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Master of Science Thesis performed in Communication Systems atLinköping Institute of Technology by

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Master of Science Thesis performed in Communication Systems atLinköping Institute of Technology by

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Advisors: 'U��)UHGULN�*XQQDUVVRQ

Linköping Institute of Technology

'U. Niclas Wiberg

Ericsson Radio Systems AB

Examiner: 3URI��)UHGULN�*XVWDIVVRQ

Linköping Institute of Technology

Linköping, 26 May 2000.

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ABSTRACT

Third generation cellular communications systems with higher datarates are under development, and the proposed radio interface isWideband Code Division Multiple Access (WCDMA). The complexityof the system makes the optimisation of the system performance adifficult process. Hiding this complexity from the net operators byproviding a self-configuring system (SCS) where the net operatorsonly have to deal with high-level parameters is highly desirable.

In this study, we have investigated a subset of plausible SCSalgorithms. The algorithms analysed are designed to adapt thesystem to achieve certain rates of denied access attempts or of forceddisconnected calls. Only speech services are considered.

The algorithms investigated achieve the specified rates. We alsopropose a novel method of measuring system performanceconcerning capacity, quality, denied accesses and forceddisconnections.

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4

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1.1 BACKGROUND 61.2 PROBLEM STATEMENT 61.3 AIM 61.4 METHOD DESCRIPTION 61.5 OUTLINE 7

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1 INTRODUCTION

1.1 BACKGROUND

Since the launch of the digital cellular communications systems (likeGSM) in the beginning of the nineties the number of subscribers hasrapidly increased. This and the desire of other services than speechhave lead to the development of a new generation of systems withlarger data rate. Ericsson is one of the companies involved in thestandardisation of this third generation of cellular communicationssystem. The proposed radio interface technique is Wideband CodeDivision Multiple Access (WCDMA). The new system is planned to bein use 2001.

Different data rates and services are supported. To improve theconnection a user can be connected to several base stations at thesame time.

1.2 PROBLEM STATEMENT

The new system will provide new features and improve the capacity.The net operators that distribute the cellular radio network services tothe subscribers may have different strategies regarding the operationand maintenance (O&M) of the system.

However, the new features also increase the complexity. Thiscomplicates the optimisation of the system and it is desired to hidethis complexity from the net operators. One solution is to provide themwith a self-configuring system (SCS) where they only have to dealwith high-level parameters. A SCS gives the net operators an easilymaintained system demanding less expertness and time to monitor.

The high-level characteristics shall easily reflect different net operatorpolicies. Capacity, Coverage and Quality (CCQ) could be such high-level parameters. An interesting subset of the SCS algorithms is thealgorithm that adapts the system to achieve certain rates.

1.3 AIM

This master thesis in telecommunications is to design algorithms for aself-configuring cellular WCDMA radio network. The aim is to developalgorithms that control the system’s radio resources in a busy hoursituation, according to some possible net-operator policies. Thepolicies are assumed to regard certain rates of denied accesses(blocking) or forced disconnections (forced dropping). The assignmentis limited to speech users only.

1.4 METHOD DESCRIPTION

7

The development of the algorithms is to be done with a simplifiedWCDMA radio model, concerning only one base station. This model isto be designed in MATLAB and necessary assumption should bemade.

The developed algorithms are then to be analysed in a moreadvanced cellular WCDMA radio model. This model is an EricssonResearch developed simulator.

1.5 OUTLINE

Chapter 2 starts with a short introduction to wireless communicationand multi access.

Chapter 3 includes a presentation of some for this study interestingparts of the WCDMA standard.

Chapter 4 introduces the models used for the simulations in thisstudy. Both the simplified environment model and the more complexrealistic model are presented.

Chapter 5 discusses the basic control theory used and a method ofcomparing the performance of different algorithms. The differentalgorithms used in the simulations are also presented here.

Chapter 6 describes the simulations and the results are presentedand commented on.

Chapter 7 provides a summary of the most important results andsome conclusive remarks. It also includes interesting topics for furtherstudies.

At the end of this thesis the references and the abbreviations arelisted. There is also an appendix with simulation parameters.

8

2 CELLULAR RADIO NETWORKS

This chapter will first give a brief introduction to radio communicationfollowed by some basic principles of wireless mobile communication.

2.1 WIRELESS COMMUNICATION

All wireless communication is about transferring information betweenusers; broadcasting or point to point, in one or both directions.

The receiver’s possibility to detect the signal is dependent of thedistance to the sender. This SDWK�ORVV is increasing with the cube ofthe distance. At a certain distance away from the transmitter thepower of the signal is negligible compared to the noise. The QRLVHcan be man-made or have a natural origin like background or thermalradiation. Man-made noise could be other radio transmitters using thesame frequency or other electrical devices. Also engines can causedisturbance. Noise originating from other radio transmissions isusually called LQWHUIHUHQFH.

Between the sender and receiver there may be objects in the line ofsight attenuate the signal strength. This is called VKDGRZ�IDGLQJ orVORZ�IDGLQJ and causes the attenuation of the signal strength to alterrelatively slowly when the users are mobile.

The signal may travel along different paths before it reaches thereceiver. The different paths can have different length, which leads toseparation in time of the signals arrival at the receiver, i.e. severalcopies of the signal will arrive at the receiver at different moments intime. This phenomenon is called PXOWL�SDWK�IDGLQJ (or IDVW�IDGLQJ)and occurs when transmitted signal has been reflected against largeobjects (like buildings in a city) before it is received. The phase of thereceived signal components decides if they contribute constructivelyor destructively to the resulting signal.

The path loss and the fading that a signal experiences through theused radio channel is often summed up and called the radio channel’sSDWK�JDLQ.

The information channel has a theoretical capacity bound. It is a limitof the maximum amount of data bits that can be sent over a channelwithout errors. It was defined by C.E. Shannon and is called theFKDQQHO�FDSDFLW\�ERXQG [15].

+⋅≤

16

%& 1log2

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9

S is the signal strength of the transmitted wave, N is the strength ofthe noise and C is the bit capacity per second. B is the amount offrequency spectrum used for the transmission (the bandwidth). If weare not able to alter the noise, the only way to increase the channelcapacity is either to increase the transmitting power or the bandwidthfor the system.

2.2 MULTIPLE ACCESS

A communication system with several users has to be constructed sothat the different transmitted signals do not interfere with each other.A way of doing this is to let the transmitted signals be orthogonal toeach other.

2.2.1 Cellular Radio Networks

For a wireless communication system, the primary goal is often toprovide as many users as possible access to the network. The userscan either be mobile or stationary and dispersed over a geographicalarea without their position priori known. Such a system is called awireless network and the area where it provides service is the VHUYLFHDUHD. The wireless network can be divided into two separate parts;the fixed network and the wireless network. The fixed networkprovides connection between the network access points (the basestations). The base stations are distributed over the service area andprovide wireless communication to the mobiles.

The area around the base station, where the transmission conditionsare good enough to keep a connection of specified quality is calledthe FRYHUDJH�DUHD. In reality, the coverage area has an irregularshape due to the geographical conditions and propagation losses, butare usually approximated by hexagons. The hexagons are calledFHOOV and ideally they cover the service area completely without anyoverlapping.

Wireless system may provide different services and the qualitymeasurement can distinguish between the different services. For areal time service like audio or video the probability of errors of datacan denote the VHUYLFH�TXDOLW\. However for services without the realtime demand like data service, no errors are accepted and the servicequality can be measured as the bit rate or the delay.

In this study, we have used the carrier-to-interference ratio (C/I) asmeasurement of the quality.

10

2.2.2 Radio Resource Allocation

As the useable frequency spectrum is limited, a wirelesscommunication system has to be carefully planned. The system getsa specified carrier frequency and a certain allowed bandwidth. Then itis up to the system to optimise the use according to the Capacity, theCoverage and the Quality of Service, CCQ.

To minimise the cost of the wireless network it is desirable to providesufficient coverage with as few base stations as possible. However,the geographic area and the available channel sets require a certainnumber of base stations. In a system with a large service area thatrequires many base stations due to propagation losses, somechannels can be reused in far apart cells. In spite of that differentinformation channels use the same carrier signal, the distancebetween the cells makes the received interference low. The smallestdistance between two base stations where a channel set is reused iscalled the reuse distance.

For mobiles that move around in the service area, they have toconnect to different base stations and cells. Different cells havedifferent signal sets so the mobile has to handle several carriersignals at the same time or it has to be able to switch between thedifferent signals as it moves through different cells. This is calledKDQGRYHU.

The user mobility creates other problems. If the users are uniformlydistributed over the service area and the system is designed tomanage the capacity, there is no problem. However if a large numberof mobiles move towards a certain area there will be a capacityproblem within that area (hot spot scenario) and the system wouldexperience problems of providing service to all users in that area.

A mobile not admitted (i.e. denied access) to the network is said to beEORFNHG. If a mobile gets too far from a serving base station and it isnot able to do a desired handover the mobile will finally lose itsconnection due to too low signal strength, and the connection isGURSSHG.

11

3 WCDMA

Wideband Code Division Multiple Access (WCDMA) is the proposedradio interface for the third generations mobile communicationstandard. Ericsson is one of the companies involved in thestandardisation work.

