Cells in a network are grouped into Location Areas (LAs). Users can move within these
LAs, updating their location with the network based upon some predefined standard.
When a user receives a call, the network must page cells within the LA (also referred to
as polling) to find that user as quickly as possible.
This creates the dynamics behind much of Location Management (LM), and many of the reports and theories discussed within this paper. The network can require more frequent Location Updates (LUs), in order to reduce polling costs, but only by incurring increased time and energy expenditures from all the updates. Conversely, the network could only require rare LUs, storing less information about users to reduce computational overhead, but at a higher polling cost. Additionally, LAs themselves can be optimized in order to create regions that require less handoff and quicker locating of users. The goal of LM is to find a proper balance between all of these important considerations.
This paper discusses LM schemes, from past and current static LM methods, to current progress and advances in dynamic LM. Much emphasis is placed on the many theoretical implementations of dynamic LM. Also discussed are future LM developments and the Enhanced 911 (E911) service, a real-world example demonstrating applications of LM and its importance.
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Presently, most LM schemes are static, where LUs occur on either periodic intervals or
upon every cell change. However, static LAs incur great costs with the ping-pong effect.
When users repetitively move between two or more LAs, updates are continuously
performed unnecessarily. In these static LAs, cells are constant in size, uniform, and
identical for each user.The current static LM standards are IS-41 and GSM MAP, which
use a hierarchical database structure and are described in 2.3.
Three simple static Location Update schemes exist in static LM, being always-update, never-update, and static interval-based. The third of these is the most commonly used in practical static LM systems.
One scheme involves the user updating its location upon every inter-cell movement, and is named always-update. This will incur significant energy and computational costs to both the network and the user, especially to the most mobile users. This may be particularly wasteful, as if a user makes frequent, quick movements within an LA, beginning and ending at the same location, many LUs will occur that might be unnecessary, especially if few or no calls are incoming. However, the network will always be able to quickly locate a user upon an incoming call, and extensive paging will not be necessary.
The converse method would be to never require the user to inform the network of intercell movements, only updating on LA changes, and is named never-update. In this scheme, resources are saved as constant updates are not required, but paging costs rise substantially. This occurs as every cell within the user’s LA may need to be checked during paging due to the lack of information, which causes excessive overhead for users with a high incoming call frequency.
These two schemes are generally unused in real-world systems, but help to provide an illustration to network administrators as to the costs of LM, the problems that occur when thoughtless LU methods are used, and a baseline that every newly developed LU scheme must show improvements over.
The final static LM technique discussed requires each user within the network to update at static, uniform intervals. This attempts to provide a balance between the extremes of the previous schemes, as the network will neither be overwhelmed with LUs nor wholly unaware of users’ locations. However, users with rapid rates of movement may move into new LAs between updates, which causes locating that user to be very difficult. Conversely, an inactive user will not move at all, but will still regularly be sending unneeded LUs. [Cowling04] While LA optimization could mitigate these problems, as discussed in the following section, such improvements are impossible under static LM schemes where LAs are uniform and constant.
Location Areas in static LM are themselves static as well. They are effectively the easiest
solution to physically dividing a network, providing the same LA to every user, without
These function as static LU schemes do: suboptimally, but sufficiently for most networks. However, their perhaps most egregious flaw is their vulnerability to the ping-pong effect. Given that these static LAs are set and cannot change, users may repetitively move between two or more adjacent LAs, which for many LU schemes will cause a large number of LUs with a small or zero absolute cell distance moved. Figure 1 demonstrates such an example, where the user may simply be moving around a city block, but may be incurring an LU on every inter-cell movement due to each movement crossing an LA boundary.
In current cellular telephone usage, there are two common standards: the Electronic and
Telephone Industry Associations (EIA/TIA) Interim Standard IS-41, and the Global
System for Mobile Communications (GSM) Mobile Application Part (MAP). Both of
these are quite similar, having two main tasks of Location Update and Call Delivery.
