report of group g

8/2/2019 Report of Group G

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IK 2511 Project in Wireless Networks

ENERGY EFFICIENCY OF OPEN-ACCESS

MACRO-FEMTO NETWORKS

Group members: Advisors:

Leon Edgar Filemon Sibel Tombaz

Nader Al-Ghazu Niklas Johansson

Yang Yanpeng

Zheng Zhihao

January 20, 2012


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ENERGY EFFICIENCY OF OPEN-ACCESS MACRO-FEMTO NETWORKS

ii

ACKNOWLEDGEMENT

We would like to begin this report by acknowledging all the people who contributed to this

project

First we are heartily thankful to our supervisor, Sibel Tombaz, whose support, guidance and

encouragement from the initial to the final enabled us to understand and implement the

project. We are indebted to Niklas from Ericsson for giving us some practical advice on the

task.

Furthermore we need to express our deep appreciation to Jens Zander and other teachers who

organized a good project course and taught us project management.

Also thanks to all the tutors and our friends who helped us.

Finally thanks to our parents for their continuous support and encouragement.


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ABSTRACT

Recently, the rising energy costs and global focus on climate changes created high interest in

energy efficiency of telecommunication systems. Wireless technology trends to the usage of

joint macro-femto networks. Macro cells provide outdoor coverage while femtocells support

indoor (and neardoor) coverage. The goal of this report is to study the effect of deploying

femtocells with open and closed access in terms of throughput and energy efficiency. The

simulation uses realistic LTE parameters to see the downlink performance in several

scenarios where different power consumption and spectrum utilization models are used. We

also investigate how the locations of the users can affect the system performance. Our

research indicates that open-access with load dependent power consumption model and

splitting spectrum utilization is most energy efficient. Furthermore, it is shown that a larger

proportion of neardoor users or indoor users lead to a higher energy efficiency gain and there

may be a tradeoff between power consumption and system coverage.

Keywords Femtocells, open/closed-access, energy efficiency, power consumption,

spectrum allocation.


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TABLE OF CONTENTS

I. Introduction .......................................................................................................................... 1

II. Problem Definition ............................................................................................................. 2

A) Closed-access and Open-access ....................................................................................... 2

B) Spectrum utilization strategies ......................................................................................... 2

C) Power consumption reduction strategies .......................................................................... 2

III. System Model and Assumptions ...................................................................................... 2

A) Network deployment ........................................................................................................ 2

B) Propagation model ............................................................................................................ 3

C) Power Consumption Model .............................................................................................. 4

D) Energy Efficiency ............................................................................................................. 4

IV. Simulation and Results ..................................................................................................... 5

A) Simulation Procedure ....................................................................................................... 5

B) Simulation Parameters ...................................................................................................... 6

C) Simulation Results ............................................................................................................ 6

Scenario I: Splitting Spectrum in Single-Cell ................................................................... 6

Scenario II: Splitting Spectrum in Multi-Cell ................................................................... 8

Scenario III: Sharing spectrum in Multi-Cell .................................................................... 9

V. Performance Analysis....................................................................................................... 10

A) Traffic Load Dependent and Independent Comparison ................................................. 10

B) Macro-Only, Macro-Femto Closed and Open Comparison ........................................... 11

C) Splitting Spectrum versus Spectrum sharing .................................................................. 12

D) Different user allocations ............................................................................................... 12

VI. Conclusion ....................................................................................................................... 13

References .............................................................................................................................. 14


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I.INTRODUCTIONThe dependency on wireless communication for voice calls and data traffic has rapidly

increased in the recent years. Mobile operators are building wireless networks for the recently

developed standards, such as LTE and WiMax. One main concern about this new generation

of mobile networks is power consumption and the energy efficiency [1]. The deployment of

more macro base stations (MBS) will dramatically increase power consumption, which will

increase the operators cost and the carbon emissions. Nowadays heterogeneous networks are

used which provide coverage by means of layers: one layer of high power macro base stations

and another layer of smaller micro base stations in dense population areas [2].

