Cognitive Access and Resource Allocation in Autonomous ... · both external (uncontrollable) and...

Cognitive Access and Resource Allocation in Autonomous Femtocell Networks

by

Leon Chung-Dai Yen

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science

Graduate Department of Electrical & Computer Engineering University of Toronto

© Copyright by Leon Yen 2010

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Cognitive Access and Resource Allocation

in Autonomous Femtocell Networks

Leon Chung-Dai Yen

Master of Applied Science

Graduate Department of Electrical & Computer Engineering University of Toronto

2010

Abstract

Femto access points (FAP) are low-power cellular base stations that are designed to be

autonomously deployed by customers indoors. Due to spectral scarcity, FAPs are expected

to share spectrum with underlying macrocells. Closed access refers to the strategy where

only Owners of the FAP are allowed access; whereas the FAP is open to everyone under

Open access. Challenges such as dead zones or excessive signaling arise when implementing

these two access strategies. Cognitive access control is a hybrid approach that would have

the FAP first senses the environment, prioritizes different classes of users, and then

reserves a portion of femtocell radio resource for Owners while distributing the remaining

to Visitors. Simulation results have shown that by utilizing the proposed Cognitive access

control and reserve resource dynamically with the surrounding environment, both Macro-

user and Owner throughputs will improve over the macrocell-only baseline, as well as both

Closed and Open access strategies.

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To my supportive family, friends, and colleagues

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ACKNOWLEDGMENT

First of all, I would like to thank my grandparents, my parents John and May, my fiancée

Helen, my brother Joe, and all other relatives for their understanding, encouragement, and

support for the past 30 years. Without them, I would not even be studying for my Master’s

degree at the University of Toronto.

Second of all, I would also like to thank my thesis supervisor, Prof. Sousa, and my Post-Doc

friend, Yang-yang, from the Graduate Department of Electrical & Computer Engineering of

the University of Toronto for their critiques, feedbacks, and recommendations for the past

three years. Without them, I would not be able to complete my graduate studies.

Finally, I would like to thank my directors, managers, and colleagues from both Radio

Engineering and Roamer Services departments at Rogers Communications Partnership for

their recognition, patience, and training. Without them, I would not be able to achieve

financial stability during my graduate program while gaining valuable engineering

experience at the same time.

Last but not the least; I would like to acknowledge the Edward S. Rogers, Sr. Scholarship and

the Natural Science and Engineering Research Council (NSERC) for providing financial

assistances during the first two years of my Master of Applied Science program.

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TABLE OF CONTENTS

ABSTRACT ----------------------------------------------------------------------------------------------- ii

ACKNOWLEDGEMENT ------------------------------------------------------------------------------ iv

TABLE OF CONTENTS ------------------------------------------------------------------------------- v

LIST OF TABLES -------------------------------------------------------------------------------------- viii

LIST OF FIGURES -------------------------------------------------------------------------------------- ix

LIST OF APPENDICES -------------------------------------------------------------------------------- xi

1 BACKGROUND INTRODUCTION

1.1 RESEARCH MOTIVATION --------------------------------------------------------------- 1

1.2 FEMTOCELL TECHNOLOGY ------------------------------------------------------------ 4

1.2.1 Spectrum Allocation ------------------------------------------------------------- 5

1.2.2 Resource Management --------------------------------------------------------- 6

1.2.3 Access Control -------------------------------------------------------------------- 9

1.3 DEPLOYMENT CHALLENGES ---------------------------------------------------------- 11

1.3.1 Closed Access -------------------------------------------------------------------- 11

1.3.2 Open Access ---------------------------------------------------------------------- 12

1.4 LITERATURE REVIEW ------------------------------------------------------------------- 13

1.4.1 3GPP Discussion ----------------------------------------------------------------- 13

1.4.2 Adaptive Access ----------------------------------------------------------------- 14

1.4.3 Limited Access ------------------------------------------------------------------- 15

1.5 THESIS CONTRIBUTION ---------------------------------------------------------------- 16

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2 PROPOSED ACCESS SCHEME

2.1 COGNITIVE ACCESS --------------------------------------------------------------------- 18

2.1.1 Environment Sensing ---------------------------------------------------------- 19

2.1.2 User Classification -------------------------------------------------------------- 19

2.1.3 Reservation Determination --------------------------------------------------- 20

2.1.4 Resource Allocation ------------------------------------------------------------ 21

2.1.5 Dynamic Reconfiguration ----------------------------------------------------- 22

2.2 OPERATION MODE --------------------------------------------------------------------- 22

2.2.1 Definitions and Criteria -------------------------------------------------------- 23

2.2.2 Relationships and Transitions ------------------------------------------------ 24

2.3 SYSTEM DESIGN ------------------------------------------------------------------------- 26

2.3.1 Design Model -------------------------------------------------------------------- 26

2.3.2 Channel Modeling -------------------------------------------------------------- 28

2.3.3 User Association ---------------------------------------------------------------- 30

2.3.4 Resource Reservation ---------------------------------------------------------- 31

2.3.5 Interference Model ------------------------------------------------------------- 31

3 SYSTEM SIMULATION

3.1 SIMULATION MODEL ------------------------------------------------------------------ 36

3.1.1 System Parameters ------------------------------------------------------------- 36

3.1.2 Random Deployments --------------------------------------------------------- 38

3.1.3 Dynamic Scheduler ------------------------------------------------------------- 41

3.2 PERFORMANCE EVALUATION ------------------------------------------------------- 44

3.2.1 Average User Throughput ---------------------------------------------------- 44

3.2.2 Cellular Operator --------------------------------------------------------------- 45

3.2.3 Femtocell Owner ---------------------------------------------------------------- 46

3.3 MACROCELL OFFLOADING ----------------------------------------------------------- 46

3.3.1 Femtocell Penetration --------------------------------------------------------- 47

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3.3.2 Indoor Percentage -------------------------------------------------------------- 48

3.3.3 Owner Probability -------------------------------------------------------------- 50

3.4 FEMTOCELL PERFORMANCE --------------------------------------------------------- 51

3.4.1 Femtocell Penetration --------------------------------------------------------- 52

3.4.2 Indoor Percentage -------------------------------------------------------------- 53

3.4.3 Owner Probability -------------------------------------------------------------- 55

3.4.4 Visitor Throughput ------------------------------------------------------------- 56

3.5 RESORUCE RESERVATION ------------------------------------------------------------ 58

3.5.1 Macro-user Throughput ------------------------------------------------------- 59

3.5.2 Owner Throughput ------------------------------------------------------------- 61

3.5.3 Visitor Throughput ------------------------------------------------------------- 63

4 CONCLUSION AND FUTURE RESEARCH

4.1 FINAL REMARK -------------------------------------------------------------------------- 66

4.2 FUTURE WORK -------------------------------------------------------------------------- 67

viii

LIST OF TABLES

1 Environment Parameters --------------------------------------------------------------------- 37

2 System Parameters --------------------------------------------------------------------------- 38

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LIST OF FIGURES

1 Evolution Path towards 4G Wireless ------------------------------------------------------- 2

2 Communication Paths for Macro-Femtocell Networks --------------------------------- 3

3 Femtocell Network Architecture ------------------------------------------------------------ 5

4 Triple Constraints in Cellular Planning ----------------------------------------------------- 6

5 Macrocell-Only Deployment ------------------------------------------------------------------ 9

6 Femtocell Deployment with Closed Access ---------------------------------------------- 10

7 Femtocell Deployment with Open Access ------------------------------------------------ 10

8 Femtocell Dead Zones ------------------------------------------------------------------------ 12

9 Procedure for Cognitive Access Control in HSDPA Femtocells ----------------------- 18

10 Relationships between Resource Reservation and Operation Modes ------------- 24

11 Transition Map of Operation Mode -------------------------------------------------------- 25

12 Pseudo Algorithm for Macro-Femtocell Network Deployment --------------------- 27

13 Resource Reservation ------------------------------------------------------------------------- 31

14 Co-channel Femtocell Networks with Signal and Interference ---------------------- 32

15 Autonomous Femtocell Deployments with Different Users -------------------------- 39

16 Random Distributions inside Circular Regions ------------------------------------------- 40

17 Simulation Process Flow ---------------------------------------------------------------------- 42

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18 Resource Reservation Process Flow ------------------------------------------------------- 43

19 Macro-user Throughput vs Femtocell Penetration ------------------------------------- 47

20 Macro-user Throughput vs Indoor Percentage ------------------------------------------ 49

21 Macro-user Throughput vs Owner Probability ------------------------------------------ 50

22 Owner Throughput vs Femtocell Penetration ------------------------------------------- 53

23 Owner Throughput vs Indoor Percentage ------------------------------------------------ 54

24 Owner Throughput vs Owner Probability ------------------------------------------------ 55

25 Visitor Throughput vs Indoor Percentage ------------------------------------------------ 57

26 Visitor Throughput vs Owner Probability ------------------------------------------------- 57

27a Macro-user Throughput vs Resource reservation (25% Owner) -------------------- 60

27b Macro-user Throughput vs Resource reservation (50% Owner) -------------------- 60

28a Owner Throughput vs Resource reservation (25% Owner) -------------------------- 62

28b Owner Throughput vs Resource reservation (50% Owner) -------------------------- 62

29a Visitor Throughput vs Resource reservation (25% Owner) --------------------------- 64

29b Visitor Throughput vs Resource reservation (50% Owner) --------------------------- 64

xi

LIST OF APPENDICES

A1 Reference ---------------------------------------------------------------------------------------- 68

A2 Abbreviation ------------------------------------------------------------------------------------ 72

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COGNITIVE ACCESS AND RESOURCE ALLOCATIONIN AUTONOMOUS FEMTOCELL NETWORKS © LCDY 2010

CHAPTER 1

BACKGROUND INFORMATION

1.1 Research Motivation

The two classes of third generation (3G) wireless communications being commercially

deployed nowadays are the High-Speed Packet Access (HSPA) from the 3rd Generation

Project Partnership (3GPP) as well as the Evolution-Data Optimized (EV-DO) from the

alternate 3GPP2. With increasing demands from customers for faster and more stable

mobile data services anytime and anywhere, a new fourth generation (4G) wireless

technology is needed. However, there is no common agreement as to what 4G is other than

that these future generation cellular networks should have larger capacities with the

capability of carrying higher user data rates. The transition from 3G to 4G is widely expected

to be very different from the evolution of the previous wireless generations up to the

current 3G [01]. As a result, there is a general agreement that new approaches to

developments of future wireless infrastructure are required.

Three different evolution paths are currently being considered as potential parallel

approaches towards 4G. The first path is by increasing the cell capacity, which can be

accomplished through implementing new cellular modulation techniques such as Long Term

Evolution (LTE) or Worldwide Interoperability for Microwave Access (WiMAX). The second

path is by improving spectral efficiencies with the utilization of Cognitive Radio (CR) or

Beam-forming communications. The third path is by intelligently deploying the radio access

networks via the implementation of autonomous wireless network architectures. A

combination of these approaches, if not all three, will eventually converge to define the

future 4G wireless standards.

While the wireless technologies continue to evolve, mobile communications have also

transformed from voice centric services to information based services. With the intention to

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bring fixed desktop user experiences to mobile devices, LTE and WiMAX have been

developed to redefine the traditional physical-layer air interface in order to bring seamless

broadband packet transmissions to mobile communications. With emerging technologies

such as Orthogonal Frequency Division Multiple Access (OFDMA) and Multiple-Input

Multiple-Output (MIMO) transmissions, both LTE and WiMAX are expected to substantially

improve end-user throughputs, network capacity, and reduce user plane latency, bringing

significantly improved user experience with full mobility [02][03][04].

Current access to radio resources is usually regulated by government agencies that execute

a nation’s right in spectrum usage. Recent studies have shown that the average spectrum

utilization was very low for frequency bands below 3GHz [05]. Cognitive radio is a paradigm

for wireless communications in which either a network or a wireless terminal changes its

transmission or reception parameters to communicate more efficiently while avoiding

unwanted interference with either licensed or unlicensed users [06]. This dynamic

alteration of operating parameters is based on the active monitoring of several factors in

both external (uncontrollable) and internal (controllable) radio environments; for example:

radio frequency spectrum, user behaviour, as well as current network state. Nonetheless,

the development of cognitive radio technology is still considered to be at its early stage.

