– assume that there are negligible changes to the channel on the

time scale of a communications packet.

• Impulse response for the channel is assumed to be of the form

– channel’s effect on signal is modeled as a series of amplitude scalings ak and time delays k.

N

kkk tath

0

)( (1)

July 2002


Slide 32

doc.: IEEE 802.15-02/240SG3a

Submission

20 40 60 80 100 120 140 160 180-2000

-1000

0

1000

2000

Am

plitu

de

Received Reconstructed

20 40 60 80 100 120 140 160 180-0.05

-0.025

0

0.025

0.05

Time (nS)

CIR

20 dB Threshold

CLEAN is a variation of

a serial correlation

algorithm Uses a template

received waveform to

sift through an arbitrary

received waveform Cross-correlation with

template suppresses

non-coherent signals

and noise Result is k’s and k’s

of CIR independent of

measurement system

CLEAN Algorithmused to deconvolve CIR from channel

record

July 2002


Slide 33

doc.: IEEE 802.15-02/240SG3a

Submission

CLEAN AlgorithmCompared to Frequency Domain De-

Convolution

0 5 10 15 20 25 30 35 40 45 50

-1000

0

1000

Channel Record (Signal + noise)

Am

plitu

de

0 5 10 15 20 25 30 35 40 45 500

0.02

0.04

0.06

0.08

0.1

Time (nS)

CLEAN Template Correlation vs Frequency Domain De-Convolution

norm

aliz

ed h

(t)

Frequency De-Convolution CIR

CLEAN CIR

July 2002


Slide 34

doc.: IEEE 802.15-02/240SG3a

Submission

CLEAN Algorithmgeometric interpretation

s

r

s-r

Original scan Error vector

Linear space of all possible reconstructed scans

CLEAN approximation to original scan (reconstructed scan)

2

2

s

r

Energy Capture Ratio:

Relative Error:

2

2

s

rs

Least Squares Condition:

12

2

2

2

s

rs

s

r(2)

July 2002


Slide 35

doc.: IEEE 802.15-02/240SG3a

Submission

CLEAN Algorithmestimation of signal distortion

• CLEAN returns the CIR in precisely the desired form (1).

• Convolution of CIR with pulse template p(t) produces the “reconstructed” channel record r(t):

• When the least squares condition (2) holds, the residual difference between the CLEAN reconstruction and original channel record is a measure of the distortion introduced by the channel (i.e. the amount of signal energy that is not of the form (1)).

N

kkk tpatphtr

0

)()(

July 2002


Slide 36

doc.: IEEE 802.15-02/240SG3a

Submission

CLEAN Residual Estimates of Signal Distortion

• Least squares condition met at 85% energy capture ratio, on average.

• Estimated signal distortion:

– NLOS, 0 to 4 meters, metal stud case: 15.5% (0.7 dB)

– LOS, 0 to 4 meters, metal stud case: 16.6% (0.7 dB)

– NLOS, 4 to 10 meters, metal stud case: 17.0% (0.8 dB)

July 2002


Slide 37

doc.: IEEE 802.15-02/240SG3a

Submission

• Introduction


• Data Analysis




July 2002


Slide 38

doc.: IEEE 802.15-02/240SG3a

Submission

Data Used for the Analysis• 429 of the 471 channel records

– all vertically polarized measurements.

– duplicate measurements removed.

Number of Measurement Locations vs Distance

130137

69 6863

4

27.60%

56.69%

71.34%

85.77%

99.15% 100.00%

0

20

40

60

80

100

120

140

160

2 4 6 8 10 MoreBin (m)

Numb

er o

f Tra

nsmi

t and

Rec

eive

Ant

enna

Pos

ition

s

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Number of Measurement Positions per Bin Cumulative %

July 2002


Slide 39

doc.: IEEE 802.15-02/240SG3a

Submission

General Remarks on the Data

• Data collection SNRs varied from about 40 dB for 1-meter boresight scans to about 15 dB for some 10-meter NLOS scans.

• LOS and NLOS channels exhibit wide variations in path loss and RMS delay spread. Some NLOS channels have lower delay spreads than some LOS channels.

– The variations can be explained by grazing angles and destructive interference for LOS channels , and low attenuation through materials for NLOS channels.