Recent studies show that in the future 60-90% of the network traffic will be originated

indoors [3, 9], which creates a need for new wireless networks architectures. Lately, high

interest has been shown in the deployment of macro-femto wireless networks (two-tier

network). Femtocells are low power nodes with small coverage, connected with an internetbackhaul which provide a good signal quality for indoor users. A large number of femtocells

can be deployed in houses and offices while umbrella MBS with high transmit power could

provide outdoor coverage. Femtocells deployment will cause co-tier interference with other

nearby femtocells and cross-tier interference with close MBSs. This may cause a reduction in

the performance of the wireless network. Meanwhile, the spectrum in heterogeneous networks

can be either shared between the two tires of the network, or can be split into different sub-

spectrums and allocated to every network tier.

Many studies on the energy efficiency of deploying femtocells have been done, some research

papers investigate the throughput of the system against the number of femtocells deployed as

in [3]. Others compare the energy efficiency with the fraction of deployed femtocells as in

[2,4,7]. In [5] the effect of interference on system performance was studied. In [8,9] the

spectrum allocation was investigated. In all these papers, they considered either an open-

access or a closed-access system, but no direct comparison was done.

In this research project the open-access and closed-access systems are compared in terms of

throughput and energy efficiency for different femtocells deployment densities. Two

spectrum utilization plans are considered: splitting spectrum and sharing spectrum. Energy

efficiency in load dependent and load independent power consumption models are compared.

All simulations are done in Matlab based on a LTE environment. All results are demonstrated

to show the downlink data-rate performance and energy efficiency.

The rest of the report is organized as follows. In section II, we briefly describe and explain

our research topic. The system model, mathematical formulas and assumptions are

demonstrated in section III. Section IV shows the simulation environment with parameters

and the results acquired. In section V we analyze and discuss the results, and section VI is the

conclusion.


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II.PROBLEM DEFINITION

A) Closed-access and Open-access

In a femtocell deployed system, two kinds of work modes are usually defined: Closed-access

and Open-access:

1) Closed-access refers that the femto base station (FBS) belongs to a Closed SubscriberGroup (CSG), which means only members of the CSG are allowed to access FBS.

2) Open-access refers that the FBS belongs to Open Subscriber Group (OSG), whichmeans the FBSs are open to public access.

As mentioned earlier, other relative papers hardly compare the performance between the two

modes. The members of the CSG may not prefer to share their FBS. But open-access

femtocells may be more green considering the benefit of the whole society. The operatorcould use some methods to persuade the members to make the FBS public. So in this project,

both modes were simulated in a LTE based system. And the gap between open-access and

closed-access systems was measured in terms of throughput and energy efficiency.

B) Spectrum utilization strategies

Consider a macro-femto cellular network with fixed bandwidth, existing spectrum utilization

strategies can be divided into two different ways. Co-channel deployment between macro and

femto networks is one method. This will make each macro or FBS occupy a wide bandwidth

but coming with serious interference between macrocells and femtocels. In the other method

the whole spectrum is split into two sub-channels. This avoids interference but the spectrumeach BS uses will be narrower. Both strategies will be simulated to figure out which one is

more suitable for a macro-femto based network.

C) Power consumption reduction strategies

In a heterogeneous cellular network, when part of the indoor (and neardoor) traffic is served

by FBSs, the transmit power of the MBS could be reduced without impairing system

performance. The reduction of transmit power should be dependent of the load of the FBSs i.e.

the percentage of the buildings with FBSs. In this project, we consider the scenario where the

power diminution is proportional to the number of users served by FBSs, and contrast it with

scenario without energy saving.

III.SYSTEM MODEL AND ASSUMPTIONS

A) Network deployment

In this report, we consider every scenario in a single hexagonal cell network with MBS in the

center of the cell, transmitting through omnidirectional antennas firstly and then extend it to a

19-sites multi-cell network as shown in Figure 1. Each cell has a radius R, inter site distance

3D R and area 23 3

2cA R .


