Protocol to control hypotheses in a network digital twin

Abstract

A network digital twin (NDT) integrates digital twins of one or more network functions (such as CNs, RANs, RAN nodes, core NFs, switches, routers, and gateways), at least one UE, and an environment of

Description

TECHNOLOGICAL FIELD

[0001] The present disclosure relates generally to telecommunications and, in particular, to control of hypotheses in a network digital twin of a telecommunications network. BACKGROUND

[0002] A telecommunications system can be seen as a facility that enables communication sessions between two or more entities such as user terminals, base stations and / or other nodes by providing carriers between the various entities involved in the communications path. A telecommunications system can be provided for example by means of a communication network and one or more compatible communication devices. The communication sessions may comprise, for example, communication of data for carrying communications such as voice, video, electronic mail (email), text message, multimedia and / or content data and so on. Non-limiting examples of services provided comprise two-way or multi-way calls, data communication or multimedia services and access to a data network system, such as the Internet.

[0003] In a wireless telecommunications system, at least a part of a communication session between at least two stations occurs over a wireless link. Examples of wireless telecommunications systems comprise public land mobile networks (PLMN), satellite based communication systems and different wireless local networks, for example wireless local area networks (WLAN). Some wireless systems can be divided into cells, and are therefore often referred to as cellular systems.

[0004] A user can access the telecommunications system by means of an appropriate communication device or terminal. A communication device of a user may be referred to as user equipment (UE) or user device. A communication device is provided with an appropriate signal receiving and transmitting apparatus for enabling communications, for example enabling access to a communication network or communications directly with other users. The communication device may access a carrier provided by a station, for example a base station of a cell, and transmit and / or receive communications on the carrier.

[0005] The telecommunications system and associated devices typically operate in accordance with a given standard or specification which sets out what the various entities associated with the communication system are permitted to do and how operations should be achieved. Communication protocols and / or parameters which shall be used for connection of the various entities are also typically defined. One example of a telecommunications system is the Universal Mobile Telecommunications System (UMTS). Other examples of telecommunications systems are Long-Term Evolution (LTE), LTE Advanced and the so-called 5G or New Radio (NR) networks. NR is being standardized by the 3rd Generation Partnership Project (3GPP). BRIEF SUMMARY

[0006] Example implementations of the present disclosure are directed to telecommunications and, in particular, to control of hypotheses in a network digital twin of a telecommunications network. The present disclosure includes, without limitation, the following example implementations.

[0007] Some example implementations provide an apparatus comprising: at least one memory configured to store instructions; and at least one processing circuitry configured to access the at least one memory, and execute the instructions to cause the apparatus to at least: access a network digital twin of a network that integrates digital twins of one or more network functions, at least one user equipment (UE), and an environment of the network, the digital twins in the network digital twin interconnected by interfaces between the digital twins; execute the network digital twin to perform one or more simulations of the network during which one or more of the digital twins create hypotheses of at least one of behavior, performance or conditions of at least one of the one or more network functions, the at least one UE, or the environment; assign hypothesis identifiers to the hypotheses at respective ones of the one or more of the digital twins; and exchange messages about the one or more simulations between the digital twins over the interfaces between the digital twins, the messages including information about the hypotheses including the hypothesis identifiers.

[0008] Some example implementations provide a method comprising: accessing a network digital twin of a network that integrates digital twins of one or more network functions, at least one user equipment (UE), and an environment of the network, the digital twins in the network digital twin interconnected by interfaces between the digital twins; executing the network digital twin to perform one or more simulations of the network during which one or more of the digital twins create hypotheses of at least one of behavior, performance or conditions of at least one of the one or more network functions, the at least one UE, or the environment; assigning hypothesis identifiers to the hypotheses at respective ones of the one or more of the digital twins; and exchanging messages about the one or more simulations between the digital twins over the interfaces between the digital twins, the messages including information about the hypotheses including the hypothesis identifiers.

[0009] These and other features, aspects, and advantages of the present disclosure will be apparent from a reading of the following detailed description together with the accompanying figures, which are briefly described below. The present disclosure includes any combination of two, three, four or more features or elements set forth in this disclosure, regardless of whether such features or elements are expressly combined or otherwise recited in a specific example implementation described herein. The present disclosure is intended to be read holistically such that any separable features or elements of the disclosure, in any of its aspects and example implementations, should be viewed as combinable unless the context of the disclosure clearly dictates otherwise.

[0010] It will therefore be appreciated that this Brief Summary is provided merely for purposes of summarizing some example implementations so as to provide a basic understanding of some aspects of the disclosure. Accordingly, it will be appreciated that the above described example implementations are merely examples and should not be construed to narrow the scope or spirit of the disclosure in any way. Other example implementations, aspects and advantages will become apparent from the following detailed description taken in conjunction with the accompanying figures which illustrate, by way of example, the principles of some described example implementations. BRIEF DESCRIPTION OF THE FIGURE(S)

[0011] Having thus described example implementations of the disclosure in general terms, reference will now be made to the accompanying figures, which are not necessarily drawn to scale, and wherein:

[0012] FIG. 1 illustrates a telecommunications system that includes one or more public land mobile networks (PLMNs) coupled to one or more external data networks, according to some example implementations of the present disclosure;

[0013] FIG. 2 illustrates a deployment of a PLMN, according to some example implementations;

[0014] FIG. 3 illustrates a network digital twin (NDT) according to some example implementations;

[0015] FIG. 4 illustrates a packaging of network functions and corresponding digital twins, and their deployments in respectively a PLMN and NDT application server, according to some example implementations;

[0016] FIGS. 5 A, 5B and 5C illustrate time steps in a simulation of a PLMN, according to some example implementations;

[0017] FIGS. 6 and 7 are signaling charts of signaling messages within a NDT between digital twins of individual network functions to establish, propagate, update and delete hypotheses, according to some example implementations;

[0018] FIG. 8 is a flowchart illustrating various steps in a method according to various example implementations; and

[0019] FIG. 9 illustrates an apparatus according to some example implementations. DETAILED DESCRIPTION

[0020] Some implementations of the present disclosure will now be described more fully hereinafter with reference to the accompanying figures, in which some, but not all implementations of the disclosure are shown. Indeed, various implementations of the disclosure may be embodied in many different forms and should not be construed as limited to the implementations set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Like reference numerals refer to like elements throughout.

