WO2018184667A1

WO2018184667A1 - Apparatus and method for performing network optimization

Info

Publication number: WO2018184667A1
Application number: PCT/EP2017/058002
Authority: WO
Inventors: Premnath KANDHASAMY NARAYANAN; Bagher Zadeh
Original assignee: Telefonaktiebolaget Lm Ericsson (Publ)
Priority date: 2017-04-04
Filing date: 2017-04-04
Publication date: 2018-10-11

Abstract

A method for performing network optimization comprises configuring a network node to collect measurement data from a plurality of user equipment devices served by one or more such network nodes in a service area, wherein the measurement data is for use with network optimization, and autonomously updating the configuration of measurement data collection at the network node based on one or more updated network parameters, such that the collection of measurement data produces a sample size of measurement data falling within a defined range.

Description

Apparatus and Method for Performing Network Optimization

Technical Field

The embodiments described herein relate to an apparatus and method for performing network optimization, and in particular to an apparatus and method for configuring measurement data collection from user equipment (UE), for example to provide autonomous UE measurement configuration for performing network optimization such as optimizing coverage and capacity in a communications network.

Background

Radio conditions are evaluated continuously in the coverage or serving area of the radio access networks, for example using Reference Signal Received Power, RSRP) signals. A coverage area can vary or become modified due to a number of factors according to the radio condition, these factors including for example the number of users, App usage and coverage optimization features (for example, using Remote Electrical Tilt, RET, or power optimization algorithms). The radio condition is pre-dominantly evaluated using the radio measurements, for example Periodic UE Measurements (PEM), Drive test reports or Minimization of Drive Test reports (MDT). The radio measurements are collected by mobile devices or radio access points, such as user equipment (UE) or Internet-of-Things (loT) devices. Drive test reports involve a test mobile equipment being driven in a car around a service area or cell, to collect information which is used offline to analyze the coverage in different locations, and based on that network parameters may be optimized. Since this is a costly exercise, MDT was proposed in 3GPP Release-10 as a way of minimizing the need for Drive test reports, by collecting measurements from UEs being used in their ordinary role, to reduce the manual drive testing that operators have to perform in their networks.

Radio measurements are collected based on configurations imposed by a network operator for the network nodes that are coupled to the mobile devices or radio access points, using element management or network management systems (e.g. Operator Support Systems, OSS). Network management functions, such as Self Organizing Network (SON) functions, evaluate the network condition using these measurements collected from the mobile devices or access point devices.

The process described above requires manual configuration at the network element for collecting UE measurements.

When a service area (for example a cell coverage area) of a telecommunication radio network is evaluated using radio measurements, the measurements depend on an activation of the measurement feature at the node. Furthermore, the measurements depend on configuration details about the measurement collection, for example details relating to the periodicity of measurements, the number or percentage of mobile devices that should collect measurements in a service area (known as "fraction"). In addition, measurement types (for example Periodic UE Measurements, PUM) are based on connections made for accessing a service (such as voice or data). Other measurement types (Eg: Minimization of Drive Test, MDT) are based on location details.

Generally, these measurements consume battery power on the mobile devices or access point devices, and as such the operator configures a network node in such a way that the minimum amount of measurement data is collected by the mobile equipment, which helps with the overall energy/power saving of the mobile equipment. It is noted that different network nodes (for example different network nodes in LTE and 5G) require different measurement configurations based on different data needs. Network operators currently configure these measurement configurations manually. These measurement configurations are static and do not follow the dynamic behavior of the network, which for example changes according to traffic and App usage in a service area. As a result, while a measurement configuration holds good for a green field network, once the network becomes mature (catering for more traffic) the data needs from each service area changes from time to time (e.g. based on traffic pattern and service demand). As a consequence the static and manual configurations relating to measurement collection does not collect the intended data with a required sample size for optimization. This can prevent optimization, or lead to poor optimization of the radio network from a coverage and capacity point of view.

This is because Coverage and Capacity (C&C) optimization algorithms (such as Remote Electrical Tilt, RET) rely on accurate measurement data, and the non-availability of enough measurement samples leads to poor or incorrect optimization for the serving area of a particular network node. Also, on the contrary, a large collection of measurement data leads to unwanted processing and battery drain on the mobile devices and access point devices that perform the measurements.

As such, since the measurement configurations are static and performed manually they are not in-line with the needs of optimization algorithms, since they do not take into account the dynamics of a network (e.g. busy periods) and leads to no optimization or bad optimization of the radio network (especially from a coverage and capacity point of view).

Also, since measurement configurations are standard across many different network nodes, they do not take into account the different configurations required for different locations, such as rural, urban and sub-urban areas. Network nodes are currently categorized approximately, based on geographical area, which means that a measurement configuration suited for one area is not suited for another area. An inaccurate measurement configuration for a particular network node can lead to the wrong or poor collection of measurement data, and as a result optimization functions may not propose any changes (where in fact they are needed) or propose unreliable changes.

In rural areas, for example, the coverage foot print of a cell or serving area is quite high and the number of connected users is generally very low. A standard measurement profile configured for such a serving area will not collect enough measurements.

In urban areas, for example, the coverage foot print of a cell or serving area is small and the number of connected users is generally very high. A standard measurement profile configured for such a serving area will collect a large amount of data which is not really required for C&C optimization algorithms.

Since measurement configurations are at a network node level, with all the mobile devices and access point devices connected to the network node following the same measurement configuration, this can lead to further problems as outlined below.

For example, all user equipment in the serving area collect data with the same measurement configuration regardless of the measurement level (e.g. good, bad or worse). For example, if more measurements are required from a specific group of cells at a specific Timing Alignment or Timing Advance (TA) bin or distance, this is not possible. Also different types of user equipment (e.g. smart phone, traditional voice phone, dongles) have different measurement capabilities and require specific configuration profiles. In addition, if there is less traffic in the service area and if a fraction profile is configured to be less, then the possibility of collecting measurements with the required sample levels is very low. This could lead to poor or no optimization at all.

The nature of measurements themselves can also lead to the following problems. Some of the measurement types (e.g. Periodic UE Measurements, PUM) are based on connections made for accessing a service (voice or data). If the users are connected in idle mode, then there is no measurement collected by the radio access device. Other measurement types (e.g. Minimization of Drive Test, MDT) are based on a location detail evaluation capability of mobile devices and/or nodes. A location calculation functionality on a user equipment device can be disabled by the user (i.e. by choice), which means that a location estimation capability by a network node about the user equipment is only able to be performed based on an approximate estimate. From the above it can be seen that existing methods of performing network optimization based on user equipment measurements have a number of disadvantages.

Summary

It is an aim of the embodiments described herein to provide a method and apparatus which obviate or reduce at least one or more of the disadvantages mentioned above.