Currently, a two level hierarchical database scheme is used. The Home Location Register (HLR) contains the records of all users’ services, in addition to location information for an entire network, while Visitor Location Registers (VLRs) download data from the HLR concerning current users within the VLR’s specific service areas. Each LA has one VLR servicing it, and each VLR is designed to only monitor one LA. Additionally, each VLR is connected to multiple Mobile Switching Centers (MSCs), which operate in the transport network in order to aid in handoffs and to locate users more easily. For LUs, IS-41 and GSM both use a form of the always-update method. All inter-cell movements cause an update to the VLR, while the HLR does not need any modification, as both the MSC and VLR that the user resides in remains constant. Inter-MSC movements within the same LA cause the VLR to be updated with the new cell address, and also cause an update to the HLR to modify the stored value of the user's MSC. Finally, Inter-VLR movements cause the new VLR to create a record for the user, as well as causing an update to the HLR where both MSC and VLR fields are updated. After this occurs, the old VLR's record for the user is removed. Figure 2 displays a symbolic high-level view of the HLR/VLR architecture, as well as demonstrating the methods of communication on a call. [Giner04]
While evaluating schemes to design LAs or determining the optimal updating standard of
LUs for users may seem of higher importance, paging and user mobility can also be
briefly examined to assist in improving LM and refining models. Although LU costs are
generally higher than paging costs, these paging costs are not insignificant. Additionally,
poor paging procedures and ineffective mobility modeling and prediction may lead to
either significantly delayed calls or decreased QoS, neither of which are acceptable to a
In the attempt to locate recipients of calls as quickly as possible, multiple methods of
paging have been created. The most basic method used is Simultaneous Paging, where
every cell in the user’s LA is paged at the same time in order to find the user. Unless
there are a relatively low number of cells within the LA, this will cause excessive
amounts of paging. Although this method will find the user quicker than the following
scheme of Sequential Paging, the costs make Simultaneous Paging rather inefficient.
An alternative scheme is Sequential Paging, where each cell within an LA is paged in succession, with one common theory suggesting the polling of small cell areas in order of decreasing user dwelling possibility. Unfortunately, this was found to have poor performance in some situations, as if the user was in an infrequently occupied location, not only might every cell be paged, but a large delay could occur in call establishment. Additionally, this method requires accurate data gathering concerning common user locations, which necessitates more frequent LUs and thereby increased costs. Consequently, most real-world Sequential Paging methods simply poll the cells nearest to the cell of the most recent LU, and then continue outward if the user is not immediately found. However, such a method will still be inefficient if the user’s velocity is high or an LM scheme is used which specifies infrequent LUs.
As an attempt to improve on previous models, another design called Intelligent Paging was introduced, which calculates specific paging areas to sequentially poll based upon a probability matrix. This method is essentially an optimized version of Sequential Paging. However, this scheme has too much computational overhead incurred through updating and maintaining the matrix, and although perhaps optimal in theory, is effectively impossible for commercial cellular network implementation. Therefore, most current schemes use one of the first two methods presented. [Cowling04] While the best paging methods can significantly speed the locating of a user, significant further improvements are possible by combining them with user mobility predictions.
For aid in effectively predicting the user’s next location, user movement patterns are
analyzed and mobility models are designed. Many such mobility models exist and can be
used by networks in LM.
The simplest of these models is random-walk, where user movements are assumed to be entirely random. While this is clearly going to lead to inaccurate predictions, it does require no knowledge of the individual user, and can be effective as a simulation tool. Frequently, random-walk is used to demonstrate the improvements a given scheme makes in comparison to this random method.
A very general scheme, ignoring individual users but considering the network as a whole, is called fluid-flow. This method aggregates the movement patterns of users, and consequently can help optimize the network’s utilization and design at a macroscopic level. However, fluid-flow provides no insight on a smaller scale, nor will it give any predictions as to specific user movements for any specific user.
Markovian mobility models also exist, where user movements are predicted through past movements. At large computational cost, every inter-cell movement probability is defined for each user. An extension of the Markovian model, created at perhaps even greater cost, is the activity-based model. In this model, parameters such as time of day, current location, and predicted destination are also stored and evaluated to create movement probabilities. However, for all the resource expenditures required in implementing these methods, in a test of a simple activity-based scheme, unstable results were returned. An even more complex activity-based scheme might provide better results, but would not be implementable on a large scale due to its immense costs. [Cowling04]
In fact, research on all current models shows that none truly does a satisfactory job of predicting user movements, demonstrating the need for further research in this area. Consequently, Consequently, [Cowling04] describes a possible enhancement as a scheme called selective-prediction, where predictions are only made in regions where movements are easily foreseeable, and a random prediction method is used elsewhere. To further this scheme, Cowling advocates a network where the base station (BS) learns the mobility characteristics of the region, in addition to the cell movement probabilities. This learning network provides the basis of a Markov model, with the full knowledge of movement probabilities and theoretically incurring low overhead.
