chapter 3: log-normal shadowing models
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Chapter 3: Log-Normal Shadowing Models. Motivation for dynamical channel models Log-Normal dynamical models Short-term dynamical models The Models are Used in the Shot-Noise Representation of Wireless Channels. Chapter 3: Motivation for Dynamical Channel Models. Mobiles move. Area 1. - PowerPoint PPT PresentationTRANSCRIPT
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Chapter 3: Log-Normal Shadowing Models
Motivation for dynamical channel models
Log-Normal dynamical models
Short-term dynamical models
The Models are Used in the Shot-Noise Representation of Wireless Channels
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Chapter 3: Motivation for Dynamical Channel Models
Short-term Fading
Varying environmentObstacles on/off
Area 2Area 1
Transmitter
Log-normalShadowing
Varying environmentObstacles on/off
Mobiles move
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Chapter 3: S.D.E.’s in Modeling Log-Normal Shadowing
Dynamical spatial log-normal channel model Geometric Brownian motion model
Spatial correlation
Dynamical temporal channel model Mean-reverting log-normal model
Space-time mean-reverting log-normal model Mean-reverting log-normal model
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Chapter 3: Log-Normal Shadowing Model
Transmittern,1
Receiver
k,1
or
d
n,3n,2
k,2
k,3
k,4 one subpath
LOS
path k
path n
d(t)
vmR(t)
n
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Chapter 3: Static Log-Normal Model
20
( )[ ]
Power path-loss in dB's:
Gaussian random variable
( )[ ] ( )[ ] , (0, ),
Signal Attenuation Coefficient:
Log-Normal random variable
( ) , - ln10 / 20
d
kPL d dB
PL d dB PL d dB X X N d d
r d e k
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d : time-delay equivalent to distance d=vc
speed of light
S(t,) and X (t,) : random processes modeled using S.D.E.’s with respect to t and
Chapter 3: Dynamical Log-Normal Model
0
0
0,
0,
( , )0
Power path-loss: Gaussian process
( ) ( , ) ,
Signal Attenuation coefficient: log-normal process
( ) ( , )
( , ) , 0, ,
t
t
kX t
PL d dB X t
r d S t
S t e t
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Chapter 3: Dynamical Spatial Log-Normal Model
Transmitter
Receiver
S(t, 3)
S(t, 2)
S(t, 1)
S(t, 4)
S(t, 5)
• Time, t: fixed (snap shot at propagation environment)• {S(t,)}|t=fixed S.D.E. w.r.t.
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Chapter 3: Dynamical Spatial Log-Normal Model
2 1 1
1
For time : fixed
, , , ,, ,
, ,
% changes: independent random variables, decompose into
a systematic (drift): evolution of local mean and
a random part (diffusion): variability aro
m m
m
t
S t S t S t S t
S t S t
und mean
( , ) Geometric Brownian Motion (GBM) w.r.t.
Percentage changes are independent and
identically distributed, log-normal distribution
( , )( , )
( , )
t fixedS t
dS tt d
S t
0 0
0
( , ) , ( , ),
0, : standard B.M., independent of S( , ).
t dW S t
W N t
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Need specific S.D.E.s for {X(t,), S(t,)} where
{X(t,)}|t=fixed => At every , B.M. with non-zero drift
{S(t,)}| t=fixed => At every , G.B.M.
: models loss characteristics of propagation environment
Chapter 3: Dynamical Spatial Log-Normal Model
0
( , )
20 0
0
( , )
Choose a S.D.E. for ( , ) as follows:
10( , ) ( , ) ,
ln10
( , ) [ ];
0, : standard B.M., independent of X( , ).
kX tS t e
X t
dX t d t dW
X t N PL d dB
W N t
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Properties of spatial log-normal model
S(t,) = ekX(t,) : Geometric Brownian Motion w.r.t.
Chapter 3: Dynamical Spatial Log-Normal Model
0 0
0
0
0 0
( , ) ( , ) 10 log 10 log
( ) ,
for given ( ) and if ( , ) is Gaussian or fixed
( , ) : evolves like a B.M. with non-zero drift w.r.t.
