Abstract:
The present invention relates to positioning in a communication network. In Long Term Evolution, LTE networks the positioning server E-SLMC needs routing information in order to communicate with individual base stations, eNodeBs. For LTE networks configured with one Mobility Management Entity, MME or one pool of MMEs, the routing information can be obtained by interrogating the MME or an arbitrary MME in the MME pool. However, if the network is configured with several MME pools serving different sets of eNodeBs, the E-SLMC has now knowledge of which MME or MME pool that is serving a certain eNodeB. The invention includes a method and a position server ( 111 ) configured to quickly determine which MME pool ( 120,130 ) is serving a certain eNodeB ( 141 - 153 ) so that the relevant routing information necessary for the positioning server ( 111 ) to communicate with the eNodeBs ( 141 - 153 ) is obtained.

Description:
TECHNICAL FIELD 
     The present invention relates to positioning in a communication network and especially to a method and a positioning server for determining associations between pools of core network nodes and base stations in said network. 
     BACKGROUND 
     Different telecommunication or data communication services may have different positioning accuracy requirements imposed by the application. In addition, some regulatory requirements on the positioning accuracy for basic emergency services exist in some countries, i.e. FCC E911 in the US. 
     In many environments, the position can be accurately estimated by using positioning methods based on GPS (Global Positioning System). Nowadays networks have also often a possibility to assist the user equipment, UE in order to improve the terminal receiver sensitivity and GPS start-up performance (such as Assisted-GPS positioning, or A-GPS). GPS or A-GPS receivers, however, may be not necessarily available in all wireless terminals. Furthermore, GPS is known to often fail in indoor environments and urban canyons. A complementary terrestrial positioning method, called Observed Time Difference of Arrival OTDOA, has therefore been standardized by 3GPP. In addition to OTDOA, the Long Term Evolution, LTE standard also specifies methods, procedures and signaling support for Enhanced Cell ID, E-CID and Assisted Global Navigation Satellite System, A-GNSS. Another method, Uplink Time Difference of Arrival, UTDOA is also under consideration for LTE. 
     Positioning in a LTE communication network involves a number of network elements. An overview is found in  FIG. 1 . The communication network  100  includes an Evolved Serving Mobile Location Center, E-SMLC  111 . The E-SMLC  111  can be connected to a plurality of Mobility Management Entities, MME  121 - 132  in more than one MME pool  120 , 130 . Each MME  121 - 132  can serve a plurality of bases stations, also called evolved Node B, eNodeB  141 - 153 . The eNodeBs served by an MME pool  120 , 130  serves terminals or user equipments, UE (not shown) in a so called MME pool area  140 , 150 . MME pool areas  140 , 150  may overlap each other  160 . 
     In the LTE positioning architecture there are three key network elements, the LCS Client, the LCS target and the LCS Server. The LCS Server is a physical or logical entity managing positioning for a LCS target device by collecting measurements and other location information, assisting the terminal in measurements when necessary, and estimating the LCS target location. A LCS Client is a software and/or hardware entity that interacts with a LCS Server for the purpose of obtaining location information for one or more LCS targets, i.e. the entities being positioned. LCS Clients may reside in the LCS targets themselves. An LCS Client sends a request to LCS Server to obtain location information, and LCS Server processes and serves the received requests and sends the positioning result and optionally a velocity estimate to the LCS Client. A positioning request can be originated from the terminal or the network. 
     Position calculation can be conducted, for example, by a positioning server (e.g. E-SMLC in LTE) or a UE. The former approach corresponds to the UE-assisted positioning mode, whilst the latter corresponds to the UE-based positioning mode. 