This chapter starts with a brief introduction to the Code DivisionMultiple Access (CDMA) concept which is the technique WCMA isbased on. This is followed by a presentation of some (for this study)vital parts in the WCDMA standard and a discussion regarding radioresource management for WCDMA.

3.1 CDMA THEORY

CDMA is a form of Spread spectrum (SS) communication technique.SS is often characterised by a bandwidth larger than 1/T, where the Tis the symbol length. There are three general approaches toimplementing SS systems; frequency hopping (FH), time hopping(TH) and direct-sequence (DS). One of the major advantages of anSS system is the robustness to interference. The information signal isspread out over a larger bandwidth.

The system’s processing gain is the ratio between system bandwidthand the information bandwidth, Typical processing gains for SSsystems lie between 20 and 60 dB. One frequently used quantity isthe Carrier-to-Interference ratio (C/I). This is the ratio between thesignal power before down conversion (i.e. the conversion of thespread signal back to the narrowband information signal) and theinterference power.

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8VHU 1

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ff

Figure 3-1 The DS-CDMA spreads the narrowband signal in thefrequency plane.

12

The WCDMA standard uses the DS-CDMA technique and the rest ofthe chapter will only concern the DS method.�In DS-CDMA the carrieris modulated by a digital code in which the code rate is much largerthen the information signal bit rate. This code is called thechannelisation code or the chip sequence and it is a binary pseudo-noise code with a chip rate, which is much higher than the data bitrate of the system. The modulated signal is a broadband noise-likesignal. After the spreading of the narrow-band information signal theradio band modulation is performed.

The DS spreads the information in both time and frequency domains,thus reducing the effects of fading and interference. Every user inCDMA systems introduces a unique level of interference that dependson its transmitted signal at the cell and its specified cross-correlationwith other signals. The number of users in CDMA systems is thendepending on the amount of interference that can be tolerated in thesystem.

3.2 THE WCDMA STANDARD

3.2.1 Power Control

For a cellular radio system, the power control is a very vital part,especially for an interference-limited system like WCDMA. It is tocontrol the C/I so that communication at acceptable quality isachieved.

Consider two users, one far away and one close to the base station. Ifthey should transmit with the same signal effect, the signal from theuser far away would drown in the signal from the user near to thebase station. This is called the near/far-problem. The power controlcould accomplish this by adjusting the transmitting power for everyuser so that the signal strength received at the base station is thesame for all active users. For connection setup and random accessthe power control is conducted according to an open loop algorithm.For the dedicated channels the power control algorithm contains twoparts, the inner and outer loop [10] and [17].

The RXWHU�ORRS is a slow algorithm and it determines the needed C/I-target. It uses the received signal strength of the control channel fordecision. Actually, the decisions are made according the bit error rate(BER) and the frame error rate (FER). In the QPSK modulation, errorprobability and the energy-per-bit to noise ratio (Eb/N0) are relatedwhich gives the relation between C/I and BER / FER, see [8] and [16].

The LQQHU�ORRS is a fast decision-feedback algorithm working at 1500Hz. Its main objective is to compensate for the fast fading. For this ituses the difference between the C/I-target and the received C/I. If thetarget is higher than the actual C/I the algorithm increases thetransmission power with 1 dB and if the target is lower the power isdecreased 1 dB.

13

In WCDMA information is sent in ten millisecond frames where eachframe is divided into 15 slots. The inner loop power control alters thetransmitting power for every slot.

3.2.2 Hand Over

For best performance of communication, the terminal and the accessnetwork should use the best available connection. In WCDMA, theterminal may be connected to several base stations at the same timetaking advantage of more transmitted signal power. The method ofbeing connected to more than one base station is called SoftHandOver (SHO).

A special case of SHO is softer handover. I.e. when the terminal isconnected to two or more cells served by the same base station site.Softer handover gives better performance.

Inter frequency handover can also be used, i.e. handover betweendifferent carrier frequencies. Handover to other systems, e.g. GSM, isalso supported in WCDMA.

3.2.3 Admission Control

The basic idea with admission control (AC) is to protect ongoing calls.In CDMA based system the number of active users in the system islimited by the interference and the available power in the radiotransmitters.

A terminals experiencing a large interference will increase itstransmitting power to assure required signal quality (see the powercontrol chapter). The other users in the cell will then experience theincreased power as an interference increase and they may have toincrease their power to compensate for the increased interference.This is often called the party effect and in a highly loaded system theincrease in interference could cause the system to enter an unstablestate leading to uncontrolled dropping of connections.

14

Figure 3-2, the interference increases rapidly when more users areadmitted into the cell.

When the transmitting power reaches its limit and the interferenceincreases the system will not be able to achieve the required C/Itarget. This will affect the terminals with worst path gain first, that isusually the terminals farthest away from the base station which givesless coverage. Since the required transmitter power and interferenceincrease rapidly with increasing load, see figure 3.2, the admissioncontrol is a vital part of a stabile CDMA system.

3.2.4 Congestion Control

The congestion control algorithms control the resource utilisation ofthe already admitted connections. If an overload situation may occurin spite of the admission control (AC) then congestion control (CC) willbe activated. To handle an overload in a WCDMA system thecongestion control can use different methods.

According to the Shannon’s channel capacity bound (Equation 2-1) acertain bit rate results in a required signal to noise (S/N), assumingfixed frequency bandwidth, B. If the demanded bit rate decreases therequired S/N will also decrease. Therefore one way to handle anoverload situation could be to decrease the bit rate for the activeconnections. I.e. lower bit rate leads to less required power due tolower S/N demand and lower power leads to less interference. Realtime audio and video services with requirements of short delayscannot queue messages like in the data packet service. Instead thedemand for lower bit rate can be solved by discarding less importantbits in the frames [2] or by letting the speech-decoder decrease its bitrate for speech services [5]. This however leads to worse speechquality.

15

Another way is letting the system to disconnect (drop) an active userby forcing it to close the connection. This is usually “the last way out”because being dropped is generally considered more annoying thanbad speech quality or being denied access when trying to connect tothe system.

Inter frequency handover may also be used to ease an overloadsituation at one carrier frequency (under the assumption that thereexist available frequencies to do handover to). The inter frequencyhandover in WCDMA is a hard handover. If the handover is done, it ispossible that the access request to the new frequency will be denied.This method will not be handled in this study.

In a downlink congestion situation the number of users and thehandover thresholds in SHO is important. If the SHO links decreasethe required BS transmitting power decreases though this will effectthe capacity. This method will not be handled in this study, for furtherinformation see [14].

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To support different speech code rate the WCDMA standard includesa multi-rate speech coder; the AMR. It is to work with eight differentrates in the range 4.75 to 12.2 kbps see [5]. The AMR modeadaptation can be asymmetric which gives the possibility to usedifferent bit rate modes in uplink and downlink during an active call. Inprinciple the speech coder should be capable of switching betweendifferent bit rates every 20 ms, i.e. for every speech frame. Howeverin practice the adaptation will have to be less frequent due tosignalling delays in the fixed network.

The WCDMA logical channel structure consists of the common controlchannels and the dedicated channels. This study will only concern thededicated control channel (DCCH) and the dedicated data channel(DDCH). They are mapped to the physical channel with the dedicatedphysical control channel (DPCCH) and the dedicated physical datachannel (DPDCH) [6]. The DPDCH carries the data bits and theDPCCH carries the pilot bits containing transmitting power controlcommands, etc.

In the uplink the DPCCH and the DPDCH are transmitted with dualchannel QPSK modulation.

16

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Figure 3-3, frame structure for uplink dedicated physical channel.Each frame of 10ms is divided into 15 slots.

The downlink divides the DPDCH and DPCCH in time, i.e. they aretime orthogonal within each slot.

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Figure 3-4, frame structure for downlink dedicated physical channel.Also in the downlink each 10 ms frame consists of 15 slots.

To minimise the interference and to maximise the capacity, no dataare sent during periods of speech silence. When the speech service isused, an information packet contains two frames, i.e. 20 ms. Thiscorresponds to the frames in the GSM system.

In the uplink the DPCCH and the DPDCH are QPSK multiplexed.When control information and no data are sent the reduction in signalpower is assumed to be 3dB. Then using the AMR and speechservice, the maximum reduction is to become 1.5 dB. Next figureillustrates the variation in uplink transmitted power using the variabledata rate.

17

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Figure 3-5, uplink physical channel with variable rate.

The downlink divides the DPDCH and DPCCH in time. The DPCCHconsists of a fix number of bits but the DPDCH varies in lengthdepending of the bit rate. The base station will send data to theconnected UE in the cell at the same frequency so the burst that mayoccur in downlink will be moderate. The maximum signal powerreduction due to the use of AMR in the downlink is also assumed 1.5dB. Figure 3-6 illustrates the variation in downlink transmitted powerusing the variable data rate.

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3.2.5 Coding

18

The spreading of the physical channels is performed in two steps.First the channels are modulated with a channelisation code. Ittransforms every data bit into number of chips. The number of chipsper bit is called the Spreading Factor (SF). After the first spreading,the signal is modulated with a scramble code. Different scramblecodes are used for cell separation in the downlink and user separationin the uplink. The channelisation codes are Orthogonal VariableSpreading Factors (OVSF) codes, which preserves the orthogonalitybetween a user’s different physical channels.

