wirelesschannelcharacterizationandmodelingkom.aau.dk/group/05gr999/reference_material/channel/thesis_slides.pdf ·...

Wireless Channel Characterization and Modeling

Masters Thesis Defense

by

Vijayalakshmi Vasudevan

Advisor : Prof.Kavitha Chandra

Center for Advanced Computation and Telecommunications

Department of Electrical Engineering

University of Massachusetts Lowell

Wireless Channel Characterization and Modeling - 1

Outline

Motivation for studying wireless channel

Performance Degradation

Channel Effects

Impact of link & control layer protocols

Thesis Outline

Indoor RF experiments

Free Space Optical (FSO) channel characterization

Modeling Indoor RF channel responses

Thesis contributions


Basic elements of communication system

LinkLayer

Link

Layer

ControlLayer

LayerControl

TCP/IP Layer

Channel

Noise+

InputEncoderSource Channel

EncoderModulator

Source

DecoderChannelDecoder

OutputDemodulator

Physical Layer


Motivation : To answer Questions:

What are the features of the channel that most influence

performance of wireless links ?

To what degree, choice of system functions

(coding/modulation) and network protocols influence

performance degradation?

Performance is measured w.r.t:

Bit/packet error rate

Power consumed (SNR required)

Average/Peak Throughput

Sensitivity to spatial and temporal placements

Robustness to link outages


Summarizing Channel effects

Channel Interference

Signal Fading(Multipath Propagation)

Flat Fading

Time varying channel

Additive Noise

Frequency SelectiveGaussian

Stationary Non−Stationary

Non−Gaussian


Thesis Objectives

Conduct Indoor RF channel measurements & analyzeexperimental data

Analysis of TCP flows on IEEE 802.11b WLANs

Analyze Free Space Optical channel data obtained fromLLNL/MIT-LL

Characterize fading amplitude distributions

Examine influence of channel (atmosphere) parameters & BER

Address parametric modeling of indoor CIR

Analyze results from computational models designed byProf.Thompson, M.Raspopovic, M.Denis

Determine pole-zero model of the transfer function


Publications

“TCP & IEEE 802.11b protocol performance in indoor

wireless channels” - V.Vasudevan, M.Parikh, K.Chandra and

C.Thompson

- In Proc. of IEEE Sarnoff Symposium, March 2003,

pg:258-261

“Models for free space optical channels” - M.Parikh,

V.Vasudevan, K.Chandra and V.Mehta

- in preparation

“Characterizing Spatial Correlation in indoor channels” -

M.Denis, V.Vasudevan, K.Chandra and C.Thompson

- In Proc. of IEEE WCNC, March 2004


Experimental analysis of indoor wireless channels


Literature Survey

Balakrishnan et.al (1995) - Reliable LL protocol with

knowledge of TCP improves performance

Valenzuela et.al (1997) - Evaluated meast. of local mean

signal strength for propagation prediction models

Kamerman & Aben (2000) - Compared throughput

performance in 802.11 networks

Gurtov (2001) - Experimented long delays in different OS,

resulting in a spurious timeout in New Reno


Link Layer: IEEE 802.11b protocol

Defines MAC and PHY layer protocols for RF transmission in

unlicensed

��

� � ��

Ghz band

PHY :

Controls multiple access to RF channel

Direct Sequence Spread Spectrum

MAC :

Distributed Coordination Function - CSMA/CA

Point Coordination Function - Circular polling mechanism

Protocol overhead - PHY/MAC/TCP - 90 bytes

Throughput - Maximum:

� �

Mbps , Achievable:

� � � Mbps


Transmission Control Protocol

Reliable transport protocol

Window size � � � �� CWND : congestion window, RWND : receiver window

� < ssthresh : Slow start : � � � �

� > ssthresh : Congestion avoidance: � = � ��

Acknowledgments - expected within a timeout value

calculated adaptively from round trip times.

Retransmissions:

Fast Retransmit after 3 duplicate Acks.

Retransmission Time out (RTO) and Exponential back off


Measurements

Setup

34 f

t56 ft

LOS

NL

OS

� ��

� � �� AP (26,34)

(26,20)

(10,18)

CACT lab

AP : Apple,Belkin

MT : Linux,Macintosh

Server : UNIX

Details

TCP flow : 5000 blocks

Data : 6000 bytes/block

TCP Version : Reno

Trace capture : tcpdump

Signal capture

iwconfig - signal

measurements

iwevent - MAC level

packet drops

Ref:Jean Tourillhes

(Wireless Tools in Linux)Wireless Channel Characterization and Modeling - 12

Measurement of signal level at MT

PDF

interferenceNLOS with

NLOS withoutinterference

P[S

< s]

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

-66 -64 -62 -60 -58 -56 -54 -52

LOS

Signal Level (s dBm)

