Methods and apparatuses for controlling one or more satellites in a network

Abstract

Embodiments described herein relate to methods and apparatuses for controlling one or more satellite in a network, wherein the one or more satellites serve a plurality of cells in a region of interest, and the plurality of cells are associated with a respective plurality of cell centres. A method (200) performed by a controlling node comprises transmitting (202), to the one or more satellites, a first configuration of the plurality of cells, the first configuration providing an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest.

Description

[0001] METHODS AND APPARATUSES FOR CONTROLLING ONE OR MORE SATELLITES IN A NETWORK

[0002] TECHNICAL FIELD

[0003] The present disclosure relates, in general, to wireless communications and, more particularly, systems and methods for load-balancing Earth-Fixed Cell (EFC) planning in Non-Terrestrial Networks.

[0004] BACKGROUND

[0005] A wireless network may comprise a non-terrestrial network (NTN) component. The NTN component may comprise a constellation of several satellites that can orbit the earth using one or more orbital planes. For example, the constellation of satellites may comprise low Earth orbit (LEO) medium Earth orbit (MEO) or geostationary Earth orbit (GEO) satellites.

[0006] FIGURE 1 illustrates an example architecture of an NTN. The example architecture of the NTN depicted in Figure 1 comprises a satellite 102 in communication with an earthbased gateway 104. The gateway 104 connects the satellite 102 to a base station or a core network, depending on the choice of architecture. A feeder link 106 refers to the link between the satellite 102 and the gateway 104. The satellite 102 is also in communication with one or more UEs 108 via a service link 110.

[0007] The satellite 102 may comprise a satellite antenna, wherein the satellite antenna may be in communication with a Radio Access Network (RAN) node. In the case of Third Generation Partnership Project (3GPP) New Radio (NR), the RAN node may be a gNodeB (gNB). Depending on the architecture, components of the nodes may be located either on the ground or onboard the satellite 102. The ground components and the onboard components may be connected through satellite gateways 104 via the feeder link 106.

[0008] It will be appreciated that in both an NTN and a terrestrial network (TN) each node (e.g. base station or satellite) in the network is expected to provide coverage to a specific territory by dividing the area of the specific territory into coverage sectors. In the case of NTN, the satellites transmit radio signals, in the form of satellite beams 112, towards those areas.

[0009] Each satellite 102 in the constellation of satellites can provide wireless network access to a user equipment (UE) 108 positioned on, or near, the Earth’s surface via the service link 110. For example, the satellites may comprise onboard antennas that can radiate beams 112 towards one or more centres of respective one or more Earth-Fixed Cells (EFCs) 114, wherein these beams 112 may be transmitter beams for the downlink (DL) and receiver beams for the uplink (UL). In the DL, the total power of the satellite antenna is shared between simultaneous DL beams. This is not true for the UL.

[0010] An EFC 114 comprises a specific and fixed geographic area with a specific centre. Compared to moving cells, an EFC 114 is especially beneficial in an NTN comprising one or more LEO satellites as the LEO satellites may steer their beams 112 to compensate the orbiting motion of the satellites. Consequently, cell selection is much simpler.

[0011] The LEO satellites may steer their beams 112 toward the centres of EFCs 114. Therefore, beam power loss occurs to UEs not located at the EFC centres. The angle between the satellite-to-EFC centre and the satellite-to-UE is called a misalignment angle. For a given satellite antenna, the larger the misalignment angle is, the more severe the beam power loss the UE 108 experiences.

[0012] SUMMARY

[0013] There currently exist certain challenges. Contrary to TN nodes, NTN nodes and satellite antennas may be required to provide coverage over an area of thousands of km2. This means that the population spread of cells covered by the same satellite 102 may be extreme, from almost no users (for example, in remote areas such as sea, mountains, forests, etc.) up to thousands of users (for example, in urban areas where an NTN is required to provide coverage in emergency scenarios). This population imbalance between NTN cells may result in a similar traffic load imbalance.

[0014] In TNs, cell planning is usually done by characterizing an area based on its population and traffic density, which define the cell radius and inter-site distance (ISD), wherein the ISD is the distance between adjacent cell centres. However, in TNs, the ISDs within a coverage area will be consistent. For example, an area may be characterized as urban or suburban based on its population and traffic density, and the ISDs within the area would then be fixed based on that characterization.

[0015] As discussed above, an NTN is unlike a TN because an area that is served by a particular satellite 102 in an NTN may span several hundreds or even thousands of cells, wherein the cells within that area may be distributed depending on cell metrics such as the population density of each cell. However, previous configurations of an NTN architecture assume a hexagonal cell layout with a specific cell radius and ISD, irrespective of the population imbalance and / or traffic load imbalance between different cells within the coverage area.

[0016] Thus, there is a need for a solution which can provide a configuration of the cell layout within a coverage area of one or more satellites in an NTN, wherein the configuration accounts for the imbalance in the population density and / or traffic load within the coverage area.

[0017] According to some embodiments there is therefore provided a method performed by a controller node for controlling one or more satellites in a network, wherein the one or more satellites serve a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres. The method comprises transmitting, to the one or more satellites, a first configuration of the plurality of cells, the first configuration providing an indication of a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest.

[0018] According to certain embodiments, there is provided a controller node for controlling one or more satellites in a network, wherein the one or more satellites serve a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres. The controller node comprises processing circuitry and memory wherein the memory contains instructions executable by the processing circuitry whereby the controller node is operable to transmit, to the one or more satellites, a first configuration of the plurality of cells, the first configuration providing an indication of a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest. According to certain embodiments, there is provided a method performed by a satellite in a network, wherein the satellite serves a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres. The method comprises receiving, from a controller node, a first configuration of the plurality of cells, the first configuration providing an indication of a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest. The method further comprises applying the first configuration to provide the plurality of cells.

[0019] According to certain embodiments, there is provided a satellite in a network, wherein the satellite serves a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres. The satellite is adapted to receive, from a controller node, a first configuration of the plurality of cells, the first configuration providing an indication of a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest. The satellite is further adapted to apply the first configuration to provide the plurality of cells.

[0020] Analogous computer programs, carriers, computer-readable media and computer program products are provided in other embodiments.

[0021] Certain embodiments may provide one or more of the following technical advantages. For example, a technical advantage may be that population density and / or traffic load of EFCs in a region of interest in an NTN are well balanced. As another example, a technical advantage may be improved performance and / or system capacity. As another example, a technical advantage may be a balanced quality of service. As another example, a technical advantage may be a reduced number of simultaneous satellite beams in the network.

[0022] Other advantages may be readily apparent to one having skill in the art. Certain embodiments may have none, some, or all of the recited advantages.

[0023] BRIEF DESCRIPTION OF THE DRAWINGS

[0024] For a better understanding of the embodiments of the present disclosure, and to show how it may be put into effect, reference will now be made, by way of example only, to the accompanying drawings, in which: FIGURE 1 illustrates an example architecture of an NTN.

