-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
�����������������
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Guidance on traffic engineering for fixed networks and telephone
service has been successfully developed by ITU-T(formerly CCITT).
This guidance has been based on a communication paradigm which has
included regulated operation
environment, operation domains matched to national boundaries,
centralized control and predictable service quality.
Theseassumptions are being more and more challenged with the
introduction of new communication modes, the increasing
popularity of mobile and personal communications services, and
deregulated operation. As a result, new paradigms areemerging for
the 21st century telecommunications. In such a framework, continued
user satisfaction and operator revenue
growth with personal communications services require that
suitable traffic engineering methods be devised. These
methodsshould help in reconciling the expected service quality with
cost-effective dimensioning and operation of networks and
infrastructure for supporting a range of services as well as
provide means for capitalizing on investments
intraditional/existing telecommunication infrastructure. The paper
notes that in order to arrive at sensible ITU-T
Recommendations on traffic engineering for personal
communications networks, practice and theory should be
mutuallysupportive.
Teletraffic Engineering for Mobile PersonalCommunications in
ITU-T Work -- The Need for
Matching Practice and Theory-----------
DAVIDE GRILLO, FONDAZIONE UGO BORDONI (+39.6.5480 3430,
[email protected])RONALD A. SKOOG, AT&T LABS (+1.732.949 7915,
[email protected])
STANLEY CHIA, AIRTOUCH COMMUNICATIONS (+1.925.210
3470,[email protected])
KIN K. LEUNG, AT&T LABS (+1.732.345 3153,
[email protected])
raffic engineering has as its ultimateobjective the
cost-effectivedimensioning of network resources tohandle the user's
demand for
telecommunications services - and hence the induceduser
information and signaling traffic streams. At theinternational
level, traffic engineering is being guidedthrough the activity of
ITU-T1 (formerly CCITT2)where the related procedures are cast
inRecommendations covering such issues as trafficcharacterization,
service quality targets, dimensioningmethods, and measurements. As
opposed toRecommendations dealing with the procedural aspectsof
interconnection/interworking in a multi-vendor andmulti-operator
environment, Recommendations ontraffic engineering provide advice
on “good practice”for network operation and are not binding.
Howevertheir recognized value is based on input
fromAdministrations, ROA's (Recognized OperatingAgencies) and
scientific bodies who collectivelyprovide a broad range of
experiences and knowledge,and who have developed an understanding
of therelationships (by a given technological scenario and
1 International Telecommunication Union
TelecommunicationStandardization Sector.2 International Telegraph
and Telephone ConsultativeCommittee.
communications paradigm) between such interrelatedaspects as
traffic demand, cost of communicationsinfrastructure, sustainable
service quality, andacceptable tariffs. The bulk of ITU-T work on
trafficengineering assumes the existence of operationdomains
comprised within national boundaries forsupporting
telecommunications services between userslocated anywhere in the
world. This work has beeninstrumental in enabling (national)
operators to conductbusiness on a global scale, and has performed
well inensuring user satisfaction and continued revenuegrowth. In
particular, traffic engineering for fixedcommunications, and
PSTN-based services, has a longtradition in ITU-T. The underlying
communicationsparadigm has been characterized, in addition
toregulated operation and operators’ domains matched togeographical
boundaries, by centralized networkcontrol, fixed bandwidth
allocation, and predictableservice quality - among others. This
paradigm whichhas held throughout the 20th century, is now
beingchallenged, [1], since: i) its cornerstones are either
nolonger valid (spread of deregulated operation,ownership of
network segments and Points-of-Presencebeyond national boundaries)
or, ii) new services andother operation modes are flanking the
traditional ones(e.g. multi-party communications, highly
variable
T
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
�����������������bandwidth requirements, on-demand
bandwidthallocation, terminal and personal mobility support 3).
An intensive study activity is being undertaken tounderstand and
model the key aspects of the emergingtelecommunications systems
associated with thischanging scenario so as to arrive at a sensible
trafficengineering methodology for them and continue tosuccessfully
operate telecommunications servicesunder the developing paradigms.
The progress of thesestudies in ITU-T shows a variegated picture,
with areaswhere investigation is approaching saturationcomplemented
with substantial traffic engineeringstandardization activity (ISDN,
ATM), areas wheretraffic engineering is progressing (IN and
signalingsystems), and areas where traffic engineering has
beenstarted but progress is not yet adequate to the advancesin
system deployment and service penetration (mobilenetworks).
Personal communications4, plays a vital role inshaping the 21st
century communications paradigms.Indeed, the associated dimensions
of space/timedependence of traffic demand, “hostile”
operationenvironment, unpredictable quality, and changing
user'snetwork attachment point represent major deviationswith
respect to the traditional communicationsparadigm. Although mobile
services have beencommercially available since the late seventies,
trafficengineering for personal communications has beenbased - and
to a great extent still is - on “currentpractice” of mobile
operators, and has been dominateduntil recently more by radio
transmission and coverageconsiderations rather than by classical
traffic loadingand service quality arguments. The justification for
thishas been that an (elite) customer base has
traded-offimpairments of service quality against ubiquitousservice
that is perceived as a major value. In addition,there are many
factors which have been hiding theinefficiency of network planning
for operators. As amatter of fact, to circumvent poor network
quality,users have been inclined to develop a pattern of 3 Personal
mobility refers to the ability of users to have flexibleaccess to
telecommunication services from any terminal, fixedor mobile, to
meet the users requirements. These requirementsmay then be
relocated from terminal to terminal. Personalmobility involves the
network capability to locate the user onthe basis of a unique
personal telecommunication identity (i.e.,"personal" number) for
the purposes of addressing, routing andcharging of the users
calls.Terminal mobility involves the ability of the user to be
incontinuous motion whilst accessing and usingtelecommunication
services and the capability of the networkto keep track of the
user's terminal. This requires thetelecommunication services to be
available as the terminalmoves within the radio coverage and
ideally at all times (ITU-TRecommendation I.114, [3]).
4 The term personal communications will be used to refer to
thecollection of terminal (PCS, Personal CommunicationsServices)
and personal (UPT, Universal PersonalTelecommunication) mobility
services.
complex avoidance behavior in order to work aroundthe perceived
network congestion and impairment.Furthermore, in order for an
operator to meet both wide-area and in-building coverage
requirements of networkroll-out, a large spare capacity has been
implicitly builtinto the network from an early day of operation.
Thisprocess is further exaggerated by the large “build-ahead”
margin adopted by most operators which isnecessary to meet the
predicted intensive subscribergrowth and to alleviate long delay in
site acquisitionfor building new base sites. Finally,
infrastructurevendors have introduced many temporary capacityrelief
features such as “directed retry”, “cell loadsharing”, [2], and
“queuing” to dynamically exploit the(temporary) unused resources
due to short-term trafficfluctuations5. Naturally, all these
processes andtechniques have adverse implications to the cost
andefficiency of a network.
In the last decade mobile services have beenexperiencing an
accelerated penetration which hasculminated in the explosive growth
to mass marketdimensions over the last few years, with outlooks
forcontinued growth. Accordingly, mobile-related traffic isforecast
to be comparable in volume with that relatedto fixed networks in a
not too distant future. Even withthe prediction of such a strong
growth in the mobilemarket, increasing competition in service
provision hasmeant that mobile operators have to be more prudent
intheir cost-management and vigilant in maximizing theirincome.
While over-dimensioning of a network isequivalent to poor capital
investment, congestion atbusy hours could mean lost calls and lost
revenues.These factors, combined with the impact that
mobile-related traffic may have on the fixed infrastructure, andthe
convergence of mobile and fixed services, drivetowards a
rationalization of the resource allocation andmanagement procedures
(both inter- and intra-operators'domains) and make it urgent to
address trafficengineering for personal communications at
theinternational standardization level. Furthermore, withthe
introduction of mobile data services such asGeneral Packet Radio
Services (GPRS) for GSM andIS-707 for cdmaOne (also known as IS-95,
[4]), theintegration of voice and data traffic is becoming
areality. This adds yet more dimensions to thecomplexity of traffic
engineering. Table 1 tries toillustrate the challenges in the
planning and operationof personal communications networks and the
expectedbenefits from a traffic engineering activity.