( , ) 10 log 10 log
c c
c c
X t X t v v
t W W
t X t
X t
E X t PL d v v
20( , ) ( )Var X t t
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Properties of spatial log-normal modelS(t,) = ekX(t,) => Using Ito’s differential rule
S(t,) = ekX(t,) : Geometric Brownian Motion w.r.t.
Chapter 3: Dynamical Spatial Log-Normal Model
0
2
( , )0
( , )
,( , ), ,
( , ) 2 2
( , )
Solution by substitution: ( , );
kX t
X t
k tdS td k t dW
S t
S t e
S t e
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Chapter 3: Spatial Log-Normal Model Simulations
• Time t: fixed• Snap-shot at propagation environment• {X(t,)}|t=fixed : increases logarithmically with d or • S(t,) = ekX(t,) : Log-Normal
Experimental Data (Pahlavan)
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Chapter 3: Spatial Correlation of Log-Normal Model
Spatial correlation characteristics:Indicate what proportion of the environment remains
the same from one observation instant or location to the next, separated by the sampling interval.
Consider
Since the mobile is in motion it implies that the above correlation corresponds to the spatial correlation.
cov ( , ) ( , ) [ ( , )]
( , ) [ ( , )]
X t t t E X t E X t
X t t E X t t
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Reported spatial correlation decreases exponentially with d
X2: covariance of power-loss process
d, t : distance, time between consecutive samples v: velocity of mobile Xc: density of propagation environment
Chapter 3: Experimental Correlation
2 2cov cov ( , ) cc v X td XX X Xt X t t t e e
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Consider the following linear process
Chapter 3: Spatial Correlation of Log-Normal Model
0
0
0
20
0
22 ( )( ) ( ) 2
( , ) ( , ) ( , ) ( , ) ,
( , ) 0; ( )
0, : standard B.M., independent of X( , ).
Consider the case: ( , ), ( , ) ( ), ( ) then
( )cov ( )
2 ( )
t
t t ttX
dX t t X t dt t dW t
X t N
W t N t t
t t
t e e e
0
0
0
2 ( )( )
22
2 ( )
1
( )Choose the variace of the initial condition: ( )
2 ( )
then cov ( )
( ) / : inversely proportional to density of the environment
t t
t
ttX
c
t e
v X
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Since the mobile is in motion, covariance with respect to t spatial covariance
Identification of parameters {(), ()}
Use experimental correlation data identify (),From variance of initial condition and () identify (),
Note: variance of initial condition of power loss process increase with distance.
equivalent to:() increases or () decreases (denser environment)
Chapter 3: Spatial Correlation of Log-Normal Model
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Chapter 3: Dynamical Temporal Log-Normal Models
Sn(tm ,)
• {S(t,)}|=fixed S.D.E. w.r.t. t
Sn(tm-1,)
Transmitter
Receiver
• T-R separation distance d or fixed
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Chapter 3: Dynamical Temporal Log-Normal Model
T-R separation distance or fixed:
( , ) Should vary around the mean predicted by
the spacial log normal model
log-normally distributed
( , ) power
fixed
fixed
d
S t
X t
loss: normally distributed
Variations are due to changes in the propagation
environment between the transmitter and the receiver
i.e. cars may obstruct the line of sight between the
transmitter and the receiver
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Need specific S.D.E.s for {X(t,), S(t,)} where
{X(t,)}|=fixed => At every instant of time t, is Gaussian
{S(t,)}|=fixed => At every instant of time t, is Log-Normal
{(t,), (t,), (t,)}: model propagation environment
Chapter 3: Dynamical Temporal Log-Normal Model
0
( , )
20
0
( , )
Choose: ( , ) mean-reverting linear S.D.E.
( , ) ( , ) ( , ) ( , ) ( , ) ,
( , ) [ ];
0, : standard B.M., independent of X( , ).
kX t
d t
S t e
X t
dX t t t X t dt t dW t
X t N PL d dB
W t N t t
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Properties of mean-reverting process
Chapter 3: Dynamical Temporal Log-Normal Model
( , ) ( , ) ( , ) ( , ) ( , ) ,
( , ) : models time-varying avg. power loss at distance
from transmitter: ( )[ ]( )
( , ) : speed of adjustment towards mean,
d
dX t t t X t dt t dW t
t
d PL d dB t
t
controls density of environment
Inversely proportional to deensity of prop. env.
