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Uplink Admission Control Based on EstimatedInterference in WCDMA Systems
Master’s Thesis completed at Communication Systems,Linköpings Universitet, Sweden
by
Erik Geijer Lundin
Reg nr: LiTH-ISY-EX-3123
Uplink Admission Control Based on EstimatedInterference in WCDMA Systems
Master’s Thesis completed at Communication Systems,Linköpings Universitet, Sweden
by
Erik Geijer Lundin
Reg nr: LiTH-ISY-EX-3123
Handledare: Gunnar Bark, Fredrik GunnarssonExaminator: Fredrik Gunnarsson
Linköping 19 Januari 2001
RapporttypReport category
Licentiatavhandling √ Examensarbete C-uppsats D-uppsats Övrig rapport
_ ________________
SpråkLanguage
Svenska/Swedish √ Engelska/English
_ ________________
TitleUplink Admission Control Based on Estimated Interference in WCDMA Systems
AuthorErik Geijer Lundin
ISBN_____________________________________________________ISRN_________________________________________________________________
Serietitel och serienummer ISSNTitle of series, numbering ____________________________________
LiTH-ISY-EX-3123
KeywordCDMA, uplink interference, estimation, admission control, measurement errors
19/1 -2001
www.rt.isy.liu.se/student/exjobb/xfiles/
AbstractWCDMA is one of the cellular radio systems that are currently being developed to enable higher data rates and abigger variety of different services to mobile users. In the fundamental technique used in WCDMA it is very valuableto have accurate knowledge of the total received power at the base station antenna.
The measurements of received power that the base stations are continuously doing are corrupted by errors that canneither be neglected nor once and for all calibrated for. In the first part of this study an online calibration algorithmbased on an estimate of the uplink interference is proposed and investigated.In the second part, an algorithm for admitting new users into the system based on a load estimate is implemented andcompared with an algorithm that simply limits the number of users.
Simulations show that the calibration algorithm should be able to provide a satisfying correction of the interferencemeasurement errors. Furthermore, by using the load estimate as input to an admission control algorithm the number ofusers may be increased with about 20 percent while maintaining the quality of service.
Institutionen för systemteknikKommunikationssystem
Uplink Admission Control Based on EstimatedInterference in WCDMA Systems
Abstract
WCDMA is one of the cellular radio systems that are currently being developed toenable higher data rates and a bigger variety of different services to mobile users.In the fundamental technique used in WCDMA it is very valuable to have accurateknowledge of the total received power at the base station antenna.
The measurements of received power that the base stations are continuouslydoing are corrupted by errors that can neither be neglected nor once and for allcalibrated for. In the first part of this study an online calibration algorithm based onan estimate of the uplink interference is proposed and investigated.In the second part, an algorithm for admitting new users into the system based ona load estimate is implemented and compared with an algorithm that simply limitsthe number of users.
Simulations show that the calibration algorithm should be able to provide asatisfying correction of the interference measurement errors. Furthermore, byusing the load estimate as input to an admission control algorithm the number ofusers may be increased with about 20 percent while maintaining the quality ofservice.
Keywords: CDMA, uplink interference, estimation, admission control,measurement errors
I wish to thank the people at LinLab for bringing me the opportunity to do mymaster thesis in what I find a very interesting area. I especially like to thank Dr.Gunnar Bark, Dr. Fredrik Gunnarsson and Dr. Niclas Wiberg for their help.
Linköping, January 2001Erik Geijer Lundin
1 INTRODUCTION ..................................................................................................................................................................7
1.1 BACKGROUND ......................................................................................................................................... 71.2 PRIOR STUDIES....................................................................................................................................... 71.3 AIM......................................................................................................................................................... 71.4 SCOPE.................................................................................................................................................... 81.5 PROBLEM STATEMENT............................................................................................................................. 81.6 OUTLINE ................................................................................................................................................. 8
2 CELLULAR RADIO SYSTEMS AND WCDMA..............................................................................................................10
2.1 RADIO TRANSMISSION PRINCIPLES ........................................................................................................ 102.2 MULTIPLE ACCESS AND CDMA.............................................................................................................. 112.3 CELLULAR NETWORKS .......................................................................................................................... 122.4 WCDMA STANDARD............................................................................................................................. 15
3 INTERFERENCE AND RELATIVE LOAD ESTIMATION ..........................................................................................17
3.1 APPROXIMATE INTERFERENCE EXPRESSION ........................................................................................... 173.2 ASSUMPTIONS REGARDING THE ESTIMATE ............................................................................................. 183.3 A CALIBRATION ALGORITHM .................................................................................................................. 193.4 UPLINK ADMISSION CONTROL ALGORITHM.............................................................................................. 20
4 SIMULATIONS AND RESULTS .......................................................................................................................................22
4.1 UPLINK INTERFERENCE MEASUREMENT CALIBRATION............................................................................. 224.1.1 Assumptions, simulation setup and initial studies............................................................................................224.1.2 The estimate as input to measurement calibration algorithm ..........................................................................234.1.3 Simulation Summary ........................................................................................................................................294.2 UPLINK ADMISSION CONTROL ................................................................................................................ 294.2.1 Assumptions and simulation setup ...................................................................................................................304.2.2 Fixed users .......................................................................................................................................................314.2.3 Mobile users.....................................................................................................................................................364.2.4 Mobile and fixed users .....................................................................................................................................374.2.5 Simulation Summary ........................................................................................................................................41
5 SUMMARY...........................................................................................................................................................................42
5.1 CONCLUSIONS ...................................................................................................................................... 425.2 SUGGESTIONS TO FURTHER STUDIES ..................................................................................................... 43
APPENDIX............................................................................................................................................................................45
APPENDIX 1: SIMULATION PARAMETERS ................................................................................................. 45
REFERENCES .....................................................................................................................................................................46
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1 Introduction
1.1 Background
The increasing demand for fast and reliable access to information that we arefamiliar with regarding fixed connections is also applicable to mobile users.To meet this demand the GSM (Global System for Mobile communication) cellularnetwork has been improved in several steps, an example of that is the dual bandthat was introduced in 1997. Today a GSM network in an urban area has to beequipped with a base station in almost every block to provide the service that theconstantly increasing number of more demanding subscribers request.
A few new systems that, for a start, will operate concurrently with GSM arecurrently being developed. One new system, that will be able to provide higherbitrates and several types of services simultaneously, is called UMTS (UniversalMobile Telecommunications Services) and will be launched sometime during2001. Several countries have already or are currently in the progress ofdistributing or selling licenses for UMTS.
Ericsson Radio Systems is one of the vendors involved in developing and settingthe standard for the new system. As one of the groups within Ericsson Research,LinLab in Linköping works with developing algorithms for radio network control.
In UMTS all users transmits a wideband signal on the same frequency. Themultiple access that is inherent to this kind of radio access is provided by the, foreach user, individual code. This technology is therefore referred to as WCDMA(Wideband Code Division Multiple Access). With this technique comes an evengreater demand of being able to control the induced power in the radioenvironment. The feedback in such control relies on a way to accurately determinethe total received power (interference) that reaches, especially, the base stationuplink antenna.
1.2 Prior Studies
The measurements of interference in uplink that are continuously done in the basestations are corrupted by errors. According to [5] the errors in measuredinterference depend mainly on component- and temperature variations in the basestations and thus vary slowly in time.
In the past LinLab has conducted some studies on different scheduling methodsof data packages in the uplink (signal from mobile station to base station), e.g. [1]and [2]. There has also been some studies on controlling the number of users inthe network, e.g. [3]. These studies rely on estimates of uplink interference andnetwork load.
1.3 Aim
This thesis consists of two parts, concerning an estimate of either uplinkinterference or, the close related, uplink load and has two partial aims.
8
One is to investigate whether or not uplink interference measurement errors canbe calibrated for by using the, in this paper proposed, algorithm which is based onaverage estimated interference.The second aim is to provide an idea of the increase in performance that anadmission control algorithm based on estimated load can give compared to analgorithm that simply limits the number of links.
1.4 Scope
The scope of this thesis is limited to the uplink only, no consideration to thedownlink is taken whatsoever. We only consider scenarios where the users arestanding still or moving slowly and are spread over the entire simulation areaaccording to a uniform distribution.
1.5 Problem Statement
The resource that primarily limits the capacity of a CDMA (Code Division MultipleAccess) network is the received interference power in the separate stations,especially in uplink. Hence, the emitted power has to be carefully controlled. Inorder to do so the base stations are continuously measuring the interferencepower.Unfortunately the measurements are corrupted by errors that can not beneglected. Furthermore, they are dependent upon the surrounding environmentand can thus not be calibrated for once and for all. However, if a reliable estimateof the average interference power can be found the measurement errors may becalibrated for by comparing the estimate with an average of correspondingmeasurements. Such measurement calibration algorithm is proposed aninvestigated in the first part of this thesis.
One way of avoiding hazardous situations is to admit users only when the systemis expected to be able to provide satisfying service throughout the new user’sentire session, both to the new user as well as to the already connected ones. Analgorithm that decides which users to admit is called an admission controlalgorithm.