Figure 1: Evolution Path towards 4G Wireless

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An autonomous wireless architecture, proposed in 2004, projected a new concept of

automatic deployment and configuration of cellular access points, which are basically low-

power base stations, resulting in the capability of organic growth of the wireless network.

The major key to the autonomous wireless architecture is a unified physical layer with

interference and power management capabilities that will allow the autonomous

deployment of cells of various sizes as described in [07][08].

Based on the idea of autonomous wireless network architecture, a new class of small base

stations called femto access points (FAP), commonly known as femtocells or Home NodeBs

(HNB) in 3GPP standards, has emerged. These devices are of the size of a regular wireless

routers, or cable modems, and provide indoor cellular coverage to mobile terminals, while

at the same time utilize existing broadband internet connections in their premises as

backbone communications [09]. Some of the key features of these FAPs are their low-cost,

low-power consumption, broadband-connectivity, and user-installability.

Figure 2: Communication Paths in Macro-Femtocell Networks

Additionally, the radio access techniques for femtocells are based on cellular standards and

are therefore compatible with existing mobile terminals. In other words, current available

cellular phones can either communicate with a nearby newly-deployed FAP or the

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underlying macro base station (MBS) as illustrated in Figure 2. The ability for femtocells to

be backward compatible with current legacy mobile devices and smartphones is considered

to be their major advantage over the other competing Fixed Mobile Convergence (FMC)

solution, the Unified Mobile Access (UMA) technology [10][11].

1.2 Femtocell Technology

Traditional low-power wireless access points are based on WiFi technology, also known as

the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard. In these

networks, a single RF channel is used and users compete for the common channel resource

via the Carrier Sensing Multiple Access-Collision Avoidance (CSMA/CA) channel access

protocol. Under CSMA/CA, performance of wireless connections in terms of user data rates

will degrade drastically as the number of user increases given that multiple users compete

for the same radio resource at the same time. Femtocells, on the other hand, will be able to

handle a larger number of users with effective interference management adapted from the

legacy cellular standards such as the 2G Time Division Multiple Access (TDMA), the 3G

Wideband Code Division Multiple Access (WCDMA), or the 3G+ OFDMA access schemes.

Femto access points (FAP) are low-cost low-power base stations that are autonomously

deployed at home (or within an indoor region) by customers, and at the same time utilize

broadband connections as their backhaul [12]. In other words, FAPs are designed to provide

high quality cellular services in both residential or enterprise environments and operate in

licensed spectrum to connect conventional mobile terminals, or user equipment (UE), to a

cellular service provider’s core backbone network as seen in Figure 3.

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Figure 3: Femtocell Network Architecture

1.2.1 Spectrum Allocation

Based on the licensed spectrum utilized by the randomly installed FAPs, two different

classes of femtocell deployments are defined: dedicated channel and co-channel

deployments. The easiest way to deploy femtocells autonomously is to allocate a separate

frequency band for FAPs, where the RF channel is different from those utilized by the

regular cellular network (macrocells); in other words, a dedicated channel femtocell

deployment. The major advantage for dedicated channel deployments in femtocells is the

fact that using an additional spectrum for deploying femtocells would minimize potential

inter-frequency interference between macrocells and femtocells. As a result, deploying

femtocells with dedicated channels will not impact the performance of the underlying

macrocell network.

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Unfortunately, the dedicated channel deployment is inefficient and hence this simple

approach is undesirable due to the fact that the frequency spectrum is a scarce resource.

Therefore, a mobile service provider (MSP) is unlikely to dedicate a separate frequency

band solely for femtocell deployments [13]. As a result, the more practical solution for MSPs

is to utilize a co-channel deployment model, which is for randomly deployed FAPs to re-use

the same RF channels as the underlying macrocells.

The major advantage of co-channel deployment is its significantly higher spectral efficiency

as discussed in [14][15]; however, this benefit is at the cost of potentially unnecessary intra-

frequency interference between the underlying macrocell and nearby femtocells [16].

Throughout this thesis, only co-channel deployments are considered and thus controlling

interference efficiently via femtocell radio resource management is the primary focus.

1.2.2 Resource Management

Resource management is one of the most important functions in an autonomous femtocell

network because the system relies on it to guarantee a certain target Quality of Service

(QoS), maintain the planned coverage area, and at the same time offer high network

capacity. These objectives often tend to be contradictory; for example: QoS levels may be

increased at the expense of coverage or capacity reductions and vice versa. Figure 4

illustrates typical triple constraints that are often considered in a traditional static cellular

planning where one element improves at the cost of the other two counterparts.

QoS

Coverage Capacity

Figure 4: Triple Constraints in Cellular Planning

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From a mobile service provider’s perspective, network planning and resource management

need to cooperate with each other in order to meet the above requirements with an

efficient use of the scarce radio resources. With network planning aiming to tune these

elements statically at a high level, radio resource management within FAPs will need to

provide efficient and effective fine-tuning mechanisms to dynamically balance these

constraints as the surrounding environment changes [17].

An inefficient resource management mechanism from a co-channel femtocell deployment

can lead to drastic degradation of the end user experience; this is due to its inability to meet

the required QoS level by not being able to provide high capacity within operator’s coverage

area. This therefore impacts overall system efficiency, operator infrastructure cost, and

business revenues.

In a generic wireless system, there are usually two resources that we need to manage

within a FAP: power and channel. Power is a physical resource that affects both coverage

and throughput; more power usually implies greater coverage and higher throughput. On

the other hand, channels are defined differently under different cellular access techniques.

In WCDMA, channels are defined by orthogonal spreading codes; however, in Orthogonal

Frequency Division Multiple Access (OFDMA), channels are defined as combinations of two

physical measures: sub-carrier frequencies and time slots. Management techniques of the

femtocell resources such as admission control, power control, and channel control will all

be described in the following paragraphs.

Before any femtocell resource is allocated, the mobile terminal must first be admitted to

the FAP. The process of admitting an UE to a femtocell is normally referred to as admission

control (or access control). The admission control of a FAP in the downlink (DL) direction is

usually affected by both the channel availability and transmission power consumption, two

radio resources that are always utilized by radio communications links.

One way to implement downlink admission control in a femtocell is based on the user

count. The user count based algorithm assumes that each UE consumes a certain fixed

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amount of radio resources and limits the maximum acceptable amount of radio resources

to be consumed. Another method of admission control implementation is to base it on cell

load in the downlink direction, because cell load tends to provide a better overall picture of

the actual loading of the cell than user count. Cell load can be calculated based on the

throughput of the femtocell, using the sum of all allocated DL channel bit rates.

In regards to power control of the resource management, the total available power in the

downlink direction is shared by all UE connections within the cell. As a result, each newly

admitted UE connection, apart from increasing the interference, reduces the total power

available to additional users [18]. Any WCDMA-based system, take HSDPA as an example,

depends on a tight power control algorithm to efficiently use the radio resources available

in the cell. Even under femtocell environment, power control is also needed to keep the air

interference level at a minimum while maintaining the required QoS level for each UE.

The objective of power control is to provide the required QoS to each UE while causing

minimum interference to other UEs of the same cell. By having an effective power control

mechanism in place, both intra-cell and inter-cell interference can be well-controlled [19]. In

most cases, the desired power level is determined based on the path loss between the UE

and the FAP. Based on the interference, the UE requests the FAP to change the desired

power level accordingly. Upon receiving power modification request, the FAP then adjusts

its downlink channel power for the requesting UE.

With respect to the channel control of the resource management, the total available

channels in the downlink direction are also shared by all UE connections within the cell.

However, the difference between power and channel is that, the definition of channel

varies among different cellular technologies. In legacy TDMA systems, channels are defined

by time slots. In the downlink direction of the current WCDMA systems, channels are

represented by Orthogonal Variable Spreading Factor (OVSF) codes. In new OFDMA

systems, channels will be combinations of time slots and sub-carrier frequencies.

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The objective for channel control is to allocate a sufficient rate to each UE such that the

required QoS is achieved. In WCDMA systems, multiple codes, or channels in general, can

be assigned to a single UE to increase the necessary throughput while reducing the total

amount of transmitted power [20][21]. The collaboration between power control and

channel control is necessary so that FAPs will not introduce interference when

autonomously deployed by users under a co-channel environment.

1.2.3 Access Control

Of all resource management mechanisms described above, admission control, or access

control, is one of the most important aspects to consider in co-channel femtocell

deployments. Before radio resources can be allocated, any UE must first be associated with

a FAP; the process of FAP association is determined by the access control algorithm. Within

3GPP standards, there are currently two types of femtocell access policies defined in [22]:

Closed access and Open access. Each of these two access policies is described in the

following paragraphs.

Figure 5: Macrocell-Only Deployment [22]

Closed access refers to the scheme where each private FAP controls its own access list

called the Allowed Access List (AAL). The AAL of a FAP may, at any time, be configured by

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the user who owns the FAP to grant network access to specific UEs within its femtocell

coverage. UEs within the femtocell coverage and with necessary access permissions (the

green UE in Figure 6) will be handed over from the original macrocell to the current

femtocell for further voice and data communications.

On the other hand, any UE not on the AAL or without permissions will be barred access by

the FAP, and will try to remain connected with its original macrocell (light blue and grey UEs

in Figure 6).However, UEs that are closer to but barred by the FAP due to Closed access

policy will experience higher signal degradation (lighter coloured UEs in Figure 6) in the

downlink communication channels due to higher intra-frequency interference, also known

as jamming, caused by the nearby FAP.

Figure 6: Femtocell Deployment with Closed Access [22]

Figure 7: Femtocell Deployment with Open Access [22]

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Open access refers to the scenario where the MSP controls the AAL on behalf of the user

who owns the FAP; in other words, femtocells with Open access are no longer privately-

operated by customers and can be considered as public cellular hot spots for the mobile

service provider. Any mobile terminal (green UEs in Figure 7) that passes through a

femtocell region with Open access policy will be handed over from the MBS to the FAP, if

granted access by the MSP. UEs coming from different mobile service providers may also be

granted access to the nearby FAP; however, premium roaming charges may be applied for

inter-operator communications.

1.3 Deployment Challenges

Before femtocells can be commercially deployed, several critical challenges will need to be

addressed and solved a priori as mentioned in [23]. In this thesis, issues regarding various

access controls in femtocell resource management are considered. The following sub-

sections explain some of the challenges associated with the two access control mechanisms.

1.3.1 Closed Access

From the definition of Closed access, only Owners (users who are listed on the AAL of the

FAP), are granted access to the FAP. The main reason for this type of deployment is to

guarantee Owner experience when they are within coverage of the FAP they acquired.

However, one critical issue arises with the deployment of Closed access when an

anonymous user enters the femtocell coverage and that user is not on the AAL.

Under such circumstance, the visiting UE will still attempt to associate itself with the FAP

due to the fact that nearby FAP pilot signal is usually much higher than MBS pilot signal

within the FAP coverage. However, this attempt will not be successful because the visiting

UE is not on the AAL of the FAP. As a consequence, the UE will become a Visitor (user who is

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not on the AAL of the FAP) and may experience severe QoS deteriorations such as call drops

within the femtocell coverage; these undesired regions caused by FAPs are known as dead

zones, as illustrated by the red areas in Figure 8.

FAP

MBS Femtocell

Macrocell

Dead Zones

FAP

FAP

Figure 8: Femtocell Dead Zones

Dead zones are small areas within macrocell coverage where all downlink communications

of non-authorized UEs are jammed by the nearby co-channel FAPs. Dead zones are often

categorized in terms of the path loss from the transmitter; a 60dB dead zone is therefore a

region around the femtocell where the path loss to the FAP is less than 60dB [24]. Unless

other RF carriers are available for hand over, otherwise the QoS within a dead zone is

typically very poor for the barred mobile terminals that it is not possible to provide the

demanded services.

1.3.2 Open Access

Contrary to the Closed access model, all users in the Open access model are granted with

full accessibility whether or not they belong to the AAL of the FAP. In other words, no AAL is

maintained to filter different classes of users; both Owners and Visitors are treated equally

assuming they belong to the same mobile service provider. One advantage of Open access is

the elimination of potential occurrences of dead zones that would deteriorate the QoS of

the overall cellular network.