July 2002


Slide 40

doc.: IEEE 802.15-02/240SG3a

Submission

Scan #1: LOS 1m distance, Antenna Boresight

1/r2 Path Loss

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20-0.06

-0.04

-0.02

0

0.02

0.04

0.06A

mpl

itude

Time (ns)

July 2002


Slide 41

doc.: IEEE 802.15-02/240SG3a

Submission

Scan #57: LOS 3.1m distance, office environment,

approximately 1/r5.28 Path Loss

0 5 10 15 20 25 30 35 40-5

-4

-3

-2

-1

0

1

2

3

4

5x 10

-3

Am

plitu

de

Time (ns)

Check this one!Check this one!

July 2002


Slide 42

doc.: IEEE 802.15-02/240SG3a

Submission

Scan #6: NLOS 1.3m distance, office environment,

approximately 1/r26.5 path loss

0 5 10 15 20 25 30 35

-2

-1

0

1

2

3

x 10-3

Am

plitu

de

Time (ns)

July 2002


Slide 43

doc.: IEEE 802.15-02/240SG3a

Submission

Scan #15: NLOS2.7m distance, office environment

approximately 1/r2.07 Path Loss

0 2 4 6 8 10 12 14 16 18 20

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

Am

plitu

de

Time (ns)

July 2002


Slide 44

doc.: IEEE 802.15-02/240SG3a

Submission

Descriptive Statistics of the Data

• CIRs and channel parameters extracted for all 429 records.

• Statistical analysis and model fitting done only for metal stud measurements.– 369 metal stud measurements.– 60 wood stud measurements not enough for statistical

breakdown.– Three scenarios considered:

• I. NLOS, 0 to 4 meters, metal stud.• II. LOS, 0 to 4 meters, metal stud.• III. NLOS, 4 to 10 meters, metal stud.

– Not enough LOS, 4 to 10 meter channels for analysis.

July 2002


Slide 45

doc.: IEEE 802.15-02/240SG3a

Submission

Explanation of Channel Statistics

• Channels characterized in terms of the following statistical parameters– RMS delay as a function of distance.

– Mean excess delay as a function of distance.

– Number of multipath components per channel.

– Occupancy probabilities as a function of excess delay.

– Mean log relative magnitudes as a function of excess delay.

July 2002


Slide 46

doc.: IEEE 802.15-02/240SG3a

Submission

delays

amplitudes

LOS delay

kth excess delay: k – 0

0 1k

a0a1 ak

amax

Channel Statistics

• Mean excess delay is a weighted average of the excess delays in the CIR.

• CIR amplitudes are the weights

• RMS delay is the standard deviation of the excess delays.• again using the CIR amplitudes as the weights.

kth relative magnitude:maxa

ak

time

multipath component

July 2002


Slide 47

doc.: IEEE 802.15-02/240SG3a

Submission

excess delay

rela

tive

mag

nit

ud

e

Mean relative magnitude over a collection of CIRs

Channel Statistics

excess delay

pro

ba

bil

ity

of

occ

up

anc

y

Probability that there is a multipath component at a given excess delay offset

July 2002


Slide 48

doc.: IEEE 802.15-02/240SG3a

Submission

Dependence of Channel Statistics on CLEAN Algorithm

Stopping Condition

• Channel statistics computed from channel impulse response as calculated by CLEAN algorithm.

• Dependence of channel statistics on stopping criteria assessed.

• The following energy capture stopping criteria were evaluated: 80%, 85%, 90%, 95%

July 2002


Slide 49

doc.: IEEE 802.15-02/240SG3a

Submission

80% Energy Capture(notional)

amplitudes

time

July 2002


Slide 50

doc.: IEEE 802.15-02/240SG3a

Submission

amplitudes

time


July 2002


Slide 51

doc.: IEEE 802.15-02/240SG3a

Submission


amplitudes

time

July 2002


Slide 52

doc.: IEEE 802.15-02/240SG3a

Submission


amplitudes

time

What is the effect on channel statistics?

July 2002


Slide 53

doc.: IEEE 802.15-02/240SG3a

Submission

Comparison of Statistics Across Energy Capture Ratios

85% energy capture 95% energy capture

Avg. RMS Delay

Mean Number of Components per Channel

Avg. Mean Excess Delay

11.57 ns8.78 ns

12.41 ns10.04 ns

36.1 86.0

I. NLOS, 0 to 4 meters, metal stud

July 2002


Slide 54

doc.: IEEE 802.15-02/240SG3a

Submission


II. LOS, 0 to 4 meters, metal stud


Avg. RMS Delay



6.36 ns5.27 ns

5.17 ns4.95 ns

24.0 42.3

July 2002


Slide 55

doc.: IEEE 802.15-02/240SG3a

Submission


III. NLOS, 4 to 10 meters, metal stud


Avg. RMS Delay



14.59 ns 16.80 ns

15.95 ns14.24 ns

61.6 117.7

July 2002


Slide 56

doc.: IEEE 802.15-02/240SG3a

Submission

85% Energy Capture Ratio Used for Statistical Analysis

• Number of multipath components per channel is the statistic that is most sensitive to changes in the stopping criteria.