     A high-level positioning architecture, as it is currently standardized in LTE, is illustrated in  FIG. 2 . The LCS target is a UE  240  and the LCS Server is an E-SMLC  111  or a Secure User Plane Location Platform SLP  112 . If both an E-SMLC  111  and an SLP  112  is used, the interface in between is normally a proprietary interface  113 . 
     Two positioning protocols operating via the radio network exist in LTE, the LTE Positioning Protocols, LPP and LLP Annex, LPPa. The LPP is a point-to-point protocol between a LCS Server  111  and a LCS target device  240 , used in order to position the target device  240 . LPP can be used both in the user and control plane, and multiple LPP procedures are allowed in series and/or in parallel thereby reducing latency. LPPa is a protocol between eNodeB  141  and the LCS Server  111  specified only for control-plane C positioning procedures, although it still can assist user-plane positioning by querying eNodeBs for information and eNodeB measurements. The SUPL protocol is used as a transport for LPP in the user plane U. LPP has also a possibility to convey LPP extension messages inside LPP messages, e.g. to allow for operator-specific assistance data or assistance data that cannot be provided with LPP or to support other position reporting formats or new positioning methods. 
     Assistance data is intended to assist a UE  240  or another network node in its positioning measurements. Different sets of assistance data is typically used for different methods. The OTDOA assistance data include a number of parameters as specified in the standard 3GPP TS 36.355. For example, some parameters may be used for determining the timing relation between a Positioning Reference Signal, received in the first sub frames of the positioning occasions of two cells. 
     In the case of combined Control Plane/User Plane, CP/UP operation, these parameters are expected to be extracted by the E-SMLC  111  from the eNodeBs  141  via the LTE Positioning Protocol LPPa and provided to the SLP  112  via interface  113 . The LPPa messages are encapsulated in Location Services Application Protocol, LCS-AP connectionless transfer procedure messages. 
     Furthermore, using the protocol extension LPPe there is also a possibility of carrying over a black-box data container meant for carrying vendor-/operator-specific assistance data from the eNodeB  141  via the MME  121 . 
     For combined CP/UP operation, it is currently not possible to obtain routing information of the connectionless transfer procedures. Although LPPa is terminated between the E-SMLC  111  and the eNodeB  141 , the E-SMLC  111  must contact the MME  121  having a S1 connection to the destination eNodeB  141  for connectionless transfer message delivery. 
     For an E-SMLC which serves one MME or one MME pool, the routing is normally not a problem because all MMEs in one MME pool can be contacted for connectionless transfer. However, if the E-SMLC  111  is connected to more than one MME pool  120 , 130 , the mapping (i.e. the associations) between the identities of the eNodeBs  141  and the MME pool  120 , 130  serving the eNodeBs  141  are necessary for such routing. 
     One solution is to import a mapping table from an Operations Support System OSS, but this may for various reasons not be possible or feasible. 
     Another approach is to make a eNodeB  141  to MME pool  120 , 130  mapping table as an E-SMLC configuration. Such configuration is however time consuming and also subject to input/manual errors. 
     SUMMARY 
     With this background, it is the object of the present invention to obviate at least some of the disadvantages mentioned above. 
     The object is achieved by a method for use in a positioning server, such as an E-SMLC, for determining associations between a plurality of pools of core network nodes (e.g. MMES) and base stations (e.g. eNodeBs) comprising the steps of, for each selected base station:
         calculate a probability index for each pool of core network nodes based on the location of the base station and the estimated locations of the pools;   determine the pool that is serving the base station by interrogating in descending order a core network node in at least one pool starting with the pool having the highest probability index until the serving pool has been identified;   receive from the core network node the associations between the first identified serving pool and the selected base station;   store the received associations.       