[0021] Unless specified otherwise or clear from context, references to first, second or the like should not be construed to imply a particular order. A feature described as being above another feature (unless specified otherwise or clear from context) may instead be below, and vice versa; and similarly, features described as being to the left of another feature else may instead be to the right, and vice versa. Also, while reference may be made herein to quantitative measures, values, geometric relationships or the like, unless otherwise stated, any one or more if not all of these may be absolute or approximate to account for acceptable variations that may occur, such as those due to engineering tolerances or the like.

[0022] As used herein, unless specified otherwise or clear from context, the “or” of a set of operands is the “inclusive or” and thereby true if and only if one or more of the operands is true, as opposed to the “exclusive or” which is false when all of the operands are true. Thus, for example, “[A] or [B]” is true if [A] is true, or if [B] is true, or if both [A] and [B] are true. Further, the articles “a” and “an” mean “one or more,” unless specified otherwise or clear from context to be directed to a singular form. Furthermore, it should be understood that unless otherwise specified, the terms “data,” “content,” “digital content,” “information,” and similar terms may be at times used interchangeably. The term “network” may refer to a group of interconnected computers including clients and servers; and within a network, these computers may be interconnected directly or indirectly by various means including via one or more switches, routers, gateways, access points or the like.

[0023] The present disclosure discusses systems and architectures that, while specific terms may be used, are broadly applicable across various technologies. For instance, while the present disclosure may reference technologies from 3GPP such as Global System for Mobile Communications (GSM), UMTS, LTE, LTE Advanced, 5G NR, 5G Advanced, and 6G, the present disclosure is equally relevant to non-3GPP technologies like IEEE 802, Bluetooth, and Bluetooth Low Energy. Example implementations of the present disclosure described herein also mention public land mobile networks (PLMNs) and mobile network operators (MNOs), but example implementations are similarly applicable to standalone non-public networks (SNPNs) and the private entities operating these networks. Furthermore, although some examples and figures focus on radio access networks (RANs) and 3GPP access, example implementations are applicable to any type of network access. This includes not only 5G or 6G 3GPP access but also non-3GPP access, such as wireline access, untrusted non-3GPP access, and trusted non-3GPP access using wireless access gateway function (W-AGF), non-3GPP interworking function (N3IWF), or trusted non-3GPP gateway function (TNGF) to connect to a 5G or 6G core network.

[0024] Further, as used in this application, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry); (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions); or (c) hardware circuit(s) and / or processor(s), such as a microprocessor s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

[0025] The above definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0026] FIG. 1 illustrates a telecommunications system 100 according to various example implementations of the present disclosure. The telecommunications system generally includes one or more telecommunications networks. As shown, for example, the system includes one or more PLMNs 102 coupled to one or more other external data networks 104 - notably including a wide area network (WAN) such as the Internet. As will be appreciated, a PLMN may be deployed in a number of different manners. Some deployments of 4G LTE and 5GNR in particular are considered standalone (SA) deployments. Other deployments combine 4G LTE and 5G technologies, and are referred to as non-standalone (NSA) deployments.

[0027] Each of the PLMNs 102 includes a core network (CN) 106 backbone, such as the Evolved Packet Core (EPC) of 4G LTE, the 5G core network (5GC) (at times referred to as the NGC) of 5G NR, and the 6G core network (6GC) of 6G; and each of the core networks and the Internet are coupled to one or more RANs 108, air interfaces or the like that implement one or more radio access technologies (RATs). Examples of these RANs include the evolved UMTS terrestrial radio access network (E-UTRAN) of 4G LTE, the next generation (NG) radio access network (NG-RAN) of 5GNR, and the 6G RAN. As used herein, a “network device” refers to any suitable device at a network side of a telecommunications network. Examples of suitable network devices are described in greater detail below.

[0028] Examples of RATs include 3GPP radio access technologies such as GSM, CDMA2000 IxEV-DO (HRPD), CDMA2000 lx (IxRTT), UTRA, E-UTRA, 5GNR, 5G Advanced, and 6G. Other examples of RATs include IEEE 802 technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.15 (including 802.15.1 (WPAN / Bluetooth), 802.15.4 (Zigbee) and 802.15.6 (WBAN)), Bluetooth, Bluetooth Low Energy (BLE), ultra wideband (UWB), and the like. Generally, a RAT may refer to any 2G, 3G, 4G, 5G, 6G or higher generation RAT and their different versions, as well as to any other RAT that may be arranged to interwork with such a mobile communication technology to provide access to the CN 106 of a MNO.

[0029] The telecommunications system 100 also includes one or more radio units that may be varyingly known as user equipment (UE) 110, terminal device, terminal equipment, mobile station or the like. The UE is generally a device configured to communicate with a network device or a further UE in a telecommunications network. The UE may be a portable computer (e.g., laptop, notebook, tablet computer), mobile phone (e.g., cell phone, smartphone), wearable computer (e.g., smartwatch), or the like. In other examples, the UE may be an Internet of things (loT) device, an industrial loT (IIoT device), a vehicle equipped with a vehicle-to-everything (V2X) communication technology, or the like. In some examples, as referenced by 3GPP, the UE may be a narrowband loT (NB-IoT) device, an enhanced machine-type communication (eMTC) device, a reduced capability (RedCap) device, an ambient loT device, or the like.

[0030] In operation, these UEs 110 may connect to one or more of the RANs 108 according to their particular RATs to thereby access a particular CN 106 of a PLMN 102, or to access one or more of the external data networks 104 (e.g., the Internet). The external data network may provide Internet access, operator services, 3rd party services, etc. For example, the International Telecommunication Union (ITU) has classified 5G mobile network services into three categories: enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC), and massive machine type communications (mMTC) or massive internet of things (MIoT).

