a hierarchical position prediction algorithm for efficient management of resources in cellular...
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A Hierarchical Position Prediction Algorithm A Hierarchical Position Prediction Algorithm for Efficient Management of Resources in for Efficient Management of Resources in
Cellular Networks Cellular Networks
Tong Liu, Paramvir Bahl, Imrich Chlamtac 22 11 33
Tellabs Wireless Systems DivisionMicrosoft ResearchErik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas
11
22
33
GLOBECOM ‘97, November 1997
Main MessagesMain Messages
… mobility prediction is a promising technique for improving resource efficiency and connection reliability in cellular networks.
… theoretical richness of stochastic signal processing field makes it feasible for predicting random intercell movement…
Bi-levelstochasticmovementmodel
Approximate pattern matching
Extended, self-learningKalman Filtering
Intercell movementprediction
OutlineOutline
Mobility prediction - Problem and Framework Related work in literature Hierarchical Position Prediction
User Mobility Model - A Global View Approximate Pattern Matching User Mobility Model - A Local View Extended, Self-learning Kalman Filtering Prediction Performance
Conclusions
Mobility Prediction - Problem DescriptionMobility Prediction - Problem Description
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Global Prediction: Next-cell(s) Crossing
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Local Prediction: Dynamic State
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Mobility Prediction - Problem DescriptionMobility Prediction - Problem Description
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Mobility Prediction - FrameworkMobility Prediction - Framework
Movement Observation
Movement Model Cell Geometry
Prediction Algorithm
Position Speed Cell siteTime
Improve lifetime connectivity and radio resource efficiency- Bandwidth Reservation - QoS Control- Optimal Routing - Position/velocity Based Handoff
Global
Local
Global
Local
Related Work in Literature Related Work in Literature
Recently Crossed Cells
Pattern Matching
Next Cell
Tabbane (JSAC, 1995)Liu and Maguire (ICUPC,1995)Liu , Munro and Barton (ICUPC, 1996)
Next-cell prediction based on movement pattern
Prediction Performance
Historical Movement Pattern
Related Work in Literature Related Work in Literature
Cell TransitionProbability Matrix
Look Up Table
Next Cell
Bar-Noy, Kessler and Sidi (Jour. Of Wireless Networks, 1995)Akyildiz and Ho(Proc. ACM SIGCOMM, 1995)Liu and Maguire (ICUPC,1995)
Prediction Performance
Next-cell prediction based on Markov Chain model
Current Cell ID
Prediction of Random Intercell MovementPrediction of Random Intercell Movement
Cell Geometry
Position Speed
Prediction Performance
Pattern Template
Linear Dynamic System
Approximate Pattern Matching
Extended Self-Learning Kalman Filter
Recent Crossed Cells
RSS Measurement
Next Cells
User Mobility Model - A Global ViewUser Mobility Model - A Global View
User Mobility Pattern
ProfileUser
7pm) - (5pm UMP
5pm) - (9am UMP
9am) - (7am UMP
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2
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Editing Process
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iUMP User Actual Path
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Editing Operation UMP
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User Mobility Model - A Global ViewUser Mobility Model - A Global View
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Approximate Pattern MatchingApproximate Pattern Matching
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User Mobility Model - A Local ViewUser Mobility Model - A Local View
S 1
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S2 MovingDynamics
CommandsTime correlatedrandom acceleration
r(t)+
U(t) a(t) F( )
Measurement noise
Nonlinear measurement
-Amax S1 S2 Sm Amax