     With this algorithm it will be possible to optionally determining routing information from the core network node and to store this information for future use. 
     In one embodiment the estimates of the pool locations are preconfigured and available beforehand by the positioning server. But if that is not the case the invention also includes different options on how to estimate the pool location by recalculating the estimated pool location based on the locations of the base stations so far identified as served by the pool. 
     One option involves the operation to set the pool location to an average of the locations for the base stations so far identified as served by the pool. In another option the pool location is defined as a geometrical shape (such as a circle, ellipse, polygon etc) where the locations for the base stations so far identified as served by the pool are within the border of that geometrical shape. 
     The object of the invention is further achieved by a positioning server (e.g. an E-SMLC) for determining the associations between the pools of core network nodes (e.g. MMES) and the base stations, comprising at least one communication interface configured to be connected to at least one core network node, a storing device and a processor device connected to the communication interface and to the storing device where the processor device is configured to execute the algorithm described above. 
     An advantage of the invention is that the method determines the association or mapping relations between the eNodeBs and the serving MME pools so that routing information for the eNodeBs, can be extracted. With the method the rate of finding the right MME pools serving the remaining eNodeB increases dramatically for each selected eNodeB. Another advantage is that the method automatically maintains the routing/mapping information during normal traffic so the information is up-to-date. No involvement of an Operations Support System OSS or any other non-standard logical functionality is necessary which makes the solution vendor independent. No involvement of manual configuration is necessary upon changes in the MME planning. Yet another advantage is that it is possible to detect planning faults e.g. an isolated island of coverage where the eNodeB is belonging to an MME pool far away from other pools. The method can also be used for other types of mobile networks (not only LTE) where access network information/configuration needs to be extracted. 
     The invention will now be described in more detail and with preferred embodiments and referring to accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating an LTE network overview. 
         FIG. 2  is a block diagram illustrating a high-level positioning architecture in LTE. 
         FIGS. 3A and 3B  is a flow chart illustrating a method according to the present invention. 
         FIG. 4  is a block diagram illustrating a positioning server according to the present invention. 
         FIGS. 5A-5D ,  6 A- 6 C,  7 A- 7 C are block diagrams illustrating a first embodiment of the present invention. 
         FIGS. 8A-8B ,  9  are block diagrams illustrating a second embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The invention comprises a method for determining associations or mappings between a plurality of pools  120 , 130  of core network nodes (such as MMEs)  121 , 131  and base stations (such as eNodeBs)  141 , 151  served by the pools  120 , 130 . The method can be implemented in a positioning server such as an E-SMLC  111 . 
     An MME pool normally covers a geographically continuous area and the number of MME pools is normally limited in one mobile Public land mobile network, PLMN. The method deploys a self-learning and self-maintaining algorithm and is based on the property that two base stations (eNodeBs) and cells that are geographically close to each other most likely belong to the same MME pool. 
     In case the serving MME pool of a certain eNodeB is not known, the method interrogates the MME pools in a certain order where the geographically adjacent implies a higher probability of association and the number of unsuccessful interrogations is iteratively minimized. 