19

4 MODELS

This chapter will describe the two different models used in thesimulations. The first part is about a simplified WCDMA radio networksimulator only simulating one radio network cell. Essential theory andthe assumptions made are presented. The code is written inMATLAB. The next part is about a more realistic WCDMA simulatordeveloped by Ericsson Research.

4.1 ONE-CELL MODEL

The simulations are made in a UNIX environment with the programMATLAB. The simulation models are created with the MATLAB editor.

4.1.1 The Radio Model

The model resembles speech traffic in a WCDMA cell. To simplify themodel perfect power control is assumed. By letting all the terminalshave the same priority and quality of service (QoS) the receivedsignal strengths from all active terminals, at the base station, can beset to have the same value. First, we will explain how the uplink wasmodelled, then the downlink.

The carrier-to-interference ratio C/I is the ratio between the signalpower before down-conversion and the interference power.

8SOLQN

The C/I-target is denoted by gamma, γ. Assuming ideal inner-looppower control in the up link, the actual C/I will be the same as C/I-target.

L8/

L8/

,

&,

, γ=

Ci is the power received from user i in the cell where it is connected.In this model, each user is connected to the same cell. Theinterference, I, is defined as

L8/WRW&,, ,−=

Itot is the total interference received at the base station.

The two previous relations give us the following expression

L8/

L8/

WRWL8/,&

,

,, 1 γ

γ+

⋅=

If all users are connected to the same cell, the up link interference inthat cell can be expressed as

∑∑ +⋅+=+=

L L8/

L8/

WRW

L

L8/WRW,1&1,

,

,, 1 γ

γ

20

(where N is the receiver noise) which can be solved into

∑ +−

=

L L8/

L8/

WRW

1,

,

,

11

γγ

The previous equation solves the total intra-interference for one cellwithout any interference from other cells. In our model, we thereforinclude an expansion factor f that represents the interference fromnearby cells. The interference from other cells is set to be a multipleof the interference in the cell. It is a very simplified model and f is afiltered normal distributed pseudo noise.

f is let to vary between 1.5 and 2.5 and its behaviour is shown infigure 4-1.

0 500 1000 1500 2000 2500 3000 3500 40001.5

1.6

1.7

1.8

1.9

2

2.1

2.2

2.3

2.4

2.5

time (s)

f

Figure 4-1 displays how f varies over 4000 seconds.

Equation 4-1 solves the total inter-interference in our one cell model.

∑ +⋅−

=

L L8/

L8/

WRW

I

1,

,

,

11

γγ

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'RZQOLQN

fn fn+1

21

Besides the uplink interference, the base-station transmitter power(downlink power) is also an important limiting factor. If the systemload gets to high, the base station will not be able to produce the rightamount of power and the C/I at the receivers will be to low. Then theterminals farthest away from the base station (the terminals with theworst path gain) will eventually lose their connection with the basestation. Just considering a one-cell scenario the transmitted power iscalculated as follows:

The γDL,i is the downlink C/I target. Assume ideal C/I-based powercontrol in the downlink, where each user is connected to the samecell, the user i is assumed to always fulfil the relation

L'/

L'/

L'/ ,

&

,

,, =γ

(TXDWLRQ����

CDL,i is the downlink signal power received at user i from the basestation where it is connected. IDL,i is defined as the receivedinterference in the cell down link.

The received power CDL,i for the terminal i is calculated according to

LLL'/J3& ⋅=,

(TXDWLRQ����

Pi is the signal power transmitted to the individual terminals and gi isthe pathgain between the terminal i and the base station it isconnected to. gi is modelled as filtered log-normal distributed pseudonoise. gi has an average of -112 dB and a variance of 10 dB. Becausethe sample time is one second the fast fading is assumed to beaveraged out during the sample and therefore neglected.

The total transmitted signal power from the base station, Ptot, iscalculated according to the following expression

∑=L

LWRW33

Ptot represents the transmitted power from the base station excludingthe power required for control information signalling (broadcastsignalling, etc.).

Further, the equations 4-2 and 4-3 give us the relation

L

LL

L

LL J,

J&3

11 ⋅⋅=⋅= γ

In addition, the expression for the total transmitted base station poweris then

∑ ⋅⋅=L L

LLWRW J,3

1γ

22

If we assume perfect down link power control and the γ to have thesame value for all terminals, we get the following expression

∑ ⋅⋅=L L

LWRW J,3

1γ

As the downlink is orthogonal, we assume the Ii to be interferencecoming from other cells and the receiver noise. To simplify thecalculations we also assume that all terminals in the cell receive thesame down link interference, I0.

L,, =0

I0 is modelled as filtered normal distributed pseudo noise. I0 is let tovary between -97 and –107 dB according to the same model as f.

This gives the power equation

∑⋅⋅=L L

WRW J,3

10γ

23

Figure 4-2 shows how interference and power are varying over time ina specific case.

0 500 1000 1500 2000 2500 3000 3500 40000

5

10

15

time (s)

Inte

rfer

ence

(dB

)

0 500 1000 1500 2000 2500 3000 3500 40000

10

20

30

time (s)

Pow

er (

W)

Figure 4-2 shows interference and power varying over time for aspecific case. The cell is here running without any admission orcongestion and there is no control over how high interference orpower gets.

4.1.2 Traffic Model

The sample time, Ts, should be sufficiently large to catch the desiredcontrol characteristics and not too small though the simulation timetends to be long. We used a Ts of one second.

New connections are entering the cell random in time andindependent of each other, i.e. a Poisson distribution [7]. Lambda (λ)is the mean intensity with which new terminals want to set up aconnection. A λ value set to one corresponds to, in mean, one newconnection entering the cell per sample. The duration of a connectionis assumed to be geometrically distributed [7] with an average speechlength of 90 seconds. The average number of users in the system isthen described with λ*90.

24

0 20 40 60 80 100 120 1400

20

40

60

80

100

120

140

lambda*90 (users)

Num

ber

of u

nits

Figure 4-3 shows the mean numbers of users for different λ and that itfollows λ*90.

4.2 MULTI-CELL MODEL

To investigate the control algorithms in a more appropriateenvironment, a WCDMA radio network simulator was used.

4.2.1 Radio model

The propagation model characterises channel qualities primarily bytheir path gain. The path gain model consists of four parts: antennagain, distance attenuation, shadow fading, and multipath fading (fastfading). These are generated based on a simple graphical modeltogether with a user mobility model.

The shadow fading is modelled by a lognormal random multiplicativefactor (normal in decibels). The fading may either be uncorrelated orcorrelated in space. The shadow fading is the same in uplink anddownlink.

The multipath fading is modelled by separate fading factors for uplinkand downlink, the mobiles are moving with average speed of threekm/h and an average acceleration of two m/s, which should resemblea pedestrian walk.

25

4.3 TRAFFIC MODEL

The geographical model is a two-dimensional environment wheremobiles and base stations are located. The area is closed with awrapping around technique to avoid borderline effects. Wrap aroundis used because otherwise the cells near to the border would have amore advantageous interference situation than cells in the middle. Toavoid these effects, imaginary cells are placed around the original cellplan. This gives that every cell experiences interference from nearbycells.

The traffic models are based on Poisson processes for user arrival.The arrival intensities can be changed for different types of users. Thespeech traffic model includes a voice activity process, which modelsalternating uplink/downlink speech with an exponential talk time. Thesession time is also exponential.

4.3.1 Our modifications to the simulator

In the simulator, power control was simulated, therefore are the timesteps very small. Logging can be done either on frame (100 Hz) or onslot (1500 Hz) level. This study wants to see the effects of admissionand congestion control, therefore longer time intervals must bestudied, much longer than the speech length of 90 sec. Thereforetime steps on 1 sec were implemented. Taking snapshots everysecond and letting the power control tune in before logging did that.

Congestion control (CC) was also implemented. In CC users eithercould be dropped or γ changed. If CC decided to drop a user, the userdemanding the highest power at the moment was dropped. Changingof γ was done by changing the bitrate in five steps.

Admission control was also modified. If the system is in a congestedstate, automatically all new users were blocked. This made it hard tocontrol the blockrate, but its no point in dropping users if at the sametime a new user is let in.

26

5 ALGORITHMS

This chapter begins with a description of the control problem followedby short introduction to the control theory concerning this study.Different operator policies will also be discussed and a way ofcomparing the simulation results, of the different algorithms, from anoperator point of view. At the end of the chapter, the algorithms thathave been used in the simulations are presented.

5.1 CONTROL PROBLEMS

The WCDMA system is limited in both interference and transmissionpower. As the downlink is orthogonal, the main restriction in thedownlink becomes the total available transmission power of the basestation. In the uplink the power limit of the terminal is of greatimportance, however assuming a perfect power control and suitableterrain the interference becomes the most limiting factor. Interferencein a cell can be divided into two types the intra- and the inter-interference. Intra-interference is the interference that the users in thecell contribute with. Inter-interference is the interference from nearbycells.

The control algorithms have to concentrate on keeping the uplinkinterference and downlink transmission power on an acceptable level.Assuming perfect power control, the control problem can be dividedinto two parts: congestion control (CC) and admission control (AC).AC is trying to control the number of users in the cell so that acongested situation never occurs and CC is used when it hasoccurred.

