service differentiation for improved cell capacity in lte...

Service Differentiation for

Improved Cell Capacity

in LTE Networks

WoWMoM 2015 – 10-13 June 2015, Boston - USA

Presenter: Mattia Carpin (carpin@dei.unipd.it)

Authors: Mattia Carpin, Andrea Zanella, Jawad Rasool,

Kashif Mahmood, Ole Grondalen, Olav Osterbo

University of Padova (Italy) – Telenor Research (Norway)

LTE High Level

2

Radio Access Network

Core Network

MME

SGW

HSS

PGW

SGi-LAN

S11

S5 IP

network

eNodeB

eNodeB, responsible for resource

allocation both in downlink and

uplink.

Scheduling problem

Opportunistic scheduling, for high cell

spectral efficiency Fair scheduling, to provide similar service

to all users

Schedulers’ metrics

CQI

Suitable constant for each user that

depends on the average channel conditions

Hyb

rid

O

pp

ort

unis

tic

Fair

Achievable bit-rate, computed

according to the CQI

Previous work

In a previous work we simulated the behaviour of a

Fair Throughput Guarantee Scheduler (FTGS)

αi is computed s.t. each UE gets in the long term the

same throughput guarantee B

What is the impact of cell edge users?

Keep the same average cell SINR μ, position UEs so

that Δ increases

Avg. SINR i-th user

Cell edge users’ impact

Class Based Scheduler

CBS assumptions

Assumptions:

Constant population (N users) over the optimization interval

Rayleigh fading channel

Average user SINR known at the eNodeB

Classification according to the average SINR

Same G (bit/s) to users belonging to same class

We guarantee Gi>Gmin using call-admission control mechanism

Gb = Gmin Gs > h Gmin Gg = k Gs

Under those assumptions we solve a system of 4N-1 equations

that gives α for each user (if such α exists!)

9

Solving equations

Adaptation mechanism

11

Call admission

control

Simulations

Circular cell of radius r meters, s.t. γ(d = r) = 2 dB

N users distributed over the cell area with uniform probability

Results obtained comparing CBS against

MTS, upper bound on spectral efficiency

PFS, simple hybrid scheduling policy

FTGS, equal guarantee to all users

Different Gmin = {50, 100, 150} Kbit/s

Results

Admission control

Short term analysis

But what in the short term?

Channel-dependent nature → short term behaviour

influenced by the fading process ↔ Doppler spread

Single-class of users, target throughput η

We introduce the average achieved throughput ϑ over a

time window of leght τ seconds

Normalized gap

Short term gap PDF

Long > Short Long < Short

Variance of the PDF

K-factor for fitting

Excess probability

Suppose we have an application that needs to

transmit L bits in T seconds

We define the excess delay probability as

This is the probability of not fullfilling the request of

the application

Example: L/WT=0.2bit/s/Hz

18

Excess probability

19

Conclusions and future

developments

Addressed Issues:

Dynamic algorithm for efficient resources allocation in LTE

Short term behaviour of the algorithm, PDF and relation with the

channel conditions

Excess delay probability

A look into the future

Full implementation of the scheduler in NS3

Impact of the scheduler decision on the E2E delay

Pre-compute and store the optimal parameters

Dynamic and real time estimation of the SINR

…

20

Service Differentiation for

Improved Cell Capacity

in LTE Networks

Presenter: Mattia Carpin (carpin@dei.unipd.it)

University of Padova (Italy) – Telenor Research (Norway)

Any questions?