Additionally, the paper [Halepovic05] provides an in-depth analysis of sample user movement and call traffic. Empirical data gathered in these experiments reveals that the 10% most mobile users account for approximately two-thirds of the total number of calls within the network. Consequently, such users must be given appropriate consideration involving resource allocation. Over half of users appeared to be stationery, and most (but not all) such users generated much less cellular activity. Therefore, only a weak correlation between user mobility and data traffic exists, as users with few cell changes do have a large variance in the number of calls they made. Further tests within the paper reveal that a majority of users have a home cell. This is a location, whether it be an actual home, office, or other place, from which a majority of their calls originate. This characteristic can be exploited by paging techniques and the customization possible in dynamic LM, as less updates may be necessary if a user is within their home area.
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Dynamic Location Management is an advanced form of LM where the parameters of LM
can be modified to best fit individual users and conditions. Theories have been and
continually are being proposed regarding dynamic LUs and LAs. Additionally, many are
reexamining paging and mobility parameters based upon these developments. Many of
these proposals in dynamic LM attempt to reduce computational overhead, paging costs,
and the required number of LUs. However, many of these proposals are excessively
theoretical and complex, and are difficult to implement on a large scale.
Many dynamic LU schemes exist, in order to improve upon excessively simple and
wasteful static LU schemes. Additionally, these schemes are intended to be customizable,
such that each user will have their own optimal LU standard, greatly reducing the overall
number of LU updates.
One of these dynamic LU formats is threshold-based, where updates occur each time a parameter goes beyond a set threshold value. One possible threshold is time, where users update at constant time intervals. This saves user computation, but increases overhead significantly if the user does not move. This time-based scheme is very similar to the common static LU scheme, with the important difference of the time value being modifiable. Another threshold-based scheme requires a user update each time they traverse a certain number of cells. This was found to work better than the time-based scheme, unless the users were constantly moving. In such a case, this method becomes quite similar to the static always-update scheme, where many unnecessary updates might occur. Consequently, a preferable scheme was found, called distance-based. This called for an update only if the user moved a certain radial length of distance. However, this scheme is not perfect, as it requires the cellular device to keep track of such distances, which added much computational complexity. [Cowling04] Figures 4, 5 and 6 provide illustrations of these threshold-based LU methods.
While static LA schemes are restrictive and inefficient, dynamic LA designs offer much
more flexibility, allowing much more customization. To improve on the past schemes, [Cowling04]
proposes several changes to the static LA methodology. Instead of viewing
the network as an aggregation of identical cells, it is now viewed as a directed graph,
where nodes represent cells, with physical adjacency shown through graph edges.
General movement patterns and probabilities, updated at predetermined intervals, are
recorded between these cells based upon handoff information. This allows low overhead
while still providing predictive power. Additionally, a smoothing factor of k is
implemented to allow individual networks to weight new data as desired, where a low kvalue
causes the network to highly weight new data, and a high k-value causes the
network to give precedence to previous data. These adapting patterns can be stored, in
order to allow further prediction based upon other data such as time.
A similar parameter examined is dwell time, which is defined as the length of a user's stay within a cell. This is used to dynamically determine the appropriate dimensions of LAs. A smoothing parameter is also used for dwell times to weight past collected data against new data. The preferable method of collecting dwell times is by having the cellular device report its dwell time to the network upon a handoff.
Within a dynamic scheme, LAs, instead of being constant and circular, can be diverse and may take different shapes, in order to be optimal for individual user parameters and network characteristics. As well, cells are organized within these LAs based upon frequency of use, with the most frequently visited cells being placed in an ordered array. This array can be used in conjunction with known physical topology to design optimal user LAs, constructed such that the users will change LAs and make handoffs as infrequently as possible. To further optimize service for the individual user, call interval times and relative mobility rates are calculated to make low-overhead predictions concerning when LA and cell changes will occur.
The primary goal of dynamic LM is to provide LU and LA algorithms to each individual
user that minimizes the costs for them. To do this, call rate and movement factor
estimations must be made. To determine the call rate, the network first determines the
call interval, by taking the average interval between calls (in seconds), and adding new
call intervals in a weighted manner. The call rate, defined as λ, is then calculated by
dividing 3600 by the call interval, in order to obtain the number of calls per hour.
Given that cells will be of different sizes and covering different types of areas in dynamic
LA, it is incorrect to base user movement properties on raw user cell dwell times. Instead,
the movement factor γ is used, where this factor specifies a user’s movement speed relative to other users. This factor is further classified based upon specific regions, as the following equation demonstrates:
As seen before, the total cost of LM is equal to the LU cost added to the paging cost. This
equation is always true, regardless of which system is used. However, different systems
will have different values for these, and the goal is to find an implementable system that
minimizes the total.