5.2 CONTROL THEORY

Interference and power are the parameters to be adjusted or to belimited. The measurable parameters are the power, the number ofactive users and, to some, extent the total uplink interference. Theavailable controllable parameters are the number of active users andtheir used data rate.

To keep a system from exceeding its limitations two basic methodscan be applied. 2SHQ�ORRS control uses predetermined parametervalues for control. I.e. the decisions are made regardless of thecurrent state of the system. E.g., the control uses fixed limits of theallowed maximum number of users in the system. The other methodis FORVHG�ORRS control, which is of interest for this report. A closedloop control uses feedback information from the system and theinformation is then used for the control decisions.

In this study, simple algorithms for admission control and congestioncontrol is to be developed. The algorithms are to make their decisionsregarding the status of individual cells.

27

Admission control is used to limit the number of users in the cell, so itis not overloaded. Assuming the AC to make its control decision fromthe number of active users in the system, the controlled system canbe described with the following block scheme.

The basic AC can then make its decision according to followingalgorithm

if Adm.Limit > New users + Old users

New users are let in

else

New users are blocked

End

The open and closed loop control differ in the way the admission limitis calculated. For an adaptive AC the admission limit is based on thecurrent state of the system, se chapter 5.4.1.

The congestion control should handle overload situations that occur inthe cell. The system overload regards the interference andtransmission power and the two primary parameters the CC uses tosolve such a situation are the bit rate and the number of users. Thefirst parameter may be altered by demanding a different coding of theAMR. However, the only way to actively alter the number ofconnections is by forcing active connections to close. The controlledsystem can be described with the following block scheme

Con.Ctrl

SYSγ

Drop

Interference

Con. Limit

Power

The basic CC can then make its decision according to the followingalgorithm

Adm.Ctrl

SYSNew

Block

#Users

Adm. Limit

New

28

if I or P > con.limits

(alter the γ or drop user or use a combination of both.)

End

The open and closed loop control differ in the way the admission limitis calculated. For an adaptive CC the limit is based on the currentstate of the system, se chapter 5.4.2.

The AC and CC algorithms described above can be classified asLQQHU�ORRS�FRQWURO algorithms. If the admission and congestion limitsare being controlled the algorithms controlling them are classified asthe RXWHU�ORRS�FRQWURO algorithms. The outer-loop algorithms are toadjust its output according to some specified criteria. This could beperformed in many ways.

The control decision may be based on the actual value of a measurederror, LQIRUPDWLRQ�IHHGEDFN, or it can be based on the sign ofmeasured variables, GHFLVLRQ�IHHGEDFN.

The algorithms proposed in this study are originally from [12] and [1].They use a variant of the decision feedback method called Jump-algorithms.

5.2.1 Jump-Algorithms

Consider a process with a control variable p and two events, A and B.Assume that for different values on p the events occur with differentprobability. Event A occurs with a large probability if p is large andevent B occurs with large probability if p is small.

0S +> Å many A and no B

0S −< Å no A and many B

For example assume the following process where

=DW�LRFFXUV%%�LI�HYHQW

LRFFXUV�DW�$$�LI�HYHQW[

L

xi varies according the value of p and we vary p according to

SYS

A

Bp SYS

29

LLLSSS ∆+=+1

where

=+=−

=∆%LI�[

$�LI�[S

L

L

L εδ

δ and ε are arbitrary constants larger than zero. At time k we get

VSSSSN

M

MN+=∆+= ∑

=0

00

p0 is the start value of p. #A and #B are the number of events A andB. Then s can be expressed as

$%V ## ⋅−⋅= δε|p| (absolute value of p) is always less than a value R. If p is largerthan M it will decrease and if it is less than –M it will increaseaccording to the assumption made earlier concerning the process inthis section. Therefore R is less than infinity and we get

∞<≤ 5V

by using

N%

%$%

E

N$

%$$

D

#

##

#

#

##

#

=+

=

=+

=

we get

5DNEN ≤⋅⋅−⋅⋅ δε

In addition, by substitution we get

N5

DE ≤⋅−⋅ δε

Letting the algorithm run over an infinite time, i.e. kÅ∞, we get

0→⋅−⋅ DE δε

Over a sufficient time, the ratio between total numbers of A and B iscontrolled by the ratio between the constants ε and δ.

5.3 OUR ALGORITHMS

5.3.1 Adaptive Admission Combination

With this algorithm our goal is to approach a certain block rate.Admission limit is increased with ε and decreased with δ.

if a users is blocked

30

increase admissionlimit

elseif a new user is let in

decrease admissionlimit

end

This will give a blockrate, ε/(ε+δ).

5.3.2 Adaptive Congestion Combination

With this algorithm our goal is to approach a given value on the droprate. The droplimit is increased with ε and decreased with δ. Droplimitis the lowest speech quality acceptable before a user must bedropped.

if a forced drop occurred

decrease droplimit

elseif a user disconnects

increase droplimit

end

This will give a droprate, ε/(ε+δ).

5.3.3 Adaptive Admission and Congestion Combination

Like in the previous algorithm, the goal is to approach a given valueon the drop rate. But here instead the Admission limit is changed ifthe cell is in a congested state or not. Admission limit is increasedwith ε if a user is disconnected normally, decreased with δ if a user isdropped.

if a forced drop occurred

decrease admissionlimit

elseif a user disconnects

increase admissionlimit

end

This will give a droprate, ε/(ε+δ).

5.4 OPERATORS POLICIES/SYSTEM PERFORMANCE

5.4.1 Operators Policies

31

Different net operators may have different policies regarding theirO&M, i.e. they can have different priorities regarding someparameters. A radio network has many parameters controlling itsperformance and many of the parameters vary over time and areindirectly or directly related to each other. This makes the planningand management of the systems very complex and a costly process.To lower both costs and the complexity it is of great interest to mapthe system parameters to some high-level radio networkcharacteristics that could easily be interpreted by the net operators.Three such parameters could be capacity, coverage and quality(CCQ).

The idea is to move the complexity of the system from net operatorsand hide them inside the system. Therefore, the system could beoptimised according to e.g. CCQ. There are many parameters relatedto CCQ and there is no obvious way of controlling the system towardsdifferent CCQ policies. Three parameters affecting the CCQ areblocking, dropping and speech quality. Of course, neither block norforced drop should occur in a perfect system, but in a “busy hour” orin a “hot spot” situation, i.e. when the system is heavily loaded,blocking and dropping do occur. High capacity in a network wouldprobably mean that blocking is low. Dropping must be considered asbad quality, but it could also be considered as capacity decreasing.Speech quality directly reflects the quality of service in the net.Speech quality also can be related to coverage, with a lower C/I-target a greater coverage can be reached.

32

5.4.2 System Performance

The differences between the simulated algorithms can be hard to seeand a relative performance measure is desired. The SystemPerformance (SP) is a way to compare how different algorithmshandle different policies and algorithm [13].

A net operator may have different CCQ requirements and if weconsider blocking, forced dropping and the speech quality as theparameters affecting CCQ, then the SP should depend of the relationbetween those parameters.

This can be done by assigning each user a SP measure, spi,t at everysample. It is calculated according to

WLWLVWLWLEORFNFIGURSE7TDVS ,,,, ⋅+⋅+⋅⋅=

The parameter qi, fdropi and blocki resemble the speech quality,forced dropping and blocking. The constants a, b and c describe theparameters importance i.e. how the parameters are related to the spi

and to each other. Ts is one, see section 4.1.2.

qi,t ∈]0 1] where 1 is representing highest speech quality and 0 worst.As the speech quality can differ in up link and down link the qi,t iscalculated according

] ]01_,_,

_

1

_

12

, ∈+

=LL

LL

WLTXOTGO

TXOTGO

T

I.e. the downlink quality, dl_qi, and uplink quality, qi, are bothnormalised to vary between 0 and 1. If both of them are good (closeto 1), the qi gets high, but if any of them is bad, the qi gets low.

fdropi is one if the user has been forced to drop and blocki is one if theuser was denied access to the system. If blocki is one, fdropi and qi

have to be zero. Else, if the user has access then fdropi and qi canboth be 0 or 1 at the same time.

The constants a, b and c describe how the parameters fdrop, blockand quality relate to each other. By setting different values on theconstants a, b and c the parameters may be differently “punished”and the SP measure can thereby resemble different policies. Sincethe spi is calculated every second/sample and the average length of aconnection is 90 second, the constant a is set to 1/90. This gives theweighted spi average for every successfully disconnected user to beone if the quality been perfect during the hole access time. Then byletting b = -1 and c= -1 the SP for every user vary between -1 to +1.However, if the user is forced by the system to disconnect, e.g.dropped, then the spi should be low. How low is depending of the netoperator policy. The same goes for blocking.

For every time sample the total SP measure for the cell is

33

( )∑∑ ⋅+⋅+∆⋅⋅==XQLWV

L

WLWLWL

XQLWV

L

WLWEORFNFIGURSEWTDVSVS

#

,,,

#

,

In our one cell model, all users are assumed to have the same C/I-target, i.e. they are assumed to have the same speech quality andtherefore the same qi. This gives

∑∑ ⋅+⋅+∆⋅⋅⋅=XQLWV

L

WL

XQLWV

L

WLWWEORFNFIGURSEWXQLWVTDVS

#

,

#

,#

The average system performance over time is then

VDPSOH

VSVS

VDPSOH

W

W

#

#

∑=

In this study, three different policies are considered. The proposedpolicies are to resemble plausible net operator policies and they areproduced in discussion with [12].