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However, major disadvantages for employing Open access is that the MSP will need to

incorporate additional billing and security components into its cellular core network

infrastructure, These network elements are added to identify and then differentiate

between various types of user traffic, either Owner (private) or Visitor (public), going

through individual FAP. Furthermore, another crucial drawback for Open access would be

the potential vast amount of signaling when a user passes through a cluster of femtocells

utilizing the Open access strategy [25]; as an undesirable consequence, unnecessary

overhead will be introduced to the overall mobile network.

1.4 Literature Review

Based on the challenges among both Closed access and Open access control mechanisms as

described in the previous section, several research initiatives have proposed potential

solutions that would address these issues. In the following sub-sections, workgroup meeting

results by 3GPP committees, along with several proposed solutions, are assessed.

1.4.1 3GPP Discussion

In August 2007, a 3GPP workgroup meeting was hosted by Nortel and Vodafone in Athens,

Greece to discuss the influence of restricting user access to femtocells [22]. Two types of

classical access control policies were defined. In Open access, all users authenticated by the

MSP can be served by the FAP; while in Closed access, only Owners or users listed on the

AAL, also defined as the Closed Access Group (CSG) by 3GPP, will be served by the FAP.

Several system simulations were performed to compare the downlink capacity of the

cellular networks utilizing either Open or Closed access control mechanism. A number of co-

channel FAPs, all with femtocell densities equivalent to 130 FAPs per macrocell, were

randomly placed in a cellular network with 19 macrocells. In addition, a total of 200 UEs

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were also randomly-deployed per macrocell. Total transmit powers were set at 43dBm for

macro base stations and 24dBm for femto access points.

Spectral efficiency is defined as the information rate that can be transmitted over a specific

bandwidth. Data rates, calculated from spectral efficiency, from both Closed and Open

access control mechanisms were compared with the baseline scenario where no FAPs were

deployed. From the simulation outcome related to the Closed access, a small portion of

users that are served by the FAP experience very high data rates, but this great

improvement is at the cost of poor data rates experienced by the larger portion of other

nearby users connected to the underlying macrocell. Collectively, the combined data rate

for the Closed access is worse than that of the no-FAP baseline setting.

On the other hand, for the Open access, half of the users served by FAPs experience better

data rates; at the same time, the other half of the users who are served by the MBS also

experience similar rates to the no-FAP baseline scenario. Based on various comparisons

from simulation results, it was concluded in [22] that the Open access is the recommended

access control mechanism because of its higher overall effective data rate over either the

Closed access or the macrocell-only baseline scenario.

1.4.2 Adaptive Access

Based on the outcome of the 3GPP workgroup meeting, several researchers at UCLA also

examined the tradeoffs relating to various access policies for WCDMA co-channel femtocells

in 2008. An adaptive access control policy to examine the benefits and tradeoffs associated

with different levels of openness in the Open access control mechanism was therefore

proposed in [26].

A scenario where a number of randomly distributed indoor and outdoor components

consisted of macrocells, femtocells, and mobile stations was simulated. Inside each indoor

region, with a radius of 10m, were two indoor users; all other locations on the map were

15


considered as outdoor regions. A total of 64 indoor regions, of which 32 have one femtocell

installed within, were placed inside the same simulation area along with 21 underlying

macrocell base stations.

A total of 456 mobile stations were randomly placed on the map where the two indoor

users within the femtocell coverage were both on the AAL of that FAP. In addition, clusters

of eight mobile stations were placed just outside the indoor regions containing the FAPs.

These mobile stations were referred to as neighbours and were allowed access only under

Open access policy. Based on simulation, it was found that as the access policy changed

toward a more Open policy with more neighbours connected to nearby FAPs, the average

femtocell user throughput decreased; however, the average throughput for macrocell users

increased because more users are redirected from the macrocell to femtocells.

Simulation results showed that completely Closed access could disrupt service because of

the interference generated by UEs that were near to, but prohibited from connecting to, a

FAP. However, completely Open access could also be problematic due to loss in data rate

arising from sharing limited femtocell bandwidth among a potential large number of users

in heavily populated areas. Therefore, rather than completely close or open femtocell user

access, better performance can be achieved by opening the femtocell to the best operating

point for a given set of connectivity and throughput criteria.

1.4.3 Limited Access

In order to overcome challenges from both Open and Closed access, an approach for access

to OFDMA femtocells called limited access control was proposed in [27]. By adapting to the

radio resource management techniques offered in OFDMA, a limited number of sub-

channels were reserved for non-authorized Visitors to make use of a small amount of the

femtocell resources when they were unable to connect to the macrocell base station due to

lack of coverage or because of high interference.

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Simulation was set in a residential area with several houses, some of which had a FAP

deployed within them according to the femtocell penetration. Each house that hosted a FAP

contained three indoor authorized Owners, while a total of five outdoor non-authorized

Visitors were randomly distributed across the street. Outdoor Visitors would attempt to

connect to the MBS first, and then to the best FAP as long as there were sufficient sub-

channels available.

It was concluded from the simulation that by limiting the number of v shared sub-channels

for outdoor Visitors, the average Visitor throughput increased; additionally, it was possible

for Visitors to achieve high average throughputs even for low values of v. In fact, setting the

value of v to 1 (i.e. one shared sub-channel) was sufficient to guarantee voice services for

Visitors in most cases. Therefore, the v parameter must be carefully adjusted by the MSP for

optimum performance prior to femtocell deployment rollouts.

In January 2010, a new concept called hybrid access, which is similar to the previously

reviewed limited access policy, was discussed in [28]. Instead of limiting to Visitors only, the

femtocell resources should be available to all users, while the remaining resources are then

reserved for Owners. This suggested hybrid access method should reach a compromise

between the performance of Owners, and the level of accessibility to Visitors. A further

study in [29] was conducted to evaluate system performance from implementing different

access control strategies. It was concluded that the sharing of femtocell resources between

Owners and Visitors will need to be dynamically tuned to achieve satisfactory performance

for both Owners and Visitors.

1.5 Thesis Contribution

From all the previous works reviewed so far, concerns related to the influence from

different access methods of co-channel femtocells still exist. FAPs that are based on Closed

access control, where only Owners of the FAP are allowed connectivity, introduce severe

17


interference to macrocell users; on the other hand, FAPs with Open access control, where

any user can connect to the FAP, do not bring any advantages to their Owners and yet

introduce additional overhead and security concern to the network.

In this thesis, a specific methodology called the Cognitive access control is proposed for

implementing the suggested hybrid access concept. The proposed mitigation is an

intermediate solution that will utilize advantages from both Open and Closed access control

mechanisms. System performance, such as throughput, of the proposed femtocell access

policy are evaluated and then compared with other access control policies under different

scenarios. Based on recommendations of practical environment and system parameters

from the 3GPP and the Femto Forum, simulations were conducted in a monte-carlo fashion.

The remaining of the thesis is organized as follows: In Chapter 2, a potential solution to the

known femtocell deployment challenges called the Cognitive access control is proposed.

Four recommended operation modes are defined for FAPs utilizing the proposed solution.

In addition, a complete system design including network deployments, channel modeling,

resource reservation, and interference calculation will also be described in Chapter 2. Then

in Chapter 3, simulations utilizing the proposed Cognitive access control are carried out

using various practical parameters; following by evaluations and comparisons of simulated

results with Open and Closed access controls as well as the macrocell-only baseline. Finally,

Chapter 4 concludes the thesis with final remarks as well as possible future research topics.

18


CHAPTER 2

PROPOSED ACCESS SCHEME

2.1 Cognitive Access

Based on the literature review discussed in the previous chapter, it can be observed that

adaptive access, limited access, and hybrid access all proposed an intermediate approach

that attempted to balance the benefits of Open and Closed methods as defined by the

3GPP. However, no concrete definitions were set for the proposed hybrid access concept as

to what the standards and procedures should be except that somehow the FAP should

reserve resource for Owners and grant partial access to Visitors.

In this thesis, a new hybrid approach called the Cognitive access control is proposed to

provide a clear definition and a step-by-step procedure that can, when implemented,

effectively balances the benefits of both Closed and Open access control strategies.

Furthermore, four operation modes are also introduced for randomly-deployed FAPs that

utilize the Cognitive access control strategy.

Environment Sensing

Dynamic Reconfiguration

User Classification

Reservation Determination

Resource Allocation

Femtocell Initialization

Figure 9: Procedure for Cognitive Access Control in HSDPA Femtocells

In Figure 9, the procedure for implementing the Cognitive femtocell access control is

illustrated in five recurring steps: Environment Sensing, User Classification, Reservation

19


Determination, Resource Allocation, and Dynamic Reconfiguration. Since this thesis is based

on the 3GPP High-Speed Downlink Packet Access (HSDPA) standards, transmit power is

utilized as the primary resource in the 4th Resource Allocation step of the Cognitive access

control strategy. In cases where 3GPP LTE or IEEE WiMAX standards are implemented,

resource blocks should be considered as the adjusting resource instead. In addition, the

step of Femtocell Initialization is included in the figure to delimit the starting point of the 5-

step cognitive procedure. The following sub-sections provide the step-by-step explanation

of the proposed Cognitive femtocell access control.

2.1.1 Environment Sensing

After the FAP is powered on and the initialization process has completed, the first step in

the Cognitive access policy is the Environment Sensing. As the name implies, the FAP in this

step of the procedure will scan the surrounding environment and listen for any UEs

requesting access in uplink (UL) channels within its femtocell coverage. Usually an UE will

attempt to access the base station, either the MBS or nearby FAP, of which the UE receives

the highest pilot signal power. Similar to the traditional Open access control, all requesting

users will be granted access in the Cognitive access policy; however, different priorities will

be assigned to different UEs based on their memberships as determined by the FAPs.

2.1.2 User Classification

The second step after environment sensing is the User Classification. During this stage, all

nearby users within the femtocell coverage are identified. Femto-users refer to active users

that are currently connected to the FAP while Macro-users refer to those connected to the

MBS. Each of Femto-user’s unique 15-digit International Mobile Subscriber Identities (IMSI)

is examined to see if it is listed on the Allowed Access List (AAL) of the FAP. If the IMSI is

indeed listed on the AAL, that Femto-user is then classified as an Owner of the FAP;

20


otherwise, the Femto-user will be classified as a Visitor of the FAP if the IMSI is not

registered on the AAL. Within a FAP, any Femto-user will be categorized as either an Owner

or a Visitor. The total number of users (NFAP) of a FAP is therefore the sum of the number of

Owners (OFAP) and the number of Visitors (VFAP) within the same FAP.

𝑁𝐹𝐴𝑃 = 𝑂𝐹𝐴𝑃 + 𝑉𝐹𝐴𝑃 (1)

Due to physical resource limitation, FAP can only support a certain number of users. In

Cognitive access control, a maximum number of Owners is guaranteed access to the FAP;

excessive Owners will be grouped as Visitors once the physical limitation of the FAP is

reached. FAP will serve all Visitors using the best effort approach where available femtocell

radio resource is equally distributed. In other words, even when the IMSI is listed on the

AAL, the UE will still be classified as a Visitor if the corresponding FAP is already serving

maximum number of Owners. For simplicity purpose, all roaming users coming from other

MSPs will always be labeled as Visitors unless barred by the serving MSP.

2.1.3 Reservation Determination

Once all Femto-users are classified as either Owners or Visitors, the Cognitive access

strategy then enters the step of Reservation Determination. In this step, the Owner density

DFAP of the FAP is calculated as the number of Owners over the total number of Femto-users

that are currently connected to the FAP. The value of the owner density DFAP will normally

be different for each FAP at different time instance; this is due to the fact that user

composition for each FAP varies under different environments and time periods.

𝐷𝐹𝐴𝑃 = 𝑂𝐹𝐴𝑃𝑁𝐹𝐴𝑃

= 𝑂𝐹𝐴𝑃𝑂𝐹𝐴𝑃+𝑉𝐹𝐴𝑃

(2)

Furthermore, another parameter called femtocell resource reservation coefficient KFAP is

defined to be the percentage of femtocell resources in FAP that are dedicated for Owners.