To accommodate the need for standardization ontraffic
engineering for personal communications, ITU-T
5 "Directed retry" means a terminal is redirected by thenetwork
to setup a call with a base station which is not the bestserver due
to network congestion reasons. "Cell load sharing"is to dynamically
move the boundary of the cells so that calls inprogress can
continue in cells which are less congested."Queuing" is used to
temporarily buffer calls e. g. on adedicated control channel when
all the traffic channels arecongested.
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
�����������������
has started a dedicated series of Recommendations, theE.750
series, [5]. Having first addressed frameworksetting aspects for a
traffic engineering activity - suchas reference configurations and
GOS6 parameters, andtarget values - the E.750 series is now giving
priority tostudies on traffic demand modeling and
dimensioningmethods.
In parallel with the increasing penetration ofpersonal
communications, a range of related studies onarchitecture, service
offerings and performance aspectshave been proliferating in the
open literature, alsofueled by the international efforts aimed at
designingadvanced, "third generation" mobile systems such
asIMT-2000, [7], [8], developed in ITU and UMTS, [9],[10], studied
in ETSI. Although these studies do not
6 Grade of Service (GOS): "A number of traffic
engineeringvariables used to provide a measure of adequacy of a
group ofresources under specified conditions; these grade of
servicevariables may be probability of loss, dial tone delay,
etc.”,ITU-T Recommendation E.600, [6].
necessarily exhibit the combination of simplicity andcoverage of
key aspects which matter for trafficengineering, and frequently
favor the analysis ofspecific technical solutions rather than
relatefundamental parameters, they have a great potential
fortraffic engineering work. The current operators’ practiceand
this wealth of literature are, in a sense, twoextremes that traffic
engineering (and related ITU-Tactivity) has to reconcile in order
to provide a sensibleguidance for a cost-effective use of network
resourceswhile meeting users expectation on service quality.
Although personal communications implies bothterminal and
personal mobility, this note concentrateson terminal mobility for
which Fig. 1 gives twofundamentally different supporting
architectures. Asshown in the figure, the key difference lies in
theorganization (signaling and database arrangement) ofthe mobility
management functions resulting in “stand-alone” (or separated
mobile and fixed network) and in“integrated” architectures. The
interfaces andfunctionality shown in the figure, in addition to
having
TERMINAL MOBILITY
Key Aspects & ProblemsRequirements & SolutionsAspects of
Traffic Engineering &Challenges
Limited amount of radio spectrumSpectrum re-use (cellular
lay-outarchitecture) to meet capacityrequirements.Interference
management, dynamicchannel allocation (DCA), use of
smartantennas
Spectrum partitioning between celllayers, considering traffic
overflow to“umbrella cells” (or mutual overflows innon-hierarchical
cell lay-outs).DCA performance models.
Hostile transmission environment forwireless communications
Channel quality monitoring andrecovery to combat
adversetransmission conditions.Smart antennas, advanced
equalization,handover and admission control, highfrequency
re-use.
Handover (combining) handling andpriority service; Call
Admission Controlfor trading off service quality againstcarried
traffic; modeling of co-channelinterference as dependent on traffic
load.
Mobility behavior of the customer baseLocation, registration and
authenticationof the customer base to track users andprovide a
seamless communicationsspace shielding from mobilityimplications
and roaming technicalities.
Mobility models. Space/time trafficdemand dependence modeling,
mappinguser mobility into signaling traffic.Signaling network
dimensioning.
Long term subscriber growth, intensivein-building coverage and
wide-areacoverage
Minimum infrastructure build and just-in-time delivery of
capacity.
Description of the traffic characteristicsand modeling of the
traffic processes.Generic dimensioning methodscomplemented with
network trafficdimensioning for specific mobiletechnology
classes.
Traffic demand fluctuationCapability of moving spare
resourcesaround in the network to accommodateperiodic and
instantaneous trafficvariations.
Traffic modeling for dynamic trunking ofshared resources.
Integrated voice and data servicesProviding additional resources
for dataservices on a dedicated basis or sharedbasis.
Service quality requirements for dataservices. Traffic
dimensioning forpacket data.
PERSONAL MOBILITY
Key Aspects & ProblemsRequirements & SolutionsAspects of
Traffic Engineering &Challenges
Registration on and interaction fromcurrent user equipment
Authentication of user data, negotiationcapabilities to cope
with actualequipment characteristics/performance,impact of actual
service providerofferings and visited networkconstraints.
Determination of traffic mix resultingfrom user-network
negotiation actions.Signaling network dimensioning.
Interaction with and personalization ofthe user profile
Manipulation capability of user data toprovide a familiar (VHE,
Virtual HomeEnvironment) and user-friendlycommunications space.
Modeling IN services; mapping usermobility into signaling
traffic.Signaling network dimensioning.
Table 1. Key aspects and problems in personal communications and
related traffic engineering implications.
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
�����������������
logical significance, also indicate the scope forteletraffic
engineering.
The thread followed in the paper is as follows.Initially, the
specifics of mobile related traffic demandare briefly introduced.
Obviously, these specifics arekey to the whole traffic engineering
process. Since
mobile services have beensupported before asystematic
trafficengineering activity hadbeen initiated in ITU-T, thecurrent
practice on whichoperation of mobilenetworks is based is
thendescribed. This descriptionhighlights the relationshipbetween
the dimensionsassociated with radiotransmission and
networkplanning, and considerssome typical actions to betaken for
estimating andaccommodating the trafficdemand assuming that
theradio coverage problem issolved. Subsequently, theorganization
of the E.750series is briefly reviewedand it is indicated that
theunderlying traffic modelingand dimensioning studiestry to
provide tools to beused for rationalizing keyphases comprising
theoperator's practice. In theface of the scope ofteletraffic
engineering asenvisaged in the E.750series, a selection
ofrepresentative theoreticalcontributions addressingboth mobility
and trafficdemand modeling as wellas typical trafficengineering
issues issurveyed. The survey isintended to provide anoverview on
the kind oftheoretical support currentlyavailable for
thestandardization activity.The paper finishes by listingand
commenting on somecommonly acceptedassumptions
underlyingtheoretical work, andstating the ITU-T needs
forprogressing a usefulstandardization activity.
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-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
��������������H��
Specifics of Mobile RelatedTraffic
n a sense, user mobility has always beenconsidered also for
fixed networks andtelephone traffic. As far as the originationand
destination of calls is concerned, a
fixed network may be viewed as a collection ofcommunication
devices rigidly associated with networkattachment points. “Fixed”
users, i.e. users of fixeddevices, may change their location over a
specificarea, for example when moving from home to theworking place
and vice versa, and then initiate (placeor receive) calls from the
fixed device location theyhave reached.
For traffic engineering purposes, the traffic loadduring “busy
hours” for a geographic region is oftenreferred to. The location of
the busy-hour during the daydepends on, for example, whether the
region is used asa business or residential area. Although users
movefrom one place to another, the peak-hour traffic load
implicitly considers such mobility. This, combined withthe
condition of sufficiently large (“infinite”)population has led to
models of the fixed trafficdemand based on characterizing call
arrival statistics(e.g., exponential law between call arrivals)
thatcapture the aggregate behavior of the users in ageographical
area.
To dimension transmission, switching andprocessing resources
bound to a geographical area, theother two needed elements are the
distribution of thecall length and the arrival rate of calls during
thereference (“busy hour”) period. Again, under the
aboveconditions, the call length distribution - at least in thecase
of telephony - has been found to be onlydependent on aggregate
models of user “behavior”which has resulted in a robust, parametric
and universalmodel with very little dependence on user
class,country, etc. In conclusion, although the basicassumptions
underlying traffic engineering of fixednetworks are continuously
challenged and tested (forexample see [11], [12] and [13]), for any
practicalpurpose the fixed traffic demand can be expressed in
I
Calldensity
x-dimension
y-dimension
Call density
x-dimension
y-dimension
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300-400200-300100-2000-100
User density at time t
User density at time t + ∆tCall density at time t + ∆t
Call density at time t Mapping user
densityinto call density
Figure 2. Illustrative example of distribution of user density
at different times and related terminal mobility traffic demandfor
a generic service.
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
�����������������terms of the distribution of call inter-arrival
times, thecall arrival rate and the distribution of call
duration,with virtually no dependence on space per se and
adependence on time dictated by the general level ofdaily and/or
seasonal activity.