&DOO�UHWHQWLRQ�SROLF\

The first policy is a call retention policy. The net operator prefers verylow dropping but he can accept some blocking. If he can accept tentimes more blocking than dropping the constant b is to be ten timesmore punished than constant c, i.e. the relation between b and cshould be 10 to 1. The constant a is set to 1/90. Some blocking isacceptable so the c is not punished so much and therefore set to –1.The constant b is then set to –10.

$XGLR�TXDOLW\�SROLF\

In this policy, the operator prefers high audio (speech) quality. Lowblocking and low forced dropping are not that important. The quality ishigh priority and the constant a is now 10 * 1/90, b and c are not to bepunished hard and they are considered equally bad, they are both -1.

&DSDFLW\�SROLF\

In the last policy, the net operator prefers high capacity. The blockingand forced dropping are to be low. The audio quality is less important.The constant a is also here kept at 1/90. The constants b and c areeven here considered equally bad but are punished more and areboth set to -10.

34

6 SIMULATIONS AND RESULTS

This chapter will present the simulations and results of this study. Thechapter is divided into two parts. The first part is about the simulationsthat have been done with the one cell WCDMA radio model. Thechapter starts with a description of the basic admission andcongestion control. Then the adaptive control will be describedfollowed by the combining of some of the algorithms. In section 6.2the algorithms that gave the best result in the one cell model will besimulated in a multi-cell model.

6.1 THE ONE CELL MODEL

6.1.1 Basic Congestion Control

To keep an acceptable signal quality and cell coverage in the uplink,the uplink interference limit is assumed 3 dB relative the backgroundnoise. In the downlink the maximum power that can be used at thebase stations for the dedicated channels is assumed to be 16 W.Therefore interference and power cannot be allowed to exceed theselimits. One way to manage this is to drop a user whenever thecongestion limits is exceeded, see figure 6-1.

0 500 1000 1500 20000

1

2

3

4

time (s)

Inte

rfer

ence

(dB

)

0 500 1000 1500 20000

5

10

15

time (s)

Pow

er (

W)

Figure 6-1 shows that by dropping a user whenever the congestionlimits is exceed, interference and power are kept below its limitations.In the bottom of the graphs, the drop instants are shown.

In our model every user is assumed to have the same γ. Then insteadfor dropping a user when the cell is congested, γ can be adjusted tomanage the problem, see figure 6-2. As described in section 4.1.2, λis the mean intensity with which new users want to set up aconnection.

35

0 0.5 1 1.5−30

−25

−20

−15

lambda (users/sek)ga

mm

a (d

B)

0 2000 40001

2

3

4

time (sek)

Inte

rfer

ence

(dB

)

0 0.5 1 1.5−20

−15

−10

−5

lambda (users/sek)

gam

ma

(dB

)

0 2000 40000

10

20

30

time (sek)

Pow

er (

dB)

Figure 6-2, In the figures at the left the worst 10% percentile of γ foruplink and downlink are shown. γ is changed so that interference andpower are kept below their limits, seen in the figures at the right.

6.1.2 Adaptive Congestion Control

The previous figure shows that the CC algorithms manage to keep therequirements of the interference and the power. Assume thosealgorithms to be perfect. I.e. the interference is assumed to alwaysfollow 3 dB and the power is assumed to always be at 16 W. Theachieved downlink and uplink γ are then calculated from the equationsin chapter 4.1. The expression for γ in the downlink is

∑⋅=

L L

'/L

J,

31

0

max,γ

and for the uplink γ is

max

max, 1

1

,1I

,18/L −−

−=γ

For different values of the noise, the down link interference, theexpansion factor f, the amount of users and their path gain conditionswill then give the required γ. When the system load increases the γ willbe decreased and instead, if the system load decreases a larger γcould be used.

36

However, limitations in the bit rate give limits for how to vary thegamma. At a certain C/I, it is not possible for the system to send anyinformation. Even at low C/I where some information could be sentthe bit error rate (BER) would be too large for the decoder to produceany usable information and at some level the synchronisation of thechannel will be impossible. For data the packet may be retransmittedbut for real-time services this is not possible due to delayrequirements. The high BER therefore leads to lost data frames forthe real time service. This leads to a lower limit of a useable γ. Tokeep a channel open, the system should at least produce a γ so highthat error less control information and some user data can becontinually sent.

A system using the method described above has to use anotherstrategy when the users already have reached their lowest γ and thesystem is still congested (have to high interference or too little poweravailable). This could be to force a certain user to close its connectionas discussed in the first algorithm in chapter 6.1.1. This combinationwill give a low drop rate characteristic until lambda (number of usersin the system) get so high that the system does not manage to keepthe limitations by just lower the γ. Then the droplimit will be increased,which is illustrated in figure 6-3.

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

0.05

0.1

0.15

lambda

drop

rat

e

−0.1dB−1.2dB−2.3dB

Figure 6-3 presents the total drop rate for both the up link and thedownlink with three different values of the drop limit. The limit isseparated with 1.1dB and the middle curve represents a drop limit atdown link γ of -21.2 dB and up link γ of -24.2 dB.

The drop rate is calculated according to the following expression

λλ

λλ +DQJXSVGURSV)RUFHG

GURSV)RUFHGUDWHGURS)RUFHG

#_#

_#__

+=

37

The different curves in figure 6-3 are very similar. Assume that whenthe droplimit is altered the drop rate curve just translates along thelambda-axis. By varying the drop limit, the system should be able tokeep a certain rate of the forced drops.

The drop rate value would be constant if the drop limit is decreasedwhen a forced drop occurs and increased for every successfullydisconnected user. I.e. the γ for both down link and uplink vary withthe system load (number of active users, noise, expansion factor anddownlink interference) and a drop occurs when the γ reaches a certainvalue (droplimit). By lowering the droplimit after the system has beenforced to drop a user at a certain value and increase it when a userhas finished a call with another value, the system should be able toreach a drop rate, chapter 5.3.2. This is illustrated in figure 6-4 with adrop rate goal of 1%.

0.2 0.4 0.6 0.8 10

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0.02

tota

l for

ced

drop

rat

e

lambda

Figure 6-4 shows that the algorithm can keep a certain drop rate, inthis case 1% forced drops.

The drop rate of 1% is in principle reached for all lambda. This ishowever not practical due to the lower limit of gamma. In addition, fordigital transmission there is an upper limit for where larger amount ofdate does not contribute to higher data quality. The AMR (AdaptiveMulti Rate) has a highest bit rate, the AMR is described in section3.2.4. At a certain C/I and data rate, the information is transferrederror less. Higher C/I does not provide any noticeable quality gain.Instead it just allocates resources. The γ can be said to have an upperlimit. With both an upper and a lower limit the algorithm is only able tocontrol the drop rate within an interval. With illustrative limitationvalues and the drop rate goal of 1% the algorithm produces a droprate according to figure 6-5.

38

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

tota

l for

ced

drop

rat

e

lambda

Figure 6-5 shows that it’s possible to keep a drop rate within aninterval in the load.

The maximum bit rate from the AMR is 12.2 kbps and it is assumed tocorrespond to a desired downlink γ of –20 dB and the required uplinkγ to –23 dB (the diversity is assumed to give a 3 dB gain at the basestation). The lowest bit rate the WCDMA-AMR may produce is 4.75kbps and this corresponds to a quality (γ) decrease of about 1.5 dB forboth downlink and uplink. The variation of γ within the intervalcorresponds to a bit rate variation, which can be seen as speechquality variation.

For realistic limitations of γ and with the same drop rate goal asearlier, the algorithm performs according to figure 6-6.

39

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

tota

l for

ced

drop

rat

e

lambda

Figure 6-6 shows that it’s not possible to keep a drop rate within aninterval in the load due to the limitations.

With that γ interval, the algorithm does not achieve the desired droprate goal of 1%. However with an effective admission control theinterval where the congestion algorithm is effective, could beincreased. The quality in the system is displayed in figure 6-7.

0 0.2 0.4 0.6 0.8 1 1.2 1.4−24.5

−24

−23.5

−23

uplin

k qu

ality

lambda

0 0.2 0.4 0.6 0.8 1 1.2 1.4−21.5

−21

−20.5

−20

dow

nlin

k qu

ality

lambda

Figure 6-7 displays the worst 10% percentile quality in the uplink anddownlink for various λ.

The quality in both uplink and downlink decreases as the system load(lambda) increases. The uplink quality is at its minimum value during90% of the time for lambda larger than 0.85 lambda. The quality in thedownlink is better. This algorithm is very interesting and will bereferred to as $OJRULWKP�,. $OJRULWKP�, can be described as:

40

if I or P > con.limit

decrease γ

if γ < droplimit drop user

end

end

When the congestion situation is over the γ will be increased in bothuplink and downlink. The drop limit is controlled according to anadaptive algorithm described below, also described in section 5.3.2.

if a forced drop occurred

decrease droplimit

elseif a user disconnects

increase droplimit

end

Both γ and the droplimit vary within certain interval. The maximumvalue of downlink γ is -20 dB and for uplink γ the maximum value is -23 dB. The droplimit’s lowest value is 1.5 dB lower than the maximumvalue of the γ. I.e -21.5 dB in the downlink and –24.5 dB in the uplink.