[0031] In various examples, a RAN 108 may be configured as one or more macrocells, microcells, picocells, femtocells or the like. The RAN may generally include one or more RAN nodes that interact with UEs 110. In various examples, a RAN node may be referred to as a base station (BS), access point (AP), base transceiver station (BTS), Node B (NB), evolved NB (eNB), macro BS, NB (MNB) or eNB (MeNB), home BS, NB (HNB) or eNB (HeNB), next generation NB (gNB), enhanced gNB (en-gNB), next generation eNB (ng-eNB), 6G NB (6gNB), or the like. The term ‘gNB’ in 5GNR may correspond to the eNB in 4G LTE. Also, a NG-RAN node may refer to a gNB or a ng-eNB. And unless otherwise specified, a gNB in 5G NR or a 6gNB in 6G may at times be more generally referred to as a gNB or a (6)gNB.

[0032] The RAN 108 may include some type of network controlling / goveming entity responsible for control of the RAN nodes. The network controlling / governing entity and RAN node may be separate or integrated into a single apparatus. The network controlling / goveming entity may include processing circuity configured to carry out various management functions, etc. The processing circuity may be associated with a memory, computer-readable storage medium or database for maintaining information required in the management functions.

[0033] FIG. 2 illustrates a deployment of a PLMN 102, such as a 4G LTE, 5G NR or 6G deployment. As shown, the RAN 108 (e.g., NG-RAN, 6G RAN) includes one or more RAN nodes 202 (RAN nodes) configured to connect one or more UEs 110 to the RAN to thereby access the CN 106 (e.g., 5GC, 6GC). In some deployments, operations of a RAN node such as a gNB may be distributed or functionally split into components including one or more remote radio head (RRHs) or radio units (RUs), and a baseband unit (BBU); and in some architectures, the BBU may be split into a central / centralized unit (CU) (central node) and a distributed unit (DU) (distributed node). The CU may be, for example, a server, host or node. In some architectures, the RRaH / RU and DU may be collocated. It is also possible that node operations may be distributed among a plurality of servers, hosts or nodes.

[0034] The CN 106 may include a number of core network functions (NFs) divided between the control plane (CP) and the user plane (UP). In particular, the CN may include, for example, NFs for mobility management (MM) (at times referred to as a MM NF) and session management (SM) (at times referred to as a SM NF), as well as a user plane function (UPF). The MM may be, for example, an access and mobility management function (AMF) in the 5GC, or a 6G MM in the 6GC. Similarly, the SM may be, for example, a session management function (SMF) in the 5GC, or a 6G SM in the 6GC. Other examples of suitable NFs include a network exposure function (NEF), a policy and charging function (PCF), a network repository function (NRF), a network slice selection function (NSSF), a unified data management (UDM), a network data analytics function (NWDAF), a management data analytics function (MDAF), or the like.

[0035] A network digital twin (NDT) may be of interest in maintaining in digital form, a copy of a network, such as a PLMN 102. In general, a digital twin is a copy of a physical system that is synchronized with its physical counterpart at a specified frequency. The digital twin serves as a digital model that accurately reflects the behavior, performance and / or conditions of the physical system represented by the digital model. In this regard, a digital twin may have a connection between the physical system and the corresponding virtual model or virtual counterpart. The connection may be established by generating measurement data in the physical system, which may then be input by the digital twin to maintain an up-to-date representation of the physical system.

[0036] In telecommunications, a NDT of a PLMN 102 may be used for a number of different purposes, such as for so-called “what-if’ analysis in which the NDT may be executed to perform simulations of the PLMN. Although NDTs have been discussed for performing what-if analysis, the discussion has only been at a high level, and in many cases involve a re-invention of classical network management and concepts of selfoptimizing networks (SON).

[0037] As shown in FIG. 3, a NDT 300 may in fact integrate digital twins of one or more network functions in the PLMN, as well as at least one UE 110, and an environment 204 of the network. Depending on a desired level of abstraction or granularity of the PLMN represented in the NDT, a network function may include, for example, a CN 106, a RAN 108, a RAN node 202, a component of a RAN node (e.g., RU, DU, CU), a core NF (e.g., MM, SM, UPF), a switch, a router, a gateway or the like. A simulation using an NDT may therefore involve a simulation of the network functions that are embedded into a simulated environment of the PLMN. For example, the behavior of a UE and RAN node may be simulated based on, e.g., a simulation of the buildings and the simulated movement of the UE. In some examples, the digital twin of a network function, UE or environment may be referred to as a simulation of the network function, UE or environment.

[0038] In order to simulate a PLMN 102 as a whole, simulations of individual network functions may be combined. For example, a simulated UE may communicate with a simulated RAN, and both may be embedded in a simulated environment (e.g. mountains, buildings, trees, other UE and other RAN nodes, external sources of interference, etc.). The simulated RAN may communicate with a simulated CN including simulated core network NF s (e.g., MM, SM, UPF, etc). The simulated network functions may use the same standardized interfaces as the network functions of the physical (“real”) network. The only difference may be that in the simulated PLMN, the messages between the simulated network functions are simulated. That is, the messages do not have an effect on actual calls, and may in fact never occur in the physical PLMN. By this approach, the overall simulated PLMN is a network of individually simulated network functions.

[0039] Simulated network functions may via standardized interfaces exchange simulated context of simulated calls. Internally, the simulated network functions may maintain their own context of the simulated calls. In this regard, in a time-discrete simulation in which the simulation is done in time steps, a simulated network function may need to create an context in one time step, and evolve that context over time as long as the network function is involved in the call. An individual network function may therefore be unable to be simulated for only one time step. The simulation may instead need to start at the time step when the context is created, and evolved for subsequent time steps until the network function is no longer involved in (e.g., “leaves”) the call.

[0040] The simulations of the individual network functions may be hosted on a specialized application server (at times referred to as aNDT application server). In cases in which the external interfaces of the network functions are standardized, the application server may offer well-defined placeholders for the standardized network functions and be able to facilitate the communication between the simulated network functions via the standardized protocols. As shown in FIG. 4, in combination with application programming interfaces (APIs) to control the lifecycle and the runtime of the simulated network functions, vendors of network functions may provide packages 402 of the actual (“real”) network functions together with their digital twins. AMNO may then deploy the actual network functions in the PLMN 102, and deploy the digital twins as software plugins in an NDT application server 404.

[0041] As indicated above, a NDT of a PLMN 102 may be used for what-if analysis. Generally, a what-if analysis means to perform multiple simulations of the same system in parallel (or sequentially, if fast enough). The different simulations may performed with different hypothetical configurations or different hypothetical starting or bordering conditions. In this regard, each simulation may represent a hypothesis of how the system might evolve based on conditions specifically set by an operator. After reaching a given criterion (e.g., a given simulation time), the results of the different simulations may be evaluated and compared. If the what-if analysis is used to optimize a system, the configuration of the “best” hypothesis may be selected and used to reconfigure the system.