By contrast, as concerns the initiation of calls, amobile
network may be viewed as a collection ofcommunication devices in no
rigid association withnetwork attachment points (radio ports), and
normallytraveling together with the (mobile) users. Toappreciate
the specifics of mobile related traffic onehas to note that:
•The user mobility is no longer confined to possiblytraveling
within a few places with long intervals ofstationariness (as in the
case of fixed users), but isgenerally characterized by wider range
usermovements and more frequent location change;
•The association between calling device and networkattachment
point is dynamic (in-between callattach/detach) and may also change
during thesame call (in-call association re-arrangement due
tohandover or combining);
•The resources to be dimensioned (e.g., radiochannels carrying
user and signaling traffic, and thefixed infrastructure for
supporting mobility) continueto be bound to a geographical
area.
As a consequence, the underlying user behavior hasa higher
traffic impact (both in space and in time)than in the fixed network
case. For example, thedistribution of population density may be
stationary, butcalls are initiated while users are “on the move”
thusgiving rise to mobility related traffic loads and
“trafficvolatility”, i.e.: i) the call initiation sites are
scatteredand dynamically changing over a geographical area;
ii)bandwidth associated with a connection may have tobe provided to
different sites throughout the call, viz.,changing radio cells
during a call.
For terminal mobility traffic, Fig. 2 illustrates anexample of
the variation of the user density in spaceand time over a
geographical area. The same figurealso suggests that the mapping of
the user density intotraffic demand may not be simply a matter of
scalingbut may require more elaborate considerations sincethe call
initiation rate may not be linearly dependenton the user density.
For example, a high user density asa result of a traffic jam may
also trigger users to placecalls at an extraordinarily high rate.
Moreover, in thecase of cellular systems this mapping may be
impactedby deep fading and/or insufficient radio coveragecausing
tides in the distribution of the traffic demandnot present in the
user density. In fact, the trafficdemand in such systems may be
conditioned (to avarying degree) by the provisions made by the
networkoperators and service providers (e.g. deployment
ofcommunication facilities, roaming agreements, tariffstructure,
marketed service features, etc.). Thisconditioning, however, shall
not impact the logicalframework for carrying out the traffic
engineeringactivities.
Finally, the in-call behavior of mobile users doesnot
necessarily align with that related to the fixednetwork, for
example as concerns the average lengthand length distribution of
the calls. This area is theobject of intensive study and
investigation, [14].
Current Practices for theOperation of Cellular
Systems
s engineering procedures for mobilesystems have only recently
started to bestandardized, the operation of cellularsystems is
frequently based on simple
rules for traffic demand estimation and resourceallocation,
complemented with monitoring and tuningthe system performance in
the field as the networkevolves, [15]. To illustrate this process,
it is instructiveto assume a “green field” situation, although in
manyinstances similar issues could be faced by operatorswho are
still evolving their infrastructure. Key aspectsare summarized in
Fig. 3. In the figure, a simplifiedview of the complex relationship
between the radioplanning and the capacity dimensioning process is
alsoshown. In addition, a numerical example of thedimensioning
process is shown in Table 2.
The dimensioning process starts with an estimationof the user
population by using the density of theinhabitants in a specific
area together with ananticipated service penetration rate. Radio
and networkplanners continue with the identification of sites
wherethe cellular infrastructure has to be laid down(typically,
base transceiver stations, base stationcontrollers and mobile
switches), and the mapping ofuser density into traffic demand. The
process thenfinishes with the allocation of the traffic
(radio)channels making judicious use of the availablespectrum7.
The accomplishment of the dimensioning cyclerequires that
numerous optimization problems be solved.These range from
minimizing the number of the basesites while guaranteeing
sufficient coverage andacceptable service quality, to planning the
re-use ofspectrum so as to accommodate the traffic demandwhile
ensuring stable system operation and usersatisfaction - to name
just a few.
7 It should be noted that the ultimate traffic capacity of a
basesite is highly dependent on the exact locations of the base
sites.While every effort is usually made during the planning stage
toensure that a base site can be well positioned, the actual
siteacquisition process is subject to many factors including
thephysical location, real estate cost, the height of the
location,the availability of equipment and antenna space, etc.
A
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
��������������I��
The success in operating a system is assessed,among others, by
the degree of control exercised oversuch phenomena as dropped
calls, repeated callattempts and handover cases. All these
phenomena willpenalize user expectations about good service
qualityand, more often than not, many of these shortcomingsare due
to poor balancing of operation parameters.The following sections
are intended to illustrate thecurrent approach to the operation and
management ofcellular systems (together with some
deficiencies).They provide a reference list of key issues to
becovered in future standardization work on trafficengineering in
ITU-T.
Figuring out the Traffic Demand
For traffic engineering of cellular systems,information on
geographical population distribution is ofvital importance to an
operator. For new entrants to amarket, this information can only be
estimated usingpublished census information. This may vary
inresolution, with the better ones being rather detailed,and may
resolve down to municipal or district level.From the census
database and the size of thegeographical area, it is possible to
estimate thepopulation density for the location. Together with
theyear-on-year user penetration forecast and the averagetraffic
intensity per subscriber, the traffic demand canbe obtained.
(Occasionally, road traffic information mayalso be available. This
information is either too detailedor very specific, which renders
its use difficult. Thereason is that this information is collected
usually in the
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Figure 3. A summary of the radio planning and capacity
dimensioning processes.
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
����������������form of number of vehicles passing a specific
junctionper 24 hour period rather than the number of vehiclesusing
the road. As urban roads could have many turn-offs, extrapolating
the information to give traffic volumefor a coverage area is by no
means a simple task).
In parallel with the traffic engineering process, radiocoverage
planning is also performed to enable networkinfrastructure
roll-out. Based on the terrain database andthe morphology database
together with the desiredsignal level necessary to provide suitable
in-buildingand outdoor services, base site locations are
identified.Frequently, contiguous coverage is required and,
hence,the coverage area of base sites are packed closelytogether in
order to eliminate coverage gaps as far as
possible. In reality, the terrain is rather undulated. Inorder
to eliminate the majority of the coverage gaps andto provide
adequate in-building and in-vehicle services,one needs to
significantly overlap the coverage betweenbase stations. Thus the
dividing lines defined by theequal signal level from two or more
base sites will formthe boundary of the “best server” region for
individualbase sites. In other words, when a mobile is within
thebest server region of a specific base site, it will receivethe
strongest signal from that base site even though thesignal from
other base sites may still be adequate forcommunications. By
associating mobile stations withthe base site of a best server
region, the highestdownlink carrier-to-interference ratio can be
obtained. In
Year 0through
10
Year 0Year 5Year 10
Traffic demand estimation
Population1,000,000
Penetration10%20%50%
Number of subscribers100,000200,000500,000
Traffic per subscriberE0.020.0120.012
Traffic demandE2,0002,4006,000
Coverage design
Size of service areakm2
1,000
Traffic distributionUniform
Nominal cell radiuskm2
Nominal cell area (assuming circular)km2
12.6
Number of base sites for coverage80
Number of cells / site (3-sector sites)3
Frequency planning and network dimensioning
Spectrum allocationMHz7.4
Radio carrier spacing (as an example)MHz0.2
Number of radio carriers37
Nominal frequency reuse12
Average number of carriers / cell3
Average number of voice channels / cell (8 channels percarrier,
excluding control channels)
22
Call carrying capacity per cell (2% blocking, Erlang B)E14.9
Capacity dimensioning
Total call carrying capacityE3,575
Average base site traffic efficiency due to terrain)60%
Actual call carrying capacityE2,145
Spare capacity (negative means insufficient
capacity)E145-255-3,855
Additional capacity
Additional capacityE02553,855
Limiting factorCoverageCapacityCapacity
Number of additional base sites0687
Adjusting for base site traffic efficiency010145
Total number of base sties8090225
Infrastructure increase-12.50%181.25%
Table 2. A simple numerical example of the radio planning and
capacity dimensioning process.
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
����������������addition, when power control is used in the
uplink, aminimum transmit power is required which could in
turnprolong battery life of the handsets.
As the best server regions are rarely regular in shapedue to
terrain undulation and geographical features, thetraffic capture
ability of each base site could be quitedifferent. By mapping the
best server region into thepopulation density map, a first order
estimation of thetraffic demand per sector can be realized.
However, thismay lead to substantial inaccuracies since users
aregenerally congregated along roads and buildings andrarely
located in open spaces. A method to obtain amore accurate
estimation of the traffic demand is topolarize the population into
areas where it is mostlikely to be located.
