WO2020099681A1

WO2020099681A1 - Method and system to minimise the signalling and delay caused by mobility management function in cellular networks

Info

Publication number: WO2020099681A1
Application number: PCT/EP2019/081665
Authority: WO
Inventors: Nicola MARCHETTI; Merim DZAFERAGIC; Irene MACALUSO
Original assignee: The Provost, Fellows, Scholars And Other Members Of Board Of Trinity College Dublin
Priority date: 2018-11-16
Filing date: 2019-11-18
Publication date: 2020-05-22
Also published as: GB201818733D0; GB202107755D0; GB2594373A

Abstract

The invention provides system to minimise the number of handover operations in a cellular network, the network comprises at least one core network entity managing the mobility function (EMMF), a plurality of base station nodes wherein each base station defines a cell, at least one moving User Equipment (UE) node, the system comprising a handover module at each of the base station nodes wherein the module is configured to: determine when a handover request occurs at a base station node; trigger an optimization process manually or whenever a handoff request occurs or on a periodic basis or if the EMMF reaches a load limit the EMMF sends a reassignment request. The invention describes a distributed and centralised approach to minimise the number of handover operations in a cellular network.

Description

Title

Method and System to Minimise the Signalling and Delay Caused by Mobility Management Function in Cellular Networks Field

The disclosure relates to the mobility management function in cellular networks.

Background

To manage mobility in cellular networks Radio Access Network (RAN) nodes in both 4G and 5G are grouped into a hierarchy of geographical areas. Different geographical areas are controlled by different core network entities managing the mobility function (EMMF), e.g. the Mobility Management Entity (MME) node and the Access and Mobility Management Function (AMF) virtual instance in the 4G and 5G architecture respectively.

Self-organizing and autonomic network management and optimization will play a key role in 5G networks in that they will be essential to fully exploit in a cost- efficient way the increased network flexibility and the dynamic on-demand deployment of virtual network functions in the NGC.

Due to the increasing densification of the cellular networks and connected mobile devices (e.g. loT devices for logistics and supply chain management), the number of handovers is significantly growing. Services like Augmented Reality, Virtual Reality, autonomous driving and smart buildings/cities are steering the development of the next generation of communication networks. These services are an important part of the Fifth Generation (5G) concept, imposing strict requirements in terms of latency, throughput, and mobility. Some of these requirements are conflicting, due to the network architecture and protocol procedures. For example, handover procedures are inevitable to ensure mobility, but at the same time they greatly affect latency. Different types of handovers (e.g., inter- Mobility Management Entity (MME), intra-MME) require distinct procedures that affect the latency differently. Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies applied to the core network elements provide very flexible resources, which should be paired with a flexible Radio Access Network (RAN) in order to exploit the full potential of these technologies.

Two types of handover approaches exist, namely network controlled (the network forces the User Equipment (UE) to move from one Base Station (BS) to another) and mobile evaluated (the UE makes the handover decision and informs the network about it). Long Term Evolution (LTE) handover procedures are a hybrid approach, meaning that the UE sends measurement information to the network and based on those measurements the network asks the UE to move to a target cell. The three main types of handovers in LTE are:

• Intra-MME/SGW: this type of handover occurs when a UE moves between two eNodeBs that belong to the same MME/SGW pool. If an X2 interface exists between these two eNodeBs the handover is completed without Evolved Packet Core (EPC) involvement, and this type of handover can be referred to as X2- handover. If an X2 interface does not exist between the serving and target eNodeB, the EPC has to be involved in the handover, and since this signalling is carried out via S1 interfaces, we refer to it as S1 -handover.

• Inter-MME/SGW: this type of handover occurs when a UE moves between two eNodeBs that belong to different MME pools or SGW service areas. In order to perform this type of handover the involvement of the EPC is necessary and therefore this type of handover can be referred to as S1 -handover.