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Figure 1. Multi-cell network with 19 cells.

Figure 1 shows the network we deploy in our simulation. All the phones connect to the MBS

when there is no FBS in the house. If there is FBS in a house, it is set in the center. Blue

phones denote indoor users which connect to the FBS when there is one in the house. Black

phones represent neardoor users which always connect to the MBS in closed-access but if

there is FBS in the house in the open case, they are forced to connect to the FBS. Red phones

model the outdoor users that always connect to the MBS.

Depending on their types, we define locations of different users: indoor users are uniformly

distributed inside a building; neardoor users are uniformly distributed outside the house but

within 10 meters from the center of the house; outdoor users are uniformly distributed in the

rest of the area. The femtocell penetration rate (Rp) models the probability with which each

building has its FBS. We assume a full traffic load in all our scenarios which means there is

no traffic model.

B) Propagation model

The path loss between a user and MBS can be calculated as [10]:

10

10

15.3 37.6log15.3 37.6log

macro

ow

R outdoor userPLR L indoor user

(1)

where R is the distance between the user and the MBS in meters andow

L is the penetration

loss of an outdoor wall. When the user connects to a FBS, the path loss can be calculated as:

10 2

10 10 2

38.46 20 log 0.7

max(15.3 37.6 log , 38.46 20 log ) 0.7

D

femto

ow D

R d indoor userPL

R R L d outdoor user

(2)

where2

0.7D

d is the penetration loss due to internal walls and2D

d is the distance inside the

house. For the reason of simplification, this report does not consider the effect of fast fading.


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C) Power Consumption Model

The relationship between transmit power and average consumed power for MBS is given by

macro t P aP b (3)

wheremacro

P andt

P represent the consumed and transmit power per site respectively. The

term a denotes the impacts which are related to the radiated power caused by PA amplifier

and feeder losses as well as site cooling. The parameter b represents fixed power which is

independent of transmit power due to signal processing, battery backup and site cooling. The

parameters we use here are a=22, b=350.

In the case where power consumption is dependent of traffic load,t

P will scale with the

percentage of users who are served by the MBS. Then transmit power can be expressed by:

(1 / )

(1 ( ) / )

t indoor FBS total

d

t indoor neardoor FBS total

P N N N closed accessP

P N N N N open access

(4)

whered

P andt

P denote the transmit power in load dependent and independent cases

respectively. Nindoor and Nneardoor represent the number of indoor or neardoor users who

belongs to one house. Ntotal means the total users in the cell. For FBSs, since the power

consumed is low, we assign the value of 9w for each of them.

D) Energy Efficiency

As an assumption, the interference from FBSs is ignored when calculating SINR, because the

transmit power of FBS is low and it needs to suffer a penetration loss due to the wall. In the

case of splitting spectrum, the SINR of macro user n can be shown as:

,

kn

i

i M i k

PSINR

P N

(5)

where M denotes the set of MBSs,i

P represents the receive power from the i-th MBS, N

denotes the thermal noise over the user bandwidth. Correspondingly, the SINR of femto user

n can be expressed as:

kn

PSINR

N (6)

wherek

P denotes the receive power from the FBS which it connects. In the case of co-channel

mode, the SINR of the macro users stays the same while that of the femto user n should

change as:

kn

i

i M

PSINR

P N

(7)


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wherek

P andi

P denote the receive power from the FBS and the i-th MBS respectively, M

denotes the set of MBSs. Given the SINR of the n-th subscriber, the throughput can be

calculated by Shannon Capacity limitation theory as:

2log (1 )n n nR W SINR (8)

where R is the throughput in Mbps, W is the bandwidth of the n-th user. The bandwidth is

equally divided by the users who share it whether in macrocell or femtocell. For the energy

efficiency factor, we use

REE

P (9)

which models how many bps we could achieve when consuming 1 watt power, i.e. bps/watt

in the entire system. Then energy efficiency gain is defined to be

open close

gain

close

EE EEEE

EE

(10)

IV.SIMULATION AND RESULTSIn this section, we give a brief simulation procedure description to reveal how the simulation

test bed is established. Then some selected system parameters are demonstrated in part B. The

results including system throughput, power consumption and energy efficiency of the two-tier

network in different scenarios are given for further analysis.