The paging cost can be fairly simply defined, as the previously as the previously calculated call rate λ, multiplied by the number of cells in the paging area, multiplied by a constant representing the cost per paging message. Consequently, reducing any of these parameters will reduce the overall paging cost. These variables can be seen below. However, the LU cost calculation is somewhat more complicated. Although we can simply say that the LU cost is equal to the cost per LU divided by the estimated dwelling time within the current LA, TLA, it is somewhat difficult to calculate this dwelling time. Through analysis and justified by simulation, [Cowling04] defines the average user’s cost as the sum of the mean dwelling times within the cell multiplied by the probability of residing within the cell for all cells, as seen below. Therefore, as also seen below, to determine an individual user’s TLA, the average TLA is divided by the movement factor γ.
In the world of dynamic LM, new theories are constantly being proposed to reduce LM
costs, or otherwise improve the network quality. Additionally, an experiment was
conducted to compare generally static industry standards and a simple dynamic LM
scheme, in order to clearly show how dynamic LM is preferable to static LM. The
following sections provide this comparative analysis as well as an overview of several of
these wide-ranging developments.
The paper [Toosizadeh05] summarizes four commonly used LM methods and compares them through C++ simulation. The first method examined was GSM Classic, an older scheme with fixed location areas, LUs occurring on each LA transition, and blanket polling of all cells on an incoming call. Next was GSM+Profiles, a method containing the
same LU scheme as GSM Classic, but where sequential paging is performed on of cell
areas of decreasing dwell times. The third is the Kyama Method, where both static and
dynamic LAs are used in order to provide easier paging. These dynamic LAs in Kyama
are a combination of the standard static LA and an extra LA created dynamically for
predicting the user’s future movements. LUs occur each time the user moves out of their
combined LA. Paging is benefited by using dwell times to determine high probability
polling regions within these dynamic LAs. Finally, the dynamic distance-based method
was also tested, where LUs occur only on exceeding user movement thresholds, and
paging follows the GSM+Profiles paging scheme.
For testing, the authors of this paper ran experiments assuming both a random-waypoint model, which assumes the parameters speed, pause time, and direction to all be random, and an activity-based model, where users move as if accomplishing normal tasks, such as moving from home to work and back at predicted intervals. The simulated space was 100 cells in a 10x10 grid, serving 100 users. Traffic was statistically generated through Poisson calculation of random traffic.
Results showed that while the GSM and GSM Profile methods incurred the largest LU costs, due to their static LA and LU methods, they had the lowest paging costs. Conversely, the Kyama method, while requiring a relatively low number of LUs, had extremely high paging costs, as their combined static/dynamic LA method proved to be somewhat unsuccessful. Overall, due to having a very low total LU cost, and also having fairly low paging costs, the best method was the distance-based algorithm. Additionally, the activity-based model proved far superior to the random-waypoint model in all performance categories concerning user movement prediction, as would be expected. [Toosizadeh05]
This experiment demonstrates that dynamic LM schemes will generally be superior static LM schemes, but also that care must be taken when creating dynamic LAs. proper analysis is not made, these dynamic LAs may become overcomplicated, thereby can incur excessively high paging costs. While paging costs are generally significantly less than LU costs, it is impossible to state that the Kyama method’s costs will entirely offset its paging costs. Regardless, the success of the distance- method in this experiment demonstrates that proper dynamic LM schemes will be preferable to static LM schemes. Furthermore, the distance-based dynamic LM scheme used is relatively simple; the theories presented in the following sections additional improvements that might lead to even higher performance in tests.
[Lam05] proposes a probabilistic approach to paging called Hand-off Velocity Prediction
(HVP). This algorithm attempts to efficiently analyze and accurately predict user
mobility for paging purposes, based upon user mobility parameters.
In HVP, the paging area for a user is calculated based upon handoff statistics, the location of the last LU, the time since the last LU, and the velocity of the user. HVP then creates handoff graphs, indicating the most likely location of the user. Complex calculations beyond the scope of this paper are used to determine the precise probabilities of user movements, but can be examined in [Lam05]. If these predictions can be made accurately, LUs would not need to be so frequent, as less user data could still have enough predictive power using HVP to avoid excessive paging costs. However, a sequential paging scheme used with HVP may delay some calls, in cases where users move in an unpredictable manner. Therefore, the authors propose a group-paging method to meet any QoS requirements, where multiple cells are polled at once to rapidly find the user.
