PresentationPresentationCS 5214CS 5214

Pin Lu

Bill Ward

““Performance analysis of cellular Performance analysis of cellular mobile communication networks mobile communication networks supporting multimedia services”.supporting multimedia services”.

AgendaAgenda

Background Information

Description of Model

Results generated by the Model

Goals of the paper: 1. Present an analytical model that simulates

a communication network that provides integrated services to a population of mobile users

2. Validate the model based on performance results

3. Use results to assess the Quality of Service.

BackgroundBackground

Authors: M. Ajmone Marsan Full Professor at the Electronics Dept, Politecnico di Torino, Italy

S. Marano Prof. of Telecommunications Networks, University of Calabria, Italy

C. Mastroianni PhD, University of Calabria, Italy

M. Meo PhD, Politecnico di Torino, Italy

Publication:Mobile Networks and Applications 5 (2000) pp. 167-177

Sponsors:Italian National Research Council

Italian Ministry for University and Scientific Research

Background ContinuedBackground Continued

Background: HistoryBackground: History

Stages of mobile communications:– Previous technical approach:

Increase capacity available for voices services.

– Current technical approach: Increase capacity available for voice and provide

a wider spectrum of telecommunication services; multimedia service (includes data and images).

– Simulation Preferred when detailed behavior of a specific

area is needed.

– Analytical Model Be able to obtain more general results.

– Combination of both.

Evaluation of cellular Evaluation of cellular telecommunication networkstelecommunication networks

– R. Steele & M. Nofal (1992) [8] Analyzed performance of personal communication

network based on microcells covering city street.

– K.K. Leung, W.A. Massey & W. Whitt (1994) [4] Fluid model to describe wireless system

– D. Hong, & S. Rappaport (1986) [2] Techniques to reduce forced terminations due to

handover failure.

– R. Beraldi, S, Marano, & C. Mastroianni (1997) [1] Hierarchical model of cellular system based on

microcells and macrocells.

Previous ResearchPrevious Research

Model DescriptionModel Description

Analytical Model is based on: Continuous time Multi-dimensional Birth-

Death Process

Descriptive Stages of the Model1. Assumptions and parameters 2. State Definition and model driving processes3. Flow balance equations and computation of

equilibrium state probabilities4. Evaluation of aggregate performance

measures.

– Cellular TeleCom provides 3 classes service

Class A – voice onlyClass B – data onlyClass C – voice and data(Multimedia)

– Number of radio channels available – N

Class A – requires 1 channelClass B – requires Cd channelsClass C – requires Cd+1 channels

Assumptions and parametersAssumptions and parameters

– The occurrence of a Handover Failure is worse than blocking a new call.The Proposed Solutions are:1. Add channels specifically for handovers

Add Ch channels

2. Decouple Multimedia calls Class D: require 1 channel

– The Analytical Model focuses on a single cell. Cell’s behavior is isolated from other cells Possible limitation: non-Homogenous cells.

Assumptions and parameters (con’t)Assumptions and parameters (con’t)

– Assumptions for a Single cell New Call request arrivals are Poisson point

processes. Handover request arrival processes from

adjacent cells are Poisson point processes. The dwell time of a Mobile Terminal is a random

variable with a negative exponential pdf. The unencumbered session duration is a random

variable with a negative exponential pdf.

Assumptions and parameters (con’t)Assumptions and parameters (con’t)

Cell A

Cell C

Cell B

Dwell time

• Cell state = # of currently active connections for each class of traffic:

s=(v,d,m,r)• v - # of active voice calls in cell• d - # of active data calls in cell

• m - # of multimedia calls in cell

• r - # of active voice components of a decoupled multimedia call

• n(s)- the function giving the total # of channels allocated to active connections when cell is in state s

• n(s) = v + d Cd + m(Cd + 1) + r

State DefinitionState Definition

– Driving processes produce different kinds of events that must be processed by the network:• requests of new voice, data and multimedia

calls• incoming handover requests for voice, data

and multimedia• outgoing handover requests for voice, data

and decoupled calls• completion of voice, data, multimedia and

decoupled calls

Driving ProcessesDriving Processes

Cell A

Cell C

Cell B

– For each driving process, it is possible to determine what are the possible successor states of s=(v,d,m,r)• New Call Requests are accepted if

• the # of free channels, excluding those reserved for handovers, is such that the call can be accommodated.