• Large change in number of multipath components causes only small changes in other statistics in going from 85% to 95% energy capture ratio.

• 85% stopping criteria also good from a least squares point of view.

July 2002


Slide 57

doc.: IEEE 802.15-02/240SG3a

Submission

Statistical Environmental Models

• Each environment characterized by statistical profile of channels collected from that environment.

• Statistical analysis and model fitting done only for metal stud measurements.– 369 metal stud measurements.– 60 wood stud measurements not enough for statistical

breakdown.– Three scenarios considered:

• I. NLOS, 0 to 4 meters, metal stud (120 channels).• II. LOS, 0 to 4 meters, metal stud (xxx channels).• III. NLOS, 4 to 10 meters, metal stud (xxx channels).

– Not enough LOS, 4 to 10 meter channels for analysis.

July 2002


Slide 58

doc.: IEEE 802.15-02/240SG3a

Submission

20

33

45

22

0

5

10

15

20

25

30

35

40

45

50

0 to 1 m 1 to 2 m 2 to 3 m 3 to 4 m

distance

nu

mb

er o

f ch

ann

els


Histogram of Number of Measurements per Meter

Total Number of Measured Channels: 120

July 2002


Slide 59

doc.: IEEE 802.15-02/240SG3a

Submission

27

41

36

14

6

0

5

10

15

20

25

30

35

40

45

1 to 20 21 to 40 41 to 60 61 to 80 81 to 83

number of components per channel

nu

mb

er o

f ch

ann

els


Histogram of Number of Multipath Components Per Channel

Mean Number of Components Per Channel: 36.1

July 2002


Slide 60

doc.: IEEE 802.15-02/240SG3a

Submission

Probability of Occupancy

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30 35 40 45

excess delay (ns)

pro

bab

ilit

y


Multipath Arrival Time Distribution

Graph of the probability that an excess delay bin contains a reflection.

July 2002


Slide 61

doc.: IEEE 802.15-02/240SG3a

Submission

-2.5

-2

-1.5

-1

-0.5

0

0 10 20 30 40 50 60

excess delay (ns)

log

rel

ativ

e m

agn

itu

de


Mean of Log Relative Magnitude vs. Excess Delay

Mean Log Relative MagnitudeMean Log Relative Magnitude

Mean + stdv.Mean + stdv.

Mean - stdv.Mean - stdv.

July 2002


Slide 62

doc.: IEEE 802.15-02/240SG3a

Submission

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3 3.5 4

distance (m)

me

an

RM

S d

ela

y (

ns

)


Mean RMS Delay vs. Distance

Mean RMS DelayMean RMS Delay



Mean RMS Delay: 8.78 ns

Standard Deviation of RMS Delay: 4.34 ns

July 2002


Slide 63

doc.: IEEE 802.15-02/240SG3a

Submission

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3 3.5 4

distance (m)

me

an

RM

S d

ela

y (

ns

)


Average Mean Excess Delay vs. Distance

Average Mean Excess Delay: 10.04 ns

Standard Deviation of Mean Excess Delay : 6.26 ns





July 2002


Slide 64

doc.: IEEE 802.15-02/240SG3a

Submission

6

27

32

14

0

10

20

30

40

0 to 1 meters 1 to 2 meters 2 to 3 meters 3 to 4 meters

distance

nu

mb

er o

f ch

ann

els




July 2002


Slide 65

doc.: IEEE 802.15-02/240SG3a

Submission

43

20

10

41 1

0

10

20

30

40

50

1 to 20 21 to 40 41 to 60 61 to 80 81 to 100 101 to 109

number of components

nu

mb

er o

f ch

ann

els




July 2002


Slide 66

doc.: IEEE 802.15-02/240SG3a

Submission


0

0.2

0.4

0.6

0.8

1

-10 0 10 20 30 40 50 60

excess delay (ns)

pro

bab

ilit

y



July 2002


Slide 67

doc.: IEEE 802.15-02/240SG3a

Submission

-2.5

-2

-1.5

-1

-0.5

0

-10 0 10 20 30 40 50

excess delay

log

rel

ativ

e m

agn

itu

de






July 2002


Slide 68

doc.: IEEE 802.15-02/240SG3a

Submission

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3 3.5 4

distance (m)

RM

S d

elay

(n

s)


Mean RMS Delay vs. Distance

Mean RMS DelayMean RMS DelayMean + stdv.Mean + stdv.