A network designed for HVP use incorporates both distance-based and time-based LUs. Users are classified into groups, depending on their approximate velocities. These velocities are found by calculating the change of signal strength of the user’s cellular device over time, hence the need for time-based LUs. Through statistics regarding handoffs and velocity, the network can calculate acceleration, which allows higherprecision predictions of movement. Probabilistic equations are used to further improve these predictions, by considering the parameters cell size, acceleration, and the number of cells previously traversed. The author’s experiments demonstrate a significant decrease in LU and paging costs, although the authors note themselves that this system is very computationally complex.
This last point is unfortunately the same for many Dynamic LM algorithms. While HVP is theoretically successful in simulations and small experiments, giving results superior to those of current systems, it is impractical to be implemented for today’s large commercial networks. However, if user movements cannot directly be used to predict handoffs, as in HVP, these movements can be used for improved LA construction as seen in the next section.
Although most efforts involving dynamic LM focus on improving LU schemes rather
than creating efficient LAs, [Fan02] proposes a scheme where three location layers are
used to ensure optimal user coverage. The main issue addressed and solved by this paper
is the ping-pong effect.
Traditional (Figure 9) and generalized (Figure 1) ping-pong effects cause a significant amount of unnecessary LUs, combining to equal to over 20% of the total LU cost. Two insufficient schemes to counter this are the Two Location Area (TLA) scheme and Virtual Layer (VLA) Scheme. The TLA scheme remembers the most recently visited areas, such that the network will know not to update if it is simply moving back and forth between the same two LAs. However, this scheme still has problems with the generalized ping-pong effect. VLA Schemes add a second LA layer to prevent unnecessary LUs from occurring when movements take place within a small region of cells. However, not only is this not fully sufficient, it simultaneously adds complexity that may end up causing unexpected ping-pong effects. Alternatively, extensive cell overlapping can be implemented, but only at a large paging cost.
Many other innovative Dynamic LM concepts exist, which although perhaps excessively
complex, theoretical, or systems-based to be fully explored, deserve brief mention.
[Lee04] proposes a scheme where LU and lookup costs are balanced, minimizing the overall performance penalty and overhead. Location information is stored within location databases called agents, which themselves are placed within BSs. Each agent contains the address of several users or other agents, thereby allowing for easier forwarding of data within the network. Instead of searching through large tables of entries or large numbers of cells, these agent trees can quickly be traversed, with the addresses stored providing quick reference for locating the recipient of a call. Additionally, through the grouping of users at similar physical locations to similar logical address, simpler paths can be created, reducing the necessary number of agents. However, this algorithm proves effective only for networks with a low call-to-mobility ratio.
[Giner04] proposes several improvements to the commonly used HLR/VLR architecture, mainly involving methods of increasing the speed of finding a called user’s VLR. One proposed theory involves using pointer chains within the HLR to store successive user VLR locations, such that updates can be made to the end of the chain, rather than requiring HLR memory to be directly accessed and updated on each movement. A similar scheme involves using local anchoring, where each user has their most frequently occupied LA designated the anchor. If the user moves, the anchor VLR is updated instead of the HLR, again saving HLR resources. Then, upon call arrival, the anchor is checked first, and then the pointer list is traversed to locate the user if necessary. Both of these schemes were shown to only be somewhat effective, working best when calls are infrequent and user mobility is high. Giner suggested using per-user location caching to fix this weakness, storing probable user locations within the MSC, but this scheme fails when users frequently move to new locations.
[Hassan03] suggests that instead using the conventional HLR/VLR architecture, all operations should be routed through several BSs connected by wireless inter-BS links, in a system called cell hopping. This system is not as decentralized as ad-hoc routing, as users do not make connections directly with each other. Users simply roam freely, registering dynamically with the nearest BS, with user requests flowing through these BSs. Cell-Based On-Demand Location Management is used, where Membership Request Query (MRQ) is used with querying and caching to determine the location of a desired user. This process may be slower than paging done in HLR/VLR architecture, but this lightweight scheme requires no central storage system. Although [Hassan03] makes an interesting proposition, it is highly unlikely that current cellular companies would desire to make this radical change and decentralize to this degree.
Additionally, [Xiao03] proposes a fractional movement-based LU scheme for cellular networks. This paper cites a deficiency of standard cell threshold-based LU methods, in that such schemes only assume VLR updates will occur after the specified number of cells are traversed, ignoring the possibility that an LU will otherwise occur due to the user crossing an LA boundary. Consequently, Xiao introduces a fractional threshold value r in addition the standard value. This is used such that after the standard threshold is reached, an LU is performed on this step with probability 1/r, or on the next cell movement with probability 1/(1 - r). [Giner05] uses a somewhat similar fractional distance-based method. In this scheme, the distance counter is reexamined on every cell movement, and zeroed if a cached cell is reached. Giner uses a fractional threshold value q similarly to Xiao's methodology, where after the standard threshold is reached, an LU is either performed immediately with probability 1/q, or on the next movement with probability 1/(1 - q). Giner also provides further research and analysis as to optimal threshold and q values.