[0025] FIGURE 2a illustrates an example architecture of an NTN according to some embodiments.

[0026] FIGURE 2b is a flow diagram showing an example method performed by a controller node, according to certain embodiments.

[0027] FIGURE 3 illustrates an example implementation of the method performed by the controller node, according to certain embodiments.

[0028] FIGURE 4 shows a comparison of the CDF of the population per EFC in a particular region of interest with and without applying the disclosed clustering process according to certain embodiments.

[0029] FIGURE 5 illustrates a controller node comprising processing circuitry (or logic).

[0030] FIGURE 6 is a block diagram illustrating a controller node according to some embodiments.

[0031] FIGURE 7 illustrates a method performed by a satellite in a network.

[0032] FIGURE 8 illustrates a satellite comprising processing circuitry (or logic).

[0033] FIGURE 9 is a block diagram illustrating a satellite 900 according to some embodiments.

[0034] DETAILED DESCRIPTION

[0035] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and / or is implied from the context in which it is used. All references to a / an / the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and / or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

[0036] The following sets forth specific details, such as particular embodiments or examples for purposes of explanation and not limitation. It will be appreciated by one skilled in the art that other examples 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 may 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 (ROM, EEPROM, Flash memory, a memory disc, RAM etc.) 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.

[0037] Hardware implementation may include or encompass, without limitation, digital signal processor (DSP) hardware, a reduced instruction set processor, hardware (e.g., digital or analogue) 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.

[0038] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art. Although particular problems and solutions may be described using new radio (NR) terminology, it should be understood that the same solutions apply to long term evolutions (LTE) and other wireless networks as well, where applicable.

[0039] As described above, an NTN may be deployed to provide coverage to a variety of territories, including open space or water areas, rural areas, suburban areas or urban areas where extra coverage is required for some emergency and / or occasional events. Due to the diversity of the possible deployment regions in NTN, and the large area over which the NTN may be deployed, an NTN faces the issue of heavily imbalanced population density and / or traffic load in a particular region of interest. It will be appreciated that a region of interest may be defined as a geographical area upon the earths surface. The region of interest may be defined in some circumstances as a region mapping to a spherical estimate of the earth’s surface, or alternatively the region of interest could map exactly to the surface of the earth. It will also be appreciated that the region of interest may also be defined as an area for which the NTN is required to provide coverage.

[0040] To this end, certain embodiments disclosed herein describe a cell planning method that provides a set of EFC centres with variable cell radiuses and inter-site distances (ISDs) that depend on metrics such as population or traffic density. In particular embodiments, for example, such a cell planning method may be performed by a controller node for controlling one or more satellites in a wireless network, wherein the one or more satellites serve a plurality of cells in a region of interest, and wherein the plurality of cells are associated with a respective plurality of cell centres. In particular embodiments, the wireless network has an NTN component. In particular embodiments, the cell planning method is transparent to NTN application scenarios and therefore the cell planning method may be applied to a scenario wherein an NTN serves certain categories of UEs independently or a scenario wherein an NTN acts in collaboration with a TN.

[0041] FIGURE 2a illustrates an example architecture of an NTN according to some embodiments. The example architecture of Figure 2a contains the same elements as those illustrated in Figure 1 (and the same reference numbers are maintained for consistency). However, the architecture of Figure 2a further comprises a controller node 250.

[0042] The controller node 250 may be in communication with (either directly or indirectly) one or more satellites 102 in a wireless network. The one or more satellites 102 serve a plurality of cells 114 in a region of interest (only one cell is illustrated in Figure 2a for clarity), wherein each satellite 102 of the one or more satellites may serve one or more of the plurality of cells 114. The controller node 250 may be located in a component of an NTN or in a component of a TN. In some embodiments the controller node 250 is located onboard the one or more satellites, for example, the one or more satellites 102 may comprise a processing element, wherein the processing element comprises the controller node. In some embodiments, the controller node 250 is located in a component of the wireless network separate to the one or more satellites and in communication with the one or more satellites.

[0043] FIGURE 2b is a flow diagram showing an example method 200 performed by a controller node, according to certain embodiments.

[0044] The method 200 of Figure 2b may be performed by a controller node (e.g. controller node 250), which may comprise a physical or virtual node, and may be implemented in a computing device or server apparatus and / or in a virtualized environment, for example in a cloud, edge cloud or fog deployment.

[0045] The method 200 begins at step 201 when the controller node 250 transmits, to the one or more satellites 102, a first configuration of the plurality of cells, the first configuration providing an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest. In other words, the distances between pairs of adjacent coordinates within the first set of coordinates are not the same across the region of interest.

[0046] In some embodiments, the first set of coordinates may belong to a two-dimensional coordinate system, for example, may comprise coordinates in a longitude-latitude coordinate system. In some embodiments, the first set of coordinates belong to a three- dimensional coordinate system, for example, a cartesian coordinate system.

[0047] In some embodiments, the indication of the first set of coordinates identifies the first set of coordinates. However, in other embodiments, the indication of the first set of coordinates comprises one or more parameters that indicate or otherwise allow the one or more satellites to determine the first set of coordinates. The one or more parameters may comprise for example information relating to azimuth and / or elevation angles for beam steering. The one or more parameters may comprise an index of a lookup table stored in a memory of the one or more satellites, wherein the lookup table comprises information which maps to angles used by the one or more satellites to steer a beam 112 towards a cell centre, and wherein the index of the lookup table maps to a subset of the angles.

[0048] In some embodiments, the first set of coordinates have a random distribution. In some embodiments, the first set of coordinates are not distributed randomly. Therefore, the first set of coordinates may be irregularly distributed according to some logic, for example, according to a clustering process as will be described later with reference to Figure 3. It will, however, be appreciated that other processes will be available to enable the distribution of the first set of coordinates to be irregular within the region of interest.

[0049] In particular embodiments, the controller node determines the first set of coordinates based on a plurality of network metrics, wherein a network metric in the plurality of network metrics is associated with a respective location within the region of interest. In particular embodiments, the network metric in the plurality of network metrics comprises information representative of one or more of: a population density at the respective location and a traffic density at the respective location. It will therefore be appreciated that herein a network metric may comprise one or more parameters. For example, a first network metric associated with a first location may comprise both the population density and the traffic density at the respective location.

[0050] By taking into account the plurality of network metrics when determining the first set of coordinates, the controller node may account for the possibility that, as the region of interest is likely to be large for an NTN, the plurality of network metrics may vary vastly across the region of interest, and the EFCs 114 may be distributed accordingly.