A) Simulation Procedure

Figure 2 illustrates the flow chart of our simulation. In the simulation, Monte Carlomethod

is adopted to obtain a reasonable result. Some assumptions are considered to simplify the

simulation, e.g. all houses are one-floor and maximum one FBS is allowed in each of them.

Femtocells are placed at a safe distance from the MBS to make sure the indoor users can

obtain a better signal from FBS instead of MBS. The simulation starts by creating a network

deployment map including the locations of MBSs and houses. Then it randomly generates the

positions of indoor users, neardoor users and outdoor users before creating a shadow fading

map. Later, within every iteration, the following process is carried out: a certain number of

houses with femtocells are chosen randomly according to different femto penetration rates;

users are moved around; Calculate the distance matrix between the users with their served

base stations; Create shadow fading matrix for all the connected paths and add it to the path

loss matrix obtained through propagation models; Calculate SINR for each users and then

throughput, power consumption and energy efficiency. After certain iterations (generally we

use 300 times), all the comparison metrics are averaged and plotted.


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Start

Read

Simulation

Parameters

Generate:

Macro BS Locations

Houses Locations

Users Locations

Generate:Shadow Fading Map

Move Users

Assign Femto Bs to Houses

Calculate:

Distances Matrix

Calculate:

Path loss

Shadow Fading

Calculate:

SINR

Calculate:

Throughput

Energy Efficiency

Power Consumption

Plot FiguresMonte Carlo

Average

END

Iteration

Figure 2. Simulation Flow Chart

B) Simulation Parameters

The selection of the simulation parameters are mostly based on 3GPP TR 36.814 [11]. The

total bandwidth is 10 MHz and they are split into two 5 MHz bands for splitting spectrum

deployment scheme. In this report most results demonstrated are using the parameters in

Table 1, unless otherwise noted.

Parameter Value

Carrier frequency 2.0 GHz

Total Bandwidth 10.0 MHz

Inter-site distance 500 m

Safe radius without houses 40.9 m

MBS transmit power 46 dBm (traffic load independent case)

FBS transmit power 20 dBm

MBS/FBS antenna gain 14/5 dBiNumber of houses per cell 20

Total users 150

Number of indoor users per house 4

Number of outdoor users per cell 30

Number of neardoor users per house 2

Femtocell penetration rate (Rp) 0~1 (varied)

MBS Shadow fading (standard deviation) 8 dB

FBS Shadow fading (standard deviation) 4 dB

Shadow fading correlation distance 20 m

Exterior wall penetration loss 15 dB

Minimum received power per user -70 dBm

Thermal noise -174 dBm/Hz

House size 10 x 10 m

2

Femtocell radius 10 m

Table 1. Simulation system parameters

C) Simulation Results

Scenario I: Splitting Spectrum in Single-Cell

i) Aggregate System Throughput

Based on the simulation model, the aggregate system throughput of all the users is shown in

Figure 3. The throughput is linearly increasing with the enhancement of femtocell penetration

rate except the starting point where no FBS is installed and all the 10MHz bandwidth isdistributed to the MBS. It increases from 300 Mbps in the macro-only case to about 3000


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Mbps when all the houses are equipped with femtocells in closed case. In addition, a higher

throughput is achieved in the closed case than the open case. The gap between the two cases

increases to a maximum of 200 Mbps when the penetration rate reaches one. The throughput

from load dependent and load independent power models show almost no difference because

the main part of throughput comes from FBS connections.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

500

1000

1500

2000

2500

3000

3500

Femtocell Penetration Rate

AggregateThroughput[Mbps]

Closed Case

Open Case

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

500

1000

1500

2000

2500

3000

3500



Closed Case

Open Case

Figure 3. Aggregate throughput comparison between closed and open case.