21


In other words, the higher value of KFAP means more resource is reserved for Owners, or less

resource is reserved for Visitors. As formulated in Equation (3), the value of KFAP in this

thesis is proposed to be set, using a utility function U(x) defined by the MSP, according to

the Owner density DFAP where any values of KFAP will be greater than or equal to the

corresponding value of DFAP.

𝐾𝐹𝐴𝑃 = 𝑈(𝐷𝐹𝐴𝑃) 𝑠. 𝑡. 𝐷𝐹𝐴𝑃 ≤ 𝐾𝐹𝐴𝑃 < 1 ∀ 𝐷𝐹𝐴𝑃 ∈ {0,1} (3)

As a result, the value of KFAP is set individually for each FAP because of the fact that Owner

density DFAP varies from one FAP to another. Besides the Owner density, other

considerations in determining the value of resource coefficient KFAP include, but are not

limited to, user traffic, cell load, QoS requirements, or even the percentage of FAP over

MBS.

2.1.4 Resource Allocation

Once the reservation coefficient KFAP is set based on the Owner density DFAP, the FAP then

allocates the determined portion of femtocell resources and dedicated them for Owners,

while the remaining portion is reserved for Visitors. In the case of WCDMA, the total

amount of power is considered as the sum of pilot and data power. Since the pilot power is

broadcast for the purpose of defining femtocell coverage, thus only the data power from

the FAP is divided between Owners and Visitors.

As seen in Equations (4), the total power PTotal|FAP is the sum of pilot power PPilot|FAP and data

power PData|FAP of the FAP. Then in Equation (5), only the data power of the FAP is split into

two portions where POwner|FAP is reserved for Owners and the other PVisitor|FAP for Visitors.

The value of KFAP in Equations (6) and (7) was used to determine how much of the data

power is reserved for Owners.

𝑃𝑇𝑜𝑡𝑎𝑙 | 𝐹𝐴𝑃 = 𝑃𝑃𝑖𝑙𝑜𝑡 | 𝐹𝐴𝑃 + 𝑃𝐷𝑎𝑡𝑎 | 𝐹𝐴𝑃 (4)

22


𝑃𝐷𝑎𝑡𝑎 | 𝐹𝐴𝑃 = 𝑃𝑂𝑤𝑛𝑒𝑟 | 𝐹𝐴𝑃 + 𝑃𝑉𝑖𝑠𝑖𝑡𝑜𝑟 | 𝐹𝐴𝑃 (5)

𝑃𝑂𝑤𝑛𝑒𝑟 | 𝐹𝐴𝑃 = 𝐾𝐹𝐴𝑃 ∙ �𝑃𝐷𝑎𝑡𝑎 | 𝐹𝐴𝑃� (6)

𝑃𝑉𝑖𝑠𝑖𝑡𝑜𝑟 | 𝐹𝐴𝑃 = (1 − 𝐾𝐹𝐴𝑃) ∙ �𝑃𝐷𝑎𝑡𝑎 | 𝐹𝐴𝑃� (7)

After sufficient femtocell resource is reserved, the remaining (1 – KFAP) portion is then

shared among all Visitors using an opportunistic scheduler as defined within the 3GPP

HSDPA standards. As mentioned earlier, power was used for dynamic distribution in

WCDMA systems; for 3GPP LTE systems that utilize the OFDMA technology, resource blocks

consisting of time slots and sub-carrier frequencies should be used as the distribution

element in femtocell resource reservation.

2.1.5 Dynamic Reconfiguration

The word cognitive refers to the ability for mobile terminals to observe the surrounding

environment, and then react accordingly to achieve better performance. Therefore, after all

femtocell resources are distributed, the FAP utilizing the Cognitive access strategy continues

to monitor the surrounding environment and then make appropriate adjustments to both

DFAP and KFAP dynamically so that the overall network performance can be maintained in an

optimum state. Therefore, this dynamic reconfiguration step can be considered as the

transitioning phase between the current and the next iteration of the Cognitive access

strategy as illustrated in Figure 9.

2.2 Operation Mode

From the definition of the Cognitive access strategy, the value of the femtocell resource

reservation coefficient KFAP is used to determine how much of the femtocell radio resource

23


to be dedicated to Owners such that their performance can be optimized. With different

values of KFAP, the corresponding Cognitive access policy is expected to behave differently.

For FAPs that utilize the Cognitive access control, four operation modes: Sleep, Private,

Public, and Hybrid, are introduced. The following sub-sections describe the requirements as

well as characteristics for each of the four proposed operation modes.

2.2.1 Definitions and Criteria

Under the Sleep mode, the power level of FAP’s pilot signal is reduced to prevent

unnecessary interference with other nearby FAPs. The reduction of pilot power will shrink

the femtocell footprint and hence reduce amount of potential undesired handovers. When

no UEs are currently connected, or when the FAP is first powered on, the idling FAP should

be operating in the Sleep mode. However, once any UE is connected, the FAP will no longer

stay in the Sleep mode and will be transited to either the Private mode or the Public mode

depending on the user class of that connected user.

Under the Private mode, the femtocell resource reservation coefficient KFAP is set to 1; in

other words, 100% femtocell resource reservation for all connected Owners. By doing this,

Owners will experience the best femtocell performance from their FAPs. When one or more

UEs are currently connected to the FAP and all connected UEs are Owners who are listed on

the AAL of the FAP, the FAP should be operating in the Private mode.

Under the Public mode, the resource reservation coefficient KFAP is set to 0; in other words,

no femtocell resource is reserved because there are no Owners connected and all available

femtocell resources are equally distributed among all connected Visitors. When one or

more UEs are currently connected to the FAP but all connected UEs are Visitors who are not

on the AAL of the FAP, the FAP should be operating in the Public mode.

Under the Hybrid mode, the value of KFAP for each FAP is dynamically adjusted based on

current environment situation as described earlier. When two or more UEs are currently

24


connected to the FAP where mixed types of user classes are observed (i.e. both Owners and

Visitors are currently connected to the FAP simultaneously), the FAP should be operating in

the Hybrid mode. FAPs utilizing the proposed Cognitive access control should mostly be

operating in the preferred Hybrid mode due to the nature of mixed UE composition of both

Owners and Visitors.

2.2.2 Relationships and Transitions

As described in the previous sub-section, the number of Femto-users connected to a FAP

and their user membership will determine that FAP’s current operation mode. The FAP

should be operating in Sleep mode if no Femto-users are connected, in Private mode if only

Owners are connected, in Public mode if only Visitors are connected, or in Hybrid mode if

two or more connected Femto-users are consisted of both Owners and Visitors. Another

important factor to consider is the femtocell resource reservation coefficient KFAP, which

also varies depending on the FAP’s current operation mode. The following Figure 10

illustrates the relationships between the resource reservation coefficient K and FAP

operation modes.

0

N

1

K0 1D

HYBRID

PRIVA

TEPUBLIC

SLEEP

Figure 10: Relationships between Resource Reservation and Operation Modes

The vertical axis in the above figure represents the total number of Femto-users currently

connected to the FAP while the horizontal axis represents the value of the femtocell

resource reservation coefficient K. Since no Femto-users are observed in Sleep mode, hence

25


the value of K is irrelevant. From definition, FAPs operating in Private mode should have

their K set to 1 so that all resource is reserved for Owners. On the other hand, the value of K

does not need to be set to 0 for FAPs operating in Public mode; in fact, the optimum value

for mode boundary between Public and Hybrid modes depends on the utility function

mentioned in Equation (3). In the thesis, Owner density D is used as the boundary condition

based on the following explanations.

Consider a scenario where a FAP is serving one Owner and three Visitors with 20% resource

reservation (i.e. K = 0.2), the Owner would only receive 20% of femtocell resources if the

FAP is operating in Hybrid mode. However, the Owner will get a higher portion of resource

at 25% if the FAP is operating in the Public mode. When the Owner density D is low, rather

than setting the reservation coefficient K small under the Hybrid mode, better performance

can be achieved by switching the FAP to the Public mode; in other words, Owners would

receive more femtocell resource in Public mode than in Hybrid mode.

Many system and environment parameters could be considered as the trigger for dynamic

adjustments of the value of K; for simplicity, the Owner density D was utilized as the

resource reservation adjustment metric in this thesis. Based on the same example (one

Owner plus three Visitors), the value of K should be set to at least the value of D, or 25%. In

a different case where the FAP is serving two Owners plus three Visitors, the value of K

should be set to at least 40%.Therefore, for a FAP operating in Hybrid mode, its value of K

should be set to a value smaller than 1 but greater than the current Owner density D, as

formulated back in Equation (3) and illustrated again in Figure 10.

Figure 11: Transition Map of Operation Mode

26


From Figure 11, it can be seen that from Sleep mode, the FAP can only transit to either

Private or Public mode depending on the membership of the newly-connected Femto-user.

When in Private or Public mode, any additional Femto-user that is of different class will

force the FAP to be switched to Hybrid mode; otherwise the FAP will remain in the same

operation mode until no more Femto-users are connected, by then the FAP will change back

to Sleep mode.

2.3 System Design

In this section, a co-channel femtocell system is introduced based on the Cognitive access

control mechanism as well as the four operation modes defined in the previous two

sections. In this proposed system, we consider only one macrocell along with a number of

randomly deployed femtocells and mobile terminals. Designs and calculations involved with

the determination of performance by implementing the Cognitive access control are

described in the following sub-sections.

2.3.1 Design Model

A co-channel macro-femtocell network was designed with one MBS, H houses, M FAPs, and

N terminals; in addition, three system coefficients were defined. The first coefficient α (FAP

Penetration) is the probability that a house has a FAP in it. The second coefficient β (Indoor

Percentage) is the probability that a user is inside a house. The third coefficient γ (Owner

Probability) is the probability that an indoor user is the Owner of the FAP inside the house.

27


Figure 12: Pseudo Algorithm for Macro-Femtocell Network Deployment

From Figure 12, the single MBS is placed in the centre of a circular area with radius R (line

10). A total of H houses, each with radius r, are then randomly placed within the circular

region (line 12); every house location is checked and regenerated if overlapped with other

houses (line 13). Also, for each of the H houses, there is a probability α that it hosts a FAP in

the centre of the house (line 17).

28


For each of the N users, there is a probability β that the user is inside one of the H houses.

Furthermore, for each of the indoor users, there is a probability γ that the indoor user is an

Owner who is located in one of the M houses that hosts the FAP “temp” (line 23 and 26).

For those indoor users who are not Owners, they will be randomly placed inside any house

(line 28), but not the house with FAP “temp” (line 29).

When the user is being randomly placed inside a house with radius r (line 31), the distance

between the user and the FAP must not be too close (line 32). For the users that are to be

placed outside, they will be randomly located somewhere within the macro-cellular region

of radius R (line 34). Again, outdoor users are also checked and see if they are inside a

house or too close to the MBS (line 35).

2.3.2 Channel Modeling

In order to determine the signal strength received by the user, signal loss is calculated with

a corresponding path loss model and then deducted from the signal power transmitted by

the MBS or the FAP. Path loss models used in the simulation, ITU P.1411 and P.1238, are

chosen based on recommendations from the Femto Forum. Between a user and a base

station (either MBS or FAP), there are three possible scenarios: outdoor-only, indoor-

outdoor, and indoor-only. Throughout the thesis, units for all parameters used in equations

are shown inside enclosing square brackets.

For outdoor-only and indoor-outdoor scenarios, the path loss model ITU P.1411 is

recommended. The only possible setting for an outdoor-only scenario is between an

outdoor user and the MBS. However, two possible settings for an indoor-outdoor scenario

are between an outdoor user and a FAP, and between an indoor user and the MBS. The

calculation of ITU P.1411 is a two-step procedure. First step of the procedure is the

calculation of the breakpoint distance RBP, which is the distance that has been observed by

measurement by the Femto Forum, beyond which the rate of change in path loss increases.

29


𝑅𝐵𝑃 [𝑚] = 4 ∙ ℎ𝐵𝑆 ∙ ℎ𝑈𝐸𝜆

(8)

𝐿𝐵𝑃 [𝑑𝐵] = � 20 ∙ log10 �𝜆2

8𝜋 ∙ ℎ𝐵𝑆 ∙ ℎ𝑈𝐸�� (9)

Once the breakpoint distance RBP is converted into the decibel form LBP using Equation (8)

and (9), the second step in determining the path loss of the channel is by comparing the

distance d between the user and the MBS (or the FAP) with the breakpoint distance. Based

on the outcome of the comparison, either Equation (10) or (11) is applied to the path loss

calculation. In other words, the path loss model ITU P.1411 is modeled by a dual slope

function resulting in two formulas.

