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8/7/2019 Call Admission Control for Multimedia Direct Sequence Code Division Multiple Access (DS-CDMA) Wireless Networks
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Chapter 1 Introduction
Call Admission Control for Multimedia Direct
Sequence Code Division Multiple Access (DS-CDMA)
Wireless Networks
CHAPTER 1
INTRODUCTION
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Chapter 1 Introduction
With rapid development and continuous expansion of mobile communications,
and explosive growth in demand for new wireless cellular services, it is expected that the
next generation wireless cellular networks will support a wide variety of services,
including voice, video, images, data, or combinations of these. Direct Sequence Code
Division Multiple Access (DS-CDMA) has emerged as the predominant radio access
technology to provide high speed multimedia services used in both wideband CDMA
(WCDMA) and cdma2000.
Great capacity gain and flexibility can be achieved with deployment of a DS-
CDMA radio interface. In narrowband time/frequency division multiple acce
(TDMA/FDMA) systems with fixed channel allocation, the capacity for each cell is time
invariant based on the specified frequency reuse pattern, ensuring the co-channel
interference level. In DS-CDMA systems, all cells share the overall frequency band, and
cell capacity is interference limited. The capacity in a DS-CDMA system depends
heavily on instantaneous traffic conditions in both home and neighboring cells, while the
transmission quality over the wireless channels can be measured in terms of the Signal to
Interference ratio (SIR) at the receiver side by taking into account both the incell and
outcell interferences.
Because of this unique interference limited soft capacity nature of the DS-CDMA
system, mechanisms such as adaptive antenna arrays and voice activity factors could be
applied to improve system capacity. Despite considerable effort and progress made in
DS-CDMA system design, the accommodation of multimedia services still poses a
number of challenges. To fully utilize the scarce resources and at the same time provide
necessary quality of service (QoS) guarantees for a variety of services, it is of great
importance to design effective resource management strategies. There are several issues
of resource management in mobile communications .
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Chapter 1 Introduction
Congestion
Cell Planning
Power and rate allocation
Call Admission Control Strategies
1.1 Problem Description
Call Admission Control (CAC) is a key element of radio resource management in
wireless and mobile communication systems. The problem of CAC is concerned with the
rules for admitting a call into the system such that the quality of service (QoS) of the call
is achieved without degrading the existing connections, fairness of resource allocation
and efficient resource usage. The problem of CAC is very challenging in multimedia
wireless systems due to the different QoS requirements, traffic asymmetry between the
uplink and downlink and differential treatment between handover and new traffic for a
given traffic class.
The objectives of this project are to:
Propose a CAC scheme for multimedia DS-CDMA wireless networks making use
of the system assumptions.
Analyze the performance of the proposed CAC algorithm using mathematical
and/or simulation techniques , using memoryless system assumptions that allow
us to model the system as a multidimensional continuous time Markov chain.
1.1.1 CAC Schemes
Call admission control is one of the most important aspects of r
management, which determines whether to admit or reject a call upon its arrival. The
objective is to maximize the utilization of resources by admitting as many new calls as
possible while maintaining the fairness and QoS of ongoing services. There are many call
admission control schemes proposed , most of which can be classified into the following
three categories.
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Chapter 1 Introduction
Number-Based CAC - assumes time invariant cell capacity and simple for
implementation and analysis.
Interference-Based CAC - considers interference limited nature of the system and
offers a better performance, suitable for FDMA and TDMA systems.
SIR-Based CAC - best characterizes transmission quality and offers QoS
guarantee with considerable difficulty in system design and measurements,
preferred for CDMA systems.
1.2 Motivation for this Project
Since the radio spectrum is a very scarce resource, resource management is one of
the most important engineering issues in wireless and mobile communications systems.
The performance of a system with a given physical resource ( for example given the
bandwidth), depends heavily on the resource management schemes.
To provide high speed multimedia services , high capacity of a system is a basic
necessity. The Direct Sequence Code Division Multiple Access (DS-CDMA) is the most
widely used for the second and third generation mobile communications systems because
of its advantage of the soft capacity and frequency planning. Here, the term call at air-
interface means not only a voice call, but also a session for any multimedia application.
The CAC for a call request is to determine whether to accept it or not. The
objective of CACis to maximize the utilization of resource (e.g., frequency spectrum in
wireless systems) as long as the required QoSs for all calls are guaranteed.
We consider two kinds of call request, new call and handoff call. In resource
sharing between the call requests, since premature termination of connected calls is
usually more undesirable than rejection of a new call request, it is widely accepted that a
system should give higher priority to handoff call requests as compared with new call
requests. The CAC scheme proposed herein guarantees the priority of handoff calls overnew calls within a service class.
Several CAC schemes have been proposed. Some proposals assumed that the cell
capacity of a system with given frequency bandwidth is time-invariant. This type of CAC
is simple and sufficient for frequency division multiple access (FDMA) or time division
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Chapter 1 Introduction
multiple access (TDMA) systems. In practice (especially in CDMA systems), even if a
fixed frequency band is used in a cell, the capacity of the cell varies with the loading of
the home and neighboring cells mainly because the co-channel interference changes
according to the loading. Thus, some CAC proposals for CDMA systems are based on the
QoS parameter such as the signal to interference ratio (SIR).
Here we consider a CAC policy under which a call can be admitted when the SIR
requirements of both the existing calls and the new call are guaranteed. The performance
of the algorithm is analyzed using the Markovian assumptions. The performance
measures that are considered are the blocking probabilities of the handoff and new calls,
throughput of the uplink and downlink and the outage probability of a call in progress
within a cell.
1.3 Tools used
The traffic is modeled using the NS2 simulator (version 2.31) and the packet
delay and packet loss are observed using the Xgraph. The performance of the algorithm is
analyzed and the results are plotted using MATLAB (version 7.1).
1.4 Overview of the thesis
To go straight to the subject, a little introduction about CAC is presented in the
motivation of the project. Then a detailed discussion about the DS-CDMA, its features,
and the various issues in resource management is presented in chapter 2. Then a brief
introduction to UMTS networks and a detailed discussion about the various CAC
schemes is presented in chapter 3. The CAC scheme is proposed and a detailed analysis
of the performance of the algorithm is presented in chapter 4 i.e.., here we will discuss
about the proposed CAC scheme with the algorithm for the performance analysis.
Chapter 5 presents the results with a brief introduction of the simulator tool used. Chapter
6 includes the conclusion part of the project and the future scope of the work.
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Chapter 1 Introduction
CHAPTER 2
DS-CDMA AND RESOURCE MANAGEMENT
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Chapter 1 Introduction
2.1 DS-CDMA
With rapid development and continuous expansion of mobile communications,
and explosive growth in demand for new wireless cellular services, it is expected that
next-generation wireless cellular networks will support a wide variety of services,
including voice, video, images, data, or combinations of these. DS-CDMA(Direct
Sequence Code Division Multiple Access) has been the predominant radio access
technology to provide high-speed multimedia services used in both wideband CDMA
(WCDMA) and cdma2000.for the present generation wireless networks(3g and 4g)
because of its unique features and soft capacity nature (compared to TDMA and FDMA).
To achieve a higher system capacity one of the most efficient methods is to reduce the
multiple access interference among the users. Great capacity gain and flexibility can be
achieved with deployment of a DS-CDMA radio interface.
2.1.1 Capacity in DS-CDMA
In DS-CDMA cellular networks, all users share the same total frequency band for
transmissions. Each user is assigned one or more distinct spreading codes, and all these
codes generally bear noise-like characteristics with very small cross-correlation to each
other. The quality of communication is primarily determined by the detected SIR level atthe receiver, with the generally accepted measurement on the bit energy to-noise density
ratio ( 0bE N), expressed as
t
N
P RSIR
I W= (2.1)
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Chapter 1 Introduction
where R is the baseband information bit rate; Wis the total radio frequency band for
transmission, so the corresponding spreading gain is G = W R ; tPis the received signal
power for the reference spreading code; and NIdenotes the total detected interference at
the receiver. In order to guarantee transmission quality, the received SIR for each service
should be maintained above a certain threshold.