• Inter-RAT: this type of handover occurs when a UE moves between two different radio technologies (e.g., a handover from LTE to WCDMA) An MME Pool Area and an SGW service area are defined as areas within which a UE may be served without the need to change the serving MME and SGW respectively, see 3GPP ®,“3gpp ts 23.401 version 8.0.0 release 8,” Tech. Rep., 2007. The X2 handover and the S1 handover without MME re-selection can be referred to as intra-region handover and to the S1 handover with MME re selection as inter-region handover. Based on 3GPP ®,“3gpp ts 123 501 version 15.3.0 release 15,” Tech. Rep., 2018. The 5G network architecture includes similar concepts. The main difference is that the functionalities are virtualized, and the equivalent to the MME Pool Area is the AMF Region. An AMF region consists of one or multiple AMF Sets. An AMF Set consists of AMFs that serve a given geographical area. The corresponding S1 and X2 interfaces are renamed and the interfaces of interest in the 5G architecture are the N2 and Xn interfaces respectively. Flence, the inter-region handovers are the N2 handovers with AMF reselection and intra-region handovers are Xn and N2 handovers without AMF re-selection.

SDN and NFV are two technologies that enable the flexible and independent up and down-scaling of the core network functionalities. The RAN should be able to respond to the dynamic resource allocation in the core network without increasing the amount of signalling (avoid the signalling storm). 5G applications like smart-cities, augmented reality and autonomous driving have strict requirements regarding latency and mobility. The handover delay has a great impact on the total latency and mobility support of users. The additional densification of the network creates BS with smaller coverage leading to an increased number of handovers, which results in an increased total latency and signalling between the RAN and the core network. The inter-region handover procedures result in higher latency. It is desirable to minimize the network latency and the amount of signalling between the RAN and the packet core by introducing an improved design of the handover regions. A number of patent applications exist in the art related to this problem. For example, US patent publication number US2014/0301200 is concerned with load distribution (load balancing) between the MMEs within a single pool area. The US patent publication considers the load on the MME and the load that is associated with a connected call, and a decision is made to forward the connection to the MMEs in a way that allows them to distribute the load among the available MMEs. Flowever this publication does address the minimization of inter-region handovers. Other applications in the art include US2005048974 and KR20080068993. It is therefore an object to provide a method and system to minimise the signalling and delay caused by mobility management function in Cellular Networks to improve handover latency.

Summary

According to the invention there is provided, as set out in the appended claims, a computer implemented method to minimise the number of handover operations in a cellular network, the network comprises at least one network entity managing the mobility function (EMMF), a plurality of base station nodes wherein each base station defines a cell, at least one moving User Equipment (UE) node, the method comprising the step of:

providing a handover module at each of the base station nodes wherein the module is configured to perform the steps of: determining when a handover request occurs at a base station node; and

triggering an optimization process manually or whenever a handoff request occurs or on a periodic basis or if the EMMF reaches a load limit the EMMF sends a reassignment request.

The invention provides a generic approach to node to x-area (where x can be handover, tracking or set of tracking areas) association. The invention provides an adaptive approach to optimize the regions in order to minimize the signalling and latency. The invention takes advantage of the existing signalling messages, meaning that no additional signalling is introduced in the network, which reduces the probability of an escalation of the signalling traffic.

It will be appreciated the invention considers the load on the MMEs and focuses on mobility management rather than load balancing. Additionally, instead of balancing the connections between the MMEs within a single pool area, the invention can create these pool areas by dynamically changing the association between the BS and the MME pool areas in a way that minimizes the amount of signalling associated with mobility management functions. The invention focuses on the dynamical reconfiguration of the association between the BS and the MME pool areas, to minimize the number of handovers between different pool areas.

In one embodiment the invention provides a system and method for the minimization of the signalling and latency related to mobility management by dynamical reconfiguration of the association between the nodes in the RAN and the core network.

In one embodiment there is provided the step of updating a base station counter when a handover is determined and calculating an energy of attraction towards the at least one EMMF.