According to a first aspect there is provided a method for performing network optimization. The method comprises configuring a network node to collect measurement data from a plurality of user equipment devices served by one or more such network nodes in a service area, wherein the measurement data is for use with network optimization. The method comprises autonomously updating the configuration of measurement data collection at the network node based on one or more updated network parameters, such that the collection of measurement data produces a sample size of measurement data falling within a defined range.

According to another aspect there is provided an apparatus for performing network optimization. The apparatus is adapted to configure a network node to collect measurement data from a plurality of user equipment devices served by one or more such network nodes in a service area, wherein the measurement data is for use with network optimization. The apparatus is adapted to autonomously update the configuration of measurement data collection at the network node based on one or more updated network parameters, such that the collection of measurement data produces a sample size of measurement data falling within a defined range.

According to another aspect there is provided an apparatus for performing network optimization. The apparatus comprises a processor and a memory, said memory containing instructions executable by said processor. The apparatus is operative to configure a network node to collect measurement data from a plurality of user equipment devices served by one or more such network nodes in a service area, wherein the measurement data is for use with network optimization. The apparatus is operative to autonomously update the configuration of measurement data collection at the network node based on one or more updated network parameters, such that the collection of measurement data produces a sample size of measurement data falling within a defined range. According to another aspect, there is provided a network node, the network node being configurable to collect measurement data from a plurality of user equipment devices served by the network node in a service area, wherein the measurement data is for use with network optimization. The network node comprises a processor and a memory, said memory containing instructions executable by said processor. The network node is operative to periodically receive updated configuration information relating to the collection of measurement data at the network node, the updated configuration information based on one or more updated network parameters, wherein the network node is operative to update the configuration of measurement data collection at the network node such that the collected measurement data produces a sample size of measurement data falling within a defined range.

Brief description of the drawings

For a better understanding of examples of the present invention, and to show more clearly how the examples may be carried into effect, reference will now be made, by way of example only, to the following drawings in which:

Figure 1 shows an example of a method according to an embodiment; Figures 2a and 2b show an example of an application of an embodiment;

Figure 3 shows an example of a method according to an embodiment;

Figure 4 shows an example of a method according to an embodiment;

Figure 5 shows an example of a method according to an embodiment; Figure 6 shows an example used for determining service area type; Figures 7a, 7b and 7c show a method according to an embodiment; Figure 8 shows an example of a method according to an embodiment; Figures 9a and 9b show a method according to an embodiment;

Figure 10 shows an example of a virtual function according to an embodiment; Figure 1 1 shows an example of an apparatus according to an embodiment; and Figure 12 shows an example of a network node according to an embodiment.

Detailed description

The following sets forth specific details, such as particular embodiments for purposes of explanation and not limitation. But it will be appreciated by one skilled in the art that other embodiments may be employed apart from these specific details. In some instances, detailed descriptions of well-known methods, nodes, interfaces, circuits, and devices are omitted so as not obscure the description with unnecessary detail. Those skilled in the art will appreciate that the functions described may be implemented in one or more nodes using hardware circuitry (e.g., analog and/or discrete logic gates interconnected to perform a specialized function, ASICs, PLAs, etc.) and/or using software programs and data in conjunction with one or more digital microprocessors or general purpose computers. Nodes that communicate using the air interface also have suitable radio communications circuitry. Moreover, where appropriate the technology can additionally be considered to be embodied entirely within any form of computer-readable memory, such as solid-state memory, magnetic disk, or optical disk containing an appropriate set of computer instructions that would cause a processor to carry out the techniques described herein.

Hardware implementation may include or encompass, without limitation, digital signal processor (DSP) hardware, a reduced instruction set processor, hardware (e.g., digital or analog) circuitry including but not limited to application specific integrated circuit(s) (ASIC) and/or field programmable gate array(s) (FPGA(s)), and (where appropriate) state machines capable of performing such functions.

In terms of computer implementation, a computer is generally understood to comprise one or more processors, one or more processing units, one or more processing modules or one or more controllers, and the terms computer, processor, processing unit, processing module and controller may be employed interchangeably. When provided by a computer, processor, processing unit, processing module or controller, the functions may be provided by a single dedicated computer, processor, processing unit, processing module or controller, by a single shared computer, processor, processing unit, processing module or controller, or by a plurality of individual computers, processors, processing units, processing modules or controllers, including cloud based computers, some of which may be shared or distributed. Moreover, these terms also refer to other hardware capable of performing such functions and/or executing software, such as the example hardware recited above.

Although in the description below the term user equipment (UE) is used, it should be understood by the skilled in the art that "UE" is a non-limiting term comprising any mobile device, communication device, wireless communication device, terminal device, Internet-of-Things (loT) device, or node equipped with a radio interface allowing for at least one of: transmitting signals in uplink (UL) and receiving and/or measuring signals in downlink (DL). A UE herein may comprise a UE (in its general sense) capable of operating or at least performing measurements in one or more frequencies, carrier frequencies, component carriers or frequency bands. It may be a "UE" operating in single- or multi-radio access technology (RAT) or multi-standard mode. As well as "UE", the general terms "terminal device", "access node", "communication device" and "wireless communication device" are used in the following description, and it will be appreciated that such a device may or may not be 'mobile' in the sense that it is carried by a user. Instead, the term "terminal device" (and the alternative general terms set out above) encompasses any device that is capable of communicating with communication networks that operate according to one or more mobile communication standards, such as the Global System for Mobile communications, GSM, UMTS, Long-Term Evolution, LTE, 5G, etc. The embodiments described herein relate to methods and apparatus for performing network optimization, that automate the seamless process of data collection (measurement data), for example based on one or more network parameters, such as different network conditions and network element types in an autonomous manner, e.g. a zero touch autonomous manner.

References will be made herein to a service area, which is intended to embrace a service area as defined in 5G in relation to an area served by one or more network nodes, or a cell or region in a traditional sense according to 3G, 4G or LTE networks.

Figure 1 shows an example of a method for performing network optimization according to an embodiment. The method comprises configuring a network node to collect measurement data from a plurality of user equipment devices served by one or more such network nodes in a service area, wherein the measurement data is for use with network optimization, step 101 . The method comprises autonomously updating the configuration of measurement data collection at the network node based on one or more updated network parameters, step 103, such that the collection of measurement data produces a sample size of measurement data falling within a defined range. The defined range may comprise, for example, a desired sample size of measurement data required for performing the network optimization. By autonomously reconfiguring the measurement collection by a network node, based on one or more updated network parameters, such that the sample size of measurement data falls within a defined range, this ensures that measurement collections are always sufficient to meet the needs of a minimum sample size required for the optimization algorithms, but also not higher than needed (which would otherwise have the disadvantage of forcing unnecessary measurements by user equipment, or transmission of measurement data from user equipment, which in turn would consume unnecessary power is such user equipment).