6.1.3 Basic Admission Control

Congestion control is used when the system already is overloaded.With admission control the number of users in the system should becontrolled before the system is congested. In this study it has beenassumed that admission control uses a number of users to measureadmission limit. Figure 6-8 illustrates the difference in mean numberof active users for a system with and without admission control.

41

0 0.5 1 1.50

20

40

60

80

100

120

140

lambda (users/sek)

Num

bers

of u

sers

Admission No Admission

Figure 6-8 shows the difference in mean number of users when anadmission limit is added. In this case, the admission limit is 60 users.

When comparing different systems it is convenient to look at theblockrate instead of the number of users blocked. The blockrate iscalculated as:

λλ

λλ QHZ%ORFNHG

%ORFNHG%ORFNUDWH

##

#

+=

Figure 6-9 illustrates the block rate for various λ.

0 0.5 1 1.50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

lambda (users/sek)

Blo

ckra

te (

%)

Figure 6-9 shows the blockrate when the admission limit is 50 users.The blockrate increases steady with a higher λ.

6.1.4 Adaptive Admission Control

42

The aim is to construct an algorithm that changes the admission limitso a given value on the blockrate can be reached. Perfect congestioncontrol is assumed to handle any problem with too many users in thesystem. The algorithm is discussed in section 5.4.1.

if a user is blocked

increase admissionlimit

elseif a new user is admitted

decrease admissionlimit

end

0 0.5 1 1.50

0.02

0.04

0.06

0.08

0.1

0.12

lambda (users/sek)

Blo

ckra

te (

%)

Figure 6-10 shows the blockrate when the self-adapting algorithm isused. It has no problems with the 10% demand on blockrate.

The algorithm has no limitations, it will always try to get 10%blockrate. Independently of how high or low the traffic is. Users willthen be blocked although the traffic is low, just because the algorithmwants to have 10% blockrate. As mentioned earlier perfect congestioncontrol was assumed, that will give a very low quality and highdropping with increasing λ. Figure 6-11 illustrates the decrease inquality.

43

0 5 10 15 20 25 30−30

−25

−20

−15

−10

−5

0

lambda (users/sek)

gam

ma

(dB

)

Figure 6-11, Here is the 10% percentile of γ, when the blockrate isheld at 10%. The price paid for keeping blockrate at 10% is that γ willget very low for high λ.

6.1.5 Adaptive Admission and Congestion Combination

By instead varying the admission limit, if the cell is congested or not,an algorithm who combines AC and CC is created. The algorithmuses a fixed drop limit and the number of normally disconnected usersand forced drops, to control the admission, see chapter 5.3.3. Thisgives the ability to control towards certain drop rates and can bedescribed according to:

if a users is dropped

decrease admissionlimit

elseif a user hang up

increase admissionlimit

end

The results on drop and block rate are illustrated in the figure 6-12.

44

0 0.2 0.4 0.6 0.8 1 1.2 1.40

0.005

0.01

0.015

drop

rat

e

lambda

0 0.2 0.4 0.6 0.8 1 1.2 1.40

0.2

0.4

0.6

0.8

bloc

k ra

te

lambda

Figure 6-12 displays the drop rate and block rate for the algorithmwith adaptive admission limit.

For low lambda values the risk of congestion is very low and there areno forced drops. As the lambda increases the algorithm starts todecrease the number of admitted calls to keep goal value of the droprate (in this case 1%) and the block rate increases. The algorithmsmanage to meet the required droprate for a rather high load on thebehalf of a very high block rate.

In a real system the load has large variations during the day andnight. The algorithm adjusts the admission limit continuouslyregardless of the load. For a long period with good conditions (lowload) the algorithm will “think” that it manages the system excellentand therefore permits more users to enter by increasing theadmission limit. However there are no new users to admit and as theinterference does not increase and there is no dropping the algorithmincreases the admission limit further. If then a lot of new users arrivethe interference can quickly rise to a severe congestion situation withmany forced drops or even a total system failure of the cell as result.A way of preventing this is to only let the algorithm adjust theadmission as long as the system is fully loaded. Then if the loaddecreases, the system only remembers the latest maximumadmission for the loaded system and when the load increases theadmission already has an acceptable value (as long as the inter-interference has increased).

45

This is implemented according to

if Load > (Admission limit – ∆) if a user is dropped

decrease admissionlimit

elseif a user hangs up

increase admissionlimit

end

end

This algorithm will be referred to as $OJRULWKP�,,, the adaptation ismade as long as the number of active users in the system is largerthan the admission limit minus a constant, ∆.

For this algorithm, it is of greater interest to look at the amount of dropconnection when the system is fully loaded. This is illustrated belowwhere the solid line represents the rates for the loaded period and thedash-dotted line represents the rates for the whole period. The ∆ isset to 2.5. Its effect of total forced drop and block is illustrated in figure6-13.

0 0.2 0.4 0.6 0.8 1 1.2 1.40

0.005

0.01

drop

rat

e

lambda

0 0.2 0.4 0.6 0.8 1 1.2 1.40

0.2

0.4

0.6

bloc

k ra

te

lambda

Figure 6-13 displays the total drop rate and block rate for the loadedand the whole period using $OJRULWKP�,,.

Letting the γ vary at high load and only drop a connection when thelow γ is reached the algorithms should give less dropping. I.e the$OJRULWKP�,, is complemented with following algorithm:

if I or P > con.limit

decrease γ

46

if γ < droplimit drop user

end

end

Otherwise, it follows:

if Load > (Admission limit – ∆) if a user is dropped

decrease admissionlimit

elseif a user hangs up

increase admissionlimit

end

end

This algorithm variant will be referred to as $OJRULWKP�,,,. Thedownlink droplimit is fixed at –21.5 dB and the uplink droplimit is fixedat-24.5 dB. The effects of using a variable γ on drop and block rateare displayed in figure 6-14.

0 0.2 0.4 0.6 0.8 1 1.2 1.40

0.005

0.01

drop

rat

e

lambda

0 0.2 0.4 0.6 0.8 1 1.2 1.40

0.2

0.4

0.6

0.8

bloc

k ra

te

lambda

Figure 6-14 displays the drop rate and block rate during the loadedperiod for various lambdas using $OJRULWKP�,,,.

Both drop and block rate is considerable lower. However the variationin γ gives a lower quality. This is illustrated in the next figure.

47

0 0.2 0.4 0.6 0.8 1 1.2 1.4−24.5

−24

−23.5

−23

uplin

k qu

ality

lambda

0 0.2 0.4 0.6 0.8 1 1.2 1.4−21.5

−21

−20.5

−20

dow

nlin

k qu

ality

lambda

Figure 6-15 displays the worst 10% percentile quality in the up linkand down link for various λ using $OJRULWKP�,,,.

The quality decrease is rather low compared to $OJRULWKP�,�(thevariable drop limit algorithm), se figure 6-7 in chapter 6.1.2.

Instead of controlling the admission with the number forced drops, thevalue on γ can be used. Whenever the system lowers the γ thealgorithm will decrease the admission limit. The γ is still decreased if itis required by the system and users are dropped if the droplimit isreached.

if Load > (Admission limit - ∆)

if γ is decreased decrease admissionlimit

elseif a user hangs up

increase admissionlimit

end

end

This algorithm will be referred to as the $OJRULWKP�,9 and its effects ofdropping and blocking is illustrated in figure 6-16.

48

0 0.2 0.4 0.6 0.8 1 1.2 1.40

0.005

0.01

drop

rat

e

lambda

0 0.2 0.4 0.6 0.8 1 1.2 1.40

0.2

0.4

0.6

bloc

k ra

te

lambda

Figure 6-16 displays the drop rate and block rate during the loadedperiod for various λ using $OJRULWKP�,9.

This gives very few drops and a much better quality but a higher blockrate than the previous algorithm. Figure 6-17 illustrates theexperienced quality.

0 0.2 0.4 0.6 0.8 1 1.2 1.4−24.5

−24

−23.5

−23

uplin

k qu

ality

lambda

0 0.2 0.4 0.6 0.8 1 1.2 1.4−21.5

−21

−20.5

−20

dow

nlin

k qu

ality

lambda

Figure 6-17 displays the 10% worst percentile and the worstexperienced quality in the uplink and downlink for $OJRULWKP�,9.

The 10 percentile is at the best quality (-23 for the uplink and –20 forthe downlink) but the worst experienced quality during the simulationis lower. This reflects the fact that there is a lowering of quality,however it is very small.

6.1.6 Result Summary

49

The chapter 6.1 starts with a discussion of the basic congestion-algorithm ideas. In section 6.1.2 two methods for limiting theinterference and power were tested, dropping and varying of γ. Insection 6.1.3 the drop limit was introduced as a good method tocontrol the drop rate. Figure 6-3 shows how the drop limit can be usedto get the specified drop rate. Moreover, it is illustrated that in an idealcase a specified drop rate always can be reached, see figure 6-4. Inthe more realistic case as with $OJRULWKP�, the desired drop rate goalis not achieved. However with an effective admission control theinterval where $OJRULWKP�, is effective, could be increased. In chapter6.1.4 it is shown that by changing admission limit a specified blockrate also can be achieved. Chapter 6.1.5 illustrates the method withadaptive admission and congestion combined. In table 6-1compilation of the interesting algorithms is done.