In one embodiment the energy of attraction towards an EMMF is the ratio between the number of handover requests that come from the EMMF and the total number of handover requests that arrive at the base station.

In one embodiment there is provided the step of deciding whether to stay assigned to the at least one EMMF or to change its assignment to another EMMF after the counter is updated. In one embodiment if the difference between the attraction towards another EMMF and the at least one EMMF is greater than a defined threshold, the base station is configured to request a change of assignment.

In one embodiment there is provided the step of utilising local information available at one or more nodes to trigger the optimization process.

In one embodiment there is provided the step of receiving at the EMMF a request from a node to get assigned to the EMMF wherein if the sum of the current load of the EMMF and the load coming from the node that is requesting the assignment is lower than a threshold (i^ , assigning the node to the EMMF.

In one embodiment there is provided the step of receiving at the EMMF a request from a node to get assigned to the EMMF wherein if the total load is greater than the threshold, the assignment can be accepted or rejected depending on the energy of attraction of the cell that is requesting the assignment. In one embodiment there is provided the step of receiving at the EMMF a request from a node to get assigned to the EMMF wherein if the energy of attraction of the cell is lower than the attraction of all other cells that are currently attached to the EMMF of interest, the assignment request will be rejected.

In one embodiment there is provided the step of receiving at the EMMF a request from a node to get assigned to the EMMF wherein if the energy of attraction of the cell is greater than the attraction of at least another cell that is currently attached to the EMMF of interest, the cell will be assigned to the EMMF and the cell with the lowest energy of attraction that was assigned to the EMMF already will be configured to request a change of assignment to another EMMF.

In one embodiment the at least one EMMF comprises a Mobility Management Entity (MME) node.

In one embodiment the at least one EMMF comprises an Access and Mobility Function (AMF) instance. In a further embodiment there is provided a system to minimise the number of handover operations in a cellular network, the network comprises at least one core network entity managing the mobility function (EMMF), a plurality of base station nodes wherein each base station defines a cell, at least one moving User Equipment (UE) node, the system comprising a handover module at each of the base station nodes wherein the module is configured to:

determine when a handover request occurs at a base station node; trigger an optimization process manually or whenever a handoff request occurs or on a periodic basis or if the EMMF reaches a load limit the EMMF sends a reassignment request.

The solution of the invention can be an online learning approach, which means that it is dynamic and adapts to any changes in the network. The method of the invention is architecture agnostic, meaning that it can be implemented with the current EPC as well as with the SDN and NFV based 5G Service Based Architecture (SBA).

The invention comprises the implementation of an architecture agnostic Network Level Mobility Management Optimization solution based on User Equipment Mobility to minimize signalling and handover latency.

In one embodiment the method optimizes the network organization in order to minimize the amount of signalling between the RAN and the core network and minimize handover delay.

In one embodiment there is provided an architecture agnostic distributed adaptive solution to minimize the number of inter-region handovers, meaning that it can be implemented with the current EPC as well as with the 5G SBA;

In one embodiment the algorithm optimizes the network by taking advantage of the existing LTE handover protocol messages, meaning that no additional signalling is involved or required; In one embodiment there is provided a system and method implementing a distributed approach, such that one or more regional central entities are in charge of performing the optimization of the handover regions. The solution allows for multiple directions when it comes to implementation: (1 ) a completely distributed self-organizing network (SON) implementation: each node in the RAN runs the algorithm locally; (2) a decentralized SON: subsets of the network are being optimized by local centres. Both implementations leverage the power of the distributed decision making which results in high performance optimization.

It will be appreciated that the invention can be applied to Tracking Area and Tracking Area List optimization, and the association between nodes in the RAN and the MME/Access and Mobility Function (AMF) can be used to describe the invention. Therefore, a distributed adaptive approach is employed, that runs on the RAN nodes and the MMEs/AMFs and takes advantage of local handover information available through the existing signalling messages, to form handover regions resulting in a minimum number of inter-region handovers.

