UNIVERSITA DI ROMA “TOR VERGATA”
Degree of Philosophy Doctor in Telecommunication and
Microelectronics
Dynamic frequency allocation infemtocells-based systems:
algorithms and performanceanalysis
by
Remo Pomposini
Supervisor Coordinator
Prof. Francesco Vatalaro Prof. Giuseppe Bianchi
Co-advisor
Prof. Franco Mazzenga
2011
I
Summary
Femtocells are small domestic low cost and low power cellular-based access points,
also known as Home Nodes B (HNB’s) or “home base stations”’, which are self-
installed by consumers and are remotely managed by operators. HNB’s transmit
with a range of tens of meters in licensed band (e.g., UMTS frequency bands), thus
avoiding the need for dual mode devices, and provide mobile handsets with high
data rate wireless access to the mobile operator network through broadband wired
connection, such as cable, xDSL or optical fiber.
The need for femtocells derives from the consideration that mobile terminals are
predominantly used within closed spaces [1]. Indeed, since most of the mobile radio
traffic is spent in the home and in workplaces, a better indoor coverage is wished
in order to increase the available bit-rate as well as to off-load macrocells [2],[3]. In
such a way, femtocells allow indoor mobile radio users to use advanced data ser-
vices, such as high quality video and audio streaming, downloads, on-line gaming
and other multimedia applications, with a higher efficiency in the use of spectrum
resources.
In the next future a multi-operator scenario is envisaged in which each network op-
erator makes available some portions of spectrum band for femtocells. In respect to
the frequencies allocated to the macrocell network, operators can assign dedicated,
common or partially common channels to femtocells [4]. Depending on the pursued
spectrum planning strategy, each operator has to face different interference scenar-
ios, referred to as cross-layer (i.e. macro-to-femto and femto-to-macro) and co-layer
(i.e. femto-to-femto) interference [5, 6, 7, 8, 9].
II
Even if a dedicated frequency band is allocated to the femtocell network to limit
cross-layer interference, harmful femto-to-femto interference levels can be generated
in a network scenario where a high density of Home Node B (HNB) sharing the same
frequency band is installed. This is due to the self-installation nature of femtocells
with no radio network planning by operators, that could lead to a random and not
coordinated deployment of HNB’s [10].
In this perspective, the Cognitive Radio (CR) concept [11] can be a possible solution
to reduce the mutual interference among femtocells. By introducing smart CR al-
gorithms accounting for different interference mitigation techniques, performance of
femtocells sharing the frequency band of their operator can be increased. However,
such algorithms can result very complicated to implement and can require a review
of the standard specifications to introduce signalling fields which permit to realize
them.
For these reasons, in this work I propose an alternative approach represented by
the opportunity for operators to share its licensed spectrum allowing users of fem-
tocells subscribed to a certain operator to exploit the frequency resources of other
operators. By assuming that operators make arrangements one with each other -
similar to roaming agreements - to allow the mutual exchange of frequency bands,
HNB’s belonging to different network operators could operate in a cognitive manner
in order to dinamically select the best operating frequency based on local interfer-
ence measurements. This solution allows to realize simple distributed algorithms
with minor revisions for the legacy femtocells, that can change operating frequency
considering also the band of other network operators. The proposed algorithms
are named Dynamic Frequency Selection (DFS) algorithms. In such a way, the in-
terference between neighboring femtocells belonging to the same operator can be
considerably reduced. In a multi-operator scenario, this approach offers advantages
to all operators in terms of achievable network capacity and quality of service pro-
vided to customers.
The aim is to evaluate the gain in terms of number of served femtocells per operator
when the channels available from operators increase, I refer to this parameter as the
III
Spectrum Sharing Gain.
In a realistic environment a hybrid scenario can be expected in which only some
HNB’s conform to the proposed DFS algorithms, while the rest of the femtocells are
unable to implement CR techniques.
For this reason in the second part of the work the performance in terms of out-
age probability and average Signal-to-Interference Ratio (SIR) when femtocells are
characterized by different behaviours are evaluated. In particular, the performance
gap with respect to the results obtained in the case in which all femtocells adopt
the proposed algorithms with reference to two situations are analyzed: in the first
one is considered that only a certain percentage of HNB’s implements the DFS algo-
rithms; in the second case, all the femtocells adopts the suggested DFS algorithms
but some of them only partially follow the rules, to the aim of maliciously exploiting
the frequency resources. These HNB’s, referred to as selfish femtocells, continue to
occupy the channel with dummy data even if their QoS is below the required level
in order to force the other DFS-conformed HNB’s to interrupt transmissions.
The work is organized in five chapters. Chapter 1 deals with basic notions con-
cerning femtocell systems. In particular, network architectures, services, standard
and security aspects are briefly discussed. Chapter 2 summarizes main mechanisms
regulating the cognitive radio concept. It aims at highlighting the main cognitive
radio characteristics, essential to understand the subsequent chapters which focus on
issues of cognitive femtocells. Chapter 3 describe the proposed cognitive algorithms
named ”Dynamc Frequency Selection Algorithms”. Along with the identification of
both the main limitation and the proposed solutions to improve femtocells perfor-
mance. A vast gamut of analytical, simulation and experimentation results, coming
from my research activity, are presented in Chapter 4. In Chapter 5 are analyzed
some security aspects concerning the proposed algorithms. Finally, conclusions are
drawn in Chapter 6.
My research activity during the PhD period focus on wireless systems, with par-
ticular interest on Dynamic Spectrum Allocation, Cognitive Radio and Femtocells
Systems. Most of the outcomes of my research activities have been collected and
IV
discussed in the following publications:
JOURNAL
1. A. Detti, P. Loreti, R. Pomposini, On the performance anomaly in WiMAX
networks, Wireless Communications & Mobile Computing, special issue on
Wireless Technologies Advances for Emergency and Rural Communications,
vol. 10, Issue 9, September 2010, pp. 1162-1172.
CONFERENCE & WORKSHOP
1. F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro, R. Giuliano, Perfor-
mance Evaluation of Spectrum Sharing Algorithms in Single and Multi Opera-
tor Scenarios, in Proceedings of IEEE 73rd Vehicular Technology Conference:
VTC2011-Spring, 15-18 May 2011, Budapest, Hungary.
2. F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro, R. Giuliano, Impact
of Control Channel Design on Cooperative Spectrum Sensing in Opportunis-
tic Spectrum Access Networks, First International Conference on Advances in
Cognitive Radio (COCORA 2011), 17-22 April 2011, Budapest, Hungary.
3. F. Mazzenga, M. Petracca, R. Pomposini, R. Giuliano, M. Vari, An Always
Available Control Channel for Cooperative Sensing in Cognitive Radio Net-
works, in Proceedings IFIP Wireless Days 2010, Venice, Italy, October 20-22
2010.
4. F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro, R. Giuliano, Algo-
rithms for Dynamic Frequency Selection for Femto-cells of Different Operators,
21st IEEE International Symposium on Personal, Indoor and Mobile Radio
Communications (PIMRC 2010), September 26-29 2010, Istanbul, Turkey.
5. F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro, Impact on QoS of
Femtocells Defecting from Dynamic Frequency Selection Algorithms, 21st In-
ternational Tyrrhenian Workshop on Digital Communications (ITWDC 2010):
Trustworthy Internet, September 2-8 2010, Ponza, Italy.
V
6. R. Giuliano, F. Mazzenga, M. Petracca, R. Pomposini, Wireless Opportunis-
tic Network Based on UWB for Preserving Environment, 19th IEEE Interna-
tional Workshops on Enabling Technologies: Infrastructures for Collaborative
Enterprises (WETICE 2010), June 28-30 2010, TEI of Larissa (Greece).
7. M. Petracca, R. Pomposini, F. Mazzenga, F. Vatalaro, ”Which Control
Channel for Cooperative Sensing in Cognitive Radio Networks?”, 1st Work-
shop of COST Action IC0902: Cognitive Radio and Networking for Coopera-
tive Coexistence of Heterogeneous Wireless Networks, November 23-25, 2010,
Bologna, Italy.
8. F. Ananasso, F. Vatalaro, A. Durantini, M. Petracca, R. Pomposini, The
femtocell: an international standard for a home base station, AGCOM techni-
cal report, April 2009, Egypt.
BOOK
1. Trustworthy Internet, Ed. Springer, F. Mazzenga, M. Petracca, R. Pom-
posini, F. Vatalaro, chapter editors of Chapter 9: Improving QoS of Femto-
cells in Multi-operator Environments. 1
1book in press
VI
VII
Acknowledgements
Per non inserire la solita lista di ringraziamenti, ho deciso di riportare i nomi delle
persone che in qualsiasi modo mi hanno aiutato a raggiungere tale traguardo tramite
una nuvola, come quelle usate per i tag dei siti web o dei blog.
A differenza delle classiche ”tag cloud” dove le parole piu importanti sono a caratteri
piu grandi, in questa nuvola l’unico distinguo tra i nomi e nel colore, che li differenzia
per la provenienza delle persone, in ambito familiare e personale, quelle nere, e le
blu in ambito professionale.
Un grazie a tutti quanti!!!.
VIII
IX
Contents
Summary II
Acknowledgements VIII
1 Femtocells: architectures, services and standards 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 A new technology and market paradigm . . . . . . . . . . . . . . . . 3
1.2.1 Benefits expected from femtocells . . . . . . . . . . . . . . . . 3
1.2.2 Femtocells usage scenarios and applications . . . . . . . . . . 5
1.2.3 Market forecasts . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 System architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.1 Femtocells and standardization . . . . . . . . . . . . . . . . . 9
1.3.2 Pre-standard proprietary architectures . . . . . . . . . . . . . 10
1.3.3 3GPP standard architecture . . . . . . . . . . . . . . . . . . . 13
1.4 Femtocell network management . . . . . . . . . . . . . . . . . . . . . 15
1.4.1 Remote control of femtocells . . . . . . . . . . . . . . . . . . . 15
1.4.2 Radio Resource Management . . . . . . . . . . . . . . . . . . 16
1.4.3 Mobility management . . . . . . . . . . . . . . . . . . . . . . . 21
1.5 Some open problems . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2 Cognitive radio 28
2.1 Cognitive Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
X
2.2 Cognitive Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3 Cognitive Femtocells . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3 Dynamic Frequency Allocation Algorithms 38
3.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2 Regulatory aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3 Start-Up Procedure in Femtocells . . . . . . . . . . . . . . . . . . . . 42
3.4 Dynamic Frequency Selection Algorithms . . . . . . . . . . . . . . . . 44
3.4.1 DFS algorithm without Power Control mechanism . . . . . . . 44
3.5 DFS algorithm with Power Control mechanism . . . . . . . . . . . . . 47
3.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4 Performance evaluation 50
4.1 Scenarios description . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2 Performance results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2.1 SIR in the regular grid topology . . . . . . . . . . . . . . . . . 53
4.2.2 Outage probability . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.3 Signal to Interference Ratio (SIR) . . . . . . . . . . . . . . . . 58
4.2.4 Spectrum Sharing Gain SSG . . . . . . . . . . . . . . . . . . . 61
4.3 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5 DFS algorithm: robustness and resilience against malicious users 66
5.1 Scenario description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.2 Performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.3 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6 Conclusions 78
Bibliography 81
XI
List of Tables
1.1 Comparison femtocell market growth. . . . . . . . . . . . . . . . . . . 9
XII
List of Figures
1.1 Cellphone usage during weekdays at home, at work and on the move
(Source: NOKIA, 2006). . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Femtocell development scenarios at home and in the office. . . . . . . 6
1.3 Possible development of femtocells in an outdoor environment. . . . . 7
1.4 Femtocell and WiFi market projections (Source: ABI Research, 2007). 8
1.5 Standard interfaces. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6 Pre-standard femtocell architecture solutions. . . . . . . . . . . . . . 11
1.7 Standard 3GPP architecture. . . . . . . . . . . . . . . . . . . . . . . 14
1.8 Femtocell CWMP monitoring and control. . . . . . . . . . . . . . . . 16
1.9 Dynamic use of macrocell frequencies. . . . . . . . . . . . . . . . . . 17
1.10 Dead zones due to the interference between femto and macro layers. . 19
1.11 Relative mean value of the bit-rate: (a) outdoor area and (b) indoor
area (from [14]) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.12 Femtocells aggregator. . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.1 Environment sensing cognitive radio network. . . . . . . . . . . . . . 29
2.2 Overlay and Underlay spectrum access techniques in CR system. . . . 30
2.3 Cognitive Cycle proposed by Mitola. . . . . . . . . . . . . . . . . . . 31
2.4 User with different channel conditions within the coverage of the same
femtocell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.1 Femtocells deployment in a small office environment. . . . . . . . . . 40
3.2 Femtocell start up procedure. . . . . . . . . . . . . . . . . . . . . . . 45
3.3 Flow chart of the DFS algorithm. . . . . . . . . . . . . . . . . . . . . 46
XIII
3.4 Flow chart of the ODFS algorithm with and without the power control
mechanism. The dashed blocks and lines are only referred to power
control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.1 Interference scenario with HNB and user terminals (indicated with
dot) belonging to different operators. . . . . . . . . . . . . . . . . . . 51
4.2 Optimal frequency arrangement among femtocells in the regular grid
scenario with 2 network operators. . . . . . . . . . . . . . . . . . . . 54
4.3 SIR distribution for femtocells in different network topology without
power control mechanism. . . . . . . . . . . . . . . . . . . . . . . . . 55
4.4 Outage Probability with 2 frequency bands for Random (solid line)
and Perturbed Grid (dashed line) topologies. . . . . . . . . . . . . . . 56
4.5 Outage Probability with 3 frequency bands for Random (solid line)
and Perturbed Grid (dashed line) topologies. . . . . . . . . . . . . . . 57
4.6 Average SIR per femtocell with 2 frequencies for Random (solid line)
and Perturbed Grid (dashed line) topologies. . . . . . . . . . . . . . . 58
4.7 Average SIR per femtocell with 3 frequencies for Random (solid line)
and Perturbed Grid (dashed line) topologies. . . . . . . . . . . . . . . 59
4.8 Average SIR per femtocell with 2 frequencies for Regular Grid with
optimal frequency assignment (solid line), Regular Grid (dashed line)
and Perturbed Grid (dotted line) topologies. . . . . . . . . . . . . . . 60
4.9 Number of active femtocells per operator vs the number of available
frequency bands for Nf = 2. . . . . . . . . . . . . . . . . . . . . . . . 62
4.10 Spectrum Sharing Gain vs the number of operators with Nf = 2. . . . 63
4.11 PDF of the minimum reuse distance for Pout = 2%. . . . . . . . . . . 64
5.1 Building scenario for the interference analysis. HNB’s are indicated
with circle and each colour represents the assignment to a different
operator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.2 Outage probability vs the percentage of non DFSA-conformed fem-
tocells in a low-medium density scenario. . . . . . . . . . . . . . . . . 70
XIV
5.3 Average SIR vs the percentage of non DFSA-conformed femtocells in
a low-medium density scenario. . . . . . . . . . . . . . . . . . . . . . 72
5.4 CDF for various percentage of femtocells that do not implemet the
DFSA algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.5 Outage probability vs the percentage of non DFSA-conformed fem-
tocells in a high density scenario. . . . . . . . . . . . . . . . . . . . . 74
5.6 Average SIR vs the percentage of non DFSA-conformed femtocells in
a high density scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.7 Bit Error Rate vs the average SNR on a Rayleigh fading channel for
different modulation schemes assuming a coding gain gc=10 dB. . . . 76
XV
XVI
Chapter 1
Femtocells: architectures, services
and standards
1.1 Introduction
The mobile radio service was designed to ensure total freedom from wires, to be
always connected anywhere with full mobility, especially outdoor, even away from
the fixed telecommunications infrastructure. But since long time we witness an
apparent paradox, on which all market analyses agree: the prevalence of the use
of mobile terminals within closed spaces. According to Northstream, the 57% of
minutes of mobile radio traffic is spent in the home and in workplaces in Western
Europe. According to Ovum from the 30 to 40% of mobile traffic originates in
the home. VisionGain foresees that by 2011 third generation (3G) traffic generated
within buildings will rise up to 75% of total traffic. In 2006, Nokia carried out an
investigation in the UK into the habits of the mobile terminal’s use by a selected users
group, finding the results summarized in Figure 1.1: over 24 hours the examined
customers sample limited within about 20-30% the phone’s use on the move. The
prevalent use of the mobile phone in indoors environment caused the gradual decline
of POTS telephony, initially in second homes and later in many principal homes too,
only partly counteracted by the requirements dictated by the use of PCs for Internet
1
1 – Femtocells: architectures, services and standards
Figure 1.1. Cellphone usage during weekdays at home, at work and on the move(Source: NOKIA, 2006).
