Computer Communications 27 (2004) 1509–1523
www.elsevier.com/locate/comcom
An alternative strategy for location update and paging in mobile networks
Alejandro Quintero*, Oscar Garcia, Samuel Pierre
Department of Computer Engineering, Mobile Computing and Networking Research Laboratory (LARIM),
École Polytechnique de Montréal, Montreal, Canada H3T1J4
Received 30 May 2003; revised 31 May 2004; accepted 4 June 2004
Available online 31 July 2004
Abstract
In mobile systems, the influence of mobility on the network performance must be strengthened, mainly due to the huge number of mobile
users and the small of cells. This paper presents a profile-based strategy combined with a built-in memory method to reduce the signaling cost
during the location update processes by increasing the intelligence of the location procedure. This strategy associates to each user a list of
cells where it is likely to be with a given probability in each period of time. The list is ranked from to the least likely place where a user is
found. When a call arrives for a mobile, it is paged sequentially in each location within the list. When a user moves between location areas in
the list, no location update is required. The implementation of this strategy has been subject to extensive tests. The results obtained confirm
the efficiency and the effectiveness of PBS to significantly reduce the costs of both location updates and call delivery procedures, in
comparison with IS-41 and other strategies well-known in the literature.
q 2004 Elsevier B.V. All rights reserved.
Keywords: Location update; Mobility management; Profile-based location; IS-41
1. Introduction
In mobile systems, a user must be able to access services
while moving from one location to another. This problem is
called mobility management which contains two components: location management and handoff management.
This paper is concerned only with the location management.
Most current networks divide the coverage area into many
location areas (LA), which are sets of many adjacent cells. In
each cell, every mobile unit (MU) communicates with a node
B through wireless links. The base stations are connected to a
radio network controller (RNC) that serves that LA.
Location management is the process that allows the
network to know the attachment point of the mobile user for
call delivery. Current location management procedures
involve a certain database architecture and the transmission
of signaling messages over the network. Third-generation
mobile networks will be characterized by high user density
and high mobility, which will increase the number of
location updates and handoff messages, thus limiting the
switching capacity and available bandwidth. Compared to
second generation systems and apart from the increased
* Corresponding author. Tel.: þ 1-514-3404711; fax: þ 1-514-3403240.
E-mail address: [email protected] (A. Quintero).
0140-3664/$ - see front matter q 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.comcom.2004.06.001
traffic demands, location and handover procedures in a
micro-cellular environment, in conjunction with the huge
number of users, will generate a considerable mobility
signaling load [15]. These bottlenecks are the motivation for
the studies of new or improved schemes to support the
increasing subscriber population [17].
User mobility information can be used to assist user
mobility management (traffic routing), to manage network
resources (resource allocation, call admission control,
congestion and flow control), and to analyze handoff
algorithms in integrated wired/wireless networks [22].
Also, users’ patterns of mobility can also be useful for
systems recovery [8].
The location management methods are classified into
two major groups [25]: non-memory-based methods and
memory-based methods. The first group includes the
methods based on algorithms and network architectures,
while the second group includes the methods based on
learning processes which require the knowledge of users’
mobility behaviour. The strategy proposed in this paper
belongs to the second group.
Location management occurs in two stages: location
update and call delivery. When an MU performs a
location update procedure, it provides the network with its
location information. Call delivery means that the network
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A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
Nomenclature
BS
CMR
HLR
ILD
LSTP
LA
MSC
MU
PBS
base station
call-to-mobility ratio
home location register
intermediate location database
local signal transfer point
location area
mobile switching center
mobile unit
profile based strategy
is queried for the location information of the called mobile.
Location update occurs when a mobile moves from one LA
to another. A location search procedure is performed when a
mobile is called. The goal is to find the called mobile’s
current cell in an accurate way.
In North America, IS-41 standard [5] is used for both
location update and call delivery procedures [7,26].
This standard deploys a two-level database architecture
consisting of one HLR (Home Location Register) and many
Visitor Location Registers (VLR). The HLR for a given
network contains the network’s subscriber profiles and the
VLR stores the profiles of the users that are currently
roaming in its associated LA.
Current cellular systems use geographic-based
registration in which a registration occurs when a user
crosses the location area [17]. Reduction of the signaling
and database access traffic constitutes an important research
challenge. Several alternative strategies have been proposed
recently to improve the performance of the IS-41 location
management scheme [10,19,21,24].
In profile-based strategy, if the position of a user is
always known in advance, then no explicit registration is
necessary. Thus, the optimum location area is a single cell
which minimizes paging cost [17]. Stationary users on fixed
schedules exhibit this type of behaviour. It may be possible
to group people into classes depending upon the
predictability of their daily routine. In Ref. [4], an user
mobility pattern (UMP) scheme is introduced for location
update and paging in wireless systems where mobile
terminals maintain their history data in a database called
user mobility history. During a location update, a UMP is
derived from user mobility history and registered to the
network. Unless the mobile terminal detects that it has
moved out of the registered UMP, it does not perform any
other location update. The dynamic location management
techniques are also proposed in Refs. [1,8,11,14,23,27].
In Ref. [30], a call admission control and bandwidth
reservation schemes in wireless cellular networks have been
developed. In order to guarantee the handoff dropping
probability, the authors propose to statistically predict user
mobility based on the mobility history of users. The mobility
prediction scheme is motivated by computational learning
theory, which has shown that prediction is synonymous with
PCR
PCS
SS7
SCP
SSP
STP
RNC
TLDN
UMP
VLR
predictive channel reservation
personal communication system
signaling system no. 7
service control point
service switching point
signal transfer point
radio network controller
temporary location directory number
user mobility pattern
visitor location register
data compression. In order to utilize resource more
efficiently, the scheme predicts not only the cell to which
the mobile will handoff but also when the handoff will
occur. Based on the mobility prediction, bandwidth is
reserved to guarantee some target handoff dropping
probability.
