U.S. patent application number 12/084040 was filed with the patent office on 2009-05-28 for detection in mobile service maintenance.
This patent application is currently assigned to Seeker Wireless Pty. Limited. Invention is credited to Malcolm D. Macnaughtan, Craig A. Scott.
Application Number | 20090135730 12/084040 |
Document ID | / |
Family ID | 37967330 |
Filed Date | 2009-05-28 |
United States Patent
Application |
20090135730 |
Kind Code |
A1 |
Scott; Craig A. ; et
al. |
May 28, 2009 |
Detection in Mobile Service Maintenance
Abstract
Disclosed is a method and system for detecting inconsistencies
between a radio communications network and a network database. In
one form, measurements from the network are provided by mobile
radio terminals. The measurements are then compared with
corresponding data on the network database to determine whether
there is an inconsistency. The methods described may be used in the
management and maintenance of the network.
Inventors: |
Scott; Craig A.; (Mortdale,
AU) ; Macnaughtan; Malcolm D.; (New South Wales,
AU) |
Correspondence
Address: |
JONES DAY
222 EAST 41ST ST
NEW YORK
NY
10017
US
|
Assignee: |
Seeker Wireless Pty.
Limited
Gordon, New South Wales
AU
|
Family ID: |
37967330 |
Appl. No.: |
12/084040 |
Filed: |
October 24, 2006 |
PCT Filed: |
October 24, 2006 |
PCT NO: |
PCT/AU2006/001577 |
371 Date: |
April 24, 2008 |
Current U.S.
Class: |
370/252 |
Current CPC
Class: |
H04W 64/00 20130101;
H04W 24/04 20130101; H04W 88/18 20130101; H04W 24/02 20130101; H04W
4/02 20130101 |
Class at
Publication: |
370/252 |
International
Class: |
G06F 11/30 20060101
G06F011/30 |
Foreign Application Data
Date |
Code |
Application Number |
Oct 24, 2005 |
AU |
2005905863 |
Nov 4, 2005 |
AU |
2005906105 |
Claims
1. A method for detecting an inconsistency between a radio
communications network and a network database, the method
comprising: receiving from a mobile radio terminal in the radio
communications network, at least one measurement of at least one
parameter from the mobile radio communications network; comparing
the at least one measurement with corresponding data in the network
database; and determining that the at least one measurement is
inconsistent if the at least one measurement is different to the
corresponding data in the network database.
2. A method as claimed in claim 1 further comprising the step of
calculating a metric associated with the at least one measurement
using data from the network database, and comparing the calculated
metric with a threshold.
3. A method as claimed in claim 2 further comprising determining
that the at least one measurement is different to the corresponding
data in the network database if the calculated metric exceeds the
threshold.
4. A method as claimed in claim 1 further comprising making a
hypothesis that a parameter of the mobile radio communications
network is not present in the mobile radio communications network
even though data in the network database indicates that the
parameter is present.
5. A method as claimed in claim 4 further comprising, if the at
least one measurement does not contradict the hypothesis,
considering data that supports the hypothesis.
6. A method as claimed in claim 4 further comprising considering
data that supports the hypothesis.
7. A method as claimed in claim 6 wherein the step of considering
data that supports the hypothesis comprises determining whether the
mobile radio terminal is in a given zone.
8. A method as claimed in claim 7 wherein, if the mobile radio
terminal is determined to be in the given zone, the method further
comprising comparing the at least one measurement with data in the
network database corresponding to one or more expected measurements
that would be expected to be obtained by the mobile radio terminal
in the given zone.
9. A method as claimed in claim 8 further comprising determining
that there is an inconsistency between the radio communications
network and the network database if the step of comparing the at
least one measurement with data in the network database
corresponding to the one or more expected measurements indicates a
difference.
10. A method as claimed in claim 9 further comprising accumulating
a plurality of measurements over time and determining that there is
an inconsistency between the radio communications network and the
network database if the difference between the accumulated
measurements and the one or more expected measurements exceeds a
predetermined threshold.
11. A method as claimed in claim 10 wherein the hypothesis is that
the radio communications network contains a non-operational
cell.
12. A method as claimed in claim 1 wherein the at least one
measurement is received from the mobile radio terminal using spare
capacity in an already established communications session.
13. A method as claimed in claim 12 wherein a plurality of
measurements are received from a plurality of mobile radio
terminals within the radio communications network.
14. A network processor in a radio communications network having at
least one radio parameter, at least one mobile radio terminal, and
a network database, the network database storing data corresponding
to the at least one radio parameter, the network processor
comprising: a receiver for receiving from the mobile radio terminal
in the radio communications network, at least one measurement of
the at least one parameter; a comparator for comparing the at least
one measurement with the corresponding data in the network
database; and a means for determining that the at least one
measurement is inconsistent if the at least one measurement is
different to the corresponding data in the network database.
15. A radio communications network comprising a network processor
as claimed in claim 14.
16. A method for detecting a non-operational cell in a radio
communications network, the method comprising: receiving at least
one measurement, including data relating to at least one cell, from
a mobile radio terminal in the radio communications network;
determining whether the mobile radio terminal is in a given zone;
determining whether the at least one cell is reported; updating
evidence that the at least one cell is not operating; determining
whether the updated evidence exceeds a predetermined threshold; and
determining that the at least one cell is not operational if the
updated evidence exceeds the predetermined threshold.
17. A method as claimed in claim 16 further comprising for each
cell reported in the at least one measurement, resetting evidence
against the at least one cell not operating.
18. A method as claimed in claim 16 wherein the step of resetting
evidence against the at least one cell not operating comprises
setting an accumulated unreported cell cost to zero and setting a
cell count to zero.
19. A method as claimed in claim 17 wherein for each cell
unreported in the at least one measurement, the step of updating
evidence that the at least one cell is not operating comprises
computing an unreported cell cost for the at least one cell and
adding the computed unreported cell cost to the accumulated
unreported cell cost and incrementing the cell count.
20. A method as claimed in claim 18 wherein the step of determining
whether the updated evidence exceeds the predetermined threshold
comprises determining whether the accumulated unreported cell cost
is greater than the predetermined threshold.
21. A method as claimed in claim 19 wherein the at least one cell
is determined to be potentially non-operational if the accumulated
unreported cell cost is greater than the predetermined
threshold.
22. A network processor for use in a radio communications network
having at least one cell in a zone and at least one mobile radio
terminal in the radio communications network, the network processor
comprising: a receiver for receiving at least one measurement,
including data relating to at least one cell, from a mobile radio
terminal in the radio communications network; a means for
determining whether the mobile radio terminal is in a given zone;
determining whether the at least one cell is reported; a means for
updating evidence that the at least one cell is not operating; a
means for determining whether the updated evidence exceeds a
predetermined threshold; and a means for determining that the at
least one cell is not operational if the updated evidence exceeds
the predetermined threshold.
23. A radio communications network comprising a network processor
as claimed in claim 22.
Description
TECHNICAL FIELD
[0001] The present invention relates to mobile communication
networks and to their management.
PRIORITY DOCUMENTS
[0002] The present application claims priority from:
Australian Provisional Patent Application No. 2005905863 entitled
"Mobile Service Maintenance" filed on 24 Oct. 2005.; and Australian
Provisional Patent Application No. 2005906105 entitled "Profile
Based Communications Service" filed on 4 Nov. 2005.
[0003] The entire content of each of these applications is hereby
incorporated by reference.
INCORPORATION BY REFERENCE
[0004] The following co-pending patent applications are referred to
in the following description: [0005] PCT/AU2005/001358 entitled
"Radio Mobile Unit Location System"; [0006] PCT/AU2006/000347
entitled "Enhanced Mobile Location Method and System"; [0007]
PCT/AU2006/000348 entitled "Enhanced Mobile Location" [0008]
PCT/AU2006/000478 entitled "Enhanced Terrestrial Mobile Location"
[0009] PCT/AU2006/000479 entitled "Mobile Location" [0010]
PCT/AU2006/001479 entitled "Profile Based Communications Service"
[0011] Co-pending International Patent Application entitled "Mobile
Service Maintenance Management" filed concurrently herewith and
claiming priority from Australian Provisional Patent Application
No. 2005905863 [0012] Section 2.7 of Mobile Radio Communications
2.sup.nd Ed. Editors Steele and Hanzo. ISBN 047197806X,J. Wiley
& Sons Ltd, 1999 [0013] Section 5.1.4 of "Radio Frequency (RF)
system scenarios" 3GPP TR25.942 [0014] Section 3.2 of "Evaluation
of Positioning Measurement Systems", T1P1.5/98-110).
[0015] The entire content of each of these documents is hereby
incorporated by reference.
BACKGROUND
[0016] Radio communication networks often use information
representing certain characteristics or parameters of different
parts of the network. One example of an application that uses this
information is a mobile radio location system. Some mobile radio
location systems operate by using radio measurements to estimate
the location of mobile terminals relative to the known locations of
the radio network access points. For the special case of cellular
mobile phone location systems these access points are the
cells.
[0017] A location system which estimates the location of a mobile
terminal relative to one or more radio network access points
requires knowledge of the relevant characteristics of those access
points. For example, in a coarse cell identifier based mobile
cellular location system, the relevant characteristics typically
include the unique identifier for the cell and the geographical
coordinates at which the cell is situated.
[0018] More accurate systems such as those which also incorporate
radio signal measurements in the calculation process require
additional configuration information. This typically includes
transmitted power, antenna gain and antenna orientation.
[0019] The performance of such systems is strongly dependent on the
integrity of the database containing this network configuration
information. This dependence increases in systems promising greater
levels of spatial resolution or accuracy. In an ideal scenario, the
configuration of the cellular network will match the network
database. In such a scenario a location system would only need to
cope with changes to the network configuration which would be
notified via an update to the network database. Experience has
shown however that typically the configuration information is
poorly maintained, distributed across multiple databases and
exhibits many errors.
[0020] Reasons for discrepancies between the supplied database and
actual configuration may lie with the network database or with the
network configuration or both. The database may be at fault due to
errors such as typographical errors, especially the transposition
of numbers, during data entry; problems with the process used to
collect and collate the network data; and failure to propagate
network configuration changes to the database. Conversely the
network configuration may not be as intended due to errors such as
typographical errors when entering configuration details and
failure to configure one or more planned network changes.
[0021] A further problem for operators is that the network
configuration is not static. Opportunities for inconsistencies to
arise between the network database and deployed configuration occur
throughout the life of the network. The network configuration
changes when sites are added to increase capacity and/or coverage.
Changes also occur when cells are decommissioned. Mobile cells
(referred to as Cells-On-Wheels) can be temporarily setup to
support the temporary capacity increases required to support events
such as significant sporting events and outdoor music concerts.