This can be achieved by assigning weightings todifferent
geographical features. Based on these
weightings, the traffic for each cell can be moreaccurately
estimated. Evidently, for a cell whichcontains lots of open spaces,
the amount of traffic isexpected to be very low. By contrast, for a
cell whichcontains buildings and shopping areas, the trafficdensity
is expected to be high. Fig. 4 tries to illustratethis principle.
Specifically, Fig. 4a shows thegeographical features and the best
server regions of anarea under study. Fig. 4b shows a 100 x 100
m
2 grid
overlay on the area. This represents the resolution of
thedigital terrain database which will eventually determinethe
resolution of the best server map as well as thetraffic
distribution map. It should be noted that both thebest server map
and the traffic maps now become anapproximation of the real life
situation. Assuming thatcensus information indicates that this 1.1
x 1.6 km
2 area
has a population of 17,600 people, this corresponds to a
0.060.170.170.110.060.060.060.060.060.060.06
0.060.170.170.110.170.170.170.170.060.060.060.060.170.170.110.170.170.170.170.060.060.060.060.170.170.110.060.170.170.170.060.060.060.060.170.170.110.170.170.170.170.060.060.060.060.170.170.110.110.110.170.170.170.110.110.060.060.170.110.060.060.110.110.110.110.110.060.060.170.110.060.000.000.060.170.060.06
0.060.060.060.170.110.060.060.170.170.170.060.060.060.060.170.110.110.170.170.170.060.06
0.060.060.060.170.110.110.060.170.170.060.060.060.060.060.170.170.110.110.060.060.060.060.060.060.170.170.060.170.110.110.110.110.06
0.060.060.170.170.060.060.060.060.110.110.110.060.060.170.060.060.060.060.060.060.060.060.060.060.170.060.060.060.060.060.060.060.06
Cell 1Cell 1
1
1
1
1
1
1
11
1
1
1
11
11
1
1
3
3
3
3
3
311
1
1
11
11
1
3
3
3
3
3
3
333
1
1
13
33
1
1
2
2
2
2
2
222
3
3
33
31
3
1
1
3
3
1
3
211
2
2
31
11
2
1
1
3
3
3
3
210
1
2
23
11
2
1
1
3
3
3
3
320
1
3
22
11
1
1
1
3
3
3
3
321
3
3
12
11
3
1
1
1
1
1
1
323
3
3
12
11
3
1
1
1
1
1
1
221
3
1
12
21
1
1
1
1
1
1
1
221
1
1
11
21
1
a)b)
d)e)f)
Road
Building
Base station
Best serverboundary
Pond
Cell 3
Cell 2
Cell 4
Cell 6Cell 5
100m
100m
Traffic estimationfor each cell
Computerpredicted bestserver regionsbased onterrain map
Determining theresolution forfiguring out thetraffic demand
Figuring out thetraffic weighting
Generatetrafficmap
c)
Mapping trafficto best serverregion
0.170.170.170.170.170.170.170.17
0.110.060.170.170.17
0.170.110.170.170.170.170.110.110.11
0.110.060.060.110.060.00
0.110.060.060.110.17
4.15Erlangs
Cell 1
Cell 2
Cell 3
KEY
a) Geographical area with a population of 17600 and a predicted
traffic of 17.6 Erlangsb) Overlaying a grid of 100x100 m
2 on the geographical area for traffic weighting
c) Signal level computer prediction of the best server regiond)
Traffic weighting map for the geographical areae) Traffic
distribution map based on the traffic weightingf) Predicted traffic
for individual cells in the geographical area
Figure 4. Estimating traffic demand from population
distribution, city lay-out and radio coverage arrangements.
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
��������������� ��population density of 1 person per 100
m
2. With a
service penetration rate of 5%, there will be 880subscribers in
the area. Given that each user willgenerate 20 mErlangs of traffic,
the total traffic in thearea amounts to 17.6 Erlangs. This
corresponds to anaverage traffic density of 1 mErlangs per 100
m
2. As the
resolution of the digital terrain database is accurate to100 x
100 m
2, computer prediction of the signal level
for the best server region will be quantized into bins ofthe
same size.
FeatureWeightRoad2Open1Water0
Road and building3Open and road2
Open and building3Open and water1
Table 3. Weighting factors for the offered traffic
inrelationship to the geographical features.
This is shown in Fig 4c. Finally, also assume someweighting
factors for the traffic load in relationship tothe geographical
features as represented in Table 3.
By mapping the best server region to the traffic bins,the
traffic demand for each cell can be calculated.Applying the
weighting to the geographic area as shownin Fig 4b, a weighting map
as shown in Fig 4d can beobtained. Summing the total weights in the
area andknowing the total traffic, the traffic demand for
eachindividual bin can be apportioned as shown in Fig 4e.Finally,
mapping the best server region to the trafficmap, the traffic
prediction for each cell can beobtained, Fig. 4f.
To show the importance of using the weightingfactor, consider
the demanded traffic of Cell 1. Withoutthe weighting factor, a
traffic load of 3.3 Erlangs ispredicted. However, with the
weighting, a traffic load of4.2 Erlangs (30% higher) can be
anticipated. It shouldbe noted that the weighting factors shown in
thisexample are indicative and for a real application
morecalibrations are necessary to ensure good accuracy. Theboundary
effects between geographical areas will alsohave to be accounted
for but this is beyond the scope ofthis discourse.
Evidently, the example shown here is related to agreen field
deployment where an operator has no “apriori” knowledge of the
actual traffic and mobilitypattern of the users. As the network
evolves, with trafficstatistics collected through the mobile
switches overtime, a much more accurate picture of the
trafficdistribution down to the resolution of a cell can be
built.With this information, the above technique can beapplied
again to refine the network optimization.Furthermore, as the radio
network is alwaysdimensioned for the peak traffic together with a
safetymargin, variations of the daily traffic due to themobility
pattern of the users are usually adequatelytaken into account as
well as extraordinary events such
as major incidents or scheduled gatherings. In the
latterinstances, operators have to employ special measures tocope
with the surge in traffic demand. An example ofthese temporary
measures is the “Cell Site on Wheels”deployed by an operator
following the Los Angelesearthquake in 1994.
Sizing the Channel Capacity of Cells
Based on the knowledge of the traffic for each cell,the number
of traffic channels - and hence oftransceivers - can be determined.
Using the GSMsystem as an example, the relationship between
offeredtraffic and the number of transceivers is indicated inTable
4. Specifically, for GSM one transceiver supportsone carrier which
in turn supports eight time slots. Thetime slots can be assigned as
traffic channels or controlchannels depending on the specific
configuration of anetwork. (The results in Table 4 consider
therequirement of control channel assignment and assumea 2%
probability of blocking for fresh calls using theErlang B
model).
Offered trafficper cell
[Erlangs]
Number of trafficchannels (or time
slots)
Number oftransceivers
2.3618.214214.922318.4264
Table 4. Examples of allocation of transceivers to a cellas a
function of offered traffic.
As a network is normally dimensioned for growthand traffic
fluctuations, it is not uncommon that a threeto six months build
ahead is incorporated in the trafficdimensioning plan in order to
accommodate safetymargins, [16]. These margins have proven to
beadequate in most cases.
In addition to normal calls, handover requests alsorequire radio
resources especially for a “make-before-break” scheme as in some
implementations of the GSMsystem, [17]. In a real network, the
number of handoversper call is dependent on the length of the calls
as wellas the mobility pattern of the users. The contribution
ofhandovers to the total traffic loading is difficult topredict and
is normally assumed as an acceptableoverhead to a system. When an
operator detects anexceptionally high volume of handovers in a
cell,measures will have to be taken to bring it under
control.Typical techniques at the disposal of an operator are
to:Increase the hysteresis margin; change the handoverthresholds;
and reduce the overlap between adjacentcells by using narrower beam
antennas. As an example,60
o antennas are frequently used among three sector
cell sites in dense urban areas for minimizing thenumber of
repeated handovers. Otherwise, the small cellsize and large
coverage overlap between neighboringsectors coupled with highly
variable shadow fading inan urban area might trigger an excessive
number ofhandover requests.