In another embodiment there is provided a computer implemented method to minimize the Tracking Area Update (TAU) and Paging signalling in a cellular network, the network comprises at least one core network entity managing the mobility function (EMMF), a plurality of base station nodes wherein each base station defines a cell, at least one moving User Equipment (UE) node, the method comprising the step of:

providing a module at each of the base station nodes wherein the module is configured to perform the steps of: determining when a TAU or Paging request occurs at a base station node; triggering an optimization process manually or whenever a TAU or Paging request occurs or on a periodic basis.

The handover is related to the user movements only when the user is in active mode (connected to the network and using a service), whereas the PU and TAU happen when the user is in active or idle mode, i.e. not using the service. The other steps are the same for the optimization process (our invention), the only different thing is that in terms of the handover optimization, the base stations calculate their attraction towards an EMMF, whereas in terms of PU and TAU optimization they calculate their attraction towards a specific Tracking Area. One EMMF covers one or more Tracking Areas.

In one embodiment the invention provides a distributed algorithm that does not involve an intermediate node, which allows the end-nodes to organize themselves into handover regions, and reduce the number of inter-region handovers, which results in reduced signalling.

In one embodiment the system and method run on the end-nodes themselves, and organizes the network in a way that results in reduced signalling. Instead of focusing on the handover procedures, we rather optimize the network organization in order to minimize delay and signalling.

In another embodiment system to minimise the number of handover operations in a cellular network, the network comprises at least one core network entity managing the mobility function (EMMF), a plurality of base station nodes wherein each base station defines a cell, at least one moving User Equipment (UE) node, the system comprising a handover module at each of the base station nodes wherein the module is configured to:

determine when a handover request occurs at a base station node; trigger an optimization process manually or whenever a handoff request occurs or on a periodic basis or if the EMMF reaches a load limit the EMMF sends a reassignment request. In one embodiment the module is configured to update a base station counter when a handover is determined and calculating an energy of attraction towards the at least one EMMF based on a predetermined criteria.

In one embodiment the calculated energy of attraction towards an EMMF is the ratio between the number of handover requests that come from the EMMF and the total number of handover requests that arrive at the base station. In one embodiment the module is configured to decide whether to stay assigned to the at least one EMMF or to change its assignment to another EMMF after the counter is updated.

In one embodiment if the difference between the attraction towards another EMMF and the at least one EMMF is greater than a defined threshold, the base station is configured to request a change of assignment.

In another embodiment there is provided a computer implemented method and system to minimise the number of handover operations in a cellular network, the network comprises at least one network entity managing the mobility function (EMMF), a plurality of base station nodes wherein each base station defines a cell, at least one moving User Equipment (UE) node, the method comprising the step of:

providing a handover module at each of the base station nodes; configuring an optimization module or a passive probe module to periodically collect handover information from the or each handover module wherein the optimization module is configured to perform the steps of:

partitioning of the base station nodes into a predefined number of non overlapping regions, each assigned to an EMMF, so as to minimize the number of inter-region handovers; and

enabling an extension of each region to allow overlap between the regions assigned to different EMMFs.

In one embodiment the passive probe module is configured in combination with an operations system support (OSS) module to periodically collect handover information from the or each handover module.

There is also provided a computer program comprising program instructions for causing a computer program to carry out the above method which may be embodied on a record medium, carrier signal or read-only memory. Brief Description of the Drawings

The invention will be more clearly understood from the following description of an embodiment thereof, given by way of example only, with reference to the accompanying drawings, in which:- Figure 1 illustrates a number of tracking areas represented in a typical cellular network;

Figure 2 illustrates a flow chart describing a cell resource allocation procedure; and

Figure 3 illustrates a flow chart describing the MME/AMF resource allocation procedure;

Figure 4 a centralised architecture can be implemented according to another embodiment of the invention; and

Figure 5 illustrate a weighted value‘w’ calculated between each adjacent base station in the network according to one aspect of the invention.