The defined range of the sample size of the measurement data may comprise a range having a minimum number of measurement data, e.g. for allowing the network optimization to be performed, and a maximum number of measurement data, e.g. so as not to cause unnecessary measurement data to be collected.

The step of autonomously updating the configuration of measurement data collection at a network node may comprise, receiving optimization status information (i.e. as the updated network parameter used to autonomously update the configuration of measurement data), and activating and/or deactivating measurement collection at the network node according to the received optimization status information.

As such, according to one embodiment there is provided a method for performing network optimization, the method comprising configuring a network node to collect measurement data from a plurality of user equipment devices served by one or more such network nodes in a service area, wherein the measurement data is for use with network optimization, and autonomously updating the configuration of measurement data collection at the network node based on one or more updated network parameters, such that the collection of measurement data produces a sample size of measurement data falling within a defined range, and wherein autonomously updating the configuration of measurement data collection at a network node comprises receiving optimization status information, and activating and/or deactivating measurement collection at the network node according to the received optimization status information. It is noted that, according to at least some embodiments described herein, deactivation of measurement collection may comprise that measurements are not carried out (e.g. frequency of measurements reduced), or measurements being carried out, but whereby the results are discarded or not included in a measurement sample. It is noted that either of these options can allow a measurement sample size falling within the defined range to be produced.

Figure 2a shows an example of an application of an apparatus 202 according to an embodiment. The example of Figure 2a shows a plurality of service areas 201 1 to 201 N. A service area 201 1 to 201 N comprises a plurality of network nodes. Referring to Figure 2b, the network nodes within a service area may include, for example, high power antennas 207 (for example forming part of base stations), low power antennas 208 (for example located on street light poles or buildings), and user equipment (UE), for example mobile devices (not shown), or connected billboards 209, connected bus stops 210, or connected driverless vehicles 21 1 , and so on.

The apparatus 202 of Figure 2a receives optimization status information relating to the plurality of service areas 201 1 to 201 N. For example, in Figure 2a the apparatus 202 receives optimization status information relating to all of the service areas 201 1 to 201 N. The optimization status information may be received, for example, from a Network Management System (NMS) 203.

In one embodiment the apparatus 202 is adapted to autonomously activate and/or deactivate measurement collection at the network nodes in a service area according to the received optimization status information.

If, for example, the service area 201 1 requires optimization, then based on the optimization status information received by the apparatus 202, the apparatus 202 is configured to activate a measurement function on the first service area 201 1 , since measurements are required for optimization. This may comprise the apparatus 202 sending an activation signal 204 to the first service area 201 1 . In one example this involves sending an activation signal to each node in the first service area 201 1. Different nodes types may have different types of settings (which may be common in nature, but have different parameters and details). Examples of different node types include Macro, Pico, Small cells and Micro.

If, for example a different service area, such as service area 2012, has met all the optimization goals, and as such no new measurements are required, then the apparatus 202 is configured to de-activate the measurement function for the second service area 2012. This may comprise the apparatus 202 sending a deactivation signal 205 to the second service area 2012, (which again may be, for example, to each node in the service area).

If, for example, another service area 201 N is already undergoing optimization, the apparatus 202 may be configured to perform no action with respect to the third service area 201 N, since measurements are already being collected and the third service area 201 N is already undergoing optimization.

From the above it can be seen that the apparatus 202 is configured to autonomously update the configuration of measurement data collection at the network nodes. The updating of the configuration may be based on optimization status information as noted above, which in turn may be based on one or more updated network parameters. The updating of the configuration of measurement data can be performed such that the collection of measurement data produces a sample size of measurement data falling within a defined range. As mentioned above, the defined range may be a range giving a sample size of measurement data required for performing network optimization. The apparatus 202 is configured to activate and/or de-activate a measurement configuration (or profile) as and when required on a particular service area, based on received optimization status information (for example received from a NMS 203). This operation is performed autonomously as and when the optimization need/status changes for a service area. The apparatus 202 can activate and/or de-activate measurement configurations on one or more network nodes in a service area, for example based on an optimization need in that service area. It is noted that, according to one embodiment, different nodes within a service area may be configured differently (e.g. different nodes within cells of a 4G network). In a similar manner, different service area levels within a 5G network may be configured differently - a service area level may have a particular measurement collection profile, while at the same time a specific profile may be used for each node, e.g. an overall profile for the service area level and specific configuration profiles extended to node level.

For example, in response to the optimization status information indicating that one or more nodes in a service area have not met an optimization goal, and/or that one or more optimization algorithms are expecting measurement data, the apparatus 202 activates measurement collection on the one or more nodes in the service area.

Likewise, in response to the optimization status information indicating that one or more nodes in a service area have met an optimization goal, and/or that one or more optimization algorithms are not expecting measurement data, then the apparatus 202 deactivates measurement collection on the one or more nodes in the service area. Figure 3 shows a method according to another embodiment for providing autonomous configuration changes, whereby the following steps may be performed.

The method comprises deriving a service area type, step 301 (service area type therefore being a network parameter being used to govern reconfiguration). For example, this may involve deriving whether a service area type relates to a rural service area, an urban service area, a sub-urban service area, or some other type of service area. In one example this comprises using a cluster machine learning technique, as will be described in more detail later in the application. In another example, the step of deriving service area type comprises autonomously deriving the service area type based on timing advance, TA, information, and distance bins, further details of which are described later in the application.

The method comprises updating the configuration of measurement data collection at one or more network nodes in the service area according to the derived service area type, step 303.

Figure 4 shows a method according to another embodiment. The method comprises determining a traffic level in a service area, step 401 (traffic level therefore being a network parameter being used to govern reconfiguration). For example, this may involve determining whether the traffic level is high or low, (e.g. above or below a certain threshold, or below a first threshold relating to a low traffic level, or above a second threshold relating to a high traffic level), based on the number of connected users in the service area, for each node in the service area.

The method comprises updating the configuration of measurement collection data at one or more network nodes in the service area according to the determined traffic level, step 403.

Figure 5 shows a method according to another embodiment. The method comprises deriving a needed measurement type in a service area, step 501 (the needed measurement type therefore being a network parameter being used to govern reconfiguration). For example, this may comprise determining whether a needed measurement type is a Periodic UE Measurement, PUM, type or a Minimization of Drive Tests, MDT, type or both. The determination may be based, for example, on the user equipment and access points connected in the service area, or the capabilities of the user equipment or access points in the service area, or the particular type of measurements needed by a particular type of optimization algorithm.

