Post on 01-Apr-2015
Maciej Stasiak, Mariusz GłąbowskiArkadiusz Wiśniewski, Piotr Zwierzykowski
Basic Definitions and Terminology
Modeling and Dimensioning of Mobile Networks: from GSM to
LTE
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Course – assigned reading
• M. Stasiak, M. Głąbowski, P. Zwierzykowski: Modeling and Dimensioning of Mobile Networks: from GSM to LTE, John Wiley and sons Ltd., January 2011.
• Iversen V.B., ed., Teletraffic Engineering, Handbook, ITU, Study Group 2,Question 16/2 Geneva, January 2005, published on-line.
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Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Arrival stream
3
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Stochastic point process
• Possible realization of the stochastic point process
t
Tn
4
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Process parameters
• Λo intensity of arriving calls
• Pk(t)
o probability of k calls arrival within time interval of length t
• f(t)o inter-arrival time distribution
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
6
Arrival Poisson processes properties
• Stationarityo stream intensity is not time-dependable
o λ(t)= λ =const
• Memorylessness (independence of all time instants)
o number of arrivals occurring within the time interval t1 is independent of the number of arrivals occurring within the time interval t2
• Singularityo in a given time point only one arrival can occur
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Lack of memory property
• Distribution function of time interval between consecutive calls (inter-arrival time) is exponential function:
• We assume that inter-arrival time interval is equal to t. Let us determine the conditional probability so that this interval lasts for at least time τ.
• So, we can
)( tTTP
)()()( tTTPtTPtTP
tetTPtF 1)()(
0 t+t T
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
8
Lack of memory property
• Taking into account the distribution function we have
• The conditional probability have to receive the following value:
)()( TPetTTP
)( tTTP
)()( tTTPee tt >>= -+- tltl
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
9
)()(1 ttPi
0)(
lim0
t
tt
)()(1 tttP
)(1)(0 tttP
Singularity property
• Let us consider time interval Δt → 0. It results from the singularity property that probability of appearance of more then one arrivals within the time interval Δt is going towards 0:o where q(Dt) is infinitely small value if compared with Δt
• Elementary probabilities:
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Poisson stream parameter
• The flow parameter L(t) at time point t is defined as the limit of quotient:
Probability of appearing at least one arrival within time interval Dt+ t
time interval length Dt ® 0:
t
tt
t
tP
t
ttPt
ttt
)(lim
)(lim
)(lim)(
0
1
0
1
0
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
11
Poisson stream - characteristics
• Probability of appearance of k arrivals at time t:
• for k=0 i k=1 we receive:
,!
)()( t
k
k ek
ttP
tetP )(0ttetP )(1
t
Tnk
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
12
Poisson stream – characteristics
• Inter-arrival time distribution:
• Mean value and variance of inter-arrival time:
• Peakedness coefficient:
,)( tetf
.1/2 TT mZ
/1)(0
dttftmT
22
0
22 /1)(
TT mdttft
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Streams operations
• Superposition of Poisson streams
• Random decomposition of Poisson stream
.213
.12 p
Strumieñ 1
Strumieñ 2
Strumieñ 3
Stream 1
Stream 2
Stream 3
Strumieñ 1p p p p
1-p1-p 1-p1-p
Stream 1
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Streams operations
• Erlang k-decomposition
o inter-arrival time distribution
o mean value of inter-arrival time
o variance of inter-arrival time
o disorder coefficient
Strumieñ 1
T1
T2
T3
T
Stream 1
Stream 2
,)!1(
)()(
1t
k
ek
ttf
,/ kmT
,/ 22 kT
./1/2 kmZ TT
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
15
Markov process definition
• A stochastic process is called the Markov process when the future trajectory of the process depends only on the present state S(t0) at the time point t0 , but is independent of how this state has been obtained.
t
przeszłość przyszłośćt0
t < t t > t0 0
past future
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Markov process in the M/M/2 system
• A service process in the M/M/2 system (trunk group with two channels) is the Markov process when:o Arrival process is the Poisson process, o Service time has exponential distribution.
States
0
1
2
time
Trajectory of the service process in M/M/2 system
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Service stream
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Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Trajectory of the Markov process
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Exponential service time
• Distribution function:
• Density function:
• Mean value and variance:
tetTPtF 1)()(
tedt
tdFtf
)()(
/1)(0
dttfth
22
0
22 /1)(
hdttfth
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Service stream
• At time point t there are k servers busy. The probability of service termination in i servers within Δt time-interval can be determined on the basis of Bernoulli distribution for i successful events, when total number of events is equal to k:
• Probability of service termination in one server within Δt time-interval:
ikii PP
i
ktkP
)1(),(
tetTPtFP 1)()(
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Service stream
• For i=0 we obtain the probability of the event that within, time interval Δt, there are no terminations among k busy servers:
• Termination probability by at least one server:
• P1 (Δt ) decomposition (into series):
• Service stream parameter:
tketkP ),(0
tketkPtP 1),(1)( 01
)(!