A major requirement for providing quality of service is that the uplink interferenceis kept at a moderate level. Therefore an effective uplink admission controlalgorithm may be that a new user is only admitted if an estimate of the currentinterference stays below some certain limit.In the second part of this thesis we compare two versions of an admission controlalgorithm based on, the to interference close related, estimated load with analgorithm that simply limits the number of links.
1.6 Outline
Chapter 2 consists of some fundamental topics in wireless communication andselected parts of the WCDMA standard.
Chapter 3 introduces the estimate that this report is built upon and assumptionsregarding the same. Two applications of the estimate are also presented here,
9
namely interference measurement calibration and admission control based onestimated uplink load.
Chapter 4 presents the simulation setup, assumptions specific to the applicationsand simulation results respectively.
Chapter 5 gives a summary consisting of conclusions and suggestions to furtherstudies.
10
2 Cellular Radio Systems and WCDMA
There are a few concepts regarding radio communication that the reader needs tobe familiar with. This chapter will provide some basic terms and knowledgeconcerning radio communication in general, cellular radio systems and CDMA.
2.1 Radio Transmission Principles
The attenuation of the transmitted radio signal between sender and receiverbasically consists of three parts.The distance between sender and receiver obviously result in an attenuation ofthe signal, this part is called distance dependent attenuation and monotonicallyincreases with increasing distance.All obstacles between sender and receiver absorb some of the signal. This resultsin attenuation that does not directly depend on the distance. Since radio wavesmay travel around objects, this part of the total attenuation may actually decreasein some situations if the receiver moves away from the sender and vice versa,increase if the receiver moves closer. This part is called shadow fading after its’origin or slow fading, because of the slow variation in this part of the attenuationwhen moving around in a radio environment.As well as absorbed, the signal is reflected on obstacles in the radio environment.These reflections are received at different time instances and hence have differentphase at the receiver. They therefore interfere with each other, eitherconstructively or destructively, and cause a third contribution to the totalinterference. This part is called fast fading because of its ability to vary rapidly.The total attenuation is, in linear domain, the product of the three contributions.The inverse of the product of the two first parts, distance dependent attenuationand shadow fading, is referred to as pathgain.
The total power that an antenna receives is called interference power or simplyinterference. Interference is compiled by background noise (thermal noise), radiotransmissions, man made noise and the signals that the mobiles and basestations in our system induces into the air. By man made noise we mean signalinduced into the air by electrical appliances other than the mobiles that we control.In CDMA the part that originates from transmitted signals may be considerabledue to several users transmitting concurrently. The ratio between interference, I,and the sum of background noise power and man made noise is called noiserise.
N
N
N
I rise Noise ∑+
==iC
(2.1)
where Ci is the power of the signal received from transmitter i (carrier power) andN is the sum of background noise, radio transmissions and other man madenoise.Since the noise rise entirely depends on the power that we deliberately emit intothe air it is an interesting ratio, especially in CDMA where the interference poweroften sets the capacity limitation.The relation between Ci, transmitted power pi and total gain gi,tot between senderi and a receiver is simply:
11
totiii gpC ,⋅=
An interesting ratio is the ratio between the power of the signal containing theinformation aimed for the receiver and interference. This carrier to interferenceratio is here denoted γ,
i
i
I
C=i
Ii is the interference that receiver i experiences.
There is a theoretical relation between the received γ and the bit error probability(BEP). One application of the relation is, among others, to set an initial γ target,i.e. the signal quality that we need to provide the BEP that we want. One usuallydoes not speak of BEP instead Bit Error Rate (BER) is commonly used whendiscussing quality of service. BER is the number of erroneous bits per second.Another measure of delivered quality is the block error rate (BLER). A block is aset of bits. A block error appears whenever one or more bits in a block are faulty,even after the error correction that is performed by the receiver.
2.2 Multiple Access and CDMA
The cost of implementing, maintaining and operating a mobile system is very high.Several subscribers must thus share this cost. Still each subscriber wants toaccess the service practically independently of other users’ activity, i.e. a systemhas to be able to provide quality of service to multiple users simultaneously.CDMA is one of the basic principles of providing multiple access, see [7] or [9] forother multiple access techniques. In all CDMA access schemes the bandwidth ofthe information signal is increased. WCDMA, in particular, uses a type of CDMAcalled DS-CDMA (Direct Sequence-CDMA). In this scheme the users areseparated by multiplying (spreading) the information bits with different quasi-random bit-sequences (channelization codes) at the transmitter. In these bit-sequences the bits are called chips. Because the chip-rate, the number of chipsper second, is much higher than the rate of the information bits, several chipsmultiply each information bit. At the receiver the same sequence of chips is usedto despread the signal and detect the information aimed for that specific user. Thisdemands that sender and receiver are carefully synchronized in time. See figure 1
12
for an explanation of how the signal is spread at the transmitter and thandespread at the receiver.Since the information is multiplied by a sequence with a much higher bit rate thanthe information bit rate its bandwidth increases significantly. The ratio between thetwo bit rates is called the processing gain (this is also the ratio betweentransmitted bandwidth and bandwidth of the original information).Unlike FDMA (Frequency Division Multiple Access) or TDMA (Time DivisionMultiple Access) where the users are orthogonal either in frequency or in time theusers send at the same carrier frequency all the time in CDMA. As a result of this,the base station receives information from several users simultaneously on thesame frequency band. The processing gain has to be larger than the ratiobetween carrier to interference ratio of user i (γi) and the sum of noise and totalinduced interference from other users with a certain margin in order to permit aseparation of the received channels.With a quite large processing gain even interference that has deliberately beeninduced into the system can be defeated. CDMA is therefore common in militaryapplications where an enemy might try to jam the signal.
The chip-rate in WCDMA is 3.84 Mchips per second and the radio signalbandwidth is 5 MHz.
The larger radio signal bandwidth helps combating effects of fading andinterference in a more direct way too. Even though some frequencies have beenattenuated there is still a fair chance that the information can be correctly receiveddue to the large bandwidth of the transmitted signal.
2.3 Cellular Networks
The service area of the new system is, just as in GSM, split into as set of so calledcells. By dividing the service area into cells the capacity of the network isincreased due to the possibility of reusing the radio frequencies.The size of the cell is dependent upon the required capacity in the area as well asthe pathgain. The cells are usually, a bit unrealistic, modeled as hexagons.One base station in every cell provides the services to the users in that cell. Onebase station may, however, control several cells.In cellular networks the users do not communicate directly with each other, theymust go through a base station. A connection to a user is therefore considered toconsist of two parts, one for delivering communication in each direction. Thechannel that delivers information from the base station to the user is calleddownlink, and opposite, the uplink passes information from the user to the basestation. The two may often be considered separately, but both are equallyessential in reality.
Unfortunately, radio signals emitted in one cell do not stay within the cell borders.This causes intercell interference, therefore we need to keep track of the pathgainbetween base stations and users that do not belong to the same cell as well as tothe same cell.
Since a user only communicates with one or several base stations and basestations do not communicate with each other through the radio interface, a
13
comprehensible way of representing all the pathgains in the network is with amatrix, G. In G, each column represents a base station and each row a user.For example, a system with two cells (two columns) and two users (two rows) willhave the following G-matrix
=
2221
1211
gggg
G (2.1)
where gij is the pathgain between user i and the base station antenna in cell j.
With knowledge of G and of the transmitted power of all the mobiles in thenetwork the interference in the base stations can easily be obtained.
As mentioned above each user has to have a minimum carrier to interferenceratio, CIR, in order to get the required quality of service that the user requests. Tocompensate for the changes in interference and attenuation the transmissionpower is constantly updated.Due to the correlation between different users’ CIR through the commoninterference it is not an easy task to control the power of all the transmitters in thesystem.For example a user near a base station will drown all other mobiles connected tothe same base station if it sends with the same power as a user on the cell edgebecause of a higher pathgain (lower attenuation). This is what is called the near-far problem.Furthermore, a user in the outer regions of one cell has to transmit with rather highpower to be heard at the base station it is connected to. Because of itsgeographical position it may be close to some other base stations as well. In thatcase the user will induce severe interference in the adjacent cells. This makes thenear-far problem one of the main issues in WCDMA.
There is a maximum capacity of all networks. This capacity is dependent on thelocation of all users or, more precisely, the pathgains between the users and thebase stations they are connected to. We will not penetrate the entire derivation,which can be found in [14] or [15], and instead merely look at a simple examplewith two users and two base stations. The G-matrix will look like the one inequation 2.1. If a user is connected to one and only one cell the availablecapacity will be
2112
22 11
gggg
* =γ
Where γ* will be shared by all users in the network when all of the system’scapacity is used. I.e. the maximum capacity depends on the ratio betweenpathgain to the base station in the own cell and pathgain to neighboring cells.Since this capacity is dependent on the radio environment it is sometimes referredto as soft capacity, [11], as opposed to hard capacity which is the amount of somefixed resource, for example time slots or a fixed maximum number of channels.