[0051] In particular embodiments, the controller node may monitor the region of interest for information relating to the network metric. For example, in some embodiments, a RAN node is in communication with the controller node. The RAN mode may be a component of the NTN and / or a component of the TN. The RAN node may determine measurements of one or more network metrics associated with the network in the region of interest. The one or more network metrics may comprise information related to the population distribution of the region of interest. The RAN node may transmit information related to the measurements of the one or more network metrics to the controller node. In some embodiments, the configuration of the plurality of cells may be dynamic, wherein the configuration of the plurality of cells may comprise, for each cell in the plurality of cells, a location of the cell centre and / or a radius of the cell. In other words, the first set of coordinates may be updated dynamically. In some examples, the first set of coordinates may be updated periodically.

[0052] In other examples, the controller node may, responsive to the monitored information relating to the network metric meeting a criterion, determine an updated configuration for the plurality of cells, and transmit, to the one or more satellites, the updated configuration.

[0053] In some embodiments, the monitored information relating to the network metric comprises information related to the population distribution of the region of interest. The population distribution of the region of interest may vary dynamically over time. In some examples, therefore, the criterion may comprise the population density of a cell in the region of interest meeting a maximum threshold criterion. In such a circumstance, it may, for example, be beneficial to update the configuration of the first set of coordinates, for example to introduce an extra cell centre within the area previously covered by the cell that has met the maximum threshold criterion.

[0054] The criterion may also comprise the population density of a cell in the region of interest meeting a minimum threshold criterion. In such a circumstance, it may, for example, be beneficial to update the configuration of the first set of coordinates, for example to remove an extra cell centre within the area previously covered by the cell that has met the minimum threshold criterion.

[0055] In other embodiments, the monitored information relating to the network metric comprises information related to the traffic demand distribution of the region of interest. The traffic demand distribution of the region of interest may vary dynamically with time. Therefore, the criterion may comprise the traffic demand of a cell in the region of interest meeting a maximum threshold criterion. In such a circumstance, it may, for example, be beneficial to update the configuration of the first set of coordinates, for example to introduce an extra cell centre within the area previously covered by the cell that has met its maximum threshold criterion.

[0056] The criterion may also comprise the traffic demand of a cell In the region of interest meeting a minimum threshold criterion. In such a circumstance, it may, for example, be beneficial to update the configuration of the first set of coordinates, for example to remove an extra cell centre within the area previously covered by the cell that has met the minimum threshold criterion.

[0057] In the example illustrated in Figure 2b, the controller node performs step 202 prior to step 201 in order to determine the first set of coordinates. However, it will be appreciated that the first set of coordinates may be obtained by the controller node in other ways. For example, the controller node may receive the first set of coordinates from another network node. In other examples, the controller node may determine the first set of coordinates using a randomisation process. In other examples, the controller node may apply a different logical process to determine the first set of coordinates. In some examples, the logical process may utilise the plurality of network metrics mentioned above.

[0058] However, in the example illustrated in Figure 2b, in step 202, the controller node applies a clustering process to a plurality of locations within the region of interest to determine the first set of coordinates.

[0059] In particular embodiments, during step 202 the plurality of locations are weighted according to the respective network metrics. The clustering process may output a set of final clusters, wherein a final cluster comprises one or more of the plurality of locations, and a respective set of final cluster centres, wherein the first set of coordinates comprises the set of final cluster centres. As will be described in more detail with reference to Figure 3, during the clustering process a number of iterations of cluster centres may be produced. However, it will be appreciated that, in some examples only the final cluster centres output by the clustering process may be utilised to actually control the EFCs provided by the one or more satellites.

[0060] In some embodiments, the plurality of locations within the region of interest comprise a uniform distribution of the plurality of locations. The plurality of locations may be sampled in a geographically equiprobable manner from the region of interest. In some embodiments, the plurality of locations within the region of interest comprise a non- uniform distribution of the plurality of locations.

[0061] It will be appreciated that any suitable clustering process may be utilised to determine the first set of coordinates, for example, the clustering process may comprise one of: a k-means clustering process (e.g. with a Lloyd’s iterative process), a Gaussian mixture model process, and a Balance Iterative Reducing and Clustering using Hierarchies (BIRCH) process.

[0062] FIGURE 3 illustrates an example implementation of step 202 of the method 200 performed by the controller node, according to certain embodiments. The example implementation comprises a method 300 for EFC planning for NTN systems in a particular region of interest. It will be appreciated that the method 300 may be transparent to NTN deployment scenarios.

[0063] It will be appreciated that the method 300 is only an example implementation of step 202 of the method 200. For example, in some embodiments some steps from the method 300 may be omitted, adjusted or re-ordered in order to perform step 202.

[0064] The method 300 will be outlined in more detail below. In the outline below, population is taken as an example network metric which may be used to determine a configuration for a plurality of EFCs. However, as described above it will be appreciated that population is not the only network metric that may be used to determine the configuration of the plurality of EFCs.

[0065] The plurality of locations within the region of interest may be obtained by sampling N points in a geographically equiprobable manner from the region of interest, wherein (N E N), and wherein each point has a location xn, wherein xne Rk,n e {1, — , N}, and an associated population pn, wherein pn,n e {1, It will be appreciated that in other embodiments, for example wherein traffic demand is used as the network metric, the traffic demand may replace the associated population pn,n E {1, ••• , / } of the N samples in the method 300. It will also be appreciated that k denotes the number dimension of the locations of the sampled points. If k = 2, then a two-dimensional coordinate system may be considered. For example, a longitude-latitude coordinate system may be considered. If k = 3, a three-dimensional coordinate system may be considered. For example, a cartesian coordinate system may be considered. X denotes the set of locations of all the sampled points in the region of interest, wherein X = {x , ■■■ ,xN}.

[0066] The method 300 may use as an input the set of sampled locations X as well as the associated populations pn, wherein n e {1, ,JV} and a number of EFC centres M, wherein (M e N). In some example, s the method 300 may also utilise as input one or more of: a maximum number of iterations of the clustering process I, a population limit per EFC centre P, a maximum ISD Dmaxand a minimum ISD Dmin. The values of M, I, P,Dmaxand Dmin maybepredefined. In some embodiments, the values of M,I, P, Dmaxand Dminmay be pre-configured by the requirements of the network. However, it will be appreciated that the values of M,l, P, Dmaxand Dminare a design choice and no limitations on these values are implied. It will be appreciated that M may be significantly smaller than N (M « N) such that the configuration of the EFCs provides an optimum number of EFCs. It will be appreciated that an optimum number of EFCs will be much smaller than the number of sampled points in the region of interest. However, how much smaller M is than N may depend on the density of the sampling of N.

[0067] The method 300 begins with step 302, by initializing an iteration index value, i, to 0. The method 300 continues to step 304, wherein it is determined whether a first iteration is being performed (wherein the first iteration is where i = 0).