These temporary additions and deletions to the network can last for
hours and in some cases days. The configuration may change
temporarily when there is a cell not operating due to scheduled
maintenance, equipment failure, or power failure. The network also
changes when technicians retune the network to improve performance
or to adapt to changes due to reasons discussed above.
[0022] Network database errors lead to corresponding errors in the
operation of the location-based system and associated services, in
some cases leading to unacceptable service quality for subscribers.
Network operators have no means of validating that the network is
configured as planned other than to perform drive tests around the
network with radio monitoring equipment. The cost of updating the
database so that it is continually up-to-date represents a
significant operational burden for the service provider.
[0023] It is an object of the present invention to detect errors
and/or inconsistencies between a configured network and
corresponding network databases.
SUMMARY
[0024] In one aspect of the present invention, there is provided a
method for detecting an inconsistency between a radio
communications network and a network database, the method
comprising: [0025] receiving from a mobile radio terminal in the
radio communications network, at least one measurement of at least
one parameter from the mobile radio communications network; [0026]
comparing the at least one measurement with corresponding data in
the network database; and [0027] determining that the at least one
measurement is inconsistent if the at least one measurement is
different to the corresponding data in the network database.
[0028] In one form, the method further comprises the step of
calculating a metric associated with the at least one measurement
using data from the network database, and comparing the calculated
metric with a threshold.
[0029] In another form, the method further comprises determining
that the at least one measurement is different to the corresponding
data in the network database if the calculated metric exceeds the
threshold.
[0030] In another form, the method further comprises making a
hypothesis that a parameter of the mobile radio communications
network is not present in the mobile radio communications network
even though data in the network database indicates that the
parameter is present.
[0031] In a further form, the method further comprises, if the at
least one measurement does not contradict the hypothesis,
considering data that supports the hypothesis.
[0032] In a further form, the method further comprises considering
data that supports the hypothesis.
[0033] In one form, the step of considering data that supports the
hypothesis comprises determining whether the mobile radio terminal
is in a given zone.
[0034] In another form, if the mobile radio terminal is determined
to be in the given zone, the method further comprises comparing the
at least one measurement with data in the network database
corresponding to one or more expected measurements that would be
expected to be obtained by the mobile radio terminal in the given
zone.
[0035] In another form, the method further comprises determining
that there is an inconsistency between the radio communications
network and the network database if the step of comparing the at
least one measurement with data in the network database
corresponding to the one or more expected measurements indicates a
difference.
[0036] In a further form, the method further comprises accumulating
a plurality of measurements over time and determining that there is
an inconsistency between the radio communications network and the
network database if the difference between the accumulated
measurements and the one or more expected measurements exceeds a
predetermined threshold.
[0037] In one form, the hypothesis is that the radio communications
network contains a non-operational cell.
[0038] In another form, the at least one measurement is received
from the mobile radio terminal using spare capacity in an already
established communications session.
[0039] In another form, a plurality of measurements are received
from a plurality of mobile radio terminals within the radio
communications network. According to another aspect of the present
invention, there is provided a network processor in a radio
communications network having at least one radio parameter, at
least one mobile radio terminal, and a network database, the
network database storing data corresponding to the at least one
radio parameter, the network processor comprising: [0040] a
receiver for receiving from the mobile radio terminal in the radio
communications network, at least one measurement of the at least
one parameter; [0041] a comparator for comparing the at least one
measurement with the corresponding data in the network database;
and [0042] a means for determining that the at least one
measurement is inconsistent if the at least one measurement is
different to the corresponding data in the network database.
[0043] In another form of the present invention, there is provided
a radio communications network comprising a network processor
according to the previous aspect of the present invention.
[0044] According to another aspect of the present invention, there
is provided a method for detecting a non-operational cell in a
radio communications network, the method comprising: [0045]
receiving at least one measurement, including data relating to at
least one cell, from a mobile radio terminal in the radio
communications network; [0046] determining whether the mobile radio
terminal is in a given zone; determining whether the at least one
cell is reported; [0047] updating evidence that the at least one
cell is not operating; [0048] determining whether the updated
evidence exceeds a predetermined threshold; and [0049] determining
that the at least one cell is not operational if the updated
evidence exceeds the predetermined threshold.
[0050] In one form, the method further comprises, for each cell
reported in the at least one measurement, resetting evidence
against the at least one cell not operating.
[0051] In one form, the step of resetting evidence against the at
least one cell not operating comprises setting an accumulated
unreported cell cost to zero and setting a cell count to zero.
[0052] In another form, for each cell unreported in the at least
one measurement, the step of updating evidence that the at least
one cell is not operating comprises computing an unreported cell
cost for the at least one cell and adding the computed unreported
cell cost to the accumulated unreported cell cost and incrementing
the cell count.
[0053] In one form, the step of determining whether the updated
evidence exceeds the predetermined threshold comprises determining
whether the accumulated unreported cell cost is greater than the
predetermined threshold.
[0054] In another form, the at least one cell is determined to be
potentially non-operational if the accumulated unreported cell cost
is greater than the predetermined threshold.
[0055] According to yet another aspect of the present invention,
there is provided a network processor for use in a radio
communications network having at least one cell in a zone and at
least one mobile radio terminal in the radio communications
network, the network processor comprising: [0056] a receiver for
receiving at least one measurement, including data relating to at
least one cell, from a mobile radio terminal in the radio
communications network; [0057] a means for determining whether the
mobile radio terminal is in a given zone; determining whether the
at least one cell is reported; [0058] a means for updating evidence
that the at least one cell is not operating; [0059] a means for
determining whether the updated evidence exceeds a predetermined
threshold; and [0060] a means for determining that the at least one
cell is not operational if the updated evidence exceeds the
predetermined threshold.
[0061] According to another aspect of the present invention, there
is provided a radio communications network comprising a network
processor according to the previous aspect.
DRAWINGS
[0062] Various aspects of the present invention will now be
described with reference to the following Figures in which:
[0063] FIG. 1--shows a system architecture of an exemplary radio
communications network to which various aspects of the present
invention may be applied;
[0064] FIG. 2--shows a network arrangement in which a cell identity
error arises from a cell coordinate error;
[0065] FIG. 3--shows a flowchart of a method of directly detecting
an inconsistency;
[0066] FIG. 4--shows a flowchart of a method of indirectly
detecting an inconsistency by accumulated observations;
[0067] FIG. 5--shows a flowchart of a method of detecting the
presence of an unknown and incorrectly identified Cell;
[0068] FIG. 6--shows a flowchart of a method of detecting
non-operational cells;
[0069] FIG. 7--shows a flowchart of a method of detecting a
non-operational cell becoming operational;
[0070] FIG. 8--shows a flowchart of a method of detecting cells
with incorrect coordinates;
[0071] FIG. 9--shows a flowchart of a method of detecting cells
with incorrect coordinates using probability metric;
[0072] FIG. 10A--illustrates the detection of incorrect cell
coordinates for cells A and B heard contemporaneously;
[0073] FIG. 10B--illustrates the detection of incorrect cell
coordinates for cells A and C heard contemporaneously; and
[0074] FIG. 10C--illustrates the detection of Cells A and C of FIG.
10B in another example.
DETAILED DESCRIPTION
[0075] Various aspects of the present invention will now be
described in detail with reference to one or more embodiments of
the invention, examples of which are illustrated in the
accompanying drawings. The examples and embodiments are provided by
way of explanation only and are not to be taken as limiting to the
scope of the invention. Furthermore, features illustrated or
described as part of one embodiment may be used with one or more
other embodiments to provide a further new combination.
[0076] Although many of the examples used to illustrate the
embodiments of the present inventions are based on the GSM mobile
phone system, the embodiments disclosed herein are readily applied
to other mobile phone systems such as UMTS, CDMA-2000, and CDMA
IS-95. This is because the parameters being measured and the
corresponding cell characteristics have equivalents in each of the
mobile phone technologies. For example, a GSM signal strength
measurement can be used in the same way as a CDMA-2000 pilot power
measurement. As another example, just as the absence of a cell from
a GSM Network Measurement Report may indicate a non operational
cell, the absence of a particular UMTS Node B from a set of intra
frequency measurements may also indicate a non-operational
cell.
[0077] It will be understood that the present invention will cover
these variations and embodiments as well as variations and
modifications that would be understood by the person of ordinary
skill in the art.
[0078] Throughout this specification, the term "mobile" or "mobile
phone" is used synonymously with terms such as "cell phones" or
"mobile radio terminal", and will be understood to encompass any
kind of mobile radio terminal such as a cell phone, Personal
Digital Assistant (PDA), lap top or other mobile computer, or
pager. Similarly the terms cell is used synonymously with the term
cell.
[0079] Throughout this specification the term "location system" is
used in its most general sense referring to systems that output
location estimates with respect to an object or coordinate frame
and to systems that output the location estimate as an indication
of the proximity to an object or an area. This includes, but is not
limited to, zone-based location systems such as that described in
PCT/AU2006/000478 entitled "Enhanced Terrestrial Mobile
Location".
[0080] The term "about" as used herein may be applied top modify
any quantitative representation that could be permissively vary
without resulting in a change in the basic function to which it is
related.
[0081] In the following description, when processing is described
as being carried out in a mobile terminal, it will be understood
that the processing could be carried out in the handset, in the
Subscriber Identification Module (SIM) that is inserted in the
handset, in an additional processing or smart card inserted into
the handset, or in a combination of two or more of these.
[0082] In this specification, use of the term network configuration
refers to the as deployed network and where relevant also includes
the operational state of each component of the network.
[0083] It will also be understood that much of the processing that
occurs in the implementation of various aspects of the present
invention can also be distributed between the handset, one or more
network elements or processors within the radio communications
network and/or one or more elements outside the radio
communications network. It will also be understood that the
invention may be applied to any application in which a location
estimate for a mobile terminal is required.
[0084] Furthermore, the network database referred to in the various
aspects of the present invention can be a central repository, a
distributed database and/or optionally with full or partial copies
distributed to one or more mobile radio terminals.
[0085] While the following description uses location and zone based
systems to exemplify the operation of the invention, it will be
appreciated that the invention is not limited to such applications.
The methods described are equally useful for other systems in which
a radio network configuration database is maintained as will be
understood by one of ordinary skill in the art. One such example is
the primary operation of the mobile network in providing voice and
data communications where problems with the network configuration
degrade the quality of service and/or coverage.
System Architecture
[0086] FIG. 1 shows an exemplary mobile radio network arrangement
in which the various processes may be applied. Shown there is a
radio communications network 10 containing a number of Base
Stations 11 for communicating with one or more radio mobile
terminals 20. Also associated with network 10 is a network
processor, such as Location Server 30 and network database 50.
