Uplink Admission Control in WCDMA Systems

Jesper Högberg T97

June 2001

Masters Thesis Supervisors: Ulf Nilsson, Per Ernström, Telia Research AB Examinator: Claes Trygger, Optimization and Systems Theory, KTH

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Abstract In future wireless communication systems there will be a traffic mix of different services with different demands on data rates and delays. In addition to speech services, which are already present in today’s systems, there will also be video telephony, e-mail, and WWW services The European 3rd generation mobile system UMTS has been designed with all of these services in mind. There will, however, be new demands on radio resource management in UMTS, which is much more flexible than, for example, GSM. In UMTS, users share the same frequency. Thus, one user will cause interference for other users in the system. New users will only be granted access to the network if they do not cause too much interference for already active users. The idea is that it is much better to block the access of a user to the network than having to drop already active users. Such decisions are made by the admission control routine. In this thesis, three different admission control algorithms are studied and compared. The aim is to compare the efficiency of the different algorithms, which is why only speech users are considered. If more services are included, the simulations become very complex and time consuming. Most of the simulations are done with a traffic intensity, which gives 10% dropping when there is no admission control, i.e. all users are admitted. Dropped means that the effect from the mobile is lower than required and the service session is interrupted. The simulations clearly show that the admission control strategies improve the system performance by decreasing the dropping of calls. As expected, some of the new users are blocked. The task for this thesis has been to study and implement new complex admission control algorithms to compare them to more simple algorithms like The Equivalent Bandwidth method.

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Contents ABSTRACT ........................................................................................................................................................... 2

CONTENTS ........................................................................................................................................................... 3

1. INTRODUCTION ............................................................................................................................................. 5

1.1 PROBLEM DEFINITION..................................................................................................................................... 5 1.2 THESIS OUTLINE ............................................................................................................................................. 6 1.3 THE THREE GENERATIONS OF TELEPHONY...................................................................................................... 6 1.4 A SHORT HISTORY OF UMTS ......................................................................................................................... 7 1.5 THE UMTS VISION......................................................................................................................................... 7

2. UMTS SERVICES AND APPLICATIONS.................................................................................................... 8

2.1 UMTS QOS CLASSES ..................................................................................................................................... 8

3. CDMA TECHNIQUE ....................................................................................................................................... 9

3.1 FDMA ........................................................................................................................................................... 9 3.2 TDMA ........................................................................................................................................................... 9 3.3 CDMA......................................................................................................................................................... 10

4. WCDMA........................................................................................................................................................... 12

4.1 SPREADING AND DESPREADING .................................................................................................................... 12

5. RADIO NETWORK SYSTEM ...................................................................................................................... 15

6. RADIO PROPERTIES ................................................................................................................................... 16

6.1 PROPAGATION PATH LOSS ............................................................................................................................ 17 6.2 SHADOWING AND SCATTERING..................................................................................................................... 18 6.4 MULTI-PATH PROPAGATION ......................................................................................................................... 18 6.5 INTERFERENCE ............................................................................................................................................. 19 6.6 THE CELL MODEL ......................................................................................................................................... 20

7. MATHEMATICAL BACKGROUND IN UMTS......................................................................................... 21

7.1 THE UPLINK.................................................................................................................................................. 22 7.2 SINGLE CELL POLE CAPACITY ....................................................................................................................... 24 7.4 NUMBER OF USERS ....................................................................................................................................... 25

8. ADMISSION CONTROL............................................................................................................................... 27

8.1 ADMISSION CONTROL PRINCIPLE. ................................................................................................................. 27

9. SYSTEM MODEL........................................................................................................................................... 28

9.1 PROPAGATION MODELS ................................................................................................................................ 28 9.2 TRAFFIC MODEL FOR SPEECH........................................................................................................................ 28 9.3 HANDOVERS................................................................................................................................................. 29

9.3.1 Macro diversity..................................................................................................................................... 29 9.4 POWER AND RATE CONTROL (PARC) ........................................................................................................... 29 9.5 CALL DROPPING MECHANISM ....................................................................................................................... 30 9.6 PERFORMANCE MEASURES ........................................................................................................................... 30

10. THE PRINCIPLE OF THE SIMULATOR ................................................................................................ 31

11. ADMISSION CONTROL ALGORITHMS ................................................................................................ 32

11.1 UPLINK LOAD FACTOR................................................................................................................................ 32 11.2 WIDE BAND POWER-BASED ADMISSION CONTROL STRATEGY..................................................................... 34

11.2.1 Approximating the rise in interference by using differentiation ......................................................... 35 11.2.2 Approximating the rise in interference by using integration .............................................................. 35

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11.2.3 Equivalent Bandwidth algorithm........................................................................................................ 36 11.3 DERIVATIVE ALGORITHM WITH EXPONENTIAL EQUALIZATION. .................................................................. 36 11.4 START APPROXIMATIONS............................................................................................................................ 37

12.SIMULATION RESULTS............................................................................................................................. 38

12.1 SYSTEM LOAD WORKING POINT .................................................................................................................. 38 12.2 ADMISSION CONTROL PILOT STUDY ........................................................................................................... 40 12.3 SIMULATIONS ............................................................................................................................................. 46

13. DISCUSSION AND CONCLUSION ........................................................................................................... 54

13.1 FURTHER STUDIES ...................................................................................................................................... 54

14. REFERENCES .............................................................................................................................................. 55

APPENDIX A. ABBREVIATIONS ................................................................................................................... 56

APPENDIX B. STARTING VALUES............................................................................................................... 57

APPENDIX C. PLOTS AND DATA.................................................................................................................. 58

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1. Introduction There are several ways to send digital information through the ether. The main techniques used in mobile communications are Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA). In FDMA the available frequency interval is divided into bands, which are assigned to individual users. Each user can then transmit in his band without significant interference from other users. In TDMA, time is instead divided into so-called slots. A user who is assigned a slot can transmit freely in it with all the system’s resources devoted to him. The GSM system uses a combination of FDMA and TDMA. In CDMA all users share the same frequency band and time slots and are identified by their orthogonal signals. This solution makes the system more flexible but may require more bandwidth for its transmissions. The next generation system, the so-called Universal Mobile Telecommunication System (UMTS) will be based on CDMA techniques. In fact, to cope with the demands for high bit rates, the allocated bandwidth for UMTS is very large (about 5 MHz). Therefore one often refers to UMTS as a Wideband CDMA (WCDMA) system. A CDMA system has in principle no absolute limit on the number of users it can support. Instead it is the interference from other users in the system that limits the number of users. This means that in order to maintain good quality of already ongoing connections, the network needs to estimate the amount of interference new users bring with them. If a new user will cause the system to be overloaded, he must not be allowed to connect to the network. This is the purpose of the admission control in CDMA systems. There are many different principles for admission control in the literature, both simple and advanced. It is necessary, however, to implement some kind of admission control, especially when the traffic will be a mix of services with different demands on data rates and delay. In this thesis we will study some proposed admission control routines in a simulated radio network. The aim of the thesis work is described in more detail in the next Section.

1.1 Problem definition As stated previously, the capacity of a WCDMA network is limited by the amount of interference in the ether, yielding a so-called soft capacity limit. This is in contrast to a so-called hard capacity limit, which is set by the available amount of hardware. If the load of the ether is allowed to increase excessively, the quality of service for existing connections can no longer be guaranteed. In practice this means that the interference from other users becomes too big. Thus, before admitting new users into the system, an estimation of the increase in interference needs to be done to ensure that the new user will not seriously affect already existing connections. This task is performed by the network’s admission control routine. If the algorithms used are not effective or not optimized, it will reflect badly upon the operator. To develop a deep competence about admission control routines is thus very important to an operator.

The objectives of the thesis are as follows. • Achieve a qualitative understanding of the functions of a WCDMA network like UMTS.

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• Implement a number of relevant admission control algorithms in a simulation environment and study their performance in relatively realistic scenarios. This should initially be done for simple traffic models.

1.2 Thesis outline Chapter 1 contains a short history of telephony and a short description of the main goal of UMTS. Ch. 2 is a short presentation of the expected services in UMTS. Ch. 3 presents the principle of CDMA, while Ch. 4 describes WCDMA in more detail. The physical network system is shown in Ch. 5 and radio properties are considered in Ch. 6. Ch. 7 describes the mathematical background, while ch. 8 is a short description of admission control. Ch. 9 describes the system model, while Ch. 10 shows the principle of the simulator. In Ch. 11 the mathematic algorithms used for the admission control are presented. In Ch. 12, the simulation results are collected. The thesis ends with a discussion and conclusion in Ch.13. 1.3 The three generations of telephony The history of mobile telephony is usually divided into three generations, see Figure 1. The first generation, which started in the middle of the 20th century and is called the telephony era, supported only speech services. It existed during the time from the innovation to about 1990. An example of a first generation system is NMT, which actually is still in operation. In the beginning of the 1990’s, the second generation was introduced. First generation Second generation Third generation 1985 1990 1995 2000 2005 2010 Figure 1. The evolution of mobile services. The second generation is called the telephony-and-data era and supported services like speech, voice mail, Short Message Service (SMS), Local Area Network (LAN) access, information services, internet/intranet access and image download. GSM (Global System for Mobile communication) is an example of a second generation system. The possibility to send packet data in a GSM network was introduced through GPRS (General Packet Radio Service), which is starting to become available to the public during 2001. Sometimes GSM together with GPRS is called a 2.5 generation network. GSM/GPRS has evolved into GSM EDGE (EnhanceD rates for GSM [or sometimes Global] Evolution), which can support higher bit rates than GSM/GPRS, but is still quite limited. Thus, in order to support really high bit rates it was decided that a new mobile system should be developed, at least within Europe. This is the third generation system. The third generation services are almost the same as those in the second generation. Some new services are high speed internet/intranet access, video retrieval and video conferencing/

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video telephony. For an overview of new multimedia services in UMTS, see [9]. In Europe the third generation system is known as UMTS. It also includes GSM EDGE, which is known GERAN (GSM EDGE Radio Access Network) within UMTS. UMTS will coexist with GSM/GPRS for quite a while, which means that today’s GSM phones will not become useless as soon as UMTS is introduced. 1.4 A short history of UMTS The story of UMTS started in 1985 in the ITU (International Telecommunications Union) with a study on FPLMTS (Future Public Land Mobile Telecommunication System). The main goals of FPLMTS were to achieve a truly mobile system with integrated speech and data services that would also be suitable as a modern telecommunication infrastructure for developing countries. In Europe FPLMTS has changed name to UMTS. Some major milestones in the development of UMTS were [2]: • A global identification of an unused frequency spectrum for FPLMTS was made by

WARC (World Administrative Radio Conference) in 1992. • The migration plans for evolving UMTS from GSM were established in 1996-1997. • The radio interface between the users and the base stations was chosen in 1998. • 3GPP (3rd Generation Partnership Project) was founded in 1998. • Test systems running in Sweden and around the world in 1999. • The first set of UMTS specifications, the so-called Release 99 of UMTS, was approved in

December 1999. 1.5 The UMTS vision UMTS will provide the mobile phone users with all kinds of services from today’s speech to future services like e-mail, Internet services, mobile video telephony and mobile multimedia conferencing. UMTS will be of great benefit to the whole of society including industry and businesses, public services, educational institutions as well as private users. The UMTS system is specified and designed to provide a flexible and future proof architecture based on standardized interfaces which will allow new services to be developed, both by operators and by third parties and offered by many different service providers. UMTS will make it possible to provide the customers with all sorts of combinations of communication and information features based on different types of media components such as audio, text, video and graphics [2].