For d ≤ RBP,

𝑃𝐿[𝑑𝐵] = 𝐿𝐵𝑃 [𝑑𝐵] + 20 + 𝟐𝟓 ∙ log10 �𝑑𝑅𝐵𝑃

� + ∑𝑊𝑎𝑙𝑙[𝑑𝐵] + 𝛿[𝑑𝐵] (10)

For d > RBP,

𝑃𝐿[𝑑𝐵] = 𝐿𝐵𝑃 [𝑑𝐵] + 20 + 𝟒𝟎 ∙ log10 �𝑑𝑅𝐵𝑃

� + ∑𝑊𝑎𝑙𝑙[𝑑𝐵] + 𝛿[𝑑𝐵] (11)

A coefficient δ is introduced to all path loss calculations to simulate the shadow fading

effect. In the simulation, δ is represented by a Gaussian random variable of variance 8σ for

outdoor-only and 12σ for indoor-outdoor scenario. When the signal goes through a wall, an

additional 20dB deduction is applied. A maximum of two walls, or a total of 40dB deduction,

is considered when calculating the path loss of the channel between an indoor user and the

FAP in another house.

For the indoor-only scenario, the path loss model ITU P.1238 is utilized. The only possible

setting for an indoor-only scenario is between an indoor user and the FAP, if any, in the

same house. The calculation of ITU P.1238 in Equation (12) works only when the distance d

between the user and the MBS, or the FAP, is greater than 1m (i.e. the gap restriction listed

in Table 1) to avoid near-field calculations:

30


For d > 1,

𝑃𝐿[𝑑𝐵] = 20 ∙ log10�𝑓[𝑀𝐻𝑧]� + 𝑁 ∙ log10�𝑑[𝑚]� − 28 + 𝛿[𝑑𝐵] (12)

For the coefficient N in the above Equation (12), the value of 22 is used for commercial

areas and 28 for residential regions. For simplicity purposes, the average value of 25 is used.

In addition, the shadow fading coefficient in the indoor-only situation is set with a Gaussian

random variable of variance 4σ.

2.3.3 User Association

When determining whether to connect to the central MBS or a nearby FAP, each user will

detect the received pilot signals and then attempt to connect itself to the base station with

the highest received pilot power. The received signal power R is calculated from the

transmitted signal power P with corresponding path loss PL and antenna gain G using the

following Equation (13):

𝑅[𝑚𝑊] = � 𝑃[𝑚𝑊]

𝑃𝐿[𝑟𝑎𝑡𝑖𝑜]� ∙ 𝐺[𝑟𝑎𝑡𝑖𝑜] (13)

Active users are users who are currently connected to a base station after the received pilot

signal power level is evaluated. There are two classes of active users: Macro-user and

Femto-user. Furthermore, two sub-classes of Femto-users are defined: Owners and Visitors.

Since all users have an identification that signifies the FAP the user owns, thus when the

identification of a user matches that of the FAP, the user is labeled as Owners. In other

words, Owners are Femto-users that are currently connected to their own FAPs. In contrast,

Visitors are Femto-users that are not connected to their own FAPs at the moment; thus,

they are likely indoor but not at home.

31


2.3.4 Resource Reservation

Based on the definitions of resource reservation from the previous chapter, a percentage of

femtocell resources KFAP is first reserved for Owners, while the remaining (1 – KFAP) portion

of femtocell resources are then shared among all Visitors. In other words, the Cognitive

access control can also be considered as a prioritized access control mechanism with

Owners getting higher priorities than Visitors. To replicate the practical hardware limitation

of a FAP in the simulation, each FAP can only support up to eight Owners, while the

remaining associated Visitors are served based on 3GPP-defined opportunistic scheduling.

Figure 13: Resource Reservation

Resources in a FAP are defined differently under different cellular standards. In 3G WCDMA

systems, power is the resource that is being distributed among Femto-users. However, in

3G+ OFDMA systems, resources are combinations of frequencies and time slots called

resource blocks. Throughout the simulation for this thesis, which is WCDMA-based, power

is the only resource that is being reserved and distributed among Femto-users.

2.3.5 Interference Model

In this sub-section, a downlink co-channel femtocell network model with one macrocell,

two femtocells, and five active users are introduced in Figure 14 as an example for

determining the received signal power, received interference level, Signal-to-Interference-

and-Noise Ratio (SINR), and user data rate for each of the five active users under the

implementation of Cognitive access control. Users in red colour are Macro-users served by

the MBS, while users in blue and green colours are Femto-users served by femtocell F1 and

F2 respectively. Additionally, the solid lines in Figure 14 represent the data signals while the

dotted lines represent interference.

32


Figure 14: Co-channel Femtocell Networks with Signal and Interference

Data power transmitted from each base station, either from the macrocell M or femtocells

F1 and F2, are evenly distributed among all connected active users as seen in the following

equations. However, for femtocells utilizing the Cognitive access control, available data

power are split into two portions by the resource coefficient KFAP, with Owners sharing the

KFAP portion while Visitors sharing the remaining (1 – KFAP). In addition, antenna gain from

each base station is added to the transmitted data signal.

𝑃1 [𝑚𝑊] = 𝑃2 [𝑚𝑊] = 𝑃𝑀 [𝑚𝑊]

# 𝑜𝑓 𝑀𝑎𝑐𝑟𝑜−𝑈𝑠𝑒𝑟𝑠∙ 𝐺𝑀 [𝑟𝑎𝑡𝑖𝑜] = 𝑃𝑀 [𝑚𝑊]

2∙ 𝐺𝑀 [𝑟𝑎𝑡𝑖𝑜] (14)

𝑃3 [𝑚𝑊] = 𝐾1∙𝑃𝐹1 [𝑚𝑊]

# 𝑜𝑓 𝑂𝑤𝑛𝑒𝑟𝑠 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑 𝑡𝑜 𝐹1∙ 𝐺𝐹1 [𝑟𝑎𝑡𝑖𝑜] = 𝐾1∙𝑃𝐹1 [𝑚𝑊]

1∙ 𝐺𝐹1 [𝑟𝑎𝑡𝑖𝑜] (15)

𝑃4 [𝑚𝑊] = 𝐾2∙𝑃𝐹2 [𝑚𝑊]

# 𝑜𝑓 𝑂𝑤𝑛𝑒𝑟𝑠 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑 𝑡𝑜 𝐹2∙ 𝐺𝐹2 [𝑟𝑎𝑡𝑖𝑜] = 𝐾2∙𝑃𝐹2 [𝑚𝑊]

1∙ 𝐺𝐹2 [𝑟𝑎𝑡𝑖𝑜] (16)

𝑃5 [𝑚𝑊] =(1−𝐾2)∙𝑃𝐹2 [𝑚𝑊]

# 𝑜𝑓 𝑉𝑖𝑠𝑖𝑡𝑜𝑟𝑠 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑 𝑡𝑜 𝐹2∙ 𝐺𝐹2 [𝑟𝑎𝑡𝑖𝑜] =

(1−𝐾2)∙𝑃𝐹2 [𝑚𝑊]

1∙ 𝐺𝐹2 [𝑟𝑎𝑡𝑖𝑜] (17)

33


Once the transmitted data power to each active users are known, received signal power R

can be computed using the link budget, or path loss, of the corresponding channel. Path

loss between an active user and the associated base station is calculated based on the

equations described in the Channel Modeling sub-sections. Similarly, antenna gain of the UE

is also added in the calculations.

𝑅1 [𝑚𝑊] = 𝑃1 [𝑚𝑊]

𝑃𝐿𝑀→𝑈1 [𝑟𝑎𝑡𝑖𝑜]∙ 𝐺𝑈1 [𝑟𝑎𝑡𝑖𝑜] (18)

𝑅2 [𝑚𝑊] = 𝑃2 [𝑚𝑊]

𝑃𝐿𝑀→𝑈2 [𝑟𝑎𝑡𝑖𝑜]∙ 𝐺𝑈2 [𝑟𝑎𝑡𝑖𝑜] (19)

𝑅3 [𝑚𝑊] = 𝑃3 [𝑚𝑊]

𝑃𝐿𝐹1→𝑈3 [𝑟𝑎𝑡𝑖𝑜]∙ 𝐺𝑈3 [𝑟𝑎𝑡𝑖𝑜] (20)

𝑅4 [𝑚𝑊] = 𝑃4 [𝑚𝑊]


𝑅5 [𝑚𝑊] = 𝑃5 [𝑚𝑊]


Received interference signal power is calculated similarly to a typical received signal power

calculation as illustrated from Equations (18) to (22). For clarifications, the term PLMU1

represents the path loss from macrocell M to UE1. In interference calculations, as seen from

Equations (23) to (32), the total transmitted signal power from an interference source is the

combination of its transmitted pilot power plus the sum of all data power transmitted to its

connected active users.

𝐼1 [𝑚𝑊] = 𝑃𝑖𝑙𝑜𝑡𝑀 [𝑚𝑊] + �𝑃1 [𝑚𝑊] + 𝑃2 [𝑚𝑊]�𝑃𝐿𝑀→𝑈3 [𝑟𝑎𝑡𝑖𝑜]

∙ 𝐺𝑈3 [𝑟𝑎𝑡𝑖𝑜] (23)


∙ 𝐺𝑈4 [𝑟𝑎𝑡𝑖𝑜] (24)


∙ 𝐺𝑈5 [𝑟𝑎𝑡𝑖𝑜] (25)

𝐼4 [𝑚𝑊] = 𝑃𝑖𝑙𝑜𝑡𝐹1 [𝑚𝑊] + 𝑃3 [𝑚𝑊]


34








𝐼8 [𝑚𝑊] = 𝑃𝑖𝑙𝑜𝑡𝐹2 [𝑚𝑊] + �𝑃4 [𝑚𝑊] + 𝑃5 [𝑚𝑊]�𝑃𝐿𝐹2→𝑈1 [𝑟𝑎𝑡𝑖𝑜]

∙ 𝐺𝑈1 [𝑟𝑎𝑡𝑖𝑜] (30)


∙ 𝐺𝑈2 [𝑟𝑎𝑡𝑖𝑜] (31)


∙ 𝐺𝑈3 [𝑟𝑎𝑡𝑖𝑜] (32)

Before the Signal-to-Interference-and-Noise Ratio (SINR) Γ can be determined, the Additive

White Gaussian Noise (AWGN) is first calculated as the product of the Boltzmann’s constant

k, room temperature T, bandwidth BW, and noise figure NF as seen in Equation (33). From

Equation (34) to (38), the SINR Γ for each of the five active users in the example model is

calculated as the ratio of received data power R over the sum of noise plus all received

interference signal I from nearby base stations.

𝐴𝑊𝐺𝑁 [𝑚𝑊] = 𝑘 ∙ 𝑇 ∙ 𝐵𝑊 ∙ 𝑁𝐹 (33)

Γ1 [𝑟𝑎𝑡𝑖𝑜] = 𝑅1 [𝑚𝑊]

𝐼𝑈1 [𝑚𝑊] + 𝐴𝑊𝐺𝑁 [𝑚𝑊]= 𝑅1 [𝑚𝑊]

�𝐼4 [𝑚𝑊] + 𝐼8 [𝑚𝑊]� + 𝐴𝑊𝐺𝑁 [𝑚𝑊] (34)



�𝐼5 [𝑚𝑊] + 𝐼9 [𝑚𝑊]� + 𝐴𝑊𝐺𝑁 [𝑚𝑊] (35)



�𝐼1 [𝑚𝑊] + 𝐼10 [𝑚𝑊]� + 𝐴𝑊𝐺𝑁 [𝑚𝑊] (36)



�𝐼2 [𝑚𝑊] + 𝐼6 [𝑚𝑊]� + 𝐴𝑊𝐺𝑁 [𝑚𝑊] (37)

35




�𝐼3 [𝑚𝑊] + 𝐼7 [𝑚𝑊]� + 𝐴𝑊𝐺𝑁 [𝑚𝑊] (38)

In the next chapter, a system simulation utilizing 3GPP’s HSDPA standards is composed

based on the co-channel femtocell system described in this chapter. In addition,

performance of the Cognitive access control under various environment settings will be

analyzed and compared with other known access control mechanisms. Throughputs of

various classes of active users are calculated following the same procedure as mentioned

earlier in this section.