$OJRULWKP,

$OJRULWKP,,

$OJRULWKP,,,

$OJRULWKP,9

)L[HG�γ - Yes - -

9DULDEOH�γ Yes - Yes Yes

)L[HGGURSOLPLW - Yes Yes Yes

9DULDEOHGURSOLPLW Yes - - -

9DULDEOHDGPLVVLRQ

�GURS�- Yes Yes -

9DULDEOHDGPLVVLRQ

�γ�- - - Yes

Table 6-1. A compilation of $OJRULWKP�,�±�,9.

In this study, it is assumed that the system is in a busy hour situation,high λ. The aim is to see how the algorithms adjust to different λ in thelong run. Mobil traffic varies over both days and weeks. It wouldprobably be possible to do some sort of prediction of the traffic anduse that information to improve the admission and congestion control.Different algorithms could then be used when the traffic is low or high.

50

Prediction of the load behaviour during shorter period is also of greatinterest. This could be very difficult in a real WCDMA system whereother services, such as packet users will be included. The wholesystem will be more complex, with different services for differentusers, like random access for packets. However short-term predictionin a mixed services system could be very difficult and the load will beburstier than in the system used in this study. However, there will alsobe some advantages, packet users have no real-time demands so thebit rate could theoretically be lowered freely, that would give a muchmore effective congestion control.

The most interesting algorithms to compare are the variable drop limitalgorithm, $OJRULWKP�,� and two of the algorithms with variableadmission limit, $OJRULWKP�,,,�and $OJRULWKP�,V. The simplest way ofcomparing the performance of the different algorithms is the SPmeasure.

The first system performance measure is to resemble a call retentionpolicy (sp1), se chapter 5.4.2. The system performance for thedifferent algorithms considering sp1 is illustrated in figure 6-18.

0 0.2 0.4 0.6 0.8 1 1.2 1.4−1

−0.5

0

0.5

sp1

lambda

Alg.I Alg.II Alg.IIIAlg.IV

Figure 6-18 illustrates the system performance according the call retentionpolicy (sp1).

Using $OJRULWKP�,, and $OJRULWKP�,9 result in a high blockrate thatleads to a low SP according sp1. $OJRULWKP�,,, handles the systembest at high load (according the criteria in sp1). $OJRULWKP�, is almostas effective as the $OJRULWKP�,,, but at higher load the algorithmsproduce high drop rate, which it gets punished for in sp1. Bothalgorithms have their SP optimum between lambda 0.4 and 0.6.However the $OJRULWKP�,,, performs overall best.

51

The second policy (sp2) is an audio quality policy where the forceddropping is considered less harmful. The algorithm performanceaccording sp2 is illustrated in figure 6-19.

0 0.2 0.4 0.6 0.8 1 1.2 1.40

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

sp2

lambda

Alg.I Alg.II Alg.IIIAlg.IV

Figure 6-19 illustrates the system performance according the audioquality policy (sp2).

Here the $OJRULWKP�, and $OJRULWKP�,,, algorithms have the best SPvalues. The $OJRULWKP�, perform better due to the lower punish of theforced drops. Still the $OJRULWKP�,, and $OJRULWKP�,9 are punished bytheir high block rate. The $OJRULWKP�, and $OJRULWKP�,,, algorithms havetheir SP optimum between lambda 0.5 and 0.7. Algorithm III is bestfor high values on λ.

The last discussed policy (sp3) is a capacity policy, dropping andblocking are to be kept low if high SP is to be reached. Figure 6-20illustrates the different algorithms performance according to sp3.

52

0 0.2 0.4 0.6 0.8 1 1.2 1.4−1

−0.5

0

0.5

sp3

lambda

Alg.I Alg.II Alg.IIIAlg.IV

Figure 6-20 illustrates the system performance according to thecapacity policy (sp3).

None of the algorithms perform well at higher load. $OJRULWKP�,performs best of the investigated algorithms according to the sp3measure. $OJRULWKP�,,, works well at lower load. $OJRULWKP�,, and$OJRULWKP�,9 are still the less suitable algorithms according to the SP.

The $OJRULWKP�,,, (variable admission limit and variable γ) performsbest in the view of the call retention (sp1) and the audio quality policy(sp2). For the capacity policy (sp3), the $OJRULWKP�, (variable droplimit) performs best of the analysed algorithms.

6.2 MULTI-CELL MODEL

To investigate the control algorithms in a more appropriateenvironment, a more advanced WCDMA radio network simulator wasused. It is more thoroughly described in section 4-2

53

Except that the radio environment is changed, our algorithms have tobe adjusted. In our one-cell model, we were able to change γcontinuously. This is not possible in this simulator and the discretechange of γ introduced a new problem, which lead to that $OJRULWKP�,which was used to get a certain drop rate could not be used.Therefore, the simulations are limited to the $OJRULWKP�,,�±�,9. Thesethree algorithms are all based on changing admission limitsdepending on how users are disconnecting from the system andwhich γ they have. The admission limit is decreased if a user is forcedto drop its connection and increased if the user is disconnecting aftera successful call. To not increase the admission limit unnecessaryhigh the admission limit was only increased if the cell was full and auser disconnected. The implemented algorithms could be divided intotwo different types of congestion control. One with changing γ and onewith γ fixed. The congestion limit is set to -129 dB (3 dB over thebackground noise).

The first simulation was performed with fixed γ and only one basestation equivalent to the DOJRULWKP�,,. The congestion control droppeda user whenever the congestion limit on three dB was exceeded. Withthis algorithm, there is no possibility to control the blockrate. Theblockrate will only depend on how high the mobile traffic intensity is, inthis simulation the traffic is always high. Admission limit is decreasedby 1, when a user is dropped and increased by 0.1, when a users isdisconnecting in a full cell. Why admission only is increased when thecell is full is because otherwise when the traffic is low the admissionlimit could increase though its not needed. This gives a droprate at10%, when the cell is full, but a much lower drop rate otherwise. 10%is perhaps a high droprate, but taken into consideration that thedroprate is calculated when the cell is fully loaded, perhaps it can beacceptable.

54

0 500 1000 1500 2000−132

−131

−130

−129

−128

Time (sek)

Inte

rfer

ence

(dB

)

0 500 1000 1500 2000145

150

155

Time (sek)

Adm

issi

on li

mit

Figure 6-21, this figure shows how interference and admission limitvary, when γ is held fixed.

A base station generally only has 128 orthogonal codes in thedownlink. If a strategy of not opening a new code tree was used, therewould be an upper limit at 128 users in a cell. Simulating with one cellin this simulator no inter-interference is included, therefore a highernumber of users is accepted. This will not occur when more cells arein use.

In the next simulation congestion control is able to vary γ in five steps.γ is decreased if interference is over the congestion limit andincreased if below. If γ can not be lowered any more a user is droppedinstead.

55

200 205 210 215 2203.5

4

4.5

5

5.5

time (s)ga

mm

a

200 205 210 215 220−131

−130

−129

−128

−127

time (s)

Inte

rfer

ence

(dB

)

Figure 6-22, this figure shows the oscillating behaviour of γ (gamma),when there is no hysteres.

This simulation discovered a problem with oscillation. When the cellgets congested γ is decreased and all new users are blocked. Then ifthat was enough to lower the interference below congestion limit newusers were allowed to enter the cell at same time as γ was increased.This often results in that the congestion limit was exceeded again andthe system starts to oscillate around the congestion limit. Therefor ahysteres is introduced on one dB. With hysteres we mean that γ is notincreased until the interference is one dB below congestion limit. Thisgives us a much smoother behaviour, when adjusting γ.

56

105 110 115 120

1

2

3

4

5

time (s)ga

mm

a

105 110 115 120−131

−130

−129

−128

−127

time (s)

Inte

rfer

ence

(dB

)

Figure 6-23, this figure shows how γ is adjusted when a hysteres isintroduced. A much smoother behaviour is reached, but with a lowerγ.

We are now letting congestion control lowering γ in five steps if theinterference is over three dB. Simulating with one cell will then give usno dropping, because lowering γ is often enough to decrease theinterference below the congestion limit.

0 500 1000 1500 2000−132

−130

−128

−126

time (sek)

Inte

rfer

ence

(dB

)

0 10 20 30 40 50 600

2

4

6

time (sek)

gam

ma

Figure 6-24, the upper figure shows interference. The lower shows γfor one user, γ=0 tells us that the user has disconnected.

57

As shown in figure 6-24, interference is kept below three dB withoutany dropping, but instead we are getting very low speech quality.Admission limit is only lowered when getting a drop, but with thisalgorithm, there are no drops because it is enough to lower γ.Therefore, the admission limit is lowered every time when the γ doesnot has its highest value.

0 500 1000 1500 2000−132

−130

−128

−126

time (sek)

Inte

rfer

ence

(dB

)

115 120 125 130 135 140 145 1500

2

4

6

time (sek)

gam

ma

Figure 6-25, the upper graph shows that the interference is heldaround three dB. The lower graph shows γ for a typical user.

Now the admission limit is more activly controlled. Therefore is thespeech quality increased. This gives us a system with lower capacity,but with high quality, low dropping, and high speech quality.