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2. UMTS services and applications The best known new feature of UMTS is the high user bit rates. On circuit-switched connections the expected maximum bit rate is 384 kbps, while on packet-switched connections it is 2 Mbps. Higher bit rates make it possible to introduce new services such as video telephony and quick downloading of data from the Internet. At the start of UMTS most of the traffic will be speech, but later the share of web browsing and emails will increase. It is difficult to predict the pace at which the share of web browsing and emails will occur; traffic will move from circuit-switched connections to packet-switched connections. At the start of UMTS service not all of the Quality of Service (QoS) functions (speech, video telephony, WEB and Email), will be implemented, and therefore delay-critical applications such as speech and video telephony will be carried on circuit switched bearers. Later, it will be possible to support delay-critical services such as packet data with QoS functions. 2.1 UMTS QoS classes Applications and services can be divided into different groups, depending on how they are considered. In UMTS four traffic classes have been identified as (see, for example, [1]): 1. Conversational class: In this class the time relation between information entities in the

stream needs to be preserved. There are also strong demands on the delays. Applications within this class are voice services, video telephony and video games. Data integrity is not always a necessity.

2. Streaming class: Here the time relation between information entities also needs to be preserved, but the requirements on the delays are somewhat less stringent than for the conversational class. Applications within this class are streaming multimedia like radio and video. Data integrity may not always be completely necessary.

3. Interactive class: In this class the transmission of information follows a request-response pattern and the data is expected within a reasonable amount of time. The data integrity is important. The applications within this class are mainly web related, like browsing and network gaming.

4. Background class: For applications within this class the data integrity is important but

there is no real requirement on the delay. The applications are, for example, e-mail. The main distinguishing factors between the different classes are data integrity and how delay-sensitive the traffic is. In the initial phase of UMTS the conversation and streaming classes will be transmitted as real-time circuit switched connections over the WCDMA air interface, while the interactive and background classes are transmitted as non-real time packet data.

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3. CDMA technique As discussed previously, UMTS will use WCDMA techniques in which all users share the same frequency band. This is only true for uplink and downlink separately, though. Uplink refers to transmissions from the user to the base station, while downlink refers to transmissions from the base station to the user, see Figure 2. In this Section we describe how this technique works in somewhat more detail. We begin, however, with a short introduction to FDMA and TDMA. uplink downlink Figure 2. The definition of uplink and downlink transmissions. 3.1 FDMA In FDMA, the entire available frequency range is divided into bands, see Figure 3. The technique is characterised by the continuous access of the users in a given frequency band without any significant interference from other users. The main drawback of FDMA is that when a user is idle, his share of the bandwidth cannot be used by anyone else. This leads to a waste of capacity. FDMA is thus not very flexible; on the other hand it relies on the use of proven techniques. time

frequency f1 f2 f3 f4 Figure 3. In FDMA the available frequency range is divided into separate bands, which are assigned to different users. There is no siginificant interference between users. The figure is taken from [5]. 3.2 TDMA In TDMA, time is divided into slots, see Figure 4. Each time slot is pre-assigned to a user. During such a slot, a user is allowed to transmit freely, and the system’s entire resources are devoted to this user. The slot assignments are periodic, and each period is usually referred to as a frame. A user could be assigned one or more time slots during a cycle thereby increasing his capacity for transmission. The drawback of TDMA is that each user has a fixed allocation

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of a time slot whether or not it has data to transmit. Again this is a waste of capacity, especially since many applications’ transmissions are quite bursty. time

frequency Figure 4. In TDMA time is divided into slots, which are allocated to different users. Within such a slot all system resources are devoted to one user. The figure is taken from [5]. 3.3 CDMA In CDMA, all users share the same frequency band all the time. This makes CDMA a much more flexible system. A user is only allocated the resources he needs, e.g. when he needs to send something he is assigned the necessary bandwidth, which is then released when the transmission is finished. Thus, when he is idle the resources are available for other users. To identify individual users, orthogonal signals in conjunction with matching filters at the receiving stations are used, see Figure 5. Each user is assigned a particular code sequence, which modulates the carrier frequency with the digital data modulated on top of it. Even when several stations employ the same code, the effect of interference is minimised by the ability of the receiver to lock onto one packet while all other overlapping packets appearing as noise. CDMA is simple to operate since it does not require any transmission synchronisation between the mobile stations, but the throughput can be low. By throughput we mean the total number of bits transmitted per second. code time frequency Figure 5. In CDMA, users share the same frequency range all the time. Instead they are identified by orthogonal codes. The figure is taken from [5].

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3.4 Power control Power control is an essential feature of all CDMA systems. A fast and accurate power control is absolutely necessary in the uplink to detect users and avoid so-called near-far problems. In the downlink, power control is used to decrease the interference but it is less critical for system operation. The near-far effect is present when an interfering transmitter is much closer to the receiver than the intended transmitter, see Figure 6. Although the cross-correlation between codes A and B is low, the resulting signal from the correlation between the received signal from the interfering transmitter and code A can be stronger than the correlated signal received from the intended transmitter. The result is that proper data detection is not possible.

Receiver, code A Intended transmitter, code A Interfering transmitter, code B Figure 6. The near-far effect in a CDMA system. The transmitter with code B makes it impossible for the receiver to hear the transmitter using code A if power control is not used. The figure is taken from [2]. Even if the power control works perfectly, some power related problems might occur. One of them is caused by the “cocktail party effect”, described by Claude Shannon in 1949 as: ”More and more people can come, and they would all pay equally, so to speak. If more people were there, gradually the noise level would increase on each channel. But everyone would still talk, even though it might be a pretty noisy ’cocktail party’ by that time” As long as base stations and terminals can increase their output power things work fine. However, when this is no longer possible the radio link will fail. Therefore, it is very important to have good power planning in order to ensure that the capacity and the coverage are stable.

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4. WCDMA WCDMA is a wideband Direct-Sequence Code Division Multiple Access (DS-CDMA) system. The user information bits are spread over a wide frequency band by multiplying the user data with quasi-random bits (called chips) derived from CDMA spreading codes. To support very high bit rates, up to 2 Mbps, the use of a variable spreading factor and multicode connection is supported. The chiprate in UMTS is 3.84 Mcps, which leads to a carrier bandwidth of about 5 Mhz. In WCDMA every bit is coded into chips and the number of chips from one bit is called the spreading factor. The inherently large carrier bandwidth of WCDMA supports high user data rates, but also highly variable data rates. Each user is allocated frames of 10 ms duration, during which the user data rate is kept constant. Every service type has its own bit rate and it is regulated every 10 ms. 4.1 Spreading and despreading Two different types of code sequences are used in order to spread the signals. They are referred to as channelization codes and scrambling codes, respectively. The channelization codes are so-called Orthogonal Variable Spreading Factor (OVSF) codes, characterized by different spreading factors (SFs). The SF also defines the length of the code. For example, a OVSF code with spreading factor 4 is four chips long, while a OVSF code with spreading factor 256 is 256 chips long. Two OVSF codes of the same length, i.e. with the same SF, are always orthogonal. For example, two 4-bit codes:

{ }1,0∈ω ( ) { }1,1−=ωX 1)0( =X 1)1( −=X ( ) { }1,1−=ωY 1)0( =Y 1)1( −=Y

( )1,1,1,1)1,1,0,0( −−== XS ( )1,1,1,1)0,1,1,0( −−==YT ( ) ( )[ ] 011111,1,1,11,1,1,1 =−+−=−−•−−=⋅TS Every user in the system is given a unique spreading code, which separates the signal from other users’ signals. To receive the coded signal, a despreading code is used. The codes for spreading and despreading are the same. All information sent in the system is coded as binary numbers. An example of a spreading and despreading procedure when an OVSF code with SF 8 is used, is shown in Figure 7.

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Bit Chip

1 -1 1 -1 1 -1 1 -1 1 -1

Figure 7. Spreading and despreading, using a spreading factor of 8. The OVSF channelization codes have spreading factors, see Figure 8, that vary from 4 to 256 in both uplink and downlink. An SF 512 may optionally be used in the downlink. The orthogonality property gives less interference between users. The number of different codes, and consequently the number of simultaneous users of a certain service, is directly related to the spreading factor: there are 256 different codes with SF 256 but only four with SF 4. A speech connection based on the 12.2 kbps AMR (the speech bitrate of UMTS) mode requires a SF of 128 whereas a 128 kbps data connection needs SF 16 to reach the QoS goals.

spreading

despreading

Data

Spreading code

Spread signal = Data * code

Spreading code

Data = Spread signal * code

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SF = 1 SF = 2 SF = 4

C ch,1,0 = (1)

C ch,2,0 = (1 ,1)

C ch,2,1 = (1 ,-1)

C ch,4,0 = (1 ,1 ,1 ,1)

C ch,4,1 = (1 ,1 ,-1 ,-1)

C ch,4,2 = (1 ,-1 ,1 ,-1)

C ch,4,3 = (1 ,-1 ,-1 ,1)

Figure 8. The code-tree for generating OVSF codes. In figure 8, the channelization codes are uniquely described as Cch,SF,k, where SF is the spreading factor of the code and k is the code number, 0 ≤ k ≤ SF-1. Each level in the code tree defines channelization codes of length SF, corresponding to a spreading factor of SF in figure 8. The generation method for the channelization code is defined as (see [14]): 1Cch,1,0 = ,

−=

−=

1111

0,1,

0,1,

0,1,

0,1,

1,2,

0,2,

ch

ch

ch

ch

ch

ch

CC

CC

CC

,

and in general we have:

( )

( )

( )

( )

( ) ( )

( ) ( )

−

−

−

=

−−

−−

−++

−++

+

+

+

+

12,2,12,2,

12,2,12,2,

1,2,1,2,

1,2,1,2,

0,2,0,2,

0,2,0,2,

112,12,

212,12,

3,12,

2,12,

1,12,

0,12,

:::

nnchnnch

nnchnnch

nchnch

nchnch

nchnch

nchnch

nnch

nnch

nch

nch

nch

nch

CCCC

CCCCCC

CC

CC

CCCC

.