Network database 50 may store any kind of data, including a model
of the network 10. A system operator 40 may also be present for
managing various aspects of the network 10. Network processor 30
may have all the required apparatus for carrying out the various
aspects of the present invention, including one or more receivers
for receiving data from various network elements such as mobile
radio terminals, comparators for comparing data received from the
network elements with data in the network database, processors for
performing various calculations and computations, and means for
outputting the results of these various calculations and
computations.
[0087] The following sections discuss the different types of
network database errors and the impact such errors have can on
location estimation and zone-based location systems.
Configuration Vs Database Errors
[0088] Any discrepancy between the as configured mobile network and
the network database that is supposed to reflect the as configured
network does not necessarily mean that it is the network database
that needs to be changed. The problem may be that an intended
change to the configuration of the network was not carried out or
was carried out incorrectly and that it is the configuration that
needs to be corrected to ensure the configured network and network
database are in synchronisation.
Operational Cell Not in Database
[0089] There is a range of scenarios in which a cell can be
operational but not in the network database. By `operational`, it
is meant that the cell is available for use by a mobile terminal in
the vicinity. While the scenarios are different, the effect of the
omission on a location system as a practical matter can cause
similar operational issues.
[0090] A cell may be operating but not necessarily in the network
database. The cell site may have been recently commissioned and the
database not updated to reflect the addition of the new cells. The
network database may have a cell ID error such that the broadcast
cell ID does not match that in the database. The cell may be due
for decommissioning and was prematurely removed from the network
database. The cell may be a temporary cell, commonly referred to as
a Cell-On-Wheels (COW), to provide coverage for a short term
localized increased in capacity requirements as would occur for
large sporting events or festivals.
[0091] In a location system, the effect on location estimates of an
operational cell being omitted from the network database can
degrade performance. As will be described in more detail further
below, the cell ID of the serving cell is, in some circumstances,
critical for determining the source of the neighbour cell
measurements. If the mobile terminal has used as its serving cell a
cell that is not in the database, then the neighbour cells may not
be able to be identified and the location transaction will not be
fulfilled. At the very least any measurements from omitted cells
cannot be used as the location of the cell cannot be determined.
This results in a drop in the accuracy of the location estimate and
could potentially lead to a failed transaction if there remain too
few measurements to enable the location estimate to be
computed.
[0092] For zone-based location systems, the effects of not updating
the network database with a new cell can adversely impact the
system performance. For a Cell ID based zone system, any subscriber
whose zone lies within the range of the new cell could find their
service degraded as whenever their mobile terminal camps on the new
cell, the mobile may be deemed to be out of the zone even though
they may physically be within the zone.
[0093] In PCT Patent Application No. PCT/AU2006/000478, the zone
computation will consider the measurement of a cell that it does
not know as being evidence that the mobile is not in the zone. The
overall effect may be to shrink the zone and if the signal strength
is sufficiently high the mobile radio terminal may not register
ever being in the zone.
Non-Operational Cell in Database
[0094] Although a cell is included in a network database it may not
necessarily be operating at a given point in time. The cell may be
new and not yet made operational. The cell may be temporarily
non-operational due to a fault or planned maintenance. The cell's
cell identity may be correctly entered and hence from the viewpoint
of measurements made the cell identity as listed in the database is
never observed and is thus assumed non-operational. The cell may
have been decommissioned but not yet removed from the database.
[0095] In certain location systems, the presence of a
non-operational cell or cells in the database may not degrade
location performance. In a system utilising unreported cells in
estimating location such as that described in PCT Patent
Application No. PCT/2006/000347, the existence of non-operational
cells in the database may degrade the accuracy of the system. By
referring to a cell as unreported, we mean that it is not reported
in a particular set of measurements from a given mobile
terminal.
[0096] For a Cell ID based zone system, the presence of
non-operational cells in the network database may not degrade the
performance. However, in a zone-based system such as that described
in the previously-mentioned PCT Patent Application No.
PCT/AU2006/000478, there may be a degradation in performance. To
illustrate, the definition of a zone measured when a dominant cell
was operational is likely to feature that cell Should that cell
become non-operational it will no longer be reported by a mobile
terminal in that zone. This in turn may lead a zone detection
system to infer that the mobile is not situated within the zone,
unless the database is updated to reflect the non-operational
status of that cell.
Cell Identification Parameter Errors
[0097] Base stations typically have a unique cell identifier. In
the case of GSM the Local Area Code (LAC) and Cell Identifier (CID)
uniquely identify a particular cell within a given network. Base
stations also have other attributes that can identify the cell to
within a subset of the cells in a network. Such attributes include
the transmission frequency (ARFCN in GSM, UARFCN in 3G UMTS), the
BSIC in GSM and PSC in 3G UMTS. When a network is retuned it is
common for the transmission frequency and BSIC/PSC to be changed
and as such a retune represents an opportunity for discrepancies to
arise between the as configured network and the network
database.
[0098] Errors in the Cell ID, in certain circumstances, will
manifest as an apparent new cell in mobile measurements and a cell
that appears to be non-operational in so far that the cell in the
database is never reported in any sets of measurements.
[0099] When measurements are made of a given cell, the unique cell
identifier is usually only obtained for the serving cell. For the
neighbour cells the measurements contain one or more of the
non-unique identifiers. For example in GSM the BSIC and ARFCN are
easily obtained; for 3G UMTS, the PSC and UARFCN are easily
obtained. Identification of the cell is achieved by finding that
cell with the observed attributes that is the most likely to be
heard given the reported serving cell. Techniques for doing this
are well known in the art and include finding the cell matching the
criteria nearest to the serving cell, and the use of propagation
models to determine the matching cell most likely to be heard given
the area where the serving cell would be expected to be strongest.
Examples of suitable propagation models include the Hata model (see
section 2.7 of Mobile Radio Communications 2nd Ed. Editors Steele
and Hanzo, ISBN 047197806X,J. Wiley & Sons Ltd, the 3GPP model
(see section 5.1.4 of "Radio Frequency (RF) system scenarios" 3GPP
TR25.942) and the T1P1.5 model (see section 3.2 of "Evaluation of
Positioning Measurement Systems", T1P1.5/98-110).
[0100] Errors in the non-unique cell identifiers may create
problems for location systems as the measurements may be associated
with the wrong cell or perhaps not associated with a cell at all.
Consequently the location estimate or zone detection may be in
error.
[0101] FIG. 2 illustrates this as an example, in which a mobile
radio terminal 20 is camped on cell 6692. Mobile radio terminal 20
also hears cell 3451 but since it is not camped, the cell is only
identified via the non-unique identifiers: ARFCN=76 and BSIC=55. In
the Location Server 30, network measurements sent by the mobile
radio terminal 20 are analyzed. One step is to uniquely identify
those cells not uniquely identified in the measurements sent by the
mobile radio. This is done by searching for a match to the
non-unique identifier that is closest to the known (serving) cell.
In this example a cell coordinate error results in cell 7587 being
selected as the best match. The cell is illustrated as a faded
tower to indicate that the cell is not actually the location
indicated in the network database. Had the cell not had incorrect
coordinates, the correct cell 3451 would have been associated with
the measurements.
Incorrect Cell Coordinates
[0102] Location estimates are particularly affected by incorrect
cell coordinates. In general the error in the location estimate is
in proportion to the error in the cell coordinates.
[0103] In a Cell ID based location system, the location estimate
may be calculated as a weighted average of the locations of the
cells heard by the mobile. The location estimate is thus corrupted
in proportion to the error in the cell location. In one example, a
mobile radio terminal 20 reports ten cells and one of those cells
has a coordinate error placing it 1 km away from its true location.
The effect of the error in this case after the averaging process is
to move the location estimate approximately 100 m in the direction
of the error.
[0104] For other types of location systems, the effect of the error
can vary significantly depending upon the relative importance of
the erroneous cell in the overall position computation. In some
cases the erroneous cell is detected and substantially ignored and
thus results in a minor loss of accuracy associated with having one
less useful measurement. In other cases the affected measurement
may play a significant role in constraining the solution; that is
it has a disproportionate effect on the location estimate. In such
cases, the resulting location error can be large and have a
significant impact on the performance of associated location-based
applications.
[0105] The effect of cell location errors on a zone-based location
system, such as that described in PCT/AU2006/000478, may vary. Once
the zone has been defined, the location of the cell is typically
less critical. During the zone registration phase there are,
however, circumstances where cell location errors may affect the
performance of a zone-based system. If for instance such a system
first computes a location estimate to validate the location of a
zone registration request against a nominal location, as described
in PCT/AU2006/001479, then cell coordinate errors could lead to the
request being rejected as not being consistent with the expected
zone location.
[0106] A zone based system that incorporates predicted measurements
into the zone definition will also typically be affected by cell
location errors. For example, in a Cell ID based system zones are
defined by the set of cells that can be heard in the zone. These
can be assigned by measurement, prediction or a combination of
both. Cells that have coordinate errors may be included in a zone
where they don't belong, artificially creating a second zone.
Conversely a cell with incorrect coordinates may not be included in
a given zone definition thus degrading the performance of the
zone.
[0107] For zones that include signal-strength as part of the
definition, the signal level predicted for a cell with incorrect
coordinates may be in error. The effect of the error can vary from
slightly different zone boundaries to significant zone performance
problems such as the mobile being declared out of the zone when
actually situated within the area intended to be enclosed by the
zone definition.
Other Cell Parameter Errors
[0108] There are other cell parameter errors that can affect
location estimate and zone performance depending upon the types of
measurements used by the system.
[0109] Location systems that rely on signal strength can be
impacted by errors in antenna azimuth, antenna down-tilt, antenna
characteristics such as gain and beamwidth, and effective transmit
power levels. A cell that is transmitting at a higher power level
than is stated in the network database typically will mean that a
location system estimating range to the cell using signal strength
will place the mobile terminal closer to the cell than it actually
is. Similarly, errors in the parameters associated with the antenna
may degrade the location estimate.
[0110] Other location systems that do not rely as directly on
signal strength may also be degraded by these types of parameter
errors. For example timing-based systems may rely on antenna
azimuth in order to associate timing measurements with the
corresponding cells where such cells were not uniquely identified.
Another example is a cell ID based system using the cell centroid.
In that system it is necessary to know the antenna characteristics
and in particular whether the antenna is directive or
omni-directional.
Process
[0111] There are various aspects to dealing with network databases
with respect to location systems. These are: detection of
inconsistencies between the as configured network and the network
database; dealing with network inconsistencies once they are
detected; and updating the system in response to network
configuration changes, corrections to the network database or in
response to a detected inconsistency.