July 2002


Slide 69

doc.: IEEE 802.15-02/240SG3a

Submission

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3 3.5 4

distance (m)

mea

n e

xces

s d

elay

(n

s)


Average Mean Excess Delay vs. Distance


Standard Deviation of Mean Excess Delay: 4.14 ns





July 2002


Slide 70

doc.: IEEE 802.15-02/240SG3a

Submission

28

22

17

22

19

11

0

5

10

15

20

25

30

4 to 5 5 to 6 6 to 7 7 to 8 8 to 9 9 to 10

distance (m)

nu

mb

er o

f ch

ann

els




July 2002


Slide 71

doc.: IEEE 802.15-02/240SG3a

Submission

7

17

41

32

13

3 3 2 1

0

5

10

15

20

25

30

35

40

45

1 to 20 21 to 40 41 to 60 61 to 80 81 to100

101 to120

121 to140

141 to160

161 to180


nu

mb

er o

f ch

ann

els




July 2002


Slide 72

doc.: IEEE 802.15-02/240SG3a

Submission


0

0.2

0.4

0.6

0.8

1

-20 0 20 40 60 80 100 120

excess delay (ns)

pro

bab

ilit

yMultipath Arrival Time Distribution


July 2002


Slide 73

doc.: IEEE 802.15-02/240SG3a

Submission

-2.5

-2

-1.5

-1

-0.5

0

-20 0 20 40 60 80 100






July 2002


Slide 74

doc.: IEEE 802.15-02/240SG3a

Submission

0

5

10

15

20

25

4 5 6 7 8 9 10

distance (m)

RM

S d

elay

(n

s)Mean RMS Delay vs. Distance


Mean RMS DelayMean RMS Delay





July 2002


Slide 75

doc.: IEEE 802.15-02/240SG3a

Submission

0

5

10

15

20

25

4 5 6 7 8 9 10

distance (m)

RM

S d

elay

(n

s)Average Mean Excess Delay vs. Distance


Standard Deviation of Mean Excess Delay: 5.97 ns






July 2002


Slide 76

doc.: IEEE 802.15-02/240SG3a

Submission

27

41

36

14

6

0

5

10

15

20

25

30

35

40

45

1 to 20 21 to 40 41 to 60 61 to 80 81 to 83


num

ber

of c

hann

els

43

20

10

41 1

0

10

20

30

40

50

1 to 20 21 to 40 41 to 60 61 to 80 81 to 100 101 to 109


num

ber

of c

hann

els

7

17

41

32

13

3 3 2 1

0

5

10

15

20

25

30

35

40

45

1 to 20 21 to 40 41 to 60 61 to 80 81 to100

101 to120

121 to140

141 to160

161 to180


num

ber

of c

hann

els

NLOS

LOS

4 – 10 m

0 – 4 m

Number of Components Per Channelcomparison across scenarios

NLOS

0 – 4 m

July 2002


Slide 77

doc.: IEEE 802.15-02/240SG3a

Submission


0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

excess delay (ns)

prob

abili

ty


0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

excess delay (ns)

prob

abili

ty


0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

excess delay (ns)

prob

abili

ty

Distribution of Multipath Arrival Timescomparison across scenarios

NLOS

LOS

4 – 10 m

0 – 4 m

NLOS

0 – 4 m

July 2002


Slide 78

doc.: IEEE 802.15-02/240SG3a

Submission

-2.5

-2

-1.5

-1

-0.5

0

0 20 40 60 80 100

-2.5

-2

-1.5

-1

-0.5

0

0 20 40 60 80 100

excess delay

log

rela

tive

mag

nitu

de

-2.5

-2

-1.5

-1

-0.5

0

0 20 40 60 80 100

excess delay (ns)

log

rela

tive

mag

nitu

de

Mean of Log Relative Magnitudecomparison across scenarios

NLOS

LOS

4 – 10 m

0 – 4 m

NLOS

0 – 4 m

July 2002


Slide 79

doc.: IEEE 802.15-02/240SG3a

Submission

0

5

10

15

20

25

4 5 6 7 8 9 10

distance (m)

RM

S d

elay

(ns)

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3 3.5 4

distance (m)

mea

n R

MS

del

ay (n

s)