The method comprises updating the configuration of measurement data collection at one or more network nodes in the service area based on the derived measurement type that is needed, step 503 (e.g. PUM, MDT). It is noted that one or more of the methods described in Figures 3 to 5 may be combined, for example whereby PUM, MDT or both can be activated with the correct configuration (e.g. Fraction, Periodicity) based on derived service area type, and/or traffic level, and/or needed measurement type. Further details will now be provided of an example for deriving service area type, as described in Figure 3 above, based on Timing Advance/Timing Alignment , TA, information, distance bins.

Referring to Figure 6, for service area type categorization, for every node in the service area the cell range is calculated based on Timing Alignment or Timing Advance, TA, bins. The X-Axis indicates the TA bin in distance (Kilometers) and Y-Axis (left) indicates the amount of RSRP samples available and Y-Axis (right) indicates the day of the distance bin (references 601 to 614 illustrating, for example, consecutive days). Based on the average samples in the bins, the amount of traffic happening in the service area for the network node is indicated (close to the network node, center of the network node and farthest distance from the network node).

Once the range of the network node is known for all the network nodes in the serving area the service area type (e.g. rural or urban or sub-urban) is determined using a clustering statistical/machine learning techniques, for example as described in a book by Pang-Ning Tan et al, entitled "Introduction to Data Mining", Chapter 8, "Defined cluster analysis are adapted as part of Service Area identification method". Additional statistical/machine learning techniques may also be used to help narrow down cell range estimation biases due to multi-path challenges in a radio network.

It is noted that in the description herein, a "cluster" refers to a "Rural, Urban or Sub-Urban" service area type, while an "object" refers to network nodes in the service area providing communication services that are capable of measuring network signal levels.

For 'n' network nodes in the service area, a partitioning method can be used to construct 'k' partitions of data (rural, urban & sub-urban). Each partition will represent a cluster. The data is classified into k groups that satisfies the constraint that each node exactly belongs to one group.

Once the network node belongs to exactly one group without any ambiguity the service area type can be determined. If a network node belongs to more than one group (e.g. rural, urban & suburban), then a fuzzy/probability clustering method is performed. In a fuzzy clustering method, every object (service area network node) belongs to every cluster (rural, urban & sub-urban) with a membership weight that is between 0 (absolutely does not belong) and 1 (absolutely belongs). Clusters are treated as fuzzy sets. Mathematically, a fuzzy set is one in which an object belongs to any set with a weight that is between 0 and 1 . Normally the sum of the weights for each object equals to 1 . Similarly, probabilistic clustering techniques compute the probability with which each point belongs to each cluster, and the probabilities of any object sum to 1 . The object is assigned to the cluster where membership weight or probability is highest. Once the network node with highest membership weight belongs to exactly one group without any ambiguity, the service area type is determined.

If a node belongs to more than one cluster/group with same membership weight, then the apparatus uses one or more clustering methods as follows.

According to a prototype based clustering method, a cluster is a set of objects in which each object is closer (more similar) to the prototype (best rural network node or urban network node or sub-urban network node) that defines the cluster than to the prototype of any other cluster. For many types of data, the prototype can be regarded as the most central point, and in such instances, one can refer to prototype-based clusters as center-based clusters.

If a prototype cannot identify the best prototype, other methods may be used.

One example is an agglomerative hierarchical clustering method. The agglomerative method starts with the points (network nodes) as individual clusters and, at each step, merges the closest pair of clusters. This requires defining a notion of cluster proximity.

A basic algorithm for cluster proximity comprises computing the proximity matrix for the network node, and repeating the steps of: merging the closest two clusters and updating the proximity matrix to reflect the proximity between the new cluster and the original clusters, until only one cluster remains. If the proximity area is too close and network node cannot cluster, then the apparatus uses other methods.

For example, one other method is to use a density-based clustering method. According to such a method a center-based approach to density allows the network node to be classified as a point being 1 ) in the interior of a dense cluster/region (a core point), 2) on the edge of a dense cluster/region (a border point) or 3) in a sparsely occupied cluster/region (a noise or background point).

An algorithm for performing such a method may comprise: labelling all points (nodes or network elements) as core, border or noise points; eliminating noise points; putting an edge between all core points that are within an indicated radius of each other; making each group of connected core points into a separate cluster; and assigning each border point to one of the clusters of its associated core points. In case of any ambiguity, based on the output of partitioning method or fuzzy/probability clustering method, the apparatus can combine the result and provide more weightage to the cluster that is identified as potential candidates by both the methods. The prototype based cluster method and agglomerative hierarchical clustering methods can be applied for the service area nodes if the combined result does not still cluster all the nodes.

The methods above are examples of how service area types may be determined autonomously, e.g. for use in a method as described in Figure 3. Next, further details will be provided of an example embodiment for deriving a traffic level (e.g. voice or data) for use in a method as described above in Figure 4, for example based on the number of connected users, and whereby the traffic level is categorized as high or low using one or more clustering techniques as discussed above.

Based on the user equipment devices served by a service area, network nodes are configured with appropriate measurement configuration profiles (with details for device specific configurations). Network nodes provide appropriate device commands (based on device specific profiles configured by apparatus) for performing measurements. Figures 7a, 7b and 7c show an example of a method according to another embodiment. In this embodiment the method uses a combination of service area type, traffic level and needed measurement type to configure the collection of measurement data at a network node within a service area.

The method of Figures 7a, 7b and 7c may be performed periodically in a loop, as indicated by box 701 a. This procedure may be performed for all nodes in a 4G network, or in a 5G service area a specific profile may be created and extended for each node, e.g. based on Figures 3, 4 and 5 above.

In step 701 of Figure 7a, for a plurality of nodes in the network, for example for every node in the network, the method comprises deriving a service area type, step 702 (for example as described above using a clustering method), deriving a traffic level, step 703, and deriving a needed measurement type, step 704. In step 705 the type of service area is determined, for example determining if the service area is rural. If so, and it is determined in step 706 that the traffic level is low, e.g. below a certain threshold, then in step 707 the method comprises reconfiguring a measurement data collection such that PUM/MDT is activated with higher fraction and lesser periodicity. By higher fraction, this means that a greater percentage of the user equipment connected to the network node are activated to collect measurement data (i.e. because the traffic level is low), and whereby measurement collection is performed with less periodicity, i.e. such that measurements are collected more frequently. A separate procedure may be performed to determine whether PUM or MDT is best suited for a particular application. The method then passes to node "c" of Figure 7b.