The leftmost value in each channelization code word corresponds to the chip transmitted first in time. In the downlink, scrambling codes are used to separate different cells and sectors from each other. All channels transmitted from one base station are synchronous at chip level and symbols can be shifted by multiples of 256 chips to each other. This preserves the good correlation characteristics of the OVSF codes (orthogonal in an environment of infinite bandwidth and no reflections). Since the same scrambling code is used in the entire cell (sector) in downlink, separation between users is in the hands of the channelisation codes.

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In the uplink, all mobiles use the same code set of OVSF codes and they are therefore distinguished only by the scrambling code, which is specific to each user. The purpose of the channelization codes in uplink is to separate different channels from the same UE (User Equipment) if necessary. There are about 242 scrambling codes. The properties and generation of these codes are outside of the scope of this thesis. For further studies see [1], [2], [14].

5. Radio network system Here is a short and simple description of the general structure of a third generation mobile network system. It is very similar to the second generation network. The base stations and the radio network controllers are new features. CN Core Network GERAN GSM Edge Radio Access Network UTRAN UMTS Terrestrial Radio Access Network MSC Mobile Switching Center SGSN Serving GPRS Support Node RNC Radio Network Controller UE User Equipment GSM Global System for Mobile communication GPRS General Packet Radio Service RNS Radio Network Subsystem Node B Base station The MSC and the SGSN function like switchboards for circuit switched and packet switched data respectively. The RNC controls basically all radio resources and, for example, adjusts the signal power sent from UEs and Node Bs.

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CN GERAN Node B Node B RNS RNS UTRAN Figure 9. Physical view of the network system. The Core Network is the already existing physical network. UTRAN is the future physical network for UMTS and RNS is one base station serving its mobiles.

6. Radio properties A problem with radio is that the available spectrum is limited. The less spectrum needed per subscriber the more subscribers can be accommodated in the network. There is a need for good modulation techniques and efficient access methods to use the ether properly. A radio signal is affected by different physical phenomenon on its way from transmitter to receiver. Here we are going to present the most important phenomena. They are path loss

MSC SGSN

GSM / GPRS

RNC RNC

UE

UE

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including shadowing, scattering and multipath propagation. We also present the meaning of interference. The cell model is also described. The models describing the propagation of radio waves in cities like New York or Hong Kong differ greatly from those that describe the propagation in the north of Sweden. Below we describe some of the effects that are relevant for path loss calculations. 6.1 Propagation path loss Path loss or attenuation of the signal causes the received signal to get weaker further away from the transmitter. Path loss makes it difficult to get sufficient signal strength levels, but it also results in a lower interference from unwanted transmitters far away from the receiver. Within the telecom business we talk about path gain and path loss. These concepts are defined as follows. dtransmittereceived PgainPathP ⋅= (6.1)

gainPath

lossPath 1= (6.2)

2dklossPath ⋅∝ (6.3) Here k is a constant and d the distance between the transmitter and receiver, see Figure 10. The path loss itself is dimensionless. It follows that ∞→lossPath as ∞→d and then

0→receivedP far away from the transmitter. The definition of path loss may seem somewhat strange, but is common in telecommunications.

d Figure 10. The path loss between transmitter and receiver depends on the distance d between them. As ∞→d , the received power will go to zero. Relation (6.3) assumes a line of sight condition between transmitter and receiver and that there are no reflections interacting with the direct radio wave. It is thus a very simple model. There are, however, more complicated models that take more phenomena into account. In general, the exponents in (6.3) are not 2’s, but general constants determined by, for example, measurements and approximations. The theoretical models are then compared to actual measurements and constants, like k in (6.3) above, can be tuned. Corrections are due to shadowing, scattering, and multipath propagation (see Figure 11), which are described below.

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6.2 Shadowing and scattering If the radio path does not have free line of sight between transmitter and receiver, the obstacles will cause shadowing. The mobile phone is normally located in a low position and the transmission will most likely be affected by shadowing objects, for example buildings and hills. When the mobile phone moves around, variations in signal strength due to the character of the objects can be measured and contributes quite a lot to the variations in the path loss. The actual received signal is often stronger than predicted. This is due to the fact that when the radio wave strikes a rough surface, the reflected energy is spread out in all directions due to scattering, thereby providing additional energy at the receiver. Flat surfaces that have much larger dimensions than the wavelength can be modelled as reflective surfaces. In other cases the roughness of the surface induces propagation effects that must be taken into account. For more details on shadowing and scattering, see [10]. The fading due to shadowing and scattering can be modelled as log-normally distributed with a zero-mean and a standard deviation of σ (see [12]). 6.4 Multi-path propagation In an urban environment with a lot of reflecting objects near the transmitter and receiver another effect occurs, called multi-path propagation. Since the transmitter is not normally transmitting directly towards the receiver but rather in a wide sector towards him/her, there will be a lot of rays reflected by obstacles and received by the receiver, see Figure 11. Different reflections will have different time delays and effects on the phase of the radio wave. Normally a user receives several waves and the resulting wave can be very strong or very weak. If two waves with the same frequency have a phase difference of 180 degrees, they will completely cancel each other out. The phenomenon is called multi-path fading and can be quite substantial. This effect is not considered in the simulator.

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Figure 11. Multipath propagation. The effects can be quite substantial. 6.5 Interference Interference is the term for the unwanted signals that the receiver experiences. Most interference is due to users in adjacent cells. Reusing a frequency in different cells is limited by co-channel interference. Co-channel interference describes the relation between the desired signal effect and the undesired signal effect, both using the same carrier frequency. Figure 12 shows the principle of interference between two base stations. The mobile wants to listen to the signal C from the carrier base station but also receives interference I from the interfering base station. The larger the interference I is, the harder it will be for the user to distinguish the desired signal C. Eventually it will become impossible. Carrier Interferer C I Figure 12. Interference. The interfering base station makes it harder for the mobile to listen to the signal from the carrier base station. Although the channelization codes are orthogonal in theory, a consequence of the effects described is that the waveforms will not be perfectly orthogonal when received. They can thus impact each other. Since all users occupy the same part of the spectrum, the effective noise will be the sum of all other users’ signals. The receiver correlates its input with the desired

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noise, enhancing the signal to noise ratio at the detector. Because the interference is summed, the system is no longer sensitive to worst case interference, but rather to average interference. 6.6 The cell model The base stations and their coverage areas can be modelled as hexagon structures with the stations in the middle. There are two types of cells: omnidirectional antenna cells and multiple sector site cells, see Figure 13. Omnidirectional antenna cells have the base station in the center of the hexagon. Multiple sector cells have the base stations located at the border of the cells. Here we present the multiple sector site as a three sector site. = base station

Omni directional cell Three sector cell Figure 13. The omnidirectional antenna cell model and the three sector antenna cell models. Only omnidirectional cells are used in the simulation. In this thesis only omni-directional cells are modelled. As an approximation of the base stations in a region the hexagons are placed close to each other in a regular pattern, see Figure 14. In reality this is not the always the case due to shadowing and multi-path propagation.

21

Figure 14. A regular cellular system in theory. Typical cell diameters for speech in UMTS with three sector sites lie between 1800 m and 1100 m for the up link and 3500 m and 1500 m for the down link, depending on the load of the cell. This shows that it is often the uplink that is the limiting link, see, for example, the simulation results in [12]. It is well known that the traffic load will affect the coverage of an UMTS cell. This is sometimes referred to as “cell breathing”, because the cell shrinks with increasing load and grows with decreasing load. This effect is inherent in all CDMA systems and it has to be reckoned with when planning the network. This adds complexity to the problem of optimising the coverage and the capacity of the system. In FDMA/TDMA systems such as GSM, the coverage is relatively independent of the capacity, which makes planning the coverage of the network much easier than in CDMA systems, where we have to take into account the interference from other users already at low traffic loads.

7. Mathematical background in UMTS As stated previously, we focus on the uplink since it is the limiting link in most cases. This is due to the relatively low output power of the mobiles. In this Section we present some mathematical formulas and expressions that are important when studying the capacity and coverage of a WCDMA network. One can do basically the same analysis for the downlink scenario as well. The mathematics will be very similar.

22

7.1 The uplink In the uplink, we receive the signals from all the UE’s at the same location, viz. at the base station antenna. It is also here that interference from other cells will be received. The limiting factor in most cases will be the maximum output power of the UE, because it has to overcome noise, other cell interference (intercell) and interference from other users within the cell (intracell) at the base station antenna. The quality of a radio link can be characterized by its Signal-to-Interference Ratio (SIR) value. This is basically the quotient between the effects of the desired signal and the interference, as received at the base station. One should note, though, that measuring these quantities in real life can be quite complicated. A service, like speech has some necessary SIR value for it to work properly. These are found through simulations and measurements in test systems. The service also has a necessary bit rate, which is determined by how much information it needs to convey. This is a known value, which we denote by R. Speech in UMTS, for example, needs a bit rate of 12.2 kbps.

i

k

Figure 15. Path gain between mobile and base station.

