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Performance Analysis of a Preemptive and Priority Reservation Handoff Scheme for Integrated Service-

Based Wireless Mobile Networksby Jingao Wang, Quing-An Zeng, and Dharma P. Agrawal

Presented by Okan YilmazCS 6204 Mobile Computing

Virginia TechFall 2005

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Abstract Analytical Model & Performance Analysis Call Types:

Originating calls Handoff requests

Service Types: Real-time Non-real-time

Partitioning based system model Real-time service calls only Non-real-time service calls only Handoff requests only

Preemptive priority handoff scheme

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Abstract (cont) Multidimensional Markov Model to estimate

Blocking probability of originating calls Forced termination probability of handoff calls Average transmission delays

Simulation and Performance Analysis Different call holding times Several cell dwell time distributions

Results Significantly reduces the forced termination

probability of real-time calls Negligible packet loss of non-real-time calls

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Introduction 2G Networks

Limited and far from acceptable Voice Short message Low speed data

3G Networks Demand for Integrated services

Business customers Any time, any place Employees, key customers e.g., brokerage, banking, emergency services, traffic

reporting, navigation, gambling, etc. Wireless and VLSI Technology

Multi-media-ready cell phones, pocket PCs, Palms

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Challenges of Integrated Services

True combination of real-time and non-real-time services

Maximize the utilization of network infrastructure

Quality of service (QoS) Handoff handling

Forced termination of an outgoing call is more annoying than blocking of a new call

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Handoffs Handoff: changing parameters of a channel

Frequency, time slot, spreading code, or combination of them

When: crossing cell boundary or deteriorating signal quality

Cell structure Support a drastic increase of demand

Microcell, picocell, hybrid cell Smaller cells More handoffs

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Handoff Design Issues Forced termination versus new call blocking Increased channel utilization in a fair manner Goal:

Minimization of forced termination of real-time service Without drastically sacrificing the other QoS parameters

Several studies based on voice based cellular networks

Need for support of multiple service types simultaneously

Keys for a good scheme: Service dependent

Delay sensitivity: non-real-time versus real-time Preemptive model: priority reservation handoff

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SYSTEM MODEL Homogenous cell with fixed number of S channels Reference cell approach Call types:

Real-time originating call: MU dials a number to place a real-time call

Real-time handoff request: MU holding a channel enters the handoff area

Non-real-time originating call: MU places a non-real-time call

Non-real-time handoff request: Non-real-time MU holding a channel approaches and crosses a cell boundary Cell boundary: The points where the received signal

strength between two adjacent cells is equal

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Notation OR: arrival rate of real-time originating calls HR: arrival rate of real-time handoff requests ON: arrival rate of non-real-time originating calls HN: arrival rate of non-real-time handoff requests RC: real-time service channels group with capacity SR

CC: common handoff channels group with capacity SC

NC: non-real-time service channels group with capacity SN

RT only: In CC, real-time service channels reserved exclusively for real-time handoff calls with capacity SE

CH: In CC, channels that can be used by both real-time and non-real-time handoff calls with capacity SC - SE

RHRQ: real-time service handoff request queue with capacity MR

NHRQ: non-real-time service handoff request queue with capacity MN

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System model for a reference cell

OR RC(SR) HR RC(SR) HC(Sc-Sc) RT(SE) RHRQ(MR) HN NC(SN) HC(Sc-Sc) NHRQ(MN) ON NC(SN)

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Algorithm for Originating Calls

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Algorithm for Handoff Requests

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System Design (cont) Preemptive procedure: real-time handoff request calls

preempt non-real-time handoff request calls if a non-real-time in CC and NHRQ is not full

Real-time handoff requests may preempt non-real-time handoff requests irrespective of NHRQ being full or not No need if very large NHRQ buffer

Real-time handoff request are dropped If RHRQ is full (both RHRQ and NHRQ are full in

preemptive scheme) If the handoff request in RHRQ cannot get service

until it moves out of the handoff area

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System Design (cont) Non-real-time handoff requests will never be dropped

If NHRQ is large enough (not necessarily be infinite) Because the non-real-time handoff request is

transferred from the reference cell to another cell Waiting time in NHRQ = dwell time of non-real-time

service subscribers Real-time handoff request calls can continue until

signal strength becomes not enough to get service This is ignored in paper. It is assumed that the call is

blocked.