[0112] The following describes various processes for detection of
inconsistencies between the as configured network and the network
database. The other two aspects are described in a co-pending
patent application entitled "Mobile Service Maintenance Management"
filed concurrently herewith.
Detecting Network Inconsistencies
[0113] By inconsistency we mean any difference between the actual
network configuration and the representation of the network in the
network. An inconsistency can include an absence of in the database
of a network element that is in the actual network, the presence of
a network element in the database that is not in the actual
network, and/or a variation in a value of a network parameter in
the database from that of the actual database.
[0114] In certain embodiments of the present invention, the
detection of such inconsistencies is based on analysis of
observations of the network signals compared against the network
database. Currently, radio network operators typically use
dedicated equipment to detect inconsistencies in the radio network.
They commonly do so by fitting out vehicles with mobile radio
receivers and GPS receivers to map the radio network signals. In
one example, taxis are used to provide spatial coverage and are on
the move for significant periods of each day. For specific problems
the operators may deploy an instrumented test vehicle to survey the
area where there is a service problem.
[0115] The following describes various processes by which certain
types of inconsistencies can be detected. The different processes
can be applied in parallel. That is, a given set of observations
can be applied to one or more of the processes at the same time
and/or in any order. Alternatively, each process may be applied one
after the other, or a combination of both.
[0116] The observations of the network 10 can come from any
receiver that is monitoring the network. This includes for instance
the large number of mobile terminals 20 in everyday use. Such
mobile radio terminals are continually making measurements of the
radio network signals. One aspect of the present invention is to
take advantage of existing measurements and use those measurements
to detect inconsistencies. This allows the operator to leverage
large numbers of temporally and spatially diverse radio
measurements available from across the network at minimum
additional cost. In the context of a home zone solution, such
measurement data is included with zone registrations and location
requests. As described herein, the measurement data, if desired,
can also be included with other messages from mobiles to the
system. This has the advantage of utilizing spare capacity and/or
existing communication sessions.
[0117] The processes by which inconsistencies are detected can be
performed in real-time or through data post-processing. Real-time
processing refers to the processing of measurements to detect
inconsistencies as soon as practically possible after the
measurements become available. For example the measurements would
be placed in a queue and the system processes the data in the queue
as fast as the system can. Post-processing refers to the
accumulation and storage of the measurements for batch processing
at a later time. For example the system could process the
measurements when it was not processing more urgent tasks
Detection Using a Single Measurement
[0118] Certain inconsistencies can be detected from a single
measurement which contains data which is inconsistent with that
held in a database. Certain inconsistencies can be detected from
multiple measurements containing data which in combination is
inconsistent with that held in the database.
[0119] Depending upon the type of hypothesized inconsistency
multiple measurements may be required in order to gain sufficient
confidence to act upon the inconsistency.
[0120] The process according to one aspect of the present
invention, of detecting an inconsistency from a single observation
is illustrated in FIG. 3. The process begins at step 100. At step
101, measurements of one or more parameters are obtained from the
network. In steps 102 and 103, for each measurement, the
measurement is compared with its corresponding data in the database
50, or if the measurement is such that a metric should be
calculated, a metric is calculated from the corresponding data in
the database. Such metrics described herein include but are not
limited to calculating the distance between two simultaneously
reported cells, computing the zone detection cost associated with a
cell. In step 104, a determination is made as to whether the
measurement is consistent with its corresponding data in the
database. In the case of a calculated metric, this can be compared
to a given threshold. If the measurement is consistent with the
database or the calculated metric is within the given threshold,
the process ends at step 106. If the measurement or the calculated
metric are not consistent with the database, then that measurement
is marked or flagged as inconsistent. A given set of observations
also referred to as measurements, of the network 10, in some forms,
will contain data pertaining to one or more cells. For each such
cell the data observed is compared against that held in the
database or against a metric computed using data from the database.
If there is a difference between the observed data and the
database, or the computed metric exceeds its associated threshold,
then an inconsistency has been detected for that cell. All cells so
identified from the set of measurements are then passed on to other
processes that take actions to resolve the inconsistencies as are
described in the above-mentioned co-pending patent application
dealing with management of the detected inconsistencies. Specific
uses of the process of detection using a single measurement include
the processes illustrated in FIGS. 5, 7, 8 and 9 as will be
described in more detail below with respect to each figure.
Detection Using Multiple Measurements
[0121] Inconsistencies can be also detected by accumulating
evidence over time or over a series of measurements to support a
hypothesis. In particular this is required for inconsistencies for
which a single measurement does not provide sufficient confidence
to conclude that the inconsistency exists. An example is the
detection of a cell being non-operational. Not hearing the cell in
any given measurement set does not prove the cell is not operating
whereas the converse of presence of a cell in a set of measurements
proves that the cell is operating.
[0122] FIG. 4 illustrates a process flow for accumulating evidence
to support a given hypothesis where the hypothesis represents the
existence of a type of inconsistency. The process begins at step
200. In step 201, a set of measurements are obtained from the
network. In steps 202 and 203, for each cell reported in a set of
measurements, the observed parameters are checked to see if the
observation contains evidence that denies the hypothesis. As stated
above, the presence of a cell in a set of measurements denies for
the time being the hypothesis that the cell is non-operational.
These steps are illustrated as steps 205 and 206 in FIG. 4. In the
case where the measurement does not deny the hypothesis, the next
step is to use the properties of a zone-based location system to
find and accumulate evidence to support the hypothesis. In steps
207 and 208, for each zone, the process determines whether the
mobile radio terminal 20 from which the measurements were obtained,
is in the given zone. If so, then in steps 209 and 210, for each
cell in the zone profile of the given zone, when the mobile radio
terminal is in a given zone, there are known expectations of what
cells are hearable, the expected signal levels and variation,
within that zone. Comparisons between the measurements and the
expected measurements from the database enable evidence to be
gathered to support or deny the hypothesized inconsistency. As an
example, the process could be used to detect a change in the
transmit power level by tracking the difference between the
expected and observed signal levels for each cell. If the evidence
for the hypothesized inconsistency exceeds a configured or
predetermined threshold (step 211), the hypothesis is accepted and
the cell is flagged as being inconsistent in step 212. In step 213,
all cells flagged as being potentially inconsistent are collected
for subsequent processing. The process then ends at step 214. A
particular example of an indirect detection process using an
accumulation of evidence is described in more detail below with
reference to detection of non-operational cells and FIG. 6.
[0123] For particular inconsistencies the step of checking
measurements for evidence to deny a hypothesis can be left out. If
there are no specific observations that can deny the hypothesis
then there is no value in this step. In the case of detecting
non-operational cells, the observation of a given cell is evidence
that denies the hypothesis. In the case where the hypothesis is
that a cell has changed transmit power levels the variability of
signal strength observations and relatively small changes made to
power levels that a single measurement cannot prove that there has
not been a change.
[0124] The process of accumulating evidence can also be applied to
inconsistencies that are detectable based on a single measurement
and hence would be suitable for detection via a direct detection
process as described above. In particular, if there is the
possibility that any given measurement may be erroneous and provide
a false indication of an inconsistency, the evidence accumulation
process can be used to make the process more robust. In one
example, there is a GSM mobile that is camped on cell 6612 and is
also hearing a cell on ARFCN 61 with BSIC 56 which resolves to cell
4459 based on proximity to cell 6612. A data error on 1 bit results
in the same cell being reported in a different set of observations
as ARFCN 61 with BSIC 57. Within the proximity of cell 6612 this
ARFCN/BSIC combination is not found in the database and thus the
observation appears as an inconsistency. A threshold could be set
such that the inconsistency is only flagged for attention if the
same inconsistency is observed one or more times. In certain
embodiments the inconsistency detection threshold may be set to
between about 1 to about 50 times, or between about 1 to about 10
times, or between about 1 to about 5 times, or between about 1 to
about 3 times. In one particular example, there is a system with
the threshold set at 3 observations. Continuing the previous
example the observation of ARFCN/BSIC 61/57 would need to be
observed in 3 separate observations before the inconsistency would
be considered to be evidence of a new cell. This is illustrated in
Table 1 below.
TABLE-US-00001 TABLE 1 Unknown Inconsistency Serving Cell
ARFCN/BSIC Count Reported 6612 61/57 1 No 2186 61/57 2 No 6612
61/57 3 Yes
[0125] A further refinement would be to make the criteria include
the serving cell. Continuing the above example the ARFCN/BSIC 61/57
would need to be observed with cell 66123 times before the
inconsistency would be treated as real. This avoids the problem of
the same inconsistency from distinct geographical locations being
treated as evidence of the same inconsistency. This example is
illustrated by expanding upon Table 1 whereby the observation with
serving cell 2186 is treated as a separate inconsistency with a
separate incident count This is illustrated in Table 2 below.
TABLE-US-00002 TABLE 2 Unknown Inconsistency Serving Cell
ARFCN/BSIC Count Reported 6612 61/57 1 No 2186 61/57 1 No 6612
61/57 2 No 6612 61/57 3 Yes
[0126] The trade-off is certainty of decision versus time delay in
reporting the inconsistency. The more certainty required, for
example a higher count threshold, the longer it will take to gather
sufficient observations. If a measurement indicates that a
previously observed inconsistency is no longer present, the
evidence is reset, for example the counter would be set to 0.
[0127] Optionally a timer could be associated with an inconsistency
such that it must be detected a number of times within a time
window before concluding that the inconsistency exists. When an
inconsistency is detected the past history of the detections is
examined over the configured time window and the number of
incidents counted. If the count equals or exceeds the threshold,
then the conclusion is that the inconsistency exists. This approach
has the advantage of inconsistencies caused by data errors which
are expected to be rare events from accumulating over a long period
and eventually causing an inconsistency to be falsely concluded to
exist reported. The time window can be configurable. Time windows
could vary over a number of ranges of time, for example, from about
0 to about 24 hours, or from about 0 to about 1 hour, or from about
0 to about 15 minutes, or from about 0 to about 6 hours, or from
about 0 to about 48 hours, or from about 10 seconds to about 3600
seconds, or from about 10 seconds to about 300 seconds.
[0128] Table 3 illustrates the method by extending the example from
table 1. The time, in the example measured in seconds from an
arbitrary epoch, at which an inconsistency is observed is noted.
The number of occurrences of the same inconsistency within the
configured time window, in this example 300 seconds, is counted
each time the inconsistency is detected. If the threshold is
reached within the time window, the inconsistency is treated as
real. In the example the threshold is 3 and this threshold is
reached within a 300 second window at time 23872 seconds. It should
be clear to one of ordinary skill in the art that the inconsistency
criteria can be extended to include the serving cell as described
above and illustrated in Table 2.