( )ibiG

( )ibkG

( )kbkG

23

The received signal power is ( ) iiib PG ⋅ , while the total amount of interference is

( )∑≠

⋅+ik

kikb PGN0 , i.e. , the background noise N0 together with the sum of all other users’

signal strengths at the base station. Pi is the transmitted power from user i. Gkb(i) is the path gain between user k and the base station user i is connected to, see Figure 15. Here b(i) is short-hand notation for the base station user i is connected to. We can now write down the equation that describes the connection between the SIR values and power, the so-called power control equations. For user i, it is (see [12])

( )∑≠

⋅+⋅

⋅=

ikkikbo

iiib

ii PGN

PGRWSIR

)(

)( . (7.1)

There is one such equation for each user in the system, which must be solved for the power Pi. W is the chiprate 3.84 MHz. To simplify the calculations we introduce the equivalent bandwidth η : iii SIRR ⋅=η . (7.2) The equivalent bandwidth can be viewed as some sort of measure of the amount of radio resources the service will need since it is directly proportional to both the bit rate and the necessary SIR. Equations (7.1) and (7.2) now yield

( ) iiibik

kikboi PGPGN

W⋅=

⋅+⋅ ∑≠

)()(η

(7.3)

( ) ( ) ( ) iiib

kkikb

ikkikb PGPGPG ⋅−⋅=⋅ ∑∑

≠)()( . (7.4)

Equations (7.3,7.4) give:

( )( ) ( ) iiibiiibk

kikboi PGPGPGN

W⋅=

⋅−⋅+⋅ ∑ )(η

( )( ) ( )

+⋅⋅=⋅⋅+⋅ ∑ WPGPG

WN

Wi

iiibk

kikbi

oi ηηη

1

( ) ( )

( )( )

( ) ( )( )( )∑

∑

⋅⋅

+⋅+

+⋅=

=⋅⋅

+⋅⋅+

+⋅⋅=

kkikb

i

i

iibi

i

iib

o

kkikb

i

i

iibi

i

iib

oi

PGWGWG

N

PG

WWG

WWG

NP

ηη

ηη

ηη

ηη

1

1

11

1

1

. (7.5)

24

This can be rewritten in matrix form according to: Β+Ρ⋅Α=Ρ , (7.6) ( )( )( )maxii P,min 1 Β⋅Α−Ι=Ρ − . (7.7)

{ } =∈ NNki ,....1, number of users Here we have introduced Pmax as the maximum output power of a UE. In UMTS, Pmax is 0.125 W. Equation (7.7) thus describes the fact that a UE can not transmit with higher power than Pmax . The matrices A and B are defined according to

( )

( )iib

ikb

i

iik G

GW

⋅

+=Α

ηη

(7.8)

( )iib

o

i

ii G

NW

⋅

+=Β

ηη

(7.9)

{ } =∈ NNki ,....1, number of users

One big problem is that it is not effective to compute the inverse of a very large matrix. In this study it is useful to make iterations to solve this system numerically. The iteration process is continued as long as the relative difference between two consecutive values is larger than ξ = 0.01.

( ) ( )

ξ≤− −

ni

ni

ni

PPP 1

(7.10)

7.2 Single cell pole capacity The pole capacity is the capacity when the background noise rise, the total interference to thermal noise ratio ( see page 33), diverges towards infinity. We will now focus on a single cell surrounded by some static interfering cells in order to understand the pole capacity better. In this situation the power control equations can be solved analytically. The solution is (see [12] for details),

( )

( )

+−+

+⋅⋅=

∑k k

ki

oii

WW

ociNWP

ηηη

ηλ

1. (7.11)

where λ is the number of users per unit time and oci is the other to own (static) cell interference ratio. We can now define the cell load X according to

25

∑ +=

i i

i

WX

ηη

. (7.12)

Equation (7.11) can now be written as

( )

( )( )XWociNW

Pi

oii −+

+⋅⋅=

1ηηλ

. (7.13)

If too many users are allowed into the system, so that X>1, the system will collapse. The output power needs to be infinite in order for all users to fulfil the SIR requirements. In this analysis, the various services that UMTS provides are differentiated through their different η values. The increment of the load when adding one user of service number s will be

s

ss W

Xη

η+

= (7.14)

If we have only one service present in the cell characterized by η, the maximum number of users in the cell is

1max +=ηWK , (7.15)

which follows from equation (7.12) and the fact that X=1 defines the maximum cell load possible. Since the maximum number of users is roughly W/η, η has a role similar to the channel bandwidth in a FDMA system, which explains the name equivalent bandwidth. Instead of taking the sum over all users in equation (7.12), we can group the users according to the service they are using. Then the load becomes

∑ +=

s s

ss W

KXη

η , (7.16)

where sK is the number of users in the cell using service number s. 7.4 Number of users It is time to introduce three levels of activity for the users: inactive, in session and active. The inactive users are of no interest to us other than as a pool of users that may start a session. The users who are in session are currently using the service, which does not necessarily mean that data is being transmitted over the radio interface. This occurs with a certain probability νs depending on the type of service. The number of active users is thus always smaller than or equal to the number of users in session. This model is based on the behaviour of speech users, where the probability of activity is called voice activity factor and has a value around 50 %. Also, the distribution of the number of users in session, Ms , is needed in order for the model to be complete. It is well known that the inter arrival times and the call holding times for speech are exponentially distributed, leading to a Poisson distribution for Ms [8]. When

26

studying statistics from measured traffic loads in the GSM network, the traffic in GSM is found to be Poisson distributed; it is therefore reasonable to choose the Poisson distribution in this model. The Poisson distribution has one parameter, referred to as the session traffic, denoted by sρ , which is equal to the mean of the distribution. This parameter is referred as the session traffic, denoted ρs. This gives

( ) sem

mMPms

sρρ −==

!. (7.17)

The subscript s is for service type. Given a certain number of users in session, the number of active users, Ks, will follow a binomial distribution:

( ) ( ) kms

ksss k

mmMkKP −−

=== νν 1| , (7.18)

which gives us

( ) ( )

( ) ( ) ( )( )

( ) ( )

( ) ( )[ ] ( )( ) ( )

ssssss

s

ss

ss

ek

ek

e

eforformulasTaylormk

e

mke

mke

kmke

kkmm

me

mMkKPmMPkKP

kss

kss

x

m

mss

kss

m

ms

msk

s

ks

m

ms

kms

ks

km

kms

ms

kskm

sks

km

ms

kmssss

νρνρρρ

ρ

ρρ

ρρ

νρρν

νρρν

νρρννρν

νρνννρ

−−−

∞

=

−

∞

=

−∞

=

+−

∞

=

−−−

∞

=

−

∞

=

==

==−

=

=−

=−

=

=−−

=−−

=

======

∑

∑∑

∑∑

∑

!!

'!

1!

!1

!!1

!

!1

!1

!!!

!

)|(

0

00

(7.19)

Here we have studied [6] to make this derivation. The result is that the number of active users also follows a Poisson distribution with mean ρsνs, i.e., ( )sss PoK νρ∈ . (7.20)

27

8. Admission control CDMA systems have no absolute limit on the number of users that can be supported in each sector. This number is determined by the multiple access interference that is generated at the base station by all the uplink signals and by the propagation conditions (path loss and shadowing). In order to maintain the quality of the radio connections an admission control is needed to prevent the system from getting overloaded. If the interference levels get too high no new users are admitted. The increase in the interference level caused by an incoming user needs to be estimated by the admission control. Multiple access interference in WCDMA can be approximated as Gaussian noise but it inherently consists of received signals of WCDMA users. Therefore, multiple access interference is highly structured, and can be taken into consideration in the receiver to improve the performance. The admission control allows a new user into the radio access network if the admission does not cause an excessive interference in the system. Thus, the admission control has a big responsibility for the stability and high capacity of the WCDMA network. The admission control is performed for the uplink and downlink transmissions separately because the traffic load can be asymmetric. A user is admitted into the system if both uplink and downlink admission control requirements are fulfilled. In this Thesis only the uplink is considered (see, for example, [3] for downlink admission control simulations). The basic admission control procedure is described in the next Section. 8.1 Admission control principle. Newly arrived real time users will be controlled by an admission control function to determine whether there is enough capacity for them to be accepted. The procedure for the new users in every cell is as follows: 1. New users in each cell will be registered in the current admission control function. 2. The already active real time users and the new user will be taken into account in the

calculation of the cell load. 3. If the new user is accepted, he/she will be included among the users. If blocked, the new

user will of course be rejected to the cellular system. 4. If there are other new users, step 2 and step 3 will be repeated. 5. The procedure will finish when there are no more prospective users in the cellular system.

28

9. System model The computer simulations are done in an environment of 16 hexagonal cells with a cell radius of 500m. A wraparound technique is used to avoid border effects. We have a base station placed in the middle of the cell and omni-directional antennas are used. 9.1 Propagation models The received power of the signal rjP from transmitter (mobile user) i at receiver (base station) j can be expressed as tiijrj PGP ⋅= , (9.1) where r stands for received and t for transmitted. The path gain G is defined as

SL

G⋅

= 1 (9.2)

where L is the propagation path loss and S is the shadow fading. The path loss is based on the Okumura-Hata formula ( ),log dLdB ⋅+= εδ (9.3)

( )

10log

10d

Lεδ +

= (9.4) whereδ is an attenuation constant based on the carrier frequency (2 GHz), the height of the transmitting and receiving antenna respectively.ε is the distance attenuation coefficient with a value of 2.5-5. Note that (9.3) of the same form as in equation (6.3), but with other exponents and in dB. Here d is the distance between a base station and a mobile user. The shadow fading S is log-normally distributed with a zero-mean and a standard deviation of σ . When computing different quantities in radio systems, the magnitudes are often so small that we use dB instead of its real physical value to simplify when doing the calculations. The relation between real values and values in dB is

1010b

a = (9.5) where a is the real value and b is the value in dB. 9.2 Traffic model for speech In GSM, speech is the dominant service and will be so also in UMTS. The service can be of different qualities. The characteristics of speech are low delay and low variations in the delay. The SIR-requirement for speech is 7.9 dB (see [7] and [12]). A constant bitrate of 12.2 kbps is needed and the service is symmetric in uplink and downlink. The voice activity is 0.5, which

29

means that we speak approximately half of the time during a call. Arrivals to the system are modelled as a Poisson process. The mean time of calls is 90 s, see [8]. 9.3 Handovers One of the fundamental RRM (Radio Resource Management) functions in the radio network is different types of handovers. For an overview of radio resource management functions in UMTS, see [13]. A handover is performed when the mobile is connected to a base station and moves and changes to another base station. If the distance between a mobile user and a base station is very large, a dropping can occur. By using handover, the radio link can be saved and the QoS requirements upheld. There are mainly three types of handovers: 1. Hard handover; the link to the “old” base station is disconnected before a link to the

“new” base station is set up. The mobile user can thus have only one radio link connection with one base station at a time.

2. Soft handover; the UE can be connected to two (or more) base stations at the same time. After moving further away from the “old” base station, the radio link is disconnected and the UE continues with the “new” base station.

3. Softer handover; basically the same as soft handover. The only difference is that the handovers are between two sectors on the same site.