Detailed Description of the Drawings

Figure 1 illustrates a number of tracking areas represented in a typical cellular network, indicated generally by the reference numeral 1. Since a User Equipment (UE) location is known by the network at a Tracking Area level, the Tracking Areas are essential for discovering the UE in the network. At least one core network entity managing the mobility function (EMMF) is configured to manage whole Tracking Areas and/or Tracking areas can also be managed by multiple EMMFs. A UE can be any mobile user equipment connected to the cellular network, e.g. a phone, car, drone, bus, Virtual Reality Fleadset. The EMMF can be embodied as a Mobility Management Entity (MME) node 2a, 2b and the Access and Mobility Management Function (AMF) virtual instance node in a 4G and 5G architecture respectively.

A Tracking Area is a logical collection of base stations, as shown in Figure 1 , used to perform tracking and reachability management functions to trace the geographical location of a UE in an idle state. The location of a UE in the idle state is known by the network on a Tracking Area List granularity. An MME always manages whole Tracking Areas and Tracking Areas can also be managed by multiple MMEs. Traditionally, a node gets assigned to a Tracking Area based on its geographical location. Due to the static deployment of the nodes (the base stations are not moving), the Tracking Area assignment is static as well. The idea behind the invention is to dynamically rearrange the handover regions, based on the moving patterns of the UEs, in order to minimize the number of inter-region handovers. As a side effect the Tracking Areas are being rearranged as well. Similarly, according to 3GPP ®,“3gpp ts 123 501 version 15.3.0 release 15,” Tech. Rep., 2018. the 5G architecture relies on the concept of Tracking Areas and Tracking Area Lists. The AMF allocates registration areas, which represent a logical grouping of Tracking Areas within a network slice.

When applied to a network organization problem, in the present case handover region organization, a distributed approach has several advantages compared to a centralized approach. The main benefits are reduced signalling overhead, scalability and on-demand resource scaling. A centralized approach assumes the existence of a node in the network which is responsible for running the optimization algorithm. The first step is to gather all the information at the centralized node, which involves a large amount of signalling between the RAN and the core network. The second step is to run an optimization algorithm, which is a NP-hard problem and therefore it does not scale well with the size of the network. As the next generation of communication networks assumes a fast changing environment, which is supported by the on-demand resource scaling (e.g., network function placement), the organization of the RAN should be able to adjust to the dynamic core network. In case of a centralized implementation this would result in more signalling and due to the computational complexity of the optimization problem, the RAN organization adaptation would be delayed.

According to one embodiment the invention a distributed implementation uses local information available at the nodes in the RAN, and therefore it does not have a need for additional signalling. Additionally, a distributed adaptive algorithm enables dynamic reorganizations according to the on-demand resource scaling in the core network. In other words each node decides based on its local information how to react to changes like new instances of core nodes, which does not involve additional computational and communication delays. The method is enabled algorithmically by a module executing software that comprises of two main components. These two parts can be running on the RAN nodes and virtual instances of the core nodes (e.g. MMEs/AMFs). The component that runs on the RAN nodes can be formalized with a first algorithm, described with respect to Figure 2. Such first algorithm, as per the following in an exemplary embodiment, can determine a cell resource allocation procedure:

Algorithm 1 Cell resource allocation procedure

while Cell is operational do

Wait for event {handoff, reassign request}

If event == handoff then

Update counters

Calculate energy of attraction

MakeAssignmentDecision( )

if event == reassign request then

MakeReassignmentf MME/AMF_id ) function MakeAssignmentDecision( )

M 4— List of available MMEs/AMFs

k - number of MMEs/AMFs that are managing the cell

A List of energies of attraction towards MMEs/AMFs

Sort A in descending order

Send assignment request to the first k MMEs/AMFs in A function MakeReassignment( MME/AMF _Jd )

