Doc.: IEEE 802.15-02/294SG3a 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 802.15-02/294SG3a

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: 08 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 802.15-02/294SG3a

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

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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 802.15-02/294SG3a

Submission

Outline

• Introduction

• Measurement Campaign

• Data Analysis

• Statistical Environmental Models

• Analytical Models

• Conclusions/Recommendations

July 2002


Slide 5

doc.: IEEE 802.15-02/294SG3a

Submission

• Introduction


• Data Analysis




July 2002


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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 7

doc.: IEEE 802.15-02/294SG3a

Submission

Overview of Results

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

• Environmental signal distortion estimated.

• Multipath channel described statistically:• Number of multipath components.• Distribution of multipath arrival times.• Average power decay profile• Distribution of RMS delay vs. distance• Distribution of mean excess delay vs. distance

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

• Recommendations made.

July 2002


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Submission

• Introduction


• Data Analysis




July 2002


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doc.: IEEE 802.15-02/294SG3a

Submission

Purpose: • Obtaining diverse set of measurements of the UWB

channel.

– 11 Different office and home environments

– LOS and NLOS channels

– Wood & Metal Studs construction

– Distances up to 10 meters

– Documentation: Methodology, location, environments

July 2002


Slide 10

doc.: IEEE 802.15-02/294SG3a

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).

July 2002


Slide 11

doc.: IEEE 802.15-02/294SG3a

Submission

Channel Measurement Test Setup

LNA

Delay Line

Preamp

Filter

f (GHz)

3

Floppy

HP54750A

Ch.1

Ch.2

Trig

DSO

5

Tx:-10dBm

TDC SG

Attenuator

37dB0-80dB

Rx:20dB Gain4.8dB NF3dBi

30dB2.2NF

Channel

July 2002


Slide 12

doc.: IEEE 802.15-02/294SG3a

Submission

Measurement Location Example13'-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

Chair

z= 48

Chair

z 19

Chair

z = 48

z =

81.3

49

z =

124.4z =

71z =

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

NLOS LOS

July 2002


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doc.: IEEE 802.15-02/294SG3a

Submission

Measurement Database• 429 included in delivered data base.• Database includes:

– Received waveform – Extracted channel impulse responses.– Calculated channel parameters (RMS delay and

path loss).– Various measurement meta-data

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

receiver.

July 2002


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doc.: IEEE 802.15-02/294SG3a

Submission

• Introduction


• Data Analysis




July 2002


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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 as a function of environment type.

July 2002


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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 17

doc.: IEEE 802.15-02/294SG3a

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 18

doc.: IEEE 802.15-02/294SG3a

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 19

doc.: IEEE 802.15-02/294SG3a

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 20

doc.: IEEE 802.15-02/294SG3a

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


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Submission

• Introduction


• Data Analysis




July 2002


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doc.: IEEE 802.15-02/294SG3a

Submission

Explanation of Channel Statistics

• Channels characterized in terms of the following statistical parameters– Number of multipath components per channel.

– Occupancy probabilities as a function of excess delay.

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

– RMS delay as a function of distance.

– Mean excess delay as a function of distance.

July 2002


Slide 23

doc.: IEEE 802.15-02/294SG3a

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 square amplitudes provide the weights

• RMS delay is the standard deviation of the excess delays.• again the CIR square amplitudes provide the weights.

kth relative magnitude:maxa

ak

time

multipath component

July 2002


Slide 24

doc.: IEEE 802.15-02/294SG3a

Submission

excess delay

rela

tive

mag

nit

ud

e

Mean relative magnitude at a given excess delay value 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 25

doc.: IEEE 802.15-02/294SG3a

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 26

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Submission

80% Energy Capture(notional)

amplitudes

time

July 2002


Slide 27

doc.: IEEE 802.15-02/294SG3a

Submission

amplitudes

time


July 2002


Slide 28

doc.: IEEE 802.15-02/294SG3a

Submission


amplitudes

time

July 2002


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doc.: IEEE 802.15-02/294SG3a

Submission


amplitudes

time

What is the effect on channel statistics?