9.3.1 Macro diversity A diversity method called macro diversity is used in softer handover. The diversity technique transports the same information along a number of communication paths between the mobile user and the base station. The mobile user will dynamically choose the transmitter with the best communication path with a combining method. It is a useful method to combat the shadow fading and other terrain effects. By using a diversity technique the performance of data transmission over fading channels can be improved and the shadowing of transmitted signals by large obstacles such as mountains, hills or buildings can be avoided. The use of soft/softer handover decreases the capacity of a network in terms of the number of users it can support. Therefore it is not advisable to have too many users in soft/softer handover. In the simulations, a hard handover strategy is used. A user can only be connected to one base station at a time. 9.4 Power and rate control (PARC) By using power control, SIR’s can be efficiently controlled. If variable transmission rates are available we can efficiently utilize the radio spectrum. Due to the different QoS requirements in UMTS, the need for power control and rate control is obvious. It is natural to use a combination of these RRM (Radio Resource Management) functions, power control and rate control. For real time services a tolerable minimum average rate must be guaranteed, while delay insensitive best effort services may temporarily reduce their transmission rates and attempt to utilize any excess network capacity. If the cellular system becomes heavily congested, the transmission rates of best effort services may be lowered even to zero. The effective

30

transmission rates are closely related to the SIR’s. If the data rate of one user is increased, the other users will experience an increased interference power for a short time. The other users will experience less interference with a decreased data rate, but for a longer time. 9.5 Call dropping mechanism A dropping condition, maxPP trequiremen > is used but the calls do not drop immediately. By using a leaky bucket technique, the calls will be dropped after a number of transmission failures. After a failed transmission the bucket will decrease by one unit and when there is no unit left in the bucket, dropping will occur. After a successful transmission the bucket will increase by two units, but only up to a given threshold. A threshold of 1 unit is used for real time services like speech corresponding to 0.1s.

50 49 48 : : : : N : : : : 3 2 1

Figure 17. The structure of the leaky bucket. 9.6 Performance measures The service quality is normally determined by the probability of blocking and dropping. Blocking occurs when a newly arrived user is denied access to the cellular radio system. Dropping occurs when an active user is disconnected from the cellular radio system. There are several reasons why dropping occurs. An active user may move into another cell, where there is no capacity available or SIR may drop below the minimum SIR requirement. Dropping of an ongoing service is in general more annoying than blocking a new user. To maintain a high QoS, the best trade-off between these two service quality measurements must be found. A lower blocking probability will result a higher dropping probability and vice versa. The dropping probability Pd is defined as the ratio between the number of dropped sessions and the number of arrivals to the system during the simulation time. The blocking probability Pb is defined as the ratio between the number of blocked sessions and the number of arrivals to the system during the simulation time.

After a successful transmission the bucket will increase by two units

After a failed transmission the bucket will decrease by one unit

31

10. The principle of the simulator The simulator is a dynamic simulator implemented in MATLAB. Each simulation is done as follows. 1. A log-normal map is created to model the effects of shadowing and scattering. 2. Initial service sessions and users are created. 3. Simulation loop: Figure 18. Principle of the simulator. The simulation ends when t = t(end of service sessions). A typical value of t(end of service sessions) is 2000 s and dt = 0.1 s. In the simulation program, the mobiles are modelled to move every time step (dt) and their new position (xy) , xy is a complex vector, depends on their velocity (vel) and acceleration (acc) at the time step n-1, where n corresponds to the current time step. The movement of the users in each time step is calculated as follows.

Create new active users (mobiles) and start new service sessions, according to a Poisson process.

Create new in-session loads. Different loads for every service type.

Move active users. Update positions of users. Add new users if there are any.

Allocate radio resources to active service sessions. Admission control. Handovers. Power control.

Calculate transmission quality (received power and SIR levels).

Remove completed and dropped service sessions.

Collect statistics.

End dropped or completed service sessions.

Compare transmission quality with quality demands.

t = t + dt

32

The acceleration is given by nnn velracc ⋅−⋅= γφ (10.1) where φ and γ are constants and r is a normal distributed random number. The user’s velocity then follows as dtaccvelvel nnn +=+1 . (10.2) This leads to the new user position dtvelxyxy nnn +=+1 . (10.3) Statistical properties of acc and vel are assumed (e.g the average of all mobile users velocity is assumed to be Rayleigh distributed) and then φ and γ can be expressed as:

dtaccσφ = (10.4)

dt

dtvel

acc ⋅

−−

=

2

11σσ

γ (10.5)

where meanacc and meanvel are the input variables to this mobility model. For further details see [15] and [16].

11. Admission control algorithms Here we present three algorithms, viz. The Derivative, The Integration and The Equivalent Bandwidth algorithms. The Derivative and The Integration methods are described in [1] and [4]. The Equivalent Bandwidth method is described in [11]. We must derive formulas for the increase in loads and interference. We must also find out values of the thresholds. In Section 11.1 the load factor for The Derivative and The Integration algorithms is presented as an equivalent bandwidth. In Section 11.2.1 and Section 11.2.2 the increase in interference levels is derived for The Derivative and The Integration methods respectively. In Section 12.2.3 the load for The Equivalent Bandwidth algorithm is presented. Start approximations for the thresholds are computed. 11.1 Uplink load factor The first step is to present the ACSIR (Admission Control SIR) for The Derivative and The Integration algorithms. The only difference from equation 7.1 is that the voice activity level is included and that the power and interference does not include the path loss. The reason that

33

path loss not is included is that we now focus on the received power at the base station. jtotal ZI − is the interference from all other users within the cell.

jtotal

j

jjj ZI

ZR

WACSIR−

⋅=ν

(11.1)

W = Chiprate (3.84 Mcps) Zj = Received signal power from user j vj = Activity factor of user j, 0.5 for speech Rj = Bit rate of user j Itotal = Total received wideband power, including thermal noise Solving with respect to Zj gives

totaljtotal

jjj

j ILdI

RACSIRW

Z ⋅=⋅

⋅⋅+

=

ν1

1 (11.2)

where we have defined the load factor of one connection jLd as

jjj

j

RACSIRW

Ld

ν⋅⋅+

=1

1 (11.3)

The total received interference, excluding the thermal noise No, can be written as the sum of the received power from all N users in the same cell

∑ ∑= =

⋅==−N

j

N

jtotaljjototal ILdZNI

1 1 (11.4)

The noise rise is defined as the ratio of the total received wideband power to the noise power

o

total

NI

riseNoise = (11.5)

This equation can also be written as

ULINTRA

N

jj

o

total

LdLdNI

riseNoise_

1

11

1

1−

=−

==∑=

(11.6)

34

Here we have made the following definition:

∑=

=N

jjULINTRA LdLd

1_ . (11.7)

When ULLd becomes close to 1, the corresponding noise rise approaches infinity and the system has reached its pole capacity. In the load factor the interference from the other cells must be taken into account by the ratio of other cell to own cell interference, oci.

ceinterferencellownceinterferencellotheroci= (11.8)

The uplink load factor including oci can then be written as

( ) ( ) ∑∑==

⋅⋅+

⋅+=⋅+=N

j

jjj

N

jjUL

RACSIRW

ociLdociLd11 1

111

ν

(11.9)

11.2 Wide band power-based admission control strategy In the interference-based admission control strategy the new user is not admitted by the uplink Admission Control algorithm if the new resulting total interference level II oldtotal ∆+_ is higher than some threshold value: thresholdoldtotal III >∆+_ (11.10) The threshold value thresholdI is set by radio network planning. The dependence of the increase in interference due to the increase in the load, ∆L, for a new user is shown in Figure 19. Interference I∆ L∆ Figure 19. Load curve and estimation of the load increase due to a new user.

Load

Max planned noise rise

thresholdI

35

11.2.1 Approximating the rise in interference by using differentiation Here we derive the increase in interference for The Derivative algorithm. It is based on the expression for the noise rise,

ULo

total

LdNI

riseNoise−

==1

1 . (11.11)

Solving this with respect to the total interference yields

UL

ototal Ld

NI

−=

1. (11.12)

Differentiating this with respect to the load then results in

( )21 UL

o

UL

total

LdN

dLddI

−= (11.13)

Here we have thus approximated the rise in interference by using derivatives. We now make the approximation

UL

total

dLddI

LdI ≈

∆∆ ,

which yields

( ) LdLd

ILd

LdN

LddLddI

IUL

total

UL

o

UL

total ∆⋅−

=∆⋅−

=∆⋅≈∆11 2 (11.14)

We refer to this way of approximating the rise in interference as The Derivative method. 11.2.2 Approximating the rise in interference by using integration Another uplink power increase estimate is based on the integration method, in which the derivative of interference with respect to the load factor is integrated from the old value of the load factor to the new as follows:

( ) ULUL

ototal dLd

LdN

dI ⋅−

= 21

36

This increase in interference follows from Eq. (11.14). Integrating this yields:

Ld

LdLdI

LdN

LdLdL

LdN

LdLdLd

LdN

LdLdNI

UL

total

UL

o

UL

UL

o

UL

UL

UL

o

UL

o

∆⋅∆−−

=−

⋅∆−−

∆=

=−

⋅

−

∆−−−

=−

−∆−−

=∆

111

11

11

11 (11.15)

The load factor of the new user, Ld∆ , is the estimated load factor of the new connection and can be obtained as:

RACSIRW

Ld

⋅⋅+

=∆

ν1

1 (11.16)

We refer to this way of approximating the rise in interference as The Integration method. 11.2.3 Equivalent Bandwidth algorithm Consider a system with a bandwidth of approximately 5 MHz comprised of a single base station to which N mobiles are connected and suppose that at an arbitrary time instant, mobile i requires a bit rate of Ri and a SIR requirement of SIRi.. Thus the equivalent bandwidth (EB) of user i is iii SIRR ⋅=η (11.17) and the sum of N users contributes to an overall equivalent bandwidthη according to

∑=

=N

ii

1ηη (11.18)

A given configuration is feasible if and only if thresholdηη ≤ (11.19) We refer to this way of approximating the rise in interference as The Equivalent Bandwidth method. 11.3 Derivative algorithm with exponential equalization. Admission Control is used to avoid dropping throughput during a complete call, i.e., on a time scale of 100 s. To base an Admission Control decision on interference, which varies on a much smaller time scale could therefore be less than optimal. In an effort to improve on this situation we have modified the interference based Admission Control algorithm so that it is based on a filtered interference value, smoothing out short time scale fluctuations. In this thesis the filter function is as follows:

37

( ) ( ) ( ) ( )tItItI received⋅−+−⋅= αα 11 (11.20) where α is the equalization factor. In this thesis we have chosen 8.0=α . 11.4 Start approximations To perform simulations, values for thresholdI and traffic intensity are needed. The threshold should be chosen to avoid dropping. To get a start approximation we assume that a mobile should be able to reach its SIR target at the cell border with shadow fading one standard deviation worse than the mean value. Here is an approximation of the thresholdI for the uplink admission control:

target

maxstartthreshold SIRR

GPWI

⋅⋅⋅⋅

=ν_ (11.21)

totL

G 1= (11.22)

( )

103log

10CBd

totL+++⋅+

=σεδ

(11.23)

=totL Total path loss including shadow fading =maxP Maximum transmission power from a mobile

=targetSIR Minimum SIR for speech =δ Attenuation constant based on the carrier frequency [dB] =ε Distance attenuation coefficient [dB] =d Cell radius [m] =σ Standard deviation for shadow fading [dB] =B Body loss, due to the human body [dB] =C Cable loss [dB]

14

_ 101469.1 −⋅=startthresholdI [W] (See Appendix B for numerical values). This approximation of startthresholdI _ is used for both The Derivative and The Integration method. A start approximation of the startthreshold _η is 0.5 MHz. The value comes from the single cell pole capacity [12]. We must also have a start approximation of the traffic intensity. The traffic intensity can be written as:

( )session

startstart t

WLd⋅⋅

+⋅=

ηνηλ (11.24)

SIRR ⋅=η , (11.25)

38

where sessiont is the mean holding time and startLd is an approximation of the load level in the cells. It follows that 3470.0=startλ users/s for one cell. (See Appendix B for numerical values).