access. Given for granted the reduction in revenues caused by VoIP (Voice-over-IP),
the operators rely, at least in part, their expectations of renewed growth of the
market for fixed telephony on the emergence of the so-called ”home networking”,
i.e. the interconnection of appliances in home networks and to the Internet. But,
despite expectations, the market for home networks evolve slowly and according to
still uncertain technological paths. The reasons for this delay are numerous:
• there are many standardization authority that operate independently to carry
out specific techniques that are not always interoperable (e.g. TISPAN/3GPP,
DSL Forum, UPnP Forum, HGI), which creates fragmentation and uncertainty
in the development of the market;
• some domestic appliances are already predisposed to connect to the Internet,
but most homes do not allow to exploit the potential due to the low penetration
both of PCs and of broadband access. Also internal wiring is inappropriate
for homes which are often old;
• most customers are not willing to face extensive domestic wiring, due to the
2
1.2 – A new technology and market paradigm
high cost of civil works, or even just the inconvenience that they entail.
Surely people prefer solutions that bypass the need to wire or rewire property, called
”no-new-wires” (power-line and wireless networks) and among them the new fem-
tocells technology, which is the subject of this article, is an interesting solution
to promote the market for home networking. Therefore it may prove suitable for
helping to build the so-called broadband ecosystem.
1.2 A new technology and market paradigm
1.2.1 Benefits expected from femtocells
The femtocell is a 2G or 3G small, low cost, self-installing radio gateway, and that
does not require maintenance. Designed to operate in licensed bands for use both in
residential areas and in SOHO (small office - home office) areas, the femtocell can
serve simultaneously a small number of terminals (less than 5, or so) and connects
to the network of a mobile operator through broadband lines (DSL, cable modem,
fiber optics).
In the mobile radio system’s evolution chain, the femtocell represents the latest
”ring”. In the past years, the technological path going from macrocells to picocells
was characterized by continuity. It combined progressive equipment miniaturization
with service, always provided by external radio towers, getting closer to the user.
On the contrary, by changing the functionalities distribution between entities, the
advent of femtocells coincides with a change of paradigm in wireless communica-
tions.
With femtocells, in fact, the capabilities of mobile radio network management move
close to the end user and are much more distributed than in traditional network
configurations. This is in line with the tendency of modern telecommunications sys-
tems that provide an increasing share of the ’intelligence’ move toward the network’s
border [12].
Many benefits are expected by the advent of femtocells. Among them:
3
1 – Femtocells: architectures, services and standards
• Use of standard terminals.
A first advantage for a mobile operators in the use of femtocells is the pos-
sibility to use standard mobile terminals, so that they can leverage the wide
dissemination of the terminal and VAS enabled.
• Ensuring QoS using licensed spectrum.
Contrary to what happens for WLAN systems, the use of licensed spectrum
may allow interference control for femtocells that is the premise for guaran-
teeing Quality of service (QoS).
• Better coverage and increased capacity.
Designed to provide good coverage in buildings without the need to increase
the number of expensive outdoor radio installations, another advantage of fem-
tocells lies in the ability to devote high-capacity radio to the customer only
if and when this is required. While the installation of a macrocell is expen-
sive and often faces considerable logistical difficulties, approaches to extending
coverage through femtocells allow to deliver radio capacity exactly where and
when it is needed.
• High bit rate values.
In a small indoor area the femtocell makes available the capacity of one base
station (BS) to a small number of potential users. Therefore, it supports
broadband traffic with values of bit rate always comparable to the peak values
expected from the standard
• Improved service offered by macrocells.
The transfer of substantial traffic to the indoor femtocell layer causes traffic
decongestion in the macrocell layer with the result that macrocells improve
the quality of service provided.
• Cost benefits.
For the operator aggregate traffic transport to the network (backhaul) is expen-
sive too. Allowing to reduce the backhaul cost, the femtocell reduces network
4
1.2 – A new technology and market paradigm
CAPEX and OPEX.
• Reduced customers churn.
The femtocell can increase customer loyalty by ensuring higher values of bit
rate and increased radio capacity, allowing the use of a single numbering system
for all applications and lower fares when calling from home.
Therefore, in the broadband access and home networking scenario the femtocell may
represent one of the key subsystems, to be associated with the home gateway with
which in the future it may also be integrated. Thanks to the femtocell the smooth
connection of the users to the home network can be ensured, thus avoiding the civil
works of internal wiring.
Today most manufacturers are producing femtocells interfaced with 3G (UMTS/HSPA).
Femtocells are also foreseen for other systems, such as WiMAX and LTE.
1.2.2 Femtocells usage scenarios and applications
Indoor coverage of a typical isolated femtocell is a few hundred square meters. Usu-
ally the cell radius varies between 30 m and 200 m depending on the propagation
characteristics in the femtocell’s environment. In principle, both isolated femtocell
coverage and multiple femtocell coverage can be implemented. The former is usu-
ally appropriate for residential use, while the latter, possibly integrated with a LAN,
is better suited in office and industrial areas (Figure 1.2). To date, however, the
major manufacturers are focusing on the product for domestic use. In the future
we can also expect femtocell use scenarios in outdoor and mixed indoor/outdoor
environments, with limited mobility conditions but in the presence of large volumes
of aggregated traffic (e.g., in malls, airports, railway stations, pedestrian areas) and
also when the end user requires high-capacity. Femtocell applications in outdoor
environments are still little investigated (although there is interest from some major
players, including Google), but we can already envisage their integration in RoF
(Radio over Fiber) architectures. These architectures are considered among the
options of interest to high capacity next generation radio communications also to
5
1 – Femtocells: architectures, services and standards
Figure 1.2. Femtocell development scenarios at home and in the office.
extend in urban areas the services of ultra broad band NGNs (next generation net-
works). One future scenario that is likely to be enabled by femtocells in dense urban
areas uses small remote units on lamp-posts and walls with backhaul over copper
pairs (DSL) or fiber optics. This may enable the operator an easier and economical
delivery of fixed and mobile services (Figure 1.3). So, the femtocell could stand out
as one of the key components of fixed-mobile convergence in the future.
There are three most promising application scenarios for the femtocell:
• Increased Quality of Service indoors.
This is the original motivation for the femtocell concept. The installation
of a femtocell in closed areas can provide the customer of one UMTS/HSPA
network data rates very close to the peak value in the immediate vicinity of the
femtocell site, so optimizing system capacity. Therefore, where DSL or fiber
optics accesses are already diffuse, femtocells can enhance the QoS provided
by the operator and can be an important enabler for the use of broadband
services on mobile terminals in indoor environments. With femtocells the QoS
6
1.2 – A new technology and market paradigm
Figure 1.3. Possible development of femtocells in an outdoor environment.
improvement offered to the customer at his residence can be obtained without
the need of dual-mode mobile phones.
• Increased traffic capacity through multiple coverage areas.
From this point of view, a femtocell network for use in large areas with a low
coverage degree is a substitute to the Distributed Antenna Systems (DAS)
technology. A DAS can extend the GSM or UMTS radio signal in shady areas
or in disadvantaged areas for one BS. Typical use cases may be represented by
shopping malls and large industrial buildings. One advantage of multiple fem-
tocell coverage areas, in addition to better coverage, is given by the increased
traffic capacity that can be permitted.
• Remote monitoring of the intelligent house.
In the future more and more household objects will be equipped with in-
tegrated computing and storage systems and wireless gateways that enable
communication with the outside world and their remote monitoring and con-
trol. The wiring of hundreds of household equipments, some of which also
7
1 – Femtocells: architectures, services and standards
arbitrarily relocatable, is not feasible and, therefore, the femtocell is the el-
ement that allows continuous monitoring without placing any constraints to
the user.
1.2.3 Market forecasts
Some analysts consider the femtocell market among the most important innovations
in mobile communications, although the projections are quite diverse. ABI Research
calculated about 52,000 units delivered in 2007, growing to about one million in 2008,
and 102 million users to be served at the end of 2011 by about 32 million access
points (Figure 1.4) [13]. If IDATE estimates about 10 million femtocells delivered in
2010 and 18 million in 2011, IDC forecasts are more conservative, reporting at least
5.7 million femtocell users of in 2011. Still with reference to 2011, OVUM envisages
17 million femtocells delivered in Western Europe only (Table 1.1).
Some mobile operators started investing in femtocells even before the 3GPP stan-
Figure 1.4. Femtocell and WiFi market projections (Source: ABI Research, 2007).
8
1.3 – System architecture
dard: e.g., the U.S. operator Sprint in September 2007 was the first to launch in
Denver and Indianapolis ”Sprint Airave”, a commercial service based on CDMA
femtocell from Samsung. In several Western countries, some operators planned to
launch commercial offerings based on specific agreements between operators and
vendors. However, the large economies of scale necessary for wide dissemination of
femtocells can be facilitated only now that a complete standard is available.
Femtocell (2011) Units (million) Customers (million)ABI RESEARCH 32 102IDATE 28IDC 5,7OVUM 38 (EU)
Table 1.1. Comparison femtocell market growth.
1.3 System architecture
1.3.1 Femtocells and standardization
An important point for the insertion of femtocells in the network of a mobile opera-
tor concerns standardization. In the tradition of GSM and UMTS mobile networks,
the complete standardization of radio interfaces, known as Um and Uu, respectively,
was essential to enable interoperability with terminals of different vendors. This
allowed achieving the necessary economies of scale in terminals production and cus-
tomers roaming. On the other hand, the need of standardization was not considered
important for the interfaces between the base station (BS) and the base station
controller (i.e. the Abis interface in the GSM and the Iub interface in the UMTS).
Therefore they exhibit proprietary features.
With the advent of femtocells as home base stations the need arises for a complete
standardization of radio interface elements towards the mobile operator network
(Figure 1.5). In order to be successful, the femtocell must be a consumer product
9
1 – Femtocells: architectures, services and standards
to ensure the high volumes of production typical of the market for terminals. Re-
cently, 3GPP completed the process of standardizing the network architecture and
protocols for 3G and for LTE .
Figure 1.5. Standard interfaces.
1.3.2 Pre-standard proprietary architectures
In the past years the industry identified several UMTS femtocell architecture con-
figurations (Figure 1.6). Though none of them was adopted as the standard they
include features which were useful for the standard definition. Every architecture
must provide for the implementation of security functions between the femtocell and
the femtocell aggregator placed on the network side, since the DSL backhaul is out
of the mobile operator’s domain and can include a path in the Internet. Therefore,
the interface must be encapsulated within an IPsec tunnel.
A first draft of architecture is based on the readaptation of the existing control el-
ements of the UMTS radio network, i.e. the RNC (radio network controller). This
is Architecture ”1” in Figure 6, named ”Iub − over − IP”.
10
1.3 – System architecture
Figure 1.6. Pre-standard femtocell architecture solutions.
According to this approach, the femtocell is connected throughout the Iub in-
terface, which is is intended to interconnect with the radio resource management
system, RNC. As is known, Iub is the 3GPP standard interface (TS 25 434) used
between the NodeB, i.e. the SRB in the UMTS system, and the RNC but, as al-
ready mentioned, is not full open, as it includes specific manufacturing features. For
this reasons, such architecture does not guarantee the interoperability of a femto-
cell network with different vendors products. In this architecture, the RNC focuses
different Iub flows deriving from different femtocells to the UMTS core network,
or CN (core network). Modern RNC are designed to accommodate at most a few
hundred of NodeBs high traffic load. For this reason the architecture does not scale
in scenarios with thousands of femtocells, or more. The changes required to imple-
ment access control and management reporting solutions in the RNC devices are
significant compared to standard solutions, and can require the re-interpretation of
the NAS (Non Access Stratum) protocols. Therefore, after the first proposal from
some manufacturing, this kind of solutions, based on the rehabilitation of the RNC,
were no more considered.