While these improvements and theories provide a sampling of current research, the continued evolution of technology and cellular networks themselves will also cause significant changes to and improvements in LM, as discussed in the following sections.
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The world of cellular networks and LM is certainly not static. Even the most recent
theories and developments may become outdated within months. With the increased use
of 3G Cellular Networks, and the E911 service providing a national standard in which
LM is absolutely essential, it is critical to observe what is to come.
Currently, the market standard for location tracking uses the cell ID to track cellular
devices through many of the methods as described previously, primarily including the
HLR/VLR architecture. However, it is not as precise as many would desire, as this
system is not always able to locate users to within a few meters.
Future possibilities for location tracking in cellular systems include Global Positioning Systems (GPS), which use line-of-sight satellite position calculation, giving five-meter range precision, but encounter problems when physical obstructions block the connection. Another possibility is Assisted Global Positioning Systems (AGPS), which is similar to normal GPS, but uses advanced searching techniques to determine user position. Unfortunately, AGPS is quite expensive, and still requires line-of-sight. Finally, there is the potential to use Broadband Satellite Networks, which use the low-earth-orbit satellites to create a global network. These give relatively high precision without line-ofsight, but are very complex to manage. [Rao03]
An example of a current commercial use for GPS is cellular phone games. These games are a larger part of the industry than might be expected, accounting for approximately $4.8 billion of revenue to providers [Rashid06]. In order to approximate the true location of the call, these networks generally use Time of Arrival (TOA) measurements and examine the difference of arrival times for multiple incoming signals, while considering signal transmission location. These calculations can be used with GPS to further resolve user location. However, these methods have not been implemented on a large scale, mainly due to GPS's costs and limitations. Alternative methods include approximating cellular device location based upon the device's relative location to other objects within the cellular infrastructure, but this is generally imprecise and unreliable. Games may seem to be a somewhat trivial example of GPS's usage and of advanced location tracking, but actually includes some methods used in the E911 service, as will be seen later.
Regardless, these are future plans, currently only used for military, private, and smallnetwork settings, and may not fully enter the cellular phone marketplace in the near future. Most of these are excessively expensive or are too difficult to maintain for a large number of cellular subscribers at this point in time. Still, for users willing to pay additional fees for Location-Based Services, such as games as described above, these technologies can be of great use and entertainment value. As technology improves and the transition to 3G continues, perhaps such services will become common.
3G Cellular Networks, while providing significant improvements over 2G Networks,
share many similarities in LM with its predecessor. However, there is one significant
difference in the HLR/VLR architecture worth noting.
This new 3G addition is the GLR (Gateway Location Register). GLRs are used to handle VLR updates in place of the HLR. The network in 3G is partitioned into Gateway Location Areas (G-LAs) that contain normal LAs. Crossing a G-LA causes an HLR update, while crossing an LA causes a GLR location update, and making a predetermined number of movements between cells causes a VLR update. It is essentially a three-level hierarchical scheme that, while significantly adding to the complexity of the network, equally improves efficiency. Also, analysis shows that dynamic and static 3G networks using this scheme outperform dynamic and static 2G networks, especially if the remotelocal- cost ratio is high, and also that both static and dynamic 3G have their purposes; static 3G is preferable if mobility is high or polling/paging cost is low, with dynamic 3G superior in other cases. [Xiao04] However, in the case of a service such as Enhanced 911, the cost of LM is less relevant; the safety of the user transcends monetary considerations.
Enhanced 911 is an emergency calling system that uses advanced LM techniques to find
the physical location of a user in distress. Public Safety Answering Points (PSAPs),
specific physical locations designed to center E911 activities around, are staffed by 911
operators who use AGPS or TDOA to aid in locating users in need of assistance.
Additionally, Wireless Location Signatures (WLS) are used with path loss and shadow
fading data, and are combined with Geographical Predicted Signature Databases (PSD)
and statistical analysis to make the most accurate predictions of user locations possible.