If it is determined in step 706 that the traffic level is not low, i.e. high, in step 709 the method comprises reconfiguring a measurement data collection such that PUM/MDT is activated with moderate fraction and moderate periodicity. By moderate fraction, this means that a moderate percentage of the user equipment connected to the network node are activated to collect measurement data (i.e. because the traffic level is not low and the service area is rural), and whereby measurement collection is performed with a moderate periodicity, i.e. at a moderate frequency. A separate procedure may be performed to determine whether PUM or MDT is best suited for a particular application. The method then passes to node "c" of Figure 7b.

If it is determined in step 705 that the service area type is not rural, then the method passes to node "c" of Figure 7b.

Turning to Figure 7b, the method continues from node "c", by determining in step 710 if the service area is urban. If so, and it is determined in step 71 1 that the traffic level is low, then in step 712 the method comprises reconfiguring a measurement data collection such that PUM/MDT is activated with moderate fraction and moderate periodicity. By moderate fraction, this means that a moderate percentage of the user equipment connected to the network node are activated to collect measurement data (i.e. because the traffic level is low and the service area is urban), and whereby measurement collection is performed with a moderate periodicity, i.e. at a moderate frequency. A separate procedure may be performed to determine whether PUM or MDT is best suited for a particular application. The method then passes to step 714.

If it is determined in step 71 1 that the traffic level is not low, i.e. high, in step 713 the method comprises reconfiguring a measurement data collection such that PUM/MDT is activated with lower fraction and higher periodicity. By lower fraction, this means that a lower percentage of the user equipment connected to the network node are activated to collect measurement data (i.e. because the traffic level is high and the service area is urban), and whereby measurement collection is performed with a higher periodicity, i.e. resulting in less frequent measurements. As before, a separate procedure may be performed to determine whether PUM or MDT is best suited for a particular application. The method then passes to step 714.

If it is determined in step 710 that the service area type is not urban, then the method passes to step 714 of Figure 7b.

In step 714 it is determined if the service area is sub-urban. If so, and it is determined in step 715 that the traffic level is low, then in step 716 the method comprises reconfiguring a measurement data collection such that PUM/MDT is activated with higher fraction and moderate periodicity. By higher fraction, this means that a higher percentage of the user equipment connected to the network node are activated to collect measurement data (i.e. because the traffic level is low and the service area is sub-urban), and whereby measurement collection is performed with a moderate periodicity, i.e. at a moderate frequency. A separate procedure may be performed to determine whether PUM or MDT is best suited for a particular application. The method then passes to node "d" of Figure 7c.

If it is determined in step 715 that the traffic level is not low, i.e. high, in step 717 the method comprises reconfiguring a measurement data collection such that PUM/MDT is activated with lower fraction and higher periodicity. By lower fraction, this means that a lower percentage of the user equipment connected to the network node are activated to collect measurement data (i.e. because the traffic level is high and the service area is sub-urban), and whereby measurement collection is performed with a higher periodicity, i.e. at a lower frequency. As before, a separate procedure may be performed to determine whether PUM or MDT is best suited for a particular application. The method then passes to node "d" of Figure 7c. Referring to Figure 7c, these steps may be performed where there is no profile activation, or where it is not possible to infer a service area type such as rural, urban, or sub-urban, for example due to classification or clustering model limitations. In step 718 it is determined whether a profile activation has been performed. A profile refers to a configuration profile for measurement data collection, whereby a set of predetermined configuration profiles may exist, i.e. such that the method can then select a configuration profile from this existing set of configuration profiles (e.g. rather than having bespoke configurations for each and every possible scenario).

If profile activation has not been performed, in step 719 it is determined if the traffic level is high, e.g. above a particular threshold. As mentioned above, this flow is performed when profile activation is not done, and where it has not been previously possible to infer a particular service area type. If the traffic level is not high, i.e. low, in step 720 the method comprises reconfiguring a measurement data collection such that PUM/MDT is activated with higher fraction and lesser (or lower) periodicity. By higher fraction, this means that a higher percentage of the user equipment connected to the network node are activated to collect measurement data (i.e. because the traffic level is low and the service area is not known as being rural, urban or sub-urban), and whereby measurement collection is performed with a lesser periodicity, i.e. at a higher frequency. As before, a separate procedure may be performed to determine whether PUM or MDT is best suited for a particular application. The method then loops back to step 722 and in turn to 701 a, such that the configuration of measurement data collection can be continually or periodically updated in a dynamic manner according to one or more changing or updated network parameters.

If it is determined in step 719 that the traffic level is high, e.g. above a particular threshold, in step 721 the method comprises reconfiguring a measurement data collection such that PUM/MDT is activated with moderate fraction and moderate periodicity. By moderate fraction, this means that a moderate percentage of the user equipment connected to the network node are activated to collect measurement data (i.e. because the traffic level is high and the service area is not known as being rural, urban or sub-urban), and whereby measurement collection is performed with a moderate periodicity, i.e. at a moderate frequency. As before, a separate procedure may be performed to determine whether PUM or MDT is best suited for a particular application. The method then loops back to step 722 and in turn to 701 a.

If it is determined in step 718 that profile activation has been done, then the method continues with the next node in the network, e.g. since based on service area categorization the nodes have been able to be configured according to the profile activation.

It is noted that references herein to "higher", "moderate" and "lower/lesser" with respect to fraction and periodicity are effectively first, second and third levels or ranges of fraction and periodicity, respectively. It is noted that the method may comprise any number of different levels of fraction and periodicity, which may be applied in a particular set of circumstances. Furthermore, the number of levels applied to fraction may differ from the number of levels applied to periodicity.

The values or levels of higher, moderate and lower may be derived by autonomous apparatus based on the nodes and service areas in the region/market, e.g. all nodes and service areas in the region/market. References to higher, moderate and lower may also comprise range values. The values, or range values, may be autonomously derived to suit a particular application.

From the above it can be seen that, according to an embodiment, configuring measurement collection comprises changing a fraction of user equipment that are configured to collect measurement data. Configuring measurement collection may also comprise changing a periodicity at which user equipment are configured to collect measurement data.

The embodiment of Figures 7a, 7b and 7c above therefore comprises configuring measurement collection whereby: if a service area type is rural:

activating a needed measurement type at a first fraction level and a third periodicity level if a traffic level is below a first threshold level, e.g. indicating a low level of traffic, and

activating a needed measurement type at a second fraction level and a second periodicity level if a traffic level is above a second threshold level, e.g. indicating a high level of traffic; or

if a service area type is urban:

activating a needed measurement type at a second fraction level and a second periodicity level if a traffic level below the first threshold level, i.e. at a low level, and

activating a needed measurement type at a third fraction level and a first periodicity level if a traffic level is above the second threshold level, i.e. at a high level; or

if a service area type is sub-urban:

activating a needed measurement type at a first fraction level and a second periodicity level if a traffic level is below the first threshold level, i.e. at a low level, and

activating a needed measurement type at a third fraction level and a first periodicity level if a traffic level is above the second threshold level, i.e. at a high level.

