cesg.tamu.edu/wp-content//uploads/2012/08/ICC2012

fishecologistΚινητά – Ασύρματες Τεχνολογίες

12 Δεκ 2013 (πριν από 3 χρόνια και 8 μήνες)

77 εμφανίσεις

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
fading


Need to consider interference between
links


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
consists of about 1000
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
fading is considered

Channel Model


Suppose base station m
allocates
P
m,z

power on
resource block
z


Received power at i is
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
-
fading
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


Propose a gradient
-
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
-
normal shadowing on each frequency



Rayleigh fast fading


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