[0042] The system itself might spawn multiple hypothesis on its further evolution, and these possible hypothetical evolutions may be used to prepare the system to react in an optimized way to the most probable hypothesis. In the case of an automatically-guided vehicle (AGV) on a factory flow, artificial intelligence (AI) / machine learning (ML) may be used to predict a future path of the AGV through corridors on the factory flow. Once the AGV approaches a crossroads, the AI / ML may spawn multiple hypothesis, such as “turn left,” “move straight,” and “turn right.” Absent further information, these hypotheses may need to be maintained, and the system may need to prepare for them. As soon as the actual AGV has moved in one direction, two of the hypotheses are obsolete. These two hypotheses no longer need to be maintained and can be deleted.

[0043] Any prediction is based on certain assumptions. In most cases it may be assumed that the configuration of the underlying system does not change between the point of time at which the system calculates the prediction, and the point of time for which the state of the system is predicted, except the intentional changes that are introduced to explore different hypotheses.

[0044] For example, predictions of a NWDAF or MDAF may be based on the assumption that the configuration of the PLMN 102 does not change. Imagine that at time T = 0, the NWDAF calculates a prediction for the handover rate from a cell = A to a cell = B at time T = 1. If between T = 0 and T = 1, the operator changes the configuration of cell = Ato “blacklist” cell = B, the prediction for T = 1 may therefore be obsolete. Similarly, at time T = 0, the NWDAF or MDAF might calculate the loads of cell = A and cell = B for T=1. If in the meantime, cell = A handovers some UE to cell = B, the prediction may no longer be valid. This may also hold for the physical PLMN, but also for the case of network simulations that integrates individually simulated network functions.

[0045] Any digital twin that uses or produces predications may therefore need to be aware of potentially changed bordering conditions that might invalidate predictions.

[0046] Multiple hypotheses may be introduced by a dedicated what-if analysis in which an operator selects the hypotheses to explore. But even for other use cases, either the simulated environment (e.g., in path prediction of an AGV) or a simulated network function (e.g., in handover from a simulated cell = Ato cell = B) may spawn multiple hypotheses if the simulations are aware that an internal decision might happen between current simulation time and time for the prediction. If an overall simulation of a PLMN 102 integrates individually simulated network functions, then hypotheses spawned in the environment (e.g., movement) or a simulated network function may trigger hypotheses in multiple other network functions.

[0047] FIGS. 5A, 5B and 5C illustrate time steps in a simulation of a PLMN 102 for a what-if analysis, according to some example implementations. The simulation shows a situation with movement of a UE digital twin 502 (a simulated UE) and three RAN node digital twins 504A, 504B, 504C (simulated RAN nodes) that host respective cells. For this analysis, a RAN node might track and predict the movement of a UE mounted on an AGV in order to predict handovers to other cells.

[0048] As shown in FIG. 5 A, at time T=0, the UE digital twin 502 approaches a crossroads while the UE is connected to cell = A. At T=0, cells = B and C are unaware of the UE and do not maintain a context for the UE. Between T = 0 and time T = 1, the UE digital twin is assumed to move with an estimated velocity a distance beyond the crossroads. At time T = 0, absent further information on the path that the UE will take for the predicted time T = 1, the simulation splits the prediction into three hypotheses with the same probability, namely, “turn left,” “move straight,” and “turn right,” as shown in FIG. 5B.

[0049] As shown in FIG. 5B, the different hypotheses regarding movement of the UE digital twin 502 have an impact on the cells. The hypothesis “turn left” leads the UE to remain in cell = A. The hypothesis “straight” leads to a handover of the UE from cell = A to cell = B. And the hypothesis “turn right” results in a handover of the UE from cell = Ato cell = C. If cells are hosted by different RAN node digital twins 504A, 504B, 504C, the “straight” and “turn right” hypotheses will trigger the source RAN node digital twin to send messages via a simulated Xn interface to the target RAN node digital twins to perform a handover of the UE digital twin. As consequence, the target RAN node digital twins may have to create a context for the arriving UE. The hypothesis “turn left” may require cell = Ato further evolve its existing context.

[0050] As long as no measurements from reality indicate the actual path of the UE represented by the UE digital twin 502, the RAN node digital twins 504A, 504B, 504C may evolve their contexts as hypotheses. As shown in FIG. 5C, at some point of time T = 6, a measurement from reality arrives and indicates that the UE moved “straight.” This renders the hypotheses “turn left” and “turn right” impossible, such that the RAN node digital twins can delete the unnecessary contexts from the digital twins of cells = A and C. Only the remaining hypothesis “straight” needs to be maintained in cell = B.

[0051] Since many UE digital twins 502 may participate in a simulation of a PLMN 102, each individual network function digital twin (simulated network function) may need to handle, e.g., create, update, delete, many different hypotheses. Since the network functions also need to consider the combination of hypotheses, it may also be important to control the number of created combinations, such as by dropping combinations of hypotheses that have a very low probability. But the currently defined interfaces between network functions are not designed for multiple hypothesis, but only for the one real context. For example, the standardized messages for handover may create one context in a target RAN node, but may be unable to address or to delete contexts once it turns out that a hypothesis needs to be updated or is no longer valid.

[0052] Signaling is also currently unaware of changed bordering conditions or intentionally introduced hypotheses. Neither network functions in the physical PLMN 102 nor network function digital twins in a simulation of the PLMN are therefore able to inform each other about hypotheses, to update hypotheses, or to delete obsolete hypotheses.

[0053] This also holds for the existing analytics functions. Neither the NWDAF nor the MDAS offers an interface to provide hypothetical configurations that may be the basis for analytic reports to assess these hypothetical configurations. For example, an analytics function may predict how long a base station is able to run when energy is supplied by the backup battery, but this prediction depends on the features that are activated in the RAN node. In this regard, the RAN node may run five hours if assumed no energy savings applied, seven hours if one cell is switched off, and eight hours if one cell and multiple-input multiple-output (MIMO) are switched off. This information may be important for a MNO to decide which measures to apply and which degradations of service to impose on subscribers.