Shift in signal levels

- due to interference

Time series

-65

-64

-63

-62

-61

-60

-59

-58

-57

0 100 200 300 400 500

Sign

al le

vel (

dBm

)

Time (seconds)

NLOS w. Interf

-61

-60

-59

-58

-57

-56

0 50 100 150 200

Sign

al le

vel (

dBm

)

Time (seconds)

NLOS w.o Interf


Measurement of signal level at MT

0.001

0.01

0.1

1

0 10 20 30 40 50 60 70

Pr[T >

τ]

Time between losses (τ seconds)

ModeratePoor

Good

Set-I exhibits faster transitions

betwn Low/High levels

uncertainty in channel sensing

packet drops at MAC level

Clear discrimination

Rx signal variance and mean

position and interference level

Time series-Poor,Moderate,Good

-65

-64

-63

-62

-61

-60

-59

-58

-57

71700 71800 71900 72000 72100 72200 72300

Sign

al lev

el (d

B)

Time (seconds)

Poor

-63

-62

-61

-60

-59

-58

-57

73200 73250 73300 73350 73400 73450 73500 73550

Sign

al lev

el (d

B)

Time (seconds)

Moderate

-61

-60

-59

-58

-57

-56

-55

50600 50800 51000 51200 51400 51600 51800 52000

Sign

al lev

el (d

B)

Time (seconds)

Good


TCP Retransmissions

Statistics

Avg. Throughput-

��

� ��

��

��

��

� �

Mbps

� � � (server)- Mean:

��

� �

��

��

��

ms- Variance:

� ��

��

��

��

��

�

ms

�� (client)- Mean:

��

� ��

��

� ��

��

� �

ms- Variance:

��

� ��

��

� �

��

� �ms

Retransmissions :

��

� ��

��

� �

��

� �

%

Single losses are acharacteristic feature

Sequence number & Losses

0

20

40

60

80

100

120

140

160

71700 71800 71900 72000 72100 72200 72300

Sequ

ence

Num

ber (

106 )

Time (seconds)

Poor

0

20

40

60

80

100

120

140

160

73200 73250 73300 73350 73400 73450 73500 73550

Sequ

ence

Num

ber (

106 )

Time (seconds)

Moderate

0

100

200

300

400

500

600

700

50600 50800 51000 51200 51400 51600 51800 52000

Sequ

ence

Num

ber (

106 )

Time (seconds)

Good


Comparative Analysis

Apple Airport

Higher Loss Rate

Periodic Losses

Loss Recovery: TCP

Fast ReTx

Belkin Base Station

Low Loss Rate

Loss of Signal Level

Multiple TCP ReTx

Loss Recovery: RTO

Expiry

0

20

40

60

80

100

120

140

0 100 200 300 400 500 600 700 800 900

Sequ

ence

Num

ber (

106 )

Time (seconds)

LOS NLOS

FReTx

RTO

RTO

RTO

RTO

RTO

0

20

40

60

80

100

120

140

160

0 50 100 150 200 250 300 350 400 450 500

Sequ

ence

Num

ber (

106 )

Time (seconds)

LOS NLOS

RTO

RTO

RTO

RTO


Performance Analysis

Channel Capacity in LOS

� � � Mbps

Link utilization < 50% in

� � ��

Mbps source rates

Sharp change in slope -

TCP performance is limited

by channel conditions

Significant variability

observed when source

rates >

��

Mbps

Packet losses -

��

� � � ��

%

TCPControlledApplication

Controlled

0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5

Num

ber o

f Los

ses

Source Rate (Mbps)

Losses

Variation

2.2 Mbps

ControlledTCP

ApplicationControlled

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

0 1 2 3 4 5

Thro

ughp

ut (M

bps)

Source Rate (Mbps)

Tput

2.2 Mbps

Variation


Conclusions

TCP throughputs of 3-6 Mbps observed in LOS and NLOS

Throughput reduction induced when Channel is time varying

Due to Channel Fades and Loss of Signal Level at Client

Apple Airport: Interference of Polling Frames with Data

Frames in AP Buffer

Belkin AP : Due to inability to capture signal at receiver

during fades

TCP Error Recovery

Fast Retransmit predominant for Airport induced errors

RTO Expiry and Backoff predominant for Belkin induced

errors.