A - List of energies of attraction towards MMEs/AMFs

Exclude the MME/AMF with MM E/AM FJd from A

Sort A in descending order

while MME/AMF not assigned do

Try to get assigned to an MME/AMF from A

Since the algorithm relies on the handover counters available on the base stations, the optimization process is triggered whenever a handoff occurs or in case of reaching the load limit of an MME/AMF the MME/AMF sends a reassignment request. In case of a handover the RAN node updates its counters and the algorithm calculates an energy of attraction towards all available MMEs/AMFs. The energy of attraction of base station _n towards the m-th MME/AMF is calculated as:

where is the number of handover requests that arrived at node « from nodes that are assigned to the m-th MME/AMF, and M is total number of MMEs in the network. Therefore, the energy of attraction towards an MME/AMF is the ratio between the number of handover requests that come from this MME/AMF and the total number of handover requests that arrived on the observed RAN node. After the counters are updated and the energy of attraction is calculated the RAN node, based on the attraction towards all available MMEs/AMFs, decides whether to stay assigned to the current MMEs/AMFs or to change its assignment. If the absolute value of the difference between the attraction towards the current MME/AMF and the attraction towards any other MME/AMF is greater than a defined threshold, the base station decides to change its assignment. This threshold value is used to tune the sensitivity of the algorithm.

In case an MME/AMF requested a reassignment of the RAN node to another MME/AMF, the RAN node starts a reassignment process. This implies sorting the list of available MMEs/AMFs based on the energy of attraction and excluding the MME/AMF that has sent the request for reassignment from this list. The next step is to get assigned to the next best (based on the energy of attraction) available MME/AMF. The second part of the algorithm is the component that runs on a virtual instance of the MME/AMF. This component can be formalized with the algorithm shown below and with respect to the flow chart of Figure 3.

Algorithm 2 MME/AMF resource allocation procedure

if L + L(n) < L_max then

Assign cell to this MME/AMF

else

if A(n) > i n i A) 4- d then

Assign i t ll to l his MME/AMF

Inform the cell with min(A ) to assign to another MME/AMF

Remove the cell with min(A ) from N and A

else

Reject the request

As shown in Figure 3 the MME/AMF waits for a request from a node that wants to get assigned to it. If the sum of the current load (amount of signalling) of the MME/AMF and the load coming from the node that is requesting the assignment is lower than a threshold

the cell is going to be assigned to the

MME/AMF. In case the total load is greater than the threshold, the assignment can be accepted or rejected depending on the energy of attraction of the cell that is requesting the assignment. If the energy of attraction of the cell is lower than the attraction of all other cells that are currently attached to the MME/AMF of interest, the assignment request will be rejected. If the energy of attraction of the cell is greater than the attraction of at least another cell that is currently attached to the MME/AMF of interest, the cell will be assigned to the MME/AMF and the cell with the lowest energy of attraction that was assigned to the MME/AMF already will be informed to get reassigned to another MME/AMF. Once the cell is reassigned it will be removed from the list of assigned cells on the current MME/AMF. An important fact, from the implementation point of view, is that all the information needed to run the algorithm is available through already existing signalling messages (e.g., Handover Request, Tracking Area Update Request), removing the need for additional signalling and at the same time simplifying the integration with the existing architecture. The information of interest are the type of handover, the number of handovers and the source MME from which the handover originated from.

Centralised Embodiment

With reference to Figure 4, a centralised architecture can be implemented according to another embodiment of the invention, is illustrated generally by the reference numeral 10. Counters are updated at one or more base stations when a handover is determined. This handover information can be collected by a passive probe 1 1 through traffic mirroring or by an Operations Support System (OSS) module 12. An optimization module 13 can be configured to periodically collect handover information from the or each handover module or receive handover information collected by the passive probe 1 1 or the OSS module 12. It will be appreciated that Figure 4 shows a separate OSS module 12 and passive probe module 1 1 , though the functionality can be carried out in a single module to periodically collect the handover information and implement the optimization process of the present invention.