[0054] Notably, since many network functions are implemented in software, in principle, this software may be deployed in the physical PLMN 102 to handle actual calls, and also deployed as its own digital twin in a NDT of the PLMN. If the software for the network function deployed in the physical network is also used as the digital twin, however, the digital twin currently may only be able to predict one hypothesis. This is because the standardized interfaces of network functions in the physical PLMN do not enable management of multiple hypotheses. If a NDT of the PLMN is to be used for what-if analysis of more than one assumption (the “if’), the currently defined standardized interfaces are insufficient.

[0055] In view of the foregoing, example implementations of the present disclosure provide a solution in which digital twins in a NDT of a PLMN 102 or other network may exchange information to create hypotheses, to modify existing hypotheses, and to inform other digital twins about deleted hypotheses. According to some examples, a source digital twin of a hypothesis may also inform one or more receiver digital twins about the probability of a hypothesis, which may enable the receiver digital twin(s) to neglect a hypothesis and combinations of hypotheses of very low probability. In some examples, the digital twins may be interconnected by interfaces that enable the digital twins to exchange information about hypotheses. These interfaces may be enhanced versions of call processing interfaces that interconnect network functions in the PLMN, or dedicated interfaces provided by the NDT application server which facilitate communication between digital twins

[0056] As compared to the signaling interfaces between the actual network functions deployed in a PLMN 102, the signaling interfaces between digital twins (e.g., simulated network functions) may be augmented by additional information, such as information elements (IEs), to control the lifecycle of a hypothesis. From standardization point of view, this may be accomplished by augmentation of existing standards. Another possibility may be to define new interfaces specifically for NDT. These specific interfaces may mirror existing interfaces of call processing and carry the additional information for the digital twins.

[0057] Since the management of a hypothesis may affect any message between any digital twins, the information about the hypothesis may be provided as part of the basic messaging infrastructure of an NDT application server. In some examples, the general message structure between the digital twins in an NDT application server may include three parts, namely, a call processing message (at times referred to as a call processing payload message), information about a hypothesis, and timing information to control simulation time relative to wall-clock time. The call processing message may be as by 3GPP, and which call processing message is included may depend on the digital twin. The call processing message may also be a message to communicate between an environment digital twin and a network function digital twin, such as where simulation of movement informs a UE digital twin 502 about its position.

[0058] In some examples, the interface between digital twins within the NDT application server may be seen as an additional protocol layer added by the NDT application server. In the general message structure, the information about the hypothesis and timing information within a NDT may represent an envelope or header for the call processing messages. This NDT-specific protocol layer or envelope may be the same for all messages within the NDT, regardless of the digital twin. In some of these examples, a message within a NDT may be structured as follows: Timing information | hypothesis information | <call processing message> The NDT-specific protocol layer or envelope may leave the call-processing messages untouched and allow for integration of basically any message of any standardization organization into the framework of an NDT. The NDT-specific protocol layer or envelope may also allow to keep the standardization of the protocol enhancements needed for an NDT separated from the standardization of the call processing protocols.

[0059] In some examples, the model of a hypothesis may include a number of attributes, such as a unique hypothesis identifier, and in some examples an optional probability. The hypothesis identifier may uniquely identify the hypothesis. In this regard, several digital twins (e.g., RAN nodes) in a RAN digital twin may each send several hypotheses to the same digital twin in a CN digital twin (e.g., a MM). The hypothesis identifier may therefore enable differentiation between different hypotheses across different network functions in the NDT. The optional probability may enable a source digital twin to inform a receiver digital twin about the probability of the hypothesis.

[0060] In some examples, replies from digital twins in the NDT may also include the hypothesis identifier to enable the digitals twin to correlate requests and replies to each other, since one digital twin may send requests per hypothesis and require replies per hypothesis.

[0061] To handle a call in a physical PLMN 102, the network functions may create contexts and move contexts by handover to other network functions, or delete contexts when the call ends. The actual network functions therefore may not need signaling messages to delete hypothetical contexts. In contrast, according to some examples, a NDT that handles multiple hypotheses may provide a dedicated message to delete hypotheses that are no longer needed. In some examples, the NDT may also provide a dedicated message to inform the digital twins that the current hypothesis is no longer the only hypothesis, but has become one of several hypotheses.

[0062] To further illustrate some example implementations, FIGS. 6 and 7 are signaling charts 600, 700 of signaling messages within a NDT between digital twins of individual network functions to establish, propagate, update and delete hypotheses, according to some example implementations. The signaling charts follow the example shown in FIGS. 5A, 5B and 5C, and the messages exchanged between the digital twins may enable the digital twins to inform each other about hypotheticals and hypothetical outcomes. As described above, this example involves a UE digital twin 502 (a simulated UE), and first, second and third RAN node digital twins 504A, 504B, 504C (simulated RAN nodes). Also involved is a mobility twin 602 in an environment digital twin, and digital twins of other network functions 604 (e.g., MM, UPF, etc.). The mobility twin may be an entity within the environment digital twin responsible for path predictions.

[0063] As shown in FIG. 6, at a time T = 0, the mobility twin 602 may at step 1 predict for time T = 1 that a path of the UE digital twin 502 splits into three hypothesis with respective positions of the UE digital twin, and with same probability. The mobility twin may then at steps 2.1, 2.2, 2.3 send three messages to the UE digital twin to create the three hypotheses, namely, “turn left,” “straight,” and “turn right.” Each hypothesis may have a respective hypothesis identifier (hypothesisld), and the hypotheses may all have the same probability = 0.33.

[0064] The UE digital twin 502 may at step 3 simulate radio frequency (RF) conditions of the respective positions of the UE digital twin. The UE digital twin may determine that the hypothetical “turn left” does not change the cell. The hypothetical “straight” may move the UE digital twin into a position of the RAN digital twin that makes cell = B better than cell = A (event “A4”). And the hypothetical “turn right” may move the UE digital twin into a position of the RAN digital twin that makes cell = C better than a threshold configured by cell = A in the UE digital twin (event “A6”).