12.Simulation results We have investigated The Derivative, The Integration and The Equivalent Bandwidth methods. The goal is to find out which of these methods is the most effective. When talking about efficiency, we mean that the method will have a low blocking probability for a given dropping probability. The first step is to find the traffic intensity to be used in the further simulations. Then we will make simulations over short intervals of time just to find out suitable thresholdI and thresholdη levels and also to study the relation between the threshold values and the number of dropped and blocked users. The main task of the thesis is to make simulations over long time intervals to investigate the dependence of different parameters and to compare the different methods. We have chosen to investigate the following: the effect thresholdη and thresholdI have for the three methods, the relation between the number of dropped and blocked users when varying the other cell to own cell interference ratio for The Derivative method, the dependence on the users’ mobility, what happens if the received power levels are computed by exponential equalization and the accuracy of the dropped and blocked users when doing the simulations. 12.1 System load working point The first step is to find the system load working point, i.e. the traffic load that gives sufficiently much dropping before adding any admission control to the simulations. This will enable us to compare the different admission control routines. Only the uplink is considered. The load is varied in order to find a dropping probability of 10%. The simulation time and parameters describing the mobility of users are shown in Table 1. Parameter Value Simulation time [s] 500 Users mean velocity [m/s] 7 Users mean acceleration [m/s^2] 0.25 Table 1. Input parameters. In real networks, dropping figures of about 1% are acceptable. The Admission Control algorithms under investigation will be used to bring the dropping down to this level. The system load is given by the call arrival rateλ / km2. From figure 20 we see that a call arrival

rate of 1.6 skm

calls⋅2 results in approximately 10% dropping. This value for the system load will

be used for all further investigations. The value 7 m/s for the mean velocity is a realistic

39

scenario, some of the users are driving cars, some walk, some are just sitting in an office etc. Every simulation in Sections 12.1 and 12.2 has the same values of these parameters.

0.6 0.8 1 1.2 1.4 1.6 1.8

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

λ / km2

drop

ped

dropping

no admission control

Figure 20. Dropped vs. cell load.

In this case the value of λ / km2 = 1.6 skm

calls⋅2 . λ is the traffic intensity, users per second.

The value 0.1 on the dropped axis corresponds to 10% dropped. The share of dropped users is

the number of dropped sessions to all sessions ratio. The traffic intensity skm ⋅2

6.1 may not be

representative for realistic scenarios, but is a good level to easily find out which of the methods can decrease the number of dropped users most effectively. The next step is to find a value of thresholdI which gives only 1% dropping. The value 1% dropped is realistic in the radio system network. To do that, the cell load is kept constant, λ / km2 = 1.6. thresholdI is the variable quantity. The results are shown in the next Section.

40

12.2 Admission Control pilot study Before doing “hard” simulations with long simulation times we perform a pilot study to find the relevant interval for the Admission Control threshold values, thresholdI and thresholdη . In figure 21 we see that we have to bring the threshold down to a tenth of our original guess in section 11.4, to achieve a dropping of 1%.

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5

x 10−13

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

I−threshold

drop

ped

dropping

derivative integration

Figure 21. Dropped vs. Interference threshold investigated for The Derivative and The Integration method. If the number of dropped users is about 1%, the thresholdI is about 13102.1 −⋅ . The dropped level is the number of dropped users divided by the number of all sessions. Here we can see that the dropping increases with increasing thresholdI , as expected.

41

In figure 22 we see that the cost of achieving a dropping level of 1% is a blocking of about 20% of all calls. The two Admission Control methods studied give indistinguishable result, given the accuracy of the simulations.

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5

x 10−13

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

I−threshold

bloc

ked

blocking

derivative integration

Figure 22. Blocked vs. Interference threshold investigated for The Derivative and The Integration method. Here the thresholdI is varied to see how the number of blocked users depends on the thresholdI . The share of blocked users is the number of blocked sessions to all sessions ratio. Here we can see that if thresholdI = 10-13 then the share of blocked sessions is approximately 0.21. Here we can see that the blocking decreases with increasing thresholdI . It is natural that the dropping increases , (see figure 21), and blocking decreases for increasing thresholdI , the total number of sessions is the same at every point on the curves, so there is a trade off between dropped and blocked users. Both methods give approximately the same results. It is not very nice to block a lot of users but it is more annoying to be dropped.

42

The efficiency of an Admission Control algorithm is best displayed in a plot of dropping versus blocking, see Figure 23.

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

blocked

drop

ped

dropping vs blocking

derivative integration

Figure 23. Dropping vs. blocking investigated for The Derivative and Integration method. Dropping vs. blocking is an excellent way to see the trade off between dropped and blocked sessions. The number of dropped is not a function of blocked users, thresholdI varies along the curves. Low dropping means low thresholdI and vice versa. For a given value of dropped sessions it is desirable to have a low blocked value. Here we can see for example that if the blocked level is 0.1, the dropped level is about 0.31. The interesting interval is where the dropped level is 0.002 – 0.03 and this interval will be studied further. We can also see that The Derivative and The Integration methods give almost the same results. The dropped level of 1% which is an acceptable value, results in a blocked value that is far from an acceptable level in real networks, but this study is just to find out which is the most effective method.

43

The same investigation must be done for The Equivalent Bandwidth method to find out the relevant interval for thresholdη . Here thresholdη is varied and the task is to find out where the dropped level is about 1%. The idea is that we would like to compare dropped versus blocked for the three different algorithms, then we have to choose an interval for the dropped level. In figure 24 we can see that our start approximation of about 6105.0 ⋅ Hz results in about 7.5% dropped calls. In the further “hard” simulations we are interested in a dropping value of approximately 1%; we must therefore use lower thresholdη values than 6105.0 ⋅ Hz in our investigations.

3 3.2 3.4 3.6 3.8 4 4.2

x 105

0.005

0.01

0.015

0.02

0.025

0.03

0.035

η−threshold

drop

ped

dropping

equivalent bandwidth

Figure 24. Dropped vs. Equivalent Bandwidth threshold for The Equivalent Bandwidth method. If the dropped level is 1% we can see that thresholdη is about 5104 ⋅ . Again the dropped level is the ratio between the number of dropped sessions and the number of all sessions. Here we can see that the dropping increases with increasing thresholdη . That is similar to the other algorithms.

44

The next step is to investigate how the blocking level depends on the variations in thresholdη .

3 3.2 3.4 3.6 3.8 4 4.2

x 105

0.15

0.2

0.25

0.3

0.35

η−threshold

bloc

ked

blocking

equivalent bandwidth

Figure 25. Blocked vs. Equivalent Bandwidth threshold for The Equivalent Bandwidth method. The resulting graph is shown in Figure 25. Here thresholdη is the variable quantity. The number of blocked users is decreasing with increasing level of thresholdη which is similar to the previous methods. The blocking versus thresholdη relation is for The Equivalent Bandwidth approximately linear when the only service is speech. To give an example we can see that a

thresholdη value of 5104 ⋅ results in a blocked level of about 0.18.

45

In figure 26 we can see the trade off between dropped and blocked users for the Equivalent Bandwidth method. The relation dropping vs. blocking can be compared for the different users even if the threshold values differ a lot. We can see our further problem as an optimization problem to find out which algorithm has the lowest blocked value for a dropped value of 1%.

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09dropping vs blocking

blocked

drop

ped

equivalent bandwidth

Figure 26. Dropping vs. blocking for The Equivalent Bandwidth method. The dropped level is not a function of blocked users, the thresholdη is varied along the curve. A low dropped level corresponds to a low thresholdη level. It is difficult to make any conclusion about the efficiency by studies of this plot, but we can see that the most interesting interval is where the dropped level is 0.002-0.03. For low blocked values, the curve is very steep, and that is a good sign of an effective method. The level of 1% dropped results in a blocked level of 0.18.

46

12.3 Simulations We have in the previous section found out the most interesting intervals for the thresholdI and the thresholdη . It is now time to make longer “hard” simulations to increase the accuracy of the dropped and blocked levels. The simulation time and parameters associated with the users’ mobility is shown in Table 2. Parameter value Simulation time [s] 2000 Users mean velocity [m/s] 7 Users mean acceleration [m/s^2] 0.25 Table 2. Input parameters. If nothing else is started, the velocity is 7 m/s in all simulations. The first investigation is to find out how the dropped levels depend on variations in thresholdI for The Derivative and The Integration methods. From figure 27 we can see that the difference between The Derivative and The Integration methods is so small that when we are going to find the most effective method we can compare The Equivalent Bandwidth method to just one of these.

0.6 0.8 1 1.2 1.4 1.6

x 10−13

0.005

0.01

0.015

0.02

0.025

0.03

I−threshold

drop

ped

dropping

derivative integration

Figure 27. Dropped vs. Interference threshold. Comparing The Derivative and The Integration method in the most interesting interval for interference threshold.

47

Here the thresholdI is varied and we can see that there is not a very big difference between the two strategies. Dropped level increases with increasing thresholdI . The aim of this investigation is to find out whether there is a big difference between these two methods. When the dropped level is 1%, the thresholdI is about 13101.1 −⋅ for the two methods. The next step is of course to see how the blocked levels depend on thresholdI . From figure 28 it is obvious that the blocked levels are almost indistinguishable. The small variation for one of the values is within the accuracy for this type of simulations. The standard deviation for dropped and blocked levels is investigated and presented at the end of this chapter.