To achieve higher system capacity, one of the most efficient means is to mitigate
multiple access interference (MAI) among multiple users.Two techniques that are
commonly used are:
i. Cell Sectorization, which deploys directional antenna arrays at the base station
and splits a cell into different sectors. Generally speaking, only signals from theusers within a sector are received at the corresponding antenna array. Thus, the
number of users one cell can serve could be increased by approximately the same
factor as the number of sectors.
ii. Voice Activity Monitoring, which switches off signal transmission when the
mobile terminal is not active to reduce interference.
In order to accommodate high-speed multimedia services and support variable
transmission rates, two mechanisms can be used:
a. Multi Code(MC) CDMA system.
b. Variable Spreading Factor(VSF) CDMA system.
In an MC-CDMA system, all data signals over the radio interface are transmitted
at a basic rate, and the spreading gain G over each code channel is a constant. Multiple
orthogonal spreading codes are transmitted simultaneously for a highspeed application.
In a VSF-CDMA system each user transmits over one single code channel.
Higher transmission rate can be achieved by varying the spreading factorG inversely
with the desired data rate. Thus, in VSF-CDMA systems, users are assigned variable
length codes and different power levels, based on data rates and QoS requirements. The
two mechanisms provide comparable performance in high-speed transmission.
2.1.2 SIR Model of a CDMA system
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Chapter 1 Introduction
In general, the uplink and downlink use different bandwidth regions
transmission in a DS-CDMA cellular system. Thus, they are usually consi
separately for capacity calculation.
Referring to the figure below , in the uplink, the received signal power at the
reference base station, BS0, from a specific mobile station, jMS , is jMS s transmitted
power jPmultiplied by the pass loss factor 0,jL ; the corresponding interference is the
Figure 2.1:The SIR model for a DS-CDMA cellular system.
received power from other active mobile stations, both within reference cell BS0 (e.g.,
iMS ) and out cells (e.g., kMS in BS1), plus the background thermal noise. The capacity
in the uplink is calculated as the maximum number of users that could be accommodated,
subject to the SIR requirement for each user in the reference cell.
In the downlink, a fraction of total transmission power at the base station is
dedicated to the control channels, and all traffic channels share the remaining power. As
shown in Figure 1, reference cell BS0 allocates a fraction of its traffic channel power to
any in-cell mobile user (e.g., iMS ); the remaining power received at iMS from its home
base station BS0 and other base stations (e.g., BS1) will appear as interference. The
capacity in the downlink is defined as the maximum number of users that could be
admitted, under the constraints that the transmission quality of each user, in terms of SIR
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Chapter 1 Introduction
threshold, is guaranteed, and the overall power needed does not exceed the maximum
power supply at the base station.
In DS-CDMA systems, all cells share the overall frequency band,and cell capacity
is interference-limited. The capacity in a DS-CDMA system depends heavily on
instantaneous traffic conditions in both home and neighboring cells, while
transmission quality over the wireless channels can be measured in terms of the signal-to-
interference ratio (SIR) at the receiver side by taking into account both the incell and out-
cell interferences. Because of this unique interference-limited soft capacity nature of the
DS-CDMA system, mechanisms such as adaptive antenna arrays and voice activity
factors could be applied to improve system capacity. Even then, the accommodation of
multimedia services poses a number of challenges.
To fully utilize the scarce resources and at the same time provide necessary
quality of service (QoS) guarantees for a variety of services, it is of great importance to
design effective resource management strategies.
2.2 Features of DS-CDMA
There are also several distinctive characteristics in a DS-CDMA system that can
be explored for better performance.
a) Universal frequency reuse
b) Power control.
c) Soft Handoff.
d) Voice Activity.
e) Propagation Model.
2.2.1 Universal Frequency Reuse
The universal frequency reuse of a DS-CDMA system allows all cells to share thesame wide frequency band. This reduces the complexity in cell planning on co-channel
bandwidth allocation and potentially leads to higher capacity gain for a cell under
asymmetric loadings and increases the dependency among neighboring cells and incurs
significant difficulty in resource management, as more frequent coordinations are
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Chapter 1 Introduction
required. Because of universal frequency reuse with interference-limited transmission,
even if the cell occupancy information is available, outage might still occur due to user
mobility, propagation variation, voice activity, and so on. As a result, more precise
control and finer tuning are generally required in resource management.
2.2.2 Power Control
Power control in DS-CDMA systems helps reduce excessive interferen
throughout the system and prolong battery life . In general perfect power control is
assumed . In the uplink, the received power from all users in the reference cell is kept
equal to overcome the near-far problem . In the downlink, the assigned power to each
user is adjusted to achieve exactly the required SIR level. In practical systems, power
control imperfection may occur occasionally and cause some misadjustment of received
power. Consequently, the received power level of both the required signal and the
interference might change over time which in turn exerts additional difficulty on power
allocation and adjustment, and requires resource management strategies to incorporate
more accurate estimation of power control imperfections.
2.2.3 Soft Handoff
Soft handoff denotes the state where a mobile transmits to and receives from more
than one base station simultaneously. On the downlink, the mobile combines the signals
from the base stations in connection and adds the different multipaths to reinforce the
received signal, which leads to improved communication quality. On the uplink, the
neighboring base stations independently decode the signals received, and the best replica
is selected. As a result, soft handoff ensures smoother user communications, better
communication quality during handoff, and larger cell coverage in DS-CDMA systems.
The trade-off lies in higher complexity and additional network resource demands. As
longer handoff transfer delay is generally allowed, it is possible for handoff traffic to be
queued. Furthermore, as more base stations become involved in communication with one
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Chapter 1 Introduction
mobile user, power transmission and allocation should be carefully adjusted to maximize
utilization and avoid excessive interference in the overall system.
2.2.4 Voice Activity
In DS-CDMA systems, cell capacity is mainly constrained by interference from
simultaneous transmissions. In such cases, the voice activity factor might be incorporated
into system design, and spectrum utilization can be significantly improved by shutting
down mobile stations during their silent periods. As a result, the system does not need to
provide full bandwidth to the admitted calls as long as their received SIR levels during
active periods exceed the required threshold. However, with partial bandwidth allocation,
outage might occur occasionally if most of the users are actively transmitting. The
additional capacity gain should be well justified to maintain the required QoS, and
congestion control is needed to combat the more varying system behavior.
2.2.5 Propagation Model
Generally, the propagation model might change greatly in a variety
environments or/and during different time intervals. This imposes great difficulty as well
as new challenges on system management. For example, under poor channel conditions,
greater power should be allocated to compensate for severe path loss. However, this may
cause overprovisioning of system resources and incur additional interference to others in
better channel conditions. On the other hand, such diversity gain might help improve
performance in terms of higher system throughput and lower inter and intracell
interference by precisely tracking channel fluctuations and scheduling transmissions
when the corresponding channel quality is near its peak. Therefore, a precise loss model
and accordingly better resource management strategies should be designed to properly
utilize the system resources.
2.3 Resource Management
Resource management strategies include how to efficiently allocate the available
resources to optimize channel utilization, how to adjust service rate to relieve congestion,
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Chapter 1 Introduction
how to provide diverse QoS requirements and how to provide priority among the various
users. There are also several distinctive characteristics in a DS-CDMA system that can be
explored for better performance. For example, universal frequency usage enables more
efficient resource provisioning; proper rate and power allocation helps to reduce
interference with greater capacity gain. On the other hand, these features inevitably
increase the complexity of system design, and thus must be carefully addressed.
2.4 Issues in Resource Management
There are several issues of resource management in mobile communications.
i. Congestion
ii. Cell Planning
iii. Power and rate allocation
iv. Call Admission Control Strategies.