• if there are no active Class D connections (ie. r = 0)

Flow Balance EquationsFlow Balance Equations

Add Voice Example: n=99, N =100, Ch=0, Cd=8 Initial state: (91,1,0,0)

(99 100 – 0 – 1) ^ r = 0Successor state: (92,1,0,0)

• Incoming handover requests for voice or data are accepted if:

• there are enough free channels to accommodate the request in the cell.

• Incoming handover requests for multimedia calls (whether decoupled or not), have 2 possible successor states.

Flow Balance Equations (con’t)Flow Balance Equations (con’t)

decoupledvoice only

Handover Add Mmedia Example: n=99, N =100, Ch=0, Cd=8 : Initial state: (91,1,0,0)

100 - (8 + 1) < 99 100 – 1Successor state: (91,1,0,1)

• Completion of Calls and Outgoing handover requests.

• Have the effect of freeing some channels in the cell.

• in some cases, this leads to the recombination of a decoupled multimedia call.

Flow Balance Equations (con’t)Flow Balance Equations (con’t)

underline on decoupled multimedia calls that are recombined

Complete 1 Mmedia add 2 MM Example: n=93, N =100, Ch=0, Cd=8 : Initial state: (82,0,1,2)

2 >1 (93 – (8 +1) 100 – 2*8Successor state: (82,0,2,0)

• The transition rate from state s to a state k is computed by summing the contributions resulting from the driving processes.

• The state space size depends on the values of Cd, rmax, and the # of radio channels available in the cell, N.

• The max # of active calls possible:• Voice: V=N

• Data: D=N/Cd

• Multimedia: M = N/(Cd + 1)

• State space size is upper bounded by:(V + 1)(D + 1)(M+1)(rmax+ 1)

Model ComplexityModel Complexity

• Four performance indices will be used in the presentation of the results:

• the average carried traffic.• the new call blocking probability.• the handover failure probability.• the probability of recovering a decoupled

multimedia call.

• Definition of transition rate(event rate). The mean frequency of events that cause a state transition, where an event can be a new call attempt, a handover request, or a call termination.

• Event rate R:

Aggregate Performance MeasuresAggregate Performance Measures

-q(s,s) is total transition out of state s

p(s) is the steady-state probability of state s

• AC: Defined as the average number of channels in use in the cell.

Average Carried TrafficAverage Carried Traffic

n(s) is the # of busy channels in state s

p(s) is the steady-state probability of state s

Voice

Average # of channels used for:

Multimedia

Data

• PB: Defined as the average fraction of new call requests that cannot be satisfied by the cell base station, due to:

• lack of free channels• to the presence of some decoupled

multimedia connections waiting to be recombined.

New Call Blocking ProbabilityNew Call Blocking Probability

Voice MultimediaData

The probability of a voice call being blocked is the least

• PH: Defined as the average fraction of incoming handover requests that cannot be satisfied, causing the forced termination of the call.

• Incoming handover requests fail if the system is in one of the states belonging to subsets:

Handover Failure ProbabilityHandover Failure Probability

Voice

Multimedia

Data

decoupling probability probability handover request of multimedia completely fails

Hdec = {s: (N - Cd-1 < n N-1) r < rmax}

• Prec: Defined as the probability that the data component of the decoupled multi-media call is resumed before the call is terminated by the user

Probability of Recovering a Decoupled Probability of Recovering a Decoupled Multimedia CallMultimedia Call

Rrec is relative rate of events that lead to

recombination of a multimedia call

Rrec(s) is recombination rate

r(s) is # of decoupled calls in state s

ACr is the average number of decoupled calls in the cell

ResultsResults

Comparisons between analytical and simulation results

Explore alternate system configurations to assess their effectiveness.

Scenario - similar to ETSI (European Telecommunications Standards Institutes)

Two classes of services– A (voice) and B (data)

N = 64 Cd = 4 (8) Ch = 0 (4, 8) rmax = Cd(0) 1/uv=1/ud=1/um=100s 1/uhv=1/uhd=1/uhm=80s 75% voice call; 25% data call

Model ValidationModel Validation

Simplify the model– Analyze one cell instead of whole network– Average incoming handover equals

outgoing handover– New call generated according Poisson

process– Call duration and dwell times according

negative exponential distributions

Model Validation (Cont’d)Model Validation (Cont’d)

Difference between analytical model and simulation – handle of handover

– Analytical model simply balance in and out handover

– Simulation provides a much detailed process about handover

Results show model and simulationResults show model and simulation are very similar to each other are very similar to each other