0.6 0.8 1 1.2 1.4 1.6

x 10−13

0.15

0.2

0.25

0.3

I−threshold

bloc

ked

blocking

derivative integration

Figure 28. Blocked vs. Interference threshold. Comparing The Derivative and The Integration methods in the most interesting interval for interference threshold. The probability of blocked sessions is again a function of thresholdI . The plot says that there is not a very big difference between the two strategies. The small difference for one of the points is within natural variations. Again the aim of this investigation is to find out whether there is a big difference between the two strategies. The blocked levels are unacceptably high. For example, if the thresholdI is 10-13, the probability of blocking is about 22%.

48

The aim of the following investigation to compare the three methods for a realistic dropped level and draw a conclusion about which is the most effective.

0.15 0.2 0.25 0.3 0.35

0.005

0.01

0.015

0.02

0.025

0.03

0.035

blocked

drop

ped

dropping vs blocking

derivative integration equivalent bandwidth

Figure 29. Dropping vs. blocking. Comparing The Derivative, The Integration and The Equivalent Bandwidth strategies. In figure 29 the threshold values vary along the curves and here we can for the first time compare the three methods. We can now find out for a given dropped level, for example 1%, which method is the most efficient. Here we can see that The Equivalent Bandwidth method is at least as efficient as the other two when the dropped level is about 1%. If the dropped level is lower, the total number of ongoing sessions is higher. The equivalent bandwidth strategy is also much simpler when doing the calculations.

49

In figure 30 we can see what happens if the other to own cell interference ratio is varied for The Derivative method. The four points are compared to the results from the previous study. Here we can clearly see the impact of variations in the other to own cell interference ratio.

0.15 0.2 0.25 0.3 0.35

0.005

0.01

0.015

0.02

0.025

0.03

0.035

blocked

drop

ped

dropping vs blocking

derivative derivative oci=0 derivative oci=0.20 derivative oci=0.40 derivative oci=0.70 equivalent bandwidth

Figure 30. Dropping vs. blocking. The other to own cell interference ratio is varied for The Derivative algorithm. Here, the other to own cell interference ratio is varied to see what happens to the trade off

between dropped end blocked levels. When varying oci, thresholdI varies like ( )thresholdIoci ⋅+

5.11 .

We can see that the threshold value is different for different oci´s, but the result is that varying oci does not improve the system. When we say that it does not improve the system, we mean that the new points coincide with the other curves. The blocked level is not much lower for a given value of dropped calls.

50

In figure 31 the we can see that the effect of neglecting the other to own cell interference ratio is the same as if the number of users within the cells were less.

0.15 0.2 0.25 0.3 0.35 0.4 0.45

0.005

0.01

0.015

0.02

0.025

0.03

0.035

blocked

drop

ped

dropping vs blocking

derivative normal derivative without oci equivalent bandwidth normal

Figure 31. Dropped vs. blocked. In one of the curves the other to own cell interference ratio is neglected. The other curves are to compare. Here we have studied what happens if the other to own cell interference ratio is neglected. The curve without inter cell interference has lower dropping and higher blocking, but principally the same appearance. The lower inter cell interference of course results in lower interference levels. The system performance is approximately the same.

51

It is now time to investigate what happens if the mobility of the users is varied. Here three mean velocities have been chosen. Low mobility is representative of an urban environment where people mostly sit or walk. Normal mobility is a mixture of all types of users. High mobility is representative if most users are sitting in cars, buses etc. thresholdI and thresholdη are varied for The Derivative and Equivalent Bandwidth methods when investigating the influence of different mobilities. Higher mobilities results in more handovers. More handovers results in larger effect fluctuations. This investigation is just another way to vary a parameter to see if The Derivative method is more efficient than The equivalent Bandwidth method. Mobility Velocity [m/s] Acceleration [m/s^2] Low 1 0.03 Normal 7 0.25 High 20 0.40 Table 3. Mobilities.

0.1 0.15 0.2 0.25 0.3 0.35

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

blocked

drop

ped

dropping vs blocking

normal derivative normal equivalent bandwidth low mobility derivative low mobility equivalent bandwidth high mobility derivative high mobility equivalent bandwidth

Figure 32. Dropping vs. blocking for The Derivative method compared to The Equivalent Bandwidth method. Investigation of the sense of different user mobilities. For each mobility level thresholdI and thresholdη are the variable quantities. Here we can see that The Equivalent Bandwidth method is at least as effective as the derivative method for all mobilities. Here it is obvious that the share of dropped calls increases with increasing user velocity.

52

The following investigation is to evaluate a possible improvement when using exponential equalization when computing the received power levels. Admission Control is used to avoid dropping throughput during a complete call, i.e., on a time scale of 100 s. To base an Admission Control decision on interference, which varies on a much smaller time scale could therefore be less than optimal.

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

blocked

drop

ped

dropping vs blocking

derivative normal derivative filter equivalent bandwidth

Figure 33. Dropping vs. blocking including The Derivative exponential equalization strategy. Here The Derivative method is investigated with its power levels computed by using exponential equalization. thresholdI and thresholdη are the variable quantities. A larger thresholdI interval is needed because adding exponential equalization has the same effect as if the thresholdI increased. This is because the cell load is under the thresholdI in average, and if a few time steps cause overload then the moving average level will still be under the thresholdI anyway. The small time scales fluctuations are smoothed out. This leads to a high increase in the number of dropped users. Again we can see that Equivalent Bandwidth method is at least as effective as The Derivative method when dropped level is around 1%.

53

This is the last investigation of the thesis and it is to investigate the accuracy of the simulations. Standard deviations and variances for dropped and blocked levels are computed for The Derivative method.

0.17 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25 0.26 0.27

4

6

8

10

12

14

x 10−3

blocked

drop

ped

dropping vs blocking

derivative statistics equivalent bandwidthmean value

Figure 34. Dropping vs. blocking. A statistic study of the accuracy for the simulations. The Derivative method is investigated. Here several simulations are done with different random seeds, which determine the arrival time, session length and the velocity for the users. The aim of this investigation is to measure the accuracy of the simulations. Ten simulations having a dropped mean value droppedµ around

1% have been performed to calculate the variance 2σ and the standard deviation σ for the number of dropped and blocked users.

0098.0=droppedµ 72 106684.2 −⋅=droppedσ

4101656.5 −⋅=droppedσ 2014.0=blockedµ

52 103542.3 −⋅=blockedσ 0058.0=blockedσ

54

13. Discussion and conclusion • Admission control clearly improves the system performance by decreasing dropping of

calls. • The Equivalent Bandwidth method is in most circumstances the most effective algorithm

when the dropped level is about 1% despite the fact that if it is simpler than The Derivative and The Integration methods.

• The Derivative and The Integration methods give essentially the same results. • The cell load increases with increasing inter cell interference in the network system. • The cell load increases with increasing velocity of the users. • The standard deviation of the number of dropped users is 5%, and for blocked users 3%. • There is not a very big difference between The Derivative and The Equivalent Bandwidth

methods when analysing the effect of different user mobilities. The Equivalent Bandwidth method is still a bit more effective in the most interesting intervals.

• If the other to own cell interference ratio is neglected, the effects for the system are the same as if the number of users were less.

• The effect of The Derivative method with exponential equalization is the same as if the thresholdI were increased. The moving average causes most effect values to be under the

thresholdI . Therefore more users are dropped. The method is not better or worse, the only difference is that the threshold value must be lower than without exponential equalization.

The Equivalent Bandwidth method is simpler than the other algorithms. When building the UMTS network, the Equivalent Bandwidth method is therefore to be preferred when implementing the Admission Control routines in the future UMTS network. When handling the best-effort users The Equivalent Bandwidth method is much better, because you can make a reservation of capacity for the best effort users. 13.1 Further studies In this thesis we have studied the UMTS network in the uplink without soft handover, with speech as the only service and a traffic intensity, which is not representative. The traffic intensity is very high just to see which algorithm is the most effective. For future work it will be natural to examine the following: • The same algorithms but under more realistic scenarios. • Simulations with less traffic intensity. • Soft handover and best effort services can be studied. • The exponential equalization can be used for all strategies and the equalization constant

can be varied. • Downlink simulations. • Measure the inter cell interference instead of having it fixed. • More comparisons of admission control vs. no admission control. • Simulations in different environments. • Try new algorithms to compare with the three algorithms.

55

14. References [1] Harri Holma and Antti Toskala, “WCDMA for UMTS”, Wiley, 2000. [2] Håkan Persson, “UMTS overview”, Technical report, Telia Research AB, 2000-07-14. [3] Kar-Lok Chan, “Downlink Admission Control for CDMA-systems in UMTS”, Masters Thesis in electrical engineering, S3 KTH. [4] Harri Holma and Janne Laakso, “Uplink Admission Control and Soft Capacity with MUD in CDMA”, IEEE VTC 99. [5] “UMTS System Overview”, APIS Training & Seminars, APIS Technical Training AB 2000. [6] Hillier and Liebermann, “Introduction to Operations Research”, 6th edition, Mc Graw Hill, 1995. [7] Peter Almers, Anders Henriksson, Christian Bergljung and Fredrik Malmström, “ A linkbudget in UTRA”, Technical report, Telia Research AB, Sweden, January 2000. [8] Magnus Aldén and Elles de Vries, “Traffic Models for 11 UMTS services”, Technical report, Telia Research AB, September 1997. [9] Tomas Ahnberg, Greger Johansson and Oskar Staf, “Multimedia in UMTS”, Technical report, Telia Research AB, Sweden, 1999. [10] Lars Ahlin and Jens Zander, “Principles of Wireless Communications”, Studentlitteratur, 1998. [11] Jamie S. Evans and David Everitt, “Effective Bandwidth Based Admission Control for Multi-Servive CDMA Cellular Networks”, Technical report, Department of Electrical and Electronic Engineering University of California, Berkeley, USA, 1995. [12] Magnus Sommer and Peter Almers, “The coverage and capacity of a UMTS network-a first study”, Technical report, Telia Research AB, 2000-07-04. [13] Enric Rovira, Johan Helge and Magnus Aldén, “Radioresurshantering i CDMA”, Technical Report, Telia Research AB, 1998-09-30. [14] 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Spreading and modulation (FDD), (Release 1999), 3GPP TS 25.213 V3.5.0, March 2001. [15] Patrik Wikström, “Real time Video Traffic Modelling and Simulations in Third Generation Mobile Networks”, Masters Thesis, Telia Research AB, May 2000. [16] L.Bergström, “RUNE 3.1 – The New Lifestyle”, Ericsson Radio Systems, 1996