[0065] As result of the RF simulation, the UE digital twin 502 may at steps 4.1, 4.2, 4.3 send messages to the first RAN node digital twin 504A for cell = A (the current serving cell where the UE digital twin is registered) to create the three hypotheses for the respective RF conditions of the respective hypotheses. The message at step 4.1 may inform the cell = A that the current context is no longer the only hypothesis. This message may not include a call processing message (as the UE remains in the cell), but include a respective hypothesis identifier and probability. The message at step 4.2 may include a call processing message that indicates an event A4 (“neighbor cell = B becomes better than threshold”) (e.g., as defined by 3GPP RAN), as well as a respective hypothesis identifier and probability. Similarly, the message at step 4.3 may include a call processing message that indicates an event A6 (“neighbor cell = C becomes amount of offset xDB better than threshold”) (e.g., as defined by 3GPP RAN), a respective hypothesis identifier and a probability.

[0066] The first RAN node digital twin 504A may at step 5 internally evaluate the three hypothesis. As the first RAN node digital twin may have been provided as twin of a vendor-specific actual RAN node, the first RAN node digital twin may apply the same vendor-specific algorithm to decide on handover as the actual RAN node, without the need to publicly disclose the details about the vendor-specific algorithm. As result of the evaluation, the first RAN node digital twin may contact other digital twins in the NDT, as appropriate.

[0067] The first RAN node 504A may at step 5.1 send a message to the second RAN node digital twin 504B (hosting cell = B), which may start an exchange of messages on the Xn interface to negotiate a handover of the UE digital twin 502 to the second RAN node digital twin. Likewise, the first RAN node may at step 5.2 send a message to the third RAN node digital twin 504C (hosting cell = C), which may start an exchange of messages on the Xn interface to negotiate a handover of the UE digital twin 502 to the third RAN node digital twin. These messages between the RAN node digital twins may all include a respective hypothesis identifier and probability. The sequence of messages between the RAN node digital twins for the handover are omitted for clarity, as are messages to the other network functions 604 (e.g., MM, UPF, etc.) for the handover. These messages, however, follow a similar message structure.

[0068] Between time T = 1 and T = 5 the UE digital twin 502 moves, and all three hypotheses are maintained in the affected RAN node digital twins 504A, 504B, 504C (and other network functions 604).

[0069] As shown in FIG. 7, at time T = 6, a measurement arrives at the mobility twin 602 at step 6 that clarifies the UE has moved “straight,” and that the path prediction of the UE digital twin 502 no longer needs to consider the hypotheses “turn left” and “turn right.” The mobility twin 602 may at steps 6.1, 6.2, 6.3 send messages to the UE digital twin, all of which include at least a respective hypothesis identifier. The messages at steps 6.1, 6.3 may inform the UE digital twin to delete the hypotheses “turn left” and “turn right,” and the message at step 6.2 may inform the UE digital twin to update the hypothesis “straight” to indicate a probability = 1 (as the hypothesis has occurred). And based on the messages, the UE digital twin may at step 7 delete the hypotheses “turn left” and “turn right,” and update the hypothesis “straight.”

[0070] The UE digital twin 502 may at steps 7.1, 7.2, 7.3 send messages to the first RAN node digital twin 504A for cell = A (the current serving cell where the UE digital twin is registered), all of which include at least a respective hypothesis identifier. The messages at steps 7.1, 7.3 may inform the first RAN node digital twin to delete the hypotheses “turn left” and “turn right,” and the message at step 7.2 may inform the first RAN node digital twin to update the hypothesis “straight” to indicate a probability = 1 (as the hypothesis has occurred). And based on the messages, the first RAN node digital twin may at step 8 delete the hypotheses “turn left” and “turn right,” and update the hypothesis “straight.” The first RAN node digital twin may at step 8.1 send a message to the second RAN node digital twin 504B to update the hypothesis “straight” to indicate a probability = 1, and the first RAN node digital twin may at step 8.2 send a message to the third RAN node digital twin 504C to delete the hypothesis “turn right.” These messages also include a respective hypothesis identifier.

[0071] In FIGS. 6 and 7, the sequence of messages between the RAN node digital twins 504A, 504B, 504C for the handover are omitted for clarity, as are messages to the other network functions 604 (e.g., MM, UPF, etc.) for the handover. These messages, however, follow a similar message structure.

[0072] FIG. 8 is a flowchart illustrating various steps in a method 800 according to various example implementations. The method includes accessing a network digital twin of a network that integrates digital twins of one or more network functions, at least one user equipment (UE), and an environment of the network, the digital twins in the network digital twin interconnected by interfaces between the digital twins, as shown at block 802. The method includes executing the network digital twin to perform one or more simulations of the network during which one or more of the digital twins create hypotheses of at least one of behavior, performance or conditions of at least one of the one or more network functions, the at least one UE, or the environment, as shown at block 804. The method includes assigning hypothesis identifiers to the hypotheses at respective ones of the one or more of the digital twins, as shown at block 806. And the method includes exchanging messages about the one or more simulations between the digital twins over the interfaces between the digital twins, the messages including information about the hypotheses including the hypothesis identifiers, as shown at block 808.

[0073] In some examples, the interfaces between the digital twins include enhanced versions of call processing interfaces that interconnect one or more network functions in the network.

[0074] In some examples, the interfaces between the digital twins include call processing interfaces that interconnect digital twins of the one or more network functions in the network digital twin, and the call processing interfaces are separate and distinct from corresponding call processing interfaces that interconnect the one or more network functions in the network.

[0075] In some examples, the messages about the simulation exchanged between the digital twins have a message structure including a call processing message for a simulation of the network, information about a hypothesis, and timing information to control simulation time relative to wall-clock time.

[0076] In some examples, the timing information and the information about the hypothesis are included in the message structure as an envelope or header for the call processing message.

[0077] In some examples, the information about the hypotheses is exchanged for management of the hypotheses, including to at least one of create, update or delete one or more of the hypotheses.

[0078] In some examples, the hypothesis identifiers include respective hypothesis identifiers for multiple different hypotheses created by one of the digital twins at one time step of simulation time, and the information about the hypotheses includes the respective hypothesis identifiers for the multiple different hypotheses.

[0079] In some examples, the information about the hypotheses includes the respective hypothesis identifiers, and further includes information that indicates respective probabilities of the multiple different hypotheses.