July 2002


Slide 30

doc.: IEEE 802.15-02/294SG3a

Submission

Comparison of Statistics Across Energy Capture Ratios

II. LOS, 0 to 4 meters, metal stud

85% energy capture 95% energy capture

Avg. RMS Delay

Mean Number of Components per Channel

Avg. Mean Excess Delay

6.36 ns5.27 ns

5.17 ns4.95 ns

24.0 42.3

July 2002


Slide 31

doc.: IEEE 802.15-02/294SG3a

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 32

doc.: IEEE 802.15-02/294SG3a

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 33

doc.: IEEE 802.15-02/294SG3a

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

I. NLOS, 0 to 4 meters, metal stud

Histogram of Number of Measurements per Meter

Total Number of Measured Channels: 120

July 2002


Slide 34

doc.: IEEE 802.15-02/294SG3a

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 35

doc.: IEEE 802.15-02/294SG3a

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 36

doc.: IEEE 802.15-02/294SG3a

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 37

doc.: IEEE 802.15-02/294SG3a

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 38

doc.: IEEE 802.15-02/294SG3a

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 39

doc.: IEEE 802.15-02/294SG3a

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 40

doc.: IEEE 802.15-02/294SG3a

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 41

doc.: IEEE 802.15-02/294SG3a

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 42

doc.: IEEE 802.15-02/294SG3a

Submission

0

5

10

15

20

25

4 5 6 7 8 9 10

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 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 R

MS

del

ay (n

s)

RMS Delay vs. Distancecomparison across scenarios

NLOS

LOS

4 – 10 m

0 – 4 m

NLOS

0 – 4 m

July 2002


Slide 43

doc.: IEEE 802.15-02/294SG3a

Submission

0

5

10

15

20

25

0 0.5 1 1.5 2 2.5 3 3.5 4

distance (m)

aver

age

mea

n ex

cess

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)

aver

age

mea

n ex

cess

del

ay (n

s)

0

5

10

15

20

25

4 5 6 7 8 9 10

distance (m)

aver

age

mea

n ex

cess

del

ay (n

s)

Mean Excess Delay vs. Distancecomparison across scenarios

NLOS

LOS

4 – 10 m

0 – 4 m

NLOS

0 – 4 m

July 2002


Slide 44

doc.: IEEE 802.15-02/294SG3a

Submission

• Introduction


• Data Analysis




July 2002


Slide 45

doc.: IEEE 802.15-02/294SG3a

Submission

Modeling Approach

• Attempted to fit two analytical 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 46

doc.: IEEE 802.15-02/294SG3a

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

• Exponential decay profile• input parameters

– TRMS : RMS delay parameter

– TS : time discretization unit

• Unable to match both RMS delay and multipath intensity profile simultaneously.

July 2002


Slide 47

doc.: IEEE 802.15-02/294SG3a

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 48

doc.: IEEE 802.15-02/294SG3a

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 49

doc.: IEEE 802.15-02/294SG3a

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 50

doc.: IEEE 802.15-02/294SG3a

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 51

doc.: IEEE 802.15-02/294SG3a

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 52

doc.: IEEE 802.15-02/294SG3a

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 53

doc.: IEEE 802.15-02/294SG3a

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

ility


Multipath Arrival Time Distribution

measured

simulated

July 2002


Slide 54

doc.: IEEE 802.15-02/294SG3a

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



July 2002


Slide 55

doc.: IEEE 802.15-02/294SG3a

Submission


0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.0 50.0 100. 150.


pro

bab

ilit

y

simulated measured


Distribution of Number of Multipath Components Per Channel

measured: 42.3

Mean Number of Components Per Channel

simulated: 43.9

July 2002


Slide 56

doc.: IEEE 802.15-02/294SG3a

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)

July 2002


Slide 57

doc.: IEEE 802.15-02/294SG3a

Submission

• Introduction


• Data Analysis




July 2002


Slide 58

doc.: IEEE 802.15-02/294SG3a

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 59

doc.: IEEE 802.15-02/294SG3a

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 60

doc.: IEEE 802.15-02/294SG3a

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

Doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time...

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Transcript of Doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time...