11
1 – Femtocells: architectures, services and standards
In Architecture ”2” the femtocell includes both the NodeB functions and the
RNC functions. Clearly, in this case the femtocell implements a different interface
solution, known as ”Iu − over − IP”, as is the UMTS Iu interface that is used.
Different integrated femtocell (NodeB + RNC) are connected to an aggregator,
which should be able to manage up to several thousand of them. The aggregator
is connected both with circuit-switched networks and with packet based networks,
through Iu − CS and Iu − PS standard interfaces, respectively. As is well known
Iu − CS is the standard interface between RNC and MSC, the mobile switching
center for services based on circuit switching, while Iu − PS is is teh interface
between RNC and SGSN, the switching center for packet based services.
The use of a truly open standard as Iu is an excellent basis for effective interop-
erability between aggregator and femtocell of different vendors.
One more alternative, represented in Figure 1.6 as Architecture ”3”, is based on
UMA (Unlicensed Mobile Access) architecture. Today, UMA is a 3GPP standard
to ensure interoperability between cellular/WiFi (TS 43318 v6.7.0 - R6, valid for
any IP access network), through an interworking entity called UNC (UMA Net-
work Controller), which allows dual-mode terminals, provided with an appropriate
client, to access to mobile network services and to perform vertical handover between
2G/3G and WiFi systems. Accprding to the UMA approach, an enabled terminal
search periodically if there is a WiFi gateway (or access point), when a compatible
one is found, it access to the network provider through an IPsec tunnel, established
between the UNC and the terminal. This tunnel ensure the communication secu-
rity, regardless of the security features offered by the technology used in the access
point. The UMA architectural approach for femtocells, is based on the use of an
Up interface toward a modified UNC. In this case, the UMA client functions are
situated in the femtocell instead of in the terminal: so any existing 2G/3G terminal
can connect with a UMA-enabled femtocell. When start-up, the femtocell use the
SIM/USIM standard with whom it is equipped and the EAP-SIM protocol to au-
thenticate within the mobile operator’s network and then to create an IPsec tunnel
12
1.3 – System architecture
between the femtocell and the UNC security gateway. Afterwards, the femtocell uses
the UMA standard procedures for the discovery of the appropriate UNC and then
to register with it. The UNC separates the voice traffic from the data traffic that
are delivered through the mobile operator circuit and packet networks, respectively.
As can be noted, in this architecture is completely missing the RNC block, whose
functions are integrated into the femtocell.
The last approach (Architecture ”4”) is based on IMS (IP Multimedia Subsys-
tem). IMS is the architecture designed to provide voice, video and multimedia
services across any kind of access networks. This standard is based on SIP (Session
Initiation Protocol) to manage the start-up, termination and modification in the
course of a multimedia over IP session. Therefore, Architecture ”4” requires femto-
cells enable SIP and connects with a gateway server that SIP IMS convergence.
1.3.3 3GPP standard architecture
As is evident, such a proliferation of architecture solutions would have inevitably
undermined any possibility of technology development and, therefore, the 3GPP
put in charge its RAN WG3 (Radio Access Network Working Group 3) with the
task of developing one single standard interface between the UMTS core network
and a femtocell. The reference configuration was approved in May 2008. Having
examined the four architecture proposals coming from the industry, RAN WG3
selected a compromise solution between Architectures ”2” and ”3” above.
The so-called 3GPP HNB Access Network (HNBAN), shown in Figure 1.7, includes
two new elements of a UMTS network, known as Home NodeB (HNB) and HNB
Gateway (HNB-GW), respectively:
• HNB (femtocell).
Connected to a broadband residential access service, this radio port provides
13
1 – Femtocells: architectures, services and standards
coverage to standard 3G terminals in the house. HNB includes NodeB func-
tions in addition to the radio resource management functionalities of a stan-
dard RNC.
• HNB-GW (femtocells aggregator).
Installed in the operator’s network, this gateway performs the task to aggre-
gate and disaggregate the traffic that comes from a large number of HNBs and
back to the CN by means of the two standard interfaces, Iu−CS and Iu−PS.
To interconnect these two network elements the standard defines the interface
Iu−h, which contains the protocols HNBAP (Home Node B Application Pro-
tocol) and RUA (RANAP User Adaptation). The first protocol is defined to
allow the creation of highly scalable HNBs, the second one provides transpar-
ent transport of RANAP (Radio Access Network Application Part) messages
and error handling functions. The new interface also introduces an efficient
and scalable method of transporting control signaling in the Internet.
Figure 1.7. Standard 3GPP architecture.
14
1.4 – Femtocell network management
1.4 Femtocell network management
The new femtocell paradigm requires rethinking all system aspects, such as those
relating to the management of operations and maintenance (O&M), in addition to
radio resource management (RRM) and mobility management (MM) aspects.
1.4.1 Remote control of femtocells
The approach to managing O&M is one of the innovative elements characterizing
the femtocell network over BS networks in traditional cellular systems. The femto-
cell, in fact, can be remotely controlled by means of mechanisms standardized in a
technical specification of the DSL Forum (TR-069). This specification, also known
as CWMP (CPE WAN Management Protocol), provides an application-level pro-
tocol for user equipment remote management (Figure 1.8). A dedicated software
installed in the O&M centralized system communicates with the home gateway to
which the femtocell is connected. It performs self-configuration, failure control, and
remote software upgrade functions.
The femtocell self-configuration functions include the choice of transmission pa-
rameters, mobility and access control parameters, and management of all cells to be
monitored. The transmission parameters of one femtocell cannot be fixed a priori
and manually configured by the operator, but must be chosen automatically during
the installation that is usually initiated by the customer. The customer simply con-
nects the femtocell to the DSL line and bears no other tasks or responsibility for its
proper functioning under normal conditions.
An UMTS femtocell may include a standard dedicated receiver, called ”macro-mode
receiver” (MMR), to receive the cell information broadcast by the overlay macro-
cells. In analogy to what happens for mobile terminals in sleep mode, the femtocell
is able to measure the level of signal and interference in surrounding cells. With this
feature, appropriate algorithms can be implemented for automatic power adjustment
15
1 – Femtocells: architectures, services and standards
Figure 1.8. Femtocell CWMP monitoring and control.
that can achieve the proper balance between minimizing co-channel interference gen-
erated by the femtocell on the overlay macrocellular network and the appropriate
coverage of the apartment. These algorithms also allow to control the interference
between adjacent cells in the case of high density femtocell installations.
1.4.2 Radio Resource Management
In a cellular system, Radio Resource Management (RRM) includes the control of
radio system characteristics and interference. As is known, strategies and algorithms
to monitor the transmission parameters are part of RRM, such as transmitted power,
choice of channels, handover criteria, and schemes for modulation and error control.
To understand the specificity of the RRM in a femtocell network we must take into
account two service features: on the one hand the user must enjoy the freedom to
place the HNB where he prefers in the house; on the other hand, the operator must
be also free to set different operating conditions for the femtocell. At operator’s
choice, the access to a HNB may be free, i.e. with no barring for any users who is
16
1.4 – Femtocell network management
within range, or it can be restricted to a Closed Subscriber Group (CSG) that is, to
a group of customers registered to the femtocell [14]. Then, the operator has several
options for spectral planning of the femtocell network. In general, there are three
cases:
1. A dedicated frequency band can be assigned to femtocells
2. The same frequencies can be used for the femtocells that are allocated to the
macrocells network
3. A frequency band can be used in common between the femtocell layer and
the macrocell layer and, in case of interference, a macrocell user is required to
perform handover to a reserved frequency (Figure 1.9).
Figure 1.9. Dynamic use of macrocell frequencies.
The use of various scenarios that derive from combinations of the operational choices
have numerous implications for both the operator, in terms of network capacity, net-
work management and service management, and for the customer, in terms of quality
and functionality. In the following we discuss interference control, considered a crit-
ical aspect for the RRM of the femtocell system. While the UTRAN was originally
conceived and implemented on the criterion of the development of an ordered and
17
1 – Femtocells: architectures, services and standards
controlled network, the presence of a femtocell layer, which also contains millions of
small HNB not coordinated with each other, can generate harmful interference lev-
els. There are three types of interference: the ”femto-macro” interference produced
by the femtocell layer the macrocell layer, the ”macro-femto” interference in the
opposite direction and, finally, the ”femto-femto” interference, internal to the layer
of femtocells. Among these, at least in the first phase of the femtocell networks de-
velopment, the interference between layers has primary interest (i.e. ”femto-macro”
and ”macro-femto”). In WCDMA cellular networks, as is for UMTS/HSPA, the
”near-far” problem can occur that produces the connections interruption of users
away from the BS due to closest ones. As is well known, in the UTRAN in or-
der to compensate for the path attenuation and the slow and fast fading, which
determine the near-far phenomenon fast power control is used. When, however, a
femtocell layer adds to the existing macrocell network, the power control can create
”dead zones” (Figure 1.10) that determine non-uniform coverage. The phenomenon
is characterized differently in uplink (UL) and downlink (DL) [15]:
• ”macro-femto” interference and dead zone in UL. In the UL connec-
tion one macrocell user located at the cell edge and who, therefore, transmits
at maximum power causes unacceptable interference to close femtocells. Con-
sequently, the femtocells located at edge of coverage experience interference
significantly greater than that which occurs in femtocells placed inside the
macrocells. In other words, under this condition the aggressor is the macrocell
terminal and the victim is the femtocell terminal.
• ”femto-macro” interference and dead zone in DL. In the DL at the
cell edge macrocell users are affected by the HNB transmissions of nearby
femtocells, since they suffer from attenuation values greater than the users who
are inside the macrocells. In other words, under this condition the aggressor
is the HNB of the femtocell and the victim is the BS of the macrocell.
To contrast the interference that may arise between the two cell layers some solu-
tions, operationally simple but not always practical, can be assumed. The use of
18
1.4 – Femtocell network management
Figure 1.10. Dead zones due to the interference between femto and macro layers.
different frequencies for the two layers eradicates the dead zone problem but it is
not a solution for UMTS operators with only one carrier. In general, it could force
the rescheduling of the macrocellular network, and can lead to possible capacity
reductions. For operators who have at least two UMTS bands, the dynamic use of
frequencies offers a solution with the transfer of an active communication within
the macrocell on the second carrier in the presence of interference arising inside the
femtocell (Figure 1.9). Finally, for the 2G/3G operators to reduce the dead zone in
the DL limitation of femtocell power can be done, jointly with the adoption of GSM
in place of UMTS in that area.
However, it is useful if each femtocell is able to monitor the RF environment, adapt
itself and change the transmission parameters (power, frequency, code, data rate) in
order to avoid, or at least contain, the interference to the macrocells and adjacent
femtocells [16]. To control the femto-femto interference, when a neighbor installs a
new femtocell, the other femtocells in the same building can be called upon to adapt
their transmission parameters. This may also be necessary to dynamically control
the interference. In principle, if two femtocells interfere, they can detect each other’s
presence and change transmission parameters at any time.
19
1 – Femtocells: architectures, services and standards
A study presented recently at the 3GPP [17] reported results of a simulation system
for UMTS/HSPA under the following conditions:
• HNB taken only with closed access (CSG),
• assumed the case of frequencies shared between HNB and macrocells,
• output power of HNB set at maximum value,
• the femto-macro interference in downlink to evaluate the impact on capacity.
The study examined the effect of interference on the P-CPICH (Primary Common
Pilot Channel). The results show (Fig. 11) that around the HNB significant inter-
ference problems may arise, where the macrocell’s power is small. The interference
could be reduced by limiting the HNB’s output power, but this also reduces the cov-
erage. The conclusion drawn is that in this scenario the HNB has to adopt adaptive
power techniques based on the interference estimate.
Figure 1.11. Relative mean value of the bit-rate: (a) outdoor area and (b) indoorarea (from [14]) .
20
1.4 – Femtocell network management
1.4.3 Mobility management
The main objective of Mobility Management (MM) in a radio network is updat-
ing frequently enough the information on clients network location to ensure proper
delivery of services. Mobility is handled through the established procedures of cell
reselection and updating of location area (LA), with the possible option of national
roaming activated in case different PLMN identifiers are used for the macrocell net-
work layer and femtocell network layer. Indeed, the addition of femtocells within
a mobile operator network requires to ask whether it is more convenient to man-
age one stand-alone layer of femtocells or to integrate it with the existing layer of
macrocells.
In a mobile network the main codes used to facilitate the terminal’s localization
are three: the MNC (Mobile Network Code), which identifies the mobile network
(PLMN), the LAC (Location Area Code) which unambiguously identifies the loca-
tion area within the PLMN and, finally, the CID (Cell Id) that identifies the cell
within the LA. The BS periodically broadcasts such identification codes. The mobile
operator can assign to femtocells the same MNC identifier chosen for the macrocell
network. In this case to discriminate if a user is within macrocell or femtocell cov-
erage they must be assigned a unique set of LAC, to be managed by a separate
planning process. Alternatively, the operator can use for femtocells a different MNC
identifier and this allows to implement different service strategies, introducing an
additional degree of flexibility, potentially reducing the complexity at radio level.
In the case of high femtocell density, even with hundreds of femtocells within range
of a single macrocell, the network must be able to discriminate under what type of
coverage the customer is located. Because of the large number of active femtocells,
some mechanisms must be implemented to manage the increased complexity of the
network and report to the management of localization. Indeed identifiers LAC and
CID may not be sufficient to handle mobility in a high number of femtocelle, espe-
cially if you use a unique identification code of the network.
To allow the network to always know the area where you parked you use the upgrade
procedure of LA (Location Area Update). Each LAC corresponding to a femtocell
21
1 – Femtocells: architectures, services and standards
must be distinguished both from that, which is associated with the macrocells over-
lapped that of the LAC femtocelle adjacent. In general the system of remote control
of femtocell’s network plays the role of aggregator and manages the complexity asso-
ciated to the significant number of LAC and CID of femtocells for the core network
(Figure 1.12). However, since the potential number of femtocells in the network is
very large, it is necessary to implement the reuse distance of LAC codes.
In the exclusive access scenario described above the femtocell or the aggregator,
Figure 1.12. Femtocells aggregator.
depending on the particular solution, stores a list of clients authorized to access to
the femtocell, called white list, based on the user’s IMSI. When a location area up-
date is started, the network simultaneously requests the terminal to send its IMSI.