Based on the collected counters handover information the base station-to- EMMF association is decided using a graph partitioning on an undirected weighted graph G = (V,E,w) that represents base stations as the set of vertices V and the occurrence of handovers between base stations as the set of edges E c v x V. As illustrated in Figure 5 the value for w is calculated between each adjacent base station in the network.

The weight w _J is the normalized number of handovers that occurred between nodes i and j: where h_itj is the number of handovers between i and j, h_max and h_min are the maximum and minimum number of handovers that occurred between any two nodes in the graph.

The graph G is partitioned into K (number of EMMF) non overlapping regions and so as to minimize the number of inter-region handovers and taking into account the maximum load each EMMF can support.

After the graph partitions are identified, each region is extended to allow overlaps according to the following procedure.

The procedure is based on associating a Tracking Area (TA) (a set of BSs) to multiple EMMFs.

Instead of assigning BSs to EMMFs, assignment of the same TA to multiple EMMFs is performed. This stage iteratively determines such assignments and ends either when a maximum number of iterations (max_itsr) has been reached or when no additional modifications have been performed. At each iteration, and for each EMMF k, the procedure identifies the TA / currently associated to another EMMF such that the number of handovers between all the TAs currently assigned to k and TA f is maximized and the additional load resulting from associating TA ) to EMMF k does not exceed the load threshold of EMMF k. The load of a TA is shared between the original assignee of the TA (as resulting from the graph partitioning stage) and any additional EMMF based on the ratio of the number of handovers between the TA and the additional EMMF over the total number of handovers between the TA and any other TA in the network (r(f,fc) = The load of additional EMMF k and the original

EMMF k is updated as follows: ffc)=E_Bife) + rlj,fc) L_TQl

Pseudocode

flag = 1

While iter £ max_itw and flag ¹ 0

flag = 0

For k G A':(randomized order)

Find the TA /corresponding to the largest value in V_k such that

the

¾ = ¾ u 0)

Update _fe taking into account that TA J is also associated with EMMF k

!j_?(fe)= (k) + r(],k L_T(j)

(¾)= (&) - r(j, k) L_T(J), k is the EMMF to which TA | was assigned by the graph partitioning stage. flag = 1

Iter ++

The embodiments in the invention described with reference to the drawings comprise a computer apparatus and/or processes performed in a computer apparatus. However, the invention also extends to computer programs, particularly computer programs stored on or in a carrier adapted to bring the invention into practice. The program may be in the form of source code, object code, or a code intermediate source and object code, such as in partially compiled form or in any other form suitable for use in the implementation of the method according to the invention. The carrier may comprise a storage medium such as ROM, e.g. CD ROM, or magnetic recording medium, e.g. a memory stick or hard disk. The carrier may be an electrical or optical signal which may be transmitted via an electrical or an optical cable or by radio or other means.

In the specification the terms "comprise, comprises, comprised and comprising" or any variation thereof and the terms include, includes, included and including" or any variation thereof are considered to be totally interchangeable and they should all be afforded the widest possible interpretation and vice versa.

The invention is not limited to the embodiments hereinbefore described but may be varied in both construction and detail.

Claims

1. A computer implemented method to minimise the number of handover operations in a cellular network, the network comprises at least one network entity managing the mobility function (EMMF), a plurality of base station nodes wherein each base station defines a cell, at least one moving User Equipment (UE) node, the method comprising the step of:

providing a handover module at each of the base station nodes wherein a module is configured to perform the steps of:

determining when a handover request occurs at a base station node; and

2. The computer implemented method of claim 1 comprising the step of updating a base station counter when a handover is determined and calculating an energy of attraction towards the at least one EMMF based on a predetermined criteria.

3. The computer implemented method of any preceding claim wherein the calculated energy of attraction towards an EMMF is the ratio between the number of handover requests that come from the EMMF and the total number of handover requests that arrive at the base station.