36


CHAPTER 3

SYSTEM SIMULATION

3.1 Simulation Model

In order to explore various advantages of the proposed cognitive solution as compared to

those from the current known access control mechanisms, a Matlab program was

composed to simulate the proposed system focusing in the downlink direction. Various

comparisons of each access control policy under different scenarios were made. Based on

current 3GPP HSDPA standards, the system parameters and simulation procedures are

described in the following sub-sections.

3.1.1 System Parameters

A circular area of radius 500m is defined with a single macrocell (or macro base station,

MBS) located in the centre of the region. A total of 100 circular houses (indoor region), each

with a radius of 10m, are randomly placed within the defined area of interest. During

random placements, these houses are separated from each other by at least 20m so that no

two houses will overlap.

Depending on femtocell penetration α (the probability that a house has a FAP installed

within), a number of FAPs are placed in the centre of each of the 100 houses. In this

simulation, it is assumed that only one FAP will be installed per house. Within the same

simulation area, a total of 300 active users, either connected to the central MBS or nearby

FAPs, are randomly placed. See the following Table 1 for a complete list of environment

parameters, recommended by Femto Forum [30] and 3GPP [31], used in the simulation.

37


Simulation Area 500 m (radius)

# of Circular Houses 100

House Diameter 20 m

House Location Uniform Distribution

Femtocell Density 1 FAP/House

Femtocell Penetration α 0 – 100%

Indoor Percentage β 0 – 100%

Owner Probability γ 0 – 100%

Resource Reservation K 0 – 100%

Cellular Standards 3GPP HSDPA

Macrocell Sectorization 3 (120o)

Femtocell Capability 8 Owners

Gap Restriction 1 m

Noise Figure 8 dB

Wall Attenuation 20 dB

Table 1: Environment Parameters

The centrally deployed MBS operates at 2GHz with a bandwidth of 5MHz (effective

bandwidth at 3.84MHz). The MBS transmits a total power of 40W, which consists of 10%

pilot power and 90% data power. An antenna gain of 14dB is applied to the signal

transmitted by the MBS. The broadcast pilot power is used to define the macrocell coverage

38


while the data power is evenly-distributed among all users who are connected to the MBS

in the centre of the region.

3.1.2 Random Deployments

All randomly deployed FAPs also operate at the same 2GHz carrier frequency and 5MHz

bandwidth as the central MBS, hence qualified as co-channel deployments. However, these

FAPs only transmit a total power of 10mW with 10% pilot power and 90% data power. No

antenna gain is added to the signal received by the FAP. The broadcast pilot power is used

to define the coverage of the femtocell while the data power is distributed to all users

connected to the FAP.

Macrocell Femtocell Terminal

Count (MAX) 1 100 300

Location Placement Centre Random Random

Frequency GHz 2 2

Bandwidth (Eff) MHz 5 (3.84) 5 (3.84)

Antenna Gain dBi 14 0 0

Antenna Height m 20 1.5 1.5

Transmit Power mW 40,000 10

Pilot : Data Ratio 1 : 9 1 : 9

Table 2: System Parameters

39


In addition, during random femtocell deployments, any two FAPs must be at least 20m

apart from each other because each house defines an indoor region with a radius of 10m.

Refer to Table 2 for a list of various important system parameters utilized by the MBS, FAPs,

as well as UEs. An example of co-channel macro-femtocell network deployments from the

simulation is shown in Figure 15.

Figure 15: Autonomous Femtocell Deployments with Different Users

During random user placements, a pre-determined number of users (i.e. indoor percentage

β in Table 1) will be placed inside random houses and be labeled as indoor users while the

remaining users will be randomly located outdoors. All users, both indoors and outdoors,

have a femtocell identification (i.e. femtoID) indicating the FAP the user owns (but may not

40


necessary be connected to at the moment), or the FAP the user is allowed access.

Furthermore, another pre-determined percentage of indoor users (i.e. Owner probability γ

in Table 1) will be labeled as an Owner and have a forced placement inside their own house

(based on user’s femtoID) during initial user placements. For simplicity, any randomly-

deployed user, either indoors or outdoors, must be at least 1m apart from either the central

MBS or any nearby FAP to avoid the possibility of near-field interference.

When deploying elements (i.e. houses, FAPs, or terminals) randomly but uniformly inside a

circular region of radius R, one should use polar coordinates but cannot naively choose a

random magnitude in the range of 0 to R and then a random angle in the range of 0 to 2π.

Such strategy will randomly place elements closer to the centre of the circular region but

not uniformly throughout, as seen in the left side of Figure 16.

Figure 16: Random Distributions inside Circular Regions

This phenomenon is due to the fact that at a certain angel interval, there needs to be more

points generated further out (at greater distance), than closer to zero. The desired radius r’

will need to be selected from a distribution that has the probability density function (PDF)

seen in Equation (39); the value of r’ can then be calculated by taking the inverse of the

cumulative distribution function (CDF) of the PDF in Equation (40).

𝑓(𝑟) = 2𝑟𝑅2

(39)

𝑟 = 𝐹−1(𝑟) = 𝑖𝑛𝑓 �𝑟′ | 𝐹(𝑟′) = 𝑟′2

𝑅2= 𝑟, 0 < 𝑟 < 1� (40)

41


𝑟′ = √𝑟 ∙ 𝑅 (41)

As a result, both distance and angle can be generated using a random generator with

uniform distribution; however, the distance generated will need to be transformed

according to Equation (41). Therefore, the remedy to this problem is to transform the

randomly generated distance-angle pairs and then convert them to rectangular coordinates

using the following equations.

𝑋 = 𝑋𝑜 + 𝑅 ∙ 𝑠𝑞𝑟𝑡(𝑟𝑎𝑛𝑑(0,1)) ∙ 𝑐𝑜𝑠(𝑟𝑎𝑛𝑑(0,2𝜋)) (42)

𝑌 = 𝑌𝑜 + 𝑅 ∙ 𝑠𝑞𝑟𝑡(𝑟𝑎𝑛𝑑(0,1)) ∙ 𝑠𝑖𝑛(𝑟𝑎𝑛𝑑(0,2𝜋)) (43)

From above Equations (42) and (43), Xo refers to the x coordinate of the centre and R equals

to the radius of the circular region; additionally, sqrt(a) and rand(b) in (42)(43) are Matlab

functions where the first function will calculate the positive square root of the variable a,

while the later will randomly generates a value in the range of 0 and b inclusive with a

normal distribution. Therefore, by utilizing the above two equations, the resulting (X,Y)

coordinates will be distributed uniformly around a circular region with centre (Xo,Yo) and

radius R, as seen in the right side of Figure 16.

3.1.3 Dynamic Scheduler

After a user has select a FAP to be connected based on the received pilot signal power, that

FAP will then execute the access control mechanism to determine whether or not to grant

access to the requesting user based on the user classification. Currently, the two widely-

recognized access control mechanisms are the Closed access control (CAC) and the Open

access control (OAC). Based on different access control mechanisms deployed, a Femto-user

may experience different treatments from the same FAP.

42


When examining the requesting user for user classification, the FAP will check the user and

see if the femtoID matches. If a match occurs, the Femto-user will then be labeled as an

Owner; otherwise, the Femto-user will be classified as a Visitor if no match is observed.

Another limitation on FAP capability is the Owner count, which is the number of Owners

that each FAP can support concurrently. When this hard limit is reached, any additional

Femto-user, no matter whether the femtoID matches or not, will be treated as Macro-users

or Visitors depending on the access control mechanism. The following Figure 17 illustrates

the process flow of the simulation under different femtocell access control mechanisms.

User ownsthe FAP?

FAP limitreached?

FAP pilot > MBS pilot?

FAP distributes KPdata evenlyto all Owners

FAP Hand-off User to MBS

FAP distributes (1-K)Pdata evenly

to all Visitors

User enters FAP coverage

FAP Access Control?

User ownsthe FAP?

FAP limitreached?

FAP distributes Pdata evenly

to all Owners

FAP distributes Pdata evenlyto all Visitors

User is classified as an Owner User is classified as a Visitor

Y

N

N

Y

N

Y

YY

NN

OPENCLOSED

COGNITIVE

Figure 17: Simulation Process Flow

43


The third access control strategy implemented in the simulation besides CAC and OAC is the

Cognitive access control mechanism (GAC). GAC is designed based on the proposed

Cognitive access strategy, but without the dynamic reconfiguration phase for simplicity of

the simulation. The major difference between GAC from CAC and OAC is the way Owners

and Visitors are treated. First, in CAC, Visitors are all handed over to the underlying

macrocell as Macro-users. Then, in OAC, both Owners and Visitors are treated equally and

share the same resource. Finally, in GAC, Visitors are granted access only after Owners are

processed and there are sufficient residual resources.

The difference between the proposed Cognitive access control and the traditional Hybrid

access strategy is that the femtocell resource reservation coefficient K is set dynamically

according to the number of users as well as their memberships within each FAP. In the

simulation, each FAP utilizing the Cognitive access procedure will first check for Femto-users.

If no Femto-users are observed, that FAP enters the Sleep mode and the value of K is

neglected. However, if there are Femto-users but none of them are Visitors, the FAP will

enters the Private mode and K will be set to 1; or if none of them are Owners, the FAP will

enters the Public mode and K will be set to 0 as illustrated in Figure 18.

Any Owner?Any Visitor?Any Active User?

SLEEP mode

K insignificant

PRIVATE mode

Set K = 1

PUBLIC mode

Set K = 0

N N N

HYBRID mode

Set K = U(D)

Y YYSTART

Figure 18: Resource Reservation Process Flow

In the case where a mixture of Owners and Visitors are observed, the FAP will then enter

the Hybrid mode. As introduced back in Equation (3) and discussed in the Operation Mode

section, the value of the femtocell resource reservation coefficient K is set according to the

value of the Owner density D where all possible values of D will be smaller than the

44


corresponding values of K. In order to fulfill these criteria, the value of K will be set as the

square root of the Owner density D in the simulation.

𝐾𝐹𝐴𝑃 = 𝑈(𝐷𝐹𝐴𝑃) = �𝐷𝐹𝐴𝑃 (44)

For example, an Owner density of 0.25 will yield a value of 0.5 as the reservation coefficient,

or 50% femtocell resource reserved for Owners. Once the value K is determined for each

FAP, the pre-determined portion of the femtocell resource will then be equally distributed

to Owners while the remaining resource is also equally allocated to all associated Visitors.

3.2 Performance Evaluation

To diminish potential influence caused by variances due to random deployments of FAPs

and terminals, a total of 1,000 independent simulations are conducted using the monte-

carlo approach; outputs from the simulations are then consolidated for averaged results. By

using this method, any potential extreme case from random number generations will

constitute only 0.1% of the averaged result.

3.2.1 Average User Throughput

Shannon’s Capacity Theorem is used to determine the theoretical maximum information

transfer data rate of the channel for a particular noise level and bandwidth. The following

Equation (45) demonstrates how the theoretical maximum user throughput Ci that can be

experienced by user i is converted from the calculated SINR Γi of user i.

𝐶𝑖 [𝑏𝑝𝑠] = 𝐵𝑊 ∙ log2�1 + 0.5 ∙ Γ𝑖 [𝑟𝑎𝑡𝑖𝑜]� (45)

For practicality purpose, a penalty gap of 3dB is applied to the SINR in the conversion as

described in [14]. Average user throughput is calculated by averaging user throughputs

45


from all users of the same type. For example, Owner throughput refers to the average user

throughput of all Owners in the entire system, regardless of the FAP they are connected to.

In the thesis, performance evaluations between different access control strategies are

conducted by comparing average user throughputs of Macro-users, Owners, or Visitors.