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
������������������
The ability to prioritize handover handling withrespect to fresh
calls is infrastructure equipmentspecific. In dense urban
environments where cells areheavily overlapped with each other in
outdoor areas dueto their short inter-site distances and the
requirement forindoor coverage, it is generally not necessary
toprioritize handover handling as calls can be maintainedeven if
the mobile moves away from the best serverregion. This large
overlap can be visualized byunderstanding that the indoor
penetration margin forurban areas is typically required to be 15 dB
or more. Ahandover failure will not normally lead to a droppedcall
as calls, which are not successfully handed-over,can be reverted
back to the originating base station.Evidently this will degrade
the service quality due tothe increased uplink interference as
calls are draggedoutside the best server region.
Similar to handover prioritization, the handoveralgorithm and
parameters are typically infrastructureequipment specific and are
usually omitted fromstandardization. For instance, for GSM, it is
possible toemploy received signal level, received signal
quality,timing advance as well as traffic reasons for
initiatinghandovers, [18]. For a properly engineered network,
themajority of the handovers should be initiated because
ofinsufficient signal level, i.e. the system operationshould be
power-limited. A high volume of handoversdue to poor received
signal quality normally indicatesthat there are interference
problems in the network(interference-limited operation). In this
situation, thedropped call rate is expected to be high as well.
Anoperator will have to minimize this by re-tuning thefrequency
plan and optimizing the base sites in thevicinity of the affected
region.
As an example, Table 5 shows a break-down ofhandover cases
according to initiating reason for a GSMsystem. In the table,
“signal criterion” signifies that thehandover is triggered by
insufficient signal level,whereas “UL quality” and “DL quality”
stand for
handover due to excessiveerror rate on the uplink anddownlink,
respectively. InGSM, signal quality isdirectly related to the
biterror rate measured prior tochannel decoding. There areeight
quality levels definedand the threshold for poorquality is between
5 and 6.Evidently, all handoverthresholds are operationparameters.
As shown in thetable, not all unsuccessfulhandovers lead to
droppedcalls, many are reverted backto the original channel wherea
repeated handover requestmay be initiated.
As it may be observedfrom the table, in an evolvingcellular
network, on theaverage about 25% of thehandover cases could be
due
to interference impairments though the scatter aroundthis value
is very significant. This is similar to whatobserved in [19]. It
should be noted that the example isfor a typical European capital
city and only the caseswhich are in excess of 300 handovers per
observationperiod are cited.
More recently, with the proliferation of the use offrequency
hopping and power control in the GSMcommunity, it was found that
quality based intra-cellhandover can help to improve the robustness
of theradio link significantly. Up to now, operators havenormally
used this more as a safety feature rather thanas an active approach
to optimize the frequency reuse.The dimensioning rules and the
relationship withinterference are still not fully understood.
As for code division multiple access (CDMA)systems such as
cdmaOne, [4], the dimensioning rulesare, in principle, similar in
many respects to othercellular systems. For instance, handovers
from basestation to base station will be largely based on thepower
level measurements from adjacent base stations.However, a CDMA
system has the flexibility of softquality degradation which allows
more room for facingtraffic fluctuations. As CDMA is inherently
aninterference-limited system, soft handover (bycombining or
selection) is required to controlinterference. To this end,
additional radio hardware isrequired at the base station. This
amounts to a 40% to70% increase in channels and this is included as
part ofthe hardware dimensioning rules. Thus, there is a
subtletradeoff between the system loading, mobility
andcapacity.
In order to maintain QOS8 objectives, CDMAadmission control can
be either enforced by physically 8 Quality of Service (QOS): “The
collective effect of serviceperformances which determine the degree
of satisfaction of auser of the service. The quality of service is
characterized by
Handover Triggering ReasonOutcome Of Handover Handling
HO’spercell
Signalcriterion
Bad ULquality
Bad DLquality
SuccessFailure
SuccessFailure:Total
Failure:Returned
to oldchannel
Failure:Dropped
calls
[%][%][%][%][%][%][%]37777.1916.186.6346.9553.0539,5212.5340766.3415.2318.4386.4913.5111,32.2148170.4810.8118.7184.4113.5912,473.11166190.015.724.2752.4447.5646,421.4464893.212.014.7854.9445.0644,60.4686556.6522.3121.0486.0113.999,5954.3961854.538.4137.0683.3316.6713,273.3931437.903.5058.6086.9413.069,5543.5058383.537.209.2689.0210.983,0877.8930276.1618.874.9750.3349.6747,022.64
Table 5. Examples of classification of handover cases for a set
of ten cells withexceptionally high volume of handover (courtesy of
AirTouch International).
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
������������������limiting the number of user codes which can be
used,or it can achieved by allowing the interference level ornoise
rise in the system to determine the capacitynaturally. However, as
most of the CDMA systems arestill in their early phase of
deployment, theeffectiveness and the adequacy of these
techniquesneed to be better understood. At present, where
capacityis expected to be a problem, usually more carriers
areadded. Thus, the relationship between admission controland QOS
control is still very much an undeterminedissue from a practical
network operation point of view.
Adjusting the System Dimensioning
As a network evolves, the number of subscribers willgrow in time
while the average traffic intensity persubscriber will gradually
decline over time. This isbecause at network start up, the initial
customers aregenerally business customers who generate a highamount
of “minutes of use”. However, as timeprogresses, more
(non-business) subscribers are addedand the minutes of use by them
are much lower than thebusiness users. This will serve to dilute
the averagetraffic intensity per subscriber across the network.
Forinstance, at network startup, the traffic per subscriber
istypically around 18-20 mErlangs per subscriber and thisdeclines
to 12-13 mErlangs as the network matures. Asfor the net effect,
although the subscriber growth mayinduce a decline in minutes of
use, the growth in thevolume of traffic demand is still quite
significant. Anoperator has to closely monitor the traffic
statistics ofthe busy hour traffic channel utilization for all the
cells.This information is obtained from the mobile switchstatistics
on an ongoing basis. When the level ofblocking reaches a
pre-determined threshold, action hasto be taken to increase the
number of transceivers percell. Of course this is subject to the
constraint of thespectrum allocation and availability. Once
themaximum number of transceivers for a specific reusepattern is
reached, the frequency reuse has to betightened in order to enable
an operator to increase thenumber of carriers per cell. This
normally takes time toplan the frequency re-tune and the
installation of newequipment at the appropriate base sites. The
overalloptimization cycle for network capacity is summarizedin Fig.
5.
When spectrum and frequency reuse become thelimiting factors,
for the longer term capacity relief, itwill be necessary to
increase the number of base sitesin order to cell split9, or to
introduce a hierarchical cell
the combined aspects of service support performance,
serviceoperability performance, service integrity and other
factorsspecific to each service.", ITU-T Recommendation M.60,
[20].
9 This is different from tightening the frequency reuse as
cellsplitting is to increase the spatial reuse of the frequency
setrather than tightening the frequency reuse pattern. Bytightening
the frequency reuse pattern, the number of carriersin the frequency
group per cluster is reduced. For instance, anominal frequency
reuse pattern for GSM is a 12 carriers
structure (HCS) with microcells overlaying themacrocell network.
The traffic dimensioning rules forestimating the number of traffic
channels requiredbecome more complex for HCS. For example,
directedretry, [21], i.e. attempting to serve a call in the
secondbest server cell when the best cell has no channelsavailable,
could improve the trunking efficiency of themicrocells but not the
macrocells 10.
A recent approach, [22], may enable efficient reuseof
frequencies between microcell and macrocells,although the new
technique is yet to be tested in thefield. In addition, the
effectiveness of speed sensitivehandover algorithms, [23], [24],
[25], etc., could alsoimpact the traffic capturing ability of the
microcells. Atthe time of writing, operators and
infrastructuresuppliers are still actively testing the viability of
thehandover algorithms and the appropriate channelallocation
strategies.
As mentioned before, for shorter term capacityrelief, it is
possible to temporarily borrow the sparecapacity in the neighboring
cells. In practice, thetemporal distribution of the traffic among
the base sitesin a dense urban area could be rather uneven and not
allthe cells neighboring to a congested cell aresimultaneously
congested. Evidently, cells have to besignificantly overlapped in
order to be able to sharecapacity with each other.