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3 3.5 4

distance (m)

RM

S d

elay

(ns)

RMS Delay vs. Distancecomparison across scenarios

NLOS

LOS

4 – 10 m

0 – 4 m

NLOS

0 – 4 m

July 2002


Slide 80

doc.: IEEE 802.15-02/240SG3a

Submission

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3 3.5 4

distance (m)

mea

n R

MS

del

ay (n

s)

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3 3.5 4

distance (m)

mea

n ex

cess

del

ay (n

s)

0

5

10

15

20

25

4 5 6 7 8 9 10

distance (m)

RM

S d

elay

(ns)

RMS Delay vs. Distancecomparison across scenarios

NLOS

LOS

4 – 10 m

0 – 4 m

NLOS

0 – 4 m

July 2002


Slide 81

doc.: IEEE 802.15-02/240SG3a

Submission

• Introduction


• Data Analysis




July 2002


Slide 82

doc.: IEEE 802.15-02/240SG3a

Submission

Modeling Approach

• Attempted to fit two different models to the data– A modified IEEE 802.11 channel model– Modified -K model

• Models evaluated on how well they reproduced the statistic distributions of the data– Bhattacharyya distance calculated between

simulated and measured distributions.

July 2002


Slide 83

doc.: IEEE 802.15-02/240SG3a

Submission

Modified IEEE 802.11 model

• Regularly spaced impulses– modified for UWB to allow for random placement

of impulses in each time bin

• Raleigh-distributed magnitudes• input parameters

– TRMS : RMS delay parameter

– TS : time discretization unit

• Was not able to match both RMS delay and multipath intensity profile simultaneously.

July 2002


Slide 84

doc.: IEEE 802.15-02/240SG3a

Submission

Bhattacharyya Distance: 0.626

0

0.1

0.2

0.3

0.4

0.5

2 4 6 8 10 12 14 16 18 20

RMS delay (ns)

pro

ba

bil

ity

simulated measured


Distribution of RMS Delay

measured: 8.85 (ns)

Mean RMS Delay

simulated: 8.58 (ns)

July 2002


Slide 85

doc.: IEEE 802.15-02/240SG3a

Submission

-7

-6

-5

-4

-3

-2

-1

0

-20 0 20 40 60 80 100 120

excess delay (ns)

log

re

lati

ve m

ag

nit

ud

e

simulated measured



July 2002


Slide 86

doc.: IEEE 802.15-02/240SG3a

Submission

-K Model

• Arrival time model– Model “clumping” of multipath arrival times by making

the probability of an arrival in a given excess delay bin dependent on whether there was an arrival in the previous bin.

– “K” value is the ratio of these conditional probabilities.• Modeling assumption is that K is constant.

– “” value is the time discretization unit.

1bin in arrival | bin in arrivalPr i-ipi

1bin in arrival no | bin in arrivalPr i-ipi

positive conditional

negative conditional

July 2002


Slide 87

doc.: IEEE 802.15-02/240SG3a

Submission

-K Model

• Amplitude model

– Log-normal model for multipath amplitudes

– Mean and standard deviation as functions of excess delay given by the statistics of the data.

July 2002


Slide 88

doc.: IEEE 802.15-02/240SG3a

Submission

• Multipath arrival times governed by statistics of data– Probability of a multipath arrival in a given

time bin depends on whether previous bin was occupied.

– Positive and negative conditional probabilities derived from statistics of data.

– No assumption that ratio of conditional probabilities is constant.

Modified-K Model

July 2002


Slide 89

doc.: IEEE 802.15-02/240SG3a

Submission

Simulation Results

• time discretization unit= = 0.1 ns for all cases.

• Empirical probabilities of occupancy and log relative magnitude data used as inputs to model.

– A -K simulation would use approximations to these quantities as its inputs, and hence could perform no better.

July 2002


Slide 90

doc.: IEEE 802.15-02/240SG3a

Submission

Occupancy Probabilities

0

0.1

0.2

0.3

0.4

0.5

-20 0 20 40 60 80 100

excess delay (ns)

pro

bab

ilit

y

simulated measured



95% Energy Capture data used.95% Energy Capture data used.

July 2002


Slide 91

doc.: IEEE 802.15-02/240SG3a

Submission

-5

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

-20 0 20 40 60 80 100

excess delay (ns)

log

rel

ativ

e m

agn

itu

de

simulated measured



95% Energy Capture data used.95% Energy Capture data used.