[0080] According to example implementations of the present disclosure, an application server may be implemented by various means. Means for implementing the application server and its components may include hardware, firmware, software, or combinations thereof. In some examples, one or more apparatuses may be configured to function as or otherwise implement the system and its components shown and described herein. In examples involving more than one apparatus, the respective apparatuses may be connected to or otherwise in communication with one another in a number of different manners, such as directly or indirectly via a wired or wireless network or the like.

[0081] According to some example implementations, at least some of the method 800 described with respect to FIG. 8 may be carried out by an apparatus comprising means for performing functions corresponding steps of the method. Examples of a suitable apparatus may include an application server which may be implemented by or on a portable computer, desktop computer, workstation computer, server (server computer) or any other suitable apparatus.

[0082] FIG. 9 illustrates an apparatus 900 in which means for performing various functions includes hardware, alone or under direction of one or more computer programs from a computer-readable storage medium or other memory, such as computer memory, according to some example implementations of the present disclosure. The apparatus may include one or more of each of a number of components such as, for example, processing circuitry 902 connected to computer-readable storage medium or other memory 904.

[0083] The processing circuitry 902 may be composed of one or more processors alone or in combination with one or more computer-readable storage media. The processing circuitry is generally any piece of computer hardware that is capable of processing information such as, for example, data, computer programs and / or other suitable electronic information. The processing circuitry is composed of a collection of electronic circuits some of which may be packaged as an integrated circuit or multiple interconnected integrated circuits (an integrated circuit at times more commonly referred to as a “chip”). The processing circuitry may be configured to execute computer programs, which may be stored onboard the processing circuitry or otherwise stored in the memory 904 (of the same or another apparatus).

[0084] The processing circuitry 902 may be a number of processors, a multi-core processor or some other type of processor, depending on the particular implementation. Further, the processing circuitry may be implemented using a number of heterogeneous processor systems in which a main processor is present with one or more secondary processors on a single chip. As another illustrative example, the processing circuitry may be a symmetric multi-processor system containing multiple processors of the same type. In yet another example, the processing circuitry may be embodied as or otherwise include one or more ASICs, FPGAs or the like. Thus, although the processing circuitry may be capable of executing a computer program to perform one or more functions, the processing circuitry of various examples may be capable of performing one or more functions without the aid of a computer program. In either instance, the processing circuitry may be appropriately programmed to perform functions or operations according to example implementations of the present disclosure.

[0085] The memory 904 is generally any piece of computer hardware that is capable of storing information such as, for example, data, computer programs, instructions 906 (e.g., computer-readable program code) and / or other suitable information either on a temporary basis and / or a permanent basis. The memory may include volatile and / or non-volatile memory, and may be fixed or removable. Examples of suitable memory include recording media, random access memory (RAM), read-only memory (ROM), a hard drive, a flash memory, a thumb drive, a removable computer diskette, an optical disk or some combination thereof.

[0086] The memory 904 is a non-transitory device capable of storing information. One example of a suitable memory is a computer-readable storage medium, which is distinguishable from a computer-readable transmission medium capable of carrying information from one location to another. Examples of suitable computer-readable transmission media comprise electronic carrier signals, telecommunications signals, or some combination thereof. As used herein, the term “non-transitory” is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM versus ROM). A computer-readable medium as described herein generally refers to a computer-readable storage medium or computer-readable transmission medium. A computer-readable medium is any entity or device capable in which information, such as one or more computer programs or portions thereof, may be stored and carried.

[0087] In addition to the memory 904 (e.g., computer-readable storage medium), the processing circuitry 902 may also be connected to one or more interfaces for displaying, transmitting and / or receiving information. The interfaces may include a communications interface 908 and / or one or more user interfaces. The communications interface may be configured to transmit and / or receive information, such as to and / or from other apparatus(es), network(s) or the like. The communications interface may be configured to transmit and / or receive information by physical (wired) and / or wireless communications links. Examples of suitable communication interfaces include a network interface controller (NIC), wireless NIC (WNIC) or the like.

[0088] The user interfaces may include a display 910 and / or one or more user input interfaces 912. The display may be configured to present or otherwise display information to a user, suitable examples of which include a liquid crystal display (LCD), light-emitting diode (LED) display, organic LED (OLED) display, active-matrix OLED (AMOLED) or the like. The user input interfaces may be wired or wireless, and may be configured to receive information from a user into the apparatus, such as for processing, storage and / or display. Suitable examples of user input interfaces include a microphone, image or video capture device, keyboard or keypadjoystick, touch-sensitive surface (separate from or integrated into a touchscreen), biometric sensor or the like. The user interfaces may further include one or more interfaces for communicating with peripherals such as printers, scanners or the like.

[0089] Execution of the instructions 906 by the processing circuitry 902, or storage of the instructions in the memory 904, supports combinations of operations for implementing example implementations of the present disclosure. In this manner, an apparatus 900 may comprise at least one processing circuitry and at least one memory coupled to the at least one processing circuitry, where the at least one processing circuitry is configured to execute instructions stored in the at least one memory. It will also be understood that one or more functions, and combinations of functions, may be implemented by special purpose hardware-based computer systems and / or processing circuitry which perform the specified functions, or combinations of special purpose hardware and program code instructions.

[0090] Some example implementations of the present disclosure may also be carried out in the form of a computer process defined by one or more computer programs or portions thereof. Example implementations of the present disclosure may be carried out by executing at least one portion of a computer program comprising instructions. The computer program may be in source code form, object code form, or in some intermediate form. The computer program may be stored in a computer-readable medium that is readable by a computer, processing circuitry or other suitable apparatus. As indicated above, for example, the computer program may be stored in a memory, such as a computer-readable storage medium. Additionally or alternatively, for example, the computer program may be stored in a computer-readable transmission medium. The coding of software for carrying out example implementations of the present disclosure is well within the scope of a person of ordinary skill in the art.