TABLE-US-00003 TABLE 3 Count Serving Unknown Time over 300
Inconsistency Cell ARFCN/BSIC (seconds) seconds Reported 6612 61/57
12876 1 No 2186 61/57 23611 1 No 6612 61/57 23732 2 No 6612 61/57
23872 3 Yes
[0129] In certain embodiments, the window may be defined in terms
of the number of opportunities presented to the system in which the
inconsistency may be detected rather than in terms of elapsed time.
An example of an opportunity to observe an inconsistency is the
receipt of a message from a mobile by the server where the message
contains radio measurements as described herein. In some instances,
the system may be configured to require the number of detections to
exceed a threshold and for that number of detections to occur
within a number of opportunities.
[0130] The detections threshold may be set to between about 1 and
about 5, between about 1 and about 20, between about 1 and about
100, between about 3 and about 10, between about 5 and about 50,
between about 20 and about 100, or between about 50 and about 1000.
The opportunities threshold may be set to between about 100 and
about 10,000,000, between about 200 and about 1000, between about
500 and about 2000, between about 1000 and about 5000, between
about 2000 and about 10,000, between about 5000 and about 20,000,
between about 10,000 and about 50,000, between about 20,000 and
about 100,000, between about 50,000 and about 200,000, between
about 100,000 and about 500,000, between about 200,000 and about
1,000,000, between about 500,000 and about 2,000,000, between about
1,000,000 and about 5,000,000, or between about 2,000,000 and about
10,000,000. As an example, consider a network with 2000 cells and
on average each message containing radio measurements contains 6
cells. On average a cell indicating an inconsistency will only be
present once every 333 messages. To be reasonable confident that a
reported inconsistency is valid and to provide a reasonable
likelihood that the inconsistency will be detected the criteria
could be specified as 3 detections within 2000 opportunities.
Detecting Unknown and Incorrectly Identified Cells
[0131] FIG. 5 illustrates a process flow for detecting the presence
of a cell that is not in the database or for which one or more cell
identification parameters does not match that in the database.
[0132] Starting at step 300, the process takes one or more
measurements at step 301 of the network signals and compares for
each measurement the cell identity information against that in the
database as shown in steps 302, 303 and 304. Using one or more of
the techniques described elsewhere in this specification the cell
identifiers included in the measurements are checked.
[0133] If the result of this processing indicates that the cell
identifiers are not in the network database, the cell is flagged as
unknown in step 307. If the result of this processing indicates
that the cell identifiers are known, then a check is made in step
305 to see if the known cell identifiers are consistent with the
network database. If not, then the cell is flagged as inconsistent
in step 306. In step 308, all of the flagged cells are gathered
together for subsequent processing according to one or more methods
of the co-pending patent application referred to above relating to
management of detected inconsistencies. This process ends in step
309.
[0134] The following provides detailed examples of carrying out
various aspects of the above method.
[0135] Detecting Unknown Cell Based on Unique Identifier
[0136] The observation of a cell ID that is not present in the
network database represents an inconsistency between the as
configured network and the network database. The unknown cell ID
could represent a cell recently made operational but not in the
database, a cell prematurely removed from the database, or a cell
assigned the wrong ID in the network or in the database.
[0137] One example is a GSM system in which a new base station site
is installed. At this site there are three cells with parameters as
shown in Table 4 below. The data associated with these new cells
has not been updated into the network database. A set of
measurements is shown in Table 5 below. The system will detect the
Cell ID 25071 as an unknown cell as the Cell ID will not be present
in the database.
TABLE-US-00004 TABLE 4 Cell ID ARFCN BSIC 25070 95 38 25071 81 59
25072 67 46
TABLE-US-00005 TABLE 5 Cell ID ARFCN BSIC RxLev Mean (dBm) 25071
Unknown Unknown -83.0 Unknown 71 61 -92 Unknown 67 46 -99 Unknown
69 43 -103 Unknown 73 34 -103
[0138] Based on the example for GSM it will be clear to those of
ordinary skill in the art how to apply the method to other radio
access technologies such as UMTS, CDMA-2000, and CDMA IS-95.
[0139] Detecting Unknown Cell Based on Non-Unique Identifier
[0140] Base stations that have only been identified by partial
identifiers are associated with cells by searching for a match to
the partial identity within the vicinity of the serving cell.
Failure to find a match for the partial identity indicates an
inconsistency between the as configured network and the network
database. The failure, however, cannot be attributed to a specific
cause. Potential causes include, but are not limited to, a new cell
in the network; premature removal of a cell from the database;
incorrect partial identifier information in the database; incorrect
coordinates for the serving cell, and incorrect coordinates for the
neighbour cell.
[0141] One example is a GSM system in which a new base station site
is installed. At this site there are three cells with parameters as
given in Table 1 above. The data associated with these new cells
has not been updated in the network database. A set of measurements
is shown in Table 6. The system will attempt to identify the unique
identity of the neighbour cells that are partially identified by
Cell ID and BSIC. A search within the vicinity of Cell 26078 fails
to find a match for the ARFCN/BSIC pair of 81/59. The failure to
match the identity of this measurement indicates the presence of an
unknown cell.
TABLE-US-00006 TABLE 6 Cell ID ARFCN BSIC RxLev Mean (dBm) 26078
Unknown Unknown -87.0 Unknown 71 61 -92 Unknown 81 59 -99 Unknown
69 43 -103 Unknown 73 34 -103
[0142] Where a system or network element, for example a mobile
radio terminal, contains a partial copy of the network database,
inconsistencies can be detected in some circumstances where the
observed identifiers contradict the data that the mobile contains.
When partial identifiers are reported in conjunction with a unique
identifier, the partial identifiers can be cross-referenced against
the unique identifier. For example, if the mobile radio terminal
contains a reference to GSM Cell ID 24141 with ARFCN 59 and BSIC 51
but observes Cell ID 24141 on ARFCN 32 and BSIC 27 then an
inconsistency has been detected.
[0143] Based on the example for GSM given above, it will be clear
to those of ordinary skill in the art how to apply the method to
other radio access technologies such as UMTS, using UARFCN and PSC
and CDMA IS-95 using the channel number/PN offset.
[0144] Detecting Unknown Cells Via Zone Detection
[0145] A mobile radio terminal will not necessarily store the
entire network database and hence may not be able to identify all
of the inconsistencies discussed above. One aspect of this
invention is a method for a mobile radio terminal to alert the
server 30 to the possibility of there being an inconsistency
between the as configured network and the network database. In a
zone-based location system the mobile contains one or more zone
profiles and each such profile contains a subset of the network
database. The zone detection process evaluates the difference
between current measurements and that expected to be seen in a
given zone as defined by the profile for that zone. Each
measurement makes a contribution to the decision as to whether the
mobile is in a given zone or not. If a single measurement is
responsible for a significant portion of the decision metric, then
the computation is repeated with that cell removed. If as a result
the mobile radio terminal is deemed to be within the zone, the cell
associated with the measurement is flagged as representing an
inconsistency. An indicative portion for a measurement's influence
to be deemed significant is 40% of the decision metric's value. The
proportion of the cost deemed significant could be in the range of
about 10% to about 80%, about 10% to about 30%, about 15% to about
40%, about 20% to about 50%, about 30% to about 60%, about 40% to
about 80%. The technique is particularly useful for detecting newly
commissioned cells and errors in the configuration of the network
that have arisen during a network retune.
TABLE-US-00007 TABLE 7 Cell ID ARFCN BSIC RxLev Mean Sigma 25068 95
38 -60.0 9 54763 81 59 -88.3 9 18322 67 46 -92.1 9 892 71 61 -98.7
9 18581 73 34 -103 9
[0146] A GSM radio zone profile is given in Table 7. In this
example, a new set of measurements is available as illustrated in
Table 8 below and these will be used to evaluate the zone status
against the profile from Table 7. The ARFCN and BSIC are not
available for the serving cell because they are not reported in the
Network Measurement Report (NMR) data.
TABLE-US-00008 TABLE 8 Cell ID ARFCN BSIC RxLev (dBm) 49844 Unknown
Unknown -83 Unknown 95 38 -89 Unknown 81 59 -90 Unknown 71 61 -92
Unknown 67 46 -99 Unknown 69 43 -102
[0147] The total cost is calculated as described in PCT Patent
Application No. PCT/AU2006/000478 by summing the costs
corresponding to the matched, unmatched and unreported cells. The
calculated values for the matched cell costs are shown in Table 9,
represented to 2 decimal places.
TABLE-US-00009 Profile Measured Cell ID ARFCN BSIC RxLev RxLev Cost
25068 95 38 -85.0 -89 0.20 54763 81 59 -91.3 -90 0.02 892 71 61
-98.7 -92 0.55 18322 67 46 -92.1 -99 0.59
[0148] The calculated value for the single unreported cost is shown
in Table 10. In this example the unreported cell is not included in
the cost because it would not be expected to be heard given the
levels that the other signals were reported at.
TABLE-US-00010 TABLE 10 Profile Cell ID ARFCN BSIC RxLev Threshold
Cost 18581 73 34 -103 -102 0.00
[0149] The calculated value for the unmatched costs is shown in
Table 11.
TABLE-US-00011 Measured Cell ID ARFCN BSIC RxLev Threshold Cost
49844 Unknown Unknown -83 -103 4.94 Unknown 69 43 -101 -103
0.05
[0150] The total cost is 6.35. The cost of the cell 49844
represents 78% of the cost. This exceeds the indicative threshold
of 40% so the test is performed. The cell is not included in the
zone detection test. The total cost is now 1.41. The threshold at
the 80% probability level, derived from a chi-squared distribution
with 5 degrees of freedom, is 2.34. The cost is less than this
threshold and thus excluding the cell with the large cost would
result in the mobile being declared in the zone. Thus the cell
49844 is declared to be a new cell. The same process can be used to
identify the presence of a new cell that was only identified by
non-unique identifiers (ARFCN+BSIC). In this instance the presence
of the unknown cell would be detected but its unique identity would
not be known.
[0151] A mobile radio terminal can only measure cells that are in
its vicinity. Base station coordinate errors may be detected by
identifying measurement sets that are incongruous. Various metrics
can be used to evaluate the likelihood that a cell's coordinates in
the database are incorrect or that a given set of measurements
contains one or more cells with suspect coordinates.
Detecting Non-Operational Cells
[0152] In any given measurement set, the existence of a cell
constitutes proof that the cell is operating. The converse is not
true; the absence of a given cell in a given set of measurements
does not necessarily constitute proof that the cell is not
currently operating. According to one aspect of the present
invention, a process to detect cells suspected of not operating is
to accumulate evidence from a series of one or more sets of
measurements until such time as a decision can be made about the
operational state for a given cell.