Fig. 1

Results show model and simulationResults show model and simulation are very similar to each other (Cont’d) are very similar to each other (Cont’d)

Fig. 2

Above validation is based on

fin = fout

Expand model validation:

fin = a*fout (a=0.6; 1.0; 1.4)

Simulation and analytical results for the average Simulation and analytical results for the average total carried traffic, with a=1.4 and a=0.6total carried traffic, with a=1.4 and a=0.6

Fig. 3

Simulation and analytical results for the average Simulation and analytical results for the average voice blocking probability, with a=1.4 and a=0.6voice blocking probability, with a=1.4 and a=0.6

Fig. 4

Results:

– The analytical model performance is still very accurate in comparison to numerical results obtained from simulation, although some loss of accuracy can be noted.

Performance EvaluationPerformance Evaluation

Class A and B services

Class C service

Class A and B services

– Previous numerical results are used– Number of channels allocated for each

data call increased from Cd = 4 to Cd = 8

– Keep all other parameters same

8 channels per data call is better than 4 channels 8 channels per data call is better than 4 channels when carrying 75% voice and 25% datawhen carrying 75% voice and 25% data

Fig. 5

Higher numbers of data channels increases Higher numbers of data channels increases possibility of blocking new userspossibility of blocking new users

Fig. 6

Results for class A and B:

– Increasing the number of channels allocated to each data call while keeping the same call rate significantly increases the data offered traffic up to a point.

– Beyond some point, system with 8 data channels offers worse carried traffic than with Cd=4.

– Blocking probabilities for both data and voice for Cd=8 are much larger than Cd=4.

To minimize the difference in blocking probability between voice and data calls, a threshold Tv is introduced.

– Number of free channels necessary for accepting new call - Tv

– If number of busy channels > N – Tv; no voice call can be accepted.

The higher threshold, the better data traffic The higher threshold, the better data traffic and the worse voice trafficand the worse voice traffic

Fig. 7

The higher the threshold, the higher probability The higher the threshold, the higher probability that new voice call will be blocked; no significant that new voice call will be blocked; no significant

impact on data blockingimpact on data blocking

Fig. 8

Results of using threshold on voice:

– This approach leads to better performance for data traffic, at the expense of a performance loss for voice traffic.

Class C services– Assumption:

60% voice call 20% data call 20% multimedia call Cd = 8 for data connection Number of channels reserved for

handover calls (Ch) Number of busy channels > Ch, new calls

are blocked

Adding handover channels reduces probabilityAdding handover channels reduces probability of handover failure for both data and voice of handover failure for both data and voice

Fig. 9

Results of adding handover channels:

– Handover failure probability decreases with the increase of Ch

Voice connection – – big decrease when Ch increased from 0 to 4, – minor decrease when Ch changed from 4 to 8,

Data connection –– biggest decrease when increasing Ch from 4

to 8

Decoupling of multimedia calls

– Multimedia call must be decoupled if available channel is not sufficient for both component but only for voicen > N – Cd –1 and n <= N –1

- Multimedia call will complete fail if no channel is freen = N

Higher #Ch reduces probability that call Higher #Ch reduces probability that call will be decoupled and reduces probability will be decoupled and reduces probability

that call will fail during handoverthat call will fail during handover

Fig. 10

Results:

– Reduction of handover failure probability is significant when Ch increased from 0 to 4

– Minor change when Ch changed from 4 to 8

– No extraordinary change for decoupling probability when Ch changes

The more channels assigned to handover, the The more channels assigned to handover, the higher the probability that the call will be higher the probability that the call will be

recombinedrecombined

Fig. 11

Results:

– The lower call rate, the higher recombination probability

– The higher Ch, the higher recombination probability

Access effectiveness of multimedia decoupling approach

(Two situations)

(1) Cd = 4 and Ch = 0, rmax = Cd(2) Cd = 4 and Ch = 0, rmax = 0

Using decoupling approach reduces Using decoupling approach reduces probability of handover failureprobability of handover failure

Fig. 12

Results:– Decoupling approach allows the

reduction of the handover failure probability by a factor between 5 and 10, depending on the cell traffic load

ConclusionConclusion

Model was based on continuous-time multidimensional birth-death process

Focuses on one cell only Improvement 1: Using handover channel

improves network performance Improvement 2: Decoupling multimedia

calls improves network performance