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Traffic Model Three characteristics:

Call arrival process Call holding time Cell dwell time

Call arrival: Poisson process Call holding time and cell dwell time

Two approaches: Traffic model: general independent identically distributed (i.i.d.)

Exponential, gamma, lognormal, hyper-exponential, hyper-Erlang Analytical model: User’s mobility, the shape and size of the cell,

and exponential distribution are used to determine cell dwell and call holding time

Paper uses the second for analytical modeling, both for numerical and simulation results

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Dwell Time Two-dimensional fluid model

fV(v): pdf of the speed V of MU E[V]: mean of the speed of MU

MU moves randomly any direction in [0,2) Assumes uniform density of users

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Cell Dwell Time

LVEA

TE

A

LVE

A

N

VEL

vvvfL

vvfLv

N

vvfLv

N

dwell

Tdwell

VE

V

VT

VO

0

0

: density of MUs in the cell NO: number of cell outgoing MUs

with moving speed v and v+v NT: total number of cell outgoing

MUs per unit time A: area of the cell L: length of the perimeter dwell: average outgoing rate of an

MU within a cell Tdwell: cell dwell time with a

random exponential distribution with mean 1/dwell

Biased sampling theory in boundaries [1]

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Handoff Area Dwell Time fV*(v): pdf of the speed of

real-time service subscribers crossing cell boundary V*

D: the length of moving path of mobile users in the handoff area

Th: dwell time of real-time service subscribers in the handoff area

E[Th]: Average handoff area dwell time

Path length and velocity of MUs are independent

VEDE

TE

VEvvf

VE

vVE

vf

vvfvV

E

VDT

VE

vvfvf

hdwellh

V

V

V

h

VV

1

11

11

1

0

0

0*

*

*

*

*

*

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Channel Holding Time Exponential distribution

TCR: Call holding time of real-time calls

TCN: Call holding time of non-real-time calls

CR: Service rate of real-time calls

CN: Service rate of non-real-time calls

TR: Channel holding time of real-time service calls

TN: Channel holding time of non-real-time service calls

dwellCNNN

dwellCRRR

dwellCNN

dwellCRR

dwelldwell

CNCN

CRCR

TE

TE

T

TT

11

11

1

11

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Arrival Process of Service Calls Poisson process OR: arrival rate of real-time originating calls HR: arrival rate of real-time handoff requests ON: arrival rate of non-real-time originating calls HN: arrival rate of non-real-time handoff requests Need to compute HR and HN from OR and ON,

respectively Homogenous mobility pattern

Mean number of incoming handoffs to reference cell = mean number of outgoing calls from the reference cell

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Arrival Process of Service Calls (cont)

E[CR]: average number of real-time calls holding channels in the reference cell

OUTR: departure rate of real-time handoff calls from the reference cell

10dwellROUTRHR CE

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Arrival Process of Service Calls (cont) E[NN]: average number of both non-real-time

service requests and calls in the reference cell E[CN]: average number of non-real-time MUs holding

channels in the reference cell E[LN ]: average length of NHRQ

)14(

)13(

)12,11(

21

21

NNN

dwellNHNHNHN

dwellNHNdwellNHN

LECENE

NE

LECE

: total arrival rate of calls

)15(HNHRONOR

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M/M/3/3

0 1 2 3

2 3

blocked/lost

M/M/3/3 [2]: M: Exponential or Poisson arrivals M: Exponential or Poisson service 3: Number of servers 3: Maximum number of customers in the system

