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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
Wireless Channel Characterization and Modeling - 2
Basic elements of communication system
LinkLayer
Link
Layer
ControlLayer
LayerControl
TCP/IP Layer
Channel
Noise+
InputEncoderSource Channel
EncoderModulator
Source
DecoderChannelDecoder
OutputDemodulator
Physical Layer
Wireless Channel Characterization and Modeling - 3
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
Wireless Channel Characterization and Modeling - 4
Summarizing Channel effects
Channel Interference
Signal Fading(Multipath Propagation)
Flat Fading
Time varying channel
Additive Noise
Frequency SelectiveGaussian
Stationary Non−Stationary
Non−Gaussian
Wireless Channel Characterization and Modeling - 5
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
Wireless Channel Characterization and Modeling - 6
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
Wireless Channel Characterization and Modeling - 7
Experimental analysis of indoor wireless channels
Wireless Channel Characterization and Modeling - 8
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
Wireless Channel Characterization and Modeling - 9
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
Wireless Channel Characterization and Modeling - 10
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
Wireless Channel Characterization and Modeling - 11
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
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
Wireless Channel Characterization and Modeling - 13
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
Wireless Channel Characterization and Modeling - 14
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
Wireless Channel Characterization and Modeling - 15
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
Wireless Channel Characterization and Modeling - 16
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
Wireless Channel Characterization and Modeling - 17
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.
Wireless Channel Characterization and Modeling - 18
Characterization of FSO channel measurements
Wireless Channel Characterization and Modeling - 19
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
Wireless Channel Characterization and Modeling - 20
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
Wireless Channel Characterization and Modeling - 21
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
Wireless Channel Characterization and Modeling - 22
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
Wireless Channel Characterization and Modeling - 23
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
Wireless Channel Characterization and Modeling - 24
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)
� � � � � � � � � � � � � �� � � �
Wireless Channel Characterization and Modeling - 25
Statistical Analysis - Parameter Estimation
Beta
����� ��� �
� � (5)
��� � �
� � � � �
� � � � � � � � � � (6)
���� �
� � � � � �
� � � �� � � � � � � � � � � � � � ��� �� � � � � �� � � � �� � � (7)
��� � � � � � � � � � � � � � � � � � �! � � � �!
� � � � � � � � � � � � � � � �
(8)
Gamma� �� � �� � �
(9)���� �
�" ��� � # � (10)
Wireless Channel Characterization and Modeling - 26
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
Wireless Channel Characterization and Modeling - 27
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
Wireless Channel Characterization and Modeling - 28
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
Wireless Channel Characterization and Modeling - 29
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
Wireless Channel Characterization and Modeling - 30
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.
Wireless Channel Characterization and Modeling - 31
Parametric Model
Wireless Channel Characterization and Modeling - 32
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
Wireless Channel Characterization and Modeling - 33
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
Wireless Channel Characterization and Modeling - 34
Derivation
Eq.14 can be represented in time domain
� � � � � � � � � � � � � � � � � � � � � � � � � � � � �
�� � � � � � � � � � � � � � � � � � �
(16)
� � � � �
���
� �� � � � �
� �� � � � � �
(17)
Coefficients � �and
� �are estimated at different � as a linear system of equations
����������
� �� �
� � � � � � � � � � � � � �
�
� � � � �� � � � � � � � � � � � � � �
�
......
......
...� � � � � � � � � � � � � � � � �
......
......
...� � � � � � � � � � � � � � � � � � � � � � � � � � �
������������������������
����������
���
�
...
...
� ��
�����������������������
�
����������
� �� �
...
� ��
...
������������������������
(18)
Wireless Channel Characterization and Modeling - 35
Derivation
�� are solved by splitting Eq.19 from
��� � � � �
row
��������������
� �� � � ��� � ��� � � � � �
� ��� � � � ��� �
� � � � � �
......
......
� � � � �� � � � � ��� � � � � �
�������
��������������
� �� �
...� �
�������
� ��
�������������
� ��� � �
� ��� � �
...
� � � �
�������
(19)
Order of system: min(
� �� � � � �� �
)
Wireless Channel Characterization and Modeling - 36
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
Wireless Channel Characterization and Modeling - 37
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
Wireless Channel Characterization and Modeling - 38
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
Wireless Channel Characterization and Modeling - 39
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
Wireless Channel Characterization and Modeling - 40
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
Wireless Channel Characterization and Modeling - 41
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
Wireless Channel Characterization and Modeling - 42
Thanks
Thanks to Prof.Chandra, Prof.Thompson, Dr.Mehta and
Prof.Krishnan
Thanks to all the help rendered by fellow students at CACT
Wireless Channel Characterization and Modeling - 43