The femtocell or the aggregator intercept the identifier which is then cross checked
with all the IMSIs stored in the white list. Therefore, the location update function
within the femtocell succeeds only if there is compliance, in which case the user is
accepted.
In Release 8 of the 3GPP standard, the handover of ACTIVE UE from UTRA Home
NB to UTRA macro cell is considered [18] and is expected to be the same as the
procedure specified in [19]. Furthermore, if there is a technical need (e.g. to limit
excessive interference) the standard envisage the handover of ACTIVE UE from
22
1.5 – Some open problems
macro UTRA cell to UTRA CSG Home NB in coverage of UTRA Home NB. As
regards the handover between femtocells, in [18] is specified that the handover pro-
cedures between allowed CSG cells with the same or different CSG ID are expected
to be the same as the procedure specified in [19].
1.5 Some open problems
Being the 3GPP standard now available, and the vendors ready with first release of
products in a few months, in the 2009-2010 biennium the first commercial femtocell
installations will presumably begin by the large mobile operators in Europe.
Some minor technological problems still to be solved relate to aspects of the system
ranging from O & M subsystem to radio resource management, to management of
mobility. We recall some of them:
• Synchronization. Synchronization is the basis of good management of pro-
cedures both at RMM and MM levels. In particular, to meet the standard
on the spectral mask for the SRB accuracy on the frequency that they must
generate is typically 50 parts per billion (0.05 ppm) or better, according to the
specification 3GPP 25.104. To meet this specific even on long term should be
used in crystal oscillators stabilized or controlled temperature requiring exter-
nal calibrations to adjust periodically (eg once a month and once a year) the
frequency that is susceptible to drift because of crystal. In conventional BTS
for periodic calibration sometimes use the GPS but more often the timing is
extracted from the physical layer of the TDM transport. In the case of femto-
celle, in many cases may be insufficient to cover the internal GPS and an IP
network is employed, which has an asynchronous different structure from the
TDM network. An alternative calibration strategy is the employment of the
IEEE 1588, Precision Timing Protocol (PTP). According to a typical master-
slave structure, this solution envisage a master clock in the network providing
timing reference to the slave clocks at the femtocells [8]. In the 3GPP Release
6 standard has released the specification of accuracy the value of 0.1 ppm for
23
1 – Femtocells: architectures, services and standards
the BTS located in closed areas and, more aware of the difficulties for fem-
tocells, in release 8 is proposed to relax the frequency error requirement for
Home BS class to 0.25 ppm [20].
• Handover. In Release 8, the handover procedure from an UTRA macro cell
to an active HNB is not defined [18].
• Access control. It’s still considering how to operate the femtocell in relation
to incidents such as the access side by a near or equipment placed in domestic
surroundings, or, finally, users of macrocells that are moving in the vicinity.
• Quality of service and boundaries of responsibility between fixed and
mobile operator. In the tradition of radio systems, one for the GSM and
also for UMTS mobile phone network owned by the mobile is fully autonomous
and does not require to rely on fixed telecommunications networks: If this is the
transport capacity of flow is restricted. But when we use the DSL connection
of customer services for the backhaul, the HNB may have to compete in the
band with the other domestic users. In this scenario of sharing the problem
of managing the quality of service. The problem is especially important if the
network is different from fixed to mobile network. Moreover, in this case we
must also ask who is responsible for the management failures.
• Emergency Services. From 2010 will be mandatory for mobile services, the
provision of emergency that involves the need to locate the user. It should be
clarified whether and how it’s possible to locate the femtocell (and if it can be
repositioned at will). Moreover, like as in the case of VoIP telephony, including
the femtocell do not have the guarantee of continuity of power: then will have
to be clarified whether the emergency service can always be assured by the
mobile equip itself with a network of femtocelle and under what conditions.
• Dynamic spectrum management. As aforementioned, the HNB can be
installed (and reinstalled as many times as you wish) by the user in domes-
tic premises without planning, differently from current mobile networks. This
24
1.6 – Chapter Summary
random distribution of HNBs can cause interference that is difficult to elimi-
nate in some configurations.
The following two possible scenarios are envisaged:
1. The Femtocells (dummy) and the Macro BS belong to the same network
operator and are considered at the same hierarchical level. So, when the
macrocell users perceive an excessive interference level caused by femto-
cells, the operator can employ spectrum
2. The Femtocells (CRs) and the Macro BS belong to the different network
operators and are not considered at the same hierarchical level. In this
case, the femtocells are seen as secondary nodes that can transmit and
receive according to constraints on interference level to the primary net-
work nodes, that are the MacroBSs. For this purpose, the HNBs have to
be endowed with Cognitive Radios features. In particular, to dynamically
manage the spectrum in an opportunistic way, the femtocells, in presence
of primary users, should implement some basic functions, which the most
important are: ”spectrum sensing”, i.e. the continuous spectrum mon-
itoring and detection of unused portions; ”spectrum management”, i.e.
the best dynamic usage of the available spectrum; ”mobility spectrum”,
i.e. the timely release of spectrum when a primary user become active.
In the second scenario, the MAC in the femtocells system will enable the
reallocation of frequencies that suffer from interference, for example due to the
presence of macrocellular radio base stations in the close proximity, possibly
even with an exchange of spectrum between different operators.
1.6 Chapter Summary
Femtocell is a new technology with the potential to achieve a paradigm shift in
radio networking. The femtocell, now a 3GPP standard, has been designed to give
solution to the problem of radio coverage in difficult spaces to high frequencies (2
25
1 – Femtocells: architectures, services and standards
GHz and beyond) where services are allocated to mobile broadband. In addition to
the coverage advantage, then, a femtocell network offers the advantage, over clas-
sical macrocell networks, that the improved radio capacity which can be obtained
in any environment, plus the ability to provide access to capacity in peak many
circumstances in which a network of macrocells can not go beyond the offer of an
average capacity of traffic. Thanks to the introduction of femtocells a traffic capac-
ity increase could be achieved that some estimate up to 500 times greater than that
achievable with traditional UMTS coverage.
But besides all of this, as seen in the chapter, there are a lot of benefits that now
generate a widespread expectation that the femtocell could potentially become an
enabler of development of the ecosystem of the home broadband, offering a gateway
to many devices without the constraints of the wired connection. The femtocell
lends itself to being a key enabler of full integration of home and SOHO environ-
ments in telecommunications networks, a major market driver for the promotion of
the broadband ecosystem so that it could be the ”missing link” to acceleration the
’evolution of intelligent technology.
The awareness of the multiple potential of this technology pushed 3GPP to complete
the standardization process in one year, or so. Since beginning of April 2009, with
3GPP UMTS Release 8 the femtocell technology is now an international standard.
As we saw in the article, with femtocell the capabilities of mobile and radio’s net-
work management, moving close to the end user are much more distributed than
in traditional network configurations. This is in line with the tendency of modern
telecommunications systems that provide an increasing proportion of intelligence to
move towards the periphery of the network. But if the femtocell technology opens
new opportunities in the development of 3G and LTE and it is also integrated in the
development of the NGN technological path, it also pose new challenges in relation
to intrinsic highly distributed nature of functionality. Some problems require careful
consideration and the solutions seem not yet fully identified. Some of these were
considered at the conclusion of the article.
26
1.6 – Chapter Summary
This work is focus on the problem of interference in a femtocells network, partic-
ularly the femto-to-femto interference. In order to mitigate such interference, the
concept of cognitive radio is applied to femtocells. More in details, two dynamic fre-
quency selection algorithms that permit to the generic femtocell the smart selection
of its operating band are proposed and analyzed by simulation.
27
Chapter 2
Cognitive radio
The Cognitive Radio (CR) [21] concept was introduced by Joseph Mitola III in 1999
[22] to indicate the concept of a wireless system which is aware of the environment
around it and of its internal structure. A CR can exploit its old experiences and its
learning ability in order to improve the standard adaptation capability (i.e. link-
adaptation based on the estimation of the channel), or for spectrum use information
in order to select the Base Station (BS), Access Points (AP) or more in general the
network nodes that are more appropriate. A CR device can even learn from the
information exchanged by the user with the aim to adapt the power consumption
and the network searching techniques to the user behaviour.
In general, a CR device operates according to two primary objectives:
1. high communication reliability;
2. efficient use of spectrum.
Mitola defines the CR as the point in which the wireless Personal Digital Assistants
(PDAs) and the networks are enough intelligent, in terms of spectrum management
and machine-to-machine communications, in order to:
1. identify the users communication needs;
28
2. provide the spectrum resource and services necessary to satisfy that needs.
On this basis, a CR can automatically select the best service for a specific trans-
mission and it can dynamically manage several transmissions based on the available
resources.
The Mitola’s definition of CR is vast, therefore a so defined device is indicated as
a “Full Cognitive Radio”. Recently the term CR is adopted with a more precise
definition. In this respect, the FCC (Federal Communication Commission) states
that every radio device able to adapt itself to the available spectrum should be ref-
erenced as a “Cognitive Radio” [23].
A CR is a device that perceives the environment around it, it collects data, it obtains
information from the collected data, it identify strategies to be adopted from the
information collected and it converts strategies into actions, as depicted in Figure
2.1.
Since cognitive radios are considered lower priority or secondary users of the spec-
Figure 2.1. Environment sensing cognitive radio network.
29
2 – Cognitive radio
trum allocated to primary users, it is necessary that these cognitive users do not
create interference for potential primary users. Different solutions can be used to
underlay, overlay or interweave the secondary user signals with the primary user
signals, in such a way that the primary signals are as little influenced as possible by
the secondary signals.
In the case of spectrum overlay, Figure 2.2 (a), a primary user receives an exclu-
sive right to spectrum access. However, at a particular time or frequency, if the
spectrum is not utilized by a primary user, it can be opportunistically accessed by
a secondary user. Therefore, to access a spectrum band, a secondary user has to
perform spectrum sensing to detect the activity of a primary user in that band. If a
spectrum hole is found, a secondary user may access the spectrum. The decision of
a secondary user whether to access the spectrum or not depends on constraints such
as the collision probability, which is defined as the probability that the transmission
from a secondary user occurs at the same time as that from a primary user.
In the underlay approach, Figure 2.2 (b), the secondary users spread their signal
Figure 2.2. Overlay and Underlay spectrum access techniques in CR system.
over a large bandwidth, minimizing the amount of interference caused to the pri-
mary users.
In the interweave approach, a cognitive radio must be capable of sensing the air
30
2.1 – Cognitive Cycle
interface and opportunistically exploit the unused spectrum by the primary users.
Since underlay techniques are more suitable for spread spectrum technologies, e.g.
Ultra Wide Band (UWB), and because it is difficult for the secondary users to obtain
the a priori knowledge of the primary user signals in order to perform an overlay
access, interweave techniques are attracting most of the attention in cellular network
environments.
2.1 Cognitive Cycle
The above described capabilities of a CR device can be explained using the ”Cog-
nition Cycle” [22], as depicted in Figure 2.3.
The ”Cognition Cycle” is the set of states, actions and interactions that the CR
Figure 2.3. Cognitive Cycle proposed by Mitola.
device makes in order to understand and know the outside world with the aim of
change the status in according to the received directions and incentives.
The first state of the cycle is named ”Observe” in which the CR device observes
31
2 – Cognitive radio
the environment around it. Clearly, the sensors and data derived from them, will
represent the environment through appropriate observable parameters in order to
be easily processed by the device.
The extracted data are pre-processed in the Orient state in order to take a decision
on the urgency of action to be performed. There are three urgency’s categories: im-
mediate, urgent and normal, which indicates the maximum time that can be waited
before performing the action. The level immediate indicates that there is no time to
perform a strategy and should be immediately taken a decision, so CR node have to
perform an action already performed in the past. The Act state will act to raise the
event. The urgent level indicates that there is no time to perform a new strategy
but it’s possible to choose a note strategy. So the next steps are: in the Decide
status the device decides the strategy to adopt and then it enter in the Act status.
Finally, the level normal provides that the device should also look at new strategies,
beyond those already known, through the state Plan. Afterwards, the device goes
into Decide mode in order to decide the strategy and then in Act state to perform
it.
The output of the states Observe, Plan and Decide, together with the data derived
from the external environment, are processed in the Learn state during which the
CR device learns from its actions. It should be noted that neither the Orient or Act
states provides input to the learning state, so there is no knowledge of the reaction
to the stimuli that occurs in those states. Alternatively, it is also possible that the
reactions to stimuli are included in the observed data in the Observe state, including
them in the Priority Status and New State blocks that indicate the observed status
before and after the action. In this case the Plan and Decide status have no longer
need to send information to the Learn state.
An important aspect that should be taken into account is the feasibility of a CR
devices. In particular, the requirements of each described phase are not trivial:
• the phase Observe is crucial for the CR device, as it allows to know the en-
vironment. A detection error at this stage may lead the device to act wrong
and even harmful for itself;
32
2.2 – Cognitive Networks
• the phase Orient plans to identify the priorities among the different stimuli
from the surrounding environment and the adaptive scheduling of the different
stimuli;
• the step Plan allows the device to determine the different strategies to use, so
it is required that there be a complete knowledge of the internal state and the
consequences of each strategy;
• in the Decide phase it is important that the device select the action to perform
taking into account the user preferences and habits as well as past experience;
• during the Act phase, the device must be able to reconfigure its internal state
in accordance with the adopted strategy;
• in the Learn phase the device must be able to store, identify and classify the
different situations (in order to reuse them in the future) taking into account
the user behavior and the obtained results. Moreover, the device must be able
to adapt the different phases of the cycle according to the past experiences.
2.2 Cognitive Networks
At the beginning the concept of Cognitive Radio was applied only to the user device,
i.e. the mobile terminal. Now, within the research and standardization offices, the
CR concept is extended to all the network devices. There is an increasing interest on
the Cognitive Radio Systems (CRS), i.e. radio systems based on cognitive concept,
both terminals and network.
An important application of the CRS are the Cognitive Networks (CN), where the
concept of CN is exyended to the netwotk domain. Therefore, a CN is a network
able to adapt its behaviour according to the environment cognition.
A CN is a network characterized by the following two entities and features:
• Cognitive Network Management;
• reconfigurable Base Stations.