[0153] FIG. 6 illustrates a process flow for detecting cells that
are in the network database 50 but which appear to be
non-operational. The process begins at step 400 to obtain
measurements from the network at step 401. For each cell that is
observed in a measurement set, the cell is deemed to be operating
and hence the evidence that it is not operational is reset to 0 at
steps 402, 403 and 404 by setting Cell Cost=0 and Cell Count=0.
[0154] If the mobile radio terminal 20 is in a zone, then there
exists the opportunity to update the evidence that cells that have
not been reported may be non-operational. From step 405 the
following steps are taken for each zone being considered. A zone is
a region within the mobile radio communications network 10 that may
be defined by various means, including those described in
PCT/AU2006/000478 entitled "Enhanced Terrestrial Mobile Location".
In step 406, a determination is made as to whether the mobile radio
terminal 20 is in the zone. Again, various methods may be used to
determine whether the mobile radio terminal is in or out of the
zone, including those described in detail in this same incorporated
reference. In steps 407 and 408, it is determined for each cell in
the zone profile, whether the cell was reported. If not, the
evidence that the cell is not operating is updated in step 409 by
computing the unreported cell cost and adding this to the
accumulated cell cost and incrementing the cell count. If the cell
is determined to be reported, the process ends at step 413.
[0155] In step 410, the collected evidence is compared with a
threshold. In this case, if the accumulated unreported cell cost is
greater than the threshold for the cell count (i.e. the threshold
is exceeded), the cell is flagged as potentially non-operational in
step 411. In step 412, al cells that have been flagged as
potentially non-operational are collected for further
processing
[0156] The following sections provide detailed examples for
detecting non-operational cells
[0157] Detecting Non-Operational Cells at Server Based on
Unreported Cost.
[0158] In one aspect of the present invention, a means and method
is provided for detecting that a particular network cell is out of
service. This is done using the measurements observed by one or
more mobile radio terminals. The detection process uses a metric
reflecting the expectation of having not observed a cell given the
other cells that were observed. With each set of measurements the
metric is accumulated for each cell. A cell is flagged as
non-operational when the accumulated metric is evaluated and deemed
to contain insufficient evidence that the cell is currently
operating. For each cell present in a set of measurements the
metric accumulator is reset.
[0159] Different metrics can be used to determine whether a cell is
operational or not. For a given measurement set the probability of
not reporting a given cell given the observed signal measurements
and assuming that the cell is operating can be calculated. These
probabilities can then be accumulated for a given cell over a
series of measurements by multiplying the probabilities. If the
accumulated probability crosses a threshold, for example 0.005, the
cell is flagged as non-operational. When a cell is present in a
measurement set, the accumulated probability is reset to 1.
[0160] Another metric is to compute a statistic such as the
chi-squared statistic, for each cell and accumulating the statistic
by adding the values over a series of measurements. When the
accumulated statistic exceeds a defined threshold the cell is
deemed to be non-operational. If the cell is present in a given
measurement set the statistic is reset to 0.
[0161] Yet another metric is to accumulate the time since a cell
was last reported. A cell that has not been included in a
measurement set for longer than the configured interval is flagged
as non-operational.
[0162] Yet another metric is to accumulate the number of
measurement sets that have been processed since a cell was last
reported. A cell is flagged as non-operational if it has not been
reported within the most recent n messages. Such a metric avoids
the false alarms that time-based metrics can generate during quiet
periods such as the early morning.
[0163] In one embodiment for a zone-based location system such as
PCT/AU2006/000478, the process at the server operates on all cells
in the network which feature in one or more zone profiles. For each
of these cells the unreported cost is accumulated each time a new
set of measurements yields a large unreported cost but the
remaining profile elements yield a good match for the measurements.
The general process is to examine each cell that relative to a
given zone profile is not reported in a given set of measurements.
If the cost of a given unreported cell is deemed significant and
when this cell is ignored the remaining measurements indicate that
the mobile is in the zone, then the cell is deemed potentially
non-operational and the evidence to support this is
accumulated.
[0164] It is common for a site to be non-operational and
consequently all cells located at that site would be
non-operational. The reasons for this are due to the resources
shared by all cells at a site; in particular power and data. It
should be clear to those of ordinary skill in the art that the
algorithm described can also be applied on a site basis. In this
mode of operation the unreported cost is accumulated on a per site
basis.
[0165] For each of these cells the unreported cost is accumulated
each time a new set of measurements yields a large unreported cost
but the remaining profile elements yield a good match for the
measurements. In physical term, such an observation indicates that
the mobile radio terminal is located in a place where the
measurements are consistent with the profile but this particular
cell is not reported. Each time a cell is reported however, the
accumulated cost is reset to zero since the cell is clearly not in
an outage state.
[0166] The following description illustrates a scenario based in a
GSM network in which a measurement yields a large unreported cost,
such that a cell outage may be indicated.
[0167] This example uses the profile defining a zone as shown in
Table 12.
TABLE-US-00012 TABLE 12 Cell ID ARFCN BSIC RxLev Mean Sigma 25068
95 38 -60.0 9 54763 81 59 -88.3 9 18322 67 46 -92.1 9 892 71 61
-98.7 9 18581 73 34 -103 9
[0168] In this example, a new set of measurements is available as
illustrated in Table 13 below. The ARFCN and BSIC are not available
for the serving cell because they are not reported in the NMR
data.
TABLE-US-00013 TABLE 13 Cell ID ARFCN BSIC RxLev Mean (dBm) 54763
Unknown Unknown -83.0 Unknown 71 61 -92 Unknown 67 46 -99 Unknown
69 43 -103 Unknown 73 34 -103
[0169] The total cost is calculated as described in
previously-incorporated PCT Patent Application No.
PCT/AU2006/000478 by summing the costs corresponding to the
matched, unmatched and unreported cells. The calculated values for
the matched cell costs are shown in Table 14, represented to 2
decimal places.
TABLE-US-00014 TABLE 14 Profile Measured Cell ID ARFCN BSIC RxLev
RxLev Cost 54763 81 59 -88.3 -83 0.17 892 71 61 -92.1 -92 0.00
18322 67 46 -98.7 -99 0.00 18581 73 34 -103 -103 0.00
[0170] The calculated value for the single unmatched cost is shown
in Table 15.
TABLE-US-00015 TABLE 15 Measured Cell ID ARFCN BSIC RxLev Threshold
Cost Unknown 69 43 -103.3 -105 0.02
[0171] In this example, since the measurement was not fully
populated, using the methods described in previously-incorporated
PCT/AU2006/000347 (referred to above and herein incorporated by
reference in its entirety), an unreported threshold value of -105
is used. The calculated value for the unreported cell cost is shown
in Table 16:
TABLE-US-00016 TABLE 16 Profile Cell ID ARFCN BSIC RxLev Threshold
Cost 25068 95 38 -60.0 -105 15.07
[0172] A suitable threshold at which an unreported cost is
considered significant enough to be accumulated for outage
detection is about 2.0 for example. However, any other suitable
thresholds could be used, such as between 0 and 0.5, 0.2 and 1.5,
1.0 and 3.0 or between 1.5 and 5.0 or between 2.0 and 6.0 for
example.
[0173] Having determined that there is an unreported cell for which
the threshold has been exceeded, there needs to be a determination
that the cell was expected to be heard before the cost can be
accumulated as evidence that the cell is not operating. Using the
process described in previously-incorporated reference
PCT/AU2006/000478, the zone status is calculated but with the
unreported cell in question not considered in the computation. The
threshold which the other costs must not exceed in order for the
unreported cost to be accumulated is defined in terms of the
chi-squared threshold in the same way as for the zone status,
taking into account the number of constraints (not counting the
unreported constraints). In the present example, the total number
of other constraints is 5. The chi-Sq threshold value is obtained
as the 90th percentile from the ChiSq cumulative density function
with 5 degrees of freedom. Using a numerical approximation to this
function, rounded to 1 decimal place, the value is 9.2. The total
of matched and unmatched costs is 0.19 which is less than the
chi-sq value of 9.2.
[0174] In the example above, the mobile is determined to be in the
zone if the unreported cell is ignored. This combined with an
unreported cost that exceeds the threshold means that the
unreported cost is accumulated towards the cell being declared
non-operational. The cost is accumulated by adding the current,
unreported cost to the accumulated cost. Once the accumulated cost
exceeds a threshold, the cell is declared non-operational. Any time
the cell is observed, the accumulated cost is reset to 0 since the
cell is clearly operational. A suitable threshold for the total
accumulated unreported cost threshold before declaring an outage to
be indicated is about 20 for example. However, any other suitable
threshold may be used, including between 10 and 15 or between 15
and 30 or between 20 and 40 for example.
[0175] Detecting Non-Operational Cells at Mobile Terminal
[0176] A limited scale version of the process illustrated above may
also be operated at each mobile terminal. In this case however the
outage analysis at a mobile terminal focuses only on the cells that
feature in a zone profile being monitored at that terminal. In this
case a historical unreported cost is maintained for cells included
in such a zone profile. In the event that for a particular profile,
all but a few elements are matched and the remaining elements
attract a large unreported cost, these unreported costs may be
accumulated.
[0177] When the accumulated unreported cost for one or more cells
reaches the threshold, a message may be sent to the server bearing
the current radio measurements. The purpose is to trigger the
server side cell outage detection processing using the current
measurements and potentially trigger the disabling of that
cell.
[0178] This mobile radio terminal focused aspect may be useful in
some cases because a mobile radio terminal may return to a zone
after a cell has been taken out of service. The lack of
measurements for that cell may prevent the mobile from ever
detecting itself as home and therefore prevent any radio
measurements being sent to the server. As a result, the server
would never have data from which to detect the outage and the zone
service will be interrupted. By performing this limited outage
analysis at the mobile radio terminal, it is possible to detect
such cases and activate the outage processing in the server.
[0179] Detecting Non-Operational Cells in Server
[0180] The elapsed time since a cell was last reported can be used
to establish whether a cell is believed to be operational or not.
In one aspect of the invention, the time at which a cell was last
reported is associated with each cell enabling the elapsed time
since last seen to be calculated for every cell at any given epoch.
Any cell for which the elapsed time since last report exceeds a
specified threshold is deemed to be non-operational. The threshold
is optionally configurable. The threshold chosen represents a
trade-off between responsiveness and false alarms. The larger the
time before reporting a cell as non-operational, the less likely it
is that the report is a false alarm. Indicative values for the
threshold are between 1 minute and 5 minutes, between 2 minutes and
20 minutes, between 5 minutes and 60 minutes, between 15 minutes
and 120 minutes.