2.4.1 Congestion Control
Congestion occurs if the system fails to find a set of power transmission levels
that satisfy users QoS requirements. Congestion might happen due to a variety of factors
such as the deterioration of the wireless environment, mobility of users, users activity,
and power control imperfection even with perfect admission control. As a result,
transmission power and cell interference tend to increase and outage occurs. This incurs
traffic loss and delay jitter, thus deteriorating transmission quality. Generally, when
congestion occurs three main mechanisms can be applied to relieve it:
Drop some ongoing calls. The system may choose to drop call(s) in outage condition, or
drop each existing call with prespecified probability, or drop call(s) that make the largestcontribution to alleviating congestion. The last scheme offers the best performance at the
greatest complexity.
Decrease transmission rate. This could be done by either proportionally reducing the
transmission rate of each user or decreasing the rates to the same maximal fair SIR level.
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Chapter 1 Introduction
Reduce the number of simultaneous transmissions. The transmission probability can be
dynamically adjusted according to occupancy information. To eliminate randomness and
obtain full control over simultaneous transmissions, cells may sequentially schedule
active transmitting periods for each service type.
To conclude, dropping ongoing calls is the most straightforward way to relieve
congestion, but it is undesirable and often unbearable from the users point of view. To
simultaneously reduce the data rate of all data services yields lower throughput than to
support fewer users with full rates at the same time. Thus, decreasing data rate results in
suboptimal system utilization. On the other hand, switching off some users leads to extra
transmission jitter and delay.
2.4.2 Power and rate allocation
A variety of criteria can be optimized, such as to minimize the
consumption, which prolongs battery life and causes less interference; or to maximize
transmission rate, which indicates maximized system throughput and resource utilization.
For a downlink, the two constraints are the SIR constraint for each service type
and the average transmission power limit among all base stations. The basic problem is
that if a mobile experiences poor channel conditions, the assigned transmission rate for
this mobile should be low.To cope with these two disadvantages, suboptimal algorithms
are proposed in which rate allocation depends on interference distribution, and powerallocation depends on traffic distribution among the cells.
For an uplink, the optimization is formulated to maximize the total normalized
transmission rate subject to the constraint on transmission power and max
allowable rate. The optimal solution yields not only higher throughput but also significant
power savings. However, this can potentially lead to starvation for users with poor
channel conditions, since users with better channel conditions will always transmit first.
2.4.3 Cell Planning
Efficient cell planning is of vital importance for service providers to reduce
network cost and maximize utilization of scarce resources. Generally, cell planning
consists of a number of issues, such as
i. Bandwidth Allocation.
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Chapter 1 Introduction
ii. Base Station Planning.
iii. Pilot Power Control, and
iv. Cell Sectorization.
Bandwidth allocation in uplink and downlink
In most systems, uplink and downlink use different bandwidth regions for
transmission, and the bandwidth allocation between them is symmetric. However, to
accommodate multimedia services, greater amounts of radio resources are required on
the downlink than on the uplink. To cope with the traffic asymmetry, unbalanced
bandwidth allocation is preferred, and great system performance gain might be achieved
by the proper assignment of bandwidth between two links according to traffic demand.
Base station planning
Since the overall frequency band is shared by all active users and the capacity of
each cell depends on the interference level, proper planning of base station locations
should consider not only cell coverage but also some other factors, such as the amount of
resources available, estimated traffic distribution in the area, and radio ch
propagation models. The main purpose of base station planning is to select the sites for
base stations by taking into account the system cost, transmission quality, servicecoverage, and so on. The optimal location of new base stations to minimize a linear
combination of installation cost and total transmitted power can be done by taking into
account traffic distribution, SIR requirements, power allocation constraints, and power
control mechanisms. More complex models also exist with considerations of the
stochastic behavior of the system or/and soft handoff.
Pilot power control
To select the proper base station(s) to connect, mobile stations need to measure
and report the 0bE N level of the received pilot power to the base stations. The pilot
power determines the cell coverage area and average number of users within a cell.
Increasing the pilot signal power of a base station expands the coverage area of the cell,
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Chapter 1 Introduction
thereby increasing the number of users in the serving cell, but resulting in higher intracell
interference. On the other hand, it may decrease the number of users in adjacent cells
with decreased intercell interference. Efficient pilot power control needs to balance the
cell load and cell coverage area among neighboring cells, with the objectives of reducing
the variation of interference, stabilizing network operation, and improving cell capacity
and communication quality, especially under nonuniform traffic loading among cells.
Cell Sectorization
Traditional sectoring approaches divide the cell into equal width sectors, which
has been shown to provide the same capacity gain under highly uniform traffic load.
However, for a system with hot spot traffic, those sectors under high density traffic load
may suffer high outage probability. Adaptive cell sectoring can be deployed to greatly
improve the performance in such a system.The optimal cell sectoring (OS) to minimize
the total transmission power can be formulated as a shortest path problem and solved by
Dijkstras algorithm. The direction and width of the sectors can also be adjusted
according to the geographic distribution of traffic. A cluster-based sectoring (CS)
algorithm can be proposed as we observe that the sector boundaries had better be across
some low density regions in order to avoid excessive oscillations,
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Chapter 1 Introduction
Figure:2.2 User distribution and cell sectoring.
also with the objective to achieve lower computational complexity. The complexity of CS
is much lower than OS, without any significant degradation of system performance.
Specifically, under the user distribution and sectoring for OS and CS shown in
Figure:2.2, the CS algorithm greatly reduces complexity from OS with only a slight
increase in total transmitted power. Furthermore, the sector boundaries generally cross
low density regions in the CS solution; while in OS, they may pass through two users
very close to each other. It is expected that dynamic cell sectoring could be designed to
incorporate the stochastic nature of the system, such as user mobility, channel fading,
power control, and sectorization imperfection, for better adaptation to real systems.
CHAPTER 3
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Chapter 1 Introduction
UMTS NETWORKS AND CAC STRATEGIES
3.1 Introduction to UMTS networks
Since their inception, mobile communications have become sophisticated and
ubiquitous. However, as the popularity of mobile communications surged in the 1990s,
Second Generation (2G) mobile cellular systems such as IS-95 and Global System for
Mobile (GSM) were unable to meet the growing demand for more network capacity. At
the same time, users demanded better and faster data communications, which 2G
technologies could not support.
Third Generation (3G) mobile systems have evolved and new services have been
defined: mobile Internet browsing, e-mail, high-speed data transfer, video telephony,
multimedia, video-on-demand, and audio-streaming. These data services had differentQuality of Service (QoS) requirements and traffic characteristics in terms of burstiness
and required bandwidth. Existing cellular technology urgently needed a redesign to
maximize the spectrum efficiency for the mixed traffic of both voice and data services.
Another challenge was to provide global roaming and interoperability of different mobile
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Chapter 1 Introduction
communications across diverse mobile environments.
Toward these ends, the International Telecommunication Union (ITU), the
European Telecommunications Standards Institute (ETSI), and other standardization
organizations collaborated on the development of the Future Public Land Mobile
Telecommunication Systems (FPLMTS). The project was later renamed International
Mobile Telecommunications-2000 (IMT-2000). The new 3G mobile cellula
communication system was set to operate at a 2 GHz carrier frequency band. For the PS
domain, the supported data rates were specified for the various mobile environments:
Indoor or stationary 2 Mbps
Urban outdoor and pedestrian 384 kbps
Wide area vehicular 144 kbps
Of the various original proposals, the two that gained significant traction were based
on Code Division Multiple Access (CDMA): CDMA2000 1X and Universal Mobile
Telecommunication System (UMTS).
i. CDMA2000 1X was built as an extension to cdmaOne (IS-95),
enhancements to achieve high data speed and support various 3G services.
ii. UMTS was based on the existing GSM communication core network (CN) but
opted for a totally new radio access technology in the form of a wideband version
of CDMA (Wideband CDMA: WCDMA). The Wideband Code Division Multiple
Access (WCDMA) proposal offered two different modes of operation: Frequency
Division Duplex (FDD), where Uplink (UL) and Downlink (DL) traffic are
carried by different radio channels; and Time Division Duplex (TDD), where the
same radio channel is used for UL and DL traffic but at different times.