Characterization of FSO channel measurements


Overview

FSO Network Principle

Based on optical line of sight communication using lasers

Current optical wireless technology provides data rates ,

-

� �

Mbps,

� � �

Mbps,

� � �

Mbps and��

� �Gbps at

� � �

nm,

and

��

Gbps at

� � � �

nm

Motivation for analyzing optical channels

Variations in Temp/Pressure

Changes in Refractive index

Signal Fades

Intensity Fluctuations


Literature Survey

Bloom et.al (2003) - Suggested common set of metrics for

performance evaluation and estimated link range based on

atmospheric conditions

Zhu & Kahn

2001 - Provided upper bounds on BER based on

log-normal channel modeling

2002 - Statistical distributions of turbulence induced

fading

2003 - Estimated error-performance bound for OOK FSO

communication

Andrews et.al (1999,2000) - Proposed gamma-gamma PDF

as mathematical model for turbulent channel


Measurements Analyzed

Location - Livermore, CA

Source - second floor balcony at

� ��

m MSLDest - Mt.Diablo at

��

m MSLPosition -

� �

-km on a

� �

slant

Ref: Source of data - LLNL + Dr.Mehta at LL, MIT

Description

January 29, 2003, 16:00-18:00 hours

Intensity Range -

��

� �

mV -

��

� �

V

Sampling time :

�

ms, Transfer rate : �

Kbps, Resolution :

� �

bits, Scale :

�

V/mW

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 1000 2000 3000 4000 5000 6000 7000

Cum

ula

tive I

nte

nsit

y (

Volt

s)

Time (seconds)

0-120 minsmean


Objective

Statistical characterization of fade amplitude

Verification of BER statistics

Analysis of temporal features

Atmospheric Interference

Variation in wind velocity

influences fading

Inverse relation to fade

amplitudes

Temperature and pressure

effects are inconclusive

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

20 40 60 80 100 120 270

275

280

285

290

295

300

305

310

Win

d ve

loci

ty (m

/sec

)

Med

ian

cros

sing

s (1

03 )

Minutes

Wind Velocity, Median Crossings

Wind SpeedMedian Crossings


Statistical Analysis - Moments

Mean (

�� ), Variance (

�� )

Skewness (

�� ) - Degree of symmetry of distribution

��

�

�� (1)

- Positive & Negative values - skewed right & left

Kurtosis (

� � � ) - Measure of flatness of data

� � � ��

�

� � � � � �� (2)

-

� � � > 3 - High peaks and heavy tails


Statistical Analysis - Hypothesized PDFs

Ref: Regress+: A compendium of probability distributions - M.

P.McLaughlin

Beta distribution

��

� � � �

� � � � � � � � � � � � ��

(3)

A-location B-scale C,D-shape

��

Gamma Distribution

��

��

� � �

��

� �� (4)

� � � � � � � � � � � � � ��


Statistical Analysis - Parameter Estimation

Beta

��

� � (5)

��

� � � � �

� � � � � � � � � � (6)

��

� � � � � �

� � � �� (7)

�� ! � � � �!

� � � � � � � � � � � � � � � �

(8)

Gamma� ��

(9)��

�" �� # � (10)


Statistical Analysis - PDF comparison

1e-05

0.0001

0.001

0.01

0.1

1

10

0 0.5 1 1.5 2

P[X

= x] (

logsc

ale)

Intensity x (Volts)

DataBeta

gamma

Hypothesis fit : Kolomogorov-Smirnov test

Beta distribution

Shifted at lower intensities

Deviation at tail probabilities

Gamma distribution

Good fit of measured inten-

sity


Statistical Analysis - BER analysis

��

� ��

� � � ��

� ��

� � � � � ��(11)

� � ��

� ��

� � � � ��

(12)

�= optimal threshold =

� � � ��! � � � � �� #" � :ON/OFF photon counts

Analysis

Intensity values transformed tophoton counts: � �

dB margin

BER suffers for lower photon

counts due to fading

Better bounds - improved Chernoff 1e-18

1e-16

1e-14

1e-12

1e-10

1e-08

1e-06

0.0001

0.01

1

0 10 20 30 40 50 60 70 80 90

Ave

rage

Pro

babi

lity

of e

rror

(log

scal

e)

Photon per bit

gamma.imCh.Pegamma.ex.Pedata.imCh.Pe

data.ex.Pegamma.Ch

data.Ch


Temporal Variation of parameters

Time series of CM

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 1000 2000 3000 4000 5000 6000 7000

µ x

Seconds

mean

0

0.02

0.04

0.06

0.08

0.1

0 1000 2000 3000 4000 5000 6000 7000

σ x2

Seconds

varianceskewkurt

Time varying - Denotesnon-stationary trend

Scatter Variation

0

0.02

0.04

0.06

0.08

0.1

0.12

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

σ x2

µx

mean-var

0

0.02

0.04

0.06

0.08

0.1

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

γ 1, γ 2µx

mean-skewmean-kurt

Non-constant variation -Heteroscedasticity


Temporal variation of parameters

ACF

0

10

20

30

40

50

60

70

0 50 100 150 200 250 300 350 400 450 500

Kx(τ)

(10-3

)

τ

12345678

Ensemble with longest fade

duration has higher

correlation

- Implies stronger degree of

relationship among

intensities of photons

PSD

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1

S x(ω) (

10-3

)

ω (10-3) radians/sec

PSD

Distribution of power with

frequency

Exhibits a periodic trend at

low frequencies


Conclusions

Signal fades occur with a higher probability, enunciated by

hypothesized PDF and photon count of BER

Moments are found to be time varying followed by

non-constant changes between parameters

Fading states exhibits pseudo random periodic features with

a strong correlation pattern

The analysis of all the features point to a non-stationarity of

the measured data.