Safa et al. [19] present a built-in memory model to
reduce the signaling and database access traffic due to users’
mobility in wireless networks. The model is based on the
IS-41 standard with the addition of a small built-in memory
to the MU and a pointer table to each LA. In other work,
Safa et al. [20] present a scheme for improving the basic
location strategy proposed in IS-41 standard. The scheme
essentially consists of adding to the current network
architecture a pointer table and a location data table.
Mobility management issues of multitier PCS networks
were presented in Refs. [2,6]. Hwang et al. [12] propose a
direction-based location update scheme with line paging for
reducing paging costs. This approach is based on a
conventional two-dimensional random walk model, where
the directions of the mobile units are assumed to be
independent and identically distributed. In Ref. [13],
an extended search is proposed for the location procedure.
In this case, the called mobile is paged first where it last was
in contact with the network, prior to search in the whole
location area. Complexity is the main problem with these
approaches, but the reductions in cost are significant.
In Ref. [22], the scheme called predictive channel
reservation (PCR) is proposed. Channel reservation packets
are sent to the adjacent cells based on the extrapolation of
MU’ mobility starting from a certain threshold of proximity
to the neighbouring cell. This results in a significant
reduction in handoff blocking rates with only small
increases in new calls’ blocking rates. The base station
(BS) uses the current location and movement direction of
each mobile and predicts its trajectory.
Aljadhai and Znati [3] deal with the question of assuring
the quality of service through the exact knowledge of the
trajectory followed by the mobile. It is crucial to have
a mechanism of prediction of the user’s trajectory.
The proposed algorithm integrates the admission and call
control to assure that QoS is going to be available during all
the communication. With the estimations of trajectories,
A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
arrival and departure times, the system is ready to support
the quality requirements. The required resources are
reserved just for the residence time in these cells. It favors
the deterministic users by updating dynamically the size of
the window of likely cells. With this approach, it is not
necessary to stock the user’s profile.
Xie and Akyildiz [29] introduce a novel distributed
and dynamic regional location management for Mobile
IP where the signaling burden is evenly distributed and
the regional network boundary is dynamically adjusted
according to the up-to-date mobility and traffic load
for each terminal. In their system, each user has its
own optimized system configuration which results in the
minimal signaling traffic. In order to determine
the signaling cost function, a new discrete analytical
model is developed which captures the mobility and
packet arrival pattern of a mobile terminal. This model
does not impose any restrictions on the shape and the
geographic location of subnets in the Internet. Given the
average total location update and packet delivery cost, an
iterative algorithm is then used to determine the optimal
regional network size.
Wu et al. [28] present a new analytic framework for
dynamic location management of PCS networks. Based on
the theory of hexagonal cellular patterns, a novel twodimensional Markov walk model with six states is proposed
to characterize the dynamic behavior of the intercell
movements for a mobile station.
Several mobility managements methods are proposed
in the literature. In most cases, they are analysed with
theorical assumptions about the user’s motion process
and call arrival process. These theoretical assumptions
can not cope with real behaviour. The classical method
that is used widely in current networks uses fixed and
network wide location areas. All subscribers have the
same cell borders to send location updating messages.
That leads to a signalling traffic burst on the signalling
channels of cells that are on the location area border,
whereas other cells have idel control channels.
The profile-based location strategy sets up a list to relate
a location probability to a time value. Each time an
incoming connecion request is coming, the system looks
in the list to find the most probable location and pages
the subscriber in order of descending probability. If the
user is not found in one of the locations recorded in the
list the whole service area has to be paged. This method
makes intensive use of the historical movement pattern
and reduces the signalling due to mobility management.
This paper proposes a profile-based strategy combined
with a built-in memory method to reduce the signaling cost
of location update by increasing the intelligence of the
location procedure. Section 2 describes the profile-based
strategy and presents some adaptation and implementation
details. Section 3 presents a performance evaluation.
Finally, Section 4 summarizes the main results.
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2. Profile-based approach
We present a profile-based strategy (PBS) with a built-in
memory method to reduce the location update by increasing
the intelligence of the location procedure. This proposed
strategy associates to each user a list of cells to determine
the user’s movement at each period of time. Users can be
divided into three categories according to the predictability
of their routines [17]. The system uses each class in a
different way to optimize resource consumption.
We distinguish three types of users: deterministic, quasideterministic and random. Deterministic users have a very
high probability of being in an area known in advance by the
system; quasi-deterministic users have a certain likelihood
to be where the system expects them to be, but they actually
could not be there; random users have positions at a given
moment which are unpredictable and the strategy proposed
here does not apply to them. The predictability of the two
first types of users can be used by the network to reduce the
number of location updates, at the expense of a somehow
higher call set up delay or cost in a purely profile-based
strategy. Because mobility and call arrival patterns vary
among users, it is highly desirable that location registration
and call delivery procedures can be adjusted dynamically on
a per-user basis.
The built-in memory model is based on Ref. [19] and the
IS-41 standard with the addition of a small built-in memory
to the MU and a pointer table to each LA (Fig. 1). In this
model, an MU’s anchor LA is defined as the LA for which
the MU’s location data has been updated at the network
database HLR. The MU built-in memory stores the address
of its anchor LA. The pointer table comprises two columns:
the MU identification number and the MU’s current LA.
The pointer table of an LA stores the current LA addresses
of the MUs which consider this particular LA as their anchor
LA. When the MU moves to a new LA, the new LA queries
the MU’s anchor LA to update the pointer table, i.e. to
create a pointer between the MU’s anchor LA and the new
LA. Consequently, no location update operation is
Fig. 1. Network architecture.
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A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
performed at the HLR. When the MU is called, its HLR is
queried to determine its anchor LA. If it no longer resides in
that LA, then the call is forwarded to the MU’s current LA
by traversing a single pointer.
When the MU is roaming outside of the set of frequent
areas for an intra-ILD move, the mobile’s anchor LA creates
a pointer to the current LA. When a mobile unit moves
towards a new ILD, the new location area becomes its new
anchor LA and the old one is deleted. While the MU is
roaming within its list of frequently visited cells, it only
registers when moving to a new ILD, to update the pointers
at distant ILD and avoid queries to HLR when a call is made
from the same ILD. The ILD can have the exact location
information or the list of cells. In the last case, a sequential
paging is performed.