     The estimated locations of the MME pools can be preconfigured and available beforehand and can be defined as average locations of the eNodeBs served by the pool or as a geographical shape where the eNodeBs served by the pool are within the border of that geometrical shape. If the estimated MME pool locations are not known beforehand, the method also includes the optional step of estimating these locations. The definitions of the MME pool locations and how to estimate these locations are described more in detail below. 
     In the embodiments below it is assumed that the method is implemented in an E-SMLC  111  connected to a plurality of pools  120 , 130  of core network nodes, MMEs  121 , 131  and where the base stations are eNodeBs  141 , 151  served by the pools  120 , 130 . However, the method may apply for any mobile network when access network information/configuration is needed by a node connected to the core network. 
     The basic method is illustrated by the flow chart in  FIGS. 3A and 3B . Again, the method is based on the property that two eNodeBs that are geographically close to each other most likely belong to same MME pool. This means that if the locations of the MME pools are known it is more likely that a certain eNodeB belongs to the MME pool that is the closest. It is therefore necessary to determine the locations of the MME pools and the geographical distances between the MME pools and the eNodeB in question. Again, the E-SMLC  111  may already know the location of some MME pools beforehand but it is also possible that E-SMLC  111  does not know the locations of any MME pool at all. 
     One embodiment of the basic method and how to estimate the location of the MME pools is exemplified below and illustrated by  FIGS. 3A-3B ,  5 A- 5 D,  6 A- 6 C and  7 A- 7 C. In  FIG. 5A , the E-SMLC  111  selects a first eNodeB N 1  (step  301  in  FIG. 3A ). In the beginning, the E-SMLC  111  has no knowledge about the locations for the MME pool(s) serving N 1 . The E-SMLC  111  therefore interrogates an arbitrary MME pool one by one until the MME pool MP 1  serving the first eNodeB N 1  has been identified. Each interrogation is sent to an arbitrary MME within each MME pool. When the MME pool MP 1  serving eNodeB N 1  is found, the E-SMLC  111  stores the association between eNodeB N 1  and MP 1 . The E-SMLC  111  calculates an estimated location of the serving MME pool MP 1  by using the location of the eNodeB N 1  (letter X inscribed in node N 1 ). Having the associations between the eNodeB N 1  and the MME pool MP 1  serving eNodeB N 1  it is possible to determine and store the routing information from the MME pool MP 1  (step  307  in  FIG. 3B ). 
     In  FIG. 5B , the E-SMLC  111  selects (step  301  in  FIG. 3A ) a next eNodeB N 2 . Again, the E-SMLC  111  has no knowledge about the locations for the MME pool(s) serving eNodeB N 2 . E-SMLC  111  now calculates (step  302  in  FIG. 3A ) a probability index Pj for the MME pool(s) based on the geographical distances between eNodeB N 2  and the estimated locations of the known MME pools (so far only MP 1 ). For each MME pool j where the estimated location is not yet determined or not available the probability index Pj is set to an initial value, preferably Pj=0. 
     As only a probability index for MME pool MP 1  is known, the E-SMLC  111  starts to interrogate (step  303  in  FIG. 3A ) MME pool MP 1 . In this example eNodeB N 2  is not served by MP 1 . The E-SMLC  111  therefore proceeds by interrogating a next MME pool MP 2  (not shown) and so on. When interrogating MME pool MP 3 , the MME pool serving eNodeB N 2  is found. The E-SMLC  111  receives and stores the received associations (steps  304  and  305  in  FIGS. 3A and 3B ). As the estimated location for MP 3  is not yet known, E-SMLC  111  calculates (step  306  in  FIG. 3B ) the estimate to be the location of eNodeB N 2  (letter Y inscribed in eNodeB N 2 ). Having the associations between the eNodeB N 2  and the MME pool MP 3  serving eNodeB N 2  it is possible to determine and store the routing information from the MME pool MP 3  (step  307 ). 
     In  FIG. 5C , the E-SMLC  111  selects (step  301 ) a next eNodeB N 3 . E-SMLC  111  now calculates (step  302 ) two probability indexes P 1 ,P 2  for N 3  where the first probability index P 1  is based on the distance between eNodeB N 3  and the estimated location X of MME pool MP 1  and the second probability index P 2  is based on the distance between eNodeB N 3  and the estimated location Y of MME pool MP 3 . 
     The used equations could for example be
 