4. The computer implemented method of any preceding claim comprising the step of deciding whether to stay assigned to the at least one EMMF or to change its assignment to another EMMF after the counter is updated.

5. The computer implemented method of claim 4 wherein if the difference between the attraction towards another EMMF and the at least one EMMF is greater than a defined threshold, the base station is configured to request a change of assignment.

6. The computer implemented method of any preceding claim comprising the step of utilising local information available at one or more nodes to trigger the optimization process.

7. The computer implemented method of any preceding claim comprising the step of receiving at the EMMF a request from a node to get assigned to the EMMF wherein if the sum of the current load of the EMMF and the load coming from the node that is requesting the assignment is lower than a threshold ( ,_nMH), assigning the node to the EMMF.

8. The computer implemented method of any preceding claim comprising the step of receiving at the EMMF a request from a node to get assigned to the EMMF wherein if the total load is greater than the threshold, the assignment can be accepted or rejected depending on the energy of attraction of the cell that is requesting the assignment.

9. The computer implemented method of any preceding claim comprising the step of receiving at the EMMF a request from a node to get assigned to the EMMF wherein if the energy of attraction of the cell is lower than the attraction of all other cells that are currently attached to the EMMF of interest, the assignment request will be rejected.

10-The computer implemented method of any preceding claim comprising the step of receiving at the EMMF a request from a node to get assigned to the EMMF wherein if the energy of attraction of the cell is greater than the attraction of at least another cell that is currently attached to the EMMF of interest, the cell will be assigned to the EMMF and the cell with the lowest energy of attraction that was assigned to the EMMF already will be configured to request a change of assignment to another EMMF.

11. The computer implemented method of any preceding claim wherein the at least one EMMF comprises a Mobility Management Entity (MME) node.

12.The computer implemented method of any preceding claim wherein the at least one EMMF comprises an Access and Mobility Function (AMF) instance.

13.The computer implemented method of any preceding claim wherein the handover module is a distributed handover module.

14.A computer implemented method to minimize the Tracking Area Update (TAU) and Paging signalling in a cellular network, the network comprises at least one core network entity managing the mobility function (EMMF), a plurality of base station nodes wherein each base station defines a cell, at least one moving User Equipment (UE) node, the method comprising the step of:

providing a module at each of the base station nodes wherein the module is configured to perform the steps of:

determining when a TAU or Paging request occurs at a base station node; and

triggering an optimization process manually or whenever a TAU or Paging request occurs or on a periodic basis.

15.A computer implemented method to minimise the number of handover operations in a cellular network, the network comprises at least one network entity managing the mobility function (EMMF), a plurality of base station nodes wherein each base station defines a cell, at least one moving User Equipment (UE) node, the method comprising the step of:

16.The computer implemented method of claim 15 wherein the passive probe module is configured in combination with an operations system support (OSS) module to periodically collect handover information from the or each handover module.

17.A system to minimise the number of handover operations in a cellular network, the network comprises at least one core network entity managing the mobility function (EMMF), a plurality of base station nodes wherein each base station defines a cell, at least one moving User Equipment (UE) node, the system comprising a handover module at each of the base station nodes wherein the module is configured to:

18. The system of claim 17 wherein the module is configured to update a base station counter when a handover is determined and calculating an energy of attraction towards the at least one EMMF based on a predetermined criteria.

19.The system of claim 18 wherein the calculated energy of attraction towards an EMMF is the ratio between the number of handover requests that come from the EMMF and the total number of handover requests that arrive at the base station.

20.The system of any of claims 17 to 19 wherein the module is configured to decide whether to stay assigned to the at least one EMMF or to change its assignment to another EMMF after the counter is updated.

21. The system of claim 20 wherein if the difference between the attraction towards another EMMF and the at least one EMMF is greater than a defined threshold, the base station is configured to request a change of assignment.