)()()( ttkj
1tk11e1t
0j
jjtk1
1P
mDDq
mND
kt
tkt
0t=ú
û
ùêë
é+=
®
)(lim)(
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Markov proces
22
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Birth and death process in M/M/2 system
• state 0 all links are free• state 1 one busy link• state 2 two links are busy
blocking state
m
l
0 1
2m
l
2 1
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Birth and death process in M/M/2 system
• Infinite number of traffic sources• Finite number of busy servers
m
l
0 1
2m
l
2 1
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
25
Kolmogorov equations
• Determination of the probability P0(t + Δt)
• Events within time Δt:o Was in state "0" and transferred into state "1": λ Δt o Was in state "0" and remained in state "0": 1- λ Δt o Was in state "1" and transferred into state "0": μ Δt
t
t
0 1 1- t
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Kolmogorov equations
Was in state "0" and remained in state "0"
).()()(
),()()()(
,)(1)()(
100
1000
100
tPtPdt
tdP
tPtPt
tPttP
ttPttPttP
t
t
0 1 1- t
Was in state "1" and transferred into state "0"
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Kolmogorov equations
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Kolmogorov equations
• Solution:
Solution of Koplmogorov equations in M/M/2/0 system for λ=μ=1, P0(0)=1
P (t)0
P (t)1
P (t)2t0 1 2
1
0.2
0.4
0.6
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Steady-state distribution
0)(
)(lim dt
tdPtPp i
itVi
.1
,
,02
02)(
,0
222120
2221
222120
2120
ppp
pp
ppp
pp
.
!2
)/(
!1
)/(1
!)/(
212
ip
i
i
Probability calculations: Solution
In the steady-state regime of the process, the state probabilities arenot time-dependable
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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a) b)
0 1 2
Steady states
• Interpretation of the probability [Pi]V:
• The state probability is interpreted as the proportion of the time in which the system remains in state i:
n timeobservatio
statein spent timelim
iP
timenobservatioi
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Stream value in state i
Stream value in the state i for : t
ii Pi
n timeobservatio
statein spenttimeintensity stream
iP
ii Pll =
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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a) b)
0 1 2
State equations in the system M/M/2
• For the M/M/2/0 system state equations take the following form:
.1
,
,02
02)(
,0
222120
2221
222120
2120
ppp
pp
ppp
pp
In state i:
Sum of incoming streams = sum of outgoing streams
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
33
Generalized birth and death process
• State transition diagram:
1 2
0
1
1
2
2
3
0
i -1
i
i+1i
i
i +1
i +2
i +1
0
1111
2211100
1100
.1
,,0)(
,
,0)(
,0
iVi
ViiViiiVii
VVV
VV
p
ppp
ppp
pp
so:
0
11
2211
1100
.1
,,
,
,
,
iVi
ViiVii
VV
VV
p
pp
pp
pp
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Local balance equation
• Streams between neighboring states are in equilibrium
• Process solution
i i+1
i
i+1
ViiVii pp 11
k
iVkV
i
iV
k
kVk pCppp
100
10
21
110
0
0 /1k
kV Cpwhere:
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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a) b)
0 1 2
Local balance equation in M/M/2
1
,2
,
222120
2221
2120
ppp
pp
pp
12
1
,2
,
20
2022
2021
p
pp
pp
21
120p
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Concept of Traffic
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Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Telecommunication traffic
• Traffic as a process of capacity units occupancy
where n(t) – number of occupied units at time T
• Units: o 1 SM (speech-minutes)
o 1 Eh (Erlang-hour)
o 1 Eh = 60 SM
T
ttnTA0
vol d)()(
37
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Telecommunication traffic intensity
• Traffic intensity:
o where n(t) – number of occupied units at time T
• Units: 1 Erlang ( 1 Erl.) o 1 Erlang = 1 call serviced during time t when observation
time is equal to t
T
ttn
A
T
0
d
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Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Telecommunication traffic and traffic intensity
321
00
vol d1d tttttttnTATT
Traffic volume:
Traffic intensity:
T
ttt
T
TAA 321vol
service time service time
t1 t3t2
T
service time
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Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Traffic intensity
service time service time
t1 t3t2
T
service time
t1 t3t2
T
busy time idle time
T=100% =time unit
% of idle time% of busy time
unittime
occupancyunittimeoffraction321
T
t
T
tttA busy
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Traffic intensity
• Parameters:o V=4 - number of channels,o N=5 - number of time periods,o tobs=5T - period under
consideration,o ti,j - occupancy of the j-th
channel during the i-th time period
o - call intensityo h - mean service time.