 Pj= 1/dist( Gj,G   —   enb ))  [1]
 
or
 
 Pj= 1/max(1,dist( Gj,G   —   enb ))  [2]
 
where dist(Gj, G_enb) is the geographical distance between the estimated MME pool location Gj and the location G_enb of the eNodeB.
 
     The estimated MME pool location Gj is here defined as the geographical center of the MME pool j i.e. Gj=(latitude for pool j, longitude for pool j) and G_enb is defined as G_enb=(latitude for eNodeB, longitude for eNodeB). Gj is calculated as an average of the locations G_enb of the eNodeBs so far identified as served by the MME pool. 
     A new average value Gj_new can be calculated as
 
 Gj _new=( Gj _old* N+G   —   enb )/( N+ 1)  [3]
 
where Gj_old is the value from the latest earlier calculation and N is the number of eNodeBs so far identified as served by MME pool j excluding the lastest eNodeB. An initial value of Gj_old can be set to the value of the location for the first identified eNodeB that is served by MME pool j. G_enb for each eNodeB can for example be obtained and stored beforehand from an Operations, Administration and Maintenance, OAM entity in the network.
 
     When calculating the probability index Pj, equation [2] above is preferred because it covers some extreme cases and limits the value range of Pj to a value between 0 and 1. Using equation [2] the first probability index P 1  will therefore have the value P 1 =1/max(1, dist(X, N 3 ) and the second probability index P 2  will have the value P 2 =1/max(1, dist (Y, N 3 ). 
     As the distance between X and eNodeB N 3  is the shortest, the first probability index P 1  is highest and the E-SMLC  111  starts to interrogate (step  303 ) MME pool MP 1 . In this case it is again MME pool MP 1  that is serving eNodeB N 3 , and the E-SMLC  111  receives (step  304 ) from an MME in the MME pool MP 1  and stores (step  305 ) the associations between eNodeB N 3  and MP 1  and recalculates (step  306 ) the estimated location of MP 1  based on both the location of eNodeB N 1  and eNodeB N 3 . In  FIGS. 5D and 6A  this new estimated location is marked with the letter X′ between eNodeB N 1  and eNodeB N 3 . The recalculated location is a ‘mean value’ of the location of eNodeB N 1  and the location of eNodeB N 3 . Having the associations between the eNodeB N 3  and the MME pool MP 1  serving eNodeB N 3  it is also possible to determine and store the routing information from the MME pool MP 1  (step  307 ). 
     As more eNodeBs remain (step  308 ), the E-SMLC  111  selects (step  301 ) a next eNodeB N 4  as illustrated in  FIG. 6B . E-SMLC  111  again calculates (step  302 ) two probability indexes P 1 ,P 2  where the first probability index P 1  is based on the distance between eNodeB N 4  and the estimated location X′ of MP 1  and the second probability index P 2  is based on the distance between eNodeB N 4  and the estimated location Y of MP 3 . As the distance between eNodeB N 4  and MP 1  is the shortest, the first probability index is highest and the E-SMLC  111  starts to interrogate (step  303 ) MME pool MP 1 . In this case it is again determined (step  304 ) that MME pool MP 1  is serving eNodeB N 4 . The E-SMLC  111  stores (step  305 ) the associations between eNodeB N 4  and MME pool MP 1  and recalculates (step  306 ) the estimated location of MME pool MP 1  based on the location of the three eNodeB N 1 , N 3  and N 4 . In  FIG. 6C  this new estimated location is marked with the letter X″ as an average location of the locations for the eNodeBs N 1 , N 3  and N 3 . Having the associations between the eNodeB N 4  and the MME pool MP 1  serving eNodeB N 4  it is also possible to determine and store the routing information from the MME pool MP 1  (step  307 ). 
     In  FIG. 7A , the E-SMLC  111  selects (step  301 ) a next eNodeB N 5 . E-SMLC  111  again calculates (step  302 ) two probability indexes P 1 ,P 2  now for N 5  where the first probability index P 1  is based on the distance between N 5  and the estimated location of MP 1  X″ and the second probability index P 2  is based on the distance between N 5  and the estimated location Y of MP 3 . As the distance between N 5  and Y is the shortest, the second probability index P 2  is highest and the E-SMLC  111  starts to interrogate (step  303 ) MME pool MP 3 . In this case it is determined (step  304 ) that MME pool MP 3  is serving eNodeB N 5 . The E-SMLC  111  stores (step  305 ) the associations between eNodeB N 5  and MME pool MP 3  and recalculates (step  306 ) the estimated location of MP 3  based on the location of the two eNodeB N 2  and N 5 . In  FIGS. 7B and 7C  this new estimated location is marked with the letter Y′ between eNodeB N 2  and N 5  as a ‘mean value’ of the location of eNodeB N 2  and the location of eNodeB N 5 . 
     If more eNodeBs exist, the algorithm carries on calculating new probability indexes and updating the location estimates for the MME pools serving the existing and remaining eNodeBs. 
     An alternative embodiment of how the algorithm is applied is illustrated by  FIGS. 8A ,  8 B and  9 . In  FIG. 8A  it is assumed that already two MME pools MP 21 , MP 22  serving six eNodeB N 21 -N 26  have been determined. In this embodiment the location of each MME pool MP 21 , MP 22  is defined as a geometrical shape. This shape could be a circle, an ellipse a polygon or some other geographical shape where the locations for the eNodeBs so far identified as served by the pool are within the border of that geometrical shape. The border can optionally be defined as circumscribing the served eNodeBs with a minimum distance between the border and the location for each eNodeB. The minimum distance can for example be a few hundred meters in order to include the coverage radius of each eNodeB. 
     In  FIGS. 8A ,  8 B and  9  the geometrical shapes are two location polygons LMP 21 , LMP 22  where the eNodeBs served by the MME pool form the vertices of that polygon. When selecting (step  301 ) an eNodeB N 27  the calculation (step  302 ) of the probability index Pj for each MME pool M 21  and MP 22  uses the equation
 
 Pj =inside( Sj,G   —   enb )  [4]
 
where inside(Sj, G_enb) is a mathematical operator indicating if the location G_enb of the selected eNodeB is within the border Sj of the location polygon j or not. The probability index Pj has here a binary value, 0 or 1. For eNodeB N 27  the location G_enb is within the polygon LMP 21 , but not within LMP 22 . The probability index P 1  for MME pool M 21  is therefore P 1 =1 and the probability index P 2  for MME pool M 22  is P 2 =0. The interrogation (step  303 ) of which MME pool that is serving eNodeB N 27  therefore starts with MME pool M 21 . If it is determined (step  304 ) that MME pool M 21  actually serves eNodeB N 27 , and as eNodeB N 27  already is within the polygon LMP 21 , no recalculation of the polygon is necessary.
 