In general, there are many optimization issuessurrounding a
cellular radio system. Most of these havemultiple variables and
constraints. Precisemathematical formulations are often difficult
if notintractable. For instance, the traffic loading for aspecific
cell is dependent on the traffic distribution, themobility pattern,
spectrum allocation, handoveralgorithm, switch parameters setting,
and so on.However, automated planning tools are beginning toemerge
to assist engineers to plan and optimize theirnetwork. An example
is the proliferation of automaticfrequency assignment tools which
put theory, [26], [27],[28], into practice. Yet there is still a
long way for toolsto evolve to become fully effective for the many
otheroptimization problems of more direct relevance to radionetwork
dimensioning.
reuse. However, operators with a generous spectrum allocationmay
relax the reuse pattern to 15 carriers or more. Although,
toincrease the network capacity, a tighter reuse pattern of
9carriers or lower may be required after the first transceivers.10
Assuming a split band arrangement for the microcell and
themacrocell, the coverage of the microcells is only a subset of
themacrocell. Splitting the spectrum allocation would imply thatthe
number of carriers for the macrocell will be reduced. Inareas where
there is microcell implementation, it is possible tohave better
trunking efficiency as the mobiles in the microcellwill be able to
access both the microcell and the macrocell.However, in areas
beyond the coverage of the microcell, themacrocell will have less
carriers and can only offer a lowervolume of traffic.
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
������������������
ITU-T Framework for TrafficEngineering of Personal
Communications
s already mentioned, trafficengineering for networks
supportingmobile and UPT services is addressed inthe ITU-T
E.750-series of
recommendations. This series covers traffic engineeringfor the
user plane, while traffic engineering for thecontrol plane is
handled under separate ITU-Trecommendations, typically those
related to commonchannel signaling systems and IN
(IntelligentNetwork)11. The characterization of
personalcommunications user traffic demand is expected todeliver
input to the demand processes for trafficengineering of the control
plane.
11 A separation between the definition of the user plane and
thecontrol plane is that the former is associated with user
datahandling, whereas the latter relates to signaling
trafficnormally consisting of short messages and packet data.
The E.750-Series ofRecommendations
The goal of the E.750-series is to recommendprocedures for
cost-effectively dimensioning networkresources for terminal and
personal mobility support.The scope of the E.750-series covers
land, maritimeand aeronautical services, as well as terrestrial
andsatellite based networks. To achieve the objectives ofthe
E.750-series the following study areas need to beaddressed:
•Mapping of user density and mobility for typicaloperating
scenarios into user traffic demand (offeredtraffic);
•Definition of GOS parameters with user perceptionsignificance
and related target values to setobjectives for traffic
engineering;
•Development of methods for meeting the GOStargets for specified
demand patterns;
•Specification of measurement procedures formonitoring the GOS
target attainment in the serviceoperations environment.
Correspondingly, the series is organized into five
majorgroupings (general aspects, traffic modeling, Grade ofService,
dimensioning methods, and trafficmeasurements). Orthogonal to this
grouping is theorganization of the series into recommendations
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Figure 5. The network capacity optimization cycle.
A
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
ÙÚÛÜÝÞßàáÚâÛãÜäßåæâçèé
addressing terminal and personal mobility12. Theorganization of
the E.750-series is summarized in Fig.6.
The development of the series has been mainlyfocused until
recently on reference connections andGOS aspects for both land and
maritime/aeronauticalservices, with particular stress on terminal
mobilityissues. The E.750-series consists currently of
twelverecommendations whose status ranges fromapproved/revised to
draft recommendations to befinalized.
The Traffic Engineering Cycle
The approach taken in ITU-T is that for both fixedand mobile
services the traffic demand should becharacterized based on those
aspects which are underno or little control of network operators
and serviceproviders. In the case of terminal mobility,
theseaspects relate essentially to the environmentalpropagation
conditioning (e.g., indoor/outdoor,metropolitan/urban), mobile
terminal speed, and userbase characteristics (e.g., mobility
behavior, fresh andrepeated call arrival processes). As an example,
Table6 lists some parameters which characterize theoperating
scenarios envisaged for IMT-2000 .
12 Recommendations on terminal mobility apply to bothexisting, -
e. g. GSM (Global System for Mobilecommunications, Europe), NADC
(North American DigitalCellular, North America), and PDC (Personal
Digital Cellular,Japan) - and developing mobile systems, e. g.
IMT-2000 andUMTS (Universal Mobile Telecommunication System,
ETSI).
ApplicationDelivery Mode
PhysicalAttributes/
Propagation
Level ofMobility
Public cellularTerrestrialIndoor and/oroutdoor
Stationary(0 km/h)
Privatebusiness
SatelliteOutdoor inurban,suburban, rural,hilly or
coastalareas
Pedestrian(up to 10 km/h)
Residentialcordless
Terrestrial orsatelliteoperation
Typicalvehicular(up to 100km/h)
Fixedsubscriber loopreplacement
Land, maritime,or aeronauticaloperation
High speedvehicular (up to500 km/h)
Residentialneighborhood
Aeronautical(up to 1,500km/h)
Mobile basestation
Satellite(up to 27,000km/h)
Paging
Table 6. Operating scenarios for IMT-2000, [29].
A key task in characterizing the traffic demand ishow to capture
the space-time relationship with dueconsideration given to the
scope of ITU-T trafficengineering. Once the traffic demand is
defined, trafficengineering for mobile systems should proceed
byexercising the dimensioning methods with specificGOS/QOS
objectives and cost constraints. Possibly, thedimensioning methods
have to be repeatedly exercisedto meet in the field GOS/QOS
objectives and to copewith the necessary adjustments of the many
operationparameters which, by necessity, cannot always beexplicitly
accommodated in the dimensioningprocedures (e.g. power control,
interleaving depth,frequency hopping patterns, source/channel
coding,etc.). With a focus on radio resources for systemssupporting
terminal mobility, Fig. 7 schematicallyshows the envisaged traffic
engineering study areas tobe covered in the E.750-series. Note that
these areas areclosely related to the stages followed in the
practice ofradio planning and capacity dimensioning as depictedin
Fig. 3. As a matter of fact, existing and envisagedrecommendations
in the E.76x (TrafficCharacterization), E.78x (Engineering
Methods), andE.79x (Measurements and Performance
Monitoring)decades, relate, respectively, to the actions
describedunder “figuring out the traffic demand”, “sizing
thechannel capacity of cells”, and “adjusting the
systemdimensioning” described in the section on the
currentpractices for the operation of cellular systems. Asindicated
in the figure, the tasks associated with trafficengineering of
cellular systems are part of a complexcycle which includes radio
coverage design andfrequency planning as key components.
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Figure 6. Organization of the E.750-series
ofRecommendations.
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
ÙÚÛÜÝÞßàáÚâÛãÜäßå�âçèé
Scope of Traffic Engineering forNetworks Supporting Terminal
Mobility
The scope of traffic engineering for networkssupporting terminal
mobility can be classified accordingto two inter-related
dimensions, i.e. thegeographical/operation-domain dimension and
thefunctional dimension. As for the former dimension, as ageneral
rule, traffic engineering in ITU-T has beenrelated to the
international segments in the
communication path. However, with the trend towardsderegulation,
competition and proliferation of roles inthe provision of
telecommunication services and theincurred dependency of service
performance oninterworking of an increasing number of subsystems
inthe communication path, the scope of trafficengineering has
widened. Correspondingly, the span oftraffic engineering for
networks supporting terminalmobility ranges from metropolitan to
internationalareas, as indicated in Fig. 8. As for the
functionaldimension, three major network segments can beidentified
(see also Fig. 1): i) the radio interface, ii) thefixed
infrastructure of the mobile network; and, iii) thefixed core
network. The radio interface comprises the
transport and signaling functionality between the mobileterminal
and the Base Transceiver Station (forsimplicity in Fig. 1, BSS,
Base Station System). Thefixed infrastructure of the mobile
network, spannedbetween the Base Transceiver Station and
MobileSwitching Center (MSC), comprises the functionalityfor
exercising/activating control on radio channelquality and
availability as impacted by user populationactivity and mobility.
Finally, the fixed core network,extending beyond the MSC, includes
the functionalityfor user location, tracking, location updating,
and callrouting. For the case of separated fixed and
mobilenetworks, [30], the figure also shows the allocation of
mobility management functions(MMF) within the mobile network,
asis typically the case with secondgeneration mobile systems
(e.g.GSM). Depending on the actualimplementations and
trafficrequirements, the MSC (MobileSwitching Center) can be
connectedwith the fixed network at the LE(Local Exchange) or TE
(TandemExchange) level. This is succinctlyindicated in Fig. 8
through thecombination LE/TE. As a matter offact, the allocation of
MMF has arange of possible options includingthe arrangements
resulting fromintegrated mobile and fixed network,[31]. The figure
indicates two obviousteletraffic interfaces at which trafficdemand
has to be characterized fortraffic engineering purposes. Onetraffic
demand relates to the radiointerface and has collected most ofthe
contributions in the literature. Theother is associated with
thecharacterization of mobile relatedtraffic which requires fixed
networkresources.