14
Since there is a maximum capacity of the network one may speak of the fractionof the maximum capacity that is currently being used. This quantity is herereferred to as load.The load is a function of all users’ γ-target and γ*. If the load is above one, i.e. weare trying to offer more service than what the radio environment can currentlyaccept, all users will increase their transmission power in order to achieve their γ-target. Remember, γ-target is the ratio between own signal power (Ci) and
interference power (I), hence a way of increasing the current γ is to increaseemitted signal power. As a result of all users increasing their transmission power,the interference power will constantly increase. Increased interference powermeans lower γ, therefore the users continues to increase their transmission power.
The result of this is simply that all users will quickly reach their maximumtransmission power and still not get their information through. The effect isreferred to as party effect.
Since all users are transmitting simultaneously the interference partially dependson the number of currently active users. The reason for the aforementioned partyeffect is that too many users have been permitted to use the radio interfaceconcurrently. All operational cellular networks are equipped with two algorithmsthat handle the number of active users in the network, Admission Control (AC)and Congestion Control (CC). These two work together to improve theperformance of the radio network and to make sure the network does not run outof resources. CC decides what action to take when the radio network actually iscongested, for example lower a users quality, delay a transfer of data packets oreven disconnect a speech user with unfavorable propagation conditions (drop auser). In order to avoid these rather unpleasant actions we need to preventcongestion in the network. This is done by the AC. AC decides if a user can begiven the right to use resources of the radio network. Before admitting a user thealgorithm somehow predicts the affect of granting the user access. The decisionmay be based on maximum number of channels in a cell, estimated load orpriority (e.g. emergency call).
As a user travels from one cell to another the user eventually enters a new celland concurrently leaves the old cell. In such a situation the near far problem isquite severe. A way of solving the problem is obviously to connect the user to thenew cell instead. This is called handover or more precisely, hard handover.WCDMA is equipped with soft handover and softer handover. When usingthese two techniques a user can be connected to several cells simultaneouslywhen located in the outer regions of two or more adjacent cells. In WCDMA amobile station is in about 25% of the time in either soft or softer handover. Thedifference between the two is that softer handover only concerns one base stationwhile a mobile in soft handover is communicating with at least two. In the case ofsofter handover the signals received in different cells are combined throughMaximum Ratio Combining. In this technique the contributions from each cell kis weighted by its pathgain, gi,k:
∑ ⋅=k
kikii CgC ,,
15
where Ci is the combined received power and Ci,k is the power received in cell k fromuser i.In the case of soft handover, when the cells belong to two or more base stations,the information is combined on a higher level of the radio network. In this case thebase stations simply receive and mark the information with a reliability tag basedon the redundancy bits of each frame before regularly passing a set of frames onto a Radio Network Controller (RNC). In the RNC, reports from several basestations are gathered and the one with highest reliability tag is passed on furtherthrough the core network.
2.4 WCDMA Standard
In this kind of multi user system it is not enough to separate information from asingle user it is also essential to separate transmission from different users.WCDMA uses two techniques that together solve these two problems, namelychannelization and scrambling.Transmission from a single source is separated by channelization codes. It is inthis process the spreading and despreading are done. Different dedicatedchannels belonging to the same user in uplink and users within one cell indownlink are separated at this first stage.Scrambling is done on top of the channelization. The scrambling does notchange the signal bandwidth. Each user and each base station has its ownscrambling code. Because both spreading and scrambling are done, differenttransmitters can use the same channelization code, i.e. the system is partly relivedof the work of keeping track of which codes are taken. Different users and basestations may choose their channelization codes from the code tree independentlyof each other, since they have different scrambling codes.
All information is sent in sections called slots. 15 slots form a frame. The possiblenumber of information bits carried during one frame depends on the degree ofspreading. A higher information bit rate requires a higher chip-rate in order tomaintain a fixed processing gain.
As mentioned above, controlling the induced interference is a matter of greatimportance in a CDMA cellular network. In WCDMA the power control basicallyconsists of two loops, inner and outer loop. The inner loop is a fast loop thatoperates at a frequency of 1500 Hz (once every slot). The aim of this loop issimply to command the mobile to increase or decrease its transmitted power with1 dB depending on weather the estimate CIR is lower or higher than the CIR-target.
The outer loop uses information gathered during a longer time and from thatinformation estimates a bit- and frame error rate (BER and FER). These numbersare then compared with what is required for the application and a new CIR targetis set. This loop operates at a much lower frequency. Since this loop does notdirectly concerns the user equipment the exact frequency is not specified in thestandard, it is up to each manufacturer, or perhaps operator, to decide how oftena new CIR-target is set. Typically a new CIR-target is set every second or so.The required CIR depends not only on the BER, but also on the speed of themobile (if the mobile travels too fast the inner loop will have problems following the
16
fast fading) and the continuously changing radio environment. If CIR-target is settoo high the corresponding channel will induce unnecessarily high interference inthe system. Thus it is important to use a CIR-target that is neither too high nor toolow.
What is mentioned above is just a fraction of all the terms and techniques that isused in WCDMA. Even so, it is covering more about WCDMA than what is neededto understand the conclusions stated in this paper. For further studies in WCDMAor wireless communication in general we refer to [7], [8] and [9].
17
3 Interference and Relative Load Estimation
This chapter will start by a brief explanation of how the interference estimate isderived. All the assumptions associated with the estimate are summarized insection 3.2. The two last sections will then present two applications of theestimate, which we will later investigate in chapter 4.
3.1 Approximate Interference Expression
The total, by the base station antenna, received interference, Ij, is
i
i
jij
i
ij pgNCNI ⋅+=+= ∑∑ ,j (3.1)
where Nj is all noise power that we can not power control, pi mobile i’stransmission power and gi,j is the pathgain between mobile i and base station j.The sum in the above equation is over all mobile stations in the system, not justthose connected to base station j.
In a model without soft handover (and therefore also without softer handover) γcan be expressed as
i
ii
I
C=γ (no soft handover) (3.2)
In our model, however, soft handover is used and we must thus consider the casewhere the mobile is represented in more than one cell. If we assume perfectpower control the following relations make sense
(3.3)
I.e. the signals are maximum ratio combined in (3.3). By using relation (3.3) weconsider all handovers as being softer handover, also those that are softhandover. The sum in (3.3) is over all cells that user i is connected to.If we combine equations (3.1) and (3.3) we get
(3.4a)
Let us, for a moment, assume that all cells have the same interference, i.e. Ijequals I. Equation (3.4a) can then be rearranged to
∑∑
+=⋅
⋅
i
kk
ki,
ji,targeti,jj
I
1g
gNI
∑∑ ⋅⋅⋅ ===
k kkii
k k
kii
Igp
I
gp 1,
,itargeti, γ
18
∑ ∑∑∑
+=
+==i
k
ki,
ji,*targeti,*j
i
k
ki,*
ji,*targeti,jj
g
gIN
gI
1g
NII (3.4b)
Solving for Îj yields
(3.5)
This expression provides an estimate of the uplink interference based onquantities that the system either knows exactly or receives occasionalmeasurements of. Lj can be seen as a practical approximation of the theoreticalrelative load estimate that can be found in reference [14]. This may be motivatedby simply looking at Îj as a function of Lj, when Lj equals zero the interferenceconsists of N alone. Conversely when Lj equals 1 the interference is infinite, whichis a theoretical maximum capacity of the network.
The strength of this estimate is that it uses information gathered in several cellswhich makes it possible to calculate the intercell interference. Since soft handoverlinks are considered the soft handover gain is to a degree captured in theestimate.
3.2 Assumptions Regarding the Estimate
For analytical convenience all cells are assumed to have approximately the sameinterference. This assumption can have an affect when a hot spot scenario isconsidered. However, in this thesis the offered traffic is always uniformly spreadover the entire simulation area and hot spot scenarios should thus not appear.Furthermore, only a subset of all cells in the simulation area is considered in themajor part of this thesis. Also, the dynamics in gi,k are expected to be much largerthan the ones in Ik. Thus, the variations in Ij in equation 3.4b should mainlydepend on the sum in the product.
Perfect power control is also assumed for analytical convenience. This is a prettygood approximation under normal situations. Examples of situations where theactual CIR is not even approximately equal to CIR-target are when the user ismoving fast or when the radio environment for some reason is rapidly changing.We will only use fixed or slowly moving users in a stationary radio environment.Furthermore, when considering an average of estimates the inaccuracies in CIRshould cancel out, assuming that the actual CIR is above CIR-target as often andmuch as it is below.
To avoid keeping track of which base station each cell belongs to, all softhandovers are regarded as softer handovers. Signals received in different cellsare therefore maximum ratio combined in the estimate.
∑ ∑
=
−= ⋅
ik
ki,
ji,targeti,
jj
g
g,
1
NI j
jL
L
19
In absence of a better model, the noise power that we can not control in thesystem is modeled as constant, equal to N.
There are also a few assumptions regarding the pathgain measurements betweenusers and base stations. These measurements are done in the user equipment.The error that is coupled with this measurement is assumed to be gaussiandistributed in logarithmic scale and independent between different measurements.According to [8] the error in relative pathgain measurement must not be greaterthan 1.5 dB with a probability of 90%, i.e. a 90% confidence interval has beenspecified. This error is implemented in the simulator that we have used.Even though the measurements are done in downlink we assume that thedifference between downlink and uplink is constant in logarithmic scale andtherefore does not affect the ratio of pathgains that we are using in the estimate.