The cost reduction depends on behavior of each class of
users. It can be expected when the user follows its expected
behavior (mostly within the list), location update cost is
reduced but paging delay can increase.
2.1. Location update procedure
When an MU moves to a new LA, a location update
procedure is performed. Different procedures are followed
depending on whether the move is intra-ILD or inter-ILD, as
well as depending on the situation in relation to the list.
Each user is assigned a set of likely location areas.
This information is a function of his past history and the
network updates it with a certain regularity. The number of
areas to keep in the list is another design factor. The list is
stored at the MU’s side in order for the mobile to know
when it leaves or enters the list to perform a location
update procedure, and at the network’s side. It can be stored
at the HLR or the ILD, as we explain later in this paper.
Let’s call {Ai }i¼1;;k ; the set of areas, A1 is the most likely
area, A2 is the second, etc. The areas are ordered in
decreasing probability.
We define local list as a set of location areas Ai which are
all under the coverage of one local signal transfer point
(LSTP). The list will be stored at the ILD associated to the
LSTP. And we define global list as a set in which there are
location areas that belong to different LSTPs. In this case, the
list is stored at the HLR. The LA where the called mobile is
roaming is stored at the ILD, and the HLR stores the list of
ILDs. In this case, the list for the user is not {Ai }i¼1;;k ; but
{Ii }i¼1;;k ; where Ii is LSTP area i where the user is likely to be.
The distribution of the location information is as follows:
† If the user is within its associated local list, the HLR
knows the current ILD. The current ILD has the local list
and the distant pointers at others ILDs have the current
ILD.
† If the user is within its associated global list, the HLR
knows the global list. The current ILD has the current
location area and the distant pointers at others ILDs have
the current ILD.
† If the user is outside its associated list, the HLR has
its current ILD. The current ILD has its anchor LA
and the anchor LA stores a pointer to the current LA.
MU’s anchor LA is defined as the LA for which the
MU’s location data has been updated at the network
database HLR.
This distribution determines the algorithm proposed in
this work. We assume that the list is stored at two places:
† The terminal itself, in a built-in memory to allow it to
know when it is entering or exiting the list (‘personalized LA’). However, the system must update the list
when the user exhibits new habits and must transmit it
to the MU.
† The switch that will conduct the search, this is the
LSTP if the list is local, or the HLR if the list is global.
A.
When the MU’s move is intra-ILD, we have to
consider the following situations:
I
Move between two zones inside the list:
1. If the list is local, no update is performed.
If it is global, the current LA is registered at
the ILD.
II. Move between two zones outside the list. We
assume that the MU has its anchor LA registered
in a built-in memory and that it is transferred to
the ILD and to the location data tables (Fig. 2);
1. The MU moves into a new LA and sends a
location update registration message to the
new RNC/VLR;
2. The RNC of the new LA registers the MU
and its anchor LA and sends a cancellation
message to the old RNC;
3. The new LA sends also a message to the
anchor LA in order to create a pointer to it.
No update is performed at the current ILD;
4. and 5.
The new LA receives an acknowledgment from
the old RNC and the anchor LA.
III. Move to exit the list
1. The RNC of the new LA registers the MU
with the new VLR as the new anchor LA
at the ILD. If the list is local, it is
deactivated;
Fig. 2. Updating procedure for an intra-ILD move outside the list.
A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
2.
B.
The ILD registers the new MU and sends a
notification message to the HLR if the list is
global;
3. The HLR sends an acknowledgment message to the current ILD and updates its table
(registering the new ILD);
4. The current ILD sends an acknowledgment
message to the current RNC/VLR.
IV. Move to enter the list:
1. The RNC of the new LA sends a registration
notification message to the current ILD;
2. The current ILD registers the current LA for
the MU if the list is global, or activates the
local list;
3. The ILD deletes the profile of the MU and
sends a cancellation message to the old
anchor LA;
4. The old anchor VLR sends a cancellation
message to the previous LA;
5. The previous LA deletes the profile of the
MU and sends an acknowledgment to the
anchor LA;
6. The old anchor VLR deletes the profile of
the user and sends an acknowledgment to the
ILD;
7. If the list is global, the ILD sends a message
to the HLR to activate the global list and
receives an acknowledgment;
8. The ILD sends an acknowledgment to the
current RNC/VLR.
When the MU’s move is inter-ILD, we have to
consider the following situations:
I. Move within the list: If this move exists, the list
can only be local, given that it contains zones
belonging to more than one LSTP. The steps to
follow are:
1. The MU moves into a new LA and sends a
registration message to the new RNC/VLR;
2. The RNC of the new LA sends a message to
register the zone;
3. The new ILD registers the zone and sends a
message to delete the profile at the old ILD;
4. The old ILD deletes the MU’s profile and
sends an acknowledgment to the new ILD;
5. The new ILD updates the distant pointers
and receives the acknowledgments;
6. The new ILD sends an acknowledgment to
the new RNC.
II. Move outside of the list:
1. The MU moves to a new LA and sends a
location update message to the RNC/VLR of
the new LA;
2. The RNC of the new LA registers the MU
and its old anchor LA and it determines the
new anchor LA. Then it sends a registration
notification message to the current ILD;
3.
1513
The current ILD registers the MU as
belonging to its coverage area and sends a
location update message to the HLR and to
the old ILD;
4. The old ILD sends a cancellation message to
the old anchor LA;
5. The old anchor VLR sends a cancellation
message to the previous LA;
6. The previous LA deletes the profile of the
MU and sends an acknowledgment to the
anchor LA;
7. The old anchor VLR deletes the profile of
the user and sends an acknowledgment to the
old ILD;
8. The old ILD and the HLR send an acknowledgment to the current ILD and update their
information;
9. If there are distant pointers:
i. The current ILD sends a location
update message to all the distant ILDs
that point to the MU;
ii. the distant ILDs update their pointers
and send an acknowledgment;
10. The current ILD sends an acknowledgment
to the current RNC/VLR.