It is noted that the first and second threshold levels can be different, or the same.

In the example of Figures 7a, 7b and 7c, the second fraction level is higher than the third fraction level, and the first fraction level higher than the second fraction level. The second periodicity level is higher than the third periodicity level, and the first periodicity level higher than the second periodicity level. In some examples the second fraction level is a moderate fraction level compared to a lower third fraction level and compared to a higher first fraction level, and the second periodicity level is a moderate fraction level compared to a lower third periodicity level and compared to a higher first periodicity level.

As noted above, a separate procedure may be performed to determine whether PUM or MDT is best suited for a particular application, i.e. as the needed measurement type. According to one embodiment, the method comprises determining whether the number of user equipment in a service area, of the type that operate in connected mode, are above a threshold level (e.g. more than 50% or predominant) and, if so, setting the needed measurement type as measurement collection based on periodic user equipment measurements, PUM. The method may comprise determining whether the number of user equipment in a service area, of the type that operate in idle mode, are above a threshold level (e.g. more than 50% or predominant) and, if so, setting the needed measurement type as measurement collection based on minimization of drive test, MDT.

Where a service area comprises a mixture of user equipment that operate in connected mode and idle mode, the method may comprise determining if a need of an optimization algorithm is from a particular area of a serving area, and, if so, performing both MDT and PUM measurement type on the user equipment based on whether a user equipment is in a connected mode or idle mode, and an availability of location estimation capability at the user equipment.

Thus, there is provided an autonomous method and apparatus to configure the network based on device type specific configurations. For example, a service area may comprise massive lOTs that normally do not send messages frequently. For such devices, the method comprises configuring the network to send further pings or posts with measurement information and time stamp (location data when applicable). This is related to user equipment, UE, or device type. The UE can be a loT device, normal smart phone or traditional voice/sms only phone. Also, UEs can be massive devices (loTs that send status once in a fortnight), relative devices (driverless cars or medical surgical equipment that is controlled remotely). Such different UEs may require different configurations based on the needs of an optimization algorithm and severity of its impact.

In one example, where the user equipment comprises critical lOTs, such critical lOTs normally require higher latency, and some of the critical IOT devices may be performing mission critical jobs (e.g. remote surgery). Such user equipment may be configured to send measurement data only in certain circumstances, for example when the network service quality is predicted to go bad and with higher fraction and periodicity values.

Figure 8 shows a method according to another embodiment. The method comprises determining the types of user equipment in a service area, step 801 . The method comprises configuring measurement collection of one or more network nodes in the service area based on the determined user equipment types within service area, step 803.

Figure 9 shows a method according to another embodiment. The method may be performed in a loop as illustrated by 901 a. For every node in the network, step 902, the method comprises deriving a traffic level, step 903. In step 904 it is determined whether a cell or service area is under optimization. If not, the method comprises de-activating measurement data collection, e.g. deactivating PUM/MDT, and then returning to the beginning of the loop.

If it is determined in step 904 that the cell or service area is under optimization, in step 906 it is determined whether enough measurement data samples exist for optimization to be performed, e.g. whether the sample size of collection of measurement data falls with a defined range, or meets a desired sample size of measurement data required for performing the network optimization. If it is determined in step 906 that there is not enough samples for optimization, in step 907 it is determined whether the traffic level is low, e.g. below a certain threshold level. If so, in step 908 the method comprises reconfiguring a measurement data collection such that the fraction value is increased and the periodicity value reduced. By increasing the fraction value, this means that a higher percentage of the user equipment connected to the network node are activated or instructed to collect measurement data (i.e. because the traffic level is low). By reducing the periodicity level, this means that the frequency at which measurement data is collected is increased (i.e. because of the lower periodicity).

If it is determined in step 907 that the traffic is not low, e.g. not below a certain threshold, in step 909 the method comprises either reconfiguring a measurement data collection such that the fraction value is increased, or reducing the periodicity value, i.e. performing one of these operations. As above, by increasing the fraction value, this means that a higher percentage of the user equipment connected to the network node are activated or instructed to collect measurement data (i.e. because the traffic level is low). By reducing the periodicity level, this means that the frequency at which measurement data is collected is increased (i.e. because of the lower periodicity).

If it is determined in step 906 that enough samples do exist for optimization, in step 910 it is determined whether traffic level is high, e.g. above a certain threshold. If it the traffic is high, in step 91 1 the method comprises either reconfiguring a measurement data collection such that the fraction value is reduced, or increasing the periodicity value, i.e. performing one of these operations. By reducing the fraction value, this means that a smaller percentage of the user equipment connected to the network node are activated or instructed to collect measurement data (i.e. because the traffic level is high and enough samples exist for optimization). By increasing the periodicity level, this means that the frequency at which measurement data is collected is reduced (i.e. because of the increased periodicity).

If it is determined in step 910 that the traffic level is not high, e.g. not above a certain threshold, the method proceeds to loop from step 912 back to 901 a, where the method is repeated again.

Referring to Figure 10, it is noted that the embodiments described above may form an apparatus that can be part of a physical node as part of operator network, or running as a virtual network function as part of the federation of network functions. In other words, some or all of the functions described above may be performed in a cloud based environment. Figure 10 shows an example illustrating how hardware resources 1000 in physical locations may be realized using one or more Virtual Network Functions/Network Functions 1001 1 to 10013 (VNF-1 to VNF-3). One or more endpoints 1007 and a virtualization layer 1005 interface between the hardware resources and virtual network functions. A Virtual Network Function Forwarding Graph 2, 1003 (VNF-FG-2) may relate, for example, to a group of node functions. The "^*" shown in Virtual Network Functions VNF-2B and VNF-3 illustrate an example of an apparatus forming part of the Virtual Network Function which is able to activate profiles on physical nodes and/or virtual nodes, such as measurement configuration profiles, wherein such apparatus is able to communicate with NMS functions for obtaining service area optimization status information.

Figure 11 shows an example of an apparatus 1 100 according to another embodiment, for performing network optimization. The apparatus 1 100 comprises a processor 1 101 and a memory 1 103, said memory 1 103 containing instructions executable by said processor 1 101 . The apparatus 1 100 is operative to configure a network node to collect measurement data from a plurality of user equipment devices served by one or more such network nodes in a service area, wherein the measurement data is for use with network optimization. The apparatus 1 100 is operative to autonomously update the configuration of measurement data collection at the network node based on one or more updated network parameters, such that the collection of measurement data produces a sample size of measurement data falling within a defined range. The apparatus 1 100 may be further operative to perform a method described in the embodiments above.