[0068] If it is determined that the first iteration is being performed, the method 300 may proceed to step 306, which comprises initialising the clustering process by randomly selecting an initial set of cluster centres from the plurality of locations. Hence, for the first iteration, initial set of cluster centres may be selected randomly from the N sampled locations. Each of the initial set of cluster centres is denoted as cm, wherein cme IRk,m e The initial set of cluster centres is denoted as C, wherein C = {c^ c2, . .. , cM}.

[0069] The method 300 then proceeds to step 312 in which the N samples are partitioned into a set of first clusters, wherein each first cluster is associated to one of the initial set of cluster centres C. For every sampled point xn, the point may be classified to the set Xm, wherein m e {1, and wherein Xmdenotes the first cluster with an initial cluster centre cm. For example, the points may be classified to the set Xmaccording to Equation 1 below. xnG Xm*, m* = arg minTOe{1;...M}|xn- cm12(1 )

[0070] Hence, step 312 may comprise, for each location in the plurality of locations: determining a distance between the location xnand each cluster centre cmin the set of cluster centres, and assigning the location xnto a selected first cluster from the set of first clusters by minimising a distance between the location xnand the cluster centre associated with the selected first cluster. In Equation 1 , the selected first cluster is denoted as wherein m* denotes the optimal solution of the minimisation problem. Equation 1 discloses that a Vector L2 Norm may be used to determine the distance between the location xnand a cluster centre cm. It will be appreciated that other calculations may be performed to determine a distance between the location xnand a cluster centre cm. By classifying the points to a set Xm(e.g. to a first cluster) using Equation 1 , the method 300 may partition the region of interest to a Voronoi diagram wherein each initial cluster centre cmis represented by the first cluster comprising the set of locations Xm.

[0071] Returning to step 304, if it is determined that the first iteration is not being performed, and hence it is determined that a second or higher iteration (up to the maximum number of iterations / ) is being performed, the method 300 proceeds to step 308.

[0072] Steps 308 to 312 may be considered to comprise performance of a “clustering process”. These steps may therefore together be considered to be an implementation of step 202 of Figure 2b. As will now be described, a plurality of iterations of these steps 308 to 312 may be performed (e.g. as controlled by use of the iteration index i). In order to perform this clustering process the input to the clustering process may effectively be a set of first clusters (e.g. the output of step 312 as described above).

[0073] Each of steps 308 and 310 are performed for a first cluster in the set of first clusters, wherein the first cluster in the set of first clusters comprises a subset of the plurality of locations.

[0074] For each first cluster therefore, the clustering process may comprise: step 308 of determining a set of weighted locations from the subset of the plurality of locations and step 310 of determining an updated cluster centre based on the set of weighted locations.

[0075] The clustering process 202 then finishes with step 312 of assigning locations in the plurality of locations to a set of updated clusters centred on the updated cluster centres.

[0076] When the next iteration of the clustering process 202 is then performed, the set of updated clusters are set as the set of first clusters. Steps 308 to 312 will now be described in more detail below. The description of steps 308 and 310 refers to the clustering process performed for a first cluster. It will be appreciated that steps 308 and 310 may be performed for each first cluster in the set of first clusters.

[0077] Step 308 comprises, for a location in the subset of the plurality of locations, determining a weighting coefficient, wherein the weighting coefficient is dependent on the network metric associated with the location, and determining the weighted location as a product of the weighting coefficient and the location.

[0078] Therefore, for the iterations other than the first iteration, the step 308 may comprise determining a weighting coefficient anfor each sampled point xnin set Xm, wherein For example, the weighting coefficient anof a sampled point xnin set

[0079] Xmmay be determined according to Equation 2 below, wherein the weighting coefficient of anmay be calculated as the ratio of the associated population pnof a sampled point xnto the sum of the respective populations pfof each sampled point xtin the set Xm.

[0080] Hence, the weighted location of the sampled point xnmay be the product of the sampled point xnand the weighting coefficient an.

[0081] Step 310may comprise determining the updated cluster centre based on the set of weighted locations, comprising determining the updated cluster centre by summing each weighted location in the set of weighted locations. For example, cluster centre cmassociated to the first cluster Xmmay be updated according to Equation 3 below. cm ~ xfEXma x(3)

[0082] The updated cluster centre may be the centroid of the first cluster represented by Xm, wherein the centroid is determined according to the weighting coefficients a{(wherein the locations xfe Xm).

[0083] Step 312 may then comprise for a location in the plurality of locations, determining a distance between the location and updated cluster centres in the set of updated cluster centres, and assigning the location to a selected cluster from the set of updated clusters by minimising a distance between the location and the updated cluster centre associated with the selected cluster. Hence, once the updated cluster centres are determined, the samples may be partitioned according to Equation 1 .

[0084] The method 300 may then proceed to step 314, which comprises increasing the iteration index by a value of 1. The method 300 may then proceed to step 316, which comprises determining whether the iteration index is greater than or equal to the maximum number of iterations of the clustering process I. If it is determined that the iteration index is less than the maximum number of iterations of the clustering process / then the method 300 returns to step 304 and the clustering process 202 is repeated.

[0085] If it is determined that the iteration index is greater than or equal to the maximum number of iterations of the clustering process / then the method 300 may proceed to step 318.

[0086] As described above steps 308, 310 and 312 may be considered to comprise a clustering process 202. Hence, application of the method 300 may comprise performing a first number of iterations of the clustering process 202, and setting a last output set of first clusters as the set of final clusters (e.g. to then be used in step 201 of Figure 1). In some examples, the method of Figure 3 may terminate here, and the set of final clusters may be utilised to control the satellites. In other words, the EFCs may be set to the centres of the set of final clusters after it is determined that the iteration index is greater than or equal to the maximum number of iterations / .

[0087] In other examples, if it is determined that the iteration index is greater than or equal to the maximum number of iterations of the clustering process / , the method 300 may comprise, prior to setting the last output set of first clusters as the set of final clusters for each first cluster in the last output set of first clusters, determining a cell metric (e.g. as in step 318 and / or step 326 as will be described in more detail below), and responsive to a cell metric associated with a first cluster of the last output set of first clusters meeting a criterion (e.g. as in step 320 and / or step 326 as will be described in more detail below), adjusting a number of cluster centres in the last output set of first clusters (e.g. as in step 322, 332 and / or step 334 as will be described in more detail below); and performing a further iteration of the clustering process 202.

[0088] By checking that a cell metric (e.g. the total population of the first cluster or the ISDs between the first cluster and its adjacent clusters) for each first cluster in the last output set of first clusters meets a specified criterion, the method of Figure 3 may ensure that certain requirements for the EFCs can be met such as a minimum or maximum ISD between adjacent cell centres or a maximum population of an EFC.

[0089] In some embodiments of the method 300, adjusting the number of cluster centres comprises adding a new cluster centre within the first cluster (e.g. as will be described in more detail with reference to steps 332, 322 and 324). For example, a location of the new cluster centre may be selected using a random process (e.g. as will be described with reference to step 322). In other embodiments of the method 300, adjusting the number of cluster centres comprises removing a cluster centre from the first cluster (as will be described in more detail with reference to step 334 and step 336).

