Femto-Matching: Efficient Traffic Offloading in Heterogeneous ... · Femto-Matching: Efficient Traffic Offloading in Heterogeneous Cellular Networks Wei Wang, Xiaobing Wu, Lei

Femto-Matching: Efficient Traffic Offloadingin Heterogeneous Cellular Networks

Wei Wang , Xiaobing Wu, Lei Xie and Sanglu Lu

Nanjing University

April 28, 2015

1/1

Heterogeneous Cellular Networks

Micro-cell

femto-cell

femto-cell

femto-cell

femto-cell

Micro-cell

femto-cell

Marco-cell

• Cellular networks use multiple layers of basestations to im-prove spatial utility

• Small cells such as femtocell or WiFi APs help offloadingthe traffic form the macro-cell

2/1

User Association Problem

• Mobile devices can connect tomultiple basestations

• Basestations provide differentservice qualities

• Traditional approach- strongest signal- lowest price- highest transmission rate

3/1

Problems for Existing Approaches

Scenarios of suboptimal solutions

• For the user- overly crowded basestations

• For the carrier- low utilization of femto-cells- overload of marco-cells

femto A

femto B

1M

1M

1M

1M

4 M

4/1

Problems for Existing Approaches

Scenarios of suboptimal solutions

• For the user- overly crowded basestations

• For the carrier- low utilization of femto-cells- overload of marco-cells

femto A

femto B

1.3M

2M

1!"M

1.3

M

2 M

4/1

Problems for Existing Approaches

Scenarios of suboptimal solutions

• For the user- overly crowded basestations

• For the carrier- low utilization of femto-cells- overload of marco-cells

femto A

femto B

1M

1.3 M

1M

1M

1.3 M

Cannot connect

to BS A due to

resource

constraints

1M

Associate to

macro-cell

1.3 M

4/1

Problems for Existing Approaches

Scenarios of suboptimal solutions

• For the user- overly crowded basestations

• For the carrier- low utilization of femto-cells- overload of marco-cells

femto A

femto B

1M

1 M

1M

1M

1 M

Cannot connect

to BS A due to

resource

constraints

1M

Associate to

macro-cell

1M

1M

4/1

Randomly Deployed Network

0 10 20 30 40 50 60 700

10

20

30

40

50

60

70

meters

mete

rs

An example for randomly deployed network

5/1

Randomly Deployed Network

0 10 20 30 40 40 60 700

10

20

30

40

50

60

70

meters

me

ters

“orphan nodes” 20%→ 5%

5/1

Key Challenges

• Misaligned objectives for users and network operators- Users want better throughput- Operators want better resource utilization

• Both the femto-cells and the users are randomly distributed• Require a global view to fully optimize the system• Mobile devices move all the time• One device usually only associates with one basestation

6/1

Problem Formulation

• Objective: maximize the overall utility- Proportional Fairness

max∑

i

log(∑

j

cij rijaij

)• Constraints:

- Basestations split their resources to associated users- Users only associate to one basestation∑

i cij ≤ 1 ∀j ∈ B,∑j aij ≤ 1 ∀i ∈ U ,

aij ∈ {0,1}, cij ≥ 0 ∀i ∈ U , j ∈ B.

7/1

Problem Transformation

• Mixed integer programming problem, but can be solved op-timally

• Key observations• Within a single cell, the resources will be divided evenly in

the proportional fairness case:

e.g., k − 1 users in BS j, with rate r1j , r2j , . . .

Each user takes 1k−1 of resources (time slots, RB,..), through-

put r1jk−1 , r2j

k−1 , . . .

Overall utility:

k−1∑i=1

logrij

k − 1=

k−1∑i=1

log rij − (k − 1) log(k − 1)

8/1

Problem Transformation

• A new user k with rate rkj joins

• Rate of existing users reduces to rijk

• Overall utility:

k−1∑i=1

logrij

k+ log

rkj

k=

k−1∑i=1

log rij + log rkj − k log k

• Marginal utility of user k :

log rkj + (k − 1) log(k − 1)− k log k

9/1

Problem Transformation

Converting the problem to an equivalent maximum weighted match-ing problem

Original network

Users

Base Stations

U1 U2 U3 U4

BS1 BS2

r r r rr11 21 31 32 42

• Splitting BS to virtualBSs

• Weights are marginalutilities

log (r /4)log (r /4)

u u u u

v v

log r11

v v v

1 2 3 4

1 1 1 2 21 2 13 2

log r log r31log r42

42

log (4r /27)11

log r 32

32log (r /4)11 log (r /4)21 log (r /4)31

log (4r /27)21 log (4r /27)31

21

10/1

Distributed Solution

• Exploit the special structure of the problem to design a dis-tributed matching algorithm

• Basic idea- Divide the edge weight to two parts, maintained separately

by the BS and user- Use the price to characterize the importance of the resource

• Auction process- Initialization: BSs set the initial prices for all virtual BSs;

Users estimate transmission rates- Iterative auction

* BSs announce the lowest price among virtual BSs* Users submit bids to the BS with highest gain* BSs select the user with highest bid and adjusts prices

• Finalize the association

11/1

Handling Mobility

• Incremental adjustment

• Node join- New node bids for the available resources in femtocells- Considering the cascading re-association

• Node leave- BS reduces the prices of the vacancy- Restart auction only when necessary

12/1

Performance Analysis

• Performance metrics- Offloading efficiency η:

ratio of users served by femtocells, reflects the efficiency offemtocells

• Both the femtocells and users are distributed as PoissonPoint Process, system parameters:

- l : Load factor l = λu/λf- κ: Number of users can be served by one femtocell

• Consider the efficiency of different schemes:- Associate to nearest BS- Matching schemes

13/1

Performance Comparation

• Associate to nearest BSOffloading efficiency around 74% due to randomness in deployment

l κ = 1 κ = 2 κ = 3 κ = 4 κ = 5 κ = 61 0.5851 0.8474 0.9483 0.9835 0.9950 0.99852 0.6636 0.8230 0.9110 0.9568 0.97963 0.6980 0.8132 0.8877 0.93414 0.7176 0.8080 0.87215 0.7303 0.80486 0.7393

• Matching schemeOffloading efficiency approaching 1, under higher network density.

14/1

Experiment Setup

• Experiments setups- Simulations with randomly generated networks- Trace-driven simulations on UIUC UIM trace

• Algorithms- Associate to nearest- RAT selection game- College admission algorithm- Femto-Matching

15/1

Experimental Results I

50 100 1500

0.05

0.1

0.15

0.2

0.25

0.3

N

1−

η

Nearest

College

RAT game

FemtoMatching

lower bound

Femto-matching has lowest ratio of not offloaded users

16/1

Experimental Results II

50 100 1500.6

0.7

0.8

0.9

1

N

Fa

irn

es

s i

nd

ex

College

RAT game

FemtoMatching

Femto-matching provides better fairness

17/1

Experimental Results III

1 2 3 4 5 6 7 80

20

40

60

Load of femtocellsNu

mb

er

of

fem

tocells

College

RAT game

FemtoMatching

50 100 150

400

600

800

N

Nu

mb

er

of

rou

nd

s

Simulation result

Curve fitting

Femto-matching provides better load balancing amongfemtocells and has low computational cost

18/1

Conclusion and Future Works

• Matching provides a good way to smooth out the random-ness in deployment

• It is possible to distributively calculate the optimal propor-tional fairness allocation

• Future researches• Detailed performance evaluation for mobility• Truthfulness in auction

19/1

Q & A

Thanks!

20/1