According to another embodiment, there is provided an apparatus for performing network optimization. The apparatus is adapted to configure a network node to collect measurement data from a plurality of user equipment devices served by one or more such network nodes in a service area, wherein the measurement data is for use with network optimization. The apparatus is further adapted to autonomously update the configuration of measurement data collection at the network node based on one or more updated network parameters, such that the collection of measurement data produces a sample size of measurement data falling within a defined range.

The apparatus may be further adapted to perform a method as described in the embodiments above.

Figure 12 shows an example of a network node 1200 according to an embodiment, the network node 1200 being configurable to collect measurement data from a plurality of user equipment devices served by the network node 1200 in a service area, wherein the measurement data is for use with network optimization. The network node 1200 comprises a processor 1201 and a memory 1203, said memory 1203 containing instructions executable by said processor 1201 . The network node 1200 is operative to periodically receive updated configuration information relating to the collection of measurement data at the network node, the updated configuration information based on one or more updated network parameters. The network node 1200 is operative to update the configuration of measurement data collection at the network node such that the collected measurement data produces a desired sample size of measurement data falling within a defined range.

The embodiments described herein provide a dynamic profile activation/de- activation and configuration, which helps to achieve energy saving goals for the network wide access points of subscribers, for example battery power on smart phones are not utilized for network performance optimization reasons.

It is noted that in some examples the methods and apparatus may be configured to periodically perform an autonomous updating of the configuration of measurement data collection at one or more network nodes, according to one or more updated network parameters. In such embodiments, it is noted that an updated network parameter may in fact have the same value or characteristic as a previous value for the network parameter, but updated to reflect that such value or characteristic is still current.

The embodiments provide effectiveness of optimization algorithms, for example coverage and capacity optimization algorithms, achieved by obtaining the correct measurement data, that leads to shorter optimization cycles, and optimization algorithms are able to adapt dynamically in the network (for example due to changes in traffic patterns).

Thus, the embodiments described above have the advantage of enabling network coverage and capacity optimization goals to be achieved due to availability of the required relevant measurement samples. In rural service area nodes, the embodiments allow more measurement samples, which can help optimization algorithms to perform effective coverage and capacity configurations. In urban service area nodes, the embodiments allow the measurement samples to be reduced, which can help optimization algorithms to process only the needed samples, and also save energy for the access points.

The embodiments described above also have an advantage of helping to reduce network operation expenditure (OPEX) due to less, or no, manual interaction being required with measurement configurations. The autonomous or zero touch configurations leads to improved optimization results in the network. Furthermore, no or poor optimization is avoided, since that is predominantly due to the non-availability of measurement data.

The autonomous activation (only when needed) and de-activation of the measurement feature provides significant power/energy savings in user equipment that perform such measurements. Overall, this also provides an energy saving for the network as a whole.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. The word "comprising" does not exclude the presence of elements or steps other than those listed in a claim, "a" or "an" does not exclude a plurality, and a single processor or other unit may fulfil the functions of several units recited in the claims. Any reference signs in the claims shall not be construed so as to limit their scope.

Claims

CLAIMS 1 . A method for performing network optimization, the method comprising:

configuring a network node to collect measurement data from a plurality of user equipment devices served by one or more such network nodes in a service area, wherein the measurement data is for use with network optimization; and

autonomously updating the configuration of measurement data collection at the network node based on one or more updated network parameters, such that the collection of measurement data produces a sample size of measurement data falling within a defined range. 2. The method as claimed in claim 1 , wherein autonomously updating the configuration of measurement data collection at a network node comprises:

receiving optimization status information; and

activating and/or deactivating measurement collection at the network node according to the received optimization status information.

3. The method as claimed in claim 2, wherein:

in response to the optimization status information indicating that one or more nodes in a service area have not met an optimization goal, and/or that one or more optimization algorithms are expecting measurement data, then activating measurement collection on the one or more nodes in the service area; or

in response to the optimization status information indicating that one or more nodes in a service area have met an optimization goal, and/or that one or more optimization algorithms are not expecting measurement data, then deactivating measurement collection on the one or more nodes in the service area.

4. The method as claimed in any one of the preceding claims, wherein autonomously updating the configuration of measurement data collection at the network node comprises:

deriving a service area type, and

updating the configuration of measurement data collection at one or more network nodes in the service area according to the derived service area type. 5. The method as claimed in claim 4, wherein the step of deriving service area type comprises autonomously deriving the service area type based on a clustering method.

6. The method as claimed in claim 5, wherein the clustering method comprises:

partitioning N network nodes in a service area into k partitions of data, each partition representing a cluster representing a possible service area type; classifying the data into k groups that satisfy a constraint that each network node, or a network node with a highest membership weight, belongs exactly to one group; and

determining the service area type based on the one group.

7. The method as claimed in any one of the preceding claims, wherein autonomously updating the configuration of measurement data collection at the network node comprises:

deriving a traffic level in a service area; and

updating the configuration of measurement data collection at one or more network nodes in the service area according to the derived traffic level. 8. The method as claimed in any one of the preceding claims, wherein autonomously updating the configuration of measurement data collection at the network node comprises:

deriving a needed measurement type; and

updating the configuration of measurement collection at one or more network nodes in the service area based on the derived measurement type that is needed.

The method as claimed in claim 8 comprising:

determining whether the number of user equipment in a service area, of the type that operate in connected mode, are above a threshold level; and

if so, setting the needed measurement type as measurement collection based on periodic user equipment measurements, PUM.

The method as claimed in claim 8 comprising:

determining whether the number of user equipment in a service area, of the type that operate in idle mode, are above a threshold level; and

if so, setting the needed measurement type as measurement collection based on minimization of drive test, MDT. 1 1 . The method as claimed in claim 8, wherein a service area comprises a mixture of user equipment that operate in connected mode and idle mode, the method comprising:

determining if a need of an optimization algorithm is from a particular area of a serving area; and

if so, performing both minimization of drive test, MDT, measurements and periodic user equipment measurements, PUM, based on whether a user equipment is in a connected mode or idle mode, and an availability of location estimation capability at the user equipment. 12. The method as claimed in any one of the preceding claims, wherein autonomously updating the configuration of measurement data collection at the network node comprises:

determining the types of user equipment in a service area; and updating the configuration of measurement data collection at one or more network nodes in the service area based on the determined user equipment types within service area.

13. The method as claimed in any one of the preceding claims, wherein configuring measurement collection comprises changing a fraction of user equipment that are instructed to collect measurement data, and/or the number of user equipment that are instructed to transmit measurement data that has been collected.

14. The method as claimed in any one of the preceding claims, wherein configuring measurement collection comprises changing a periodicity at which user equipment are configured to collect measurement data.