III. Move to exit the list:
1. The MU moves to a new LA and sends a
location update message to the RNC/VLR of
the new LA;
2. The RNC of the new LA registers the MU
and sends a registration notification message
to the current ILD;
3. The current ILD registers the profile of the
MU, deletes the old LA or deactivate the
local list and sends a location update
message to the HLR;
4. The HLR sends an acknowledgment to the
current ILD and updates its table;
5. If there are distant pointers:
i. The current ILD sends a location
update message to all the distant ILDs
that point to the MU;
ii. the distant ILDs update their pointers
and send an acknowledgment;
6. The current ILD sends an acknowledgment
to the current RNC/VLR.
IV. Move to enter the list:
1. The MU moves to a new LA and sends a
location update message to the RNC/VLR of
the new LA;
2. The RNC of the new LA knows that it is the
point of entry in the local list. Then it sends
a registration notification message to the
current ILD;
3. The local ILD registers the LA of the MU or
activates the local list, sends a location
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A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
4.
5.
6.
7.
8.
9.
update message to the HLR (to register the
list) and to the old ILD (to delete the profile
of the MU);
The old ILD sends a cancellation message to
the old anchor LA;
The old anchor VLR sends a cancellation
message to the previous LA;
The previous LA deletes the profile of the
MU and sends an acknowledgment message
to the anchor VLR;
The old VLR deletes the user’s profile and
sends an acknowledgment to the old ILD;
The HLR updates the list and the old ILD,
then sends an acknowledgment to the current
ILD;
If there are distant pointers:
i. The current ILD sends a cancellation
message or an update message if the list
is local to the distant ILDs that store a
pointer;
ii. The distant ILDs update their pointers
and send an acknowledge message;
3.
The ILD forwards the message to the anchor RNC
of the called MU;
4. The anchor RNC assigns a TLDN (temporary
location directory number) to the called mobile
and forwards it to the calling RNC;
5. The calling RNC sets up a connection to the called
RNC using this TLDN.
Scenario 2
addresses the case where the called MU and the calling MU
are under an area covered by the same ILD but the called
MU is not roaming in its anchor LA (Fig. 3). A pointer has
to be crossed to reach the current LA which assigns a TLDN
to the call and transmits it to the calling RNC.
Scenario 3
addresses the case where there is a pointer at the calling ILD
towards the called unit and this one is roaming in its anchor
LA. The called MU is out of its associated list. The distant
pointer is crossed to reach the ILD that has the information
on the MU. This one is roaming in its anchor LA.
Scenario 4
addresses the case where the called mobile is outside the list
but it has a distant pointer at the location data table of the
calling ILD. The called mobile is not roaming in its anchor LA.
The current ILD updates the profile and sends an
acknowledgment to the current RNC.
3. Performance evaluation
2.2. Location search procedure
This procedure involves the determination of the current
LAs serving the cell where the MU is roaming We present
four possible scenarios according to the proposed location
management strategy:
Scenario 1
addresses the case where the called MU and the calling MU
are under an area covered by the same ILD and the called
MU is roaming in its anchor LA:
1. A call is initiated to an MU and forwarded to the
RNC of the calling unit;
2. The RNC sends a location request message to the
ILD that determines the anchor LA of the called
mobile;
The performance of location management schemes is
higly dependant on user’ mobility and incoming calls
characteristics [19]. All users can be classified according to
their call-to-mobility ratio (CMR). We assume that the link
costs and database access costs are defined, respectively, by
the delays of message transmission and updating. Besides,
we assume that an RNC/VLR covers only one location area.
The total cost of our strategy is the sum of the location
search cost, location update cost and list maintenance cost.
3.1. Analytical model
The cost of list update and maintenance must be
considered in order to compare the proposed strategy with
other existing strategies. We consider the following
Fig. 3. Location search procedure (Scenario 2).
A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
maintenance costs: (a) Processing of the billing information
of the user to build the list; (b) Transfer of this list to the
place where it will be stored (ILD for the local lists, HLR for
the global list) and (c) Notification of the modifications in
the list to the mobile terminal.
We assume that the link costs and database access costs
are defined by the delays of message transmission and
updating or query, respectively. For each mobile unit, we
define the following quantities:
l:
m:
average number of calls to a target MU per time unit;
average number of times the user changes LA per time
unit;
v: probability that a pointer exists from the calling ILD to
the called ILD;
q: probability of an intra LSTP move (old and new LAs
are under the same ILD);
r: probability that the called MU is in its anchor LA;
t: probability that the calling and the called mobiles are
connected to the same LSTP;
m: probability that a user’s associated list consists of
location areas which are all under the same LSTP;
z: probability that a user is in one of the zones of its
associated list.
The size of the list has to be optimized for each particular
user, depending on his mobility patterns, in order to
minimize the total cost.
For analysis purposes, we consider the following costs:
Uintra : cost of a location update operation when the MU’s
move is intra-LSTP;
Uinter : cost of a location update operation when the MU’s
move is inter-LSTP;
Sx : cost of the location search operation using Scenario x;
UT : total cost of the location update procedure;
ST : total cost of the location search procedure;
CG : total cost per time unit for the location search and
location update.
