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12 Δεκ 2013 (πριν από 4 χρόνια και 7 μήνες)

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Self
-
Organized Resource
Allocation in LTE Systems with
Weighted Proportional Fairness

I
-
Hong Hou and Chung Shue Chen

Motivation

4G LTE networks are being deployed

With the exponentially increasing
number of devices and traffic,
centralized control and resource
management becomes too costly

A protocol for self
-
organizing LTE
systems is needed

Challenges

LTE employs OFDMA

Link gains can vary from subcarriers to
subcarriers due to frequency
-
selective

Need to consider interference between

A protocol needs to achieve both high
performance and fairness

Our Contributions

Propose a model that considers all the
challenges in self
-
organizing LTE
networks

Identify three important components

Propose solutions for these components
that aim to achieve weighted
proportional fairness

Outline

System Model and Problem Formulation

An algorithm for Packet Scheduling

A Heuristic for Power Control

A Selfish Strategy for Client Association

Simulation Results

Conclusion

System Model

A system with a number
of base stations and
mobile clients that
operate in a number of
resource blocks

A typical LTE system
resource blocks

Each client is associated
with one base station

Channel Model

G
i,m,z

:= the channel gain
between client
i

and base
station
m

on resource
block
z

G
i,m,z

varies with
z
, so
frequency
-
selective

Channel Model

Suppose base station m
allocates
P
m,z

power on
resource block
z

G
i,m,z
P
m,z

The power can be either
signal or interference

SINR of
i

on
z

can be
hence computed as

S
I
N
R
i
,
z

P
i
,
m
,
z
G
i
,
m
,
z
N
i
,
z

P
l
,
z
G
i
,
l
,
z
l

m

Signal

Interference

Channel Model

H
i,m,z

:= data rate of
i

when
m

serves it on
z

H
i,m,z

depends on SINR

Base station
m

can serve
i

on any number of
resource blocks

ø
i,m,z

:= proportion of time
that
m

serves
i

on
z

Throughput of
i
:

r
i

i
,
m
,
z
H
i
,
m
,
z
z

Problem Formulation

Goal: Achieve weighted proportional
fairness

Max (
w
i

:= weight of client)

Choose suitable
ø
i,m,z

(Scheduling)

Choose
P
m,z

(Power Control)

Each client is associated with one base
station (Client Association)

w
i
l
o
g
r
i
i

An Online Algorithm for
Scheduling

Let
r
i
[
t
] be the actual throughput of
i

up to
time
t

Algorithm: at each time
t
, each base
station
m

schedules
i

that maximizes
w
i
H
i,m,z
/
r
i
[
t
] on resource block
z

Base stations only need to know
information on its clients

The algorithm is fully distributed and can
be easily implemented

Optimality of Scheduling
Algorithm

Theorem: Fix Power Control and Client
Association,

The scheduling algorithm optimally solves
Scheduling Problem

Can be extended to consider fast
-
channels

l
i
m
t
®
¥
w
i
l
o
g
r
i
[
t
]
i
å

m
a
x
w
i
l
o
g
r
i
i
å
Challenges for Power Control

Find
P
m,z

that maximizes

Challenges:

The problem is non
-
convex

Need to consider the channel gains
between all base stations and all clients

Need to consider the influence on
Scheduling Problem

w
i
l
o
g
r
i
i

Relax Conditions

Assume:

The channel gains between a base station
m

and all its clients are the same,
G
m

The channel gains between a base station
m

and all clients of base station
o

are the
same
G
m,o

We can directly obtain the solutions of
Scheduling Problem

A Heuristic for Power Control

-
based heuristic

The heuristic converges to a local optimal
solution

The heuristic only requires base stations to
know local information that is readily
available in LTE standards

Can be easily implemented

Client Association Problem

Assume that each client aims to choose
the base station that offers most
throughput

Consistent with client’s own interest

In a dense network, a client’s decision has
little effects to the overall performance of
other clients

Estimating Throughput

To know the throughput that a base station
offers, client needs to know:

H
i,m,z

: throughput on each resource block,
can be obtained by measurements

ø
i,m,z

: amount of time client is scheduled

Develop an efficient algorithm that
estimates
ø
i,m,z

Solves Client Association Problem

Simulation Topology

500 m

X25

X16

X16

X9

Simulation Settings

Channel gains depend on:

Distance

Log
-

Compared Policies

Default

Round
-
robin for Scheduling

Use the same power on all resource blocks

Associate with the closest base station

Fast Feedback: has instant knowledge of
channels

Slow Feedback: only has knowledge on
time
-
average channel qualities

Simulation Results

0
50
100
150
200
250
300
Default
Slow Feedback
Fast Feedback
Total Throughput (Mbps)

Simulation Results

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
2
4
6
8
10
CDF

Throughput (Mbps)

Default
Slow Feedback
Fast Feedback
Conclusion

We investigate the problem of self
-
organizing LTE networks

We identify that there are three important
components: Scheduling, Power Control,
Client Association

We provide solutions for these problems

Simulations show that our protocol
provides significant improvement over
current Default policy