Parametric Model


Literature Survey

Pillage et.al (1990) - Proposed Asymptotic Waveform

Evaluation method for solving linear electric circuits

Haneda et.al (1994) - Modeled room transfer function using

common acoustical poles and zeros in the channel as an

ARMA system

Celik et.al (1995) - Modeled frequency response as an

multipoint Padè approximation using Taylor series moments


Rational Function

The channel transfer function in discrete time domain

� ��

� � � � �

� � ��

� � � � �� (13)

� � � � �

: complex random variable of magnitude and phase

�

� � � ��

: transforms of input and output of the system

��

� �� (14)

� �- zeros of the CTF

��

� �� (15)� �- poles of the CTF

Causal system is assumed


Derivation

Eq.14 can be represented in time domain

� � � � � � � � � � � � � � � � � � � � � � � � � � � � �

��

(16)

� � � � �

��

� ��

� ��

(17)

Coefficients � �and

� �are estimated at different � as a linear system of equations

��

� ��

� � � � � � � � � � � � � �

�

� � � � ��

�

......

......

...� � � � � � � � � � � � � � � � �

......

......

...� � � � � � � � � � � � � � � � � � � � � � � � � � �

��

��

��

�

...

...

� ��

��

�

��

� ��

...

� ��

...

��

(18)


Derivation

�� are solved by splitting Eq.19 from

��

row

��

� ��

� ��

� � � � � �

......

......

� � � � ��

��

��

� ��

...� �

��

� ��

��

� ��

� ��

...

� � � �

��

(19)

Order of system: min(

� ��

)


CIR calculation

Computational model based on method of images

CIR computed using electric field vector derived in terms ofHertzian potential �

� � ��

�� (20)

Ref:C.Thompson,M.Raspopovic,M.Denis

Experimental Setup

Dimensions (

��

� ��

� �

) m

Configurations [ � � � � ]

� � �

CIR/configuration

Channel Resolution :

��

�

ns

Narrowband

� � � ��

�

Ghz

8 m

12 m

T1

T21 (2,10.2)

(2,9.8)

T1

T21 (2,6.2)

(2,5.8)R1

R21(7,6.2)

(7,5.8)

R21

R1(7,1.8)

(7,2.2)

α1

α2

α3


Wideband Response

-2

-1.5

-1

-0.5

0

0.5

1

0 100 200 300 400 500

h[n]

(10-3

)

n

a3,TR11

Order :[ �� ] = [

� ��

� �

]

Stable pole-zero model

FR of model follows a smoothpattern

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Imag

z

Real z

zeros

0

0.5

1

1.5

2

2.5

3

3.5

4

0 10 20 30 40 50 60

|H(ω

|

ω/Ts (GHz)

WB ModelWB Exact


Narrowband Response

-200

-150

-100

-50

0

50

100

150

200

250

0 10 20 30 40 50 60 70

h[n]

(10-6

)

n

a3,TR11

Order :[ �� ] = [

��

�

]

Stable poles, all zeros areoutside UC

Model’s FR follows the FFT ofinput CIR

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Imag

z

Real z

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.5 1 1.5 2 2.5 3 3.5 4

|H(ω

|

ω/Ts (GHz)

NB ModelNB Exact


Conclusions

Pole-zero model is derived by matching coefficients of CTF

with � CIR points

Narrowband and Wideband systems analyzed -

Representation of system with lesser parameters

Smoothed FR of model is obtained

Drawbacks - Unable to match the tail region of CIR


Thesis Contributions

RF channel -

Conducted measurements and showed the influence of signal level andMAC layer interaction with TCP flows

Presented throughput variation due to spatial and temporal positioningof mobile receivers

Implemented LINUX kernel modules essential for IEEE 802.11b

networking in infrastructure mode

FSO channel -

Hypothesized gamma distribution to represent the fluctuation inintensity

Showed time varying characteristics of observed moments which

indicate non-stationary behavior of measured data

Higher correlation of data and sharp peaks at low frequencies

enunciates the need to capture the fading states


Contributions & Future Work

Parametric model -

Identified pole-zero model for studying the impulse

response characteristics of a channel

Presented a method to understand the spatial variation

of CIR using fewer parameters of the channel

Future Work

Estimating FR of CIR using multipoint Pade

approximation from the Taylor series moments


Thanks

Thanks to Prof.Chandra, Prof.Thompson, Dr.Mehta and

Prof.Krishnan

Thanks to all the help rendered by fellow students at CACT


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