33
2 – Cognitive radio
The Cognitive Network Management functionalities concerns the Radio Access Tech-
nologies (RAT), it manage and control the network nodes in order to adapt them
to the optimal configuration for the radio resources exploitation. This feature act
on the base of some input information, such as the available resources, the traffic
demands, the terminal characteristics in each cell (supported RAT, frequency bands,
etc.) and required services from each user (bandwidth, QoS, etc.). Moreover, this
feature may adopt a cooperative radio resource management scheme, where decision
functions are shared between the network nodes.
The reconfigurable Base Stations are the nodes that physically set up the CN. The
main feature of these nodes is to have hardware and computing resources that can
be reconfigured dynamically, in order to be used with other RATs, different frequen-
cies, different channels, etc., they can also operate in multi-RAT mode, based on a
dynamic load management.
An interesting aspect of the CN is the possibility of introducing the so-called ”Cog-
nition Radio Enablers”, i.e. devices that support the observing process of the ter-
minals, in order to know the radio spectrum, such as the Cognitive Pilot Channel.
In conclusion, the availability of reconfigurable radio Base Stations, together with
the Cognitive Network Management capabilities can give to the operators an addi-
tional management tool for radio resources and for processed resources, in order to
achieve a greater efficiency in their use.
2.3 Cognitive Femtocells
Cellular telephone companies are continually under pressure to provide new services
and devices to their users and as result they are using up increasing amounts of
scarce bandwidth.
For network operators is not simple to supply new radio spectrum, for this rea-
son are necessary new technical solutions that provide more effective use and reuse
of bandwidth. Some new technologies like LTE cell standards using MIMO show
significant promise by improving spectrum efficiency. However the viability of this
34
2.3 – Cognitive Femtocells
4G technology to meet the demand over the long term is also questionable. The
infrastructure costs will be even larger than 3G as LTE base stations will need to be
deployed every 500 meters or less and must be capable of supporting multi-megabyte
downloads to video Smartphones at these distances. Alternatives, such as limiting
user capacities, will not make for happy customers nor address the demand issue.
Faced with such concerns cellular systems designers have been discussing other tech-
nologies that offer alternative bandwidth distribution approaches. Martin Cooper,
one of the pioneers of cellular noted that bandwidth use could be multiplied by up
to 1600 times provided that radio cells became sufficiently small.
Femtocells can be used to create such a scenario. Moreover, their ability to support
efficient MIMO technology would be a better bet. There are drawbacks however.
With Femtocells, as their density increases so does the potential for radio interfer-
ence, Figure 2.4.
One solution to these problems could be to use Cognitive Radio technology in
Femtocells.
In a similar way as in the interweave approach, described above, femtocells must be
able to search the radio channel and estimate which resources are free among the
available ones in order to avoid cross-layer and co-layer interference.
Cognitive radio techniques can be implemented in the femtocell device, but they
must not be implemented into femtocell user terminals due to legacy constraints.
Femtocells must operate using legacy mobile terminals that do not depend on new
user equipment.
Despite having different methods of learning about the air interface, what informa-
tion should be used and how it should be combined is still an open issue. Moreover,
different trade-off has to be taken into account. As an example, measurement re-
ports coming from the user terminals will provide accurate information about the
user environment at the expense of raising the overhead information and processing
time.
Another important problem that can be arise with the diffusion of these devices is
the increasing amount of interference.
35
2 – Cognitive radio
Figure 2.4. User with different channel conditions within the coverage of the samefemtocell.
It is envisioned that Cognitive Femtocells could be installed at the user’s premises
and would interface with the DSL and cable links that already form an internet dis-
tribution infrastructure within cities. How this infrastructure is to be used and what
demands will be made on it by Cognitive wireless systems is both a regulatory and
technical concern requiring careful study and the drafting of new standards. The
internet was not designed to meet the mobility requirements of cellular applications.
The use of Cognitive radio technology will affect our thinking about how we license
spectrum. For example, should we continue to sell spectrum if Cognitive Radio
technology allows us to rent it? The technology could enable incumbent owners to
sub-license their spectrum and retain revenue doing this. Should there be a method
36
2.4 – Chapter Summary
of penalizing Femtocells that create excessive interference and deny access to adja-
cent users? Also what roles do independent wireless services providers have in these
models? In Europe for example, some wireless service providers have no spectrum
licenses but are providing services based on license-exempt spectrum alone.
2.4 Chapter Summary
Cognitive radio is viewed as a novel approach for improving the utilization of a
precious resource: the radio spectrum.
The cognitive radio, is defined as an intelligent wireless communication system that
is aware of its environment and uses the methodology of understanding- by-building
to learn from the environment and adapt to statistical variations in the input stimuli,
with two primary objectives in mind:
• high communication reliability;
• efficient use of spectrum.
The immediate interest to regulators in fielding cognitive radios is to provide new
capabilities that support new methods and mechanisms for spectrum access and
utilization now under consideration by international spectrum regulatory bodies.
Considering that for network operators is not simple to supply new radio spectrum
and that cellular telephone companies are continually under pressure to provide new
services and devices to their users and as result they are using up increasing amounts
of scarce bandwidth. The adoption of Cognitive Radio technology in Femtocells can
be a solution to these problems.
Unfortunately, with the diffusion of femtocells we have to face the problem of in-
terference, that can be arise between femtocells. This problem can be addressed
adopting appropriate cognitive algorithms.
37
Chapter 3
Dynamic Frequency Allocation
Algorithms
In this chapter are described the devised algorithms for dinamically selecting fre-
quency based on local interference measurements.
Two different approaches for femtocells that differ in the preference or not to use
the frequency band of their operators are proposed. In the first case, each femtocell
takes into account its subscription to the specific operator and attempts to use its
own band until the Signal-to-Interference Ratio (SIR) is above the required thresh-
old ρ0. In the second case, the femtocells are absolutely greedy and aim to maximize
their SIR careless of which operator offers the less interfered channel. It means that
also if the femtocell measures a SIR enough to guarantee the desired QoS level on its
frequency, it searches for another band that can maximize the throughput. The two
algorithms are named Greedy Dynamic Frequency Selection (GDFS) algorithm and
Operator-oriented Dynamic Frequency Selection (ODFS) algorithm, respectively.
As aforementioned, in the analysis are considered both the scenario where all the
femtocells transmit at the maximum power and the scenario in which the power con-
trol mechanisms are implemented. In general, according to the spectrum planning
an operator could allocate one or more frequency channels to femtocells. Here it is
38
3.1 – Problem statement
assumed that each operator provides its femtocells with a maximum of two dedi-
cated shared channels. The proposed algorithms can be easily extended to the case
in which the number of channels per operator assigned to femtocells is increased.
In the following sections we focus on the motivation that lead us to introduce the pro-
posed algorithms for cognitive femtocells, i.e. the problem statement, after which we
detail how the two algorithms work for both maximum power and power-controlled
transmissions.
3.1 Problem statement
The “home base stations”, known as femtocells, are the cellular-based access points.
They give the possibility to connect standard mobile devices to the network of a
mobile operator through a broadband wired connection (e.g. ADSL, cable broad-
band connections, optical fibres) or dedicated wireless point-to-point link [5].
Femtocells generally transmit in licensed bands (e.g. those for UMTS), thus avoid-
ing the usage of dual mode devices. In the next future a multi-operator scenario is
envisaged in which each network operator makes available some portions of spec-
trum band for femtocell installations. In respect to the frequencies allocated to the
macrocell network, operators can assign dedicated, common or partially common
channels to femtocells [24]. Depending on the applied spectrum planning strategy,
each operator has to face different interference scenarios, characterized by cross-
layer (i.e. macro-to-femto and femto-to-macro) and co-layer (i.e. femto-to-femto)
interference [6], [9].
In a first phase for femtocell deployment in an area, the mobile operator can assign
two dedicated bands taken from its licensed spectrum depending on the required ser-
vices by subscribers. In this way it is possible to avoid cross-layer interference and
to reduce the co-layer interference among femtocells. Nevertheless, to limit the cost
and time of frequency planning phase and maintaining the self-installation nature of
femtocells, a non-coordinated deployment of femtocells can be favoured [10]. Unfor-
tunately, when subscriber installations of femtocells become more dense, the mutual
39
3 – Dynamic Frequency Allocation Algorithms
interference could be harmful for offered services, limiting the femtocell capacity, as
visible in Figure 3.1 where is depicted a possible future office scenario. The use of
Cognitive Radio (CR) techniques is a viable solution to solve the interference prob-
lem by implementing and installing on femto Home Node B (HNB) complicated CR
algorithms possibly requiring a deep revision of the standard specification. As an
Figure 3.1. Femtocells deployment in a small office environment.
example the implementation of CR may require the introduction of signalling fields
which allow interference measurements that are required to enable CR procedures.
For this reason, simple but effective distributed algorithms aimed to dynamically
redistribute the available spectrum belonging to different network operators among
femtocells just based on local interference measurements are proposed.
The algorithms are based on the assumption that operators share their licensed
spectrum allowing users of femtocells subscribed to a certain operator to exploit
the frequency resources of other operators. Each femtocell is able to select one of
the possible channels available from all the operators in order to experience the
40
3.2 – Regulatory aspects
best Signal-to-Interference Ratio (SIR). This approach realizes a simple distributed
algorithm requiring minor revisions for the legacy femtocells.
3.2 Regulatory aspects
The proposed solution to limit co-tier interference in femtocell networks is based
on the assumption that network operators make arrangements one with each other
(similar to roaming agreements) to allow the reciprocal exchange of operating fre-
quency channels. However, while the infrastructure sharing among telecom service
providers is a mandatory policy by the European Commission (EC) [25], the si-
multaneous mutual interchange of spectrum bands among network operators is not
currently permitted. Nevertheless, the guidelines proposed by some regulatory bod-
ies open interesting perspectives in this regard.
The proliferation of wireless services and devices for uses such as mobile communica-
tions, public safety, WiFi, and TV broadcast serve as the most indisputable example
of how much modern society has become dependent on radio spectrum. While land
and energy constituted the most precious wealth creation resource during the agri-
cultural and industrial eras, respectively, the radio spectrum has become the most
valuable resource of the modern era [26].
Access to the radio spectrum is a key requirement for continuous wireless growth
and deployment of new mobile services. Given the fast-growing demand for radio
spectrum, regulators around the world (e.g., the Federal Communications Commis-
sion, FCC) are analyzing the way the spectrum is currently used and, if appropriate,
make recommendations on how to improve radio resource usage.
In particular they are implementing much more flexible and liberal forms of spec-
trum management, often referred to as dynamic spectrum management. This new
model dynamically redistributes and reassigns spectrum within and across different
wireless systems, adapting spectrum usage to actual demands and achieving much
more efficient use of the precious spectrum resource. Within the new model, two
prominent approaches are being considered by the regulators: spectrum trading and
41
3 – Dynamic Frequency Allocation Algorithms
cognitive spectrum access.
Spectrum trading is a market-based approach for spectrum redistribution that en-
ables a spectrum license holder (for example, a cellular operator) to sell or lease all
or a portion of its spectrum to a third party [27].
Note that this is an important departure from the command and control manage-
ment model, where spectrum licenses are granted by regulators for the provision of
a specific service using a predefined technology, and license holders were not allowed
to reallocate their spectrum to different technologies or other users.
Even the policy programme for the use of the European Union’s radio spectrum
foresees that spectrum should be managed on the basis of principles including spec-
trum efficiency and flexibility, technology and service neutrality and competition.
In addition, collective use of spectrum and spectrum trading would be promoted,
also encouraging convergence of authorization conditions and procedures for bands
tradable across Europe [28].
Moreover, the Radio Spectrum Policy Programme (RSPP) encourages the develop-
ment of standards able to avoid harmful interference or disturbance by other radio or
non-radio devices by means of efficient spectrum usage techniques, especially when
high density of radio devices occurs [29].
In this perspective, the demonstration of the benefits deriving from the sharing of
licensed frequency bands among operators can contribute to review the communi-
cations regulatory framework.
3.3 Start-Up Procedure in Femtocells
After acquiring the femtocell, the customer only needs to plug the femtocell into
a power source and Internet connection to start using it. The customer cannot be
assumed to have the knowledge to install or configure the femtocell, hence these
processes need to be automatic. Therefore, after power on, femtocells begin the
start-up procedure as described in [30].
This procedure envisages that when powered on the femtocell connects to the own
42
3.3 – Start-Up Procedure in Femtocells
operator network through the backhaul connection to carry out operations such as
registration, authentication, software updating.
In this phase, femtocells have also to set their RF parameters to a default config-
uration. This should be done with the information provided by operators through
the backhaul link, such as:
• frequency for DL and UL,
• scrambling code list, or
• radio channel bandwidth,
• location, routing and service area code information,
• neighbouring list,
• physical cell ID,
• RF parameters (pilot and maximum data power . . .)
The last four parameters can be be automatically calculated by femtocell from in-
formation on the macrocell layer provided by the operator (OSS data), and from
information on the femtocell layer provided by the users (registration data).
However, when the core network can not support this configuration phase, in partic-
ular for the first parameters of the list, femtocells can perform an auto-configuration
procedure based on sensing of the radio environment.
In the core network assisted configuration, HNB receive from the operator the in-
formation about the possible radio frequencies to be used for data transmission and
transmit it on the HNB control channel to the Mobile Terminals (MTs). Then, in
order to determine the operative frequency, the MTs perform power measurements
on the indicated radio channels and report results to the HNB.
In the second case, due to the lack of assistance from the operator via backhaul con-
nection, femtocells execute the network configuration by self-setting RF parameters
based on monitoring of the radio channels. Hence, the information needed for initial
43
3 – Dynamic Frequency Allocation Algorithms
configuration are transmitted on the less interfered frequency.
Once the operative frequency is determined according to one of the described start-
up procedures, femtocells enter into the self-optimization phase [30], since the femto-
cell needs dynamically to adapt its parameters to the changing environment condi-
tions. Using the network listening mode and other inputs, e.g. broadcast messages,
measurement reports, cognitive radio (Section citesec:CognitiveFemtocells) the fem-
tocell will collect statistics to optimize its performance dynamically (coverage and
capacity).