To model the user equipment’s incapability of measuring pathgain to all basestations in the network, we have chosen to simply use the five strongest pathgainsfrom every user. Also, instead of updating the entire pathgain matrix five times asecond the users were asked to send their report randomly distributed in time withan average intensity of one report in every two-second period (i.e. one every200:th frame). The reason for decreasing the frequency of the reports is to easethe payload on the radio interface and the reason for spreading them in time issimply to model the coincide between the time of measurement reports andhandover events.In reality, a user who is travelling fast will report new pathgain measurementsmore frequently than one standing still due to more frequent handovers. On theother hand, as we are just using the slow fading, the need for frequently updatedpathgain measurements is not as big for a slow moving user since the slow fadinggenerally does not change substantially for small movements.
3.3 A Calibration Algorithm
In a CDMA network it is crucial to maintain the total interference at a moderatelevel, especially in uplink. To know when to take appropriate actions the basestations are continuously measuring the uplink interference. Unfortunately themeasurements are corrupted by errors that can not be neglected. These errorsare a combination of static component deviations as well as temperaturedependent variations in the components and are thus depending on the constantlychanging surrounding environment.
The idea behind the first part of this master thesis is that the measurement errorsvary slowly and can thus be considered approximately constant over a shortperiod of time. During this time a new calibration can be done through comparisonwith a reliable estimate of the average interference. We have used an average ofinterference estimates according equation 3.5. If a new estimation is doneregularly, e.g. every minute, the system can follow fluctuations in the error due totemperature variations as well as compensate for static component deviations.
The estimate depends on variables that change rather quickly and therefore sodoes the estimation error. Even though single estimates of the uplink interferencemay not be considered good compared to the measurements, an average of
20
estimates may still coincide well with the corresponding theoretical averageinterference. If so, the estimate can be used to periodically calibrate for themeasurement errors.
Provided that the average estimation error is small, an average of estimates canbe used as follows.Let εest and εmeas denote the estimation and measurement errors. Let Imeas,Iest, and I represent measured, estimated and actual uplink interferencerespectively. With these symbols the estimated and measured interference canmodeled as:
meas
est
εε+=
+= II
II
meas
est[dB]
Consider the following mean value over several estimates and measurements:
measestmeasestmeas εεεεε =−=−−+= )mean()I mean(I)I-mean(I estmeas
Thus, the difference between measured and estimated interference is a functionof the errors only. The last equality sign is true provided that the averageestimation error is zero and that the measurement error is approximately constant.Neither of these requirements is, in reality, true. But, if the estimate can produce asatisfactory estimated average interference in a period of time that is considerablyshorter than the period of time in which we can consider the measurement erroras constant, the estimate may be used to compensate for the measurement erroras long as it can be considered constant.We have to regularly calculate a new average measurement error to compensatefor the always-changing measurement error.
3.4 Uplink Admission Control Algorithm
An effective way of limiting the induced interference is simply to temporarily denysome users access to the network, either users who want to enter the network orusers who are already active. In other words, an AC algorithm based on theestimated interference should be quite successful. A criterion in such admissioncontrol may be that a new user is admitted only if the new estimated interferencein one or some of the surrounding cells is below a fixed limit.As we shall see, it turns out that the uplink load estimate in equation 3.5 is moreaccurate than the interference estimate. An AC algorithm based on the uplinkload, instead of the interference, should be just as powerful since the two areclosely related.
21
The three AC algorithms that we have chosen to compare may be explained bythe following pseudo codes:
Hard Capacity (HC)
Admission decision = HC(userdata, δHC)For every user that wants to access the systemConsider the number of soft handover links in the cell theuser wants to connect to.
If the number of SHO links is less than or equal to δHC – 1Admission decision = yes
ElseAdmission decision = no
End if
Next userEnd
Soft Capacity 1 (SC1)
Admission decision = SC1(userdata, δSC1)For every user that wants to access the system
Estimate the resulting load in the cell that the userwants to connect to provided that the user is admitted.
If the load is less than or equal to δSC1 in the cell theuser wants to connect to
Admission decision = yesElse
Admission decision = noEnd if
Next userEnd
Soft Capacity 2 (SC2)
Admission decision = SC2(userdata, δSC2)For every user that wants to access the system
Estimate the resulting load in the five cells with highestpathgain to the new user provided that the user isadmitted.
If the load is less than or equal to δSC2 in all five cellsAdmission decision = yes
ElseAdmission decision = no
End ifNext user
End
SC2 thus considers the situation in what are probably the neighboring cells.
22
4 Simulations and Results
We have done all our simulations in Casper, [10]. From a set of parametersconcerning radio propagation, base station settings, user equipment settings,traffic etc it generates and handles traffic in a WCDMA network. Since thesimulator is implemented entirely in MATLABTM it is fairly easy to adjust bothsettings and outputs to suit ones specific needs.
All simulations in the first part of the paper have been conducted with the defaultsettings of the simulator. The modifications that are done in the second part arestated in section 4.2.1.The most important parameters are stated in Appendix 1.
4.1 Uplink Interference Measurement Calibration
At first the aim was simply to see if the estimate could be used as mentioned insection 3.3. The requirement is that the average estimate error is not too big, i.e.significantly lower than the statistical measurement error.
4.1.1 Assumptions, simulation setup and initial studies
The entire idea of compensating for measurement errors in the base stations withan average of estimates is based on the assumption that the estimate error variesmuch faster than the measurement error and can thus be filtered by using anaverage over several estimates. Based on [5] and [6], the errors in measureduplink interference are Gaussian distributed in logarithmic scale with a mean valueof zero and variance such that a 95% confidence interval has the limits ± three dB.Of these three dB, roughly one dB is due to component variations and can thus beconsidered constant and the other roughly two dB are due to temperaturedependency of the components. One half of the temperature dependent part (onedB) is from temperature variations in the tower-mounted amplifier and one half isdue to temperature variations in the container where the logic circuits are. Thisapproximate model of the measurement errors is developed under the assumptionthat the temperature is varying within an interval of 60 °C.Based on these properties of the measurement error we have assumed that theerror is approximately constant over a period of a few minutes.
The offered load we are using consists of an equal mix of speech and packetusers. The reason for using slightly higher percentage packet users than onemight expect is realistic is that packet users demands more radio resources percapita and hence increases the load while keeping the computational burdendown.
In our simulations we have calculated an average of interference estimates overten seconds. A new estimation is done every tenth frame. Since a frame is 10 mslong, the average is over 100 estimates.Since we are only doing one estimate every tenth frame that estimate willrepresent the interference during the last ten frames, even though it is based onthe situation in the end of the ten frame period only.
23
In this initial study we assume that we have knowledge of the entire pathgainmatrix, i.e. all users report pathgain measurements to all base stations in theentire network. Furthermore, all pathgains in the entire network are sampledsimultaneously five times a second. Under these conditions, an average ofestimates from ten seconds gives a fairly accurate estimate of the average uplinkinterference, see figure 4.1 where average simulated interference is compared tocorresponding estimate of the average interference. Equation 3.5 is used asinterference estimate. The purpose of this simulation is simply to see how theestimate performs under rather generous circumstances. Ideally all dots would beon the solid line which corresponds to an average estimate error of zero. The factthat all averages are above the solid line exposes the over-estimation of theinterference under these circumstances.
-133 -132.5 -132 -131.5 -131 -130.5-136
-135
-134
-133
-132
-131
-130
-129
-128
Average over 10 seconds
Mea
n(es
timat
ed u
plin
k in
terf
eren
ce)
[dB
W]
Mean(simulated uplink interference) [dBW]
Figure 4.1 Each dot corresponds to an average of 100 consecutive interferenceestimates.
The dotted lines represent the three dB error that the measurement are originallyassociated with.The found resemblance between estimated and by simulator calculatedinterference motivates to make the inputs to the estimate more realistic.
4.1.2 The estimate as input to measurement calibration algorithm
In this section we will use the more realistic assumptions stated in section 3.2regarding the pathgain report.
Not only are the reports less frequent, each report is also limited to the fivestrongest pathgains. Consequently, not all users contribute to the estimatedinterference in all cells.
24
In order to make the limitation in pathgain reports have an affect we haveincreased the number of cells from three to 21, see Appendix 1.
At this stage there are three separate ways of combining CIR target values withpathgain values. We can either
• use the CIR targets that are present at the time of the estimation, i.e. at theend of each 100 ms period (see “Current CIR-target”),
• use the CIR targets that were present at the time of the pathgainmeasurements, i.e. use the, to the pathgain values, corresponding CIR-targetvalues (see “Corresponding CIR-target”) or
• use an average of the CIR-targets that the user has had since the lastestimation, i.e. the user’s CIR-targets of the last 100 ms (see “Average CIR-target”).
All three methods use the current link configuration. Hence, even the secondmethod above, “corresponding CIR-target”, may change from estimate to estimatesince some users may have gained or released channels due to soft handover.