3.2 UMTS Network Topology
When deploying a WCDMA network, most operators already have an existing 2G
network. WCDMA was intended as a technology to evolve GSM network toward 3G
services.
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Chapter 1 Introduction
3.2.1 GSM Network Architecture
Figure 3.1 illustrates a GSM reference network , showing both the nodes and the
interfaces to support operation in the CS and PS domains. In this reference network, three
sub-networks can be defined
Figure: 3.1 GSM Reference Network
Base Station Sub-System (BSS) or GSM/Edge Radio Access Network (GERAN)
This sub-system is mainly composed of the Base Transceiver Station (BTS) and
Base Station Controller (BSC), which together control the GSM radio interface , eitherfrom an individual link point of view for the BTS, or overall links, including the transfers
between links for the BSC. When data functionality was added to GSM with the
deployment of General Packet Radio Service (GPRS), an additional node was added to
the interface between the GPRS-CN and the radio interface, that is the Packet Control
Unit (PCU).
Network and Switching Sub-System (NSS)
This sub-system mainly consists of the Mobile Switching Center (MSC) that
routes calls to and from the mobile. For management purposes, additional nodes are
added to the MSC, either internally or externally. Their main purpose is to keep track of
the subscription data, along with associated rights and privileges, in the Home Location
Register (HLR), or to keep track of the subscribers mobility in the HLR and Visitor
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Chapter 1 Introduction
Location Register (VLR). Two other nodes manage security issues: the Equipment
Identity Register (EIR) verifies the status of the mobile phone (i.e., the hardware), while
the Authentication Center (AuC) manages the security associated with the Subscriber
Identity Module (SIM). The last node is the Gateway-MSC (GMSC). The MSC and
GMSC are differentiated only by the presence of interfaces to other networks, the Public
Switched Telephone Network (PSTN) in the GMSC case. Typically, the MSC and the
GMSC are integrated.
General Packet Radio Service, Core Network (GPRS-CN)
Within the NSS, two specific nodes are introduced for the GPRS operation: the
Serving GPRS Support Node (SGSN) and the Gateway GPRS Support Node (GGSN). In
the PS domain, the SGSN is comparable to the MSC used in the CS domain. Similarly, in
the PS domain, the GGSN is comparable to the GMSC used in the CS domain. These
nodes rely on existing BSS or NSS nodes, particularly the VLR and HLR, to manage
mobility and subscriptions .
3.2.2 UMTS Overlay Release 99
UMTS is based on the GSM reference network and thus shares most nodes of the
NSS and General Packet Radio Service, Core Network (GPRS-CN) sub-systems. The
BSS or GERAN is maintained in the UMTS reference network as a complement to the
new Universal Terrestrial Radio Access Network (UTRAN), which is composed of
multiple Radio Network Systems (RNS) as illustrated in Figure 3.2. Compared to the
GSM reference network, the only difference is the introduction of the Radio Network
Controller (RNC) and Node Bs within the newly formed RNS. Essentially, these two
nodes perform tasks equivalent to the BSC and BTS, respectively, in the GSM
architecture.
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Chapter 1 Introduction
Figure: 3.2UMTS Reference Network
3.2.3 UMTS Network Architecture beyond Release 99
The initial deployments of WCDMA networks comply with Release 99 of the
standard. This standard, or family of standards, began to evolve even before being fully
implemented, to address the limitations of the initial specifications as well as to include
technical advancements. At a higher level, migrating from Release 99 to Releases 4, 5,
and then 6 does not change the structure of the network.
3.3 Call Admission Control (CAC)
Call admission control is one of the most important aspects of r
management, which determines whether to admit or reject a call upon its arrival. The
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Chapter 1 Introduction
objective is to maximize the utilization of resources by admitting as many new calls as
possible while maintaining the fairness and QoS of ongoing services. There are many call
admission control schemes proposed , most of which can be classified into the following
three categories.
a. Number-Based CAC.
b. SIR-Based CAC or
c. Interference-Based CAC.
3.3.1 Number-Based CAC
In number-based CAC, the QoS requirement for the upper bound on packet error
probability is mapped into the maximum number of voice/data calls that can be
simultaneously accommodated in the system, denoted v dK K. To reflect voice calls
capability to tolerate higher bit error rates, vKis set greater than dK. As shown in
Figure:3.3, the admission of voice calls depends on a threshold value v . Newly arriving
data either joins the backlog pool or is discarded if the pool is full.
To provide priority to voice calls, the admission threshold v can be set equal to
vK(i.e., admit as many voice calls as the system can support). Some fairness can be
offered to data traffic by setting v less than vK, taking into account the number of
backlogged data packets. At any time slot t, voice calls can transmit without delay and
the backlogged data packets occupy the silence period of voice calls for transmission
with probability tP . tP is calculated according to a function with the parameters for
maximum allowed simultaneous data transmissions dK, current active voice calls, and
number of backlogged data packets obtained from system feedback at the beginning of
each time slot.Based on the time-invariant cell capacity assumption, the operation of number-
based call admission strategies is very similar to those in narrowband systems, in which
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Chapter 1 Introduction
call admission only depends on current cell loading. Such a scheme is simple for
Figure: 3.3 Number-based admission control.
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Chapter 1 Introduction
implementation and analysis. However, in a DS-CDMA system, the capacity varies with
the interference level, and the number-based schemes completely ignore the soft nature of
DSCDMA capacity, so the control is generally inaccurate and nonadaptive.
3.3.2 Interference-Based CAC
In DS-CDMA cellular networks, all users share the same wide frequency band.
When a call ( MS_Arrival ) is admitted into a cell, it transmits to and receives information
from the corresponding base station ( BS0), as shown in Figure: 3.4. In the uplink the
transmitted power from MS_Arrival increases interference levels on other in-cell (
MS_In ) and out- cell ( MS_Out ) users receivers at their base stations BS0 and BS1. In
the downlink the power allocated fromBS0 to MS_Arrival causes additional
interference for all other users (MS_In and MS_Out ) in the system.
The interference level depends heavily on overall system conditions. As a result,
the admission of a new call can gracefully degrade the performance of all users currently
in service. Accordingly, in a DS-CDMA system the number of calls that can be admitted
to a cell is not a fixed value. Thus, a more reasonable measurement should be the
interference level at the receivers.
An admission control scheme based on the received interference at base stations
was proposed for the uplink DS-CDMA system. Three interference margins are defined:
the total interference margin ( TIM ), current interference margin ( CIM ), handoff
interference margin ( HIM ).
Here, TIM is the maximum acceptable link interference such that the QoS in
terms of lower bound b 0E N is guaranteed; CIM is the estimated interference level
taking into account the assignment of the channel to the newly arrived call; HIM is the
interference estimation further considering the reserved channels for handoff calls. The
BS interprets the current interference from the measured power strength and calculates
CIM and HIM accordingly. If there is a call request and HIM < TIM (i.e., handoff
arrival can be safely reserved), the call will be admitted. Otherwise, the base station will
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Chapter 1 Introduction
check whether it is a handoff call and CIM < TIM . If so, the base station will assign a
Figure: 3.4 MS Admission
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Chapter 1 Introduction
new channel for the handoff call; otherwise, the call will be rejected. Compared with
number-based schemes, interference based call admission control better represents the
inherent interference limited nature of DS-CDMA systems at the expense of extra
complexity in interference detection.