( , ) : instantaneous variance or variability of processt
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Properties of mean-reverting process
Chapter 3: Dynamical Temporal Log-Normal Model
0 0
0
0
0 0
0
, , , ,
0
0 0 0
, , , ,
0
2 ,
( , )
, , , ( )
, , , , ( , )
At every instant of time ( , ) is Gaussian
( , ) , ,
( , )
tt t u t
t
t
t
tt t u t
t
t
X t e X e
u u du u dW u
t t u du X X t
X t
E X t e X e u u du
Var X t e
0 0
00
, 2 , , 2 2,tt u t
tte u du
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S(t,) = ekX(t,) => Using Ito’s differential rule
Chapter 3: Dynamical Temporal Log-Normal Model
02 ( , )
0
( , )
1( , ) ( , ) ( , ) ( , ) ln ( , )
1 ( , ) ( , ) , ( , )
2
Solution by substitution: ( , );
kX t
X t
dS t S t k t t S tk
k t dt k t dW t S t e
S t e
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Illustration of mean reverting model
(t,) high: not-dense environment(t,) low: dense environment
Chapter 3: Temporal Log-Normal Model Simulations
low
high
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Chapter 3: Dynamical Temporal-Spatial Log-Normal Model
vmT (t)
d
d(t)x
y
vmR (t)
(0,0)
n
Transmitter
Receiver
(t)
Propagation environment varies (t) Transmitter-Receiver relative motion d(t)
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Chapter 3: Temporal-Spatial Log-Normal Model Sim.
0
0 0
20
Define average power path-loss ( , )
10 log ( )
( ): temporal variations of the environment
( , ) ( , ) ( , ) ( , ) ( , ) ,
( , ) [ ];
( , ) : Normally distri
d
d t
t PL d t dB
PL d dB d t d t
t
dX t t t X t dt t dW t
X t N PL d dB
X t
0
( , )
2 ( )
buted
( , ) : Log-normally distributed
( , ) ( , ) ( , )
cov ( )
kX t
ttX
S t e
X t X t E X t
t e
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Chapter 3: Temporal-Spatial Log-Normal Model Sim.
n (t)
vm (t)
d
Transmitter
d(t)
Receiver
d2
d3
d1
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() : inversely proportional to the density of the propagation environment
Chapter 3: Spatial Correlation of Log-Normal Model
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M. Gudamson. Correlation model for shadow fading in mobile radio systems. Electronics Letters, 27(23):2145-2146, 1991.D. Giancristofaro. Correlation model for shadow fading in mobile radio channels. Electronics Letters, 32(11):956-958, 1996.F. Graziosi, R. Tafazolli. Correlation model for shadow fading in land-mobile satellite systems. Electronics Letters, 33(15):1287-1288, 1997.A.J. Coulson, G. Williamson, R.G. Vaughan. A statistical basis for log-normal shadowing effects in multipath fading channels. IEEE Transactions in Communications, 46(4):494-502, 1998.R.S. Kennedy. Fading Dispersive Communication Channels. Wiley Interscience, 1969.S.R. Seshardi. Fundamentals of Transmission Lines and Electromagnetic Fields. Addison-Wesley, 1971.L. Arnold. Stochastic Differential Applications: Theory and Applications. Wiley Interscience, New York 1971.D. Parsons. The mobile radio propagation channel. John Wiley & Sons, New York, 1992.
Chapter 3: References
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C.D. Charalambous, N. Menemenlis. Stochastic models for long-term multipath fading channels. Proceedings of 38th IEEE Conference on Decision and Control, 5:4947-4952, December 1999.C.D. Charalambous, N. Menemenlis. General non-stationary models for short-term and long-term fading channels. EUROCOMM 2000, pp 142-149, April 2000.C.D. Charalambous, N. Menemenlis. Dynamical spatial log-normal shadowing models for mobile communications. Proceedings of XXVIIth URSI General Assembly, Maastricht, August 2002.
Chapter 3: References