4.1.2.1 Current CIR-target
In the case where we use the CIR-target values that are present at the time of theestimation the only change compared to previous simulations is to use lessknowledge of the pathgain matrix due to limited reports from the user equipment.Results from a simulation where current CIR-target values have been used isshown in figure 4.2. The resemblance between that result and the one shown infigure 4.1 confirms our theory that less frequent pathgain reports does notsubstantially impair the result. On the contrary, the slight decrease in estimatedinterference in some cells, which may be explained by the limited pathgainreports, is an improvement since the estimate under these circumstances is anover-estimation.Even though this simulation is shorter (20 seconds compared to 50 in figure 1),the number of cells is increased by a factor 31, consequently the number of dots isincreased from five to six per base station.
1 We had to increase the number of cells in order to make the impact of limited pathgain reports realistic. Instead of using 7cells (one per base station) we are now using 21, see Appendix 1.
25
-131.8 -131.6 -131.4 -131.2 -131 -130.8 -130.6 -130.4
-135
-134
-133
-132
-131
-130
-129
-128
-127
Average over 10 seconds
Mea
n(es
timat
ed u
plin
k in
terf
eren
ce)
[dB
W]
Mean(simulated uplink interference) [dBW]
Figure 4.2. Average interference based on current CIR-target.
4.1.2.2 Corresponding CIR-target
If the, to the pathgain measurements, corresponding CIR-target values are usedthe estimation becomes in general below the theoretical interference, i.e. itbecomes an underestimation. The change from over- to under-estimation shownin figure 4.3 may be explained by the variations in the packet users’ CIR-target.
Due to implementation limitations a user only contributes to the estimatedinterference if the user is active
• when the pathgain measurement is done and (req. 1)
• at the time of the estimation2. (req. 2)
A user that was active during a major part of the time between two estimates butdoes not fulfill the above requirements does not contribute to the estimatedinterference.This together with the fact that all users contribute to the theoretical interference,regardless of their activity at the end of the 10-frame period, makes the estimatean under estimation.
In figure 4.3 each mark corresponds to an average estimated interference in onebase station (i.e. three cells), hence each mark can be seen as an average over30 seconds.
2 This is a limitation of the current implementation; we only know what stations a user is connected to if the user is active.
26
-131.55 -131.5 -131.45 -131.4 -131.35 -131.3 -131.25 -131.2 -131.15 -131.1
-135
-134
-133
-132
-131
-130
-129
-128
-127
Average over 30 seconds
Mea
n(es
timat
ed u
plin
k in
terf
eren
ce)
[dB
W]
Mean(simulated uplink interference) [dBW]
Current CorrespondingAverage
Figure 4.3. Performance of the three different ways of combining CIR-target values withpathgain values. All data is taken from the same simulation.
In the simulations depicted in figure 4.3 half of the users are packet users, whoseactivity varies quite rapidly, even over such a short period as 10 frames.
4.1.2.3 Average CIR-target
If an average of CIR-targets that each user has had since the last estimation3 isused, the fast variations in CIR-target values may be captured and counted for.This method of combining input data gives a higher average estimatedinterference compared with using corresponding CIR-target values. Theimprovement may be explained by a relaxation of the requirement that a user hasto be active when delivering the pathgain report (req. 1).It is still an underestimation because of the fact that not all users are considered inthe current implementation (due to req. 2), see figure 4.3.See figure 4.4 for an explanation of why not all users are considered.
3 In this scheme each user still has its own CIR-target, but we are now using an average of the ten latest CIR-target values.
27
690 691 692 693 694 695 696 697 698 699 7000
0.02
0.04
0.06
0.08
0.1
CIR-target of a typical packet user
CIR
-tar
get
[]
Frame no. []
CIR-target mean CIR-target
Figure 4.4 Clearly the average of CIR-targets during frame number 690 to 700 should bemore than zero. Still the average CIR-target that the estimate uses is zero because thecurrent CIR-target for this user at the end of the period happens to be zero and hence theuser is not considered in the current simulator implementation.
4.1.2.4 Comparison of the three versions of the estimate
Packetusers have a CIR-target that is varying quite a lot. This variation decreasesthe estimated interference when using average or corresponding CIR-targetvalues, and thus impairs the result. In the case where current CIR-target valuesare used the variations are, in average, not affecting the estimate; even thoughsome users affect the average simulated interference without affecting theestimated, the vice versa is also occurring. So an average of estimates based oncurrent CIR-targets is not affected by the fast alternation in transmission activity.
Figure 4.5 shows the performance of the estimates for different load values. Theload in cell j is estimated by
∑ +=
i i
ijL
γγ
1 (4.1)
where the sum is over all users represented in cell j. Lj in equation 4.1 dependsonly on the users connected to cell j, it does not consider any intercellinterference. It may be a bad estimate, but it is still independent of the threeestimates considered here and therefore fulfils its purpose.
28
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.5
1
1.5
2
2.5
Estimated load []
Noi
se r
ise
[dB
]
Current CIR-target Corresponding CIR-targetAverage CIR-target Simulated
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.2
0.4
0.6
0.8
1
Estimated load []
Var
ianc
e[es
timat
ed-s
imul
ated
]
Figure 4.5 Noise rise estimates for varying load and a mix of users (see equation 2.1 for anexplanation of noise rise). The load is estimated by using equation 4.1. The lower figureshows the variance in errors of single estimates as a function of the estimated load.
As can be seen in the lower part of figure 4.5, the estimate based on current CIR-target values has the highest variance at high load when considering singleestimates. The increased number of users and their varying CIR-targets mayexplain the increase in variance for high load. However, we are interested in anaverage of several estimates and therefore the lower part is of less interest for us.It is provided as future reference to investigations regarding other applications ofthe estimate.
The upper half of figure 4.5 is constructed by taking average of estimates withapproximately the same estimated load. The figure is a compilation of severalsuch averages.
When considering an average of estimates the algorithm based on current CIR-targets and average CIR-targets perform equally well when there is an evenamount of packet users and speech users in the system. The method based oncorresponding CIR-target provides a stable but misleading estimate of the uplinkinterference, especially under high load and is thus not recommended at all, seefigure 4.5.
The fact that when using average CIR-target results in an average estimate that isalways approximately 0.25 dB below the simulated one inspires to try some kindof correction of the estimate. Unfortunately this offset depends on the mix of usersin the system and a constant correction, independent of the kind of users currentlyactive, is thus not recommended.
29
The underestimation of the estimate when using corresponding or average CIR-targets is also exposed in figure 4.5.
The fact that the variance of the simulated noise rise is fairly low independently ofestimated load gives credits to the assumption that there is some relation betweenthe estimate of the load and the uplink noise rise.
4.1.3 Simulation Summary
The performance of the estimate based on average CIR-target values ispractically independent of the load. This one is also the only one that considersthe fast changes in packet users’ CIR-target during a ten-frame period.However, since an average of estimates based on, at the time of the estimation,current CIR-targets has an equally small error while being an overestimation it isbetter suited for use as input to a measurement-calibration algorithm.
Using the CIR-targets that are present at the time of estimation gives a better orequally good result under moderate load and is probably easier to implement. Adrawback for this method is that it gives an unreliable estimate under heavy loadand is therefore not well suited for applications where single estimates areinteresting.
In absence of a good model for the part of the interference that we can not powercontrol, Nj, we have modeled it as constant. This together with the fact that ourestimate of the uplink interference is proportional to Nj probably results in a meanerror that is bigger than the three dB that are associated with the measurements.A basic requirement when calibrating for measurement errors is that onesomehow can compare the measurements with data that have better accuracythan the measurements currently give. Since this is not the case here, we can notuse the estimate as a measurement calibrator in its current version.
In our simulations we have used constant N in both ways of calculating the uplinkinterference (simulation and estimation), therefore the bad performance of themodel does not shine through.
4.2 Uplink Admission Control
The second part of this thesis concerns admission control. A natural admissioncontrol is based on simply counting the number of links in each base station. Thistype of capacity is here referred to as hard capacity. A capacity that has to bemeasured or estimated is sometimes referred to as soft capacity, defined in [11].We will compare the AC algorithms stated in section 3.4 with each other.It is only in the conversion from estimated load to estimated interference that weneed to know Nj, the part of the interference that we can not power control. Theproblem discussed above does therefore not appear here.
30
4.2.1 Assumptions and simulation setup
The algorithm that considers the number of links in a base station regards linksthat belong to users in soft handover as one over the number of links the usercurrently has. So the sum of all soft handover links associated to a specific user isalways one, regardless of whether the user is in soft handover or not.
Information regarding approved admission requests is instantly distributed to allbase stations, i.e. all base stations always know all links in the entire simulationarea.
We have used a special type of users that simulates a 192 kbits streamingservice. This type of users has been chosen to produce a considerable load whilemaintaining the simulation burden on a moderate level.
The choice of quantity to represent quality of delivered service is tricky. The blockerror rate, BLER, is often used. Still, there are several ways of using it, see forexample [12] where a user is considered satisfied if its average BLER is belowsome limit. The number of satisfied users is then used as a measurement onquality of service.We will look at the average BLER over an entire simulation while keeping an eyeon the peaks in uplink interference. By BLER we here mean the percentage ofincorrect received frames. We will consider the quality of service satisfying if theaverage BLER is below 2 percent.In order to get a good estimate of the average BLER one needs a significantamount of data. To reduce the simulation times, we will extract the average BLEP(Block Error Probability) from the simulations and use that as an estimate of theaverage BLER.