3.3.3 SIR-Based CAC
Admission control based on the uplink SIR requirements was proposed . A crucial
measurement, residual capacity, is defined as the additional number of calls that can be
accepted by a base station so that the systemwide outage probability will not exceed a
predefined SIR level. In the localized algorithm, the residual capacity is calculated solely
based on the SIR measurement at the local base station, and a call will not be admitted
unless the residual capacity is greater than zero. In the global algorithm, all adjacent
cells SIR levels and residual capacities will be calculated at call admission. A call is
accepted if and only if the minimum of all cells residual capacity is greater than zero.
This ensures that the admission of the new call will not affect the QoS in all surrounding
cells, which is particularly important in a nonuniform traffic scenario. This can be
extended to support multimedia services, where uplink and downlink were considered
separately. The average SIR level for each call class is measured periodically for both
links. Accordingly, for each class i, the system estimates the expected i,jSIRat a class j
call arrival. Distinct admission thresholds i,j are set for admission if i,jSIR i,j , for all
call classes. Priority is provided for handoff calls by setting higher SIR thresholds for
new arrivals.
To better reflect the stochastic system behavior and guarantee long-t
transmission quality, an admission control scheme for the downlink MC-CDMA system
supporting multiple classes of traffic was proposed. The lower-bound SIR requirement
for each service type is formulated, incorporating statistical factors such as voice activity
and log-normal shadowing in propagation. The long-term outage probability
calculated, and the capacity constraint is derived for admission control. Reservation
based on an iteratively estimated handoff arrival rate is performed to provide priority
support for handoff traffic.
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Chapter 1 Introduction
In summary, number-based call admission control assumes a time-invariant cell
capacity and is simple for implementation and analysis. However, it does not consider the
inherent soft capacity nature of DS-CDMA system and could lead to inaccurate results.
On the other hand, the SIR-based schemes best characterize the transmission
quality and thus offer QoS guarantee. But the varying system environment poses
considerable difficulty in system design and measurements.
Interference-based call admission control also considers the interference-limited
nature of the system. The trade-off lies in simpler implementation for
measurements of QoS.
The CAC scheme that is proposed here is based on the measured Signal to
Interference Ratio (SIR).
3.4 System Model
CDMA systems are interference-limited systems. Thus, the capacity of a cell that
is, the total resource in the cell) varies with the loading of the home and neighboring cells
because the co-channel interference changes according to the loading. On the other hand,
for guaranteeing adequate call quality, the SIR of a call should be maintained to be higher
than a predefined value. To accomplish this objective, a call request is admitted only
when even though it is accepted, the SIR of an ongoing call is expected to be not smaller
than a threshold value. This type of CAC scheme is called the SIR-based scheme. The
proposed scheme here decides whether to admit or reject a call request based on the
measured bit-energy-to-noise density ratio 0bE Nat receiver.
3.4.1 Model of Multiple Class Calls
Assume that there are L classes of calls in the system. While a call is connected,
it alternates between active and dormant states according to the characteristics of traffic
source. Let us assume that only the active calls generate traffic. The data rate of a call
varies according to its state with this model. Let us denote the uplink and downlink data
rates of a class i call in active withu
iR andd
iR ( 0 1i L ) respectively.
Call requests are classified into handoff call and new call requests. The proposed
scheme gives higher priority to handoff calls over new calls within the same class. If
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Chapter 1 Introduction
i j< the admission priority of a call with class i is higher than that of a class j call
which means a handoff (new) call of class i has a higher priority over a handoff (new)
call of class j . A new call request of high-priority class may have higher priority as
compared with a handoff call of low priority class.
3.5 Proposed Call Admission Control Scheme
In practical systems, a base is equipped with a finite number of receiver elements
and there can be only a finite number of spreading codes for users in a cell. When all
receiver elements have been already assigned to ongoing calls, any call connection
request is rejected. In this system, the number of calls accommodated simultaneously in a
cell is limited to the number of OVSF codes.
When a class- i call request (either a new call or a handoff call) arrives at a base,
the base decides whether to admit or reject the request. If all receiver elements have been
already assigned to ongoing calls or there is no available spreading code, the call request
is rejected. If both receiver elements and spreading codes are available, the following call
admission scheme is used.
The class- i call request can obtain an admission only when even though the
request is accepted, a bit energy-to-noise density ratio of an ongoing active call within a
home cell is expected to be not lower than a threshold.
The CAC procedure consists of three stages.
3.5.1 Stage 1
Let us first consider uplink. The base measures the uplink 0bE Nfor each active
call and calculates the average of the measured 0bE Ns for each class periodically (e.g.,
every power control step). Letu
kMbe the average of the measured uplink 0bE Ns for
class- kactive calls. Then the base estimates how much the mean uplink 0bE Nof class
kcalls decreases, due to the acceptance of a class- i call request.
Let ,u
k iEdenote the estimate of the resulting 0bE Nfor class k,when the class- i
call is accepted which is given by equation (3.1).
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Chapter 1 Introduction
1
,
1{ . }
u uu i ik i u u
k u k
RE
M W
= + (3.1)
where uWis the uplink spreading bandwidth andu
kdenotes the required value of
uplink 0bE Nfor class- kcalls, for maintaining adequate transmission quality.
3.5.2 Stage 2
Consider downlink. Assume that each active mobile measures its downlink
0bE Nand can report it to the base periodically. Then the base calculates the average of
the reported 0bE Ns using the reported information, separately for each class which is
denoted byd
kM.Then the base determines ,
d
k iE, denoting the estimate of the resulting
0bE Nfor class kwhen the class- i call is accepted, given by equation (3.2)
1
,
1{ (1 ) . }
d dd i ik i d d
k d k
RE
M W
= + (3.2)
where dW is the downlink spreading bandwidth and denotes the average
orthogonality factor in downlink.
3.5.3 Stage 3
Let ,uk iand ,dk i , respectively, denote the uplink and downlink threshold 0bE Ns
of a class- kcall for controlling admission of a class- i new call request. Let ,u
k i and
,
d
k i ,respectively denote the uplink and downlink threshold 0bE Ns of a class- kcall for
controlling admission of a class- i handoff call request.
The proposed CAC scheme accepts a new call request with class- i if the
following condition (i.e.., equation (3.3)) for any class- kis satisfied.
, ,u uk i k iE and , ,d dk i k iE for0 k L (3.3)
If a class- i handoff call arrives , then the base decides admit the call if the
following condition (i.e.., equation (3.4)) for any class- kis satisfied.
, ,u u
k i k iE and , ,d d
k i k iE for0 k L (3.4)
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Chapter 1 Introduction
3.6 Threshold values for CAC
Conditions for supporting CAC priorities among call classes are as follows:
Between Handoff and New Calls Within a ClassThe possibility that a connection request is admitted increases as the threshold
0bE Nbecomes lower. In the proposed CAC scheme, handoff requests have higher
priority over new call requests of the same class. This is supported by the following
inequalities (i.e.., equation (3.5))for any class- i ( 0 1i L ).
, , , ,,
u u d d
k i k i k i k i < < for0 k L (3.5)
Between Calls With Different Class
Let us consider new calls. From our assumption that if , i j< the priority of a
new (handoff) call with class- i is higher than that of a new (handoff) call with class- j ,
the higher priority of a class- i new call as compared with a class- j new call means that
the condition (3) is more easily satisfied for class- i than for class- j .For example, let us
examine uplink condition in (3). As shown in (1), the estimated 0bE N, ,u
k iE, is
dependent on the data rateu
iR and the required 0bE N,
u
i . Ifu u u u
i i j jR R < , then
, ,
u u
k i k jE E> . In this case, even though ,u
k i is equal to ,u
k j, uplink condition of (3) is
satisfied with higher probability for a class- i new call than for one with class- j . If
u u u u
i i j jR R = , then ,u
k i should be smaller than ,u
k j . In summary, CAC threshold values
should be determined so as to guarantee the priority between call classes, considering the
data rates and the required 0bE Ns of all call classes.