Table 1 summarizes the location update scenarios
Table 1
Location update scenarios
Move
From
To
ILD
Uintra1
Uintra2
Uintra3
Uintra4
Uinter1
Inside the list
Outside the list
Inside the list
Outside the list
Inside the list
Inside the list
Outside the list
Outside the list
Inside the list
Inside the list
Intra-ILD
Intra-ILD
Intra-ILD
Intra-ILD
Inter-ILD
Uinter2
Uinter3
Uinter4
Outside the list
Inside the list
Outside the list
Outside the list
Outside the list
Inside the list
Inter-ILD
Inter-ILD
Inter-ILD
It has to be a
global list
1515
It can be seen that the following relations hold between
these quantities:
UT ¼ qUintra þ ð1 2 qÞUinter
Uintra ¼ qintra1 Uintra1 þ qintra2 Uintra2 þ qintra3 Uintra3
þ qintra4 Uintra4
Uintra ¼ qinter1 Uinter1 þ qinter2 Uinter2 þ qinter3 Uinter3
þ qinter4 Uinter4
where:
qintra1
qintra2
qintra3
qintra4
qinter1
qinter2
qinter3
qinter4
¼ Pr{stay within the list provided that the MU has
made an intra-ILD move} ¼ z2
¼ Pr{stay out of the list provided that the MU has
made an intra-ILD move} ¼ ð1 2 zÞ2
¼ Pr{move out of the list provided that the MU
has made an intra-ILD movement} ¼ zð1 2 zÞ
¼ Pr{enter the list provided that the MU has made
an intra-ILD movement} ¼ zð1 2 zÞ
¼ Pr{stay within the list provided that the MU has
made an inter-ILD move} ¼ z2
¼ Pr{stay out of the list provided that the MU has
made an intra-ILD move} ¼ ð1 2 zÞ2
¼ Pr{move out of the list provided that the
MU has made an intra-ILD movement} ¼
zð1 2 zÞ
¼ Pr{enter the list provided that the MU has
made an intra-ILD movement} ¼ zð1 2 zÞ
The total cost for the location search procedure is:
ST ¼ tð1 2 zÞðrS1 þ ð1 2 rÞS2 Þ þ ð1 2 tÞðvðð1 2 zÞ
ðrS3 þ ð1 2 rÞS4 ÞÞÞ
The total cost per time unit for location search and
location update is given by:
CG ¼ mUT þ lST
3.1.1. List maintenance
The goal is to achieve a list with an information as much
accurate as possible. This is a condition for the proposed
scheme to perform better than other standards. The system
should have two kinds of information, as stated in Ref. [26]:
(a) Long-term information, stored in a relatively static list,
obtained after the first month of subscription to the service,
for instance. This list would be updated when new
patterns of movements are detected by the system
(or customized by the user) and transmitted to the built-in
memory at the handset; (b) Short-term information:
changes depending on the recent behavior of the user
(last hours or days).
We consider the following maintenance costs:
† Processing of the billing information of the user to build
the list.
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A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
† Transfer of this list to the place where it will be stored
(ILD for the local lists, HLR for the global list).
† Notification of the modifications in the list to the mobile
terminal.
The location update costs when the move is intra-ILD
are:
Uintra1 ¼ ð1 2 mÞ £ ðA1 þ Cb Þ
ð3:1Þ
We call CE1 ðkÞ the long-term cost and CE2 ðkÞ the shortterm cost. Both depend on the size k of the list.
Uintra2 ¼ 2Cv þ 8A1 þ 4L
ð3:2Þ
lO
Uintra3 ¼ Cv þ Cb þ 2A1 þ L þ ðCh þ 2Ar þ 2R þ 2D
is the number of calls originated by the mobile per
time unit;
is the number of received calls per time unit;
is the observation period to recalculate the
probabilities.
l
T
We suppose that the mobile terminal visited nj times the
location area Aj during the observation period.
The proposed list update algorithm is as follows:
† The long-term list is updated when the systems estimates
that a new pattern of movement exists. If the system
changes the “static” list every M1 location update
operations, the normalized cost for the long term list is:
SE1 ¼ ðlO þ lÞ £ CE1 ðkÞ=M1
† The short-term list is based on users’ recent events,
and may be re-ordered dynamically. If the mobile
is located frequently in a given area, this is placed
first in the list until the next update. This re-evaluation
is done every M2 location update operations, so the
cost is:
SE2 ¼ ðlO þ lÞ £ CE2 ðkÞ=M2
þ LÞ £ ð1 2 mÞ
ð3:3Þ
Uintra4 ¼ 8A1 þ Cb þ 4L þ 2Cv þ ðCh þ L
þ 2ðD þ R þ Ar ÞÞ £ ð1 2 mÞ
ð3:4Þ
The estimated location update cost for an intra-ILD move
can be deduced from Eqs. (3.1) –(3.4):
Uintra ¼ z2 Uintra1 þ ð1 2 zÞ2 Uintra2 þ zð1 2 zÞUintra3
þ zð1 2 zÞUintra4 ¼ z2 ½ð1 2 mÞðA1 þ Cb Þ
þ ð1 2 zÞ2 ð2Cv þ 8A1 þ 4LÞ
þ zð1 2 zÞ½3Cv þ 10A1 þ 2Cb þ 5L
þ ð1 2 mÞð4ðD þ R þ Ar Þ þ 2Ch þ 2L
ð3:5Þ
Let Uk be the cost for updating a distant pointer (located
at another distant ILD), from another ILD, including the
acknowledgment:
Uk ¼ k £ ð2 £ ðL þ 2 £ D þ RÞ þ Cb Þ
ð3:6Þ
The total maintenance cost of the list is:
SE ¼ SE1 þ SE2
and the total cost for the PBS strategy is:
For inter-ILD moves, the costs are expressed:
Uinter1 ¼ 2A1 þ 3L þ 2R þ 4D þ 2Cb þ
The costs for traversing various network elements are:
A1 :
D:
Ar :
L:
R:
Cv :
Cb :
Ch :
Cost of transmitting a message on A-link between
service switching point (SSP) and LSTP;
Cost of transmitting a message on D-link between
LSTP and RSTP;
Cost of transmitting a message on A-link between
RSTP and service control point (SCP);
Cost of processing and routing a message by LSTP;
Cost of processing and routing a message by
RSTP;
Cost of a database update or query at the VLR;
Cost of a database update or query at the ILD;
Cost of a database update or query at the HLR.