The proposed Dynamic Frequency Selection algorithms refer to this ”loop” phase of
the femtocell cycle. In particular, if the interference conditions cause a QoS degra-
dation on the operator channel (i.e. SIR ≤ ρ0), femtocell can select the frequency
band of another operator for data transmission.
However, in this case a problem of femtocell identification can arise when MTs be-
longing to the Closed Subscriber Group (CSG) of the HNB are temporarily not
linked to the femtocell (e.g. a mobile user coming back home with his terminal in
idle-state). As a result MTs can not recognize the presence of femtocell and remain
on the macrocell network without exploiting the better indoor coverage provided by
their HNB. For this reason HNBs should periodically transmit the control informa-
tion containing its physical cell ID on the beacon frequency of the operator. This
can be done either by temporarily interrupting transmissions on the new selected
channel to switch to the original frequency or by endowing HNB with an additional
dedicated beacon transmitter, as proposed in [31].
3.4 Dynamic Frequency Selection Algorithms
3.4.1 DFS algorithm without Power Control mechanism
In a multi-operator scenario with N network operators, the flow chart for modeling
the ODFS algorithm by femtocells transmitting at the maximum power level is re-
ported in Figure 3.3.
44
3.4 – Dynamic Frequency Selection Algorithms
Figure 3.2. Femtocell start up procedure.
Once femtocells are deployed in the subscriber premises and after the start-up pro-
cedure, they search for the less interfered channel (or bandwidth) among those
activated by its mobile operator. In particular, they measure SIR on all the Nf
available operating frequencies, expressed as:
SIRfop =Cfi
ITOTfi
(3.1)
where Cfi is the received power at the i-th frequency band (1 ≤ i ≤ Nf) and ITOTfi
is
the total amount of interference power on that channel. Then femtocells select the
frequency corresponding to the highest experienced SIR for their MTs. Note that
in our analysis the measured SIR is related to the downlink transmission. It means
that each MT uses the pilot signal received from its HNB to calculate the initial SIR.
Since MT can not measure the signal power level on the other available frequency
bands, it can just report to its HNB the interference power ITOTfi
sensed on such
channels. However, as the proposed algorithms consider SIRmeasurements to select
the operating frequency, a correction factor βλ can be introduced accounting for the
signal propagation at different bands. By assuming that the path loss exponent is
the same on the considered channels, with respect to the current operating frequency
45
3 – Dynamic Frequency Allocation Algorithms
fi the SIR on the j-th channel can be calculated as:
SIRfj = SIRfi · αi · βλ (3.2)
where βλ = (λi/λj)2 is the propagation correction factor and αi = ITOT
fi/ITOT
fjis the
ratio between the interference power levels measured on the considered frequency
bands. In (3.2) λi and λj are the wavelenghts of the i-th and j-th channels, respec-
tively. Then femtocell determines which channel maximizes the throughput of the
MT based on the received measurement reports.
Since in a realistic scenario multiple operators are present in the area (Mop oper-
SIRfop
< ρ0start
SIRfi = max{
SIRf1,SIRf2 ,...,SIRfMop·Nf
}
SIRfi
≥ ρ0
Turn OFF(sensingmode)
select fc = fi end
yes
no
yes
no
Figure 3.3. Flow chart of the DFS algorithm.
ators in Figure 3.3), femtocells periodically measure SIR not only on their mobile
operator frequency bands but also on the channels of other operators, i.e. SIRfi
with 1 ≤ i ≤ Nf ×Mop (we assume that all the mobile operators allocate an equal
number Nf of channels). Femtocell selects one of the channels of its operator until
the SIRfop level is above the required threshold ρ0. It means that femtocells con-
tinue to transmit on their own operator frequency bands even when the channels of
46
3.5 – DFS algorithm with Power Control mechanism
other mobile operators are less interfered (i.e. they provide a higher SIR). Only
if the SIRfop becomes less than ρ0, the femtocell is enabled to select the frequency
band of another operator. In particular, each femtocell selects the channel that
maximize its SIR.
However, if also the SIR on the selected channel does not match the QoS require-
ments (i.e. SIRfi < ρ0 ∀i), the femtocell sets its status to sensing mode, i.e. it
just continues to perform spectrum sensing and interference measurements with-
out accessing the channels. Sensing mode operation can be envisaged in order not
to damage the transmission of other femtocells: since the unrecoverable interfer-
ence conditions prevent the femtocell from transmitting, it temporarily disables its
transmission and switches into sensing mode. Since each HNB accounts for its sub-
scription to the specific operator, femtocells attempt to use its own spectrum band
until the measured SIRfop is above the required threshold ρ0. It means that during
the sensing mode period, femtocells first and foremost schedule their channels for
interference measurements. This is also valid when a channel of another operator is
selected and femtocells periodically check other frequency bands.
The GDFS algorithm can be seen as a special case of the ODFS algorithm, since
the only difference is that each femtocell immediately selects the channel that maxi-
mizes its SIR regardless of which operator has license for that frequency band. This
means that the first conditional block is skipped in Figure 3.3.
The described cognitive algorithm is very simple and easy to implement. As high-
lighted by the flow chart in Figure 3.3, with respect to the ODFS, the GDFS algo-
rithm allows the femtocell to maximize SIR.
3.5 DFS algorithm with Power Control mecha-
nism
The behaviour of femtocells implementing the ODFS algorithm with power-controlled
transmissions is depicted in the flow chart of Figure 3.4, considering all the blocks
47
3 – Dynamic Frequency Allocation Algorithms
and lines. The algorithm differs from the previous one in the following aspects.
Given the SIR on the frequency band of the home operator the single femtocell
adjusts its transmission power in order to reach the target quality level ρ0. The
transmission power is decreased if the SIR is greater than ρ0 otherwise it is in-
creased up to the maximum transmission power level, Pmax, if necessary to reach
ρ0. If the target ρ0 is reached the algorithm stops.
Otherwise, the femtocell searches for the operating band providing the greatest
SIR and it tries to adjust its transmission power following a procedure similar to
that used for the home operator (see previous steps). Finally, if the transmission
power required to reach ρ0 is greater than Pmax than the femtocell switches into
sensing mode, since it can not reach quality. Otherwise, the femtocell selects the
new operating frequency belonging to one of the other operators.
3.6 Chapter Summary
The self-installation nature of femtocells sharing the same frequency band can lead
to harmful femto-to-femto interference levels.
The possibility for operators to share its licensed spectrum allows femtocells of one
operator to exploit the frequency resources of other operators.
In this chapter is described the devised algorithms for dinamically selecting fre-
quency, among those available from every operator, based on local interference mea-
surements. Two different approaches for femtocells that differ in the preference or
not to use the frequency band of their operators are proposed.
With the Greedy Dynamic Frequency Selection (GDFS) algorithm femtocells aim
to maximize their SIR careless of which operator offers the less interfered channel.
Adopting the Operator-oriented Dynamic Frequency Selection (ODFS) algorithm,
the femtocell takes into account its subscription to the specific operator and at-
tempts to use its own band until the Signal-to-Interference Ratio (SIR) is above the
required threshold ρ0.
48
3.6 – Chapter Summary
SIRop
< ρ0start Decrease Tx Power
Increase Tx power
Ptx ≤Pmax
SIRi = max {SIRf1,SIRf2 ,...,SIRfN}
SIRi
> ρ0
Increase Tx power
Decrease Tx Power
Ptx ≤Pmax
select fc = fi
Turn OFF (sensing mode) end
yes
no
yes
no
yes
yes
no
no
Figure 3.4. Flow chart of the ODFS algorithm with and without the power controlmechanism. The dashed blocks and lines are only referred to power control.
49
Chapter 4
Performance evaluation
4.1 Scenarios description
In order to evaluate the performance of the proposed algorithms, three different
network topologies are considered, obtained as:
• random positioning of femtocells in the area;
• positioning of femtocells over a regular grid;
• positioning of femtocells over a regular grid with random 2D displacement
around their original point of the grid.
The last topology, referred to as perturbed grid, is a trade-off between the other ones
and it can represent a typical case of femtocells installed within adjacent apartments.
The interference scenario for the random network topology is shown in Figure 4.1.
A multi-operator scenario where femtocells are randomly distributed in an area of
100 × 100m2 is considered, in accordance to an uniform spatial distribution. This
also reflects a typical case of HNB’s randomly positioned by subscribers in a resi-
dential area. User terminals are assumed to be located at the femtocell border.
HNB’s can or can not implement power control mechanisms. In the second case,
femtocells are sources transmitting at the maximum power level set according to the
50
4.1 – Scenarios description
Figure 4.1. Interference scenario with HNB and user terminals (indicated withdot) belonging to different operators.
standard specification, i.e. Ptx = Pmax = 20 dBm [32]. This assumption is aimed
to evaluate an operating boundary condition in which power control algorithms are
not adopted. To take into account for the distance between HNBs and subscriber
terminals (namely r), the indoor segment and the outdoor segment are considered
separated and different propagation models depending on the type of link are used.
The ITU-R P.1238 model [32] is assumed for pathloss. It is expressed as:
LIN (r)[dB] = L50(r) + LFM + LW (4.1)
51
4 – Performance evaluation
where LFM is the additional shadow fade margin and LW is the penetration loss
due to the outer wall of the building. From [32], LFM = 7.7 dB and LW = 0 dB,
since we are only interested in indoor propagation. L50(r) is the median path loss
at a distance r assumed equal to 10 m (femtocell border), i.e. the loss exceeded at
50 % of positions at that distance, given by the following expression:
L50(r)[dB] = 20 · Log10fc + 10γ · Log10r + Lf(nf )− 28 (4.2)
In equation (4.2) fc is the operating frequency, γ is the indoor path loss exponent
and Lf(nf ) is the floor penetration loss, which varies with the number of pene-
trated floors nf . As recommended by the ITU-R [32], both γ and Lf(nf ) depend
on the frequency and the environment. We assume that femtocells are in residen-
tial environment with nf = 0 (ground level apartments) and operate at frequencies
fc around 1800 MHz, with a channel spacing between different network operators
∆fc = 10 MHz. Based on this assumption, from [32] we set γ = 2.8 and Lf(nf ) = 0.
For the outdoor attenuation model, the following expression for path loss is consid-
ered:
LOUT (d)[dB] = MCL[dB] + 10γout · Log10
(
d
d0
)
+ 2 · LW [dB] (4.3)
where d > d0 is the distance in m from the considered femtocell, MCL[dB] > 0 is
the minimum coupling loss in decibel for d0 = 1 m, γout is the outdoor path loss
exponent assumed to be equal to 3.2. For sake of simplicity we considered just two
external walls crossed by the interfering signal and we set LW = 10 dB.
4.2 Performance results
Performance are evaluated in terms of outage probability and average SIR, which
is directly related to the maximum achievable throughput. When the received SIR
52
4.2 – Performance results
from a subscriber terminal (downlink case) experiences a value lower than the re-
quired threshold, ρ0, an outage event occurs as defined in (4.4):
Pout = Pr {SIR < ρ0} = Pr
{
C
I< ρ0
}
(4.4)
where C is the received power on the reference link, which is equal to the product
between the transmitted power, PT , and the propagation loss, LIN (r); I is the
interference term which takes into account for the overall power transmitted by
other femtocells operating in the same bandwidth (i.e. at the same frequency of the
reference femtocell k) and its expression at frequency fi:
Ik = Ik(fi) =
Nfemto,fi∑
j 6=k
PT
Ltot(rj,fi)(4.5)
where Nfemto,fi is the number of transmitting femtocells in the area at the frequency
fi, Ltot > 1 is the overall pathloss at frequency fi accounting for both indoor and
outdoor losses and rj is the distance between the k-th reference femtocell and the
j-th femtocell. Note that in (4.4) we do not consider the thermal noise η since in gen-
eral for mature cellular systems it can be neglected with respect to the interference I.
4.2.1 SIR in the regular grid topology
In the regular grid topology the optimal frequency arrangement among femtocells in
terms of throughput and outage probability is depicted in Figure 4.2. The achievable
SIR for each femtocell is unique and can be calculated in a closed form as follows:
SIR =LIN(r)
4 ·MCL · L2W
(
α
(d√2)γ
+ β
2dγ
) (4.6)
where α and β are defined as:
α =
∞∑
k=1
1
kγ(4.7)
β =∞∑
k=1
1
(2k)γ. (4.8)
53
4 – Performance evaluation
and LIN (r), MCL and LW are smaller than 1.
To obtain the summations in (4.7) and (4.8) we considered an infinite number of
interferers over an infinite grid. In the absence of shadowing the critical density of
Figure 4.2. Optimal frequency arrangement among femtocells in the regular gridscenario with 2 network operators.
femtocells leading to the condition SIR < ρ0 for every femtocell in the area can
be easily calculated from (4.6). In this case (regular grid scenario with optimal
frequency assignment) femtocells simultaneously fall below the threshold like a sort
of avalanche effect (break of equilibrium). The last consideration is valid if we do
not consider the border effects, i.e. the femtocells located in the close proximity
of the border of the area. According to this assumption, in the regular grid case
54
4.2 – Performance results
with optimal frequency assignment the SIR distribution could be approximated as
a delta function. However, the femtocells located at the borders and the vertices
of the scenario lead to a spread of the SIR distribution with respect to the average
value, as shown in Figure 4.3, where the empirical probability density functions of
SIR for the other network topologies are also reported. As we can note, the pdf’s
show a more and more larger variance passing from the regular grid up to the ran-
dom network topology.
The regular grid topology with optimal frequency allocation represents the baseline
Figure 4.3. SIR distribution for femtocells in different network topology withoutpower control mechanism.
for comparing the behavior and the performance of the proposed frequency adjust-
ment techniques. In this scenario the frequency assignment remains unchanged after
the application of the proposed algorithms, proving their stability. For this reason,
shadowing is not considered in our analysis in order to compare results with this ref-
erence case. However, the application of the proposed algorithms does not allow to
reach the optimal frequency/power adjustment when it is applied by starting from
a random frequency assignment on the regular grid case due to their decentralized
55
4 – Performance evaluation
nature. This means that a local minimum of the outage probability as well as a local
maximum of the SIR are reached (i.e. the femtocell system has several equilibria
points).
4.2.2 Outage probability
In Figure 4.4 and Figure 4.5 is reported the outage probability obtained without the
chance to exchange frequency channels among operators compared with the curves
related to GDFS and ODFS algorithms for 2 and 3 available channels, respectively.