     When selecting (step  301 ) an eNodeB N 28  the same calculation is used. As eNodeB N 28  is not within any polygon at all the probability indexes P 1  and P 2  are both P 1 =0, P 2 =0. In this situation, the equation [1] or [2] described above can be used in addition to equation [4]. If it is determined (steps  303  and  304 ) that eNodeB N 28  is served by MME pool MP 22  the border Sj of the location polygon LMP 22  for that pool MP 22  is recalculated (step  306 ) by adding eNodeB N 28  as a new vertex to the polygon as can be seen from  FIG. 8B . 
     When selecting an eNodeB N 29  the same calculation is used again and new probability indexes P 1  and P 2  are calculated. For eNodeB N 29  the result is P 1 =1, P 2 =0 so the interrogation of which MME pool that is serving eNodeB N 29  starts with MME pool M 21 . In this case it is however determined that it is MME pool MP 22  that is serving eNodeB N 29 . The border for location polygon LMP 22  for MME pool MP 22  is therefore recalculated by adding eNodeB N 29  as a new vertex but the border of location polygon LMP 21  is also recalculated by excluding eNodeB N 29 . This is illustrated in  FIG. 9 . 
     Again, if more eNodeBs exist, the algorithm carries on calculating new probability indexes and recalculating the shape of the location polygons for the MME pools serving the existing and remaining eNodeBs. 
     In yet another embodiment, the calculation of the probability index Pj for MME pool j is using the equation
 
 Pj= 1/max(1 ,Dj )  [5]
 
     Where the Dj is defined as the geographical distance between the location G_enb of the selected eNodeB and the location G_cej of the eNodeB already identified to be served by MME pool j and that is closest to the selected eNodeB. 
     If the Tracking Area Code, TAC i.e. the identity of the tracking area, TA to which the eNodeB belongs is known by the E-SMLC  111 , the algorithms described above can optionally be enhanced. 
     According to LTE standards, all eNodeBs that belong to the same tracking area are also served by the same MME pool. This means that as soon as an MME pool is determined for a selected eNodeB, the E-SMLC  111  can interrogate (in step  310  in  FIG. 3A ) the same MME pool and receive (in step  311 ) for each eNodeB known to belong to the same TA the associations between these eNodeBs and the MME pool which then are stored (in step  305 ). The location information for all these eNodeBs can also be used to recalculate the estimated MME pool location (step  206 ). 
     For all embodiments of the method it is also possible to run the algorithm in parallel for different eNodeBs. 
     An embodiment of a positioning server  111  according to the present invention is illustrated in  FIG. 4 . The positioning server is here an E-SMLC  111  located in a core network. The E-SMLC  111  comprises at least one communication interface  401  where one or several interfaces  401  are configured to be connected to MMEs  121 , 131  in at least one pool  120 , 130  of MMEs  121 , 131  in the core network. Each MME pool  120 , 130  is connected to and serving at least one base station, eNodeB  141 - 153  in the radio access network. The E-SMLC  111  also comprises a computing unit  402  including a storing device  4022  for storing among others the determined associations and MME pool locations and a processor device  4021  connected to the communication interface  401  and to the storing device  4022 . 
     The processor device  4021  is configured to execute the algorithms described above and illustrated for example by the flow chart in  FIGS. 3A and 3B . That is, to select in step  301  an eNodeB  141  and to calculate in step  302  a probability index Pj for each MME pool  120 , 130  based on the location G_enb of the selected eNodeB  141  and the estimated locations Gj of the MME pools  120 , 130 . The probability indexes can be calculated according to any of the equations [1]-[3] or [5] described above. The processor device  4021  is further configured to determine in step  303  the MME pool  120  that is serving the selected eNodeB  141  by interrogating in descending order an MME  121  in at least one MME pool  120  starting with the MME pool  120  having the highest probability index Pj until the serving MME pool  120  has been identified. The processor device  4021  is further configured to receive in step  304  from the MME  121  the associations between the first identified serving MME pool  120  and the selected eNodeB  141  and to store in step  305  the received associations in the storing device  4022 . The processor device  4021  is further configured to recalculate in step  306  the estimated pool location Gj of the first identified serving MME pool  120  based on the locations G_enb of the eNodeB  141  so far identified as served by the pool  120  and to repeat the above listed steps for each remaining eNodeB  142 - 153 . 
     The positioning server  111  can also be further configured to determine routing information from the MMEs  121 , 131  in the MME pools  120 , 130  and to store that routing information in the storing device  4022 .