Because of simulation difficulties when using high bit rate users with low BLER theouter loop power control is from hereon disengaged and consequently the CIR-target for active users will be constant throughout the entire simulation. An effectof this is that the BLER will be more sensitive to any instability of the system,instability that may be caused by too many active users in the network. The CIR-target in the following simulations has been set to provide a BLER ofapproximately 1,3 percent.During the entire simulation the system is being flooded with users. To provide justthe right amount of users, enough to flood the system but without creating toomuch computational burden for the simulator, the user creation algorithm originallyimplemented in the simulator is manipulated somewhat. The followingpseudocode explains the new user creation algorithm:
If any base station without users in lineCreate 5 new users
ElseCreate users according to a Poisson process such thatthere will be an average of 8 users in each cell
End
All users are spread uniformly over the simulation area, even those created whenthere is a base station without users in line.
31
Each user is transmitting for an exponentially distributed time with an averagelength of 5 seconds. We have chosen such short time to enable higher userconsumption without drastically increasing the simulation length.
We want to compare the performance of different Admission Control algorithms;therefore the congestion control that is implemented in the simulator has alsobeen disengaged. Due to this, there is no way of repairing a mistake done by theadmission control algorithm. One simply has to wait for some users to voluntarilydisconnect and free resources or move out of the cell range.
There is no admission control in downlink. In fact, no downlink calculations aredone at all.
Since we are interested in system capacity and not coverage, the maximumtransmission power in uplink is omitted. This way coverage is not limited by theusers’ equipment. Any coverage limitations will thus be due to the AC algorithm’schoice of users since we are totally ignoring the downlink.
Since we are only considering the uplink, linkadmission, which only considersdownlink restrictions, is disengaged. I.e. all soft handover requests are approved.
We have done a whole range of simulations with different admission limits, all witha simulation setup according to the above.
This study may be compared to [13], where an AC based on measuredinterference is compared with an AC based on number of users in the cell, and [4]where a quality measure based on estimated interference is used as input to anadmission control algorithm. The estimate is based on an empirical model thatconsiders the situation in the cell and the surrounding cells.
4.2.2 Fixed users
We started by simulating with all users practically standing still.
A study of the average BLER shows that SC2 admits approximately 130 users inthe 21-cell network before breaking the 2 percent limit, the corresponding numberfor HC is 115. Thus, SC2 admits approximately 15 percent more users when usingfixed users. SC1 performs not as good as SC2 and admits approximately 120, seefigure 4.12.
However, by analyzing results from simulations a bit more in detail, one sees thateven though the average BLER is low there are other warning signals that the limitis set too high, for example uplink interference.After having studied the interference, we only consider simulations where the limitis set to δHC =3 or δHC =4 users and δSC1 = δSC2 = 0,7 in the cases where estimatedload is used as input to the admission control.
Since all users are practically standing still throughout their entire session the onlytime a resource can be released is when a user voluntarily terminates its call.Furthermore, the only change a user does to its set of soft handover links isadding links in the very beginning of the session, never disconnecting any. Hence,
32
the small dips in figure 4.6 are caused by admitting a new user instantly after orbriefly before a channel is released.
0 200 400 600 800 1000 1200 1400 1600 1800 20000
1
2
3
4
5
min
(no
of li
nks)
frame no.
Hard Cap. 4 Hard Cap. 3 Soft Cap. 0.7 (SC2)
Figure 4.6. Minimum number of links over all cells. Fixed users.
The hard capacity algorithms admit a new user as soon as there is a wholechannel free in a cell. The soft capacity algorithm is less naive and uses theopportunity to lower the estimated load in the system, consequently there are noshort dips in the thick solid line in figure 4.6.Figure 4.6 also shows how the soft capacity algorithm adapts to slow changes inthe radio environment caused by user creation and termination.
0 200 400 600 800 1000 1200 1400 1600 1800 20000.4
0.5
0.6
0.7
0.8
0.9
1
1.1
frame no.
max
(est
imat
ed lo
ad)
Hard Cap. 3 Hard Cap. 4 Soft Cap. 0.7 (SC2)
Figure 4.7 Maximum estimated load, maxj Lj(t). Fixed users.
Compared to the hard capacity algorithms, SC2 maintains the maximumestimated load at a fairly constant and comforting level throughout the entiresimulation, see figure 4.7 where equation 3.5 has been used to estimate the load.SC2’s behaviour is achieved by admitting new users only when the radioenvironment permits. The hard capacity ones, on the other hand, stubbornlyadmits new users regardless of the radio environment and position of the newusers; consequently their load-estimate either occasionally reaches hazardouslevels or the limit has to be set so low that expensive resources are left unused.
33
The hard capacity algorithm with a limit of three users in figure 4.7 pays the priceof admitting fewer users (approximately totally 60 in the 21 cells), but the load ison a more comfortable level compared with HC with a limit of four users.SC2 with a threshold of 0.7 admits approximately 85 users in the entire simulationarea that consists of 21 cells. This is slightly more than the approximately 80 usersthat the hard capacity algorithm with a threshold of four users admits in the samearea, even though the estimated load is in general lower, see figure 4.8.
In figure 4.8 a comparison between three different algorithms is shown, each onerepresenting one of the three major ways of admission control in this study.
0 200 400 600 800 1000 1200 1400 1600 1800 20000.5
0.6
0.7
0.8
0.9
1
max
(est
imat
ed lo
ad)
0 200 400 600 800 1000 1200 1400 1600 1800 200065
70
75
80
85
90
95
frame no.
Tot
al n
o of
use
rs
Soft Cap. 0.7 (SC2)Soft Cap. 0.7 (SC1)Hard Cap. 4
Figure 4.8 upper figure: max(Lj(t)) lower figure: total no. of users
SC1 admits a new user as soon as the load is below the threshold minus the loada new user will bring, i.e. the threshold minus γ. This requirement is very much likethe one in the hard capacity algorithms, the number of links simply has to be lessthan the threshold minus one. In the above cases, where the users are standingpractically still, there should be a palpable resemblance. This can be seen in theupper graph in figure 4.8, the plots belonging to the hard capacity algorithm andthe simpler version of soft capacity (SC1) behaves similarly. Especially the softcapacity algorithm behaves unsatisfactory; max load is generally not even close tothe limit of 0,7. So does HC 4 too, but it does not aim for a load limit of 0.7.
As a result of relaxed requirements the average load is higher for a giventhreshold in SC1 compared to SC2, see figure 4.9. Even so, the more complexsoft capacity algorithm admits more users in the system.A possible explanation to this is that it chooses “cheap users”. Examples of suchusers are users who are, with respect to propagation characteristics, close to a
34
base station. A user that stands close to a base station is probably just connectedto that base station, whilst a user who is located almost in the middle of two ormore cells probably has several links simultaneously.
0.4 0.5 0.6 0.7 0.8 0.9 1 1.1
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
Set load limit
mea
n(m
ean(
estim
ated
load
))
SC1SC2
Figure 4.9 Average load for the soft capacity algorithms. meant(meanj(Lj(t)).
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100001
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4
frame no.
aver
age
no o
f lin
ks p
er u
ser
SC1 0,7SC2 0,7HC 4
35
Figure 4.10 Average number of soft handover links per user.
Figure 4.10 shows the average number of links per user for each frame. After justa few hundred frames the average number of soft handover links is considerablyless in SC2 than in the two others. Thus, provided that the system is alwaysflooded with users, we are likely to end up with islands of active users surroundingeach base station after a longer period of operation.
-150 -140 -130 -120 -110 -100 -90 -80 -70 -600
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Pathgain between user and base station [dB]
Per
cent
age
of a
dmitt
ed u
sers
SC1 0,7SC2 0,7HC 4
Figure 4.11 Estimated density function of pathgain of admitted users
Figure 4.11 shows an estimate of the density function for pathgain between theadmitted user and the base station the user was connected to. SC2 has a slighttendency to choose users that have a higher pathgain. Not only is SC2’s plotslightly shifted to the right, the considerably higher percentage of users with apathgain equal to or greater than the max pathgain of –70 dB indicates that usersclose to a base station are preferred by SC2. The data is taken from rather longsimulation (10000 frames) where the first hundred frames have been omittedsince these represent a rather unnatural situation.
The resemblance between SC1 and HC under these circumstances is againexemplified in figures 4.10 and 4.11, SC1 shows no tendency to prioritize “cheap”users. This is quite natural considering the fact that SC1, just as HC, does notconsider the distance to the user. SC1 considers the relative pathgain, i.e. theratio of pathgains from the user to the cell we are interested in and to cells that theuser is connected to. Since the user only tries to connect to one cell initially, SC1always find this ratio for a prospective user to one, independently of the user’slocation within the cell.
36
When using SC2 users that are approximately in the middle of two base stationswill have a longer average waiting-time, since they produce more interference inneighboring cells. In the other two algorithms the “first in, first served” principlerules since all users are considered equal regardless of their location.SC2 thus, to some degree, trades coverage for capacity.
4.2.3 Mobile users
We shall now consider more realistic cases where the users move around in thesimulation area. The comparison will again be focused on soft versus hardcapacity. For a comparison between mobile and fixed users see section 4.2.4.
