service differentiation for improved cell capacity in lte...
TRANSCRIPT
Service Differentiation for
Improved Cell Capacity
in LTE Networks
WoWMoM 2015 – 10-13 June 2015, Boston - USA
Presenter: Mattia Carpin ([email protected])
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 ([email protected])
University of Padova (Italy) – Telenor Research (Norway)
Any questions?