Reduction in the Number of CAC Threshold Values
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Chapter 1 Introduction
As seen in (3) and (4), there are 4 thresholds for each class and 24L in total.
Therefore, the work to determine CAC threshold values may be very hard. To reduce the
complexity, we set the CAC threshold values using the required 0bE Ns as follows,
given by equation (3.6).
, , , ,, , ,u u h u u n d d h d d n
k i k i k i k i k i k i k i k i = = = =
for0 1i L and 0 k L (3.6)
wheren
iandh
iare, respectively, the CAC parameters for the new call requests and
handoff call requests with class- i , and are larger than one. As seen in (6), the number of
CAC parameters, s, is reduced to 2 in total and this makes us determine CAC
thresholds more easily. The priority of handoff calls over new calls within any class- i is
guaranteed byh n
i i < .
CHAPTER 4
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Chapter 1 Introduction
PERFORMANCE ANALYSIS AND
IMPLEMENTATION
4.0 Introduction
A cellular system consists of several cells. We assume that the overall system is
homogeneous in statistical equilibrium. For a homogeneous system , a cell is statistically
the same as any other cell. Thus, for each class, the mean handoff arrival rate to a cell
should be equal to the mean handoff departure rate from the cell. With this observation,
we can decouple a cell from the rest of the system and evaluate the system performance
by analyzing the performance of that cell.
4.1 Assumptions
The following assumptions regarding memoryless properties allow us to model
the system as multidimensional continuous time Markov chain.
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Chapter 1 Introduction
A1) New calls of class- i , 0 1i L , arrive at the cell according to Poisson process
with rate i .
A2) The dwell time of a class- i call in a cell is exponentially distributed with mean 1 i.
A3) The service time of a class- i call is also exponentially distributed with mean 1 i.
A4) During a connection, a class- i call alternates between active state and dormant state,
and the data activities of uplink and downlink channels of a call are independent of each
other. The amount of time which the class- i call spends in each of active and dormant
states for an uplink (downlink) channel are exponentially distributed with mean 1u
i(
1 di) and 1u
i(1d
i), respectively.
According to the above assumptions A1)A3), handoff calls of class- i arrive
from adjacent cells according to Poisson process. We denote this arrival rate of class- i
handoff calls by i. From the assumption A4), we can obtain the uplink and downlink
activity factorsof a class- i call, denoted byu
iandd
i, respectively. That is,
uu ii u u
i i
=
+and
dd ii d d
i i
=
+.
4.2 Flow Balance Equations
When in ( 0 1i L ) denotes the number of class- i calls in progress within the
cell, the system state can be defined by a row vectors
s 0 1 1( , ,......, )Ldef n n n .
When a call request arrives at a base, the base performs two tests. It first checks
whether there are available receiver elements and available spreading codes for the
request. Let us define a feasible state as a possible state for the given number of
receiver elements and the spreading code assignment scheme.We denote the state space
of all feasible states by S. Then, the first test implies that the base checks whether the
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Chapter 1 Introduction
state will be within S, after admitting the call request. If this simple test is passed, the
base secondly decides whether to admit or reject the request, using the proposed CAC
scheme. The CAC scheme influences the state transition rate
between feasible states .
Within the feasible state space S, any state transition is caused by one of the
following events:
1. Arrival of a new call.
2. Arrival of a handoff call.
3. Termination of an ongoing call and
4. Handoff of an ongoing call toward neighboring cells.
Let us denote the possible successor states from state s as
is + 0 1 1 1 1( , ,...., , 1, , ...., )i i i Ldef n n n n n n + +
is 0 1 1 1 1( , ,...., , 1, ,...., )i i i Ldef n n n n n n +
.
Consider the transition from state s to stateis + , caused by the origination of a
class- i new call. Let us denote this transition rate by ( , )nq s i . When ( , )
nA s i is the
probability which a base admits a class- i new call in state s , is expressed as
( , ) ( , )n n
iq s i A s i= .
Consider the transition from state s to stateis + , caused by the origination of a
class- i handoff call. Let us denote this transition rate by ( , )hq s i . When ( , )
hA s i is the
probability which a base admits a class- i handoff call in states , is expressed as
( , ) ( , )h h
iq s i A s i = .
Let us consider the state transition from state s to stateis , due to the
completion of a class- i call in process. This state transition rate, denoted by ( , )cq s i , is
expressed as
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Chapter 1 Introduction
( , )c
i iq s i n = .
Let us consider the state transition from state s to stateis , due to the
departure of a class ongoing call toward adjacent cells. This state transition rate, denoted
by ( , )xq s i , is expressed as
( , )x i iq s i n= (4.5)
Let ( )s be the stationary probability of state s . The stationary probabilities
should then satisfy the following flow balance equations:
1
0
1
1
1
( ) { ( , ) ( , ) ( , ) ( , )}
. ( ){ ( ) ( , )}
. ( ){ ( ) ( , )}
i
i
Ln h c x
i
L n h
n i i i
i o
Lc x
s S i i i
i o
s q s i q s i q s i q s i
I s q s q s i
I s q s q s i
+
=
=
+ + +
=
+ + +
= +
+ +
for all s S (4.6)
with the additional normalization equation that
( ) 1s S
s
= (4.7)
In (4.7), cIis an indicator function of which value is one if the condition is true;
otherwise, the value is zero.
4.3 Call Admission Probability
Here we calculate the call admission probabilities of new calls and handoff calls.
In the proposed scheme, the call admission decision is based on measurement and
estimation of 0bE N. Since the calls in dormant state do not generate traffic, we should
know the number of active calls of each class for calculating 0bE N. Let us assume that
the system state is s . Let us introduce a state s, which explicitly describes the number
of active calls in each of two links for all classes. Let ildenote the number of class- i calls
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Chapter 1 Introduction
whose uplink channel state is active. And let im be the number of downlink active calls
with class- i . Then, sis defined as
s 0 0 1 1( , ,....., , ,....., , )i i L Ldef l m l m l m (4.8)
Let us call sas an activity state ofs .
Let ( )Q s be the state space of all feasible activity states of
system state s . Then
( ) { : (0 )s i iQ s l n= and (0 )i im n
for0 i L } (4.9)
Let us calculate the mean0b
E Ns of uplink and downlink channels of class- k
call in state swith the background noise ignored.
4.3.1 Uplink Bit energy to noise density ratio
Consider the uplink channel of an ongoing call with class- k.Let jCbe the average
of the signal powers that the base receives from mobiles with class- j call. Then, the
average home-cell interference of class- kuplink channel in state s, denoted by
( )uk sH, is as
1
( )L
u
k s j j k
j o
H l C C
=
= (4.10)
Let jNbe the mean number of class- j calls in process within a cell. Then, the
mean number of class- j calls whose uplink channel state is active isu
j jN. Whenu
denotes the ratio of the uplink interference from other cells to that from home cell, the
mean interference of the class- kuplink channel coming from other cells in state s,
( )uk sO , is as
1
0
( )L
u u u
k s j j j
j
O N C
=
= (4.11)
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Chapter 1 Introduction
The average of total interference for class- kuplink channel is a sum of ( )u
k sH
and ( )u
k sO . Then, the mean 0bE Nof the class- kuplink channel in state s, ( )
u
k sM,
is as
( ) .( ) ( )
u k uk s u u u
k s k s k
C WMH O R
=+
(4.12)
The mean received signal power at a base for any class call is proportional to the
uplink data rate and the required 0bE Nof the class. Therefore, ( ) ( )u u u u
j k j j k k C C R R = .