When using mobile users slightly higher load limits in some cases still providereasonable interference. But, to enable comparison between fixed and mobileusers we have chosen to study a load limit of 0,7 here too.
0 200 400 600 800 1000 1200 1400 1600 1800 20000.5
0.6
0.7
0.8
0.9
1
max
(est
imat
ed lo
ad)
0 200 400 600 800 1000 1200 1400 1600 1800 200065
70
75
80
85
90
95
frame no.
Tot
al n
o of
use
rs
Soft Capacity 0.7 (SC2)Soft Capacity 0.7 (SC1)Hard Capacity 4
Figure 4.12 Maximum estimated load, maxj Lj(t), and total number of users.Mobile users. Equation 3.5 has been used to estimate the load.
In broad outline, the soft capacity algorithms shown in figure 4.12 still admits moreusers with less estimated load than the hard capacity algorithm.
However, since the users are now moving around their contribution to the loadchanges continuously after admission. Thus, SC2 produces a maximum estimatedload that varies a bit more than before. Figure 4.12 shows the maximum estimatedload and therefore only conveys changes where users interfere more thanexpected at the time of admission. The average load is still at approximately thesame level.
37
0 200 400 600 800 1000 1200 1400 1600 1800 2000-140
-130
-120
-110
-100
-90
-80
-70
-60
-50
frame no
max
(upl
ink
inte
rfer
ence
) [d
BW
]
SC2 0,7 mobileSC1 0,7 mobileHC 4, mobile
Figure 4.13 Maxj(simulated uplink interference).
Figure 4.13 shows the maximum interference power at each frame for the threedifferent algorithms. All three algorithms manage to keep the interference atreasonable levels. Furthermore, SC2 always maintains a maximum uplinkinterference power below –110 dBW (noise rise of approximately 20 dB) while theother two have peaks that reach considerably higher. The difference may beexplained by the fact that SC2 considers the situation in several cells, not just theone that the user tries to connect to. Hence, SC2 does not admit more users ifthere is a high interference power in the neighboring cells.
4.2.4 Mobile and fixed users
In this section we will study the impact that the change in mobility has on theadmission control algorithms.
38
50 60 70 80 90 100 110 120 130 140 1500
0.005
0.01
0.015
0.02
0.025
0.03
no of users
aver
age
BLE
RHC mobile HC fixed SC1 mobileSC1 fixed SC2 mobileSC2 fixed
Figure 4.14 Average BLER for entire simulations
Figure 4.14 shows a compilation of all simulations done in the second part of thisthesis. The hard capacity algorithms break the two-percent barrier earlier than thesoft capacity ones, especially for mobile users. When using this type of users SC1performs approximately equal regardless of whether the users are standing still orslowly moving around. When using fixed users, the limit on number of users thatcan possibly fit in to the network has probably been reached when there is around130 users concurrently transmitting, hence the rapid increase in average BLER forSC2 at around 130 users. When using mobile users, on the other hand, evenmore users may be admitted before the BLER breaks the two-percent barrier.The impairment in performance for HC when using mobile users may be explainedby the temporary hot spots that occasionally appear due to users moving intowards one cell. An increase in number of users in one cell means a performancedecrease in that cell. It also means less number of users in the surrounding cells,therefore these cells admits more users. Hence an increase in both the totalnumber of users and average BLER.This effect may be avoided by using some kind of link admission control combinedwith congestion control, i.e. have some requirements on soft handover requestsand drop one or several users when needed.
In figure 4.14 to 4.16 the 100 first frames have been omitted to avoid theunrealistic situations that are associated with the start-up of a simulation.
When users are moving around their pathgain will continuously change, thereforethe rare pathgain reports should impair the result. However, that does not seem tobe the case, the soft capacity algorithms perform equally well or better despite thechange in mobility pattern.
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20 40 60 80 100 120 140
-140
-135
-130
-125
no of users
aver
age
inte
rfer
ence
[dB
W]
HC, fixed HC, mobile SC1, fixed SC1, mobileSC2, fixed SC2, mobile
Figure 4.15 Average interference for entire simulations
20 30 40 50 60 70 80 90 100 110
-140
-130
-120
-110
-100
-90
-80
-70
-60
no of users
max
inte
rfer
ence
[dB
W]
HC, fixed HC, mobile SC1, fixed SC1, mobileSC2, fixed SC2, mobile
Figure 4.16 Max interference for entire simulations
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Figure 4.15 and 4.16 shows the average and max interference versus the numberof users respectively. Even though the BLER is around or below 2 percent theinterference is so high in some of the simulations that the situation would notappear in reality where the transmission power of the user equipment is limited.The average interference for the hard capacity algorithm reaches abnormal levelsbefore 85 users, which corresponds to approximately 4 users in each cell.SC2 once again performs best and first at around 110 users the interferencebecomes unrealistic.
The rapid increase in interference power is a typical symptom of party effect, assoon as the number of users is just a bit too much the interference power risesrapidly both in time and with increasing number of users. In the absence of bothcongestion control and transmission power limit the increase in interference powerwill continue unrestrained.
The system manages to maintain the BLER relatively low despite the highinterference due to the almost perfect power control; the user equipmentaccurately receives all power control commands without any delay.
Figure 4.17 shows SC1 and SC2 when using mobile and fixed users separately.The estimated load for mobile users in figure 4.12 are shown here again to enableeasy comparison.The high load for ‘SC1 mobile’ between 850 and 1120 in figure 4.17 is entirelycaused by one base station. Cell neighbors admitting a lot of users, which thanmoved in towards the over loaded cell, may have caused this situation. Since thecells only consider the situation in their own cells, they do not take notice of thesituation nearby.The instant drop at the around 1150 is due to a user finally disconnecting andsaves the situation.
The sharp peaks in the plots that correspond to mobile users in figure 4.17 maybe caused by updated pathgain measurements.This change is not alarming, after all it is the number of users and theirtransmission quality that interests us in reality.
0 200 400 600 800 1000 1200 1400 1600 1800 20000.6
0.65
0.7
0.75
0.8
0.85
0.9
frame no
max
(est
imat
ed lo
ad)
SC1 mobileSC1 fixed SC2 mobileSC2 fixed
41
Figure 4.17 Comparison between mobile and fixed users for the soft capacityalgorithms. Load limit is 0,7 in all four plots.
Other than the sharp peaks in estimated load in figure 4.17, the soft capacityalgorithms seem to be independent of the change in the users’ mobility that wehave applied here. The load has approximately the same mean values andapproximately the same numbers of users are admitted. As both algorithms makenew estimates of the load whenever there is a user that wants to enter the system,it does not come as a surprise that the change in the users’ mobility just slightlyincreases the average estimated load.
More users can be admitted before breaking the 2-percent barrier in the casewhere the users are not standing still compared to the case where the users arestanding still. This, together with the fact that any given load limit results inapproximately the same number of admitted users, concludes that the load limitmust be dependent on the mobility of the users.The improved quality of service for mobile users may be explained by the fact thata user who is travelling with moderate speed statistically has a better radiochannel than one standing still. The change in a slow moving user’s total pathgainis mainly due to the fast fading. The one used in the estimate, the slow fading,stays approximately the same during the time between pathgain measurementsand hence the accuracy of the estimate is not substantially affected.
4.2.5 Simulation Summary
The two soft capacity algorithms that we have tested either considers the load injust the cell that the new users wants to access or the load in the cell that the newusers wants to access as well as some of the neighboring cells. Of these two, theone considering the situation in just one cell is simpler; in order to predict the newload in more than one cell the RNC needs more information, more than the userequipment can provide before admission. It is, however, a bit pessimistic, usuallythe RNC has some knowledge of the situation in neighboring cells. Also, a newuser may be granted a simple dedicated channel on which more information canbe delivered to the RNC. In that case the RNC (Radio Network Controller) can begiven knowledge of for example pathgain to neighboring cells as well, not just tothe cell the user tries to connect to.
It is clear that the soft capacity algorithms use the resources more effectively andtherefore admits more users before the average block error rate becomes toohigh.In addition to comparing soft capacity with hard capacity we have also comparedthe performance of the admission control algorithms when using mobile usersversus fixed users. When using slowly moving users the soft capacity algorithmsenables even more users to be admitted compared to fixed users before theaverage block error rate becomes more than two percent. The hard capacityalgorithm, on the other hand, performs slightly worse when using slowly movingusers.Hence the difference between the soft capacity and hard capacity algorithmsincreases when using slowly moving users of the kind used here.
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5 Summary
5.1 Conclusions
We have presented an estimate of the uplink interference based on pathgainmeasurements made by the user equipment and, to the system, known data (theusers’ activity and target values on received signal quality, CIR-target).As packet users’ activity varies rapidly, the choice of how to represent the pastactivity becomes an issue. We have proposed and studied a set of methods ofcombining the inputs to the estimate and arrived at a suggestion that will provide asatisfactory result under most conditions when considering an average of severalestimates.When using an average of estimates as input to an interference measurementcalibration algorithm one should base every single estimate on the average CIR-target since the last single estimate (“Average CIR-target”). A drawback for thisway of combining the inputs to the estimate is that it probably results in the mostcomplex implementation of the three proposed.