[0181] The threshold can optionally change based on the time of day
to reflect that the expected time between reports will be longer
when there is less people movement such as early in the morning. If
the rate of observations varies significantly throughout the day or
by day-of week, a more appropriate threshold is one based on the
number of elapsed transactions since the last observation rather
than the elapsed time. As each transaction arrives it is assigned
an value that is incremented with each transaction. Each cell is
then assigned the value of the most recent transaction in which it
was observed. Once the number of transactions since a given cell
was last observed is exceeded it is deemed non-operational. The
threshold is again a trade-off between responsiveness to a cell
becoming non-operational and false alarms. Consider a network with
3000 cells and on average 6 cells are reported per set of
observations. In such a network a minimum 500 sets of observations
are required for every cell to have the possibility of being
reported once. Taking into account the random nature of which cells
are reported, a reasonable value is 3 times the minimum setting the
threshold at 1500.
[0182] In another aspect of the invention, elapsed time is measured
relative to the rate of transactions coming into the location
server. The location server maintains a transaction counter.
Associated with each cell is the transaction counter value
associated with the transaction in which the cell was last
detected. The elapsed time since a cell was last reported is
measured as the difference between the current transaction counter
value and that stored for the cell. A given cell is deemed
non-operational if the number of transactions since last update
exceeds a specified threshold. Optionally the threshold is
configurable. For example in a network with 2000 base stations and
a GSM network in which mobile reports at most 7 cells at any time,
it would take approximately 300 messages to see each cell reported
once ignoring the randomness of such reports. The threshold could
be set at 3000 to allow for the random distribution of which cells
were reported. Using the elapsed transaction count metric has the
advantage of adapting to the rate at which transactions are being
gathered and hence automatically handles the periods where the
actual elapsed time is expected to be larger due to fewer incoming
transactions.
[0183] In another aspect of this invention, the server can
optionally seek to obtain further evidence that a cell is
non-operational by requesting certain mobiles send measurements to
the server. If a given cell is suspected of having failed, the
server can search zone profile definitions to find zones which
include the suspected cell. Optionally the server could prioritise
the list of zones based on the signal strength order of the zones.
Zones where the suspect cell is expected to be highest are given
preference where the suspect cell is second highest and so on. From
this zone list the server seeks mobile radio terminals that are in
the zone. Such mobile radio terminals, optionally based on zone
preference, are then requested to send a set of measurements, for
example by forcing a status update. The number of mobiles so
targeted is configurable.
Detecting Non-Operational Cell Becoming Operational
[0184] FIG. 7 illustrates a process flow for detecting cells that
have been flagged as non-operational (for example, by one or more
of the previously-described methods), but have been re-activated.
Whenever a cell that has been flagged as non-operational is
observed in a measurement set the cell is flagged as being
operational. The process starts from step 500 to collect
measurements from the network at step 501. For each reported cell,
the operational status is checked at steps 502 and 503. If in step
504, the cell has been flagged as non-operational (for example by
the previously-described method), the cell is then flagged for
reporting in step 505. If the cell has not been flagged as
non-operational, no further action is taken.
[0185] In step 506, the cells flagged in step 505 are gathered
together for potential reinstatement as operational. The process
then ends in step 507.
[0186] The following provides a detailed example of performing the
above described method.
[0187] The server can detect the reappearance of a non-operational
cell using the same algorithms used to detect non-operational
cells. When monitoring the network for non-operational cells the
presence of a cell deemed non-operational in a set of measurements
indicates that the cell is operating again.
[0188] As an example consider a set of measurements made on a GSM
network as shown in Table 17. Using the network database and
proximity to the cell 25652 the cell with ARFCN 68 and BSIC 51 is
resolved to be cell ID 54312. This cell is flagged in the server
database as non-operational (Table 18). The detection of cell 54312
infers that it is again operational and consequently the
operational status is changed.
TABLE-US-00017 TABLE 17 Cell ID ARFCN BSIC RxLev Mean (dBm) 25652
Unknown Unknown -87.0 Unknown 56 66 -94 Unknown 61 54 -95 Unknown
68 51 -95 Unknown 29 46 -102
TABLE-US-00018 TABLE 18 Cell ID ARFCN BSIC Operational 25652 31 39
Yes 38821 56 66 Yes 49731 61 54 Yes 54312 68 51 No 54311 29 46
Yes
Detecting Cells with Incorrect Coordinate
[0189] FIG. 8 illustrates a process flow for detecting cells that
have incorrect coordinates. From step 600, the process obtains the
network measurements in step 601. In step 602, the process
determines the distance between all pairs of cells that have been
measured contemporaneously based on the coordinates of the cells in
the network database. For each cell a metric relating to the
relative proximity of the cells is computed and if the metric
exceeds the criteria as determined in steps 603 and 604, then the
cell is flagged in step 605 as potentially having incorrect
coordinates.
[0190] In step 606, the flagged cells are collected for subsequent
further processing, and the process ends at step 607.
[0191] The following provides detailed examples of performing
various aspects of the above method for detecting a cell with
incorrect coordinates.
[0192] A mobile radio terminal can only measure cells that are in
its vicinity. Base station coordinate errors can be detected by
identifying measurement sets that are incongruous. Various metrics
can be used to evaluate the likelihood that a cell is in the
incorrect location or that a given set of measurements contains one
or more cells with suspect coordinates.
[0193] Detection Using Distance Metric
[0194] The cells measured by a mobile radio terminal typically come
from the same geographic area. As such, the average or median
distance from each cell in a measurement set to the other cells in
the set should be comparable. A cell having a distance metric much
higher than the others may be an indication of a cell with a
coordinate error. One such metric that may be used is the median
distance.
[0195] In one embodiment, for each cell the median distance to the
nearest n cell sites is computed. As an example, n could be 8,
although any value in the range of about 2 to about 20, about 2 to
about 5, about 3 to about 8, about 4 to about 12, or about 2 to
about 8, could be used. Any contemporaneously reported pair of
cells that is more than m times the average of the two median
distances apart is deemed to indicate a cell potentially in the
incorrect location. As an example m could be 2 although any value
in the range of about 1 to about 20, about 1 to about 3, about 2 to
about 5, about 3 to about 8, or about 5 to about 20 could be
used.
[0196] Table 19 shows a section of a network database with the
median distance from each base station to the nearest 8 base
station sites using a metric for the separation of base stations in
the vicinity of each base station. A set of contemporaneous
measurements reports cell IDs 26078 and 4415. The distance between
these cells is 2002 m. This distance is under the median distance
for both cells so the measurement provides no indication of an
incorrectly located base station. A different set of
contemporaneous measurements reports cell IDs 26078 and 5617. The
distance between these cells is 18006 m. This metric is 2 times the
larger of the median inter-site distances involved which is 11202
m. Hence the measurement indicates that a cell may have incorrect
coordinates.
TABLE-US-00019 TABLE 19 Median Distance to Cell ID Nearest 8 Sites
(m) Easting Northing 26078 5601 6495885 2662920 5617 4321 6478631
2673922 8173 7840 6482814 2672664 4415 3400 6493345 2663641
[0197] Detection Using Signal Detection Likelihood
[0198] Where a set of measurements contains more than one unique
cell identifier, cell coordinate problems can be detected by
evaluating a metric that measures the likelihood that all such
identified cells could be contemporaneously heard at the reported
signal strengths at a given location in the network. By evaluating
this metric over the network coverage area the maximum likelihood
can be found. The maximum likelihood is compared against a
threshold. If the likelihood is below a threshold, then the set of
observations indicates that there is a potential problem with the
location of one or more cells.
[0199] FIG. 9 illustrates steps of a method for determining the
detection of a cell in the wrong location using a probability
metric. The process begins at step 700, to obtain network
measurements at step 701. In step 702, a probability that each
reported cell is in the correct location is computed (described in
more detail below). For each cell (step 703), a comparison is made
between the computed probability and a threshold in step 704. If
the probability is less than the threshold, the cell is flagged as
being potentially in the wrong location in step 705. In step 706,
the flagged cells are gathered together for subsequent processing.
The process then ends in step 707.
[0200] For any given location x, the expected signal strength at x
for each cell can be estimated using techniques well known in the
art such as the Hata model (see section 2.7 of Mobile Radio
Communications). The difference between the measured signal
strength and the estimated signal strength is affected by the
difference between x and the true location of the mobile radio
terminal, the accuracy of the cell location, the accuracy of the
model, and the variability of signal strength measurements. Using
optimization techniques well known in the art the maximum
likelihood can be estimated. The point x at which this occurs is
the maximum likelihood estimate of the mobile radio terminal's
location. This location is not required in this instance as it is
the maximum likelihood value itself that is the quantity of
interest. It is well known in the art that if only two cells are
identified, the location estimate is ambiguous; there will be two
equally likely locations at which the cost is minimised. This is
not relevant in the problem being addressed in this example--it is
simply an artifact of the process. To which location the algorithm
converges does not matter as it is the minimized cost and not the
location that is of interest.
[0201] For a signal strength model using Gaussian errors, it is
well known in the art that the maximum likelihood calculation is
equivalent to finding the location x that minimizes the following
equation:
.chi. 2 = i ( S i - f i ( x ) ) 2 .sigma. i 2 ##EQU00001##
Where
[0202] S.sub.i is the measured signal strength for cell i,
f.sub.i(x) is the estimated signal strength at x for cell i, and
.sigma..sub.i.sup.2 is the variance of the signal strength for cell
i due to the type of radio environment. The measurements S.sub.i
are contemporaneous. .OMEGA..sup.2 is the cost that is minimized
and is a chi-squared statistic for which the number of
degrees-of-freedom is the number of cells heard. The .OMEGA..sup.2
statistic is converted to a probability and it is this probability
that is compared to the threshold. If the threshold is exceeded,
then the scenario indicates that one or more cells involved
potentially have a coordinate error. The threshold is configurable
and is a trade-off between reliably detecting coordinate errors and
the number of false alarms. Since the coordinate error is static,
the detection threshold can be set reasonably large to reduce the
number of false alarms. Such a threshold will simply increase the
expected time it will take for a given error to be detected. The
threshold may be set to any desired value. In one example, the
threshold could be in the range about 95% to about 99.99%, and
including about 96%, 97%, 98%, 99% or 99.5% or about 99.99%. The
threshold may even be set lower than 95%, for example in the range
from about 70%-about 90% or about 80%-about 95%. Having determined
that one or more cells are potentially in the wrong location, the
next step is to determine which cells to flag for further action.
