IMPACT OF MOBILITY IN DENSE LTE-A NETWORKS WITH SMALL CELLS M. Bruno Baynat (Université Pierre et...

IMPACT OF MOBILITY IN DENSE LTE-A NETWORKS

WITH SMALL CELLS M. Bruno Baynat (Université Pierre et Marie Curie – LIP6)

Mme. Raluca-Maria Indre (Orange Labs)

M. Narcisse Nya (Université Pierre et Marie Curie – LIP6)

M. Philippe Olivier (Orange Labs)

M. Alain Simonian (Orange Labs)

1

PLAN

Context Motivations Goal Network Model Network assumptions Modeling assumptions Markovian Model Fixed Point approximation Performance results Conclusion and future works

2IDEFIX MEETING 26-27 Mars 2015

CONTEXT

Constant increase of data in mobile networks

Massive deployment of small cells

Increase the proportion of mobile users

Impact of this increase on the performance of

LTE-A


MOTIVATIONS

Evaluate and quantify the impact of mobility

on the performance of small cells


GOAL

Simple analitical models

Influence of mobile users on the performance

of static users

Amount of generated handovers


NETWORK MODEL


7

Macro Cell

NETWORK MODEL

IDEFIX MEETING 26-27 Mars 2015

NETWORK MODEL

8

Macro Cell


9

ASSUMPTIONS


10

NETWORK ASSUMPTIONS Cell with constant capacity C

Two types of users

Static users

Mobile users

Equitable ressources sharing : Round-Robin

Each users download data of size Σ

Full transmission for static users

Mobile users remain in the cell for a limited time θ


11

MODELING ASSUMPTIONS Requests for transmission is generated according

to Poisson processes Rate λs for static users

Rate λm for mobile users

Exponential distribution of service time

Exponential remaining sojourn time of an active

mobile user θ

Exponential distribution of data to download Σ


12

MARKOVIAN MODEL


MARKOVIAN MODEL

13

ns, nm

13

ns, nm+1

ns+1, nmns-1, nm

ns, nm -1

Inverse of mean sojourn time

Arrival rate of static users’

requests

Arrival rate of mobile users’

requestsService rate of the

cell


MARKOVIAN MODEL

1414

The model is exact

Stability condition

Does not depend on the mobile users

Numerical resolution Truncating both dimensions of state space

Gauss-Seidel or Least mean square


MARKOVIAN MODEL


Mean time to transfer the average volume E(Σ)

Performance indicators of interest Average throughput obtained by any user

Propotion of handover

MARKOVIAN MODEL

1616

Limitations of the model :

Exponential distribution of mobile users

sojourn time

Exponential distribution of data to transmit

Resolution complexity

Scalability


17

FIXED POINT APPROXIMATION


1818


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Capacity of the cell

Average size of the downloaded

file

Average size downloaded by a

mobile user ?

Two classes of users with different service rate

Multi-class Processor-Sharing queue with two

classes of customers


1919



Stability condition

Multi-class PS queue

Thus necessary that

For this system

is sufficient

2020



How to calculate ?

Depends on sojourn time and average throughput

of the user

If the parameter is known

Standard results for the stationary multi-class

processor sharing

2121

FIXED POINT APPROXIMATION21

Knowing the distribution of Σ

Fixed pointProbability Density

Throughput of the user given by the PS

queue

Probability density of sejourn time \

Probability density of sejourn time \

If is known


FIXED POINT APPROXIMATION Performance indicators of interest

Average throughput obtained by any user

handover probability

Exponential distribution of and

and

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PERFORMANCE RESULTS


242424

PERFORMANCE RESULTS

Θ and Σ are both exponentially distributed The Markovian model is exact


Static users throughput Mobile users throughput

252525

PERFORMANCE RESULTS

Θ and Σ are both exponentially distributed The Markovian model is exact


262626

PERFORMANCE RESULTS

Impact of sojourn time distribution


272727

PERFORMANCE RESULTS

Impact of key parameters

User throughput with differrent cell size

User throughput with different speed


28

CONCLUSION & FUTURE WORKS


292929

CONCLUSION AND FUTURE WORKS


Markovian model

Exponential distribution of θ and Σ

Resolution complexity

Not extensible

Exact

303030



Fixed point approximation

Approximate model

Very simple

Easily extensible

313131



Future Works

Macro-cell with several coding zones

Several neighboring cells

THANK YOU FOR YOUR

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IMPACT OF MOBILITY IN DENSE LTE-A NETWORKS WITH SMALL CELLS M. Bruno Baynat (Université Pierre et...

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