Uk
ð3:7Þ
K
CPBS ¼ CG þ SE
3.2. Cost analysis
X
Uinter2 ¼ 3Cv þ 8A1 þ 2Cb þ 7L þ 6D þ 4R þ 2Ar
X
þ Ch þ Uk
ð3:8Þ
k
Uinter3 ¼ Cv þ 2A1 þ Cb þ 3L þ 4D þ 3R þ 2Ar
X
þ Ch þ Uk
ð3:9Þ
k
Uinter4 ¼ 8A1 þ 2Cb þ 7L þ 6D þ 4R þ 2Ar þ 2Cv
X
þ Ch þ Uk
k
ð3:10Þ
A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
From Eqs. (3.7) – (3.10), the cost for an inter-ILD
location update operation is given by:
The equation for the global cost per time unit is the
following:
CG ¼ mUT þ lST
Uinter ¼z2 Uinter1 þ ð1 2 zÞ2 Uinter2 þ zð1 2 zÞUinter3
þ zð1 2 zÞUinter4
X ¼ z2 2A1 þ 3L þ 2R þ 4D þ 2Cb þ Uk
1517
ð3:18Þ
3.3. Cost comparison
k
þ ð1 2 zÞ 3Cv þ 8A1 þ 2Cb þ 7L þ 6D
X þ 4R þ 2Ar þ Ch þ Uk þ zð1 2 zÞ
Pollini and Chih-Lin [17] proposed a strategy, which we
call Pollini, for location management, and Safa et al. [19]
proposed another strategy, which we call Safa, for solving
the same problem. To be able to compare our strategy to
IS-41, Pollini and Safa, we have to calculate the costs for the
location update and location search operations.
2
k
þ Cv þ 2A1 þ Cb þ 3L þ 4D þ 3R
X þ 2Ar þ Ch þ Uk þ zð1 2 zÞ
3.3.1. Comparison with IS-41
Uis41 : Cost for a location update operation
Sis41 :
Cost for location search operation
Cis41 : Total cost per time unit for location search and
location update operations.
k
þ 8A1 þ 2Cb þ 7L þ 6D þ 4R þ 2Ar
X þ 2Cv þ Ch þ Uk
ð3:11Þ
k
where
Scenarios 1 and 2 deal with the situation in which the
called and calling mobiles are under the same ILD.
In Scenario 1 ðS1 Þ; the called mobile is roaming under its
anchor LA:
Uis41 ¼ 4ðA1 þ L þ Ar þ D þ RÞ þ 2Cv þ Ch
ð3:19Þ
Sis41 ¼ 4ðA1 þ L þ Ar þ D þ RÞ þ Cv þ Ch
ð3:20Þ
and
Cis41 ¼ mUis41 þ lSis41
S1 ¼ 4A1 þ 2L þ Cv þ Cb
ð3:12Þ
In Scenario 2 ðS2 Þ; the called mobile is not found in its
anchor LA. The cost of Scenario 2 is equivalent to the cost
of Scenario 1 plus the cost of traversing a pointer, this is
S2 ¼ S1 þ ð2A1 þ LÞ
ð3:13Þ
In scenarios 3 and 4 (S3 ; S4 ), a distant pointer exists at the
calling ILD towards the current ILD of the called mobile.
The difference is, once again, the cost of traversing a pointer
at the anchor LA:
S3 ¼ 4A1 þ 4L þ 2Cb þ Cv þ 4D þ 2R
ð3:14Þ
S4 ¼ S3 þ ð2A1 þ LÞ
ð3:15Þ
The final expression for the cost of a location update
operation is:
UT ¼ qUintra þ ð1 2 qÞUinter
ð3:16Þ
Using Eqs. (3.12) – (3.16), the location search cost can be
written as:
ST ¼ tðð1 2 zÞðrS1 þ ð1 2 rÞS2 ÞÞ þ ð1 2 tÞðvðð1 2 zÞ
ðrS3 þ ð1 2 rÞS4 ÞÞÞ
ð3:17Þ
ð3:21Þ
We define the relative cost of the proposed scheme as the
ratio of the total cost per time unit to that of the IS-41
scheme, as a function of the call-to-mobility ratio (CMR)
CG
Ut þ CMR £ St
¼
CIS41
Uis41 þ CMR £ Sis41
ð3:22Þ
where CMR ¼ l=m
3.3.2. Comparison with Pollini
To compare with Pollini strategy, we have to consider
that a location update operation is performed only when the
user exits his associated list. So, if z denotes the probability
of being inside the list, the average cost for a location update
operation according to the scheme presented in Ref. [17],
that we call Upollini ; is the following:
Upollini ¼ ð1 2 zÞð4ðA1 þ L þ Ar þ D þ RÞ
þ 2Cv þ Ch Þ
ð3:23Þ
In Ref. [17], an expression for the paging cost is deduced:
CPI ¼ CP ½1 þ vf ðE½K21 Þ
ð3:24Þ
where CP is the cost of paging when the user registers
explicitly, as in IS-41. It is also the cost of paging when the
call is completed in the first location area of the list. vf is the
cost of each paging attempt; E[K ] is the expected number of
location areas in which the system pages the user. It is clear
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A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
that the probability of incurring CPI is z (i.e. the probability
of the user being inside the list), and the probability of
incurring CP is (1 2 z). Thus, the paging cost per location
search operation is as follows, using Eq. (9) in Ref. [17]:
Spollini ¼ zCPI þð12zÞCP ¼ CP ½1þzvf ðE½K21Þ
ð3:25Þ
We assume in this paper, for comparison purposes with
the proposed scheme, CP to be the paging cost to find an user
in a single location area in the IS-41 standard, without any
addition of intermediate tables or databases and CP ¼ LþA1 :
In order to obtain the location search cost, we have to add
the fixed cost to the HLR that will conduct the search, and
the cost to establish the TLDN communication channel after
the user has been found. The subsequent paging attempts
should not include these fixed costs. The final expression of
the paging cost per location search operation is:
Spollini ¼ ð4ðA1 þLþAr þDþRÞþCv þCh Þ
ð1þzvf ðE½K21ÞÞ
ð3:26Þ
The global cost for the Pollini strategy with the metrics
used in this paper is:
Cpollini ¼ mUpollini þ lSpollini
ð3:27Þ
3.3.3. Comparison with Safa
Let:
Usafa :
Ssafa :
Csafa :
Cost for a location update operation
Cost for location search operation
Total cost per time unit for location search and
location update operations
The following costs are calculated in Safa et al. [19]:
Usafa ¼ 8A1 þ 4L þ 2Cv þ ð1 2 qÞ
2L þ 4Ar þ 4D þ 4R þ Ch þ
X
!