The simplest choice is the default case where all cells involved
are flagged as potentially being in the wrong location and the
problem of identifying which, is left for an external system, for
example a network operations team. Another choice is based upon the
examination of the cost that each cell contributes to the total and
if it exceeds a threshold, it is flagged as being potentially in
the wrong location. The ability of this approach to detect the cell
at fault improves with the number of cells included in the
computation. As described above, the cost will be a .OMEGA..sup.2
statistic but with one degree of freedom. Again the statistic is
converted to a probability and compared against a threshold
probability, for example, 98%, or any other ranges as described
above. As described elsewhere in this specification, evidence can
be accumulated over multiple measurement sets and the decision
based upon the accumulated evidence. In this scenario, the
.OMEGA..sup.2 cost contribution for each cell can be accumulated
and compared against a probability threshold.
[0203] The method used to detect cells in the wrong location using
measured signal strengths can also be applied to timing
observations. Given the description of the method above it should
be clear how to apply the method to timing measurements.
[0204] Table 20 shows part of a network database. Table 21 shows an
excerpt of a set of measurements that illustrate cells A and B
heard contemporaneously, as shown in FIG. 10A. The signal levels
have a range correspondence, using the Hata model, of 2415 m and
4930 m respectively for cells A and B. Circular arcs centred on A
and B using these ranges intersect at two distinct points P and P'.
At either of these two points the cost function evaluates to 0
which is clearly less than any chosen chi-squared threshold. As
such, there is no evidence that either cell has a significant
coordinate error.
[0205] Table 22 shows an excerpt of a set of measurements that
illustrate cells A and C heard contemporaneously, as illustrated in
FIG. 10B. Note however that the true location of the cell is
distinctly different to that in the database which the following
analysis will reveal. The signal levels have a range
correspondence, using the Hata model, of 2415 m and 203 m
respectively for cells A and C. Circular arcs centred on the
coordinates of A and C, as defined in the database using these
ranges, do not intersect. Using numerical optimization techniques
well known in the art, the point at which the cost is minimized is
determined and shown as point X in FIG. 10B. For a standard
deviation of 8 dB, appropriate for a suburban radio environment,
the minimum cost is found to be 19.02. The chi-squared threshold
for 99.9% is 14.1. Thus the cost exceeds the threshold and the
observed signal strength is deemed to have arisen from variations
due to noise. Thus either both of the cells are deemed to
potentially have a coordinate error. Further measurement sets
involving A or C would indicate which was the more likely to be in
error. Note that had the coordinates for C been correct in the
database, the circular loci would not intersect, at the optimal
estimate X' as shown in FIG. 10C. The resulting minimized cost
would be 0.28, well below the threshold.
[0206] It should be clear to those of ordinary skill in the art
that the technique can be extended to situations where multiple
signals are uniquely identified. The more signals so available
makes the technique better able to distinguish which cell actually
has the wrong coordinates.
TABLE-US-00020 TABLE 20 Cell ID Easting Northing A (in 455161
6654541 DB) B (in 457832 6654541 DB) C (in 478751 6651368 DB) C
(True) 455511 6651368
TABLE-US-00021 TABLE 21 Cell ID RxLev Mean (dBm) A -84.4 B
-96.7
TABLE-US-00022 TABLE 22 Cell ID RxLev Mean (dBm) A -84.4 C
-46.5
[0207] Detection Based on Zone Location
[0208] Described in PCT/AU2006/001479, is the association of a
nominal location to a zone. Similarly when a zone is measured, the
measurement data can also be used to estimate the location of a
zone. Either of these locations can be used to assist the detection
of a cell with incorrect coordinates.
[0209] The methods described earlier detect the presence of
potential cell coordinate errors with no prior information
pertaining to the location where the measurements were made. Using
the zone location, the evaluation metrics can be further refined.
If measurements are known to be made within the vicinity of a zone
with a known location, for example the measurements were triggered
via a zone transition, then the measurements can be evaluated
assuming they came from the location of that zone.
[0210] Distance Metric
[0211] With regard to the distance metric, the distance from the
zone location to each cell can be computed. This distance is then
compared to a multiple of the inter-cell distance metric for that
cell. The comparison can explicitly include an allowance for the
accuracy of the zone location or implicitly include such an
allowance via a larger multiple of the distance metric. In one
example, there is a cell with ID 38761 for which the inter-cell
median distance is 1540 m. If the validation multiplier is set to
2.5, that is, in this network, a cell is not expected to be
hearable at a distance from the cell of 2.5 times the inter-cell
distance which in this example is 3850 m. If a cell is reported
from in or near a zone that is 10561 m away from the cell, this
distance exceeds the maximum expected range for the cell and thus
the cell is flagged as potentially having incorrect
coordinates.
[0212] Signal Detection
[0213] With regard to the signal detection likelihood metric, the
computation is constrained to be evaluated at the known location.
An allowance made for any uncertainty in the location of the zone
can be made by increasing the standard deviation of the signal
level. Using a signal propagation model an appropriate allowance
can be computed. If for example the zone location has an
uncertainty of 500 m 2DRMS, the T1P1.5 propagation model in a
suburban environment at a range of 3000 m from the base station
indicates that an appropriate allowance would be to increase the
signal strength by 2 dB.
[0214] Reusing the example in Table 10, Table 11 and Table 12, the
use of the zone location can be illustrated. The nominal location
of the zone is coordinates (455411, 6651528). There is now no need
to minimize the cost function as there is a reliable estimate of
the location of the mobile. At the estimated location the estimated
signal strengths are -87.8 dBm, -91.6 dBm, and -119.1 dBm for cells
A, B, and C respectively.
[0215] In the scenario where cells A and B are heard
contemporaneously and with the solution constrained to the nominal
location of the zone the cost is 0.54. This is well below the 99.9%
threshold and the test results do not indicate any detectable
problem with cell coordinates.
[0216] In the scenario where A and C are heard contemporaneously
and with the solution constrained to the nominal location of the
zone the cost is 82.5 which is significantly higher than the 99.9%
chi-squared threshold as chosen in the previous example. Thus the
measurements indicate that there is a potential problem with the
coordinates of one or both base stations. Had cell C had the
correct coordinates, the cost would have been 0.2 and well below
the threshold.
[0217] Signal Hearability
[0218] The nominal zone location can also be combined with a signal
propagation model to determine if a cell has incorrect errors. For
a cell to be detectable at a given location, the received signal
strength, including any receiver and processing gains, needs to be
above the receiver noise floor and it must be sufficiently strong
to be detectable above the interference. At the nominated location
the signal strength can be estimated based on a radio propagation
model. Optionally the model can include the effects of co-channel
interference. Optionally the model can take into account the effect
of adjacent channel interference. The estimated signal strength is
compared to the receiver sensitivity. If the signal is weaker than
this value, then the cell potentially has incorrect coordinates. If
the signal is sufficiently strong, it is then compared to the
combined estimated effects of co-channel and adjacent channel
interference. If the signal is not sufficiently strong relative to
the interference, then the cell coordinates may be in error.
[0219] In the above example for a GSM network, the estimated signal
strength at the nominated location is -119.1 dBm. The receiver
sensitivity for a GSM mobile is approximately -104 dBm. Thus the
estimated signal strength is 15.1 dBm, which is too weak to be
detected and thus the cell may have incorrect coordinates. If the
signal were above the receiver sensitivity, then the interference,
if being estimated, could then be evaluated. In GSM the signal
needs to be 9 dB stronger than the nett interference to be
detected.
[0220] Incidental Detection
[0221] Measurements made of cell signals are commonly reported only
with partial cell identifiers. For example in GSM, neighbour cells
are usually identified only by a BSIC and ARFCN. Serving cells are
identified via their Cell ID. The actual cell associated with each
partially identified measurement is determined by searching for a
match to the partial identity within the vicinity of the serving
cell that has been fully identified via a cell ID. If the neighbour
cell has incorrect coordinates, then the search to find the cell
may fail or result in the measurement being associated with the
wrong cell. As such, the failure to find a cell to match reported
cell identifiers can be an indication of a cell with coordinate
errors. Similarly, if a serving cell has incorrect coordinates, one
or more neighbour cells may not be able to be fully identified
based on the BSIC and ARFCN because of the coordinate error. Thus a
coordinate error may manifest via the detection of an unknown cell
which will be resolved via a correction to a cell's coordinates
once the root cause is identified.
[0222] Efficient Collection of Network Measurements
[0223] In one aspect of the invention, the system can optionally
leverage the spare capacity in existing messages and/or use already
established communication sessions to report information about the
radio network for use in one or more of the methods described
above. In many communications networks the protocols available for
transmitting the zone status updates or location data are fixed in
size, for example SMS in GSM. The status update and location
messages do not necessarily use all of the available space. There
are also session based communications protocols wherein there is a
network bandwidth cost associated with setting up the session, for
example USSD in GSM. Having set up a session to send a message, the
marginal cost of sending extra data is low. An advantage of the
present invention is to leverage the available space or session to
send information about the observed radio network at no extra cost
in terms of network capacity. In systems where the message length
is variable, the extra information required to support methods
according to aspects of the present invention can still be appended
to status and location update messages for a small marginal cost.
The network capacity cost of setting up a connection is often such
that sending a small amount of extra data will not significantly
impact the system. This information can be used to support the
detection of inconsistencies between the network database and the
actual configuration. The information sent can include the identity
if serving cell being used by the mobile, and for each cell heard
by the mobile: full (e.g. CID+LAC) and partial cell identifiers
(e.g. BSIC, PSC), channel/frequency, signal strength, and/or
variation in signal strength. The data can be the raw measurements
or filtered (e.g. averaged).
[0224] In the process of operating a location or zone based
service, a subscriber's handset or mobile terminal periodically
exchanges messages with a network based server. For example, in a
home zone service operating as described previously, typically each
time the subscriber moves either into or out of the zone, a message
is sent notifying the server. A further advantage of combining data
into this message is the spatial coverage that such a spatial
trigger provides. The network data gathered will derive from a
cross the network coverage area.
[0225] For a location service there will be a message sent to the
server containing either the data in support of a location request
or the coordinate estimate generated in the mobile.
[0226] As an example of the spare capacity available in a fixed
size message format, consider a zone-based location system in which
the mobile notifies the status (in/out) of its zones to a server
using SMS. In GSM the SMS payload is a fixed size of 140 octets. As
illustrated in Table 23 below, 1 octet is reserved to indicate the
type of message, one octet enables up to 8 zone statuses (IN or
OUT) to be reported leaving 138 octets available for reporting
observations of the radio network.
TABLE-US-00023 TABLE 23 Msg Zone Type State Radio network
observation data 1 octet 1 octet 138 octets
[0227] Throughout the specification and the claims that follow,
unless the context requires otherwise, the words "comprise" and
"include" and variations such as "comprising" and "including" will
be understood to imply the inclusion of a stated integer or group
of integers, but not the exclusion of any other integer or group of
integers.
[0228] The reference to any prior art in this specification is not,
and should not be taken as, an acknowledgement of any form of
suggestion that such prior art forms part of the common general
knowledge
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