When simulating with more than one cell, handover between cellsmust be considered. Our algorithm depends on that we can limit thenumbers of users in a cell, which is done with admission limit. Newusers are blocked if the cell is full, but if a user wants to do ahandover to a cell, we can not block him. That is because the user willinterfere with the cell although it is not connected to it. If the cell is theuser’s best alternative and he is denied he probably have to increasethe power to get sufficient C/I with the help of its other links and thiswill only increase the interference further. However, if the user isallowed to do the desired handover he does not have to increase histransmitting power, he may lower it instead. Therefore is no user, whowants do a handover, blocked. The problem still exists and someother method than blocking is desired.

58

0 500 1000 1500 2000−135

−130

−125

Time (sek)

Inte

rfer

ence

(dB

)

0 500 1000 1500 200070

80

90

100

Time (sek)

Adm

issi

on li

mit

Figure 6-26, seven base stations fixed γ. Upper figure shows theinterference and the lower admission limit for the different cells.

Figure 6-26 can be hard to understand, but it illustrates that theinterference is about –129 dB (3 dB over the background noise) andthat admission limit for the cells varies, the algorithm seems to work.The drop rate for the whole simulation was 3%, about 10% if onlyconsidering when the cell is fully loaded. That is, because thealgorithm is only active when the cell is full or congested. I.e. we arenot controlling the system if there is no problem.

0 500 1000 1500 2000−134

−132

−130

−128

−126

Time (sek)

Inte

rfer

ence

(dB

)

0 500 1000 1500 200060

70

80

90

100

Time (sek)

Adm

issi

on li

mit

Figure 6-27, seven base stations varied γ. Here is admission limitlowered, when γ is decreased. Almost no dropping occurred, but thecapacity in the cells is decreased.

59

Here is admission limit controlled with γ, which gives us lowercapacity, but with high speech quality and low dropping.

0 20 40 60 80 100 120 140 1600

1

2

3

4

5

6

Time (sek)

Gam

ma

Figure 6-28, this figure shows gamma for one typical case.

60

6.2.1 Result Summary

In the multi-cell model, it was not possible to control droprate bychanging γ, mostly because of that the discrete steps in γ are too few.Therefore, only DOJRULWKP�,,�,9 was simulated. When simulating,$OJRULWKP�,,, with fixed γ and only one cell, it seemed to work properly.The drop rate was controlled to 10% in a busy hour situation.

With varied γ we noticed that no dropping occurred, which leads tothat our algorithm that was supposed to control admission limit did notwork. Therefore many users in every cell got low speech quality.

Simulating instead with algorithm IV (admission limit is lowered if γ isbelow its highest value) gave a much better result. The number ofusers in every cell was decreased and therefore was the speechquality increased for every user.

$OJRULWKP�,,, and ,9 give us no specified drop rate, but are of interestanyway. If comparing the two algorithms, two different operatorpolicies can be found. When decreasing admission limit every timethe speech quality is not perfect, the system will get fewer users buteverybody will get good quality. Lowering admission limit first when γis so low that you have to drop a user will give a system with manyusers but with low speech quality. Consequently, these algorithms aremore a way to relate capacity to quality.

In the one-cell model, every user who entered the cell was consideredas a new user but with soft handover, users may be connected toseveral base stations. A denied access will not decrease theinterference in a soft handover situation. This lead to problems withthe AC used in this study and in the multi-cell model every user whowanted to switch cell was allowed to do so, even if the cell hadreached its admission limit. In this study, there is no correlationbetween hand-over algorithms and admission. All control decisionsare taken at cell level. If instead the decisions were to be made frommeasures over a larger area (more cells) the problem with softhandover and AC in the Multi-cell model would be possible to solve.The possibility to predict the individual cell load would probably alsoincrease. This would open the door for more interesting CC. E.g.algorithms handling hotspot scenarios and the use of hierarchy cellarchitectures.

Some studies of such operating AC algorithms have been made [9]and [10].

61

7 SUMMARY

7.1 CONCLUSIONS

The aim for the study was to develop algorithms that would control theWCDMA system in a busy hour situation (i.e. when the system load ishigh during a longer period). More specifically the algorithms were tocontrol the system according to different policies. Those policies werecharacterised by different rates of denied system access or forceddisconnection.

For the first part with the simplified simulation model, the goals wereachieved in controlling the system towards the different rates. Thealgorithms were also analysed with the proposed system performancemeasure. Three different possible policies were used in the analyse.The designed algorithms performed differently well for different policyviews.

The algorithms that where implemented in the more complex model,showed result that were interesting. Certain rates were achieved bythe algorithms, however with a more complex model other policiesmay be more interesting to study.

7.2 FURTHER STUDIES

In this study, it is assumed that the system is in a busy hour situationand has many users. However, mobile traffic varies over both daysand weeks. It would be interesting to do some sort of prediction of thetraffic and use that information to improve the algorithms, maybe byusing different algorithms for different situations.

Prediction of the load behaviour during a shorter period is also ofgreat interest. In a real WCDMA system other services will beincluded, such as packet users. The whole system will then be morecomplex and short-term prediction could be very useful forimprovements of the algorithms.

In this study, all control decisions are taken at cell level. If instead thedecisions were to be made from measures over a larger area (morecells), it would improve the ability to control the system. In addition,some problems occurred in this study could be solved. Algorithms foradmission control and congestion control including hierarchy cellarchitectures and handover situations would be interesting.

With the different services, it could be interesting with a priority list forhow to treat different services and even different users in an overloadsituation. More important customers (e.g. price differentiated serviceswill most likely be implemented by the operators) may be given morebit rate and less chance to be blocked or dropped. However, this ismostly a policy issue.

A. ABBREVIATIONS

62

λ Lambda, mean numbers of mobiles arriving eachsecond

γ Gamma, C/I-target

AC Admission Control

AMR Adaptive Multi Rate speech codec

CC Congestion Control

CDMA Code Division Multiple Access

DPCCH Dedicated Physical Control Channel

DPDCH Dedicated Physical Data Channel

FDMA Frequency Division Multiple Access

GSM Global System for Mobile communication

O&M Operations and Maintenance

QoS Quality of Service

SF Spreading Factor

SHO Soft HandOver

SP System Performance

TDMA Time Division Multiple Access

WCDMA Wideband CDMA

63

B. APPENDIX SIMULATION PARAMETERS

Unless stated otherwise, the simulation parameters in Table 1 areused throughout the simulations with the multi-cell model.

3DUDPHWHU 9DOXHCell radius [m] 1000

Distance independent gain in the Hata formula(β) 15,3Distance dependent gain in the Hata formula 3.76

Shadow fading standard deviation [dB] 5Shadow fading correlation distance [m] 110

BS maximum output power [W] 20MS maximum output power [W] 0.25MS power dynamic range [dB] 85

Average MS speed [m/s] 0.83Average MS acceleration [m/s2] 2

Average call length [s] 90Receiver noise UL [dB] -132,8764Receiver noise DL [dB] -128,8764

64

REFERENCE

[1] Simplified radio network management: algorithms ideas,ERA/LVA/RN Niclas Wiberg, docnr LVA/R-99: 130.

[2] TS 26.101 V1.2.0, “3GPP”, AMR Speech Codec Frame Structure.

[3] IEEE Transactions on Vehicular Technology, “WCDMA – TheRadio Interface for Future Mobile Multimedia Communication”, ErikDahlman, Per Beming, Jens Knutsson, Fredrik Ovesjö, MagnusPersons and Christiaan Roobol, November 1998.

[4] TS RAN 25.942 V1.0.21, “3GPP”, RF System Scenarios,September 1999.

[5] TS RAN 25.922 V0.5.0, “3GPP”, Radio Resource ManagementStrategies, Mars 2000.

[6] TS 25.211 V3.1.0, “3GPP”, Physical channels and mapping oftransport channels onto physical channels, December 1999.

[7] Sannolikhetsteori och statistikteori med tillämpningar, GunnarBlom, Studentlitteraturen, fjärde upplagan 1989.

[8] WCDMA System Level Performance with Fast Fading and Non-Ideal Power Control, Bo Engström and Mårten Ericson, IEEE VTS50th Vehicular Technology Conference, Amsterdam, the Netherlands,September 1999.

[9] Intercell Interference in WCDMA Uplink Packet Date Scheduling,Antonella Gioia, M.SC.Thesis, June 1999.

[10] Power Control in Cellular Radio System; Analysis, Design,Estimation, Fredrik Gunnarsson, PhD Thesis, University of Linköping,April 2000.

[11] Uplink Packet Access Control in WCDMA, Dr. Niclas Wiberg andAntonella Gioia, Internal report.

[12] In discussions with Dr.Niclas Wiberg ERA/LVA/RN.

[13] In discussion with Magnus Almgren ERA/T/B.

[14] Evaluation of Transmission Load Dependent Soft HandoverAlgorithms in WCDMA Systems, Per Skillemark M SC Thesis, March1999.

[15] Applications of CDMA in Wireless/Personal Communication, VijayK. Garg, Kenneth Smolik, Joseph E. Wilkes, Prentice-Hall, USA,1997.

[16] Digital Communication, Simon Haykin, John Wiley & sons, USA,1988.

[17] TS 25.214 V3.1.0, “3GPP”, Physical layer procedures,