Then
1 1
0 0
( )u
u u kk s L L
u u u u u u u
j j j k k u j j j jj j
WM
l R R N R
= =
=
+ (4.13)
4.3.2 DownlinkBit energy to noise density ratio
Consider the downlink channel of a class- kcall in state s. Let jp be mean
transmission power at a base for a class- j user, xPbe total transmission power of a base
in cell x , and let zdenote the portion of overhead channel (e.g., pilot channel) power (
ohP) for the maximum transmission power ( maxP ). That is, maxohz P P= and is assumed to
be a fixed value. When the home cell is numbered as cell 0, 0( )ohP P z and
1
0
0
L
oh j j
j
P P m p
=
= . So,1
0
0
(1 )L
j j
j
z P m p
=
. As the load increases and 0Papproaches to
maxP , 0Pmay be regarded as1
0
( ) (1 )L
j j
j
m p z
=
. Since admission control becomes full of
interest mainly at heavy load, we approximate the transmission power of the home cell
base as follows:
1
0
0
1.
1
L
j j
j
P m pz
=
=
(4.14)
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Chapter 1 Introduction
We know that in the proposed CAC , each active mobile reports the measured
0bE Nfor its downlink channel periodically and the base calculates the avera
downlink using the reported 0bE Nvalues. Let us calculate the average downlink 0bE N
for class- kcalls. We assume that the average of the measured downlink 0bE Ns for
class- k calls is equal to the measured 0bE Nof a class- kmobile at an average
location (the tagged mobile). This is known as the concept of average location. We
assume that the transmission power required for the tagged mobile is same as mean
transmission power, kp , for class- kcalls. We consider the simplest model for radio
channel, which is a propagation loss inversely proportional to the distance between
transmitter and receiver. Let 0dbe the distance from the base of home cell (cell 0) to the
average location. When the average orthogonality factor in downlink is represented by ,
home cell interference of the tagged mobile at average location in state sis as
0 0( ) ( )(1 )
d
k s kH cd P p = (4.15)
where is the path-loss exponent, and and c are constants.
Let us evaluate other cell interference of the tagged mobile. Assume that only
cells in two tiers are considered as interference source. There are six cells in first tier and
12 cells in second tier, which are numbered from 1 to 18. When xddenotes the distance
from the base of cell x to the tagged mobile at average location, the interference at the
tagged mobile from the base of cell x is x xcd P
.Then, other cell interference at the
tagged mobile is18
1
x x
x
cd P
= . Since the mean number of class- j calls in downlink active
state isd
j jN
1
0
1.
1
Ld
x j j j
j
P N pz
=
=
(4.16)
Therefore, the downlink 0bE Nof the tagged mobile in state s, denoted by
( )dk s
M, is given by
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Chapter 1 Introduction
0
1 1 18
0 0 1
( ) .1 1
(1 )( ) ( )( )1 1
d d kk s L Ld
dkj j k j j j x x
j j x
W cd pM
Rm p p N p cd P
z z
= = =
=
+
(4.17)
Let
18
0
1( )d x
xd d
== .
drepresents the average ratio of the downlink
interference from other cells to that from home cell and since the mean transmission
power at a base for a call is proportional to the data date and the required 0bE Nof the
call, ( ) ( )d d d d
j k j j k k p p R R = . Therefore, ( )
d
k sMis
1 1
0 0
(1 )( )
(1 ) (1 )(1 )
dd d kk s L L
d d d d d d d
j j j k k d j j j j
j j
z WM
m R z R N R
= =
=
+ (4.18)
4.3.3 Call Admission Probability
Let us calculate the probability that the activity state is swhen the system is in
state s . This probability is denoted by ( )sP s| . Since the activity of a call is
independent of any other call and the uplink activity of a call is also independent of its
downlink activity , ( )sP s| is given by
( ) ( )
1
0
! !( ) { ( ) (1 ) }{ ( ) (1 ) }
! ! ! !
j j j j j j
Ll n l m n mj ju u d d
s j j j j
j j j j j j j
n nP s
l n l m n m
=
| =
(4.19)
In the proposed CAC scheme, a new call request and a handoff call request with
class- i are accepted if the conditions on the estimates of uplink and downlink 0bE Ns
for any class- k, (3.3) and (3.4) are respectively satisfied. When a class- i call request
arrives, the base calculates the estimates using (3.1) and (3.2), respectively. That is, if a
class- i call requests a connection in state s, the base calculates , ( )u
k i sE and , ( )
d
k i sE for
any k, as follows:
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Chapter 1 Introduction
1
,
1( ) { . }
( )
u uu i ik i s u u
k s u k
RE
M W
= + (4.20)
1
,
1( ) { (1 ) . }
( )
d dd i ik i s d d
k s d k
RE
M W
= + (4.21)
Let us introduce a function, , ( , )u
k i sg x , whose value is zero if the estimate of
average uplink 0bE Nfor class- kwith at least one active call in state sis smaller than
x ; otherwise the value is one.
, ( , )u
k i sg x = {0, if0kl> and , ( )u
k i sE x< ; 1, otherwise}
Similarly, , ( , )d
k i sg x denotes a function which has a value of zero when the
estimate of average downlink 0bE Nfor class- kin state sis smaller than x ; otherwise
the value is one. That is
, ( , )d
k i sg x = {0, if 0km > and , ( )d
k i sE x< ; 1, otherwise}
We know that ,u
k iand ,d
k i, respectively, denote the uplink and downlink threshold
0bE Ns of a class- kcall for controlling a class- i new call request. Then, the probability
that a class- i new call obtains an admission in state s , ( , )nA s i , is
1
, , , ,
( ) 0
( , ) { ( ) ( , ). ( , )}i
s
Ln u u d d
s S s k i s k i k i s k i
Q s k
A s i I P s g g
+
=
= | (4.24)
Similarly, we know that ,u
k i and ,d
k i , respectively, denote the uplink and
downlink threshold 0bE Ns of a class- kcall for controlling a class- i handoff call
request. Then, the probability that a class- i handoff call
obtains an admission in state s , ( , )hA s i , is
1
, , , ,
( ) 0
( , ) { ( ) ( , ). ( , )}i
s
Lh u u d d
s S s k i s k i k i s k i
Q s k
A s i I P s g g
+
=
= | (4.25)
whereis S
I+ checks whether there are available receiver elements and spreading codes.
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Chapter 1 Introduction
4.4 Performance Measures
The various performance measures that are considered are
1. Blocking probability of class- i new calls.
2. Failure probability of class- i handoff calls.3. Outage probabilities of class-i call in progress for both the uplink and the
downlink.
4. Throughputs of the uplink and the downlink.
4.4.1 Blocking probabilities of handoff and new calls
Let ibe the blocking probability of class- i new calls. The connection request of
a class- i new call in state s is rejected with the probability {1 ( , )nA s i }. Thus, iis
given by
{1 ( , )}. ( )n
i
s S
A s i s
= for0 1i L (4.26)
Similarly, let ibe the failure probability of class- i handoff arrivals. The class- i
handoff call in state s is forced to terminate with the probability {1 ( , )hA s i }. Thus,
iis given by
{1 ( , )}. ( )h
i
s S
A s i s
= for0 1i L (4.27)
4.4.2 Outage Probability of a call in progress
Let us calculate the outage probability of a call in progress. The outage
probability of a call is the probability that the measured 0bE Nof the call is smaller than
the required 0bE Nfor maintaining adequate transmission quality. Letu
iandd
ibe the
uplink and downlink outage probabilities of class- i calls. We know that ,u
k iand ,
d
k i,
respectively, denote the required 0bE Ns for uplink and downlink traffic of class- i calls.
Then
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Chapter 1 Introduction
0 { ( ) }( )
0
( )
( )
( )( )
u ui i s i
s
i
s
s l MQ su
is S s l
Q s
P s I I
sP s I
>
|
=|
(4.28)
0 { ( ) }( )
0
( )
( )
( )( )
d di i s i
s
i
s
s m MQ sd
is S s m
Q s
P s I I
sP s I
>
|
=|
(4.29)
4.4.3 Throughput of uplink and downlink
Let uand dbe the throughputs for uplink and downlink, respectively. Here, the
throughput is a data rate under the condition that the measured 0bE N
is larger than or
equal to the required 0bE N. Therefore
1
{ ( ) }( ) 0
( ) ( ) u ui s i
s
Lu
u s i iMs S Q s i
s P s I l R
=
= | (4.30)
1
{ ( ) }( ) 0
( ) ( ) d di s i
s
Ld
d s i iMs S Q s i
s P s I m R
=
= | (4.31)
Then, the total system throughput is a sum of uand d.