We assume ρ0 = 9.4 dB. In the random network topology the GDFS and ODFS
16 25 36 49 64 81 100 121 14410
−3
10−2
10−1
100
Number of femtocells
Out
age
Pro
babi
lity
Initial random assignment
Greedy algorithm
Operator algorithm
Greedy PC algorithm
Figure 4.4. Outage Probability with 2 frequency bands for Random (solid line)and Perturbed Grid (dashed line) topologies.
algorithms show similar performances, whereas in the case of perturbed grid the
56
4.2 – Performance results
81 100 121 144 169 196 22510
−3
10−2
10−1
100
Number of femtocells
Out
age
Pro
babi
lity
Initial random assignment
Greedy algorithm
Operator algorithm
Greedy PC algorithm
Figure 4.5. Outage Probability with 3 frequency bands for Random (solid line)and Perturbed Grid (dashed line) topologies.
GDFS slightly outperforms the ODFS. As we expected, the power control imple-
mentation leads to the best performance. Considering an outage probability equal
to 5 %, with respect to a network capacity of 14 and 66 femtocells with the initial
frequency assignment in the random and perturbed grid topology, respectively, the
algorithms allow to reach a network capacity of about 68 and 120 femtocells. As
shown in Figure 4.4 and Figure 4.5, an increase of the number of available frequency
bands results in an improvement in the number of active femtocells per frequency.
As an example, given an outage probability of 5 %, in the case of random net-
work topology with 2 frequency bands, we obtained about 34 served femtocells per
frequency, while in the same scenario with 3 network operators we had 47 active
femtocells per channel. In the regular grid topology, the instability effects due to
the aforementioned break of equilibrium for the optimal frequency assignment case
57
4 – Performance evaluation
are also present: a sort of smooth on-off behavior of the outage is observed with
respect to the random network topology, i.e. a relatively small change in the fem-
tocell density leads to a sudden increase in the outage probability. However, the
instability effects are more gradual than those related to the optimal frequency as-
signment case, that means that it can not be said that all femtocells collapse at the
same moment when equilibrium is reached using the proposed algorithms.
4.2.3 Signal to Interference Ratio (SIR)
In Figure 4.6 and Figure 4.7 are reported the obtained results for the average achiev-
able SIR per femtocell. The results show that random topology always provide the
16 25 36 49 64 81 100 121 144
10
12
14
16
18
20
22
24
26
28
Number of active femtocells
Ave
rage
SIR
per
fem
toce
ll [d
B]
Initial random assignmentGreedy algorithmOperator algorithmGreedy PC algorithm
Figure 4.6. Average SIR per femtocell with 2 frequencies for Random (solid line)and Perturbed Grid (dashed line) topologies.
worst achievable performances both in terms of outage and SIR distribution for low
58
4.2 – Performance results
81 100 121 144 169 196 2259
10
11
12
13
14
15
16
17
18
Number of active femtocells
Ave
rage
SIR
per
fem
toce
ll [d
B]
Initial random assignmentGreedy algorithmOperator algorithmGreedy PC algorithm
Figure 4.7. Average SIR per femtocell with 3 frequencies for Random (solid line)and Perturbed Grid (dashed line) topologies.
density of femtocells within the area. Conversely, when the number of femtocells
increase, the achievable average SIR in the random topology is slightly better than
the SIR obtained in the perturbed grid case. This is due to the fact that the outage
probability of the perturbed grid scenario becomes not null but smaller than the
outage obtained for the random topology, so there are more femtocells in sensing
mode. As a consequence, the average SIR per femtocell is higher until the two val-
ues of the outage probability become comparable. As we expected, the achievable
throughput with the implementation of the power control mechanism is constant,
since each femtocell continuosly adjusts its trasmission power level in order to reach
the required quality threshold ρ0. As regards the other curves, an increase of the
number of femtocells in the area corresponds to a reduced achievable SIR since,
contrary to the power control case, the transmission power is constant and so the
59
4 – Performance evaluation
interference level outgrows. We can observe that the proposed algorithms perform
considerably better than the initial random frequency assignment. The GDFS algo-
rithm shows an improvement with respect to the ODFS algorithm due to the greedy
nature of femtocells aiming at maximizing their throughput.
In Figure 4.8 we report the comparison between the average achievable SIR of femto-
cells in the regular grid scenario with optimal frequency assignment and the regular
and perturbed grid topologies where the proposed algorithms are applied. The re-
16 25 36 49 64
10
12
14
16
18
20
22
24
26
28
Number of active femtocells
Ave
rage
SIR
per
fem
toce
ll [d
B]
Optimal frequency assignmenton Regular Grid topology
Figure 4.8. Average SIR per femtocell with 2 frequencies for Regular Grid with op-timal frequency assignment (solid line), Regular Grid (dashed line) and Perturbed
Grid (dotted line) topologies.
sults show that if only the initial random frequency assignment is considered in
the regular grid topology, we obtain a difference of about 3 dB with respect to the
optimal case, but with the implemetation of our GDFS algorithm we improve the
performance, leading the achievable throughput very near to the optimal curve. In
general, the obtained results show that when the density of femtocells increases the
60
4.2 – Performance results
cases related to the different network topologies tend to converge to the same per-
formance in terms of SIR and outage probability.
4.2.4 Spectrum Sharing Gain SSG
Given a certain value of the outage probability Pout and maintaining the number of
femtocells per frequency band constant, we define the Spectrum Sharing Gain (SSG)
as:
SSG|Pout=
Tf∈F
Tf∈Fop
(4.9)
where Tf∈F and Tf∈Fopare the numbers of active femtocells after the implementa-
tion of the proposed DFS algorithm considering the set of channels available from
all the operators F ={
f1,...,fNf×Mop
}
and just the set of own operator frequency
bands Fop ={
f1,...,fNf
}
, respectively. This parameter represents the increase in
the number of active femtocells for a fixed outage probability, obtained by means of
DFS algorithm and when operators permit the mutual exchange of their frequency
bands with respect to the case in which each femtocell can dinamically select only
the channels available from its own operator. In the following sub-section we provide
results concerning the SSG. To the aim of evaluating the SSG, in our simulations
we mantain the ratio between the density of femtocells in the area and the number
of available channels constant.
As for the simulations described in the previous section, the interference measure-
ments and the status update is randomly scheduled by each femtocells. The simula-
tion time is long enough to ensure that the equilibrium in the allocation of frequency
bands has been reached, i.e. each femtocell remains on the last selected operating
channel.
In order to evaluate an operating boundary condition, it is assumed that HNBs
do not implement power control mechanisms, i.e. femtocells are sources transmit-
ting at the maximum power level set according to the standard specification, i.e.
Ptx = Pmax = 20 dBm [32]. Moreover, we assume ρ0 = 16.4 dB for a 16-QAM
61
4 – Performance evaluation
connection.
In Figure 4.10 and Figure 4.9 are reported the SSG and the number of active fem-
tocells per operator, respectively, for different values of the outage probability. An
2 3 4 5 6 7 810
15
20
25
30
35
40
45
50
Number of frequencies
Num
ber
of fe
mto
cells
per
ope
rato
r
Pout
= 2%
Pout
= 5%
Figure 4.9. Number of active femtocells per operator vs the number of availablefrequency bands for Nf = 2.
increase in the number of available frequency bands results in an improvement in
the allowed number of active femtocells. As an example, for an outage probability
of 2 % the DFS algorithm allows to obtain about 31, 37 and 42 active femtocells
per single operator when 4, 6 and 8 frequency bands are allocated, respectively,
while we experiment 14 served femtocells in the case of 2 available channels. As
shown in Figure 4.10, the lower the outage probability the more evident the SSG.
In general, the achieved results show that a marked gain of the active HNBs per
operator is obtained with the sharing of the spectrum bands among operators. The
SSG increases up to likely reach a saturation point for higher values of the num-
ber of available channels. This is due to the fact that in our analysis the number
62
4.2 – Performance results
1 1.5 2 2.5 3 3.5 40
20
40
60
80
100
120
140
160
180
200
Number of operators
SS
G [%
]
Pout
= 2%
Pout
= 5%
Figure 4.10. Spectrum Sharing Gain vs the number of operators with Nf = 2.
of femtocells in the considered scenario is proportional to the number of spectrum
bands. This leads to a very high density of femtocells in the considered scenario
which prevents DFS algorithm from overcoming the achieved performance.
The obtained gain can be explained looking at the formula in (4.5). Indeed, the in-
terference caused by the other femtocells transmitting on the same frequency band
mainly depends on the distance between the considered HNB and the nearest inter-
ferer(s). With the possibility to exploit the sharing of spectrum bands of different
operators, the proposed DFS algorithm performs an autonomous redistribution of
the minimum frequency reuse distance among femtocells, as highlighted by Fig-
ure 4.11. In particular, with the increase of the available frequency bands, the DFS
algorithm tends to shrink the variance of the obtained distributions. This allows
to reduce or completely eliminate the lower values of the distributions, i.e. the tail
on the left side (see results from 4 to 8 available frequencies in Figure 4.11) which
63
4 – Performance evaluation
correspond to the most harmful interferers for the considered femtocell. Hence, the
required outage probability is reached with a higher allowed number of served fem-
tocells per operator, which explains the SSG, especially for small outage probability.
Figure 4.11. PDF of the minimum reuse distance for Pout = 2%.
4.3 Chapter Summary
The proposed algorithms provide marked improved performance with respect to the
random frequency assignment resulting from the self-installation nature of femto-
cells. In particular, the suggested GDFS algorithm performs better than the ODFS
algorithm in terms of achievable SIR, while we obtained similar results for the out-
age probability. Moreover, the results show that the GDFS algorithm allows to
reach performance very close to the optimal case in terms of achievable throughput
also in a typical residential scenario with femtocells randomly installed in adjacent
apartments.
64
4.3 – Chapter Summary
In addiction, the algorithm also shows a marked gain in terms of active femtocells per
operator with the increase of the number of shared frequency bands. The obtained
results lead to a significant consideration from the operator point of view: spectrum
sharing among operators is advantageous to ensure QoS to their subscribers.
65
Chapter 5
DFS algorithm: robustness and
resilience against malicious users
In this chapter is analyzed the performance gap with respect to the results obtained
in Chapter 4 with reference to two situations:
1. in the first one it is considered that only a certain percentage of HNB’s imple-
ments the DFS algorithms;
2. in the second case, all the femtocells adopts the suggested DFS algorithms
but some of them only partially follow the rules, to the aim of maliciously
exploiting the frequency resources. These HNB’s, referred to as selfish femto-
cells, continue to occupy the channel with dummy data even if their QoS is
below the required level in order to force the other DFS-conformed HNB’s to
interrupt transmissions.
The first case is merely aimed at evaluating the impact on QoS of a percentage of
HNB’s defecting from the DFS algorithm. This can be expected in a more realistic
scenario where failures or a transient state towards the complete adoption of DFS
algorithm can occur. For this purpose we consider femtocells installed in a residen-
tial/offices building, in order to obtain a realistic environment.
In the second situation, the selfish behaviour assumed by femtocells is aimed to
66
5.1 – Scenario description
decrease the interference level by forcing the other DFS-conformed HNB’s to turn
into sensing mode, which means to interrupt their transmissions.
In the previous chapters the proposed DFS alhorithms are analyzed considering
both maximum power and power-controlled transmissions. Since in this chapter
we are just interested in evaluating the impact of different behaviours assumed
by HNB’s on the network performance in terms of outage probability and average
achievable throughput, we assume that HNB’s do not implement power control
mechanisms. It means that all the femtocells transmit at the maximum power level
allowed by the standard specification, i.e. Ptx = Pmax = 20 dBm [32], but each HNB
can or can not conform to the DFS algorithm.
In addition, in this chapter we do not refer to a specific DFS algorithm, for this
reason, we refer to them as Dynamic Frequency Selection Algorithm (DFSA).
5.1 Scenario description
In Figure 5.1 the interference scenario is depicted. Femtocells installed in a six floor
residential building are considered. As regards the number of femtocells within the
building, we consider two cases corresponding to different levels of HNB’s density. By
assuming one HNB for each apartment, in the first case each floor has two apartments
(low-medium density scenario), whereas in the second case four apartments are
considered for each floor (high density scenario). According to the typical self-
installation by users, HNB’s are randomly located inside the apartments. We assume
that the floor area is 200 m2 and each floor is 3 m heigth. Equal-area apartments
are assumed in which user terminals are located at a distance of 4 m from the HNB.
A typical multi-operator scenario is considered where each femtocell is subscribed
to one of the N network operators. In the analysis is assumed both N = 2 and
N = 3 operators providing services in the considered building area. The initial
assignment of each femtocell to an operator (and therefore to an operating frequency)
is randomly performed in accordance to an uniform distribution. We assume that
each operator allocates one dedicated frequency band for femtocell communications.
67
5 – DFS algorithm: robustness and resilience against malicious users
Residential building
3 m
floor area = 200 m2
femtocell of operator A
femtocell of operator B
Figure 5.1. Building scenario for the interference analysis. HNB’s are indicatedwith circle and each colour represents the assignment to a different operator.
Hence HNB’s subscribed to a same operator do not suffer from cross-tier interference,
whereas they interfere one with each other due to the sharing of a single radio
channel.
In this analysis we are just interested in evaluating the impact of different behaviours
assumed by HNB’s on the network performance in terms of outage probability and
average achievable throughput, we assume that HNB’s do not implement power
control mechanisms. It means that all the femtocells transmit at the maximum
power level allowed by the standard specification, i.e. Ptx = Pmax = 20 dBm [32],
but each HNB can or can not conform to the DFSA. In particular, we analyze the
following two different cases:
1. femtocells can or can not adopt the DFSA;
2. all the femtocells implement DFSA, but some of them are selfish and do not
turn into sensing mode, i.e. even if the condition SIR < ρ0 is verified they
access the channel with dummy data instead of setting their transmission
status to off and just performing interference measurements.
68
5.2 – Performance analysis
The selfish behaviour assumed by femtocells is aimed to decrease the interference
level by forcing the other DFSA-conformed HNB’s to turn into sensing mode, which
means to interrupt their transmissions.