July 2002


Slide 92

doc.: IEEE 802.15-02/240SG3a

Submission


0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 50 10 15


pro

bab

ilit

y

simulated measured


Distribution of Number of Multipath Components Per Channel

measured: 42.3

Mean Number of Components Per Channel

simulated: 43.995% Energy Capture data used.

95% Energy Capture data used.

July 2002


Slide 93

doc.: IEEE 802.15-02/240SG3a

Submission


0

0.1

0.2

0.3

0.4

0.5

2 4 6 8 10 12 14 16 18 20 22 24 26

RMS Delay (ns)

pro

bab

iity

simulated measured


Distribution of RMS Delay

measured: 6.36 (ns)

Mean RMS Delay

simulated: 11.70 (ns)95% Energy Capture data used.

95% Energy Capture data used.

July 2002


Slide 94

doc.: IEEE 802.15-02/240SG3a

Submission

• Introduction


• Data Analysis




July 2002


Slide 95

doc.: IEEE 802.15-02/240SG3a

Submission

Conclusion• Modeling channel response as a sum of scaled/delayed

versions of channel input provides a good fit to data.

• Wide variety of channel characteristics, even within the same environment.

• Multipath arrival times and average power decay profiles follow linear or piece-wise linear trends.

• Exact parameter values for arrival times and decay profiles are dependent on the environment type.

• Occupancy probabilities and decay profiles do not completely characterize the channel data, since two models can have the same statistics for these quantities, and yet differ in the statistics of RMS delay.

July 2002


Slide 96

doc.: IEEE 802.15-02/240SG3a

Submission

Recommendations

• IEEE 802.11 and -K model should not be used, because they do not provide good fits to the statistical models of the environments.

• Selected SG3A model should fit the collected data.– Number of multipath components per channel– Probability of occupancy – Average power decay profile– Distribution of RMS delay vs. distance– Distribution of mean excess delay vs. distance

July 2002


Slide 97

doc.: IEEE 802.15-02/240SG3a

Submission

R.A. Scholtz, Notes on CLEAN and Related Algorithms, Technical Report to Time Domain Corporation, April 20, 2001

Homayoun Hashemi, “Impulse Response Modeling of Indoor Radio Propagation Channels”, IEEE Jornal on Slected Areas in Communications, VOL. 11, No. 7, September 1993

Theodore S. Rappaport, “Wireless Communications Principles and Practice”, 1996

Intelligent Automation, Inc., “Channel Impulse Response Modeling: Comparison Analysis of CLEAN algorithm and FT-based Deconvolution Techniques, Technical Report to Time Domain Corporation, November 21, 2001

Bob O’Hara and Al Petrick, “IEEE 802.11 Handbook A Designer’s Companion”, 1999

References

July 2002


Slide 98

doc.: IEEE 802.15-02/240SG3a

Submission

Definitions/Terminology

July 2002


Slide 99

doc.: IEEE 802.15-02/240SG3a

Submission

• LOS

– Line of Sight (transmit and receive antenna have a clear visible field of view relative to each other)

• NLOS

– Non-Line of Sight

• CIR

– Channel Impulse Response

• Waveform Template

– correlation template used in the correlation process (CLEAN Algorithm)

• LTI

– Linear Time Invariant

Terminology

July 2002


Slide 100

doc.: IEEE 802.15-02/240SG3a

Submission

N

kkk tath

0

)()(

1

-1

a3

Example CIR

a2

a1

1

2

3

Where: ak are the impulse amplitudesk are the impulse delays

• CLEAN1 – Variant of a serial correlation algorithm

• Channel Modeled as LTI filter, with impulse response h(t) of the form:

Terminology

July 2002


Slide 101

doc.: IEEE 802.15-02/240SG3a

Submission

• RMS Delay Spread can be expressed as:

N

kk

N

kkk

N

kkk

a

aa

0

2

0

2

0

22

Terminology

July 2002


Slide 102

doc.: IEEE 802.15-02/240SG3a

Submission

• Mean Excess Delay can be expressed as:

N

kk

N

kkk

a

a

0

2

0

2

Terminology

July 2002


Slide 103

doc.: IEEE 802.15-02/240SG3a

Submission

• Relative Magnitude can be expressed as:

max

)()(a

am

Where:

)(max0

max kNkaa

Terminology

July 2002


Slide 104

doc.: IEEE 802.15-02/240SG3a

Submission

• Average Multipath Intensity Profile (MIP) (or Average Power Decay Profile (APDP) can be expressed as:

)(ln()( 2 mEP

Terminology

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Doc.: IEEE 802.15-02/240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time...

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