4.4.4 Determination of Handoff Call Arrival Rates and Carried Load
The flow balance equations are derived using the arrival rate of class- i handoff
calls, i( 0 1i L ). Since the overall system is assumed to be homogeneous in
statistical equilibrium, the arrival rate of handoff calls with class- i should be equal to the
departure rate of class- i calls toward neighboring cells. That is
( )i i is S
s n
= for 0 1i L (4.32)
As seen in (4.32), the handoff arrival (departure) rates are dependent on state
probabilities, while the state probabilities are derived using the handoff arrival rates. On
the other hand, as seen in (4.13) and (4.18), the mean number of ongoing calls for each
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Chapter 1 Introduction
class, jN( 0 1j L ), is used to calculate the average uplink and downlink 0bE Ns
and these are in turn used to derive the state probabilities. Conversely, the mean number
of ongoing calls is obtained from the state probabilities. That is, when calculating the
mean number of ongoing calls for each class, we meet the same problem as whendetermining handoff arrival rates. To solve these problems, we use the following iterative
algorithm that begins with the initial guess for handoff arrival rates and the mean number
of ongoing calls.
4.5 Algorithm
Figure 4.1 shows the flowchart for the algorithm to be implemented for the
performance analysis of the proposed CAC scheme.
Step 1: Set an initial value for i( 0 1i L ) which is calculated using the blocking
probabilities of handoff arrivals and new call arrivals with class-i by iand i,
respectively as follows:
, { (1 ) (1i h i i i iP = + (4.33)
where ,h iPis the probability that the connection is released by handoff departure and
, ( )h i i i iP = + . Assuming that 1i
= and 1i
= ,
,
,(1 )
h i ii i i
h i i
P
P
=
; (4.34)
We set the initial value ofito i i i .
Step 2: Estimate an initial value of jN( 0 1j L ). Let us assume that all cells are in
the same state. Then, (19) and (24)are replaced by the following equations:
1
0
( )(1 )
uu u k
k s Lu u u u
u j j j k k
j
W
Ml R R
=
=+
(4.35)
1
0
(1 )( )
(1 ) (1 )(1 )
dd d kk s L
d d d d
d j j j k k j
z WM
m R z R
=
=
+ (4.36)
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Chapter 1 Introduction
We obtain the initial values for iteration from (4.34)(4.36).
Figure 4.1 Flowchart showing the performance analysis.
Step 3: Compute the stationary probabilities ( )s s using (4.2)(4.25).
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Chapter 1 Introduction
Step 4: Compute the mean rate of handoff arrivals, ,i new (0 1i L ), using (4.32)and
compute the mean number of ongoing calls, ,i newN ( 0 1i L )
, ( )i new is S
N n s
= (4.37)
Step 5: Let ( 0) > be a predefined small value. We introduce a function iforisuch
that
i= {1, if,
1i new
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Chapter 1 Introduction
CHAPTER 5
RESULTS
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Chapter 1 Introduction
5.0 Introduction
In this chapter, first we will discuss about the modeling of the traffic with
different bit rates and their priorities set at different levels. Then the corresponding
performance is also evaluated in terms of throughput, packet loss and packet delay. This
is done using the Network Simulator NS2 (version 2.31). Then secondly, the performance
of the proposed CAC algorithm is analyzed and the corresponding plots are plotted using
MATLAB ( version 7.1).
5.1 NS2 Simulation Results
Ns-2 is an open source discrete event simulator used by the research community
for research in networking .It has support for both wired and wireless networks and can
simulate several network protocols such as TCP, UDP, multicast routing, etc. More
recently, support has been added for simulation of large satellite and ad hoc wireless
networks. The ns-2 simulation software was developed at the University of Berkeley. The
standard ns-2 distribution runs on Linux. However, a package for running ns-2 on
Cygwin (Linux Emulation for Windows) is available.
5.1.1 Simulation scenarioThe simulation scenario evaluates the performance of the traffic model in terms of
the throughput, packet delay and packet losses. The simulation topology of this scenario
is simple. It consists of 8 mobile nodes : 4 source nodes and 4 destination nodes. Each
node is transmitting with a different priority. With respect to the source nodes, Node 0 is
given a higher priority than Node 2, which is given also a higher priority than Node 4,
which, in its turn, is given a higher priority than Node 6 .
Each source is a Constant Bit Rate (CBR) source over UDP (User Datagram
Protocol). The size of a transmitted packet is 512 bytes. Transmission rate of the nodes is
set at different values. Node0 transmits at 400 Kbps, Node2 at 500 Kbps, Node4 at
600Kbps and Node6 at 800 Kbps. We assumed that the nodes are in the transmission
range at a constant distance of 195 m. The simulation time lasted for 80 sec. Table 5.1
shows the specifications of the traffic model used.
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Chapter 1 Introduction
Channel Wireless Channel
Radio Propagation Model Two ray ground propagation
Network Interface Wireless Physical
MAC Type 802.11 MAC
Antenna Model Omni antenna
Maximum Queue size 50Queue type Drop tail/Priority queue
Number of mobile nodes 8
Routing Protocol DSDV
802.11 MAC RTS threshold 3000
802.11 MAC Basic rate 1 Mbps
802.11 MAC Data rate 2 Mbps
CBR Packet size 512 Bytes
Node 0 CBR rate 400 Kbps
Node 2 CBR rate 500 Kbps
Node 4 CBR rate 600 Kbps
Node 6 CBR rate 800 KbpsSimulation time 80 sec
Table 5.1 Traffic Model Specifications
Figure 5.1 shows the NAM layout of the simulation topology
Figure 5.1: NAM layout of the topology.
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Chapter 1 Introduction
Node 0 starts transmitting at time T=1.4 seconds as shown in figure 5.2.
Figure 5.2 Node0 starts transmitting.
Node 2 starts transmitting at time T=10 seconds as shown in figure 5.3
Figure 5.3 Node 2 starts transmitting.
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Chapter 1 Introduction
Node 4 starts transmitting at time T=20 seconds as shown in figure 5.4
Figure 5.4 Node 4 starts transmitting.
Node 6 starts transmitting at time T= 40 seconds as shown in figure 5.5.
Figure 5.5 Node 6 starts transmitting
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Chapter 1 Introduction
In the next section we clearly analyze the performance of the above traffic model
in terms of the packet throughput, average packets end to end delay and the packet drop
rate.
5.1.2 Performance Evaluation
5.1.2.1 Throughput
Figure 5.6 Throughput plot using Xgraph
Figure 5.6 shows the throughput plot for the above designed traffic model. Node 0
starts transmitting at time T =1.4 sec while Node 2 starts transmitting at time T=10 sec.
During the period of time [1.4 sec, 10 sec] Node 0 is the only transmitting node using the
entire available bandwidth. This justifies the high performance of Node 0 during the
specified interval of time. At time T=10 sec, Node 2 starts transmission hence sharing
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Chapter 1 Introduction
channel resources with Node 0. This explains the heavy reduction of bit rate. In addition,
the bit rate plot experiences heavier oscillations and reduction as the number of
transmitting nodes increases. Oscillations are reflected in heavy disorders in network
performance.
5.1.2.2 Average packets end to end delay
Figure 5.7 Average Packets end to end delay plot using Xgraph
Figure 5.7 shows the average packets end to end delay plot for the above designed
traffic model. When the number of nodes that are sharing the network resources
increases, then the delay significantly increases and readjusting CW of each node takes a
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