56

Appendix A. Abbreviations AMR Adaptive Multi-Rate BER Bit Error Rate CDMA Code Division Multiple Access CN Core Network FDMA Frequency Division Multiple Access FPLMTS Future Public Land Mobile Telecommunication System EB Equivalent Bandwidth TDMA Time Division Multiple Access GERAN GSM Edge Radio Access Network GPRS General Packet Radio Service GSM Global System for Mobile communication ITU International Telecommunications Union LAN Local Area Network MSC Mobile Switch Center Node B Base station OVSF Orthogonal Variable Spreading Factor QoS Quality of Service RNC Radio Network Controller RNS Radio Network Subsystem SF Spreading Factor SGSN Serving GPRS Support Node SIR Signal to Interference Ratio SMS Short Message Service UE User Equipment UMTS Universal Mobile Telecommunication System UTRAN UMTS Terrestrial Radio Access Network WCDMA Wideband Code Division Multiple Access

57

Appendix B. Starting values To make simulations, values for thresholdI and traffic intensity are needed. For the uplink admission control:

target

maxstartthreshold SIRR

GPWI

⋅⋅⋅⋅

=ν_

totL

G 1=

( )

103log

10CBd

totL+++⋅+

=σεδ

61084.3 ⋅=W Hz 125.0=maxP W 5.0=ν 2.12=R kbps 79.010=targetSIR 26=δ dB 35=ε dB 500=d m 7=σ dB 6=B dB 3=C dB 14

_ 101469.1 −⋅=startthresholdI [W] This approximation is used for both The Derivative and The Integration methods. The traffic intensity:

( )t

WLstart ⋅⋅

+⋅=ην

ηλ

SIRR ⋅=η 3.0=startLd 84.3=W MHz 5.0=ν 2.12=R kbps 79.010=SIR 90=sessiont s 3470.0=startλ users/s for one cell.

58

Appendix C. Plots and data The information in this appendix gives a hint about values of the number of blocked and dropped users, and also the values of thresholdI and thresholdη . This might be of help in further studies and when doing further simulations. The present thesis includes only a few Admission Control studies.

0.6 0.8 1 1.2 1.4 1.6

x 10−13

0.005

0.01

0.015

0.02

0.025

0.03

I−threshold

drop

ped

dropping

derivative integration

0.6 0.8 1 1.2 1.4 1.6

x 10−13

0.15

0.2

0.25

0.3

I−threshold

bloc

ked

blocking

derivative integration

Figure 35. Dropped and blocked vs. I-threshold. Figure 35 shows the dropping and blocking versus thresholdI for The Derivative and The Integration methods. We can see the same curves as in figures 27 and 28; the numerical values for each point are given in table 4.

59

3 3.2 3.4 3.6 3.8 4 4.2

x 105

0.005

0.01

0.015

0.02

0.025

0.03

0.035

η−threshold

drop

ped

dropping

equivalent bandwidth

3 3.2 3.4 3.6 3.8 4 4.2

x 105

0.15

0.2

0.25

0.3

0.35

η−threshold

bloc

ked

blocking

equivalent bandwidth

Figure 36. Dropped and blocked vs. Equivalent Bandwidth threshold. Figure 36 shows the dropping and blocking versus thresholdη for The Equivalent Bandwidth method. This investigation is the basis for figure 29. The numerical values for each point are shown in table 4. Derivative I-threshold [W] 0.0573e-12 0.0803e-12 0.1032e-12 0.1262e-12 0.1491e-12 0.1720e-12Dropped 0.0020 0.0037 0.0089 0.0145 0.0243 0.0336Blocked 0.3435 0.2701 0.2223 0.1613 0.1415 0.1184Integration I-threshold [W] 0.0573e-12 0.0803e-12 0.1032e-12 0.1262e-12 0.1491e-12 0.1720e-12Dropped 0.0018 0.0043 0.0088 0.0143 0.0238 0.0315Blocked 0.3423 0.2625 0.2200 0.1610 0.1602 0.1198Equivalent Bandwidth Eta-thr. [cps] 287500 312500 337500 362500 387500 412500 437500Dropped 0.0012 0.0018 0.0024 0.0044 0.0064 0.0151 0.0354Blocked 0.3907 0.3498 0.3139 0.2624 0.2134 0.1657 0.1152Table 4. Input values.

60

7 8 9 10 11

x 10−14

3

4

5

6

7

8

9

10

x 10−3

I−threshold

drop

ped

dropping

oci=0 oci=0.20oci=0.40oci=0.70

7 8 9 10 11

x 10−14

0.2

0.21

0.22

0.23

0.24

0.25

0.26

0.27

0.28

0.29

I−threshold

bloc

ked

blocking

oci=0 oci=0.20oci=0.40oci=0.70

Figure 37. Dropped and blocked vs. I-threshold for varied oci. Figure 37 shows the dropping and blocking versus thresholdI for The Derivative method. Here we have investigated the behaviour when varying the other cell to own cell interference ratio. This investigation is the basis for figure 30. The numerical values for each point are shown in table 5. Derivative with varying oci Oci 0 0.20 0.40 0.70 I-threshold [W] 6.88e-14 8.26e-14 9.63e-14 1.170e-13 Dropped 0.0028 0.0047 0.0084 0.0110 Blocked 0.2930 0.2492 0.2233 0.1915 Table 5. Input values.

61

0.6 0.8 1 1.2 1.4 1.6

x 10−13

2

4

6

8

10

x 10−3

I−threshold

drop

ped

dropping

derivative without oci

0.6 0.8 1 1.2 1.4 1.6

x 10−13

0.25

0.3

0.35

0.4

0.45

I−threshold

bloc

ked

blocking

derivative without oci

Figure 38. Dropped and blocked vs. I-threshold. Figure 38 shows the dropping and blocking versus thresholdI for The Derivative method. Here we have investigated the behaviour when neglecting the other cell to own cell interference ratio. This investigation is the basis for figure 31; the numerical values for each point can be found in table 6. Derivative without oci I-threshold [W] 0.0573e-12 0.0803e-12 0.1032e-12 0.1262e-12 0.1491e-12 0.1720e-12Dropped 0.0021 0.0016 0.0028 0.0050 0.0074 0.0108Blocked 0.4705 0.3710 0.2930 0.2596 0.2171 0.2002Table 6. Input values.

62

0.6 0.8 1 1.2 1.4 1.6

x 10−13

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

I−threshold

drop

ped

dropping

normal derivative low mobility derivative high mobility derivative

0.6 0.8 1 1.2 1.4 1.6

x 10−13

0.1

0.15

0.2

0.25

0.3

0.35

I−threshold

bloc

ked

blocking

normal derivative low mobility derivative high mobility derivative

Figure 39. Dropped and blocked vs. I-threshold for varied mobilities. Figure 39 shows the dropping and blocking versus thresholdI for The Derivative method. Here we have investigated the meaning of different user mobilities. This investigation is part of the basis for figure 32. Numerical values for each point are given in table 7.

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3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6

x 105

0.02

0.04

0.06

0.08

0.1

η−threshold

drop

ped

dropping

normal equivalent bandwidth low mobility equivalent bandwidth high mobility equivalent bandwidth

3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6

x 105

0.1

0.15

0.2

0.25

0.3

0.35

η−threshold

bloc

ked

blocking

normal equivalent bandwidth low mobility equivalent bandwidth high mobility equivalent bandwidth

Figure 38. Dropped and blocked vs. Eq. Bandwidth threshold for varied mobilities. Figure 38 shows the dropping and blocking versus thresholdη for The Equivalent Bandwidth method. Here we have investigated the meaning of different user mobility. This investigation is part of the basis for figure 32. The numerical values for each point are given in table 7.

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Derivative normal mobility I-threshold [W] 0.0573e-12 0.0803e-12 0.1032e-12 0.1262e-12 0.1491e-12 0.1720e-12Dropped 0.0020 0.0037 0.0089 0.0145 0.0243 0.0336Blocked 0.3435 0.2701 0.2223 0.1613 0.1415 0.1184Derivative low mobility I-threshold [W] 0.0573e-12 0.0803e-12 0.1032e-12 0.1262e-12 0.1491e-12 0.1720e-12Dropped 0.0001 0.0006 0.0021 0.0051 0.0093 0.0117Blocked 0.3504 0.2589 0.2148 0.1856 0.1599 0.1389Derivative high mobility I-threshold [W] 0.0573e-12 0.0803e-12 0.1032e-12 0.1262e-12 0.1491e-12 0.1720e-12Dropped 0.0416 0.0510 0.0605 0.0703 0.0813 0.0885Blocked 0.3063 0.2153 0.1614 0.1320 0.0976 0.0805Eq. Bandwidth normal mob.

Eta-thr. [cps] 287500 312500 337500 362500 387500 412500 437500Dropped 0.0012 0.0018 0.0024 0.0044 0.0064 0.0151 0.0354Blocked 0.3907 0.3498 0.3139 0.2624 0.2134 0.1657 0.1152Eq. Bandwidth low mob. Eta-thr. [cps] 312500 350000 375000 400000 425000 462500Dropped 0.0001 0.0007 0.0014 0.0037 0.0107 0.0367Blocked 0.3564 0.2747 0.2178 0.1846 0.1427 0.0750Eq. Bandwidth high mob. Eta-thr. [cps] 312500 350000 375000 400000 425000 4625000Dropped 0.0409 0.0469 0.0520 0.0600 0.0760 0.1091Blocked 0.3175 0.2529 0.2012 0.1512 0.1208 0.0574Table 7. Input values.

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1 2 3 4 5 6 7 8 9 10

x 10−14

0.02

0.04

0.06

0.08

I−threshold

drop

ped

dropping

derivative filter

1 2 3 4 5 6 7 8 9 10

x 10−14

0.2

0.4

0.6

0.8

I−threshold

bloc

ked

blocking

derivative filter

Figure 41. Dropped and blocked vs. I-threshold for The Derivative strategy with exponential equalization. Figure 41 shows the dropping and blocking versus thresholdI for The Derivative method when using exponential equalization. Here we have investigated the behaviour when using exponential equalization when computing the received power levels. This investigation is the basis for figure 33 and the numerical values for each point are given in table 8. Derivative filter Dropped Blocked I-threshold [W]

0.0002 0.9616 0.06e-14 0.0002 0.9616 0.11e-14 0.0004 0.8923 0.34e-14 0.0011 0.6007 0.57e-14 0.0020 0.3435 1.15e-14 0.0052 0.2471 1.72e-14 0.0113 0.1937 2.29e-14 0.0210 0.1673 2.87e-14 0.0414 0.0902 4.01e-14 0.0550 0.0695 4.59e-14 0.0728 0.0416 5.73e-14 0.0918 0.0160 8.03e-14 0.0984 0.0154 10.32e-14

Table 8. Input values.

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