The traffic engineering tasks fornetworks supporting terminal
mobilityrelate to all three above functionalsegments. For the
traffic engineeringof the radio interface, the key GOSparameters
are “probability of call
blocking” and “probability of handover failure”, [32],[33]. The
traffic engineering problems that must beaddressed include (see
also [34]):•A tradeoff of radio spectrum reuse with call
blocking and handover failure;•Estimating the signaling load and
allocating
adequate bandwidth to handle mobility relatedsignaling functions
(e.g., paging and locationupdates);
•Radio resource allocation policies;•Admission control
strategies.
For the fixed part of the network, the key GOSparameters related
to mobility are:
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-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
ÙÚÛÜÝÞßàáÚâÛãÜäßåéâçèé•Post selection delay (consisting of
authentication
delay, paging/alerting delay, time to obtain therouting number,
and the fixed network ISDN/PSTNdelays);
•Call blocking and lost signaling transactions;•Profile lookup
and update response times.
Fig. 8 shows the two parts of the fixed network: Thefixed
infrastructure of the mobile network (the middleband in Fig. 8
consisting of the BS, MSC, MMF andassociated trunking and
signaling) and the core network(the upper part of Fig. 8).
The mobility related traffic engineering problemsthat must be
addressed for the fixed network are:
•Dimensioning the mobile infrastructure real-timeresources in
BS, MSC, and MMF systems to handlethe mobility processing
needs;
•Developing database architectures anddimensioning database
real-time capacity in boththe mobile infrastructure and core
network to handlemobility functions;
•Dimensioning signaling and trunking capacity inboth the mobile
infrastructure and core network.
To characterize the traffic demand for theseproblems, models of
user behavior must be developed.These models must characterize user
density andmobility, user calling behavior (e.g., arrival,
destinationand holding time statistics), and user
re-attemptbehavior. These user behavior models are then coupledwith
system characteristics and operations to derive
traffic load parameters such as rate of handovers, rate
oflocation updates, channel occupancy time, etc. Thesetraffic loads
are then used to dimension the system andmeet specified GOS
targets.
Probing the Literature
Much research efforts have been spent tocharacterize user
density and mobility, calling behaviorand their performance impacts
on wireless networks.Given the large volume of results in the
literature, it isimpossible to give a comprehensive review of
themhere. Rather, the purpose here is to present a briefoverview of
some of the models and results that, in ourview, are
representative. Readers can find other workon the subject
referenced by the papers cited here.When appropriate, areas that
require further study willalso be pointed out.
The basic purpose of the mobility and teletrafficmodels is to
capture the movement and callingbehavior of subscribers as a means
to predict orevaluate capacity and performance of wirelessnetworks.
Specifically, user movement in terms ofdirection and speed affects
the time duration in whichusers stay in a cell or location area. In
turn, a shorttime duration results in a frequent call handover
whenusers are making calls, or a frequent update to
locationdatabases for call delivery even when users are notmaking
calls. Highly mobile users may also require
MetropolitanArea
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Teletraffic Interface
Teletraffic Interface
otherLE/TE’s
otherMSC’s
KEYMTMobile TerminalBSBase StationMSCMobile Switching
CenterLELocal ExchangeTETandem ExchangeISCInternational Switching
CenterMMFMobility Management Function
Figure 8. Scope of traffic demand characterization for cellular
networks(separated mobile and fixed network, mobile-to-mobile
communication).
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
ÙÚÛÜÝÞßàáÚâÛãÜäßåRâçèémore network resources for paging and
other signalingfunctions for call delivery than is required for
slowermoving users. Furthermore, as discussed earlier, userdensity
and service penetration play an important rolein network planning
and engineering. An area with ahigh user density is likely to yield
high traffic demands,for which sufficient equipment has to be
deployed tohandle the projected traffic load. Last but not
least,these models should capture the calling behavior interms of
call arrival rate, re-attempt, and call holdingtime distribution
for proper system dimensioning. Astraffic load of new and handover
calls, locationupdates, call delivery and other signaling load
havesignificant impacts on service quality (e.g., callblocking and
dropping probability), there is always astrong demand for mobility
and teletraffic models foranalyzing and engineering cellular
networks.
Radio Interface
Mobility and teletraffic models related to the radiointerface
typically address traffic load characterizationand handover
performance. By assuming uniform userdensity, and randomly chosen
fixed movementdirection and speed, the model proposed by
[35]expresses the cell-crossing (i.e., handover) rate as afunction
of mobile density, mean velocity, and cellperimeter. The model is
often referred to as the fluid-flow model. Although it neglects
many aspects ofpractical situations, it appears to be the
simplestmobility model with closed-form formulas. In fact,
theassumption of uniform user density and movementdirection or its
variant are commonly used to studynetwork performance; for example,
see [36], [37], [38],and [39]. As pointed out below, [40] shows
that thefluid model can closely approximate certain
practicalscenarios, but fails to do so in others.
In [38], the authors prove that the uniformassumption leads to a
biased sampling condition. Thatis, the speeds of cell-crossing
terminals are statisticallydifferent from those that remain within
a cell. Theprobability distribution function for the speed of
cell-crossing terminals are derived for use in performanceanalysis
and simulation to obtain consistentcomparisons among different
design alternatives. Usingthe same assumption, [39] shows that (a)
the handoverrate (i.e., the mean number of handovers per
call)increases as the square root of the increase in the cellsper
unit area; and (b) the handover rate is given by theratio of mean
call duration to the mean cell sojourntime. These results offer an
understanding of designtrade-offs and sizing issues in evolving
wirelessnetworks.
Although [41] also assumes uniform user density,the author
proposes that the speed and direction of amobile terminal are
regenerated randomly andindependently after an exponentially
distributed randomtime (i.e., a random traveling distance). The
mobilitymodel is motivated by the need of reducing thecomplexity of
simulation models for studying channeloccupancy time, but [41] also
presents arguments tojustify the mobility assumption in certain
settings. It is
found that the channel occupancy time can be closelyapproximated
by an exponential distribution inpractical situations.
Recently, [42] develops a mathematical formulationfor tracking
movement of mobile terminals, whichmove randomly with degrees of
freedom matching themobility conditions in practical networks. The
modelcan be used to characterize cell residence time,channel
holding time, and the average number ofhandovers per call. It is
found that cell residence timecan be described by generalized gamma
distributions,while channel holding time can be approximated
byexponential distributions. The latter is consistent withthe
findings in [41], although the formulation in [42]provides
additional modeling flexibility and capability.
Instead of tracking user movement, [43] studiesvarious channel
assignment strategies for handovercalls by assuming that the
residence times of a mobileterminal staying in different cells are
independent andidentically distributed. Such an assumption is made
fortractability reasons. If it can be validated by actualfield
measurements, the mobility model will be usefulin system design and
engineering.
It is evident that the assumption of random usermovement is not
appropriate for cases such as usersplacing phone calls while
driving. For this reason,researchers have observed the need of
combiningteletraffic theory and vehicular traffic theory [44]
toestimate call and signaling traffic load. For example,[40]
proposes a simulation method, which keeps trackof terminal
locations in cellular networks by using avehicular traffic model
based on realistic relationshipsamong vehicle speed, density and
volume. For uniformstreet layouts such as the Manhattan street
patterns, itis found that the fluid-flow model [35] yields
accuratecell/area boundary crossing rate when compared withthe
detailed simulation. However, the fluid-flow modelcan over or
under-estimate the crossing rate in non-uniform street patterns.
Furthermore, [45] proposes asimulation and analytic model to
consider teletrafficload and vehicle movement. The authors also use
themodels to study the impacts on call blockingprobability in case
of sudden change of vehiculardensity in a ring-shaped service
area.