Load dependent (left), and load independent (right).

ii) Power consumption

Figure 4 shows the difference of power consumption between load dependent and

independent cases. For the former case, both open and closed access modes consume less

power when more houses are equipped with femtocells. Meanwhile, the open case is

decreasing much more than closed case. While in the traffic load independent case, all thetransmit power of MBS and FBS are fixed, so the power consumption boosts when more

femto access points are used.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1700

800

900

1000

1100

1200

1300

1400

1500


PowerConsumption[Watt]

load indepdent

load dep. in closed case

load dep. in open case

Figure 4. Power consumption comparison between load dependent (closed and open case) and load independent.

iii) Energy Efficiency

The energy efficiency is increasing with the growth of femtocell penetration rate in both

scenarios (Figure 5). The open case is more energy efficient than close case with load


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dependent power control algorithm implemented. That is because the power consumption

gain achieved by the open system is larger than the throughput difference.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.5

1

1.5

2

2.5

3

3.5

4


EnergyEfficiency[Mbps/Watt]

Open Case

Closed Case

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.5

1

1.5

2

2.5

3

3.5

4



Closed Case

Ppen Case

0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52

1.25

1.3

1.35

1.4

1.45

1.5

1.55

1.6



Open Case

Closed Case

Figure 5. Energy efficiency comparison between closed and open case.

Load dependent (left), and load independent (right).

The right plot in Figure 5 shows the traffic load independent energy efficiency comparison.

The close case is slightly higher than open case. This is triggered by the throughput

comparison since the power consumption is the same for both cases.

In most of the figures, the value corresponding to the penetration rate shows the mean value

after a Monte-Carlo average. To see whether the fluctuation of the values could affect the

conclusion, a confidence interval (CI) is made for each value. According to the bottom one in

figure 5, the fluctuant range of the value is very small comparing with the value itself. The

CIs are made in all the figures and they turns out small all the time, so in the rest figures onlymean values are considered.

Scenario II: Splitting Spectrum in Multi-Cell

A multi-cell environment with two tiers of interference (19 cells) is simulated in this section.

The difference from the single cell scenario comes mainly from the interference part. Because

the whole bandwidth is reused in every macrocell, the macro users will experience extra

interference from its neighbor MBSs. As a result, the aggregate throughput is slightly reduced

due to decreased SINR at the receivers (shown in Figure 6 left). The power consumption

remains the same in load dependent scenario. Figure 6 right reveals the energy efficiency

developing trends are the same with single cell case. The only difference comes from

throughput.


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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

500

1000

1500

2000

2500

3000


AggregateThroughp

ut[Mbps]

Closed Case

Open Case

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.5

1

1.5

2

2.5

3

3.5

4



closed case

open case

Figure 6. A comparison of Throughput (left) and Energy efficiency (right) between closed and open case

in multi-cell system with split spectrum (load dependent case)

Scenario III: Sharing spectrum in Multi-CellIn this Scenario, a sharing spectrum is considered, which means the whole bandwidth 10

MHz is reused by all macro and femto BSs. The simulation result is shown in Figure 7.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

200

400

600

800

1000

1200

1400

1600

1800

2000



Closed Case

Open Case

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.5

1

1.5

2

2.5


Ener

gyEfficiency[Mbps/Watt]

Closed Case

Open Case

Figure 7. A comparison of Throughput (left) and Energy efficiency (right) between closed and open case

in multi-cell system with sharing spectrum (load dependent case)

The left plot indicates a larger gap between the throughput in open and closed cases. The

maximum difference is 400 Mbps which is almost twice the difference in the splitting

spectrum case. This is because the neardoor users are forced to connect to FBSs in open-

access case, so the throughput decreases due to the large interference coming from MBSs. A

comparison of the resulting energy efficiency is shown in Figure 7 right. The open-access is

no longer better than closed-access until a large portion of FBS is deployed. In other words,

the open-access system is not as energy efficient as in the splitting spectrum scenario.