In E911, calls follow this process: the BS forwards the call to the MSC, where a voice path is set up to the access point. The MSC requests the BS locate the caller, which forwards the location request to the Serving Mobile Location Center (SMLC). The SMLC uses network measurement reports to approximate the location of the user, and then forwards the approximation to the Gateway Mobile Location Center (GMLC), which then finally transfers a refined estimate to the PSAP. It is a very complex process, unusable for normal communication, but essential in these extreme cases. [Feuerstein04]
However, as recent a development as E911 is, may already be in the process of being phased out. The National Emergency Number Association (NENA) report [NENA05] states that E911 must be upgraded to a Next Generation 911 (NG911) scheme in the near future. New devices must implement systems to handle NG911 calls, and LM must continue to be improved. The new NG911 must be expanded to VoIP architecture, and better integration of national databases and stations must occur. However, E911 still stands as an example of the importance of LM as well as the rapidity of change within cellular networks.
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As the comparisons within the papers included in this survey indicate, static LM schemes
are becoming increasingly out of date. While they are still used where cost or resource
availability is an issue, upgrading to dynamic schemes is preferable. However, dynamic
LM schemes have not yet become a panacea, as many schemes are still only theories,
being insightful developments, but not useful under real-world conditions. Consequently,
dynamic LM must continue to be researched and improved.
Given the increasing number of cellular users in an on-demand world, and transitions occurring from E911 to NG911 and 2G to 3G and beyond, Location Management must and will continue to improve both Location Update and paging costs, while allocating appropriate Location Areas in a practical, implementable fashion.
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Please note - some references may require user authentication.
[Cowling04] James Cowling, "Dynamic Location Management in Heterogeneous Cellular Networks," MIT Thesis. http://people.csail.mit.edu/cowling/thesis/jcowling-dynamic-Nov04.pdf
Extensive overview of Static and Dynamic LM as well as paging and several other parameters.
[Giner04] Vicenete Casares Giner, "State of the art in Location Management procedures," Eurongi Archive. http://eurongi.enst.fr/archive/127/JRA151.pdf
Provides an informative view of the HLR/VLR architecture, as well as providing an overview of LM techniques.
[Kyantakya04] K. Kyantakya and K. Jobmann, "Wireless Networks - Location Management in Cellular Networks: Classification of the Most Important Paradigms, Realistic Simulation Framework, and Relative Performance Analysis."IEEE transactions on vehicular technology 2005: v.54, no.2 687-708. http://ieeexplore.ieee.org/document/1412086/
Similar to Cowling's Paper, provides an extensive overview of Static and Dynamic LM schemes.
[Toosizadeh05] Navid Toosizadeh and Hadi Bannazadeh, "Location Management in Mobile Networks: Comparison and Analysis," University of Toronto Report. http://www.eecg.toronto.edu/~navid/ProjectReport.pdf
Compares 4 Static/Dynamic LM schemes.
[Halepovic05] Emir Halepovic and Carey Williamson, "Characterizing and Modeling User Mobility in a Cellular Data Network," University of Calgary. http://portal.acm.org/ft_gateway.cfm?id=1089969&type=pdf&coll=GUIDE&dl=ACM&CFID=67915996&CFTOKEN=36591408
Provides an analysis of user mobility, including examination of data traces.
[Lo04] Shi-Wu Lo, Tei-Wei Kuo, Kam-Yiu Lam, Guo-Hui Li, "Efficient location area planning for cellular networks with hierarchical location databases," Computer Networks. Amsterdam: Aug 21, 2004.Vol.45, Iss. 6; pg. 715. http://dx.doi.org/10.1016/j.comnet.2004.02.012
Discusses the SCBLP theory.
[Xiao04] Y. Xiao, Y. Pan, J. Li, "Design and analysis of location management for 3G cellular networks," IEEE Transactions on Parallel and Distributed Systems, v.15 i.4 April 2004 p.339-349. http://ieeexplore.ieee.org/document/1271183/
Provides insight as to 3G-specific LM modifications.
[Giner05] Vicenete Casares Giner and Pablo Garcia-Escalle, "On the Fractional Movement-Distance Based Scheme for PCS Location Management with Selective Paging," Lecture Notes in Computer Science, Volume 3427, Jan 2005, Pages 202 - 218. http://www.springerlink.com/index/M87GJA20CLFMVX9Q.pdf
Discusses a Fractional-Movement scheme using Distance as the main parameter.
[Xiao03] Yang Xiao "Optimal fractional movement-based scheme for PCS location management ," Communications Letters, IEEE, v.7 i.2 Feb 2003 p.67-69. http://ieeexplore.ieee.org/document/1178889/
Discusses a Fractional-Movement scheme using Cell Movement as the main parameter.
