Dynamic Bandwidth Reservation in Cellular Networks Using Road Topology Based Mobility Predictions InfoCom 2004

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Dynamic Bandwidth Reservation in Cellular Networks Using Road Topology Based Mobility Predictions InfoCom 2004. Speaker : Bo-Chun Wang 2004.4.21. Outline. 。 Motivation 。Relative work 。Road topology based mobility prediction 。Dynamic bandwidth reservation scheme 。Simulations and results.

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### Dynamic Bandwidth Reservation inCellular Networks Using Road Topology Based Mobility PredictionsInfoCom 2004

Speaker : Bo-Chun Wang

2004.4.21

Outline

。Motivation

。Relative work

。Dynamic bandwidth reservation scheme

。Simulations and results

Motivation

Forced termination

is worse than blocking

a new call !!

Insufficient bandwidth

Forced termination

i.e., handoff “dropped”

• Prioritize handoffs by reserving bandwidth
• Tradeoff  more news calls blocked
Motivation

PFT = Forced termination probability

PCB = New call blocking probability

Static: PFT = 0.01, PCB =0.15

Reservation

Dynamic: PFT = 0.01, PCB = 0.10

time

• Handoff arrivals are random
• Dynamic reservation more efficient
• No knowledge of future: use prediction
• Accuracy   reservation efficiency 
Outline

。Motivation

。Relative work

。Dynamic bandwidth reservation scheme

。Simulations and results

Relative Work
• Signal Strength
• Mobility(direction,speed)
• History=>probability(user,BS)
Shortcoming in Previous Work
• Assumes hexagonal/circular boundary
• Actual cell boundary fuzzy & irregularly shaped
• Road topology information not utilized
• Could potentially yield better accuracy
Outline

。Motivation

。Relative work

。Dynamic bandwidth reservation scheme

。Simulations and results

Candidate Cell A

Candidate Cell B

Handoff regions

Reserve more in Cell A!

Probability 0.1

Probability 0.9

Where to reserve bandwidth?

B

A

Preliminaries
• Each BS keeps a database of the roads within its coverage area
• Topology extracted from digital maps

All segments

Handoff-probable

segments only

Database Entries
• Neighboring segments
• Transition probability to each neighbor
• Statistical data:
• Transit time
• Probability of handoff
• Time spent before handoff
• Handoff locations
• Target handoff cell
Modeling Segment-transition
• Transition between road segments modeled as 2nd order Markov processes

F

F

D

D

E

E

MT1 & MT2 have different probabilities of entering EF

MT1

MT1

C

C

MT2

MT2

B

B

J

J

I

I

A

A

Prediction Output

[ctarget, w, tLPL(L), tUPL(U)]

4-tuple:

Lower prediction limit

Predicted target handoff cell

Upper prediction limit

Prediction weight

Derived using previously observed prediction errors

Time tLPL(L):P [actual handoff time  tLPL(L)] = L

Time tUPL(U):P [actual handoff time  tUPL(U)] = U

Prediction Output

Can have multiple 4-tuples per MT

[ctarget, w, tLPL(L), tUPL(U)]

4-tuple:

(One for each possible path to each handoff region)

Handoff region

w

• ctarget: Target cell if handoff occurs on EF
• w:P(ABBEEF, handoff at EF)
• tLPL(L), tUPL(U): Prediction limits of time from handoff if ABBEEF occurs

F

D

E

pdf of time

from handoff

C

B

A

Outline

。Motivation

。Relative work

。Dynamic bandwidth reservation scheme

。Simulations and results

Reservation Scheme

Two processes:

1) Compute Rtarget periodically: using predictions falling within the next T

2) Adapt T : to achieve desired PFT

arrival

time

t0

t0+T

departure

T PFT

Logic Behind the Scheme

Suppose:

• Have precise handoff information

Question:

• How much bandwidth should we reserve to prevent any incoming handoff from being dropped within T?
Perfect Knowledge Over T

Bandwidth change due to incoming/ outgoing handoffs

Time T

Rtarget increases monotonically with T

2

1

0

time

1

T PFT

Sum of

bandwidth changes

Peak=1

1

Set Rtarget to peak

0

time

1

A More Realistic Scenario
• Previous example assumes perfect knowledge of handoff timings
• Examine a more realistic scenario: only predictions available
• Prediction errors in handoff timings

Under-reservation occurs when predicted order is reversed

Use prediction limits to introduce biases

Choose L & U experimentally

[ctarget, w, tLPL(L), tUPL(U)]

Outline

。Motivation

。Relative work

。Dynamic bandwidth reservation scheme

。Simulations and results

Simulation Model
• 19 wireless cells
• Uncertain handoff regions
• Traffic lights
• Capacity = 100 BUs
• Voice (1 BU) & video (4 BUs) calls
Other Schemes for Comparison
• Benchmark:knows MT’s nextcell & handoff time
• Static:fixed reservation target
• Reactive:reacts to forced terminations
• Choi’s AC1:uses MT’s previous cell, & time in current cell
• LE:linear extrapolation (Infocom’01)
• RTB with Path Knowledge (RTB_PK):knows future path
Summary
• Mobility predictions incorporate both positioning info & road topology knowledge
• No cell geometry assumption
• Adaptive reservations use both incoming & outgoing handoff predictions
• Prediction accuracy, reservation efficiency
• Lesser new call blocking while meeting handoff prioritization target