[0090] In some embodiments of the method 300, the criterion comprises a maximum threshold criterion on the cluster population.

[0091] In these examples, in step 318, after I iterations are finished, a population per first cluster pmmay be determined. For example, the population per cluster pmmay be determined according to Equation 4 below.

[0092] Pm = xfexmP (4)

[0093] The method 300 may then proceed to step 320, which comprises determining whether the population per first cluster pmmeets a maximum threshold criterion. In these example, the maximum threshold criterion is implemented as whether the population per first cluster is pmless than or equal to the population limit P. If it is determined that the population per first cluster is greater than the population limit (e.g. if pm> P occurs), then the method 300 may proceed to step 322, in which one more cluster centre may be created by randomly selecting a location from the set Xm.

[0094] The method 300 then proceeds to step 324, in which the newly selected cluster centre is added to the set of cluster centres and the iteration index may be reset to to 0. The method 300 then returns to step 312 and a new set of first clusters is created to reinitialise the clustering process 202.

[0095] If it is determined that the population per first cluster meets the maximum threshold criterion, , the method 300 may proceed to step 326, which comprises determining inter- centre distances between any two adjacent cluster centres cmiand cm2(where m1#= m2). For example, the inter-centre distances may be determined according to Equation 5 below.

[0096] It will be appreciated that a distance other than the distance described in Equation 5 may be considered, for example, a chordal distance.

[0097] The method 300 may then proceed to step 328, which comprises determining whether the inter-centre distance meets a threshold criterion. In this example, the threshold criterion may comprise a maximum threshold criterion and / or a minimum threshold criterion. In particular, in this example, it is determined whether the inter-centre distance dmi,m2is greater than or equal to Dminand less than or equal to Dmax. If it is determined that dm mis less than Dmir, or it is determined that mis greater than Dmax, then the method 300 proceeds to step 330, comprising determining if dmi,m2is greater than Dmnr. If it is determined that dm mis greater than Dmar(if dm m> Dmnroccurs), the method 300 may proceed to step 332, which comprises adding one more cluster centre to the set of cluster centres. In other words, when the inter-site distance does not meet the maximum threshold criterion, adjusting the number of cluster centres comprises adding a new cluster centre within the first cluster. For example, the one more cluster centre, cM+1, may be determined according to Equation 6 below. cM+1= ^^ (6)

[0098] The method 300 may then proceed to step 324, which comprises increasing the number of cluster centres M by 1 and re-initializing the iteration index to 0. The method 300 then returns to step 312 and a new set of first clusters is created to re-initialise the clustering process 202.

[0099] On the other hand, if in step 330 of the method 300 it is determined that dmi,m2is not greater than Dmax(if dmi,m2< Dminoccurs), the method 300 may proceed to step 334, which comprises excluding cluster centre cmior cluster centre cm2from the set of cluster centres. In other words, when the inter-site distance does not meet the minimum threshold criterion, adjusting the number of cluster centres comprises removing either the cluster centre of the first cluster or the cluster centre of the adjacent cluster. This removal of a cluster centre may be denoted as C = C\{cml} (or C = C\{cm2} as appropriate). The method 300 may then proceed to step 336, which comprises decreasing the number of EFC centres M by 1 and re-initializing the iteration index to 0. The method 300 then returns to step 312 and a new set of first clusters is created to reinitialise the clustering process 202.

[0100] Hence, once a cluster centre is added to or removed from the set C, the method 300 returns to step 312 and reapplies the clustering process 202 for the maximum number of iterations I, as illustrated in Figure 3.

[0101] Returning to step 328 of the method 300, if it is determined that the inter-centre distance dmi,m2isgreater than or equal to Dminand less than or equal to Dmax(e.g. if the threshold condition is met) the method 300 may proceed to step 338, wherein the cluster centres cmof the set of first clusters, wherein cm,Vm e are output as the set of final cluster centres.

[0102] It will therefore be appreciated that the clustering process provided: and wherein the clustering process provides: a set of final clusters, wherein a final cluster comprises one or more of the plurality of locations, and a respective set of final cluster centres. The first set of coordinates may comprise the set of final cluster centres. In other words, set of final cluster centres may be used as the first set of coordinates for the EFCs, and may be indicated to the satellites in step 201 of Figure 2b.

[0103] It will be appreciated that the value of the maximum number of iterations / may be carefully chosen such that convergence can be achieved before either the population or the inter-centre distance are determined. The value of / may also be adjusted throughout the process. For example, I may be lower for subsequent sets of performing the clustering process 202 (e.g. after I has already been reached once, it may be lowered for subsequent sets). In other embodiments, the population limit per cluster centre P may be selected based on the network considering some factors such as deployment scenario.

[0104] In some embodiments, when the population distribution changes considerable, the N sampled points may be updated and the method 300 may be repeated. Therefore, new EFC centres are generated which provides timely results to the network. FIGURE 4 compares the cumulative distribution function (CDF) of population per EFC in a given region of interest when applying the method 300 and when using a hexagonal grid of EFC centres. In this example, M = 100, / = 50, P = 104, Dmax= 110km and Dmin= 25km are used when applying the method 300. In Figure 4, the population per EFC is significantly well-balanced when the method 300 is applied, with the highest population per EFC always under the population limit and the population per EFC at 50 percentile almost the same as the population per EFC of the hexagonal layout.

[0105] FIGURE 5 illustrates a controller node 500 comprising processing circuitry (or logic) 501 . The processing circuitry 501 controls the operation of the controller node 500 and can implement the method described herein in relation to a controller node (e.g. controller node 250). The processing circuitry 501 can comprise one or more processors, processing units, multi-core processors or modules that are configured or programmed to control the controller node 500 in the manner described herein. In particular implementations, the processing circuitry 501 can comprise a plurality of software and / or hardware modules that are each configured to perform, or are for performing, individual or multiple steps of the method described herein in relation to the controller node 500. It will be appreciated that the controller node 500 may comprise one or more virtual machines running different software and / or processes. The controller node 500 may therefore comprise, or be implemented in or as one or more servers, switches and / or storage devices and / or may comprise cloud computing infrastructure that runs the software and / or processes.

[0106] Optionally, the controller node 500 may comprise a memory 503. In some embodiments, the memory 503 of the controller node 500 can be configured to store instructions (e.g. program code) executable by the processing circuitry 501 of the controller node 500 whereby the controller node 500 is operable to perform any of the methods described herein (e.g. the methods described with reference to Figures 2b and 3).