[0091] As will be appreciated, any suitable instructions may be loaded onto a computer, a processing circuitry or other programmable apparatus from a memory or a computer-readable medium (e.g., computer-readable storage medium, computer-readable transmission medium) to produce a particular machine, such that the particular machine becomes a means for implementing the functions specified herein. The instructions may also be stored in a computer-readable medium that can direct a computer, a processing circuitry or other programmable apparatus to function in a particular manner to thereby generate a particular machine or particular article of manufacture. In some examples, the instructions stored in the computer-readable medium may produce an article of manufacture, where the article of manufacture becomes a means for implementing functions described herein. The instructions may be retrieved from a computer-readable medium and loaded into a computer, processing circuitry or other programmable apparatus to configure the computer, processing circuitry or other programmable apparatus to execute operations to be performed on or by the computer, processing circuitry or other programmable apparatus.

[0092] Retrieval, loading and execution of instructions comprising program code instructions may be performed sequentially such that one instruction is retrieved, loaded and executed at a time. In some example implementations, retrieval, loading and / or execution may be performed in parallel such that multiple instructions are retrieved, loaded, and / or executed together. Execution of the program code instructions may produce a computer-implemented process such that the instructions executed by the computer, processing circuitry or other programmable apparatus provide operations for implementing functions described herein.

[0093] Many modifications and other implementations of the disclosure set forth herein will come to mind to one skilled in the art to which the disclosure pertains having the benefit of the teachings presented in the foregoing description and the associated figures. Therefore, it is to be understood that the disclosure is not to be limited to the specific implementations disclosed and that modifications and other implementations are intended to be included within the scope of the appended claims. Moreover, although the foregoing description and the associated figures describe example implementations in the context of certain example combinations of elements and / or functions, it should be appreciated that different combinations of elements and / or functions may be provided by alternative implementations without departing from the scope of the appended claims. In this regard, for example, different combinations of elements and / or functions than those explicitly described above are also contemplated as may be set forth in some of the appended claims. Although specific terms are 5 employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

1. An apparatus comprising:at least one memory configured to store instructions; andat least one processing circuitry configured to access the at least one memory, and execute the instructions to cause the apparatus to at least:access a network digital twin of a network that integrates digital twins of one or more network functions, at least one user equipment (UE), and an environment of the network, the digital twins in the network digital twin interconnected by interfaces between the digital twins;execute the network digital twin to perform one or more simulations of the network during which one or more of the digital twins create hypotheses of at least one of behavior, performance or conditions of at least one of the one or more network functions, the at least one UE, or the environment;assign hypothesis identifiers to the hypotheses at respective ones of the one or more of the digital twins; andexchange messages about the one or more simulations between the digital twins over the interfaces between the digital twins, the messages including information about the hypotheses including the hypothesis identifiers.

2. The apparatus of claim 1, wherein the interfaces between the digital twins include enhanced versions of call processing interfaces that interconnect one or more network functions in the network.

3. The apparatus of claim 1 or claim 2, wherein the interfaces between the digital twins include call processing interfaces that interconnect digital twins of the one or more network functions in the network digital twin, and the call processing interfaces are separate and distinct from corresponding call processing interfaces that interconnect the one or more network functions in the network.

4. The apparatus of any of claims 1 to 3, wherein the messages about the simulation exchanged between the digital twins have a message structure including a call processing message for a simulation of the network, information about a hypothesis, and timing information to control simulation time relative to wall-clock time.

5. The apparatus of claim 4, wherein the timing information and the information about the hypothesis are included in the message structure as an envelope or header for the call processing message.

6. The apparatus of any of claims 1 to 5, wherein the information about the hypotheses is exchanged for management of the hypotheses, including to at least one of create, update or delete one or more of the hypotheses.

7. The apparatus of any of claims 1 to 6, wherein the hypothesis identifiersinclude respective hypothesis identifiers for multiple different hypotheses created by one of the digital twins at one time step of simulation time, and the information about the hypotheses includes the respective hypothesis identifiers for the multiple different hypotheses.

8. The apparatus of claim 7, wherein the information about the hypotheses includes the respective hypothesis identifiers, and further includes information that indicates respective probabilities of the multiple different hypotheses.

9. A method comprising:accessing a network digital twin of a network that integrates digital twins of one or more network functions, at least one user equipment (UE), and an environment of the network, the digital twins in the network digital twin interconnected by interfaces between the digital twins;executing the network digital twin to perform one or more simulations of the network during which one or more of the digital twins create hypotheses of at least one of behavior, performance or conditions of at least one of the one or more network functions, the at least one UE, or the environment;assigning hypothesis identifiers to the hypotheses at respective ones of the one or more of the digital twins; andexchanging messages about the one or more simulations between the digital twins over the interfaces between the digital twins, the messages including information about the hypotheses including the hypothesis identifiers.

10. The method of claim 9, wherein the interfaces between the digital twins include enhanced versions of call processing interfaces that interconnect one or more network functions in the network.

11. The method of claim 9 or claim 10, wherein the interfaces between thedigital twins include call processing interfaces that interconnect digital twins of the one or more network functions in the network digital twin, and the call processing interfaces are separate and distinct from corresponding call processing interfaces that interconnect the one or more network functions in the network.

12. The method of any of claims 9 to 11, wherein the messages about the simulation exchanged between the digital twins have a message structure including a call processing message for a simulation of the network, information about a hypothesis, and timing information to control simulation time relative to wall-clock time.

13. The method of claim 12, wherein the timing information and the information about the hypothesis are included in the message structure as an envelope or header for the call processing message.

14. The method of any of claims 9 to 13, wherein the information about the hypotheses is exchanged for management of the hypotheses, including to at least one of create, update or delete one or more of the hypotheses.

15. The method of any of claims 9 to 14, wherein the hypothesis identifiers include respective hypothesis identifiers for multiple different hypotheses created by one of the digital twins at one time step of simulation time, and the information about the hypotheses includes the respective hypothesis identifiers for the multiple different hypotheses.

16. The method of claim 15, wherein the information about the hypotheses includes the respective hypothesis identifiers, and further includes information that indicates respective probabilities of the multiple different hypotheses.

17. An apparatus comprising means for performing the method of any of claims 9 to 16.

18. A computer-readable medium comprising instructions that, in response to execution by at least one processing circuitry, causes an apparatus to perform the method of any of claims 9 to 16.

19. A computer-readable storage medium comprising instructions that, in response to execution by at least one processing circuitry, causes an apparatus to perform the method of any of claims 9 to 16.

20. A computer program comprising instructions that, in response to execution by at least one processing circuitry, causes an apparatus to perform the method of any of claims 9 to 16.

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