Uk
ð3:28Þ
k
Ssafa ¼ 6A1 þ 5L þ 4Ar þ 4D þ 4R þ Cv þ Ch
2 pð2tðL þ R þ 2DÞ þ 4Ar þ 2R þ Ch Þ
2 rð2A1 þ LÞ
ð3:29Þ
And the total cost per time unit is
Csafa ¼ mUis41 þ lSis41
ð3:30Þ
model, which captures all relevant aspects of such strategy
in a concise way.
4.1. Comparison with others strategies
In this section, we present the numerical results of the
comparison between our solution and IS-41, Pollini and
Safa, in the cases of the deterministic and quasi-deterministic users. For these purposes, we will vary those
parameters that are more linked to the design of the
profile-based strategy:
† z; the probability of a user being roaming within his
associated list. Its value depends on the degree of
predictability of the customer’s behavior. Deterministic
users have a value of z which is close to 1.
Quasi-deterministic users range between 0.5 and 0.8.
Random users cannot be assigned a list, as we have
already seen. Their values for z would fall below 0.5.
† m; the probability of a user having a local list. It depends
on the geographical characteristics of the network and on
the mobility of the customer. When the size of an LSTP
is large because of the scattered location of mobile users,
we can estimate that the probability of a user moving
under the coverage of such an area is high; on the other
hand, if the LSTP covers a small area, the user is likely to
have his probable location areas among several LSTPs.
The behavior of the user also has an influence depending
on the mobility habits of the customer. The more often
and away he moves, the less likely he is to have a local
list. Table 2 illustrates these relations.
† n; the average number of tries when paging a user within
its list. This depends on the probability distribution of the
user and the size of the list. Uniform, linear and
exponential models are discussed in Ref. [3]. For each
of them, an average value for n is given as a function of k;
the size of the list in number of location areas.
Considering that a good number is 5 location areas in
the list for a user, and a linear-distribution, we find an
average value of 2. This is in fact a pessimistic
approximation, since we are assuming that the first
paging is never fruitful, but at the same time we propose
in this work an algorithm to take advantage of the
short-term events to improve the accuracy of the list.
† vf is the fraction of cost we incur into when paging after
the first time. We will consider always the most
pessimistic case, i.e. vf ¼ 1:
Table 2
Values for m according to users mobility and size of LSTP
Mobility of the user
4. Numerical results
This section presents a comparison with others
strategies for solving the same problem. Then, we present
the performance evaluation of PBS as well as a detailed
Size of LSTP
Small
Large
High
Low
m , 0:4
0:4 # m , 0:7
0:4 # m , 0:7
m $ 0:7
A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
† We assume the following simulation parameters:
Cv ¼ 3; Ch ¼ 6; Cb ¼ 1; R ¼ 1; L ¼ 1:
4.1.1. Quasi-deterministic users (z ¼ 0.8)
For low values of CMR (high mobility), location update
cost dominates over location search cost. The proposed
strategy reports to HLR only when an inter-LSTP move is
performed and the list is local; if the list is global, the HLR
has to be informed when the user enters his ‘personal’ LA.
Thus, the cost reduction is greater for higher values of m
(local list). This reduction can reach 30% of the IS-41.
When CMR is high, the cost of location search dominates
and the saving in location update diminishes. When m
increases, so does the likelihood of a local search that does
not query the HLR but the ILD, resulting thus in a lower cost
in this scenario. In every case, a great amount of reduction is
obtained for the decrease in queries to the HLR when the
profile of the called unit is stored locally (decentralized
architecture). Fig. 4a shows the evolution of the relative cost
for the IS-41 scenario as a function of the CMR.
Due to the fact that Safa exploits the ‘locality’ of many
calls, the cost reduction achieved by our algorithm
comparatively to Safa is not as high as the reduction achieved
comparatively to IS-41. For low values of the CMR, the cost
reduction reaches 50% of Safa algorithm, and for high values
of the CMR the cost reduction is 85%. In this case,
when location search procedure dominates, the saving in
1519
cost from the decentralization is common to the two schemes.
In fact, if the list is local, the sequential paging degrades the
savings in costs (Fig. 4b). Cost reduction compared to Pollini
algorithm can reach 60% for high values of the CMR.
The reduction degrades for low values of CMR and if the list
is global. Whenever location search cost dominates
(CMR greater than 1), the proposed algorithm performs
better than Pollini. Actually, it has to be noted that Pollini
only considers local lists (Fig. 4c).
We can conclude that, with the presented cost structure,
PBS is less costly than IS-41, Safa and Pollini for users with
a high CMR, and that PBS performs significantly better than
IS-41, Safa and Pollini for CMRs lower than 5.
Now we study the variation of n, the number of tries in
the sequential paging that takes place whenever the user is
roaming in its associated list and receives a call.
As expected, when n increases the location search cost is
higher and the relative cost increases too. This effect is more
relevant for high values of CMR, when the location search
cost dominates. For lower CMRs, the savings are more
important for any value of n; but the curves diverge as
CMR increases. In comparison to IS-41, even with n ¼ 4
the reduction is 40%, and goes to 50%.
4.1.2. Deterministic users (z ¼ 0.99)
For deterministic users, the improvement obtained by
PBS is better than the case of quasi deterministic users.
Fig. 4. Comparison in the case of quasi-deterministic users when m varies. (a) Comparison between PBS and IS-41. (b) Comparison between PBS and Safa.
(c) Comparison between PBS and Pollini.
1520
A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
Fig. 5. Comparison in the case of deterministic users when m varies. (a) Comparison between PBS and IS-41. (b) Comparison between PBS and Safa.
(c) Comparison between PBS and Pollini.