[0107] Alternatively or in addition, the memory 503 of the controller node 500, can be configured to store any requests, resources, information, data, signals, or similar that are described herein. The processing circuitry 501 of the controller node 500 may be configured to control the memory 503 of the controller node 500 to store any requests, resources, information, data, signals, or similar that are described herein. In some embodiments, the controller node 500 may optionally comprise a communications interface 502. The communications interface 502 of the controller node 500 can be for use in communicating with other nodes, such as other virtual nodes. For example, the communications interface 502 of the controller node 500 can be configured to transmit to and / or receive from other nodes requests, resources, information, data, signals, or similar. The processing circuitry 501 of controller node 500 may be configured to control the communications interface 502 of the controller node 500 to transmit to and / or receive from other nodes requests, resources, information, data, signals, or similar. The communications interface 502 can use any suitable communication technology.

[0108] The controller node 500 may be configured operate in the manner described herein in respect of an controller node.

[0109] FIGURE 6 is a block diagram illustrating a controller node 600 according to some embodiments. The controller node 600 may control one or more satellite in a network, wherein the one or more satellites serve a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres. The controller node 600 comprises a transmitting module 602 configured to configured to transmit, to the one or more satellites, a first configuration of the plurality of cells, the first configuration providing an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest. In some examples, the controller node 600 may further comprise an applying module 604 configure to apply a clustering process to the plurality of locations within the region of interest to determine the first set of coordinates.

[0110] The controller node 600 may operate in the manner described herein in respect of an controller node (e.g. controller node 250).

[0111] FIGURE 7 illustrates a method performed by a satellite in a network. The method may be performed by the satellite 102 illustrated in Figure 2. It will be appreciated that the method of Figure 7 may be performed by any satellite that the controller node 250 is in communication with. Each satellite may be providing a plurality of cells within the region of interest considered by the controller node. However, each satellite may only be serving part of the full region of interest considered by the controller node 250. The satellite serves a plurality of cells within a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres.

[0112] In step 701 the method comprises receiving, from a controller node (e.g. controller node 250 illustrated in Figure 2b), a first configuration of the plurality of cells.

[0113] The first configuration provides an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest. It will be appreciated that in some examples the indication comprises the first set of coordinates. In other examples, the satellite may determine the first set of coordinates from the indication. For example, the indication may comprise one or more parameters such as information relating to azimuth and / or elevation angles for the plurality of cells; and an index of a lookup table stored in a memory of the satellite, wherein the lookup table comprises information which maps to angles used by the satellite, and wherein the index of the lookup table maps to a subset of the angles from which the satellite can determine the first set of coordinates.

[0114] In step 702 the method comprises applying the first configuration to provide the plurality of cells.

[0115] In some examples, step 702 may comprise applying the first configuration comprises steering a plurality of beams (112) provided by the satellite towards respective coordinates in the first set of coordinates of the plurality of cell centres. A beam may comprise a transmitter beam for downlink, DL, and a receiver beam for uplink, UL.

[0116] FIGURE 8 illustrates a satellite 800 comprising processing circuitry (or logic) 801. The processing circuitry 801 controls the operation of the satellite 800 and can implement the method described herein in relation to a satellite (e.g. satellite 280). The processing circuitry 801 can comprise one or more processors, processing units, multi-core processors or modules that are configured or programmed to control the satellite 800 in the manner described herein. In particular implementations, the processing circuitry 801 can comprise a plurality of software and / or hardware modules that are each configured to perform, or are for performing, individual or multiple steps of the method described herein in relation to the satellite 800. It will be appreciated that the satellite 800 may comprise one or more virtual machines running different software and / or processes. The satellite 800 may therefore comprise, or be implemented in or as one or more servers, switches and / or storage devices and / or may comprise cloud computing infrastructure that runs the software and / or processes.

[0117] Optionally, the satellite 800 may comprise a memory 803. In some embodiments, the memory 803 of the satellite 800 can be configured to store instructions (e.g. program code) executable by the processing circuitry 801 of the satellite 800 whereby the satellite 800 is operable to perform any of the methods described herein (e.g. the methods described with reference to Figures 2b, 3 and 7).

[0118] Alternatively or in addition, the memory 803 of the satellite 800, can be configured to store any requests, resources, information, data, signals, or similar that are described herein. The processing circuitry 801 of the satellite 800 may be configured to control the memory 803 of the satellite 800 to store any requests, resources, information, data, signals, or similar that are described herein.

[0119] In some embodiments, the satellite 800 may optionally comprise a communications interface 802. The communications interface 802 of the satellite 800 can be for use in communicating with other nodes, such as other virtual nodes. For example, the communications interface 802 of the satellite 800 can be configured to transmit to and / or receive from other nodes requests, resources, information, data, signals, or similar. The processing circuitry 801 of satellite 800 may be configured to control the communications interface 802 of the satellite 800 to transmit to and / or receive from other nodes requests, resources, information, data, signals, or similar. The communications interface 802 can use any suitable communication technology.

[0120] The satellite 800 may be configured to operate in the manner described herein in respect of a satellite.

[0121] FIGURE 9 is a block diagram illustrating a satellite 900 according to some embodiments. The satellite 900 may serve a plurality of cells within a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres. The satellite 900 comprises a receiving module 902 configured to receive, from a controller node, a first configuration of the plurality of cells, the first configuration providing an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest. The satellite 900 further comprises an applying module 904 configure to apply the first configuration to provide the plurality of cells.

[0122] There is also provided a computer program comprising instructions which, when executed on a least one processor (such as the processing circuitry 501 of the controller node 500 described earlier), cause the processor to carry out at least part of the method(s) described herein. According to some embodiments there is provided a carrier containing the computer program. In some embodiments, the carrier can be any one of an electronic signal, an optical signal, an electromagnetic signal, an electrical signal, a radio signal, a microwave signal, or a computer-readable medium. There is also provided a (for example, tangible and / or non-transient) computer-readable medium comprising instructions which, when executed by at least one processor, cause the at least one processor to perform at least part of the method(s) described herein.

[0123] Certain embodiments have several advantages. One advantage is population density and / or traffic load of EFCs in a region of interest in an NTN are well balanced. Another advantage is improved performance and / or system capacity. As the EFC centres resulting from the proposed method are placed in the region of interest based on information relating to population distribution and / or traffic demand, the EFC centres are geographically closer to areas with higher population density and / or traffic load. Thus, as there are shorter distances between the UEs and the serving satellite, and there are lower misalignment angles, the UEs experience less pathloss. Moreover, throughput among different UEs is improved on average and throughput per beam is more stable which improves the overall system performance.

[0124] Another advantage is a balanced quality of service. As the proposed method may impose a maximum and minimum limit on ISDs, it avoids the situation where some UEs located on the edge of a large EFC experience very poor connection due to a large misalignment angle. By partitioning large EFCs into multiple smaller EFCs, the proposed method provides a more balanced quality of service among all UEs in the region of interest.