As for the signal propagation, the path loss model described in section 4.1 is con-
sidered. As recommended by the ITU-R [32], in equation (4.2) both γ and Lf(nf )
depend on the operating frequency and the environment. In this analysis is as-
sumed that femtocells are in residential environment with nf = 6 and operate at
frequencies fc around 1800 MHz, with a channel spacing between different network
operators ∆fc = 10 MHz. Based on this assumption, from [32] we set γ = 2.8 and
Lf(nf ) = 15 + 4 · (nf − 1).
5.2 Performance analysis
We run simulations to evaluate the impact of non DFSA-conformed femtocells on
the QoS performance. The outage probability and the average SIR per femtocell
are assessed as a function of the percentage of non DFSA-conformed HNB’s. As
describe in the previous chapter, each femtocell is randomly scheduled for the inter-
ference measurements and the status update. Furthermore, the simulation time is
appropriately set to guarantee the convergence of the DFSA, i.e. at the end of the
test each femtocell has selected the best operating frequency channel. Simulation
results are obtained using a Monte Carlo based approach.
As regards the low-medium density scenario (i.e. 2 femtocells per floor), in Figure
5.2 and Figure 5.3 are reported the outage probability and the average SIR per fem-
tocell, respectively, as a function of the percentage of femtocells whose behaviour is
not to conform to the proposed DFSA, for different number of available frequency
channels. When 2 operators provide services in the considered residential area,
the complete defection from the DFSA causes an increase of the outage probability,
which ranges from about 8,4 % (which is equivalent on average to 1 outage femtocell
in the building) when all the femtocells adopt the proposed algorithm up to about
69
5 – DFS algorithm: robustness and resilience against malicious users
0 20 40 60 80 10010
−3
10−2
10−1
100
Non "DFS−conformed" femtocells [%]
Out
age
prob
abili
ty
Femtocells without DFS, 2 fc
Femtocells without DFS, 3 fc
Selfish femtocells, 2 fc
Selfish femtocells, 3 fc
Figure 5.2. Outage probability vs the percentage of non DFSA-conformed femto-cells in a low-medium density scenario.
50 % in the all non DFSA-conformed HNB’s case. This outstanding degradation
is due to the impossibility to perform an efficient allocation of frequency channels
by means of the proposed algorithm. Conversely, the selfish behaviour of femtocells
does not cause a marked decrease of performance. This is due to the fact that in
a low density scenario as long as less than about 85 % of HNB’s are selfish there
is on average always one DFSA-compliant femtocell which experiments a SIR less
than the required threshold ρ0 and turns into sensing mode, i.e. it sacrifices itself
in behalf of network capacity. When all the femtocells are selfish we note a little
decrease of performance due to the increase of the average interference level caused
by the continuos trasmission by each femtocell. The obtained results show similar
trends also when 3 operators are considered. However, in this case the gap between
an all DFSA-compliant scenario and the case of 100 % non DFSA-conformed HNB’s
is larger than the 2 operators scenario. Indeed, when 3 operating frequencies are
70
5.2 – Performance analysis
available for femtocells, a smart cognitive selection of the channel lead to a marked
improvement of outage probability with respect to the initial random assignment.
In this case, the selfish behaviour of femtocells is completeley negligible due to the
very low values of outage probability.
The performance related to the outage probability are reflected in the average SIR
experimented by femtocells which are not in outage, as shown in Figure 5.3. As
expected, for 2 available frequency bands with respect to the all DFSA-conformed
HNB’s case a slight worsening of average SIR is observed when both all femtocells do
not implement the DFSA and all the femtocells are selfish. This trend is reflected
in Figure 5.4, which reports the cumulative distribution functions (CDFs) of the
SIR for different percentage of HNB’s defecting from the DFSA. This results are
obtained considering the SIR of all the femtocells in the area. We can note that in
general it is better to adopt the DFSA, but with the increase of the percentage of
non DFSA-conformed femtocells the probability of obtaining higher values of SIR
increases. Anyway, it is worthwhile to note that by considering only the femtocells
whose SIR ≥ ρ0 the achievable average SIR is almost the same in both situations
with a very slight difference in favour of the selfish case when the percentage of
non DFSA-conformed HNB’s increases. This can be explaned by considering the
low-medium density scenario which permits a better redistribution of frequency re-
sources by adopting the DFSA compared with the random channel assignment. This
is more evident when 3 operators provide services in the considered area, resulting
in an average SIR difference of more than 2 dB between the selfish behaviour and
the defection from DFSA. As shown in Figure 5.3, in this scenario it’s much better
for femtocells to conform to the proposed DFSA since the average SIR per HNB’s
increases of more than 2 dB. Conversely, when HNB’s implement the DFSA with-
out sensing mode the same performance are obtained regardless of the percentage
of femtocells taking the selfish attitude.
As regards the high density scenario (i.e. 4 femtocells per floor), the obtained
results in terms of outage probability and average SIR per femtocell are shown in
71
5 – DFS algorithm: robustness and resilience against malicious users
0 20 40 60 80 10019.5
20
20.5
21
21.5
22
22.5
23
23.5
Non "DFS−conformed" femtocells [%]
aver
age
SIR
per
fem
toce
ll [d
B]
Femtocells without DFS, 2 fc
Femtocells without DFS, 3 fc
Selfish femtocells, 2 fc
Selfish femtocells, 3 fc
Figure 5.3. Average SIR vs the percentage of non DFSA-conformed femtocells ina low-medium density scenario.
Figure 5.5 and Figure 5.6, respectively. As in the previous scenario, the implemen-
tation of DFSA always allows to maximize the network capacity and the achievable
average SIR. In particular, starting from higher values of outage probability due to
the increased number of femtocells per area, the consideration related to the trends
of the outage curves are the same of the low-medium density scenario, with the
exception that the higher is the percentage of selfish femtocells the more evident is
the decrease of network capacity. For example, even when 3 carrier frequencies are
available, the selfish behaviour by all the femtocells causes an increase of the outage
probability of about 3 % with respect to the all DFSA-conformed HNB’s case.
As for the average SIR of femtocells in quality, interesting results are shown in Fig-
ure 5.6, where we can observe worst performance in the case of selfish femtocells with
respect to the case of defection from the DFSA. This is due to the high density of
femtocells in the area, which in the random frequency assignment for the considered
72
5.2 – Performance analysis
0 10 20 30 40 500
20
40
60
80
100
SIR [dB]
Pro
b(S
IR<
absc
issa
) [%
]
25%
0 10 20 30 40 500
20
40
60
80
100
SIR [dB]
Pro
b(S
IR<
absc
issa
) [%
]
50%
0 10 20 30 40 500
20
40
60
80
100
SIR [dB]
Pro
b(S
IR<
absc
issa
) [%
]
75%
0 10 20 30 40 500
20
40
60
80
100
SIR [dB]
Pro
b(S
IR<
absc
issa
) [%
]
100%
Figure 5.4. CDF for various percentage of femtocells that do not implemet theDFSA algorithm.
scenario statistically lead to a greater value of the average SIR of a few femtocells
that are not in outage (see Figure 5.5) since with the random channel distribution
single femtocells of one operator neighbour to cluster of femtocells belonging to the
other operator can occur. This implies that a lot of femtocells measures SIR < ρ0,
while a few “lucky” femtocells can experiment high values of SIR, as visible in the
last curve of Figure 5.4. Conversely, with the increase of HNB’s density the smart
allocation of the available operating channels still permits to preserve the outage
performance when the percentage of selfish femtocells increases, but on average the
achievable SIR is lower than that obtained when the same percentage of HNB’s
defects from the proposed algorithm.
In general, we can argue that when the percentage of selfish femtocells increases the
performance in terms of network capacity are preserved against a little reduction in
73
5 – DFS algorithm: robustness and resilience against malicious users
0 20 40 60 80 10010
−2
10−1
100
Non "DFS−conformed" femtocells [%]
Out
age
prob
abili
ty
Femtocells without DFS, 2 fc
Femtocells without DFS, 3 fc
Selfish femtocells, 2 fc
Selfish femtocells, 3 fc
Figure 5.5. Outage probability vs the percentage of non DFSA-conformed femto-cells in a high density scenario.
the average SIR measured by those femtocells that are in quality; to the contrary,
in the case of femtocells defecting from DFSA the average SIR of femtocells which
are not in outage shows a very slight decrease with respect to the best performance
(i.e. the all DFSA-conformed HNB’s case) to the detriment of marked worse outage
probability. Furthermore, the obtained results point out that for low percentage of
HNB’s that do not turn into sensing mode the selfish behaviour is advantageous
for them and at the same time, even if an unfair situation occurs, it does not sub-
stantially debase the performance in terms of network capacity as well as average
SIR per femtocell, even in high density scenario. However, this is valid only when
less than about 25 % of femtocells are selfish. Hence, the best situation is verified
when all the femtocells implement the proposed DFSA without assuming a selfish
behaviour.
As an example, with reference to the Bit Error Rate (BER) curves shown in Figure
74
5.2 – Performance analysis
0 10 20 30 40 50 60 70 80 90 10017.5
18
18.5
19
19.5
20
Non "DFS−conformed" femtocells [%]
aver
age
SIR
per
fem
toce
ll [d
B]
Femtocells without DFS, 2 fc
Femtocells without DFS, 3 fc
Selfish femtocells, 2 fc
Selfish femtocells, 3 fc
Figure 5.6. Average SIR vs the percentage of non DFSA-conformed femtocells ina high density scenario.
5.7 for different modulation schemes, we give an indication of the possible benefits
in terms of achievable throughput deriving from the implementation of the DFSA.
By assuming a symbol rate of 1 Msymbol/s we can obtain a bit rate of 2, 4 and
6 Mbit/s for QPSK, 16QAM and 64QAM, respectively, based on the available av-
erage SNR on a Rayleigh fading channel. Considering a required BER of 10−3, in
the considered low-medium density scenario a bit-rate of 6 Mbit/s is always avail-
able for active femtocells regardless of their behaviour. Conversely, in the described
high density scenario we can note a decrease of the achievable throughput from 6
to 4 Mbit/s both passing from 3 to 2 available carrier frequencies and considering
3 channels when all the femtocells are selfish.
Hence, the higher is the density of femtocells in the area the greater is the number of
75
5 – DFS algorithm: robustness and resilience against malicious users
−5 0 5 10 15 20 2510
−4
10−3
10−2
10−1
100
average SNR [dB]
BE
R
QPSK16QAM64QAM
Figure 5.7. Bit Error Rate vs the average SNR on a Rayleigh fading channel fordifferent modulation schemes assuming a coding gain gc=10 dB.
operating channels needed for enjoying high data rate services and the more conve-
nient is to entirely implement the DFSA, i.e. without assuming selfish behaviours.
5.3 Chapter Summary
In this chapter is analyzed the impact on the QoS performance of different behaviour
of HNB’s with respect to the case in which all the femtocells adopt the proposed
algorithm. The outage probability and the average SIR per femtocell is evaluated
by simulations as a function of the percentage of femtocells that defect from the
proposed DFSA or behave like selfish, i.e. they maliciously adopt the DFSA without
turning into sensing mode. Both cases of 2 and 3 operators is assumed providing
services in the considered indoor scenario. Each operator allocates one dedicated
76
5.3 – Chapter Summary
frequency channel for femtocell communications. Results show that in order to
maximize the network capacity and the average throughput it is better for femtocells
to fully conform to the proposed DFSA, i.e. by also implementing the sensing mode.
Moreover, it is observed that with the increase of the available carrier frequencies
and the decrease of the density of HNB’s in the area the selfish behaviour tends not
to affect the QoS performance.
77
Chapter 6
Conclusions
In this work the problem of co-tier interference for femtocells is faced. Different net-
work topologies are considered, ranging from a pre-planned deployment to a random
placement of femtocells.
Based on the assumption that operators agree on the sharing of their spectrum
bands, two simple distributed algorithms are proposed aimed to dynamically redis-
tribute the available frequency resources belonging to different network operators
among femtocells just based on local interference measurements.
The proposed algorithms provide marked improved performance with respect to
the random frequency assignment resulting from the self-installation nature of fem-
tocells. In particular, the suggested GDFS algorithm performs better than the
ODFS in terms of achievable SIR, while we obtained similar results for the outage
probability. Moreover, the results show that the GDFS algorithm allows to reach
performance very close to the optimal case in terms of achievable throughput also in
a typical residential scenario with femtocells randomly installed in adjacent apart-
ments.
In order to assess the real performance of the proposed algorithms I define the Spec-
trum Sharing Gain (SSG), as the number of served femtocells per operator when
the channels available from operators increase.
78
The performance analysis carried out to obtain this parameter show that the pro-
posed algorithm achieves a marked gain in terms of active femtocells per operator
with the increase of the number of shared frequency bands.
The obtained results lead to a significant consideration from the operator point of
view: spectrum sharing among operators is advantageous to ensure QoS to their
subscribers.
In the second part of this thesis I have analyzed the robustness of the Dynamic
frequency Selection Algorithms (DFSA) and the impact on network capacity of
Femtocells defecting from the DFSA. For this scope, the impact on the QoS per-
formance of different behaviour of HNB’s with respect to the case in which all the
femtocells adopt the proposed algorithm are analyzed.
The outage probability and the average SIR per femtocell as a function of the per-
centage of femtocells that defect from the proposed DFSA or behave like selfish, i.e.
they maliciously adopt the DFSA without turning into sensing mode, are evaluated
by simulations.
In order to analyze a more realistic scenario a residential/offices building is consid-
ered. Both cases of 2 and 3 operators providing services in the considered indoor
scenario are assumed. Each operator allocates one dedicated frequency channel for
femtocell communications.
Results show that in order to maximize the network capacity and the average
throughput it is better for femtocells to fully conform to the proposed DFSA, i.e. by
also implementing the sensing mode. Moreover, it is observed that with the increase
of the available carrier frequencies and the decrease of the density of HNB’s in the
area the selfish behaviour tends not to affect the QoS performance.
Summarizing, the obtained results highlights that:
• adopting the proposed DFSA we obtain a marked improvement on QoS per-
formance;
• for operators is better to adopt the proposed algorithms and to share spectrum
each other;
79
6 – Conclusions
• it’s not necessary that all femtocells in an area adopt the DFSA in order to
obtain a performance improvement;
• DFSA performs a natural resilience against malicious users.
80
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