P( a(t)/S1 ) P( a(t)/S2 ) P( a(t)/Sm )
+
Dynamic EquationsDynamic Equations
Continuous-time dynamic equation:
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Observation ModelObservation Model
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Adaptive Dynamic State EstimatorAdaptive Dynamic State Estimator
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Prediction of Next Cell Prediction of Next Cell
Trajectory
Direction
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cell 1
cell 2
cell 3
cell 4
cell 5
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Hierarchical Position PredictionHierarchical Position Prediction
User Profile)( 21 nbbbUMP Approximate Pattern Matching
Global Prediction
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UAP Forming)( kLk aa
)( 1
^
kkLk aaaUAP
Optimum AdaptiveFiltering
Local Prediction of Next Cell
Dynamic state
1
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RSS measurement
LocalPrediction
Global Prediction
Significance of Local PredictionSignificance of Local Prediction
MovementPattern 2
MovementPattern 1
---Crossed Cell ---Uncrossed Cell
A practical situation necessitates looking-ahead mode for movement pattern identification
Prediction Performance - Simulation ParametersPrediction Performance - Simulation Parameters
Mhz 50.........9....................signal.... RF of Wavelength
dB 1..........antenna...station mobile ofgain Power
dB ....6..........antenna...station base ofgain Power
w......20..........power..... ansmissionstation tr Base
dB .....5shadowing. random ofdeviation Standard
] 80 ......[30,........................................range..... Speed
10.....)........./constant(1on accelerati Random
/.5.........0).(on accelerati random of Variance
/7.5) 2.5, ...(0,input..... driving ticdeterminis theof States
/ ..10..............................on accelerati Maximum
5t.......0.measuremen RSS of interval Sampling
km .....2..............................cell...... a of Radiance
..30..............................sites..... cell ofNumber
.............................................Geometry Cell
22
2
2
miles/hr
s
sm
sm
sm
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lsHexagonCel
m
Prediction Performance - Trajectory TrackingPrediction Performance - Trajectory Tracking
2000 0 2000 4000 6000 8000 10000 12000 14000 16000 180000.5
0
0.5
1
1.5
2
2.5x 10
4
Xdirection [meter]
Ydi
rect
ion
[met
er]
Uesr1
Uesr2
Uesr3
Actual trajectory
Predicted trajectory
1
2
3
4
5
6
7
8
9
12
1314
Cell ID
20
19
Prediction Performance - Speed EstimationPrediction Performance - Speed Estimation
0 500 1000 1500 2000 2500 3000 3500 4000 45000
5
10
15
20
25
Time second
Speed
mete
r/secon
d
Predicted Speed
Actual Speed
Local Prediction of Next CellLocal Prediction of Next Cell
User1 Current cell ID 1 2 3 4 20 Prediction ratio
Predicted cell ID 6 3 4 20 75%
User2 Current cell ID 1 6 5 8 9 19
Predicted cell ID 6 5 7 9 19 80%
User3 Current cell ID 1 6 7 12 14
Predicted cell ID 6 7 12 14 100%
Local Prediction of Next CellLocal Prediction of Next Cell
Parametric Behavior of Next-cell Prediction
dB4
dB5
dB6
dB7
Local Prediction of Next CellLocal Prediction of Next Cell
Global PredictionGlobal Prediction
d(UAP,UMP1)
2
2
2
Current Cell
C8
C9
C10
d(UAP,UMP2)
3
Global Prediction
C9
C10C18C17C16
C17C16
Determine Edit Distance:
Prediction Result:
UAPUMP1
1
2C
5C
8C
10
^
C
9C
2C 5C 8C 12C 14C
0 0 0 0 0
0 1 2 3 4
1
0
1
1
1
1 2 3
0 1 2 3
10 2 3
UAPUMP2
1
2C
5C
8C
10
^
C
9C
2C 5C 4C 9C 19C
0 0 0 0 00 1 2 3 4
1
0
1
1
1
2 1
0 1 2
21
18C 17C 16C
0 0 0
5 6 74 5 6
4 5 6
2 3 4 5
2 2 3 4 5
3
ConclusionConclusion
Hierarchical Movement Model Top level: Movement Pattern subject to random editing
operations Bottom level: A linear dynamic system driven by the
combination of a semi-Markovian process and Color Gaussian Noise.
Hierarchical Position Prediction Algorithm Approximate Pattern Matching Extended Self-learning Kalman Filter