[0125] Another advantage is a reduced number of simultaneous satellite beams in the network. As the proposed method is adjustable to metrics such as population or traffic, the method can result in requiring fewer cells within a region of interest, and therefore fewer simultaneous beams being generated by the satellites. This in turn can increase the power per beam and improve the downlink link budget, and also relaxes the feeder link capacity requirement.

[0126] 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.

[0127] The following are some example embodiments of aspects disclosed herein:

[0128] Example 1 . A method performed by a controller node for controlling one or more satellites in a network, wherein the one or more satellites serve a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres, the method comprising: transmitting (201), to the one or more satellites, a first configuration of the plurality of cells, the first configuration providing an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest.

[0129] Example 2. The method as in example 1 further comprising: determining the first set of coordinates based on a plurality of network metrics, wherein a network metric in the plurality of network metrics is associated with a respective location within the region of interest.

[0130] Example 3. The method as in example 2, wherein a network metric in the plurality of network metrics comprises information representative of one or more of: a population density at the respective location and a traffic density at the respective location.

[0131] Example 4. The method as in example 2 or 3 further comprising: applying (202) a clustering process to the plurality of locations within the region of interest to determine the first set of coordinates, wherein the plurality of locations are weighted during the clustering process according to the respective network metrics, and wherein the clustering process provides: a set of final clusters, wherein a final cluster comprises one or more of the plurality of locations; and a respective set of final cluster centres, wherein the first set of coordinates comprises the set of final cluster centres.

[0132] Example 5. The method as in example 4, wherein the clustering process comprises one of: a k -means clustering process with a Lloyd’s iterative process, a Gaussian mixture model process, and a Balance Iterative Reducing and Clustering using Hierarchies, BIRCH, process.

[0133] Example 6. The method as in example 4 or 5 wherein applying the clustering process to the plurality of locations within the region of interest comprises: for a first cluster in a set of first clusters, wherein the first cluster in the set of first clusters comprises a subset of the plurality of locations: determining (308) a set of weighted locations from the subset of the plurality of locations; and determining (310) an updated cluster centre based on the set of weighted locations; assigning (312) locations in the plurality of locations to a set of updated clusters centred on the updated cluster centres; and setting the set of updated clusters as the set of first clusters.

[0134] Example 7. The method as in example 6, wherein determining the set of weighted locations comprises, for a location in the subset of the plurality of locations: determining a weighting coefficient, wherein the weighting coefficient is dependent on the network metric associated with the location; and determining the weighted location as a product of the weighting coefficient and the location.

[0135] Example 8. The method as in any one of examples 6 or 7, wherein determining the updated cluster centre based on the set of weighted locations comprises: determining the updated cluster centre by summing each weighted location in the set of weighted locations.

[0136] Example 9. The method as in examples 6 to 8, wherein assigning locations in the plurality of locations to the set of updated clusters comprises: for a location in the plurality of locations: determining a distance between the location and updated cluster centres in the set of updated cluster centres; and assigning the location to a selected cluster from the set of updated clusters by minimising a distance between the location and the updated cluster centre associated with the selected cluster.

[0137] Example 10. The method as in any one of examples 6 to 9 further comprising initialising the clustering process by randomly selecting (306) an initial set of cluster centres from the plurality of locations.

[0138] Example 11 . The method as in example 6 to 10 further comprising: performing a first number of iterations of the clustering process, and setting a last output set of first clusters as the set of final clusters.

[0139] Example 12. The method as in example 11 , further comprising: prior to setting the last output set of first clusters as the set of final clusters: for each first cluster in the last output set of first clusters, determining a cell metric, and responsive to a cell metric associated with a first cluster of the last output set of first clusters meeting a criterion: adjusting a number of cluster centres in the last output set of first clusters; and performing a further iteration of the clustering process.

[0140] Example 13. The method of example 12, wherein the cell metric comprises a cluster population and wherein the criterion comprises a maximum threshold criterion.

[0141] Example 14. The method as in example 12 or 13, wherein adjusting the number of cluster centres comprises adding (322) a new cluster centre within the first cluster.

[0142] Example 15. The method as in example 14 wherein a location of the new cluster centre is selected using a random process.

[0143] Example 16. The method of example 12 wherein the cell metric comprises an inter-site distance between the first cluster centre and an adjacent cluster centre and wherein the criterion comprises a maximum and / or a minimum threshold criterion.

[0144] Example 17. The method of example 16 wherein, when the inter-site distance does not meet the maximum threshold criterion, adjusting the number of cluster centres comprises adding (332) a new cluster centre within the first cluster.

[0145] Example 18. The method of example 16 or 17 wherein, when the inter-site distance does not meet the minimum threshold criterion, adjusting the number of cluster centres comprises removing (334) either the cluster centre of the first cluster or the cluster centre of the adjacent cluster.

[0146] Example 19. The method as in any preceding example, further comprising monitoring the region of interest for information relating to the network metric.

[0147] Example 20. The method as in example 19 further comprising: responsive to the monitored information relating to the network metric meeting a criterion, determining an updated configuration for the plurality of cells, and transmitting, to the one or more satellites, the updated configuration.

[0148] Example 21 . A controller node (500) for controlling one or more satellites in a network, wherein the one or more satellites serve a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres, the controller node comprising processing circuitry (501) and memory (503) wherein the memory contains instructions executable by the processing circuitry whereby the controller node is operable to: transmit (201), to the one or more satellites, a first configuration of the plurality of cells, the first configuration providing an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest.

[0149] Example 22. The controller node as in example 21 wherein the memory contains further instructions executable by the processing circuitry whereby the controller node is operable to perform the method as in any one of examples 2 to 20. Example 23. A method performed by a satellite (102) in a network, wherein the satellite serves a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres, the method comprising: receiving (701), from a controller node, a first configuration of the plurality of cells, the first configuration providing an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest; and applying (702) the first configuration to provide the plurality of cells.

[0150] Example 24. The method of example 23, further comprising determining the first set of coordinates of the plurality of cell centres from the indication of the first set of coordinates.

[0151] Example 25. The method of example 24, wherein the indication of the first set of coordinates comprises one or more parameters.

[0152] Example 26. The method of example 25, wherein the one or more parameters comprise one or more of: information relating to azimuth and / or elevation angles for the plurality of cells; and an index of a lookup table stored in a memory of the satellite, wherein the lookup table comprises information which maps to angles used by the satellite, and wherein the index of the lookup table maps to a subset of the angles.

[0153] Example 27. The method as in examples 23 to 26, wherein applying the first configuration comprises steering a plurality of beams (112) provided by the satellite towards respective coordinates in the first set of coordinates of the plurality of cell centres.

[0154] Example 28. The method of example 27, wherein the beam comprises a transmitter beam for downlink, DL, and a receiver beam for uplink, UL.