P0 + P1 + P2 + P3=1 (+) P1 = P0+ 2 P2

Pblocking = P3

Throughput = (1-P3) *

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PERFORMANCE ANALYSISi

j

k

l

m

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Stable State diagram for (i=1, j=1, k=1, l=2, m=0)

S = SR + SC+ SN =12SR = 6; SC=SN=3; SE=1MR=5; MN=50; NT=3162

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Total number of states Four cases to consider:

1. Both RHRQ and NHRQ are empty: 0≤ i ≤SR;0 ≤ j ≤ Sc - k; 0≤ k ≤ Sc - SE ; 0≤ l ≤ SN ; m = 0

k=0 j=(0 .. Sc) : Sc +1 possibilities k=1 j=(0 .. Sc -1) : Sc possibilities … k= Sc-SE j=(0 .. SE) : SE +1 possibilities Total = [(Sc-SE +1) * (Sc + SE +2)]/2 states

N1=[(SR+1)*(Sc-SE +1)*(Sc + SE +2)*(SN+1)]/2 states2. RHRQ is not empty while NHRQ is empty:

i = SR; Sc < j + k i =SR; Sc-k+1≤ j ≤ Sc + MR + k; 0≤ k ≤ Sc-SE ; 0≤l ≤SN ; m=0

k=0 j=(Sc + 1 .. Sc + MR) : MR possibilities k=1 j=(Sc .. Sc + MR + 1) : MR possibilities … k= Sc-SE j=(SE + 1.. Sc + MR) : MR possibilities Total = [(Sc - SE +1) * MR] states

N2=[(Sc - SE +1) * MR * (SN + 1)]/2 states

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Total number of states (cont)3. RHRQ is empty NHRQ is not empty:

Sc-SE ≤ j + k; l = SN; 0≤i ≤SR; Sc-SE-k ≤ j ≤Sc-k; 0≤k ≤Sc-SE ; l=SN; 1≤m ≤MN

k = 0 j=(Sc - SE .. Sc) : (SE +1) possibilities k = 1 j=(Sc – SE - 1 .. Sc - 1) : (SE +1) possibilities … k = Sc - SE j=(0 .. SE) : (SE +1) possibilities Total = (Sc - SE +1) * (SE +1) states

N3 = (SR + 1) * (Sc - SE + 1) * (SE + 1) * MN

4. Both RHRQ and NHRQ are not empty i = SR; Sc < j + k; l = SN; i = SR; Sc-k+1 ≤ j ≤Sc+ MR - k; 0≤k ≤Sc-SE ; l=SN; 1≤m ≤MN

k = 0 j=(Sc+1 .. Sc + MR) : MR possibilities k = 1 j=(Sc .. Sc + MR - 1) : MR possibilities … k = Sc-SE j=(SE +1.. SE + MR) : MR possibilities Total = [(Sc-SE +1) * MR]/2 states

N4 = [(Sc-SE+1) * MR * MN]/2 states

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Normalizing Condition

1. Both RHRQ and NHRQ are empty

2. RHRQ is not empty while NHRQ is empty

3. RHRQ is empty while NHRQ is not empty

4. Both RHRQ and NHRQ are not empty

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Average number of calls E[CR]: average number of

real-time calls holding channels in the reference cell

1&3: i + j: real-time calls 2&4: RC is full; SC-k real-time

calls

E[NN]: average number of both non-real-time service requests and calls in the reference cell

1&2: k + l: non-real-time calls 3&4: RN is full; SN+k real-time

calls; m calls in NHRQ

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Pseudo-code to solve (NT+2) independent nonlinear equations

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Blocking Probabilities Originating real-time

calls are blocked when i= SR

Forced termination of real-time service handoff requests BHR: Blocking

probability MR is full

DR: dropping probability MR is not empty

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Channel and RHRQ buffer utilizations