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V.PERFORMANCE ANALYSIS

A) Traffic Load Dependent and Independent Comparison

Figure 8. Power consumption difference with different femtocell penetration rate under load independent and

dependent cases.

The histogram (Figure 8) shows that a vast amount of power can be saved if load dependent

algorithm is used. The maximum power consumption saved from load dependent (open case)

compared to load independent case is 13.3 kw. While inside the load dependent scenario,closed case consumes more than open case with a maximum gap of 4.2 kw and the gap is

increasing as penetration rate rises. That is because fewer users are connected to MBS,

leading to less MBS transmit power needed. But at the same time, we may get an outage area

at the edge of cell.

FBS Penetration Rate 0 0.2 0.4 0.6 0.8 1

Closed

Ratio of users served by FBS 0.00% 10.67% 21.33% 32.00% 42.67% 53.33%

MBS transmit power(dBm) 46 45.5 44.95 44.33 43.58 42.69

Cost in Outage 0.00% 0.00% 0.00% 0.00% 0.33%0.01% 0.00%

Open

Ratio of users served by FBS 0.00% 16% 32% 48% 64% 80%

MBS transmit power(dBm) 46 45.24 44.33 43.16 41.56 39.01

Cost in Outage 0.00% 0.00% 0.00% 0.74%0.17% 2.52%0.32% 0.00%

Table 2. MBS transmit power and outage with different femtocell penetration rates.

Table 2 shows the relationship among the number of femtocell users, MBS transmit power

and outage probability. According to the Power Consumption Model, the more users

connected to FBS, the less transmit power is needed for MBS. But in the other side, we may

lose some coverage when too low transmit power is used. It is shown in the table that when

42% of users are served by FBSs, some indoor users without FBS at the cell edges mayexperience a bad signal quality (received power less than -70dBm). When the ratio arrives to


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64%, the outage ratio rises to around 2.5%. For the case that all the houses are equipped with

FBS, 39.01 dBm is enough for the macro base station to cover the whole cell area and provide

a good received power for outdoor users. (Notice that when outage is mentioned in last

paragraph, it refers to the indoor users without FBS. Because the transmit power of macro

base stations is large enough to ensure a good signal quality for outdoor users.)

B) Macro-Only, Macro-Femto Closed and Open Comparison

Houses 20 Outdoor 30

Rp 0.6/ 1 Indoor 4

Users 150 Neardoor 2

Table 3. User allocation

Macro-only Close Open

Rp=0.6TP (Mbps) 15.611 1727.2 1624.2

EE(bps/w) 670.2492 95564 105390

Rp=1TP (Mbps) 15.611 2833.5 2627.5

EE(bps/w) 670.2492 194130 258660

Table 4. Throughput and energy efficiency comparison between three scenarios

Table 4 shows the results of throughput and energy efficiency comparison between macro-

only, closed-access and open-access with user allocation in Table 3. The throughput has a

huge improvement when FBSs are deployed compared with macro-only case whether in

closed or open-access. This is mainly because the users served by FBS enjoy a large

bandwidth and do not suffer interference from the MBS when FBSs exist.

TP (Mbps)Indoor Near door Outdoor Sum

Femto Avg Macro Avg Femto Avg Macro Avg Macro Avg

Close 1714.7 428.68 3.63 0.11 4.24 0.11 4.59 0.15 1727.2

Open 1166.6 291.65 4.75 0.15 444.61 222.31 2.33 0.15 6 0.2 1624.2

Gap -548.1 1.12 444.61 -1.92 1.41 -102.88

Table 5. Throughput analysis for indoor, neardoor and outdoor users under open and closed cases.