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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.25
)12214(/)1.(
1 1., Erl
T
TttDefA
N
i
V
jobsji
t
1T 2T 3T 4T 5T0
Traffic intensity Def. 1
• Traffic intensity is equal to the average number of simultaneously occupied channels during a given period of time under considerations.
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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.25
)3223(/)2.(
1 1., Erl
T
TttDefA
N
i
V
jobsji
t
1T 2T 3T 4T 5T0
Traffic intensity Def. 2
• Traffic intensity is the ratio of the sum of channel occupancy time during a given period of time under considerations with respect to this period.
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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t
1T 2T 3T 4T 5T0
.28
10
5
8)3.( Erl
T
TchDefA
Traffic intensity Def. 3
• The product of the average number of o calls (offered traffic)
o connections (carried traffic)
• per time unit and the average time of connection.
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
t
1T 2T 3T 4T 5T0
Traffic intensity Def. 4
The mean number of calls (connections) per mean service time
offered traffic carried traffic
.28
10
5
8)4.( Erl
T
TsDefA
45
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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SystemtelekomunikacyjnyRuch oferowany Ruch obsłużony
Ruch tracony
A Y
A’
'AYA
SystemOffered traffic Carried traffic
Rejected traffic
ATTENTION!Conventionally, under the notion of traffic we understand traffic intensity
Kinds of traffic
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Kinds of traffic
• Carried traffic o the traffic carried by the group of servers during the time
interval T
• Offered traffic o the traffic which would be carried if no calls were rejected
due to lack of the capacity, i.e. unlimited number of servers. The offered traffic is a theoretical value and it cannot be measured
• Lost (rejected) traffic o the difference between offered traffic and carried traffic
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Quality of service in telecommunication
systems
48
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Call and packet level in networks
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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),(
),(),(
),(
),(),(
21
2121
21
2121 ttN
ttNttN
ttN
ttNttB
offerd
carriedoffered
offered
lost
Concept of blocking
• Call congestion (Call loss probability) B(t1, t2 ) in time interval (t1, t2) is the fraction of all calls which are rejected due to lack of capacity Nlost(t1, t2 ) with respect to all calls which are offered in the system Noffered(t1, t2 )
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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),(
),(),(
21
2121 ttT
ttTttE blocking
Concept of blocking
• Time congestion (Blocking probability) E(t1, t2 ) in time interval (t1, t2) is the fraction of the time Tblocking(t1 , t2) when all servers are busy with respect to the total time of observation T(t1, t2)
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Basic notions and parameters
• Traffic load capacityo the value of the offered traffic (traffic intensity) which can be
serviced with the adopted value of blocking probability (loss probability)
• Loado the value of the carried traffic (traffic intensity) in the system
• Blocking o the state of system in which a call arriving at the input of the
system cannot be serviced due to occupancy of all servers in the system
• Throughput o the probability of event that the given call will be serviced in the
system
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
Quality of service in communication systems (QoS)
• Packet delay (cell delay)o A delay considered between the moment of sending and
receiving the packet (in appropriate nodes)
• Delay parameters (for example of ATM network)
o CDTmean - Mean Cell Transfer Delay - statistical average delay of packet
o CTDmax - Maximum Cell Transfer Delay – maximum delay of packet, guaranteed by network with probability 1-α
o CDVpeak-peak - Peak to Peak Cell Delay Variation – maximum delay decreased by constant system delay (i.e. propagation time, processing time in node)
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Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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constant max delay variation
max delay
Del
ay d
istr
ibut
ion
α= loss ratio
delay
1-α
Quality of service in communication systems
(QoS)
• Interpretation of delay parameters
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Reasons for networks delay
• Constant and independent of network loado propagation time in physical layer
o processing time in network node
o minimum time the node wait for packet acknowledgement
o bit rate of outgoing link bigger than incoming link
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Reasons for networks delay
• Dependent on network loado queuing in the buffers
o queuing discipline,
o priorities for given packet classes
o mechanisms for packet streams shaping
o resources reservation for given packet classes
server
outgoing stream
buffer
incoming stream
Modeling and Dimensioning of Mobile Networks: from GSM to LTE
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Kinds of traffic at the packet level
• In majority of packet networks kinds of traffic are associated with parameters of offered services
• We can always distinguish the following traffic streamso Constant bit rate traffic
o Variable bit rate traffic• stream traffic, constant parameters of transmission• adaptive traffic• elastic traffic