Another mobility model used to capture time andspace dynamics in
cellular networks along a highway ispresented in [46]. Assuming
that each vehiclealternates between calling and non-calling
staterandomly, the model uses differential equations todescribe the
movement of both vehicle types. As aresult, call traffic load for
each cell along the highwayand handover rates at cell boundaries
can be obtained.With vehicles arriving to the system according to
time-dependent Poisson processes, new call load andhandover also
form Poisson processes. The model canbe viewed as a traffic demand
and useful in studyingthe time and spatial dynamics in mobile
networks. Forinstance, their analysis shows that a
significantincrease of offered load results if users initiate
callswith a rate inversely proportional to their speed (e.g., ina
traffic jam). In fact, this coupling effect betweenmovement and
calling behavior has not been fully
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
ÙÚÛÜÝÞßàáÚâÛãÜäßåSâçèéunderstood. Additional study will be
worthwhile on thesubject as well as on re-attempt behavior, and
two-dimensional traveling space. These aspects areaddressed in
other studies, although not in theframework of one comprehensive
model. As examplesof recent work: The impact of the user
re-attemptbehavior on protecting handovers against fresh calls
isaddressed in [47] (although disregarding the spatialdynamics); a
two-dimensional space is addressed in[48] by reducing the problem
to a combination of one-dimensional geometries (but assuming, among
others,that the distribution of users on a segment is
uniform);“spatial point patterns” modeling (two-dimensional)space,
time-frozen distributions of mobile users whileaccommodating in a
flexible and computation-friendlyway a range of possible
geometrical constraints areconsidered in [49] as a
multi-dimensional extension ofthe Markovian Arrival Process (MAP),
[50], based onarrival rates dependent on the “environment state”
ofthe system.
It is worth noting the potential importance of theconcept of
different realms of traffic and mobilitymodels with different
levels of details for street, region,metropolitan and national
areas, as reported in [51] and[52]. This is so because different
amounts of detailssimplify the models, while adequately capturing
themajor essence of mobility and traffic situations inquestion. As
a result, the simple models may beproven to be sufficient and
useful in planning andengineering practical networks. Towards this
goal,additional research work will be highly desirable.Recently,
[52] introduces a set of mobility models withscope ranging from
city to street level. Three modelsthat cover city, zone and street,
respectively, areintended to capture mobility at different scales
as ameans to estimate mobility and traffic parameters
forengineering procedures. (As discussed later, [51] alsomakes a
similar observation that mobility models withdifferent levels of
details are needed.)
Before leaving the discussion on the radio interface,it must be
pointed out that mobility and traffic modelsfor overlaid,
micro/macrocell architecture [53], [54] areeven more complicated
than those for the single layerarchitecture. Due to the overlapping
coverage of microand macrocell, a call can be served either by
micro ormacrocell. This brings about new approaches ofdynamic
channel assignment according to the mobilityof users; see e.g.,
[19], [20], [55], [56] and [21]. Further,efficient reuse of radio
spectrum in the overlaidnetworks also becomes an issue [57], [18].
In terms ofteletraffic issues, when a call is blocked by
amicrocell, it can be “overflowed” to the associatedmacrocell to
see if the latter has spare radio channels.Existing methods such as
[58], [59] and [60] may beuseful for analyzing the call overflow,
but additionalfactors such as mobility need also be considered
to
balance performance for new and handover calls [61].A survey of
some of these issues for micro/macrocelloverlays can be found in
[62].
It is clear that additional research will be needed tofully
address the mobility and teletraffic issues for theoverlaid
networks.
Fixed Infrastructure
In the study of teletraffic issues for the signalingfunctions
and the fixed infrastructure, terminal orpersonal mobility need
not, in general, be consideredas detailed as in most of the models
discussed above.Rather, models with large scales will be
adequate.
Adding new base stations and reducing cell size area common
approach to meet increased traffic demand.Small cell and
registration areas however tend toincrease signaling traffic. Using
the fluid-flow model[35], [63] reveals a potentially significant
increase oftraffic load on the signaling links due to a
combinationof high terminal density and mobility, and smalllocation
area in PCS networks. Using the number oflocation updates between
two calls, [64] proposes aframework for estimating the signaling
load. Similarly,[65] and [66] predict a large increase of workload
forthe network databases to support mobility whencompared with IN
network services.
To avoid the performance impacts of signaling anddatabase load
due to mobility, many new mobilitymanagement or location tracking
algorithms have beendevised and analyzed. The common goal of these
newmethods is to reduce the network signaling (includingpaging over
the radio channels [67], [68], [69]) anddatabase load, thus
improving call-setup delay, networkcapacity and perceived service
quality, whileefficiently delivering calls to mobile users. Since
thedetails of the algorithms lie beyond the main scope ofthis
paper, instead of discussing them here, readers arereferred to the
papers and their cited work on thesubject that are published
recently in two special issuesof IEEE JSAC [70] and [71].
Last but not least, it is worth noting that [51]proposes a
realistic teletraffic modeling framework,which consists of
topology, call and mobility model.The call model is characterized
by actual call data inan existing telephone network. The mobility
modelconsiders user movement at three different scales,resulting in
metropolitan, national, and internationalsubmodel. The mobility
parameters in the firstsubmodel is estimated from personal
transportationsurveys, while those for the latter two are
approximatedfrom the airplane passenger traffic data. Using
thisframework, workload for the location database can bestudied by
simulation. Potentially, it can also be usefulfor evaluating
various mobility management algorithmsand network topology
design.
-
D. Grillo, R. A. Skoog, S. Chia, and K. K. Leung, "Teletraffic
Engineering for Mobile Personal Communications in ITU-TWork -- The
Need for Matching Practice and Theory”, to appear in IEEE Personal
Communications
ÙÚÛÜÝÞßàáÚâÛãÜäßåTâçèé
A summary of these mobility and traffic models,and related
teletraffic issues for mobile networks isgiven in Table 7. The
purpose there is to show thepotential of the models in studying
networkperformance and design issues.
Where Do We Stand?
espite a large volume of (at times)quite sophisticated mobility,
trafficdemand and dimensioning models,theorists, for the sake of
tractability,
often make simplifying assumptions about user density,and assume
certain statistical properties of channelholding time, cell
residence time and other mobilityrelated parameters, when modeling
mobilecommunication networks. As the mobile and UPTservices will
undoubtedly provide users with richfeatures and multimedia
capability, mobility andteletraffic issues for the future networks
will becomemore complicated than those in the current
secondgeneration networks. It will be a challenge to theteletraffic
community to provide engineering tools fordifferent system
generations meeting the robustness andsimplicity requirements
demanded for a smooth systemoperation.
Some Popular Assumptions: TrafficEngineering "Myths"?
In the area of terminal mobility and cellularsystems, there has
been increasing consensus in theopen literature on a series of
working assumptionswhich have led to mathematically tractable
problems.Given the complexity of mobile system operation andthe
need for traffic engineering procedures with ITU-Tsignificance, it
is important to revive considerations ofhow well the models being
used represent “real world”systems. This is by no means meant to
undermine thevalue of traffic models, but rather to stress the need
ofvalidating with field data the indications from themodels and
determine that the models are accurateenough to justify their
adoption in a sound teletraffic
engineering practice. A point to note is that trafficstatistics,
and in general information on networkoperation, for a specific
cellular radio network arehighly sensitive proprietary information
for an operatordue to the competitive nature of the industry.
Thisinformation is rarely published in the public domain norshared
outside the companies. (Infrastructure suppliersmay occasionally
have access to a limited amount ofthis information, but this could
mostly be restricted tothe start-up phase of a network). Since
access to thewealth of information on real life network operation
iseffectively very restricted, contributions on trafficengineering
to the open literature usually have to workfrom abstractions whose
rationale and impact may notbe backed in all cases by deep
knowledge about realsystem operation and needs. These factors
contribute tosome of the disconnects between theory of
trafficengineering and the real world.
Some of the most popular assumptions related totraffic and
mobility modeling have included:
•Radio cells have regular (hexagonal) shape.Cells are defined in
terms of radio coverage asprovided by the power emitted by the
basetransceiver station/radio port antenna around whicha cell is
constituted. As such, the cell “boundary” isassociated with the
limiting distance from theantenna site beyond which communication
with amobile terminal becomes troublesome. Due toterrain
characteristics and existence of obstacles ofvarious nature
interfering with propagation of radiowaves, the boundary of a cell
is normally fuzzy anda cell coverage may even be jeopardized over
anarea.
•Handovers (in FDMA/TDMA systems) areaccomplished as soon as a
user crosses theboundary between adjacent cells).Since cells are
usually not regularly shaped, theirradio coverage must overlap to
some extent. Thisoverlap provides a window during w