[0155] Example 29. A satellite (800) in a network, wherein the satellite serves a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres, and wherein the satellite comprises processing circuitry (801) and memory (803), the memory containing instructions executable by the processing circuitry whereby the satellite is operable to: receive (701), from a controller node, a first configuration of the plurality of cells, the first configuration providing an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest; and apply (702) the first configuration to provide the plurality of cells.

[0156] Example 30. The satellite of example 29, further adapted to perform the method according to any of examples 23 to 28. Example 31. A computer program, comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out a method according to any of examples 1 to 28.

[0157] Example 32. A carrier containing the computer program according to example 31 , wherein the carrier comprises one of an electronic signal, optical signal, radio signal or computer readable storage medium.

[0158] Example 33. A computer-readable medium comprising instructions that, when executed on at least one processor, cause the at least one processor to perform the method according to any of examples 1 to 28. Example 34. A computer program product comprising non transitory computer readable media having stored thereon a computer program according to example 31.

Claims

CLAIMS1 . A method (200) performed by a controller node for controlling one or more satellites in a network, wherein the one or more satellites serve a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres, the method comprising: transmitting (201), to the one or more satellites, a first configuration of the plurality of cells, the first configuration providing an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest.

2. The method as claimed in claim 1 further comprising, determining the first set of coordinates based on a plurality of network metrics, wherein a network metric in the plurality of network metrics is associated with a respective location within the region of interest.

3. The method as claimed in claim 2, wherein a network metric in the plurality of network metrics comprises information representative of one or more of: a population density at the respective location and a traffic density at the respective location.

4. The method as claimed in claim 2 or 3 further comprising: applying (202) a clustering process to a plurality of locations within the region of interest to determine the first set of coordinates, wherein the plurality of locations are weighted during the clustering process according to the respective network metrics, and wherein the clustering process provides: a set of final clusters, wherein a final cluster comprises one or more of the plurality of locations; and a respective set of final cluster centres, wherein the first set of coordinates comprises the set of final cluster centres.

5. The method as claimed in claim 4, wherein the clustering process comprises one of: a k -means clustering process with a Lloyd’s iterative process,a Gaussian mixture model process, and a Balance Iterative Reducing and Clustering using Hierarchies, BIRCH, process.

6. The method as claimed in claim 4 or 5 wherein applying the clustering process to the plurality of locations within the region of interest comprises, for a first cluster in a set of first clusters, wherein the first cluster in the set of first clusters comprises a subset of the plurality of locations: assigning (312) locations in the plurality of locations to a set of updated clusters centred on the updated cluster centres; and setting the set of updated clusters as the set of first clusters.

7. The method as claimed in claim 6, comprising determining (308) a set of weighted locations for a location in a subset of the plurality of locations, comprising: determining a weighting coefficient, wherein the weighting coefficient is dependent on the network metric associated with the location; and determining the weighted location as a product of the weighting coefficient and the location.

8. The method as claimed in claims 6 or 7, wherein assigning locations in the plurality of locations to the set of updated clusters comprises, for a location in the plurality of locations: determining a distance between the location and updated cluster centres in the set of updated cluster centres; and assigning the location to a selected cluster from the set of updated clusters by minimising a distance between the location and the updated cluster centre associated with the selected cluster.

9. The method as claimed in claim 8, further comprising: determining a cell metric, and responsive to a cell metric meeting a criterion: adjusting a number of cluster centres; and performing a further iteration of the clustering process.

10. The method of claim 9, wherein the cell metric comprises a cluster population and wherein the criterion comprises a maximum threshold criterion or the cell metric comprises an inter-site distance between the first cluster centre and an adjacent cluster centre and the criterion comprises a maximum and / or a minimum threshold criterion.11 . The method as claimed in claim 9 or 10, wherein adjusting the number of cluster centres comprises: adding (322) a new cluster centre within the first cluster or removing (334) either the cluster centre of the first cluster or the cluster centre of the adjacent cluster.

12. The method as claimed in any preceding claim, further comprising: monitoring the region of interest for information relating to the network metric; and responsive to the monitored information relating to the network metric meeting a criterion, determining an updated configuration for the plurality of cells, and transmitting, to the one or more satellites, the updated configuration.

13. A controller node (500) for controlling one or more satellites in a network, wherein the one or more satellites serve a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres, the controller node comprising processing circuitry (501) and memory (503) wherein the memory contains instructions executable by the processing circuitry whereby the controller node is operable to: transmit (201), to the one or more satellites, a first configuration of the plurality of cells, the first configuration providing an indication of a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest.

14. The controller node as claimed in claim 13 wherein the memory contains further instructions executable by the processing circuitry whereby the controller node is operable to perform the method as claimed in any one of claims 2 to 12.

15. A method performed by a satellite (102) in a network, wherein the satellite is configured to provide wireless network access to a user equipment, UE, (108)positioned on, or near, the Earth’s surface via a service link (110) and the satellite serves a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres, the method comprising: receiving (701), from a controller node, a first configuration of the plurality of cells, the first configuration providing an indication of a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest; and applying (702) the first configuration to provide the plurality of cells.

16. The method of claim 15, further comprising determining the first set of coordinates of the plurality of cell centres from the indication of the first set of coordinates.

17. The method of claim 16, wherein the indication of the first set of coordinates comprises one or more parameters comprising one or more of: information relating to azimuth and / or elevation angles for the plurality of cells; and an index of a lookup table stored in a memory of the satellite, wherein the lookup table comprises information which maps to angles used by the satellite, and wherein the index of the lookup table maps to a subset of the angles.

18. The method as claimed in claims 15 to 17, wherein applying the first configuration comprises steering a plurality of beams (112) provided by the satellite towards respective coordinates in the first set of coordinates of the plurality of cell centres wherein the beam comprises a transmitter beam for downlink, DL, and a receiver beam for uplink, UL.

19. A satellite (800) in a network, wherein the satellite is configured to provide wireless network access to a user equipment (UE) 108 positioned on, or near, the Earth’s surface via a service link 110 and the satellite serves a plurality of cells in a region of interest, wherein the plurality of cells are associated with a respective plurality of cell centres, and wherein the satellite comprises processing circuitry (801) and memory (803), the memory containing instructions executable by the processing circuitry whereby the satellite is operable to:receive (701), from a controller node, a first configuration of the plurality of cells, the first configuration providing an indication of: a first set of coordinates of the plurality of cell centres, wherein the first set of coordinates are irregularly distributed in the region of interest; and apply (702) the first configuration to provide the plurality of cells.

20. The satellite of claim 19, further configured to perform the method according to any one of claims 16 to 18.

21. A computer program, a carrier containing a computer program, , wherein the carrier comprises one of an electronic signal, optical signal, radio signal or computer readable storage medium, a computer program product comprising non transitory computer readable media having stored thereon a computer program, the computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out a method according to any of claims 1 to 13 and 15 to 18.

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