3.2.2 Cellular Operator

The comparison between the three access control mechanisms, Closed, Open, and

Cognitive, should be a two-fold process: first from cellular operators’ perspective and then

from femtocell owners’ point of view. For a cellular operator, the most important aspect of

a femtocell is its ability to improve the overall system capacity by introducing a small

cellular region that covers indoor users; in other words, the advantage of offloading indoor

user traffic from the underlying macrocell network [32][33].

Indoor users normally require higher power to maintain their QoS due to higher link

budgets from being inside of a building. By handing over these high-demand indoor users to

the FAP, the MBS can re-distribute extra resources to serve more Macro-users, which in

turn increases the overall network capacity and spectral efficiency. Since FAPs will be

autonomously deployed by users with an organic growth over time, the need for deploying

additional macrocells will be reduced significantly.

From operators’ perspective, Macro-user throughput is the most important evaluation

among the three. By comparing the throughput improvement among the three femtocell

access control mechanisms to the macrocell-only baseline (i.e. no FAPs deployed) scenario,

a relative macrocell offloading percentage can be estimated. In other words, a higher

Macro-user throughput improvement usually refers to a better macrocell offloading ratio.

Typically, a higher macrocell offloading ratio is preferred by mobile service providers.

46


3.2.3 Femtocell Owner

On the other hand, successful femtocell deployments will also rely heavily on their

popularity among customers (i.e. femtocell owners). From customers’ point of view, how

well a FAP can offload user traffic from the underlying macrocell is irrelevant; rather, it is

the performance of individually acquired FAPs that they care about. One way to compare

the performance of an access control mechanism is via the Owner throughput. Typically, a

customer would prefer the access control mechanism which can provide a higher Owner

throughput for better user experience.

In reality, Macro-user throughput and Owner throughput would tend to mutually affect

each other; in other words, higher Owner throughput typically incurs lower Macro-user

throughput and vice versa. A good femtocell resource management and access control

mechanism should thus generate a well-balanced performance from the two throughput

considerations so that both mobile service operators and femtocell owners can be satisfied.

In the next section, the two evaluations on macrocell offloading (Macro-user throughput)

and femtocell performance (Owner throughput) will be done for all three femtocell access

control mechanisms under various system settings; simulation results will be plotted for

comparisons and further analyses.

3.3 Macrocell Offloading

As mentioned in the previous section, the ability to offload indoor user traffic from the

macrocell is one of the most critical performance qualities in autonomous femtocell

deployments. To analyze macrocell offloading under various environments for all three

femtocell access control mechanisms, three system parameters were individually adjusted

for evaluation: femtocell penetration, indoor percentage, and Owner probability.

47


3.3.1 Femtocell Penetration

The term femtocell penetration refers to the probability that a house has a FAP installed

inside; in other words, the percentage of houses that have FAPs deployed in them. Usually,

this parameter is important to MSPs because it illustrates the chronological trend of

femtocell growth for a specific region of interest. A 0% in femtocell penetration means no

FAPs are currently deployed in any house within the region, while a value of 100%

represents that at least one FAP is deployed in every house. With femtocell penetration

being the variable, the other two environment parameters, indoor percentage and Owner

probability, are fixed.

Figure 19: Macro-user Throughput vs Femtocell Penetration

From Figure 19, it can be observed that as femtocell penetration increases from 10% to

90%, Macro-user throughputs for both OAC and GAC increase drastically for almost 170%

from 70kbps up to 190kbps.With more FAPs deployed in the region, fewer Macro-users will

compete for access to the MBS, hence the significant improvement. Additionally, it appears

that OAC and GAC generate similar performance as the lines overlap with each other; this is

48


due to the fact that all users attempting to access the FAP will be granted access regardless

and thus the total numbers of Macro-users are equal under either OAC or GAC. In other

words, both OAC and GAC perform similarly and their macrocell offloading abilities improve

exponentially as more FAPs are deployed within the same region.

On the other hand, regardless of femtocell penetration, CAC does not have much Macro-

user throughput improvement over the baseline performance. In fact, as more FAPs are

deployed, the Macro-user throughput actually decreases and drops to nearly the same

performance as the no-FAP baseline. This is because in CAC, only Owners are allowed access

to the FAP; as more FAPs are deployed, more indoor Macro-users will experience downlink

interference from the FAP and thus decreases the overall Macro-user throughput. In other

words, for regions with low Owner probability (i.e. commercial areas), CAC will only provide

limited improvement to the overall network.

3.3.2 Indoor Percentage

The term indoor percentage refers to the probability of an active user being inside a

building, either at home or not. In other words, an indoor percentage of 100% means that

all active users are indoors while 0% refers to all active users being outdoors. Besides

femtocell penetration, indoor percentage is another interesting indicator for cellular service

provider. For example, an office area during a weekday will tend to have a higher indoor

percentage than the same area over the weekend. With indoor percentage being the

variable, the other two parameters, femtocell penetration and Owner probability, are fixed.

49


Figure 20: Macro-user Throughput vs Indoor Percentage

As seen in Figure 20, the Macro-user throughput for the no-FAP baseline scenario

decreased as more active users are placed indoors. As expected, the Macro-user throughput

for FAPs utilizing CAC also decreases as the indoor percentage increases. This outcome is

anticipated because when active users are inside a building, link budget will rise and SINR

will drop due to signal attenuation through the walls; thus, as more Macro-users are placed

indoors, lower Macro-user throughput will result. With deployment of FAPs under CAC, the

improvement in macrocell offloading is marginal under this system setting because only

25% of the indoor users who are Owners of FAPs are offloaded.

However, Macro-user throughputs for both OAC and GAC actually increase for low indoor

percentage until a critical point is reached, after which the performance begins to decrease

as more active users are placed inside the building. This convex phenomenon is due to the

fact for low indoor percentage, the effect of less outdoor Macro-user sharing the same

amount of macrocell resource dominates the effect of additional indoor Macro-users (inside

50% of the 100 houses that do not have FAP deployed) generating low SINR and vice versa

for high indoor percentage. The critical point at 0.7 indicates that the effect of losing

50


outdoor Macro-users and adding indoor Macro-users are balanced; this is also the point

where the improvement in Macro-user throughput (110kbps) from the no-FAP baseline

scenario (50kbps) reaches its plateau at around 120%, as seen Figure 20.

3.3.3 Owner Probability

The term Owner probability refers to the probability that an indoor active user is in the

house that hosts a FAP of which that user owns (i.e. listed on the AAL of the FAP); in other

words, Owner probability can be used to estimate the Owner density. However, unless

femtocell penetration is at 100%, Owner probability is close, but not equal to, Owner

density of a FAP in the system. In general, Owner probability is a system parameter that can

usually be used to simulate various environments; as an example, a residential area will

tend to have a higher Owner probability than a commercial area. Again, with Owner

probability as the variable, the other two parameters, femtocell penetration and indoor

percentage, are fixed.

Figure 21: Macro-user Throughput vs Owner Probability

51


From Figure 21, since Owner probability is a system parameter that is closely related to

femtocell penetration, thus it has no impact on the performance from the no-FAP baseline

scenario and hence the flat horizontal line observed. However, Macro-user throughputs for

all three access control mechanisms increases exponentially as the Owner probability

increases from 10% to 90%. When deploying FAPs using CAC, the improvement observed in

Macro-user throughput can reach a maximum of 240% increase over the baseline scenario

from 50kbps at 10% up to 170kbps at 90% Owner probability. This huge increase is due to

the fact that as Owner probability increases, more indoor active users will become Owners

with access to the FAP, and therefore the total number of Macro-users competing for the

macrocell resource will be smaller.

In the case where FAPs are deployed in a region with 50% femtocell penetration, 60%

indoor percentage, and 50% Owner probability, indoor Macro-users will achieve a

throughput improvement of around 160% over the baseline scenario from 50kbps to

130kbps with either OAC or GAC. Even with low Owner probability at 0.1, the Macro-user

throughput can still be improved by 100% from 50kbps to 100kbps. The huge improvements

in OAC and GAC are because as Owner probability increases, more indoor users are being

offloaded to the FAP and therefore fewer outdoor Macro-users are competing for the same

macrocell resource.

3.4 Femtocell Performance

From the three scenarios examined in the macrocell offloading evaluation, it can be

concluded that both OAC and GAC are capable of providing better macrocell offloading for

the cellular operator than what CAC can under all circumstances. Although MSPs would

prefer the access control mechanism that provides the best macrocell offloading result, it is

eventually the consumers who are the main catalysts with respect to femtocell

deployments. From customers’ point of view, it is the performance of the femtocell in terms

52


of Owner throughput, and not the macrocell offloading, that is of their most essential

criterion when considering whether or not to install a FAP in their own premises.

Again, in the following sub-sections, the evaluation for femtocell performance is conducted

based on the Owner throughput from the three access control mechanisms; the baseline

scenario where no FAPs are deployed is also included for various comparisons. The same

three environment parameters: femtocell penetration, indoor percentage, and Owner

probability, are adjusted in a similar fashion as the evaluation for macrocell offloading.

3.4.1 Femtocell Penetration

From the definition of femtocell penetration as described in Sub-Section 3.3.1, all indoor

users are randomly but uniformly distributed among all houses. Therefore, by fixing both

indoor percentage and Owner probability, as more FAPs are being deployed within the

same region, the average number of Femto-users that a FAP needs to support decreases. As

a result, Owner throughputs for all three access control mechanisms should increase as

femtocell penetration increases.

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Figure 22: Owner Throughput vs Femtocell Penetration

Under CAC, all FAP resources are reserved for connected Owners only, thus it should

generate the highest Owner throughput. Whereas under OAC, all femtocell resources are

shared by all Femto-users, both Owners and Visitors, and therefore it should generate the

lowest Owner throughput. However, only a portion of the resource is reserved for Owners

under GAC while the remaining is shared among Visitors. As a consequence, the Owner

throughput by GAC outperforms OAC but underperforms CAC, which is shown in Figure 22.

3.4.2 Indoor Percentage

From the definition of indoor percentage as described in Sub-Section 3.3.2, as the number

of indoor users in the simulation increases, the total amount of Owners should also

increases if the Owner probability is fixed. As a result, more Owners are now sharing the

same amount of femtocell radio resources (considering the femtocell penetration also

remains unchanged) and thus the Owner throughput is expected to drop as indoor

percentage increases.

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Figure 23: Owner Throughput vs Indoor Percentage

Again, from Figure 23, it can be seen that the average Owner throughput decreases as the

indoor percentage increases, which reflects our expectation. In addition, under the

environment with 50% femtocell penetration and 25% Owner probability, the performance

for GAC in terms of Owner throughput lies between both CAC and OAC. This result is also

anticipated considering GAC was purposely designed to be the intermediate solution

between the other two access control strategies.

In this simulation, the resource reservation K for each FAP under GAC is adjusted

individually to the positive square root of its Owner density as described in Equation (44).

With Owner probability of the system fixed at 25%, the average amount of resource

reserved for Owners will be around 50% for each FAP. As seen in Figure 23, the average

Owner throughput of GAC lie in the middle between both CAC and OAC, which correspond

to the 50% resource reservation in GAC.

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3.4.3 Owner Probability

From the definition of Owner probability as described in Sub-Section 3.3.3, as the number

of Owners in the simulation increases, the average number of Owners each FAP needs to

support should also increases if both femtocell penetration and indoor percentage remain

fixed. As a result, Owner throughputs for all three access control mechanisms should

decrease as the Owner probability increases.

At low Owner probability, the Owner density for each FAP should also be low as well since

there are only a limited number of indoor users who are at home. In this situation, the

Owner throughput for OAC should be the highest since only the few Owners are consuming

all the resource; while the Owner throughput for CAC would be the lowest since most

Visitors are competing for the same resources. However, at high Owner probability where

majority of indoor users are Owners, the Owner throughput for GAC should approach OAC

as Owner probability increases.

Figure 24: Owner Throughput vs Owner Probability

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From Figure 24, it can be observed that average Owner throughputs for CAC, OAC, and GAC

do decline as Owner probability increases. Furthermore, as initial anticipated, the Owner

throughput for GAC does indeed lies between CAC and OAC at low Owner probability and all

three converge at high Owner probability. This is because when Owner probability is really

high, the resulting Owner density and the subsequent resource reservation coefficient for

each FAP should also be high as well. Under this situation, nearly all resources will be

reserved for Owners in GAC while very little Visitors are competing for resources in OAC;

this is why both lines, along with CAC, would overlap as Owner probability approaches 1.