The estimate should be well suited to be used in an interference powermeasurement calibration algorithm where an average of estimates is comparedwith an average of corresponding measurements. The only thing holding such analgorithm back is the absence of a satisfying model for the noise power receivedby the uplink antenna that we can not power control (e.g. background noise andelectrical appliances not under our control). Today this part of the interferencepower is modeled as constant.
We have also compared a couple of different admission control algorithms basedon the estimate (soft capacity) with one that simply considers a quantity thatrepresents the amount of users in the cell (hard capacity). The soft capacityalgorithms consider the estimated load in the system, and are thereforeindependent of the noise that we can not power control.
The soft capacity algorithms admit more users than the hard capacity one doesbefore the block error rate becomes too high. When considering high capacitypedestrian users the more advanced algorithm which considers the situation inmore than one cell admits approximately 25 percent more users than the hardcapacity one before the quality becomes to low. The less advanced soft capacityalgorithm, which considers the situation in one cell only, admits approximately tenpercent more users.The improvement for the more advanced soft capacity algorithm can be dividedinto two parts, one half originating from the estimate itself and one half from thefact that the algorithm uses global knowledge. The less advanced soft capacityalgorithm does not use the global knowledge and consequently does not performequally well.There is, however, depending on the mobility of the users, a possibility of endingup with an area around each base station where the density of active users isincreased when using the more advanced soft capacity algorithm due to thealgorithm’s predilection for users close to a base station.
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The increase in number of users is thus partly at the price of impairment incoverage. Reserving resources for users that are assumed to be far from a basestation could solve this.But then again, in a case of capacity shortage one may choose to prioritize usersclose to a base station since that enables us to admit more users and onetherefore gladly pays the prize of reduced coverage.
The performances of both soft capacity algorithms are practically independent ofwhether the users are standing still or slowly moving around according to a patternsimulating a pedestrian walk. Interestingly enough the maximum load limit may beset higher when using mobile users. This may be due to the fact that the slow usermovement enhances the radio transmission without substantially deteriorating theestimation that is done in the admission control algorithm.
A realistic AC algorithm may be a compromise between the algorithms presentedhere. Such an algorithm, which uses some knowledge of the neighboring cells toestimate the resulting uplink interference power in those cells, will probablyprovide an increase in number of users by somewhere around 15-20 percentcompared to the hard capacity algorithm used here.
5.2 Suggestions to further studies
The current MATLABTM implementation requires that a user is active at the time ofthe estimate in order to contribute to the estimated interference power. This hasan impact on the estimate when considering packet users. An improvement of theimplementation could be to log the cells a user has been connected to since thelast estimation. Especially the version of the estimate that uses knowledge ofusers’ activity since the last estimation (Average CIR-targets) would gain fromsuch modification.As the result arrived at in the first part can not be used today anyway, due toabsence of a reasonable model for the background noise, this improvement hasnot been done here. In the second part, where admission control is investigated,all users have constant CIR-target and the estimate would thus hardly gain fromsuch modification.
The updating of measurement data should coincide with the time of the actual softhandover events. Today these instances are modeled through a simple randomprocess.
In the process of deriving the estimate one assumes, for a moment, that all cellshave the same uplink interference. In this study we have only consideredscenarios where the users are uniformly spread over the entire simulation area. Acomplimentary study may be to use a hot spot scenario, i.e. increase the numberof users within a small area. Such scenario will produce considerable differencesin interference between the cell with a hot spot in it and adjacent cells.To ease the computational burden we have used quite demanding users in thesimulations regarding admission control. One cell alone can not serve more than4, max 5 users. Thus, approximately 25% of a cell’s resources have to be freebefore a new user can be admitted.
44
A set of simulations where more realistic, less demanding users are used canperhaps indicate a greater difference between admission controls based on softand hard capacity respectively.Simulations where a mix of different services is offered may also be interesting.
There is yet another approach to make the admission control algorithms morerealistic. The two versions of soft capacity admission control that has beenpresented are either a bit too pessimistic or too optimistic. A realistic compromisebetween the two would be that the new user only reports the measured pathgainbetween him and the cell he wants to connect to, but the AC still considers up tofive cells in the area. A fixed load value may be added to the surrounding cellsduring admission decision, or more advanced, if the reported pathgain is small ahigher load value is added to the neighboring cells.Another approach is to set up a temporarily dedicated signal link on which theuser may report pathgain to several cells.When studying admission control, simulations should last longer than a fewseconds. We have tried to get around this problem by choosing the averagesession time as low as five seconds, which is not realistic but enables us to studymore user arrivals and departures.
A major modification that we did in the AC simulation setup is that we omitted themobile stations’ maximum transmission limit in order to relieve the simulationsfrom the coverage limitation.Without a maximum transmission power limit, BLER is just a function of how wellthe fast power control performs. When using a maximum transmission power limitthe coverage problem also contributes to the total BLER. A separate set ofsimulations where the transmission limit is again enabled will probably show theeffect that the unlimited transmission power had on the simulation results.Furthermore, the fast power control used here is quite idealistic. Using a powercontrol in which power command delays and random errors in the commands areimplemented will probably change the result considerably.
Finally we should once again point out that this study is limited to the uplink only, afurther step towards reality is obviously to consider the downlink as well. However,the limiting resource in downlink is usually not the interference power.
45
6 Appendix
6.1 Appendix 1: Simulation parameters
Unless stated otherwise, these parameter values have been used in thesimulations.
Parameter First Part (measurementerror calibration)
Second Part(AC)
Initially1 Finally1
No of cells [] 7 21 21No of base stations [] 7 7 7Cell radius [km] 1 1 1MS maximum output power [dBW] -9 -9 ∞Average no of speech users [] 120 3502 0Average no of packet users [] 120 3502 dynamicAverage Session Time, speech [s] 90 90 -Average Session Time, packet [s] 12 12 5Discontinuous Transmission (DTX) Yes Yes NoAverage user speed[m/s] 3/3.6=0.83 3/3.6=0.83 3/3.6=0.83Average user acceleration [m/s2] 0.2 0.2 0.2/0.002Background noise [dBW] -132.8764 -132.8764 -132.8764Confidence interval, interferencemeasurements
95%, 3 dB 95%, 3 dB 95%, 3 dB
Confidence interval, relativepathgain measurements
90%, 1.5 dB 90%, 1.5 dB 90%, 1.5 dB
Estimation frequency [Hz] 10 10 -Pathgain measurement updatefrequency [Hz]
2 0.5 0.5
Outer Power Control frequency[Hz]
100 100 -
Congestion Control Yes Yes No
1 Each base station initially controlled one cell, in the later part, where limited pathgain reports are used, each base stationcontrols three cells.
2 The increased number of users is to compensate for the increase in number of cells
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7 References
[1] A. Gioia, ERA/LVA/R, “Intercell interference in WCDMA uplink packet datascheduling”, Jun 1999
[2] N. Wiberg and A. Gioia, “Uplink Packet Access Control in WCDMA”, Proc.VTC Spring, Tokyo, Japan, May 2000
[3] E. Sigurdsson Rosenborg, P. Nilsson, “Adaptive Admission and CongestionControl in WCDMA”, M. Sc. Thesis, Linköpings universitet, Linköping, Sweden,May 2000
[4] B. Timus, EPL/T/RP, “Uplink admission control with multicell information”,June 2000
[5] J. Lundstedt, ERA/RR/TR, “UL AND DL POWER MEASUREMENTINACCURACIES, RBS WITH TMA”, Oct 1999
[6] Discussion with J. Lundstedt, ERA/RR/TR, Sep 2000
[7] H. Holma, A. Toskala and others, “WCDMA FOR UMTS”, John Wiley &sons, June 2000, ISBN 0 471 72051 8
[8] 3GPP Organizational Partners, “3G TS 25.133 V3.2.0 (2000-06), TechnicalSpecification”
[9] Lars Ahlin, Jens Zander, “Principles of Wireless Communications”,Studentlitteratur, second edition, 1998, ISBN 91-44-00762-0,
[10] N. Wiberg, ERA/LVA/R-00:040, “Casper 1.0”, Feb 2000
[11] H. Holma, J. Laakso, Uplink admission control and soft capacity with MUDin CDMA. In Proc. IEEE Vehicular Technology Conference, Amsterdam, theNetherlands, Sep 1999.
[12] Luca Vignali, ERI/R/B, “Basis service management in WCDMA: SpeechAMR 12.2 kbit/s”, Doc. No. T/B – 00:182, Sep 2000
[13] Yoshihiro Ishikawa, Narumi Umeda, “Capacity Design and Performance ofCall Admission Control in Cellular CDMA systems”, IEEE Journal on SelectedAreas in Communications, vol. 15, no. 8, Oct 1997
[14] F. Gunnarsson, “power control in cellular radio systems: Analysis, Designand Estimation”, PhD Thesis, University of Linköping, Apr 2000, ISBN 91-7219-689-0
[15] J. Zander. Performance of optimum transmitter power control in cellularradio systems. IEEE Transactions on Vehicular Technology, 41(1), Feb. 1992
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