The reason why throughput in closed-access is higher than in open-access could be found in

Table 5. For the indoor users served by FBS, their throughput will decrease in open-access

because some neardoor users will share the spectrum with them. Other indoor users who

connect to the MBS get a little more bandwidth also due to those neardoor users going for

FBSs. For the neardoor users who connect to FBSs in open-access case, their throughput rises

a lot as a result of sharing a wideband with indoor users only. Other neardoor users just get a

small increase owing to the former ones going for FBSs. The outdoor users throughput also

improves slightly because of the same reason. The most difference comes from the change of

the indoor and neardoor users throughput in the two scenarios. The data in Table 5 proves

that the decreasing part from indoor users is larger than the increasing part from neardoor

users, from which we conclude that closed-access is better than open-access in terms of

throughput. While open-access performs better at the aspect of energy efficiency because it

consumes less energy than in the closed case.


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C) Splitting Spectrum versus Spectrum sharing

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

500

1000

1500

2000

2500

3000


AggregateThroughput[M

bps]

Splitting Spectrum

Spectrum Sharing

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.5

1

1.5

2

2.5

3

3.5

4


EnergyEfficiency[Mbps/

Watt]

Splitting Spectrum

Sharing Spectrum

Figure 9. Aggregate throughput and energy efficiency comparison within two spectrum utilization scheme

with different femtocell penetration rate.

Figure 9 shows the throughput and energy efficiency comparisons when different spectrumutilization schemes are applied in the open-access mode. The general trend is increasing and

the splitting spectrum case gives a better system throughput (energy efficiency). The gap

between them is increasing as penetration rate is growing. The reason for this is obvious: in

the co-channel scheme, the advantage is that users can enjoy a larger bandwidth (double), but

they also experience severe interference between femtocells and macrocells. Because of the

high transmit power from MBS, femtocell users especially those who are outside houses can

only get a low SINR which leads to bad performance in throughput. From the result, we can

see the throughput improvement from spectrum increase is much less than the throughput loss

caused by bad received SINR.

D) Different user allocations

The main difference in throughput comes from neardoor users. So in this section, we want to

first illustrate the effect of different number of neardoor users on aggregate throughput and

energy efficiency. In Table 1, indoor users per house are set to 4, the total number of users is

still 150, and the femtocell penetration rate is 0.6. With a different number of neardoor users

per house, we can see different throughput and energy efficiency gains.

Open Closed

Neardoor TP [Mbps] EC [Watt] EE [Mbps/W] TP [Mbps] EC [Watt] EE [Mbps/W] EE_Gain

1 1646.7 983.5 1.67 1719 1053.6 1.63 0.03

2 1619.6 913.4 1.77 1738.3 1053.6 1.65 0.07

3 1553 843.4 1.84 1709.1 1053.6 1.62 0.14

Table 6. System performance in open and closed cases with different number of neardoor users.

From previous analysis, neardoor users experience a worse SINR value when they are forced

to connect to femtocells. The decrease in throughput from indoor users is much larger than

the throughput increase of neardoor users although they can share a better spectrum. As a

result, the more neardoor users, the worse throughput is obtained under our fixed simulation

environment. This can be proved through the second column in Table 6. An obvious

advantage of introducing neardoor users is saving huge power consumption under open-

access and traffic load dependent mode (as shown in the third column). In the closed case,


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both the throughput and power consumption remain almost the same. At last, energy

efficiency gain reveals our system is more green with more users are near the houses.

Open Closed

Indoor/Ratio TP [Mbps] EC [Watt] EE [Mbps/W] TP [Mbps] EC [Watt] EE [Mbps/W] EE_Gain

3/40% 1587.6 983.5 1.61 1722.2 1123.6 1.53 0.05

4/53.3% 1607.6 913.4 1.76 1727.9 1053.6 1.64 0.07

5/66.7% 1662.3 843.4 1.97 1764.7 983.5 1.79 0.10

Table 7. System performance in open and close cases with different number of indoor users.