Fig. 5 shows the comparison between PBS, IS-41, Safa, and
Pollini when m ¼ varies. In terms of costs, PBS provides
better results than IS-41, Safa and Pollini, and the average
improvement rates are 55, 30 and 48%.
4.1.3. Random users (z ¼ 0.5)
The reduction in cost is more important if the list is local
when m ¼ 1: In terms of costs, PBS provides better results
than IS-41, Safa and Pollini when m varies, and the average
improvement rates are 30, 5 and 40% respectively (Fig. 6).
In the case m ¼ 0:5 and n varies, PBS still provides better
results than Pollini and IS-41 and the average improvement
rates are 30 and 25%, respectively. The cost is the same as in
Safa, no significant improvement is achieved.
4.2. Simulation results
To assess the accuracy of the presented analytical model,
this section presents a performance evaluation of PBS as
well as a detailed model, which captures all relevant
aspects of our approach in a concise way. The simulation
models both the call delivery and mobility behavior of
users offering the ability to consider different service types
and different MT groups over a range of cell-layout
scenarios. Three different cell layout scenarios have been
investigated. The first assumes macrocells only,
the second medium-size microcells while the third smallsize microcells [16].
In our experiments, 500 MTs are simulated and we
generated 1000 samples for each cell layout scenarios
assuming normal distributions using the statistics
estimated from the real data. We run simulations for the
probability of a user being roaming within his associated
list from 0.5 to 0.99. We assume that a user is within that
area covered by its list at least half of time. In practice, it
would not be performer to have a list of likely positions in
which a user is not found at least half of the time. In the
case of users whose position at a given moment is
unpredictable, and the past knowledge of their location
cannot predict their future location our strategy is not
applicable.
Finally, when the size of an LSTP is large because of
the scattered location of mobile users, we can estimate
that the probability of a user moving under the coverage
of such an area is high; on the other hand, if the LSTP
covers a small area, the user is likely to have his
probable location areas among several LSTPs.
Voice model: A stochastic process can describe the voice
traffic model, with arrival times corresponding to the
beginning times of sessions. The call arrival process is
assumed to be Poissonian while the call duration is
exponentially distributed [9,18]. The call arrival rate
(calls/MT/hour) is 3.
A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
1521
Fig. 6. Comparison in the case of random users when m varies. (a) Comparison between PBS and IS-41. (b) Comparison between PBS and Safa. (c) Comparison
between PBS and Pollini.
Each session describes a complete phone call and
contains ON-OFF period. ON periods occur when voice is
generated whereas no voice is generated during OFF
periods. Fig. 7 illustrates call sessions. The ON period
distribution following an exponential law with mean 352 ms
and the OFF period also following an exponential law with
mean 650 ms. Within the ON period, voice arrive at a fixed
rate 1=t: Therefore, 1=t is the sampling rate. The call mean
time duration is 5 min. The exponential distribution has
the following probability density function:
fVoIP ðxÞ ¼ l·e
2 lx
;x $ 0
where l ¼ ð300xÞ21 ; x represents the time.
Mobility model: In this research the MTs are assumed
to be moving at the same velocity V; and their direction
movement is uniformly distributed over [0,2p].
The average number of LA crossing, C, of a zone of
perimeter P is given by C ¼ rPV=p; where r is the users
density in the LA. To perform this evaluation, we define
three classes of users depending on their velocity and the
cell radius that they are roaming in. These categories:
(a) users located in buildings, (b) pedestrians with a
speed following the Gaussian distribution with mean
value 5 km/h and variance 30%, (c) car passengers with
a speed following the Gaussian distribution with mean
value 20 km/h and variance 30%, and (c) car passengers
Fig. 7. Voice model.
with a speed following the Gaussian distribution
with mean value 100 km/h and variance 30% (Table 3)
[16,21]. Finally, we assume that a LA is a square of
5 £ 5 cells and we consider a network that contains 20
LAs. We set the density r to 1000 users per cell.
In Fig. 8a the PBS success probability is presented for the
varying number of LAs in the a user list. When a call arrive
for a mobile, it is paged sequentially in each location
within the list. If the MU is under an area covered by the list,
Table 3
Classes of users
Velocity (km/h)
Cell radius (km)
Class 1
Class 2
Class 3
Class 4
0.1
1
5
2
20
5
100
10
1522
A. Quintero et al. / Computer Communications 27 (2004) 1509–1523
Fig. 8. PBS performance. (a) Success probability for varying number of Las in the list and varying the user profile. (b) Normalized cost for varying number of
Las in the list and varying the user profile.
it is a success. Fig. 8b shows the normalized cost of the our
strategy for the varying number of LAs in the a user list.
a movement pattern which is repeated at the same time
of the day. Table 4 summarizes the average improvement
rates. These values are calculated as: average-improvement-rate ¼ (100- cost ratio) in percentage.
5. Conclusions
In this paper, we proposed a profile-based strategy with
a built-in memory method to reduce the signaling cost
location updates by increasing the intelligence of the
location procedures in mobile systems. The proposed
strategy associates to each user a list of cells to predict the
users’ movement at each period of time. The list is ranked
from to the least likely place where a user is found. When
a call arrives for a mobile, it is paged sequentially in each
location within the list. When a user moves between
location areas in the list, no location update is required.
The results showed that the profile-based strategy has
several advantages over the IS-41, Safa’s solution and
Pollini’s solution, i.e. if the position of a user is always
known in advance, then no explicite registration is
necesssary. Thus, the optimun location area is the one
that minimzes paging cost, a single cell. Deterministic
users exhibit this typs of behavior. We carried out
analytical, statistical and simulation based experiments
to evaluate the performance of our strategy. The results
obtained confirm the efficiency and the effectiveness of
PBS compared with IS-41 and other strategies well known
in the literature. The performance in comparison with the
classical is better for all possible user movement statistics.
This strategy is useful when the subscriber has
Table 4
The average improvement rates
IS-41
Strategy Safa
Strategy Pollini
Deterministic
users (%)
Quasi-deterministic
users (%)
Random
users (%)
65
35
60
55
30
48
30
5
40
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