3.4.4 Visitor Throughput

Another interesting measure, although not of interest to FAP owners but still of importance

to cellular operators that aim to guarantee QoS for all of their customers, is the average

Visitor throughput. In the following evaluation, indoor percentage and Owner probability

are altered to analyze potential influences on this measure.

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Figure 25: Visitor Throughput vs Indoor Percentage

Figure 26: Visitor Throughput vs Owner Probability

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From Figure 25 and 26, it can be seen that the average Visitor throughput for CAC remains

zero, which confirms that only Owners are allowed access and all Visitors will be handed

over to the underlying macrocell as Macro-users. In other words, the performance of CAC in

terms of Visitor throughput is independent of indoor percentage, Owner probability, or any

other system parameters; therefore, this evaluation is basically a performance comparison

between GAC and OAC.

In Figure 25, Visitor throughputs for both GAC and OAC decreases as more active users are

indoors. With Owner probability being fixed, more indoor users implies more Visitors are

distributed within each FAP and therefore lower Visitor throughput. On the other hand, in

Figure 26, Visitor throughput increases as Owner probability increases (i.e. fewer Visitors for

each FAP). Although in both figures that OAC outperforms GAC in terms of Visitor

throughput, the difference between the two is only at most 15%.

3.5 Resource Reservation

From previous evaluations of macrocell offloading and femtocell performance, it can be

concluded that GAC is the preferred access control mechanism because it not only provides

matching macrocell offloading effects as OAC, but also achieves similar Owner throughput

as CAC given the right amount of femtocell resource reservation.

Now the major question arises: Is the current method of square-rooting the Owner density

a good way in determining the resource reservation coefficient K for each FAP? In other

words, using the Cognitive access control, how much of the femtocell radio resources

should be reserved for Owners in order to achieve desirable macrocell offloading levels for

operators while at the same time still managing to provide satisfactory Owner throughputs

for customers?

In the following sub-sections, performance differences and potential impacts on Macro-

user, Owner, and Visitor throughputs will be analyzed for more insights. Additional

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simulations will be conducted by turning off the cognitive configuration functionality of the

FAP under GAC. In other words, instead of adjusting the value of KFAP individually for each

FAP, the resource reservation coefficient will now be fixed to a pre-determined value K for

all FAPs in the simulation. Comparisons and analyses will be done by varying the resource

reservation coefficient K and Owner probability γ, while keeping femtocell penetration α

constant at 50% and indoor percentage β fixed at 60%.

3.5.1 Macro-user Throughput

Since the resource reservation coefficient K is used to determine the amount of femtocell

resources to be reserved for Owners of the FAP, thus it should not have any impact on

Macro-user throughputs. In other words, the ability to offload indoor user traffics from

macrocell to femtocells will neither improve nor degrade under all three access control

mechanisms when the value of K is varied.

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Figure 27(a): Macro-user Throughput vs Resource reservation (25% Owner)

Figure 27(b): Macro-user Throughput vs Resource reservation (50% Owner)

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As expected, average Macro-user throughputs were not affected with changes in resource

reservation for CAC, OAC, and GAC as seen in Figure 27(a)(b). Based on current simulation

settings, at 25% Owner probability in Figure 27(a), CAC achieves an average Macro-user

throughput of 58kbps, which is an improvement of 14% over the baseline scenario at

51kbps. However, when the Owner probability is increased to 50% as in Figure 27(b), the

Macro-user throughput improvement over baseline jumps up to 60% at 82kbps; this is due

to the fact that in an environment with higher Owner probability, fewer Visitors are being

handed over to the macrocell as Macro-users.

In addition, when Owner probability is increased from 25% to 50%, average Visitor

throughputs from both GAC and OAC also increase from 108kbps to 136kbps respectively,

which is an improvement over baseline from 112% to 167%. In Figure 27(a)(b), both GAC

and OAC appear to have the same Macro-user throughput performance because all Femto-

users are allowed access to nearby FAPs; thus the total number of Macro-users remains

constant regardless of the value of resource reservation.

3.5.2 Owner Throughput

Even though the Macro-user throughput is independent from resource reservation, both

Owner and Visitor throughputs in GAC are directly related to the amount of femtocell

resource reserved. It is expected that with more femtocell resource being reserved just for

Owners, the average Owner throughput under GAC should also improve accordingly. Owner

throughputs for CAC and OAC, on the other hand, should not be affected by the reservation

coefficient since neither strategy has the resource reservation functionality implemented.

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Figure 28(a): Owner Throughput vs Resource Reservation (25% Owner)

Figure 28(b): Owner Throughput vs Resource Reservation (50% Owner)

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From Figure 28(a)(b), it can be observed that the average Owner throughput from GAC

clearly improves as the value of the femtocell resource reservation coefficient K increases.

However, average Owner throughputs for both CAC and OAC remain unchanged with the

variation of resource reservation as expected. The difference between Figure 28(a) and

28(b) is the Owner probability, which was set at 25% and 50% respectively. It can be

observed that average Owner throughputs for CAC, OAC, and GAC all experience

performance drops when Owner probability was raised from 25% to 50%; this is because

more Owners are now competing for the same femtocell resources.

From all the evaluations so far, it can be concluded that in terms of macrocell offloading

from operator’s perspective, both OAC and GAC have clear advantage over CAC; but on the

other hand, in terms of femtocell performance from customer’s point of view, either CAC or

GAC is preferred. Since macrocell offloading is independent of resource reservation; thus, it

can be considered that for GAC to be preferred over OAC, the reservation coefficient K

should be set to a value greater than the intersection point between OAC and GAC, which is

close to 0.5 in Figure 28(a) and 0.7 in Figure 28(b). These two numbers happen to be close

to the positive square root of the Owner probabilities used in Figure 28(a) and 28(b);

therefore, it can be concluded that the square-root utility function considered in the

simulation, as described in Equation (44), is a suitable choice for autonomous FAP

deployments (using GAC) under an environment with 50% femtocell penetration and 60%

indoor percentage.

3.5.3 Visitor Throughput

Another interesting performance to look at is the Visitor throughput. It is expected that the

Visitor throughput in GAC will decrease as the value of femtocell resource reservation

coefficient K increases, which happens to be the opposite effect as the Owner throughput in

GAC. Again, this is due to the fact that as more femtocell resources are dedicated to

Owners, less will be available for Visitors.

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Figure 29(a): Visitor Throughput vs Resource Reservation (25% Owner)

Figure 29(b): Visitor Throughput vs Resource Reservation (50% Owner)

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Since no Visitors are granted access in the case of CAC, the average Visitor throughput

remains zero under all situations. Additionally, the performance of Visitor throughput for

OAC remains unaffected by the variations in resource reservation as seen in Figure 29(a)(b).

These two outcomes are expected because neither CAC nor OAC utilize the ability to

reserve femtocell resources. However, Visitor throughputs for both OAC and GAC rise as a

whole from Owner probability of 25% in Figure 29(a) to 50% in Figure 29(b) because fewer

Visitors are sharing the femtocell resource.

Upon further observations, the average Visitor throughput from GAC decreases as the

reservation coefficient K increases and the lines for GAC and OAC intercept at K equals to

0.3 at 25% Owner probability and 0.5 at 50%. In other words, when considering average

throughputs for Visitors, GAC will have advantage over OAC only if the value of K is smaller

than the interception point.

When implementing GAC in random FAP deployments, the increase of Owner throughput as

seen in Figure 28(a)(b) comes with the decrease of Visitor throughput as seen in Figure

29(a)(b); this is because both Owners and Visitors are competing for the same femtocell

resources within the FAP. It is therefore crucial for FAPs to adjust their resource reservation

coefficients KFAP dynamically under different environments such that GAC will not only

outperforms CAC in terms of macrocell offloading, but at the same time also achieves

higher femtocell performance than OAC.

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CHAPTER 4

CONCLUSION AND FUTURE RESEARCH

4.1 Final Remark

Femto access points (FAP), or commonly known as femtocells, are low-cost low-power user-

deployed cellular base stations that are proposed to be the indoor radio access media for

future generation wireless technologies. Femtocell deployments are expected to be in a co-

channel manner due to scarcity of the licensed spectrum. Two femtocell user access policies

have been defined but each has a drawback: potential dead zones in Closed access policy

and poor ownership performance in Open access policy. Various studies have attempted to

provide a solution to balance between the two access control strategies but none of which

have provided an effective mechanism.

In this thesis, a new approach called the Cognitive access control is proposed to provide a

clear mitigation between the current Closed and Open access strategies. In Cognitive access

control, radio resource such as the transmit power of a FAP is divided into two parts where

pilot power is broadcasted for administrations and data power for actual communications.

Two classes of users are defined: Owners are private Femto-users that are listed on the AAL

of the FAP while Visitors are public Femto-users that are not listed on the AAL. Controlled

by the resource reservation coefficient K, partial femtocell resource is reserved for Owners

while residual resource is shared among nearby Visitors connected to the same FAP.

A simulation framework with three access control mechanisms, Closed, Open, and

Cognitive, along with a baseline (no FAP), was developed under 3GPP HSDPA autonomous

co-channel femtocell networks. Three system parameters, femtocell penetration, indoor

percentage, and Owner probability, have been defined to replicate different environments.

Simulation results in terms of the Macro-user, Owner, and Visitor throughputs have shown

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relationships across different access control strategies. From evaluations of macrocell

offloading, femtocell performance, and resource reservation, it was confirmed that by

setting the value of K dynamically according to the square root of Owner density for each

FAP, the Cognitive access control offers an effective compromise that would satisfy both

operators in terms of macrocell offloading, and customers in terms of user experience.

4.2 Future Work

Although the Cognitive access control proposed in this thesis is an ideal dynamic resource

management mechanism for cellular operators to roll out femtocells autonomously, more

works should be done to further improve various aspects of the Cognitive femtocell model.

In the following sub-sections, three approaches for potential enhancements are proposed.

First step that can be done is to convert the simulation used in this thesis from the 3GPP

HSDPA standards into LTE standards; in other words, from WCDMA to MIMO and OFDMA.

With a new radio access technology, it is expected that the new simulation will need to

have different methods in allocating radio resources and calculating channel SINR.

Another approach is to use an alternative resource reservation mechanism. Instead of using

the current proposed Owner density, other environment parameters such as femtocell

penetration, indoor percentage, traffic load, QoS requirements, or a combination of any or

all of the above, can be utilized to formulate a more effective access control strategy.

Finally, instead of using the current opportunistic scheduling for allocating resource to

Femto-users, a new scheduling mechanism called proportional fair can be utilized for the

simulation. The proportional fair scheduling is a compromise-based scheduling algorithm

that aims to balance the two competing interests by assigning each user a scheduling

priority that is inversely proportional to its desired resource consumption [34]. It is

anticipated that by adapting to the proportional fair scheduling, the overall femtocell

network performance can be greatly improved.

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ABBREVIATIONS

3GPP 3rd Generation Partnership Project

3G/4G 3rd/4th Generation Wireless

AAL Allowed Access List

CAC Closed Access Control

CR Cognitive Radio

CSMA/CA Carrier Sensing Multiple Access/Collision Avoidance

DL Downlink

DSL Digital Subscribers Line

FAP Femto Access Point

FMC Fixed Mobile Convergence

GSM Global System for Mobile

GAC Cognitive Access control

HNB Home NodeB

HSDPA High-Speed Downlink Packet Access

IEEE Institute of Electrical & Electronics

IMEI International Mobile Equipment Identity

LTE Long Term Evolution

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MBS Micro Base Station

MIMO Multiple-Input Multiple Output

MSP Mobile Service Provider

OAC Open Access Control

OFDMA Orthogonal Frequency Division Multiple Access

OVSF Orthogonal Variable Spreading Factor

PL Path Loss

QoS Quality of Services

RF Radio Frequency

RRM Radio Resource Management

SINR Signal to Interference and Noise Ratio

SDT Squared Distance-adjusted Throughput

SLA Service Level Agreement

UE User Equipment

UL Uplink

UMA Universal Mobile Access

UMB Ultra Mobile Broadband

WCDMA Wideband Code Division Multiple Access

WiMAX Worldwide Interoperability for Microwave Access

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