[Lee04] Kevin Lee, Hong Wing Lee, Sanjay Jha, and Nirupama Bulusu, "Adaptive, Distributed Location Management in Mobile, Wireless Networks." http://www1.cse .unsw.edu.au/~sjha/papers/icc04lee.pdf
Proposes an agent-based LM system.
[Lam05] Kam-Yiu Lam, BiYu Liang, ChuanLin Zhang, "On Using Handoff Statistics and Velocity for Location Management in Cellular Wireless Networks," The Computer Journal Jan 2005: 48, 1. http://vnweb.hwwilsonweb.com/hww/jumpstart.jhtml?recid=0bc05f7a67b1790e3761dd0af148832703413c5fdbb17b22a1747616f6aa0b3ead887cd28ffac928
Proposes HVP, discussing use of Velocity and other parameters for user movement prediction.
[Rao03] Bharat Rao, Lous Minakakis, "Evolution of Mobile Location-Based Services." http://portal.acm.org/citation.cfm?doid=953460.953490
Discusses Evolution of Location-Tracking mechanisms, such as GPS/AGPS.
[Rashid06] Omer Rashid, Ian Mullins, Paul Coulton, Reuben Edwards, "Extending Cyberspace: Location Based Games Using Cellular Phones." http://portal.acm.org/ft_gateway.cfm?id=1111302&type=pdf&coll=GUIDE&dl=ACM&CFID=67915996&CFTOKEN=36591408
Discusses LM and systems used in current cell-phone games.
[Feuerstein04] Marty Feuerstein, "The Complex World of Wireless E911," Polaris Wireless. http://search.epnet.com/login.aspx?direct=true&db=buh&an=14114819
Provides an overview of E911 service and call process.
[NENA05] Unsigned, "NENA NG E9-1-1 Program 2005 Report," National Emergency Number Association Report. http://www.nena.org/media/files/ng_final_copy_lo-rez.pdf
Presents NENA's opinions on E911 in 2005 and future recommendations.
[Hassan03] Jahan Hassan and Sanjay Jha, "Cell Hopping: A Lightweight Architecture for Wireless Communications," IEEE Wirelesss Communications October 2003. http://www.comsoc.org/livepubs/pci/private/2003/oct/hassan.html
Presents a LM scheme which avoids centralization and focuses on BS use.
[Fan02] Guangbin Fan, Ivan Stojmenovic, and Jingyuan Zhang, "A Triple Layer Location Management Strategy for Wireless Cellular Networks." http://www.site.uottawa.ca/~ivan/TripleIC3N.pdf
Presents a LA scheme which uses three layers to avoid the ping-pong effect.
[Yu05] F. Yu, V.W.S. Wong, and V.C.M. Leung, "Performance Enhancement of Combining QoS Provisioning and Location Management in Wireless Cellular Networks," IEEE transactions on wireless communications 2005: v.4 no.3 943-953. http://ieeexplore.ieee.org/document/1427684/
Presents a scheme where QoS and LM are considered simultaneously.
[Varshney03] Upkar Varshney, "Location Management for Mobile Commerce Applications in Wireless Internet Environment." http://www.sis.pitt.edu/~dtipper/3955/acm_paper.pdf
Presents a LM scheme for Commercial (high-priority) applications.
Back to Table of Contents
AGPS: Assisted GPS
BS: Base Station
E911: Enhanced 911
EIA/TIA: Electronic and Telephone Industry Associations
HLR: Home Location Register
HVP: Hand-off Velocity Prediction
IS: Interim Standard
G-LA: Gateway Location Area
GLR: Gateway Location Register
GMLC: Gateway Mobile Location Center
GPS: Global Positioning Systems
GSM: Global System for Mobile Communications
LA: Location Area
LM: Location Management
LU: Location Update
MAP: Mobile Application Part
MRQ: Membership Request Query
MSC: Mobile Switching Center
NENA: National Emergency Number Association
NG911: Next-Generation 911
PSAP: Public Safety Answering Point
PSD: Predicted Signature Databases
QoS: Quality of Service
SCBLP: Set-Covering Based Location Area Planning
SCP: Service Control Point
SMLC: Serving Mobile Location Center
SSP: Service Switching Point
SS7: Signaling System 7
STP: Signal Transfer Point
TOA: Time of Arrival
TDOA: Time Difference on Arrival
TLA: Two Layer Area
VLA: Virtual Layer Area
VLR: Visitor Location Register
VoIP: Voice over IP
WLS: Wireless Location Signatures
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