Recently, more and more data traffic is generated from indoor users, so we want to examine

the system performance under different indoor user ratios. In Table 7, the number of indoor

users per house is changed with a fixed neardoor user which is 2. The total number of users is

150, and the femtocell penetration rate is still 0.6. It is clear that, a higher throughput is

obtained in both open and closed cases, because indoor users can share a larger bandwidthcompared with outdoor users. Plus, the power consumption decreases. Hence, higher energy

efficiency is obtained if a larger indoor user ratio is set, which is good for urban areas. As is

shown in this table, 10% gain can be achieved when 67% of users are inside houses. If more

houses are installed with femtocell, this figure will improve more.

VI.CONCLUSIONIn this report, we have analyzed different performance in a Marco-Femto network between

open and closed-access in terms of throughput and energy efficiency in several scenarios. We

introduce load independent and load dependent modes at the aspect of power consumption

and prove that the latter one could save energy dramatically. Meanwhile we find that there is

a tradeoff between transmitter power and network coverage even though the outage

probability is very small (up to 2.5%). Spectrum utilization schemes are taken into account as

well. Dividing the bandwidth between macro and femto users shows better results than

sharing the whole bandwidth because the interference is quite small when splitting the

spectrum. Generally a femto based network performs much better than a Marco-only network

in all aspects. Furthermore, our research indicates that in the considered simulation setup,

open-access with load dependent power consumption model and splitting spectrum utilization

is most energy efficient. Closed-access performs better in throughput but need to consume

more power. Different user allocations have impact on the results. It is proved that a larger

proportion of neardoor users or indoor users lead to a higher energy efficiency gain.


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REFERENCES

[1] J. Hoydis, M. Kobayashi, and M. Debbah, Green Small-Cell Networks, IEEEVehicular Technology Magazine, vol. 6, pp. 37-43, Mar. 2011.

[2] L. M. delApio, E. Mino, L. Cucala, O. Moreno, and B. Ignacio, Energy Efficiency andPerformance in mobile networks deployments with femtocells, presented at the 22ndIEEE Personal Indoor Mobile Radio Communication, 2011, pp. 107 - 111.

[3] M. Wang, X. Zhu, Z. Zeng, S. Wan, and W. Li, System Performance Analysis ofOFDMA-based Femtocell Networks.

[4] H. Claussen, L. T. W. Ho, and F. Pivit, Effects of Joint Macrocell and ResidentialPicocellDeployment on the Network Energy Efficiency, presented at the 19th IEEE

Personal Indoor Mobile Radio Communication (PIMRC), Cannes, 2008, pp. 1-6.

[5] F. Cao and Z. Fan, The tradeoff between energy efficiency and system performance offemtocell deployment, in Wireless Communication Systems (ISWCS), 2010 7thInternational Symposium on, pp. 315319.

[6] D. Chee, M. suk Kang, H. Lee, and B. C. Jung, A study on the green cellular networkwith femtocells, in Ubiquitous and Future Networks (ICUFN), 2011 ThirdInternational Conference on, 2011, pp. 235240.

[7] K. Dufkov, M. Popovic, R. Khalili, J. Y. Le Boudec, M. Bjelica, and L. Kencl,Energy Consumption Comparison Between Macro-Micro and Public Femto

Deployment in a Plausible LTE Network.

[8] W. Cheng, H. Zhang, L. Zhao, and Y. Li, Energy Efficient Spectrum Allocation forGreen Radio in Two-tier Cellular Networks, in Proceedings of the IEEE GlobalTelecommunications Conference (GLOBECOM 2010), 2010, pp. 15.

[9] A. D. Domenico, E. C. Strinati, and A. Duda, Ghost Femtocells: an Energy-EfficientRadio Resource Management Scheme for Two-Tier Cellular Networks, EuropeanWireless 2011, 2011.

[10] Femto Forum white paper, Interference Management in OFDMA Femtocells,www.femtoforum.org/.

[11] 3GPP TSG RAN1, TR 36.814 v9.0.0, Evolved Universal Terrestrial Radio Access (E-UTRA); Further advancements for E-UTRA physical layer aspects, March 2010.

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