Utilization=mean channel used/ S

E[CN]: average number of calls holding channels 1&2: k+l: non-real-time

calls 3&4: NC is full; SN+k real-

time calls; m calls in NHRQ RHRQ utilization = mean

number of channels in RHRQ/MR

E[LR]: average length of RHRQ 1&2: j+k-SC real-time

handoff requests waiting in RHRQ

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NHRQ Buffer Utilization and Forced Termination probability

NHRQ utilization = mean number of channels in LHRQ/MN

E[LN]: average length of NHRQ 1&2: m non-real-time handoff

requests waiting in NHRQ Ph: Probability that a real-

time service call triggers a handoff request in the reference cell Real-time service call holding

time > the cell dwell time Phf: Forced termination

probability of real-time handoff calls (l-1) successful handoff followed

by a forced termination

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Transmission Delay of non-real-time service

Td : The lifetime transmission delay of non-real-time service Sum of Tws

Tw : transmission delay on non-real-time service in each cell

Little’s Law Mean waiting time = mean

number of customers in queue / throughput

BON : blocking probability of originating non-real-time calls 1 - P[NCSN]

E[TS]: Average serving time of non-real-time calls (mean number of calls getting

service + in queue) / (total throughput)

BHN: blocking probability of non-real-time service handoff requests NHRQ is full: m = MN

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Average transmission delay of non-real-time service (cont)

Nh: average number of handoff per a non-real-time handoff request (delay due to Nh

handoffs + call holding time) by average serving time

E[TN]: average transmission delay of non-real-time service Handoff arrival

probability times average delay each handoff request ecounters

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Numerical and Simulation Results Integrated service homogenous cellular system Call arrivals

Poisson Call holding and cell dwell times

Scenario 1: exponentially distributed as in performance analysis

Scenario 2: iid with Gamma distribution Cell and handoff area dwell times with = 1.5 Call holding time with = 2

Same mean value Cell shape: hexagonal Each neighbor has equal probability to receive handoff

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Simulation Results: Comparison of QoS Parameters

BOR, BON: blocking probability of real-time & non-real-time service

Phf: Forced termination probability of real-time service calls

TN: Transmission delay of non-real-time service calls

Scen#1 and analytical analysis results are consistent < 4% difference in BOR, BON,

and Phf Accuracy of analysis is

substantiated Scen#1 and Scen#2 results

are comparable Phf: Scen#2 is 20 less BOR, BON: Scen#2 is 6% and

2% larger, respectively TN: Scen#2 is 28% less

Reasonable: Gamma has smaller standard deviation

Parallel trend: Analytical formula with

tolerable error margins

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Simulation Results: Performance Comparison of real-time calls

Fhr & Phr: Priority and preemptive have 14.7% and 30.9% improvements

over guard channel, respectively BOR: almost the same Priority especially with preemptive procedure is effective in

decreasing forced terminations

Schemes: Standard guard channel

(base) Priority reservation Preemptive priority handoff

Higher QoS parameters when higher arrival rates (lower service quality)

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Simulation Results: Performance comparison of non-real-time calls

TN increases with higher traffic

Guard channel performs better Channels available for non-

real-time decreases due to lower priority

Largest TN is 3.91sec.; 6.5% of whole service time

31% decrease in forced termination probability is more important

7% increase in blocking probability of originating non-real-time calls

Forced termination probability of non-real-time is negligibly small

Proposed scheme is better in terms of the performance

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Conclusions A handoff scheme is proposed

Priority reservation Preemptive priority policy

Analytical model for performance analysis has been proposed

Simulation results match the analytical model Several QoS parameters have been evaluated Forced termination probability of handoff requests of

real-time calls can be decreased Non-real-time service handoff requests do not fail

A reasonable 6.5% transmission delay increase

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References [1] Priority handoff analysis, Vehicular Technology

Conference, 1993 IEEE 43rd, Xie, H.; Kuek, S., Page(s): 855-858, Digital Object Identifier 10.1109/VETEC.1993.510945

[2] CS5214 Course notes, Ing-Ray Chen, 2004.