15. The method as claimed in any one of the preceding claims, wherein configuring measurement collection comprises:

if a service area type is rural:

activating a needed measurement type at a first fraction level and a third periodicity level if a traffic level is below a first threshold level, and

activating a needed measurement type at a second fraction level and a second periodicity level if a traffic level is above a second threshold level; or

if a service area type is urban:

activating a needed measurement type at a second fraction level and a second periodicity level if a traffic level is below the first threshold level, and

activating a needed measurement type at a third fraction level and a first periodicity level if a traffic level is above the second threshold level; or

if a service area type is sub-urban:

activating a needed measurement type at a first fraction level and a second periodicity level if a traffic level is below the first threshold level, and

activating a needed measurement type at a third fraction level and a first periodicity level if a traffic level above a second threshold level. 16. The method as claimed in any one of the preceding claims, wherein autonomously updating the configuration of measurement data collection at the network node comprises:

configuring a network node using a configuration profile selected from a set of configuration profiles.

17. An apparatus for performing network optimization, the apparatus comprising a processor and a memory, said memory containing instructions executable by said processor, whereby said apparatus is operative to:

configure a network node to collect measurement data from a plurality of user equipment devices served by one or more such network nodes in a service area, wherein the measurement data is for use with network optimization; and

autonomously update the configuration of measurement data collection at the network node based on one or more updated network parameters, such that the collection of measurement data produces a sample size of measurement data falling within a defined range.

18. The apparatus as claimed in claim 17, wherein in autonomously updating the configuration of measurement data collection at a network node said apparatus is operative to:

receive optimization status information; and activate and/or deactivate measurement collection at the network node according to the received optimization status information.

19. The apparatus as claimed in claim 18, wherein:

in response to the optimization status information indicating that one or more nodes in a service area have not met an optimization goal, and/or that one or more optimization algorithms are expecting measurement data, said apparatus is operative to activate measurement collection on the one or more nodes in the service area; or

in response to the optimization status information indicating that one or more nodes in a service area have met an optimization goal, and/or that one or more optimization algorithms are not expecting measurement data, said apparatus is operative to deactivate measurement collection on the one or more nodes in the service area.

20. The apparatus as claimed in any one of claims 17 - 19, wherein in autonomously updating the configuration of measurement data collection at the network node said apparatus is operative to:

derive a service area type, and

update the configuration of measurement data collection at one or more network nodes in the service area according to the derived service area type.

21 . The apparatus as claimed in claim 20, wherein in the operation of deriving service area type said apparatus is operative to autonomously derive the service area type based on a clustering method.

22. The apparatus as claimed in claim 21 , wherein in executing the clustering method said apparatus is operative to:

partition N network nodes in a service area into k partitions of data, each partition representing a cluster representing a possible service area type; classify the data into k groups that satisfy a constraint that each network node, or a network node with a highest membership weight, belongs exactly to one group; and

determine the service area type based on the one group.

23. The apparatus as claimed in any one of claims 17 - 22, wherein in autonomously updating the configuration of measurement data collection at the network node said apparatus is operative to:

derive a traffic level in a service area; and

update the configuration of measurement data collection at one or more network nodes in the service area according to the derived traffic level.

24. The apparatus as claimed in any one of claims 17 - 23, wherein in autonomously updating the configuration of measurement data collection at the network node said apparatus is operative to:

derive a needed measurement type; and

update the configuration of measurement collection at one or more network nodes in the service area based on the derived measurement type that is needed.

25. The apparatus as claimed in claim 24 further operative to:

determine whether the number of user equipment in a service area, of the type that operate in connected mode, are above a threshold level; and

if so, set the needed measurement type as measurement collection based on periodic user equipment measurements, PUM.

26. The apparatus as claimed in claim 24 further operative to:

determine whether the number of user equipment in a service area, of the type that operate in idle mode, are above a threshold level; and

if so, set the needed measurement type as measurement collection based on minimization of drive test, MDT.

27. The apparatus as claimed in claim 24, wherein a service area comprises a mixture of user equipment that operate in connected mode and idle mode, and said apparatus is operative to:

determine if a need of an optimization algorithm is from a particular area of a serving area; and

if so, perform both minimization of drive test, MDT, measurements and periodic user equipment measurements, PUM, based on whether a user equipment is in a connected mode or idle mode, and an availability of location estimation capability at the user equipment.

28. The apparatus as claimed in any one of claims 17 - 27, wherein in autonomously updating the configuration of measurement data collection at the network node said apparatus is operative to:

determine the types of user equipment in a service area; and update the configuration of measurement data collection at one or more network nodes in the service area based on the determined user equipment types within service area.

29. The apparatus as claimed in any one of claims 17 - 28, wherein in configuring measurement collection said apparatus is operative to change a fraction of user equipment that are instructed to collect measurement data, and/or the number of user equipment that are instructed to transmit measurement data that has been collected.

30. The apparatus as claimed in any one of claims 17 - 29, wherein in configuring measurement collection said apparatus is operative to change a periodicity at which user equipment are configured to collect measurement data.

31 . The apparatus as claimed in any one of claims 17 - 30, wherein in configuring measurement collection said apparatus is operative to:

if a service area type is rural:

activate a needed measurement type at a first fraction level and a third periodicity level if a traffic level is below a first threshold level, and

activate a needed measurement type at a second fraction level and a second periodicity level if a traffic level is above a second threshold level; or

if a service area type is urban:

activate a needed measurement type at a second fraction level and a second periodicity level if a traffic level is below the first threshold level, and

activate a needed measurement type at a third fraction level and a first periodicity level if a traffic level is above the second threshold level; or

if a service area type is sub-urban:

activate a needed measurement type at a first fraction level and a second periodicity level if a traffic level is below the first threshold level, and

activate a needed measurement type at a third fraction level and a first periodicity level if a traffic level above a second threshold level.

32. The apparatus as claimed in any one of claims 17 - 31 , wherein in autonomously updating the configuration of measurement data collection at the network node said apparatus is operative to:

configure a network node using a configuration profile selected from a set of configuration profiles. 33. A network node, the network node being configurable to collect measurement data from a plurality of user equipment devices served by the network node in a service area, wherein the measurement data is for use with network optimization, the network node comprising a processor and a memory, said memory containing instructions executable by said processor, whereby said network node is operative to:

periodically receive updated configuration information relating to the collection of measurement data at the network node, the updated configuration information based on one or more updated network parameters, wherein the network node is operative to update the configuration of measurement data collection at the network node such that the collected measurement data produces a sample size of measurement data falling within a defined range.

34. An apparatus for performing network optimization, the apparatus being adapted to: