Method, apparatus and computer program

Abstract

The application relates to requesting and obtaining synthetically generated data, that is data that has been artificially manufactured, possibly to replicate real-world traffic but that has not been generated by real-world events. An apparatus 501 of a network entity, which receives, from a consumer 505 (such as a service consumer or synthetic data consumer), at least one request associated with a first dataset; determining, based on the at least one request, first data that has been generated synthetically, wherein the first data is comprised in the first dataset; and providing, to the consumer, the first data. The apparatus may comprise a Synthetic Data Generation Function (SDGF) which provides synthetic data sets to authorised consumers on request. The SDGF may be a Network Function in a 5G core network. The synthetic data may be used for AI / ML network management operations. The SGDF may register data sets in a repository with an identifier and allow requests to be defined based on statistical properties or scope of the synthetic data set(s). The SGDF may also generate the first dataset on request. The dataset(s) may be generated by synthetic data producers 507 and stored at the SGDF.

Description

Technical Field Various examples of this disclosure relate to methods, apparatuses, and computer programs for a communication network. Background A communication network can be seen as a facility that enables communications between two or more communication devices, or provides communication devices access to a data network. A mobile or wireless communication network is one example of a communication network. A communication device may be provided with a service by an application server. Certain communication networks operate in accordance with standards, such as those promulgated by 3GPP (Third Generation Partnership Project) or ETSI (European Telecommunications Standards Institute). Examples of standards include the so-called 5G (5th Generation) standards promulgated by 3GPP. Summary Some examples of this disclosure will be described with respect to certain aspects. These aspects are not intended to indicate key or essential features of the various examples of this disclosure, nor are they intended to be used to limit the scope of thereof. Other features, aspects, and elements will be readily apparent to a person skilled in the art in view of this disclosure. For example, it should be appreciated that further aspects may be provided by the combination of any two or more of the various aspects described below. According to an aspect, there is provided an apparatus of a network entity, wherein the apparatus comprises means for the network entity to perform: receiving, from a consumer, at least one request associated with a first dataset; determining, based on the at least one request, first data that has been generated synthetically, wherein the first data is comprised in the first dataset; and providing, to the consumer, the first data. In some examples, the at least one request comprises an identifier of the first dataset. In some examples, the determining comprises: retrieving, based on the at least one request, the first data from a repository. In some examples, the at least one request associated with the first dataset comprises a request for the generation of the first dataset according to at least one capability. In some examples, the determining comprises: generating, based on the at least one request, the first data for the first dataset. In some examples, the determining comprises: providing, to a producer, a further request for the generation of the first dataset; and receiving, from the producer, the first data that has been generated synthetically. In some examples, the means are further for the network entity to perform: providing, to the consumer, an identifier of the first dataset. In some examples, the means are further for the network entity to perform: receiving, from a producer, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically; validating the request for registration, so that at least one of the following: an identity of the dataset, an identity of the producer, or information associated with the dataset, are stored for the dataset at a repository; and providing, to the producer, a response comprising an identity of the dataset. In some examples, the means are further for the network entity to perform: receiving, from a producer, a request for updating the first dataset, wherein the request for updating comprises an identity of the first dataset; causing the first dataset to be updated at a repository where the first dataset is stored based on the request for updating. In some examples, the means are further for the network entity to perform: receiving, from the consumer, a request for information related to datasets comprising data that has been generated synthetically; retrieving information related to the first dataset; and providing, to the consumer, the information related to the first dataset, wherein the information comprises an identity of the first dataset. In some examples, the means are further for the network entity to perform: receiving, from a producer, a request for registration of the producer, wherein the request for registration comprises: an identity of the producer, and information related to at least one capability associated with synthetic data generation of the producer; and registering the at least one capability of the producer. In some examples, the means are further for the network entity to perform: receiving, from a consumer, a request for discovery of at least one capability associated with generating synthetic data; and based on the request, providing, to the consumer, a response comprising information related to data that can be generated according to the at least one capability. In some examples, the network entity is a network function. In some examples, the consumer is a further network entity, the further network entity configured as a consumer. In some examples, the network entity is a service producer. In some examples, the network function is a synthetic data generation function. According to an aspect, there is provided an apparatus of a further network entity, wherein the apparatus comprises means for the further network entity to perform: providing, to a network entity, at least one request associated with a first dataset; and receiving, from the network entity, first data that has been generated synthetically, wherein the first data is comprised in the first dataset. In some examples, the at least one request comprises an identifier of the first dataset. In some examples, the at least one request associated with the first dataset comprises: a request for the generation of the first dataset according to at least one capability. In some examples, the means are further for the further network entity to perform: receiving, from the network entity, an identifier of the first dataset. In some examples, the means are further for the further network entity to perform: providing, to the network entity, a request for information related to datasets comprising data that has been generated synthetically; and receiving, from the network entity, information related to the first dataset, wherein the information comprises an identity of the first dataset. In some examples, the means are further for the further network entity to perform: providing, to the network entity, a request for discovery of at least one capability associated with generating synthetic data; and receiving, from the network entity, a response comprising information related to data that can be generated according to the at least one capability. In some examples, the further network entity is a network function. In some examples, the further network function is configured as a consumer. In some examples, the further network entity is configured as a service consumer. In some examples, the further network entity is configured as a synthetic data consumer. According to an aspect, there is provided a method comprising: receiving, from a consumer, at least one request associated with a first dataset; determining, based on the at least one request, first data that has been generated synthetically, wherein the first data is comprised in the first dataset; and providing, to the consumer, the first data. In some examples, the at least one request comprises an identifier of the first dataset. In some examples, the determining comprises: retrieving, based on the at least one request, the first data from a repository. In some examples, the at least one request associated with the first dataset comprises a request for the generation of the first dataset according to at least one capability. In some examples, the determining comprises: generating, based on the at least one request, the first data for the first dataset. In some examples, the determining comprises: providing, to a producer, a further request for the generation of the first dataset; and receiving, from the producer, the first data that has been generated synthetically. In some examples, the method comprises: providing, to the consumer, an identifier of the first dataset. In some examples, the method comprises: receiving, from a producer, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically; validating the request for registration, so that at least one of the following: an identity of the dataset, an identity of the producer, or information associated with the dataset, are stored for the dataset at a repository; and providing, to the producer, a response comprising an identity of the dataset. In some examples, the method comprises: receiving, from a producer, a request for updating the first dataset, wherein the request for updating comprises an identity of the first dataset; causing the first dataset to be updated at a repository where the first dataset is stored based on the request for updating. In some examples, the method comprises: receiving, from the consumer, a request for information related to datasets comprising data that has been generated synthetically; retrieving information related to the first dataset; and providing, to the consumer, the information related to the first dataset, wherein the information comprises an identity of the first dataset. In some examples, the method comprises: receiving, from a producer, a request for registration of the producer, wherein the request for registration comprises: an identity of the producer, and information related to at least one capability associated with synthetic data generation of the producer; and registering the at least one capability of the producer. In some examples, the method comprises: receiving, from a consumer, a request for discovery of at least one capability associated with generating synthetic data; and based on the request, providing, to the consumer, a response comprising information related to data that can be generated according to the at least one capability. In some examples, the method is performed by a network entity. In some examples, the network entity is a network function. In some examples, the consumer is a further network entity, the further network entity configured as a consumer. In some examples, the network entity is a service producer. In some examples, the network function is a synthetic data generation function. According to an aspect, there is provided a method comprising: providing, to a network entity, at least one request associated with a first dataset; and receiving, from the network entity, first data that has been generated synthetically, wherein the first data is comprised in the first dataset. In some examples, the at least one request comprises an identifier of the first dataset. In some examples, the at least one request associated with the first dataset comprises: a request for the generation of the first dataset according to at least one capability. In some examples, the method comprises: receiving, from the network entity, an identifier of the first dataset. In some examples, the method comprises: providing, to the network entity, a request for information related to datasets comprising data that has been generated synthetically; and receiving, from the network entity, information related to the first dataset, wherein the information comprises an identity of the first dataset. In some examples, the method comprises: providing, to the network entity, a request for discovery of at least one capability associated with generating synthetic data; and receiving, from the network entity, a response comprising information related to data that can be generated according to the at least one capability. In some examples, the method is performed by a further network entity. In some examples, the further network entity is a network function. In some examples, the further network function is configured as a consumer. In some examples, the further network entity is configured as a service consumer. In some examples, the further network entity is configured as a synthetic data consumer. According to an aspect, there is provided an apparatus comprising: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform: receiving, from a consumer, at least one request associated with a first dataset; determining, based on the at least one request, first data that has been generated synthetically, wherein the first data is comprised in the first dataset; and providing, to the consumer, the first data. In some examples, the at least one request comprises an identifier of the first dataset. In some examples, the determining comprises: retrieving, based on the at least one request, the first data from a repository. In some examples, the at least one request associated with the first dataset comprises a request for the generation of the first dataset according to at least one capability. In some examples, the determining comprises: generating, based on the at least one request, the first data for the first dataset. In some examples, the determining comprises: providing, to a producer, a further request for the generation of the first dataset; and receiving, from the producer, the first data that has been generated synthetically. In some examples, the network entity is caused to perform: providing, to the consumer, an identifier of the first dataset. In some examples, the apparatus is caused to perform: receiving, from a producer, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically; validating the request for registration, so that at least one of the following: an identity of the dataset, an identity of the producer, or information associated with the dataset, are stored for the dataset at a repository; and providing, to the producer, a response comprising an identity of the dataset. In some examples, the apparatus is caused to perform: receiving, from a producer, a request for updating the first dataset, wherein the request for updating comprises an identity of the first dataset; causing the first dataset to be updated at a repository where the first dataset is stored based on the request for updating. In some examples, the apparatus is caused to perform: receiving, from the consumer, a request for information related to datasets comprising data that has been generated synthetically; retrieving information related to the first dataset; and providing, to the consumer, the information related to the first dataset, wherein the information comprises an identity of the first dataset. In some examples, the apparatus is caused to perform: receiving, from a producer, a request for registration of the producer, wherein the request for registration comprises: an identity of the producer, and information related to at least one capability associated with synthetic data generation of the producer; and registering the at least one capability of the producer. In some examples, the apparatus is caused to perform: receiving, from a consumer, a request for discovery of at least one capability associated with generating synthetic data; and based on the request, providing, to the consumer, a response comprising information related to data that can be generated according to the at least one capability. In some examples, the apparatus is configured to implement, at least in part, a network entity. In some examples, the network entity is a network function. In some examples, a further network entity is configured as the consumer. In some examples, the network entity is a service producer. In some examples, the network function is a synthetic data generation function. According to an aspect, there is provided an apparatus comprising: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform: providing, to a network entity, at least one request associated with a first dataset; and receiving, from the network entity, first data that has been generated synthetically, wherein the first data is comprised in the first dataset. In some examples, the at least one request comprises an identifier of the first dataset. In some examples, the at least one request associated with the first dataset comprises: a request for the generation of the first dataset according to at least one capability. In some examples, the apparatus is caused to perform: receiving, from the network entity, an identifier of the first dataset. In some examples, the apparatus is caused to perform: providing, to the network entity, a request for information related to datasets comprising data that has been generated synthetically; and receiving, from the network entity, information related to the first dataset, wherein the information comprises an identity of the first dataset. In some examples, the apparatus is caused to perform: providing, to the network entity, a request for discovery of at least one capability associated with generating synthetic data; and receiving, from the network entity, a response comprising information related to data that can be generated according to the at least one capability. In some examples, the apparatus is configured to implement, at least in part, a further network entity. In some examples, the further network entity is a network function. In some examples, the further network function is configured as a consumer. In some examples, the further network entity is configured as a service consumer. In some examples, the further network entity is configured as a synthetic data consumer. According to an aspect, there is provided a computer program comprising instructions, which when executed by an apparatus, cause the apparatus to perform at least the following: receiving, from a consumer, at least one request associated with a first dataset; determining, based on the at least one request, first data that has been generated synthetically, wherein the first data is comprised in the first dataset; and providing, to the consumer, the first data. According to an aspect, there is provided a computer program comprising instructions, which when executed by an apparatus, cause the apparatus to perform at least the following: providing, to a network entity, at least one request associated with a first dataset; and receiving, from the network entity, first data that has been generated synthetically, wherein the first data is comprised in the first dataset. According to an aspect, there is provided a computer program comprising instructions, which when executed by an apparatus, cause the apparatus to perform at least the following: providing, to a network entity, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically; and receiving, from the network entity, a response comprising an identity of the dataset. According to an aspect, there is provided an apparatus of a network entity, wherein the apparatus comprises means for the network entity to perform: providing, to a network entity, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically; and receiving, from the network entity, a response comprising an identity of the dataset. In some examples, the network entity is configured as a service producer and / or a synthetic data producer. According to an aspect, there is provided a method comprising: providing, to a network entity, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically; and receiving, from the network entity, a response comprising an identity of the dataset. According to an aspect, there is provided an apparatus comprising: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform: providing, to a network entity, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically; and receiving, from the network entity, a response comprising an identity of the dataset. In some examples, the network entity is configured as a service producer and / or a synthetic data producer. According to an aspect, there is provided an apparatus comprising circuitry configured to perform: providing, to a network entity, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically; and circuitry configured to perform: receiving, from the network entity, a response comprising an identity of the dataset. A computer product stored on a medium may cause an apparatus to perform the methods as described herein. A non-transitory computer readable medium comprising program instructions, that, when executed by an apparatus, cause the apparatus to perform the methods as described herein. An electronic device may comprise apparatus as described herein. Various other aspects and further examples are also set forth in the detailed description and in the claims. According to some aspects, there is provided the subject matter of the independent claims. Some further aspects are defined in the dependent claims. Any examples(s) that do not fall under the scope of the claims (if any) are to be interpreted as examples useful for understanding this disclosure. List of Abbreviations: AF: Application Function ADRF: Analytics data repository function AnLF: Analytics logical function AMF: Access and Mobility Management Function AN: Access Network BS: Base Station CN: Core Network DCCF: Data collection coordination function DL: Downlink eNB: FL: GDPR: eNodeB Federated learning General data protection regulation gNB: gNodeB 5 lloT: Industrial Internet of Things LTE: Long Term Evolution MS: Mobile Station MFAF: Messaging framework adaptor function ML: Machine learning 10 MnS: Management service MTLF: Model training logical function NDT: Network digital twin NEF: Network Exposure Function NG-RAN: Next Generation Radio Access Network 15 NF: Network Function NR: New Radio NRF: Network Repository Function NW: Network NWDAF: Network data analytics function 20 GAM: Operations administration and management PCF Policy Control Function PDU: Protocol data unit PLMN: Public Land Mobile Network QoS: Quality of service 25 RAN: Radio Access Network RF: Radio Frequency SDGF: Synthetic data generation function SMF: Session Management Function UC: Use case 30 UE: User Equipment UDR: Unified Data Repository UDM: Unified Data Management UL: Uplink UPF: User Plane Function 35 3GPP: 3rd Generation Partnership Project 5G: 5th Generation 5GC: 5G Core network 5G-AN: 5G Radio Access Network 5GS: 5G System Brief Description of Drawings Some examples will now be described, by way of illustrative and non-limiting example only, with reference to the drawings in which: FIG. 1 shows a schematic representation of a 5G communication system; FIG. 2 shows a schematic representation of an apparatus for the 5G communication system of FIG. 1; FIG. 3 shows a schematic representation of a communication device for the 5G communication system of FIG. 1; FIG. 4 shows a schematic representation of a network analytics architecture for a 5G communication system; FIG. 5 shows a schematic representation of a network entity that is able to provide data generation related services and data publication related services for synthetic data; FIG. 6 shows an example signalling diagram for synthetic dataset registration; FIG. 7 shows an example signalling diagram for a synthetic dataset update; FIG. 8 shows an example signalling diagram for synthetic dataset discovery; FIG. 9 shows an example signalling diagram for a synthetic dataset request; FIG. 10 shows an example signalling diagram for a synthetic dataset capability request; FIG. 11 shows another example signalling diagram for a synthetic dataset capability request; FIG. 12 shows an example signalling diagram for synthetic data generation capability registration; FIG. 13 shows another example signalling diagram for synthetic data generation capability registration; FIG. 14 shows an example signalling diagram for synthetic data generation capability discovery; FIG. 15 shows a schematic representation of an information model for synthetic data generation; FIG. 16 shows a schematic representation of synthetic data generation inheritance relations; FIG. 17 shows an example method flow diagram performed by an apparatus; FIG. 18 shows another example method flow diagram performed by an apparatus; FIG. 19 shows another example method flow diagram performed by an apparatus; and FIG. 20 shows a schematic representation of a non-volatile memory medium storing instructions which when executed by a processor allow, enable, or otherwise facilitate a processor to perform one or more of the steps of the method of FIGS. 17 to 19. Detailed Description Artificial intelligence (Al) and machine learning (ML) -based network management may be utilised to achieve ‘zero touch’ input and autonomous networks. Each individual management scenario and use case is different, and would benefit from a well-designed solution. Depending on the use case, AI / ML solutions may be provided by a solution provider or ML model designer. Such solutions may be purchased and utilised to solve a specific problem or improve a certain aspect of a network. AI / ML is used in a 5G system (5GS) to provide network analytics in the 5G core (5GC). Analytics consumers include network functions (NFs) (e.g. PCF, AMF, SMF, etc.) and operations, administration and management (OAM) which may use the analytics to optimize the network. A network data analytics function (NWDAF) comprising one or more of a Model Training logical functions (MTLFs) and an Analytics Logical Function (AnLF) gathers data from data sources including, for example, NFs, OAM, the RAN (via OAM) and the UE (via an application function). Data and analytics may be gathered via a data management framework comprising a Data Collection Coordination Function (DCCF) and a Messaging Framework Adaptor Function (MFAF) (e.g. as seen in FIG. 4). Data, analytics and models may be stored in an Analytics Data Repository function (ADRF). 3GPP defines the analytics produced by the NWDAF (e.g. slice load level, observed service experience, NF load, network performance, UE mobility, etc.) by specifying input data, output analytics (comprising statistics and or predictions) and procedures for producing the analytics. A fully functional ML based solution may use a specifically prepared set of data in the scope of relevance where the ML based solution will be deployed. This data set is used to train the ML based solution before deployment. For such training, it is beneficial to use real-world data. Real-world data is data that is measured and tracked from ‘real’ communications happening in networks. However, it is not always possible to obtain sufficient real-world data for such ML training. In this situation, it may be justified to use synthetic data in place of, or to supplement the real-world data. Synthetic data is data that is generated synthetically or artificially. Synthetic data may be information that is artificially manufactured rather than generated by real-world events. Synthetic data may be created algorithmically and may be used as a stand-in for test data sets of production or operational data, to validate mathematical models and to train ML models. Synthetic data generation is becoming increasingly utilized to overcome the limitations of real-world data availability. Real-world data is challenging because of the lack of labelled data, bias, incompleteness, lack of variety, cost and privacy / security constraints. Synthetic data is an alternative to such real-world data. Although artificial, the data statistically mimics the patterns and characteristics of real-world data. Synthetic data may be created using algorithms and statistical models that replicate the patterns, characteristics, and relationships found in real-world data. In the case of the NWDAF, the availability of applicable real-world data may be limited due to several factors, including the time available to gather data, service area / area-of-interest constraints, and the number of relevant UEs, PDU Sessions, QoS Flows, network slices, etc. For these cases, synthetic data may improve the quality of models and analytics. Some of the main advantages of synthetic data generation include: i) the data may be generated in large scale and with different characteristics, making it possible to create diverse data sets that can be used to train machine learning models when real data is scares or hard to obtain, ii) the data may be generated to help in avoiding bias and unfairness in generated data sets; real-world data can often be biased or unbalanced, leading to machine learning models that are biased or unfair, iii) the data may be used for enhancing privacy and security; real-world data often contains private or sensitive information that cannot be shared, and iv) the data may be used for reducing costs and avoiding penalty for non-compliance with data protection schemes (e.g. the General Data Protection Regulation (GDPR) in Europe). Synthetic data generation (SDG) is also useful for data alignment in the context of collaborative AI / ML or federated learning (FL). Some uses cases have been identified in the context of 3GPP Rei.19. SDG is useful for privacy and defence against attacks in case of collaborative AI / ML or FL (e.g. “anonymization” of data before sharing them with 3rd parties). SDG is useful for balancing data imbalances and also for providing better data diversity. In collaborative AI / ML or FL, some participants may have more data than others, leading to imbalanced contributions to the global model. Synthetic data can be used to balance the dataset, ensuring that all participants have an equal impact on model training. Participants might have limited and non-representative data. Synthetic data generation can help create diversity, ensuring that the federated model is more robust and generalizes well to various scenarios. SDG is useful for data augmentation, as the model may be more robust and perform better on new / rare scenarios. One or more of the following examples relates to synthetic data generation in 5G networks and 6G networks. It should be understood that the examples are equally applicable to other 3GPP standards such as 5G advanced, etc. One or more of the examples below aim to address one or more of the problems explicitly identified herein or otherwise apparent to the skilled person in the relevant art(s) from this disclosure. In some examples, there is provided a network entity (e.g., a network function), wherein the network entity is configured to perform: receiving, from a consumer (e.g., a service consumer and / or synthetic data consumer), at least one request associated with a first dataset; determining, based on the at least one request, first data that has been generated synthetically, wherein the first data is comprised in the first dataset; and providing, to the consumer, the first data. In the following examples, an ‘authorised consumer’ may be a consumer (or client) of a synthetic data generation function (SDGF), wherein the consumer has been previously identified and authorised to interact with the SDGF. The consumer may have been authorised using, for example, credentials, a token, a certificate, etc. A ‘service consumer’ may be a client of the synthetic data generation function (SDGF). A ‘service producer’ may be a service hosting the synthetic data generation function (SDGF). An ‘authorised consumer’ may be termed a ‘synthetic data consumer’. A ‘synthetic data consumer’ may be a user of the synthetic data. A ‘synthetic data producer’ may be a service that is able to generate synthetic data. In some examples, there is provided a network entity (or function) for generating data that is synthetic, or a network entity (or function) for data that is generated synthetically. The network entity / function may be referred to as a synthetic data generation function (SDGF), or any other suitable name. An SDGF may enable an authorised consumer to discover available synthetic data sets, characteristics of the data sets, statistical information, and request labelled or unlabelled synthetic data sets. A synthetic data set is a data set that comprises synthetic data. The SDGF may provide means for an authorized consumer to provide a smaller data set and receive an extended / larger data set with the same statistical characteristics of the smaller set in the request. In some examples, an SDGF exposes (or provides) synthetic data services and capabilities. The services may be consumed by authorized consumers (e.g. ML entities, ML based NFs). Data sets comprising synthetic data (which may be termed ‘synthetic data sets’) may be generated and provided to a consumer. In some examples, an SDGF may be an NF in the 5GC. An SDGF may be incorporated within an existing NF (e.g. an NWDAF). An SDGF may be another type of network entity. An SDGF may reside in the management plane. In some examples, the registration and discovery of synthetic data sets may be provided by a different network function from the network function that provides the synthetic data. For example, registration and discovery may be considered as a separate logical function within the SDGF from providing the synthetic data. In some examples, an authorized consumer is able to register and store synthetic data sets (or updates, or versions thereof). The stored synthetic data may then be discovered and requested by other authorized consumers. In some examples, an SDGF is able to represent different versions of synthetic data sets according to their statistical properties, features, processing performed, scope / context and / or date / time generated. In some examples, an authorized consumer is able to discover and be informed about information related to the available registered synthetic data sets in an SDGF. The information may comprise statistical properties of the synthetic data set, and / or a scope of the synthetic data set, e.g., geographical location, identities of cells (e.g. cell IDs), key performance indicators (KPIs) included, etc. In some examples, an SDGF represents available data sets in terms of their statistical properties and their scope (or context). In some examples, an SDGF registers and exposes available synthetic data sets in a repository, wherein the data sets may be represented by their statistical properties and / or scope. In some examples, an SDGF may register synthetic data sets into a repository where the SDGF may expose the data from that repository. In some examples, an authorized consumer is able to request and receive, from the SDGF, a synthetic data set according to a specified criteria, such as the statistical properties of the synthetic data set, or scope of the synthetic data set. In some examples, a consumer (e.g. an authorized management service (MnS) consumer or a 5GC consumer) is able to register, to a SDGF, the capability of the consumer as a synthetic data producer able to generate synthetic data sets. The capability may be for a specific type, for a specific use case (UC), or list of UCs, for specific geographical locations / cells, or according to a specific generation method (e.g. statistic, simulation based). In some examples, an SDGF registers a capability of a consumer that is able to generate synthetic data, which may then be exposed to potential synthetic data consumers. In some examples, an authorized consumer is able to discover and be informed about information related to the available registered synthetic data generation capabilities in the SDGF. The information may include information on the data which may be generated by the specific synthetic data generation capability. In some examples, an authorized consumer is able to request the generation of a synthetic data set using a synthetic data generation capability and according to a capability criteria provided by a synthetic data producer. In this manner, in some examples, (authorized) consumers / synthetic data consumers and (authorized) synthetic data producers are provided. The consumer may discover generated and registered datasets, and subsequently request / retrieve a generated and registered dataset using an identity (e.g., a "datasetObjectldentifier"). When a dataset generation is to be utilized (e.g., when the registered datasets are not suitable), the consumer first discovers registered synthetic data generation capabilities via a SDGF. Following this, the consumer requests the generation of a new dataset. A synthetic data producer may first register with the SDGF. The synthetic data producer may register available / generated synthetic datasets to the SDGF, and may update datasets that are already registered to SDGF. These examples will be described in more detail below (e.g. alongside FIGS. 5 to 16). Examples of networks and systems that may utilise synthetic data and may be suitable for synthetic data generation are described below (e.g. as seen in FIG. 1). In the following, various examples of this disclosure are explained with reference to communication devices capable of communication (e.g., configured to communicate) via a wireless cellular system and mobile communication systems serving such communication devices. Before explaining in detail the various examples, certain general aspects of a wireless communication system and communication devices are briefly explained with reference to FIGS. 1 to 4 to assist in understanding the technology underlying the described examples. FIG. 1 shows a schematic representation of a 5G communication system 100. The wireless communication system 100 comprises one more communication devices 102 such as user equipments (UEs), or terminals. The wireless communication system 100 comprises a 5G system (5GS). The 5GS comprises a 5G radio access network (5G-RAN) 106, a 5G core network (5GC) 104 comprising one or more network functions (NF), one or more application functions (AFs) 108, and one or more data networks (DNs) 110. The 5G-RAN 106 may comprise one or more gNodeB (gNB) distributed unit (DU) functions connected to one or more gNodeB (gNB) centralized unit (CU) functions. The 5GC 104 comprises an access and mobility management function (AMF) 112, a session management function (SMF) 114, an authentication server function (AUSF) 116, a user data management (UDM) 118, a user plane function (UPF) 120, a network exposure function (NEF) 122 and / or other NFs. Some of the examples as shown below may be applicable to 3GPP 5G standards. However, some examples may also be applicable to 5G-advanced, 4G, 3G and other 3GPP standards. In a wireless communication system 100, such as that shown in FIG. 1, communication devices 102, such as for example, terminals, user apparatuses, user equipments (UE), and / or machine-type communication devices are provided with wireless access via at least one base station or similar wireless transmitting and / or receiving node or point. The communication device 102 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 devices. The communication device 102 may access a carrier provided by a base station or access point, and transmit and / or receive communications on the carrier. FIG. 2 shows a schematic representation of an apparatus for the 5G communication system of FIG. 1. The apparatus 200 may be for controlling a function of one or more network entities and / or network functions, such as the entities of the 5G-RAN or the 5GC as illustrated on FIG. 1. The apparatus 200 comprises at least one random access memory (RAM) 211a, at least one read only memory (ROM) 211b, at least one processor 212, 213 and an input / output interface 214. The at least one processor 212, 213 is coupled to the RAM 211a and the ROM 211b. The at least one processor 212, 213 may be configured to execute an appropriate software code 215. The software code 215 may, for example, allow, enable, or otherwise facilitate performance of one or more steps to perform one or more of the aspects or examples disclosed herein. The software code 215 may be stored in the ROM 211 b. The apparatus 200 may be interconnected with another apparatus 200 controlling another entity / function of the 5G-AN or the 5GC. In some examples, each function of the 5G-AN or the 5GC comprises an apparatus 200. In alternative examples, two or more functions of the 5G-AN or the 5GC may share an apparatus 200. The apparatus 200 may comprise one or more circuits, or circuitry (not shown) which may be configured to perform one or more of the aspects or examples disclosed herein. FIG. 3 shows a schematic representation of a communication device for the 5G communication system of FIG. 1. The communication device 300 may be similar to the communication device 102 illustrated in FIG. 1. The communication device 300 may be provided by any device capable of sending and receiving (e.g., configured to send and receive) radio signals. Non-limiting and illustrative examples of a communication device 300 include a user equipment, a terminal, a mobile station (MS) or mobile device such as a mobile phone or what is known as a ’smart phone’, a computer provided with a wireless interface card or other wireless interface facility (e.g., USB dongle), a personal data assistant (PDA) or a tablet provided with wireless communication capabilities, a machine-type communications (MTC) device, a Cellular Internet of things (CloT) device, or a terrestrial / maritime / aerial vehicle such as a car, a truck, a boat, an air plane, or a drone, or any combinations of these or the like. The communication device 300 may provide, for example, communication of data for carrying communications. The communications may be one or more of voice, electronic mail (email), text message, multimedia, data, machine data and so on. The communication device 300 may receive signals over an air or radio interface 307 via appropriate apparatus for receiving and may transmit signals via appropriate apparatus for transmitting radio signals. In FIG. 3, a transceiver apparatus is designated schematically by block 306. The transceiver apparatus 306 may be provided for example by means of a radio part and associated antenna arrangement. The antenna arrangement may be arranged internally or externally to the mobile device. The communication device 300 may be provided with at least one processor 301, at least one memory ROM 302a, at least one RAM 302b and other possible components 303 for use in software and hardware aided execution of tasks it is designed to perform, including control of access to and communications with access systems and other communication devices. The at least one processor 301 is coupled to the RAM 302b and the ROM 302a. The at least one processor 301 may be configured to execute an appropriate software code 308. The software code 308 may, for example, allow, enable, or otherwise facilitate performance of one or more of the aspects or examples disclosed herein. The software code 308 may be stored in the ROM 302a. The communication device 300 may comprise one or more circuits, or circuitry (not shown) which may be configured to perform one or more of the aspects or examples disclosed herein. The processor, storage and other relevant control apparatus may be provided on an appropriate circuit board and / or in chipsets. This feature is denoted by reference 304. The communication device may optionally have a user interface such as keypad 305, touch sensitive screen or pad, combinations thereof or the like. Optionally one or more of a display, a speaker and a microphone may be provided depending on the type of the device. FIG. 4 shows a schematic representation of a network analytics architecture for a 5G communication system. There is provided a first NWDAF 401 and a second NWDAF 403. The first and second NWDAFs 401 and 403 have interfaces with an MFAF 405. The MFAF 405 also has an interface with a first DCCF 407. The first DCCF 407 has an interface with ADRFs 409 and a first NF 411. The first NWDAF 401 also has an interface with the ADRFs 409 and the first NF 411. The second NWDAF 403 has an interface with a second DCCF 413. The second DCCF 413 also has an interface with the ADRFs 409 as well as a second NF 415. The second NF 415 also has an interface with the first NWDAF 401. It should be understood that in other examples there may be more, or fewer than two NWDAFs, two DCCFs, or two NFs. The NWDAF 401,403 is a component of the analytics architecture which will harvest raw data, process the raw data and produce analytical data. ML trained models may be generated by the NWDAF and utilized by other NWDAFs in the network. The DCCF 407, 413 may be utilized when multiple NWDAFs are to use the same raw data (the input to the NWDAF). Moreover, multiple NFs may utilize the same analytics data (the output of the NWDAF). To avoid duplicate requests for the same data and hence avoid identical raw data or analytics data being generated, requests for data may be coordinated by the DCCF. The MFAF 405 is a messaging framework function by which analytics or event notifications (e.g., carrying raw data for the NWDAF to process) may be distributed around the network. The ADRF 409 instances may be deployed in the network to store raw data or associated analytics that have been performed on that data. The NWDAF, DCCF or MFAF may store, access and delete data and analytics using the ADRF (e.g., as required). These functions are considered to be part of the service based architecture of 5G and as such, the 3GPP have defined each of their Service Based Interfaces (SBIs). For example, the Nadrf SBI defines the format a request to store analytics data should follow. The Nnwdaf SBI defines the way in which an NF subscribes to the delivery of specific analytics data, amongst many other capabilities. Network analytics is particularly useful in 5G networks, 6G networks and beyond, by offering features such as, for example, self-healing, autonomous network optimization, predictive fault diagnosis and user experience optimization. FIG. 5 shows a schematic representation of a network entity that is able to provide data generation related services and data publication related services for synthetic data. There is provided a network entity (ora function) for generating synthetic data, herein referred to as an SDGF 501. The SDGF 501 may be able to function as an service producer 503 (also referred to as a management service (MnS) producer), in some examples. In other examples, the SDGF 501 is integrated with an NF that is able to function as a service producer 503. In some examples, the SDGF 501 resides in the management plane. The SDGF 501 may be an NF in the 5GC, in some examples. In other examples, the SDGF 501 is integrated with / incorporated within an existing NF (e.g., the existing NF may be an NWDAF). The SDGF 501 is able to communicate with consumers 503. In this context, the consumers 503 may be referred to as SDGF consumers. The consumers 503 are associated with data generation services. The consumers 503 are associated with the data generation services as the consumers 503 may request data sets comprising synthetic data, in additional to other services. The SDGF 501 is also able to communicate with producers 505. In this context, the producers 505 may be referred to as SDGF producers. The producers 505 are associated with data publication services. The producers 505 are associated with the data publication services as the producers 505 may generate data sets comprising synthetic data and provide the data sets to the SDGF 501 for storing. Signalling and operations involving the SDGF 501 for several different data generation related services and data publication related services are shown in more detail in FIGS. 6 to 14. In the following examples of FIGS. 6 to 14, signalling is shown between Service consumers and producers. In other examples, the Service consumers and Service producers may be 5GC NFs. For example, the SDGF may be a 5GC network function, or an NWDAF with an embedded SDGF. Furthermore, in the following examples of FIGS. 6 to 14, signalling is shown including a synthetic data consumer, which may be an NWDAF, a DCCF or MFAF. FIG. 6 shows an example signalling diagram for synthetic dataset registration. In FIG. 6 there is signalling between a synthetic set producer (e.g. a producer that is able to generate synthetic data) and an SDGF. The synthetic data producer is referred to as a ‘producer’ herein. The synthetic data producer may be considered a service consumer in some examples. The SDGF may be considered a service producer in some examples. It should be understood that in the examples herein, the terms ‘dataset’, ‘data set’ and ‘set of data’ may be used interchangeably. At S601, the producer provides, to the SDGF, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically. The request for registration of the dataset may be termed a ‘Register Synthetic Dataset Request’, or any other suitable message name. The request may comprise an identity of the producer (e.g., data producer ID). The request may comprise descriptive information of the data. The descriptive information of the data may include at least one of the following: a type of the data, an associated use case of the data, an identity of analytics (e.g. analytics ID), an indication of the whether the data is labelled or unlabelled, set size of the data, labels for the data, or metadata. The analytics ID corresponds to an ID for a use case, indicating a specific scenario (or purpose) to which the data is related, generated or utilized. The SDGF validates the request for registration provided by the producer. An identity of the data set (e.g., a data set ID, or unique identifier (UID)), the data producer ID, and the descriptive data are stored for the data set. This allows, enables, or otherwise facilitates the data set to be exposed at the SDGF to authorized consumers. At S602, the SDGF provides, to the producer, a response comprising the identity of the data set. The response may comprise an identity of a registration of the data set (in a repository). An example procedure (‘example T) associated with FIG. 6 is as follows: Example 1: i) A consumer provides, to a SDGF, a ‘Register Synthetic Dataset Request’ which may comprise (DataProducerlD, DataSetDescriptor, Metadata). ii) The SDGF provides, to the consumer, a ‘Register Synthetic Dataset response’ which may comprise (Registration!D, datasetObjectldentifier). Attributes related to ‘example T are shown in Tables 1 and 2 below. In the following 5 “S”=Support Qualifier, "M”=Mandatory, “O”=Optional, “CM”=Conditional mandatory: Register Synthetic Dataset Request Attribute Name S Information Type / Legal Value Description dataProducerlD M String UID of the producer of the data set datasetDescriptor M Json = { datasetName, dataType[lnt, Float,String], dataRepresentation[timeSeries, tuples, triples] } Data descriptor with main information about the data set. Provides an initial set of information about the data needed for the discovery process. Allows the consumers to request the correct synthetic dataset for their need. metadata M Json = { Processing Done [Labelled, Clean, Quantized], relevantUC / Analyticsl D, geographicalArea / CelllDs } Provides further detailed information on the dataset including processing done on the set, relevant geographical areas, cell IDs, analyticsID, etc. Data 0 String Data set provided by the data producer Table 1: Attributes related to ‘example T for inclusion in a register synthetic dataset request message. Register Synthetic Dataset Response Attribute Name III Information Type / Legal Value Description registration! D M String ID of the successful registration of the dataset at the SDGF datasetObjectldentifier M String UID of the registered data set at the SDGF Table 2: Attributes related to ‘example T for inclusion in a register synthetic dataset response message. FIG. 7 shows an example signalling diagram for a synthetic dataset update. In FIG. 7 there is signalling between a synthetic data producer (e.g. a producer that is able to generate synthetic data) and an SDGF. The synthetic data producer is referred to as a ‘producer’ herein. The synthetic data producer may be considered a service consumer in some examples. The SDGF may be considered a service producer in some examples. At S701, the producer provides, to the SDGF, a request for updating a dataset (that is has been stored / previously stored). The request may comprise at least one of the following: an identity of the dataset to be updated, an identity of the producer, descriptive information of the data (that may have been updated), or metadata (that may have been updated). At S702, based on the request for updating, the SDGF causes the dataset to be updated. For example, the SDGF causes the descriptive information of the data to be updated at a repository where the dataset is stored. At S703, the SDGF provides, to the producer, a response / notification indicating that the update has been performed (or that the update is successful). An example procedure (‘example 2’) associated with FIG. 7 is as follows: Example 2: i) A consumer provides, to an SDGF, a ‘Synthetic Dataset Update Request’ which may comprise (DataProducerlD, DataSetDescriptor, Metadata). ii) The SDGF performs a Synthetic Dataset Update. iii) The SDGF provides, to a consumer, a ‘Synthetic Dataset Update Notification’. Attributes related to ‘example 2’ are shown in Table 3 below: Synthetic Dataset Update Request: Attribute Name S Information Type / Legal Value Description dataProducerlD M String UID of the producer of the data set datasetObjectldentifier M String UID of the registered data set at the SDGF datasetDescriptor CM* Json = { datasetName, dataType[lnt, Float,String], Data descriptor with main information about the data set. dataRepresentation [timeSeries, tuples, triples] } Provides an initial set of information about the data needed for the discovery process. Allows the consumers to request the correct synthetic dataset for their need. Metadata CM* Json = { ProcessingDone [Labelled, Clean, Quantized], relevantUC / Analyticsl D, geographicalArea / CelllDs } Provides further detailed information on the dataset including processing done on the set. Data CM* String Data set provided by the data producer Table 3: Attributes related to ‘example 2’ for inclusion in a synthetic dataset update request message. Attributes marked with an asterisk (*) indicate that at least one of these attributes are to be included in the message. FIG. 8 shows an example signalling diagram for synthetic dataset discovery. In FIG. 8 there is signalling between a synthetic data consumer (e.g. a consumer that is able to consume synthetic data) and an SDGF. The synthetic data consumer is referred to as a ‘consumer’ herein. The synthetic data consumer may be considered a service consumer in some examples. The SDGF may be considered a service producer in some examples. At S801, the consumer provides, to the SDGF, a request for information related to datasets comprising data that has been generated synthetically. In this manner, an authorized synthetic data consumer requests information about the available registered synthetic datasets in the SDGF. The request may comprise at least one attribute for synthetic data of the dataset (e.g. as seen in Table 4 below). At S802, the SDGF retrieves, based on the request, information related to at least one dataset that has been stored / registered at a repository. In some examples, a plurality of datasets (relevant to the request) are stored at the repository. In other examples, one or zero datasets are stored at the repository. The retrieving by the SDGF may comprise retrieving datasets that match information comprised within the request. At S803, the SDGF provides, to the consumer, the information related to the at least one dataset. The information may comprise an identity of each dataset of the at least one dataset. The information may comprise at least one of: statistical properties of the data set, or a scope of the data set. In this manner, the SDGF receives a request (e.g., S801), retrieves related information from the data repository (e.g., S802), and shares a response (e.g., S803) with the consumer 5 comprising, in some examples: statistical properties of the data set, and a scope of the data set. Examples of the scope of the data include: a geographical location, identities of cells, key performance indicators that have been included. An example procedure (‘example 3’) associated with FIG. 8 is as follows: 10 Example 3: i) A consumer provides, to an SDGF, a ‘Synthetic Datasets Information Request’ which may comprise (dataConsumerlD, datasetDescriptor, metaData). ii) The SDGF performs an information retrieval. iii) The SDGF provides, to the consumer, a ‘Synthetic Datasets Information Response’ 15 (datasetObjectldentifierList). Attributes related to ‘example 3’ are as shown in Tables 4 and 5 below: Synthetic Dataset Discovery Request: Attribute Name S Information Type / Legal Value Description dataConsumerlD M String UID of the consumer requesting the data set datasetDescriptor 0 Json = { datasetName, descript[lnt, Float,String], dataRepresentation[timeSeries, tuples, triples] } Data descriptor with main information about the data set. Provides an initial set of information about the requested data (filter). metadata 0 Json = { ProcessingDone [Labelled, Clean, Quantized], relevantUC / Analyticsl D, geographicalArea / CelllDs } Optional field providing further detailed information on the dataset including processing statistical properties of the data set, scope, geographical areas, cell IDs, KPIs, etc. (filter). Table 4: Attributes related to ‘example 3’ for inclusion in a synthetic dataset discovery request message. In some examples, the consumer may leave some fields in datasetDescriptor or metadata empty indicating that there is no paritcular preference for that field value. 5 Synthetic Dataset Discovery Response: Attribute Name S Information Type / Legal Value Description datasetObjectldentifierList M String[O..*] A list of UI Ds of the data sets matching the consumer request description and metadata datasetDescriptorList 0 Json[0..*] = { datasetName, dataType[lnt, Float,String], dataRepresentation [timeSeries, tuples, triples] }[0.‘] Data descriptor with main information about the data set. Provides an initial set of information about the requested data. metadataList 0 Json[0..*] = { ProcessingDone [Labelled, Clean, Quantized], relevantUC / Analyticsl D, geographicalArea / CelllDs }[0.*] Optional field providing further detailed information on the dataset including processing statistical properties of the data set, scope, geographical areas, cell IDs, KPIs, etc. Table 5: Attributes related to ‘example 3’ for inclusion in a synthetic dataset discovery response message. FIG. 9 shows an example signalling diagram for a synthetic dataset request. In FIG. 9 10 there is signalling between a synthetic data consumer (e.g. a consumer that is able to consume synthetic data) and an SDGF. The synthetic data consumer is referred to as a ‘consumer’ herein. The synthetic data consumer may be considered a service consumer in some examples. The SDGF may be considered a service producer in some examples. At S901, the consumer provides, to the SDGF, a request associated with a dataset. 15 The request may be for the dataset which is stored at (or stored at a location accessible by) the SDGF. The request may comprise an identity of the dataset. At S902, the SDGF determines, based on the request, data of the first dataset, the data comprising data that has been generated synthetically. The determining may comprise retrieving the data from a repository. The SDGF may use the identity of the dataset to retrieve the data. The SDGF may also determine / retrieve information related to the dataset. In this context, ‘the data of the first dataset’ means, ‘data that is comprised in the first dataset’, or ‘data that is part of the first dataset'. These terms may be used interchangeably. At S903, the SDGF provides, to the consumer, the data of the dataset. In this manner, an authorized consumer is able to request (e.g., S901) a synthetic data set registered at the SDGF. The consumer provides the identity of the dataset / unique identifier (UID) of the data set in the request. The SDGF retrieves or extracts (e.g., S902) the requested data set from the repository, and then sends (e.g., S903) a response to the consumer containing the required data and associated descriptive information of the dataset. An example procedure (‘example 4’) associated with FIG. 9 is as follows: Example 4: i) A consumer provides, to a SDGF, a ‘Synthetic Dataset Request’ which may comprise (dataConsumerlD, datasetObjectldentifier). ii) The SDGF performs a Data Extraction based on (datasetObjectldentifier). iii) The SDGF provides, to the consumer, a ‘Synthetic Dataset Response which may comprise (Data, datasetDescriptor, metadata). Attributes related to ‘example 4’ are as shown in Tables 6 and 7 below: Synthetic Dataset Request: Attribute Name HI Information Type / Legal Value Description dataConsumerlD M String UID of the consumer requesting the data set datasetObjectldentifier M String UID of the registered data needed by the consumer Table 6: Attributes related to ‘example 4’ for inclusion in a synthetic dataset request response message. Synthetic Dataset Response: Attribute Name S Information Type / Legal Value Description Data M String Data set requested by the Consumer datasetDescriptor O Json = { datasetName, dataT ype[l nt, Float, String], dataRepresentation [timeSeries, tuples, triples] } Data descriptor with main information about the data set. Provides an initial set of information about the requested data. Metadata O Json = { Processing Done [Labelled, Clean, Quantized], relevantUC / Analyticsl D, geographicalArea / CelllDs } Optional field providing further detailed information on the dataset including processing statistical properties of the data set, scope, geographical areas, cell IDs, KPIs, etc. Table 7: Attributes related to ‘example 4’ for inclusion in a synthetic dataset request response message. FIG. 10 shows an example signalling diagram for a synthetic dataset capability request. In FIG. 10 there is signalling between a synthetic data consumer (e.g. a consumer that is able to consume synthetic data) and an SDGF. The synthetic data consumer is referred to as a ‘consumer’ herein. The synthetic data consumer may be considered a service consumer in some examples. The SDGF may be considered a service producer in some examples. Two options are possible for synthetic data generation requests, depending on a producer registration mode, which may either be single or multiple synthetic data generation capabilities. FIG. 10 depicts the single synthetic data generation capability, while FIG. 11 depicts the multiple synthetic data generation capability. At S1001, the consumer provides, to the SDGF, a request for the generation of a dataset. The request may be according to a capability and / or at least one criteria associated with data. The capability may be a synthetic data generation capability exposed by the SDGF. The request may comprise at least one attribute for synthetic data to be included in the dataset (e.g. as seen in Table 8 below). At S1002, in a first alternative, based on the request, the SDGF generates the dataset, wherein the dataset comprises data that is generated synthetically (by the SDGF). In some examples, the SDGF determines whether the SDGF is capable of generating (e.g., configured to generate) the dataset of the request. When the SDGF determines that the SDGF is capable (e.g., based on the SDGF determining the SDGF is configured to generate the dataset of the request), the SDGF generates the dataset. At S1003, in a second alternative, the SDGF provides, to a synthetic data producer, a further request for the generation of the dataset. The further request may comprise at least one attribute for synthetic data to be included in the dataset (e.g. as seen in Table 10 below). At S1004, based on the further request, the synthetic data producer generates synthetic data for the dataset. At S1005, the synthetic data producer provides, to the SDGF, data of the dataset, wherein the data comprises data that has been generated synthetically by the synthetic data producer. S1002 is of the first alternative of FIG. 10, while S1003, S1004, S1005 are of the second alternative of FIG. 10. In this manner, the dataset is either generated by the SDGF, or by an external producer to the SDGF. At S1006, the SDGF provides, to the consumer, a response comprising an identity of the dataset (that has been generated). The identity of the dataset may be a UID. In this manner, an authorized consumer requests (e.g., S1001) the generation of a synthetic dataset using one of the synthetic data generation capabilities exposed by the SDGF. Dependent on the requested generation criteria, the SDGF either: generates (e.g., S1002) synthetic data (internally) and sends the UID of the generated dataset to the synthetic data consumer (e.g., S1006); or requests (e.g., S1003) data generation from a registered synthetic data producer that sends (e.g., S1005) the SDGF the generated dataset. The SDGF registers the generated dataset then, sends (e.g., S1006) the UID to the consumer. Two options are possible depending on the producer registration mode: single or multiple synthetic data generation capabilities. An example procedure (‘example 5’) associated with FIG. 10 is as follows: Example 5: i) A consumer provides, to a SDGF, a ‘Synthetic Dataset Generation Request’ which may comprise (dataConsumerlD, dataGenerationCapabilitylD, dataScope, selectedUC, dataGenerationModel, dataAttributesList,dateRange, timeGranularity, dataSize). In a first alternative {}: ii) {The SDGF performs a Data Generation (according to the ‘Synthetic Dataset Generation Request’).} In a second alternative {}: ii){The SDGF provides, to a producer, a ‘Synthetic Dataset Producer Request’ which may comprise (dataProducerlD, dataScope, selectedUC, dataGenerationModel, dataAttributesList, dateRange, timeGranularity, dataSize). The producer performs a Data Generation (according to the ‘Synthetic Dataset Generation Request’). 5 The producer provides, to the SDGF, a ‘Synthetic Dataset Producer Response’ which may comprise (Data, datasetDescriptor, metadata).} iii) The SDGF provides, to the consumer, a ‘Synthetic Dataset Generation Response’ which may comprise (datasetObjectldentifier). 10 Attributes related to ‘example 5’ are as shown in Tables 8 to 11 below: Synthetic Data Generation Request: Attribute Name S Information Type / Legal Value Description dataConsumerlD M String UID of the consumer requesting the data set dataGenerationCapabilityl D M String UID of the data generation published capability dataScope or dataContext M string Data generation scope / context (e.g. geographical location) selectedllC M string Selected UC dataGenerationModel 0 string Generation model / algorithm dataAttributesList 0 String[O..*] Selected attributes (features and KPIs) dateRange 0 [Int, Int] in ms Date range timeGranularity 0 Int in s Time interval between data rows dataSize 0 Int Data set size (number of rows) Table 8: Attributes related to ‘example 5’ for inclusion in a synthetic dataset generation requesi message. Synthetic Data Generation Response: Attribute Name S Information Type 1 Legal Value Description datasetObjectldentifier M String UID of the generated data set Table 9: Attributes related to ‘example 5’ for inclusion in a synthetic dataset generation response message. Synthetic Data Producer Request: Attribute Name S Information Type / Legal Value Description dataProducerlD M String UID of the producer requesting the data set dataScope or dataContext M string Data generation scope / context (e.g. geographical location) selectedllC M string Selected UC dataGenerationModel 0 string Generation model / algorithm dataAttributesList 0 String[O..*] Selected attributes (features and KPIs) dateRange 0 [I nt, I nt] in ms Date range timeGranularity 0 Int in s Time interval between data rows dataSize 0 I nt Data set size Table 10: Attributes related to ‘example 5’ for inclusion in a synthetic dataset producer request message. Synthetic Data Producer Response: Attribute Name S Information Type / Legal Value Description Data M String Data set requested by the Consumer datasetDescriptor 0 Json = { datasetName, dataT ype[l nt, Float, String], dataRepresentation[timeSeries, tuples, triples] } Data discriptor with main information about the data set. Provides an initial set of information about the requested data. Metadata 0 Json = { ProcessingDone [Labelled, Clean, Quantized], relevantUC / Analyticsl D, geographicalArea / CelllDs Optional field providing further detailed information on the dataset including processing statistical properties of the data set, scope, geographical areas, cell IDs, KPIs, etc. Table 11: Attributes rela ted to ‘example 5’ for inclusion in a synthetic dataset producer response message. FIG. 11 shows another example signalling diagram for a synthetic dataset capability request. As discussed herein, FIG. 10 depicts the single synthetic data generation capability, while FIG. 11 depicts the multiple synthetic data generation capability. In FIG. 11 there is signalling between a synthetic data consumer (e.g. a consumer that is able to consume synthetic data) and an SDGF. The synthetic data consumer is referred to as a ‘consumer’ herein. The synthetic data consumer may be considered a service consumer in some examples. The SDGF may be considered a service producer in some examples. At S1101, the consumer provides, to the SDGF, a request for the generation of a dataset. The request may be according to capabilities and / or at least one criteria associated with data. The capabilities may be a synthetic data generation capabilities exposed by the SDGF. The request may comprise at least one attribute for synthetic data to be included in the dataset (e.g. as seen in Table 12 below). At S1102, in a first alternative, based on the request, the SDGF generates the dataset, wherein the dataset comprises data that is generated synthetically (by the SDGF). In some examples, the SDGF determines whether the SDGF is capable of generating (e.g., configured to generate) the dataset of the request. When the SDGF determines that the SDGF is capable (e.g., based on the SDGF determining the SDGF is configured to generate the dataset of the request), the SDGF generates the dataset. At S1103, in a second alternative, the SDGF provides, to a synthetic data producer, a further request for the generation of the dataset. The further request may comprise at least one attribute for synthetic data to be included in the dataset (e.g. as seen in Table 14 below). At S1104, based on the further request, the synthetic data producer generates synthetic data for the dataset. At S1105, the synthetic data producer provides, to the SDGF, data of the dataset, wherein the data comprises data that has been generated synthetically by the synthetic data producer. S1102 is of the first alternative of FIG. 11, while S1103, S1104, S1105 are of the second alternative of FIG. 11. In this manner, the dataset is either generated by the SDGF, or by an external producer to the SDGF. At S1106, the SDGF provides, to the consumer, a response comprising an identity of the dataset (that has been generated). The identity of the dataset may be a UID. In this manner, an authorized consumer requests (e.g., S1101) the generation of a synthetic dataset using one of the synthetic data generation capabilities exposed by the SDGF. Dependent on the requested generation capabilities / criteria, the SDGF either: generates (e.g., S1102) synthetic data (internally) and sends the UID of the generated dataset to the synthetic data consumer (e.g., S1106); or requests (e.g., S1103) data generation from a registered synthetic data producer that sends (e.g., S1105) the SDGF the generated dataset. The SDGF registers the generated dataset then, sends (e.g., S1106) the UID to the consumer. Two options are possible depending on the producer registration mode: single or multiple synthetic data generation capabilities. An example procedure (‘example 6’) associated with FIG. 11 is as follows: Example 6: i) A consumer provides, to a SDGF, a ‘Synthetic Dataset Generation Request’ which may comprise (dataConsumerlD, dataGenerationCapabilitiesProducerlD, capabilityName, dataScope, selectedUC, dataGenerationModel, dataAttributesList,dateRange, timeGranularity, dataSize). In a first alternative {}: ii) {The SDGF performs a Data Generation (according to the ‘Synthetic Dataset Generation Request’)} In a second alternative {}: ii) {The SDGF provides, to a producer, a ‘Synthetic Dataset Producer Request’ which may comprise (dataProducerlD, capabilityName, dataScope, selectedUC, dataGenerationModel, dataAttributesList, dateRange, timeGranularity, dataSize). The producer performs a Data Generation (according to the ‘Synthetic Dataset Generation Request’). The producer provides, to the SDGF, a ‘Synthetic Dataset Producer Response’ which may comprise (Data, datasetDescriptor, metadata).} iii) The SDGF provides, to the consumer, a ‘Synthetic Dataset Generation Response’ which may comprise (datasetObjectldentifier). Attributes related to ‘example 6’ are as shown in Tables 12 to 15 below: Synthetic Data Generation Request: Attribute Name S Information Type / Legal Value Description dataConsumerlD M String UID of the consumer requesting the data set dataGeneration CapabilitiesProducerlD M String UID of the data generation published capabilities of the producer capabilityName M String Capability Name dataScope or dataContext M string Data generation scope / context (e.g. geographical location) selectedllC M string Selected UC dataGenerationModel 0 string Generation model / algorithm dataAttributesList 0 String[O..*] Selected attributes (features and KPIs) dateRange 0 [Int, Int] in ms Date range timeGranularity 0 Int in s Time interval between data rows dataSize 0 Int Data set size (number of rows) Table 12: Attributes related to ‘example 6’ for inclusion in a synthetic data generation reques message. Synthetic Data Generation Response: Attribute Name S Information Type / Legal Value Description datasetObjectldentifier M String UID of the generated data set Table 13: Attributes related to ‘example 6’ for inclusion in a synt hetic data generation response message. Synthetic Data Producer Request: Attribute Name S Information Type / Legal Value Description dataProducerlD M String UID of the producer requesting the data set capabilityName M String Capability Name dataScope or dataContext M string Data generation scope / context (e.g. geographical location) selectedUC M string Selected UC dataGenerationModel 0 string Generation model / algorithm dataAttributesList 0 String[O..*] Selected attributes (features and KPIs) dateRange 0 [Int, Int] in ms Date range timeGranularity 0 Int in s Time interval between data rows dataSize 0 Int Data set size Table 14: Attributes related to ‘example 6’ for inclusion in a synthetic data producer request 10 message. Synthetic Data Producer Response: Attribute Name S Information Value Type / Legal Description Data M String Data set requested by the Consumer datasetDescriptor 0 Json = { datasetName, dataType[lnt, Float,String], dataRepresentation[timeSeries, tuples, triples] Data descriptor with main information about the data set. Provides an initial set of information about the requested data. Metadata 0 Json = { ProcessingDone [Labelled, Clean, Quantized], relevantUC / Analyticsl D, geographicalArea / CelllDs Optional field providing further detailed information on the dataset including processing statistical properties of the data set, scope, geographical areas, cell IDs, KPIs, etc. Table 15: Attributes related to ‘example 6’ for inclusion in a synthetic data producer response message. 5 FIG. 12 shows an example signalling diagram for synthetic data generation capability registration. In FIG. 12 there is signalling between a synthetic data producer (e.g. a producer that is able to generate synthetic data) and an SDGF. The synthetic data producer is referred to as a ‘producer’ herein. The synthetic data producer may be considered a service consumer 10 in some examples. The SDGF may be considered a service producer in some examples. A producer may have a single synthetic data generation capability, or multiple. FIG. 12 depicts the single capability, while FIG. 13 depicts the multiple capabilities. At S1201, the producer provides, to the SDGF, a request for registration for the producer. The request for registration comprises an identity of the producer. The request for 15 registration may comprise information related to the synthetic data generation capability of the producer. Examples of information related to the capability is shown in Table 16, under the attribute ‘dataGenerationCapabilityDescriptor’. The SDGF registers the capability of the producer. This allows, enables, or otherwise facilitates the SDGF to expose the capability of the producer to authorised consumer when requested to do so. At S1202, the SDGF provides, to the producer, a response indicating that the capability 5 of the producer has been registered. The response may comprise an identity of the capability that has been registered. An example procedure (‘example 7’) associated with FIG. 12 is as follows: 10 Example 7: i) A consumer provides, to an SDGF, a ‘Synthetic Data Generation capability Registration Request’ which may comprise (dataProducerlD, dataGenerationCapabilityDescriptor, metadata). ii) The SDGF provides, to the consumer, a ‘Synthetic Dataset Producer Registration 15 Response’ which may comprise (dataGenerationCapabilitylD). Attributes related to ‘example 7’ are as shown in Tables 16 and 17 below: Synthetic Data Generation Capability Registration Request: Attribute Name S Information Type / Legal Value Description dataProducerlD M String UID of the producer dataGeneration CapabilityDescriptor M Json ={ CapabilityName, dataScope, UCs, dataGenerationModel, dataAttributes, dateRange, timeGranularity, dataSize } Data generation capability description metadata 0 Json = { relevantUC / Analyticsl D, geographicalArea } Optional field providing further detailed information on the generation capabilities geographical areas, cell IDs, KPIs, etc. Table 16: Attributes related to ‘example 7’ for inclusion in a synthetic data generation capability registration request message. Synthetic Dataset Producer Registration Response: Attribute Name S Information Type 1 Legal Value Description dataGenerationCapabilityl D M String UID of the data generation published capability Table 17: Attributes related to ‘example 7’ for inclusion in a synthetic data generation capability registration response message. FIG. 13 shows another example signalling diagram for synthetic data generation capability registration. As discussed herein, FIG. 12 depicts a single capability for data generation by a producer, while FIG. 13 depicts multiple capabilities for data generation by a producer. In FIG. 13 there is signalling between a synthetic data producer (e.g. a producer that is able to generate synthetic data) and an SDGF. The synthetic data producer is referred to as a ‘producer’ herein. The synthetic data producer may be considered a service consumer in some examples. The SDGF may be considered a service producer in some examples. At S1301, the producer provides, to the SDGF, a request for registration for the producer. The request for registration comprises an identity of the producer. The request for registration may comprise information related to the synthetic data generation capabilities of the producer. Examples of information related to the capabilities is shown in Table 18, under the attribute ‘dataGenerationCapabilityDescriptorList’. The SDGF registers the capabilities of the producer. This allows, enables, or otherwise facilitates the SDGF to expose the capabilities of the producer to authorised consumers when requested to do so. At S1302, the SDGF provides, to the producer, a response indicating that the producer has been registered. The response may comprise an identity of the capability / capabilities that have been registered. An example procedure (‘example 8’) associated with FIG. 13 is as follows: Example 8: i) The consumer provides, to an SDGF, a ‘Synthetic Data Generation capability Registration Request’ that may comprise (dataProducerlD, dataGenerationCapabilityDescriptorList, metadata). ii) The SDGF provides, to the consumer, a ‘Synthetic Dataset Producer Registration Response’ which may comprise (dataGenerationCapabilitiesProducerlD). Attributes related to ‘example 8’ are as shown in Tables 18 and 19 below: Synthetic Data Generation Capability Registration Request: Attribute Name S Information Type / Legal Value Description dataProducerlD M String UID of the producer requesting the data set dataGenerationCapabilityDescriptorList M Json[1..*]={ capabilityName, dataScope, UCs, dataGenerationModel, dataAttributes, dateRange, timeG ranulari ty, dataSize }[1..*] Data generation capability description metadata M Json[0. *] = { relevantUC / Analyticsl D, geographicalArea }[0.*] Optional field providing further detailed information on the generation capabilities geographical areas, cell IDs, KPIs, etc. Table 18: Attributes related to ‘example 8’ for inclusion in a synthetic data generation capability registration request message. 10 Synthetic Dataset Producer Registration Response: Attribute Name S Information Type / Legal Value Description dataGenerationCapabilitiesProducerlD M String UID of the data generation published capabilities of the producer Table 18: Attributes related to ‘example 8’ for inclusion in a synthetic data generation capability registration response message. FIG. 14 shows an example signalling diagram for synthetic data generation capability discovery. In FIG. 14 there is signalling between a synthetic data consumer (e.g. a consumer that is able to consume synthetic data) and an SDGF. The synthetic data consumer is referred to as a ‘consumer’ herein. The synthetic data consumer may be considered a service consumer in some examples. The SDGF may be considered a service producer in some examples. At S1401, the consumer provides, to the SDGF, a request for discovery of capabilities associated with synthetic data generation. The request for discovery may comprise information including at least one descriptor associated with synthetic data generation capabilities. Examples of the at least one descriptor are shown in Table 19 under the attribute ‘dataGenerationCapabilityDescriptorList’. At S1402, based on the request, the SDGF provides, to the consumer, a response comprising information related to data that can be generated according to the at least one capability. In this manner, an authorized consumer discovers the registered synthetic data generation capabilities in the SDGF. The SDGF sends a response comprising information on the data which can be generated by the synthetic data generation capability, including at least one of: specific type of data, the specific use case (UC) I list of UCs, the specific geographical locations / cells or the specific generation method (e.g. statistic, simulation based) that is applicable to the synthetic data generation capability. An example procedure (‘example 9’) associated with FIG. 14 is as follows: Example 9: i) A consumer provides, to an SDGF, a ‘Synthetic Data Generation Capability Discovery Request’ which may comprise (dataConsumerlD, dataGenerationCapabilityDescriptor, metadata). ii) The SDGF provides, to the consumer, a ‘Synthetic Data Generation Capability Discovery Response’ which may comprise (dataGenerationCapabilityList, dataGenerationCapabilityDescriptorList, metadataList). 5 Attributes related to ‘example 9’ are as shown in Tables 19 and 20 below: Synthetic Data generation capability Discovery Request: Attribute Name S Information Type / Legal Value Description dataConsumerlD M String UID of the consumer requesting the data generation dataGenerationCapabilityDescriptorList 0 Json[0..*]={ dataScope, UCs, dataGenerationModel, dataAttributes, dateRange, timeG ranulari ty, dataSize } Data generation capability description (Filter) metadata 0 Json[0. *] = { relevantUC / Analyticsl D, geographicalArea }[0.*] Optional field providing further detailed information on the generation capabilities geographical areas, cell IDs, KPIs, etc. Table 19: Attributes related to ‘examples’ for inclusion in a synthetic data generation capability discovery request message. In some examples, the consumer may leave some fields in datasetDescriptor or metadata empty. 5 Synthetic Data generation capability Discovery Response: Attribute Name S Information Type / Legal Value Description dataGenerationCapabilityl DList M String[O..*] List UI Ds of the data generation published capability / ies dataGenerationCapabilityDescriptorList 0 Json[0..*]={ CapabilityName, dataScope, UCs, dataGenerationModel, dataAttributes, dateRange, timeG ranulari ty, dataSize }[0.*] List of Data generation capabilities description metadataList 0 Json[0..*] = { relevantUC / Analyticsl D, geographicalArea }[0.*] Information on the generation capabilities geographical areas, UCs, KPIs, etc. Table 20: Attributes related to ‘example 9’ for inclusion in a synthetic data generation capability discovery response message. In some examples, service management and orchestration (SMO) I non real-time (non-10 RT) RAN intelligent controller (RIC) framework shall support functionality to allow (service) consumers to register, update and delete synthetic data and associated descriptive information. In some examples, the SMO / non-RT RIC framework shall support functionality to allow (service) consumers to discover registered synthetic data sets. In some examples, the SMO I Non-RT RIC framework shall support functionality to allow service consumers to request registered synthetic data sets. In some examples, the SMO / Non-RT RIC framework shall support functionality to allow (service) consumers to register, update and delete their synthetic data generation capability and associated descriptive information. In some examples, the SMO / Non-RT RIC framework shall support functionality to allow (service) consumers to discover registered synthetic data generation capabilities and associated descriptive information. In some examples, the SMO / Non-RT RIC framework shall support functionality to allow (service) consumers to request the generation of synthetic data sets according to specified characteristics. Synthetic data management services, including data management and exposure services may enable at least one of the following capabilities: synthetic data registration, update, deletion; synthetic data discovery; synthetic data request; synthetic data generation capability registration, update, deletion; synthetic data generation capability discovery; or synthetic data generation request. In some examples, synthetic data generation functions (e.g. SDGF) are logical functions that produce synthetic data generation services and capabilities to allow authorized service consumers to request the generation of synthetic data according to specified characteristics by the consumer. In some examples, synthetic data for AI / ML training, testing and inference may be a example use case. A fully functional ML based solution uses a specifically prepared set of data collected by the operator in the scope that is relevant to where the ML based solution will be deployed. Typically, this data set is to be real-world data to guarantee training of the ML based solution on real-world data before deployment. Real-world data is challenging because of the lack of labelled data, bias, incompleteness, lack of variety, cost and privacy / security constraints. Moreover, the operator may not want to share the data sets collected in the network with a 3rd party solution provider due to NDA agreements with RAN vendors covering performance KPIs. In such scenarios, synthetic data is to be provided to the training functions to ensure the training can still be achieved. The expectation is that although artificial, Synthetic data should statistically mimic the patterns and characteristics of real-world data. This presents a use for network management systems to provide mechanisms to allow, enable, or otherwise facilitate ML training while overcoming the limitations and challenges of real-world data., e.g., for network management systems to have mechanisms to generate synthetic, including means to allow, enable, or otherwise facilitate the users of such synthetic data to define the characteristics and features of the generated synthetic data. Some potential features to be standardised include: REQ-SynthData-1 - The 3GPP management system should have a capability of allowing, enabling, or otherwise facilitating an authorized consumer to register and store synthetic data sets or updates or versions thereof, which can then be discovered and requested for by other authorized consumers. REQ-SynthData-2 - The 3GPP management system should have a capability of allowing, enabling, or otherwise facilitating an authorized consumer to discover and be informed about the available registered synthetic data sets in the SDGF, the information including statistical properties of the synthetic data set, and scope, such geographical location, cell IDs, KPIs included, etc. REQ-SynthData-3 - The 3GPP management system should have a capability of allowing, enabling, or otherwise facilitating an authorized consumer to request and receive from the SDGF Service producer a synthetic data set according to a specified criterion, such as the statistical properties of the synthetic data set, or its scope, including geographical location, cell IDs, KPIs included, etc. REQ-SynthData-4 - The 3GPP management system should have a capability of allowing, enabling, or otherwise facilitating an authorized Service consumer to register to the SDGF Service consumer’s capability as a synthetic data producer able to generate synthetic data sets of a specific type, for a specific UC / list of UCs, for specific geographical locations / cells or according to a specific generation method (e.g. statistic, simulation based). REQ-SynthData-5 - The 3GPP management system should have a capability of allowing, enabling, or otherwise facilitating an authorized consumer to discover and be informed about the available registered Synthetic data generation capabilities in the SDGF, the information including information on the data which can be generated by the specific Synthetic data generation capability, including specific type of data, the specific UC I list of UCs, the specific geographical locations / cells or the specific generation method (e.g. statistic, simulation based) that is applicable to the Synthetic data generation capability. REQ-SynthData-6 - The 3GPP management system should have a capability of allowing, enabling, or otherwise facilitating an authorized consumer to request the generation of a synthetic data set using one of the synthetic data generation capabilities and according to the capability criteria provided by the synthetic data producer. In some examples, there is an introduction of an information object class (IOC) for a synthetic data generation function, which may be called the SyntheticDataProducer. The SyntheticDataProducer may have one or more external data producers, so has an attribute for external data producers, which are themselves SyntheticDataProducers. So the SyntheticDataProducer may have a recursive relationship. An SDGF registers the capabilities of these external Synthetic data producers which can be exposed to potential Synthetic data Service consumers. In some examples, there is an introduction of a datatype for identifying synthetic data instances, which may be called SyntheticData. This allows, enables, or otherwise facilitates representation of different versions of synthetic data sets according to their statistical properties, features, processing performed, scope / context and date / time generated. As such, the SDGF may expose available synthetic data sets in a repository where the data sets can be represented by their statistical properties and scope (e.g., including geographical location, cell IDs, KPIs included, etc.) In some examples, there is an introduction of an IOC for a request to generate and deliver synthetic data. It may be an extension of a generic request to deliver data, which may be called the DataDeliveryRequest. It includes a description of the data that is requested in terms of their statistical properties and their scope / context. In some examples, there is an introduction of an IOC for a request to register data, which may be called syntheticDataRegistration In some examples, there is an introduction of an IOC for the delivery of data, which may be called DataDelivery. The data delivery may be used when requested by a service consumer or when the data is being delivered to the SDGF by some external data producer. The SyntheticDataProducer, as discussed herein, may be capable of data compression (e.g., configured to compress data) and may be modelled as depicted in FIGS. 15 and 16. FIG. 15 shows a schematic representation of an information model for synthetic data generation. There is provided an information object class (IOC) for a synthetic data generation function, which is called SyntheticDataProducer 1503. There is provided a proxy class for a managed entity, which is called ManagedEntity 1501. The SyntheticDataProducer 1503 is a managed object name-contained in either a subnetwork, a ManagedFunction or a ManagementFunction. The ManagedEntity 1501 represents at least one of the following lOCs: subnetwork, ManagedFunction, or ManagementFunction. The SyntheticDataProducer 1503 is a type of ManagedFunction, i.e., the SyntheticDataProducer is a subclass of and inherits the capabilities of a ManagedFunction. The SyntheticDataProducer 1503 may have one or more external data producers, so the SyntheticDataProducer 1503 may also be associated to other SyntheticDataProducers (not shown). An IOC for a dataDeliveryRequest 1505, a data type of produced data 1507, and an IOC for a DataDelivery 1509 are each associated with the SyntheticDataProducer 1503. FIG. 16 shows a schematic representation of synthetic data generation inheritance relations. There is provided an IOC for a dataDeliveryRequest 1601 and an IOC for a dataDelivery 1603. Both the dataDeliveryRequest 1601 and the dataDelivery 1603 are accoiciated with an IOC for a ‘Top’ 1605. There is also provided an IOC for a SyntheticDataProducer 1607 which is asscoaietd with an IOC for a ManagedFunction 1609. The objects depicted in FIGS. 15 and 16 are discussed in more detail below. In some examples, an IOC (SyntheticDataProducer «IOC») represents the properties of SyntheticDataProducer. Each SyntheticDataProducer is a managed object name-contained in either a Subnetwork, a ManagedFunction or a ManagementFunction. The dataProducer is a type of managedFunction, i.e., the SyntheticDataProducer is a subclass of and inherits the capabilities of a managedFunction. The SyntheticDataProducer may have one or more external data producers, so the SyntheticDataProducer may also be associated to other SyntheticDataProducers. The SyntheticDataProducer IOC may include the following attributes in Table 21: Attribute name Support Qualifier isReadable isWritable islnvariant isNotifyable dataProducerlD M T F F F availableData M T F F F producibleData M T F F F Table 21: Attributes for the SyntheticDataProducer IOC. T = true, F = false. In some examples, an IOC (dataDeliveryRequest «IOC») represents the properties of dataDeliveryRequest. For each request to have data delivered, a service consumer may create a new dataDeliveryRequest object on the SyntheticDataProducer, i.e., dataDeliveryRequest shall be an information object class that is instantiated for each request for data including the request for compressed data. The dataDeliveryRequest contains the consumer’s needs for the delivery of compressed data. The dataDeliveryRequest IOC may include the following attributes in Table 22: Attribute name Support Qualifier isReadable isWritable islnvariant isNotifyable dataDeliveryRequestID M T F F T dataDescri ption M T F F T Table 22: Attributes for the dataDeliveryRequest IOC. In some examples, an IOC (syntheticDataRegistration «IOC») represents the properties of syntheticDataRegistration. For each intent to register synthetic data, a service consumer may create a new syntheticDataRegistration object on the SyntheticDataProducer, i.e., syntheticDataRegistration shall be an information object class that is instantiated for each intent to register data. The syntheticDataRegistration contains the description of the data to be registered by the Service consumer (external data producer). When the registration succeeds, the Service producer may notify the consumer of the success upon which the Service consumer may then deliver the registered data. The syntheticDataRegistration IOC may include the following attributes in Table 23: Attribute name Support Qualifier isReadable isWritable islnvariant isNotifyable DataRegisrationlD M T F F T dataDescri ption M T F F T Table 23: Attributes for the syntheticDataRegistration IOC. In some examples, an IOC (DataDelivery «IOC») represents the properties of DataDelivery. For each synthetic data that has been successfully registered, a service consumer may deliver the data by creating a new DataDelivery object on the SyntheticDataProducer. Besides the data content to be delivered, the DataDelivery may specify the identifier of the corresponding successful data registration instance. Alternatively if the delivery follows a request from the consumer. The DataDelivery may specify the identifier of the corresponding data delivery request instance. The dataDelivery IOC may include the following attributes: in Table 24: Attribute name Support Qualifier isReadable isWritable is Invariant isNotifyable dataDeliverylD M T F F F DeliveryRegisrationlD CM T F F F dataDeliveryRequestID CM T F F F dataContent M T F F F Table 24: Attributes or the DataDelivery IOC. A cond itions is that either the DataRegistrationlD or the dataDeliveryRequestID should be stated. In some examples, for syntheticData « datatype », this datatype represents the 5 properties of a specific synthetic data dataset. SyntheticData may be available at a certain producer (e.g. already generated), or SyntheticData may be producible by a synthetic data producer. The DataSet IOC may include the following attributes in Table 25: Attribute name Support Qualifier isReadable isWritable islnvariant isNotifyable datasetID M T F F F dataScope M T F F F dataSetDataType M T F F F candidateFrequecy Range M T F F F 10 Table 25: Attributes for the DataSet OC. Attributes associated with the examples described herein may include one or more of the following in Table 26: Attribute Name Documentation and Allowed Values Properties datalnstancelD It indicates the specific instance(s) of data for which compression is required or has been undertaken prior to delivery or storage type: string multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: false dataDeliveryRequestID It indicates the identifier of a specific request to deliverdata type: string multiplicity: 1 isOrdered: N / A Attribute Name Documentation and Allowed Values Properties isUnique: N / A defaultvalue: None isNullable: False dataScope It indicates the types of entitesi for which the data relates, e.g. cells, geography, etc type: context[see TS28.312] multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: False datasetID It indicates the identifier or name of data set type: string multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: True dataSetDataType It indicates the type of the data in the data set Allowed values: [Int,Float,String], type: Enum multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: False candidateFraquencyRang e It indicates the applicable periodicity of the time series as time in seconds. If only one value is stated, it indicates that only that value is available or supported. Type: integer multiplicity: 1...* isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: False availableData It indicates the specific dataset that are available at a certain synthetic data producer type: SyntheticData multiplicity: * isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: False Attribute Name Documentation and Allowed Values Properties ProducibleData It indicates the specific dataset that can be produced by a certain synthetic data producer type: synthticData multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: True DataRegisrationlD It indicates the identifier or name of an instance of data registration type: string multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: True dataDescri ption It describes the data that should be delivered for a given the request or that shall be delivered if the registration is successful type: synthticData multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: True DataDeliverylD It indicates the identifier or name of an instance of data that was earlier on registered and is being delivered type: string multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: True DeliveryRequestOrRegisra tionl D It indicates the identifier of the specific data deliver request or the successful data registration instance for the specific dataDelivery type: string multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None isNullable: True dataContent It is the data content being delivered in a specific data delivery instance type: binary multiplicity: 1 isOrdered: N / A isUnique: N / A defaultvalue: None Attribute Name Documentation and Allowed Values Properties isNullable: True Table 26: Attribute definitions according to some examples. One or more of the examples discussed herein have the advantage that functionality is provided to allow, enable, or otherwise facilitate network entities, such as NFs, to determine and retrieve synthetic data. The synthetic data may be used by the network entities for use cases such as AI / ML training. Real-world data is challenging to use because of the lack of labelled data, bias, incompleteness, lack of variety, cost and privacy / security constraints. Therefore, using the synthetic data has a number of advantages. One or more of the examples allow, enable, or otherwise facilitate producers / generators of synthetic data to register their capability, such that consumers of synthetic data may discover the capabilities, and request synthetic data accordingly. Synthetic data may be requested according to a number of criteria such as statistic properties of the data, and scope of the data. This accurate / precise nature of the generated synthetic data may not be easy / possible to obtain in real-world data. FIG. 17 shows an example method flow performed by an apparatus. In some examples, the example method flow of FIG. 17 may be performed by a network entity or network function. The network entity / network function may be implemented, at least in part, by the apparatus. In some examples, the apparatus comprises one or more means for the network entity / network function to perform the method of FIG. 17. For example, the network function may be an SDGF. In S1701, the method comprises receiving, from a consumer, at least one request associated with a first dataset. In S1703, the method comprises determining, based on the at least one request, first data that has been generated synthetically, wherein the first data is comprised in the first dataset. In S1705, the method comprises providing, to the consumer, the first data. FIG. 18 shows an example method flow performed by an apparatus. In some examples, the example method flow of FIG. 18 may be performed by a network entity or network function. The network entity / network function may be implemented, at least in part, by the apparatus. In some examples, the apparatus comprises one or more means for the network entity / network function to perform the method of FIG. 18. The network entity / network function may be configured as a service consumer or synthetic data consumer. In S1801, the method comprises providing, to a network entity, at least one request associated with a first dataset. In S1803, the method comprises receiving, from the network entity, first data that has been generated synthetically, wherein the first data is comprised in the first dataset. FIG. 18 shows an example method flow performed by an apparatus. In some examples, the example method flow of FIG. 18 may be performed by a network entity or network function. The network entity / network function may be implemented, at least in part, by the apparatus. In some examples, the apparatus comprises one or more means for the network entity / network function to perform the method of FIG. 18. FIG. 19 shows an example method flow performed by an apparatus. In some examples, the example method flow of FIG. 19 may be performed by a network entity or network function. The network entity / network function may be implemented, at least in part, by the apparatus. In some examples, the apparatus comprises one or more means for the network entity / network function to perform the method of FIG. 19. The network entity / network function may be configured as a service producer or synthetic data producer. In S1901 the method comprises providing, to a network entity, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically. In S1903 the method comprises receiving, from the network entity, a response comprising an identity of the dataset. FIG. 20 shows a schematic representation of non-volatile memory media 2000a (e.g. computer disc (CD) or digital versatile disc (DVD)) and 2000b (e.g. universal serial bus (USB) memory stick) storing instructions and / or parameters 2002 which when executed by a processor allow, enable, or otherwise facilitate the processor to perform one or more of the steps of the methods of FIGS. 17 to 19. It is noted that while various examples are described in this disclosure, there are several variations and modifications which may be made herein without departing from the scope of this disclosure. The examples may thus vary within the scope of the claims. In general, some examples may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the various examples described herein are not limited thereto. While the various examples may be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is understood that these blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting and illustrative examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof. The examples may be implemented by computer software stored in a memory and executable by at least one data processor of the involved entities or by hardware, or by a combination of software and hardware. Further in this regard it should be noted that any procedures may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on such physical media as memory chips, or memory blocks implemented within the processor, magnetic media such as hard disk or floppy disks, and optical media such as for example DVD and the data variants thereof, CD. The term “non-transitory”, as used herein, 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 vs ROM). As used herein, “at least one of the following:” and “at least one of: ” and similar wording, where the list of two or more elements are joined by “and”, or “or”, mean at least any one of the elements, or at least any two or more of the elements, or at least all of the elements. As used herein, the expression “and / or” also includes any and all combinations of the listed terms, including at least any one of the elements, or at least any two or more of the elements, or at least all of the elements. As used herein, the term “or” refers to a non-exclusive “or” unless otherwise indicated (e.g., use of “or else” or “or in the alternative”). As used herein, unless stated explicitly, performing a respective feature, step, or functionality “in response to A” does not indicate that the respective feature, step, or functionality is performed immediately after “A” occurs as one or more intervening features, steps, or functionalities may be performed (at least in part) between an occurrence of the respective feature, step, or function and “A”. Analogously, performing a respective feature, step, or functionality “based on A” does not indicate that the respective feature, step, or functionality is performed solely based on “A” as the respective feature, step, or functionality may be further based on one or more other features, steps, or functionalities in addition to “A”. The memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory. The data processors may be of any type suitable to the local technical environment, and may include one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASIC), gate level circuits and processors based on multi core processor architecture, as non-limiting and illustrative examples. As used herein, the term “means for”, or “means configured to perform” (or similar) may be any means that are suitable for performing the feature. The “means” may be configured to perform one or more of the functions and / or method steps previously described. For example, the “means” may include one or more of: at least one processor, at least one memory, transceiver circuitry, antenna circuitry, etc. It should be understood that these are provided as non-limiting and illustrative examples. Alternatively, or additionally some examples may be implemented using circuitry. The circuitry may be configured to perform one or more of the functions and / or method steps previously described. That circuitry may be provided in the base station and / or in the communications device. As used herein, the term “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analogue and / or digital circuitry); (b) combinations of hardware circuits and software, such as: (i) a combination of analogue 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 the communications device or base station to perform the various functions previously described; and (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. This definition of circuitry applies to uses of the term “means” herein, including in any claims. As a further example, as used herein, 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 integrated device. 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 a server, a cellular network device, or other computing or network device. The foregoing description has provided, by way of non-limiting and illustrative examples, a full and informative description of the various examples of this disclosure. However, various modifications and adaptations may become apparent to those skilled in the relevant arts in view of the foregoing description, when read in conjunction with the drawings and the claims. However, all such and similar modifications of these teachings are intended to fall within the scope of this disclosure.

Claims

1. An apparatus of a network entity, wherein the apparatus comprises means for the network entity to perform:receiving, from a consumer, at least one request associated with a first dataset;determining, based on the at least one request, first data that has been generated synthetically, wherein the first data is comprised in the first dataset; andproviding, to the consumer, the first data.

2. The apparatus according to claim 1, wherein the at least one request comprises an identifier of the first dataset.

3. The apparatus according to claim 1 or claim 2, wherein the determining comprises: retrieving, based on the at least one request, the first data from a repository.

4. The apparatus according to any of claims 1 to 3, wherein the at least one request associated with the first dataset comprises a request for the generation of the first dataset according to at least one capability.

5. The apparatus according to claim 4, wherein the determining comprises: generating, based on the at least one request, the first data for the first dataset.

6. The apparatus according to claim 4, wherein the determining comprises: providing, to a producer, a further request for the generation of the first dataset; and receiving, from the producer, the first data that has been generated synthetically.

7. The apparatus according to any of claims 1 to 6, wherein the means are further for the network entity to perform: providing, to the consumer, an identifier of the first dataset.

8. The apparatus according to any of claims 1 to 7 wherein the means are further for the network entity to perform:receiving, from a producer, a request for registration of a dataset, wherein the dataset comprises data that has been generated synthetically;validating the request for registration, so that at least one of the following: an identity of the dataset, an identity of the producer, or information associated with the dataset, are stored for the dataset at a repository; andproviding, to the producer, a response comprising an identity of the dataset.

9. The apparatus according to any of claims 1 to 8, wherein the means are further for the network entity to perform:receiving, from a producer, a request for updating the first dataset, wherein the request for updating comprises an identity of the first dataset;causing the first dataset to be updated at a repository where the first dataset is stored based on the request for updating.

10. The apparatus according to any of claims 1 to 9, wherein the means are further for the network entity to perform:receiving, from the consumer, a request for information related to datasets comprising data that has been generated synthetically;retrieving information related to the first dataset; andproviding, to the consumer, the information related to the first dataset, wherein the information comprises an identity of the first dataset.

11. The apparatus according to any of claims 1 to 10, wherein the means are further for the network entity to perform:receiving, from a producer, a request for registration of the producer, wherein the request for registration comprises: an identity of the producer, and information related to at least one capability associated with synthetic data generation of the producer; andregistering the at least one capability of the producer.

12. The apparatus according to any of claims 1 to 11, wherein the means are further for the network entity to perform:receiving, from a consumer, a request for discovery of at least one capability associated with generating synthetic data; andbased on the request, providing, to the consumer, a response comprising information related to data that can be generated according to the at least one capability.

13. The apparatus according to any of claims 1 to 12, wherein the network entity is a network function.

14. An apparatus of a further network entity, wherein the apparatus comprises means for the further network entity to perform:providing, to a network entity, at least one request associated with a first dataset; andreceiving, from the network entity, first data that has been generated synthetically, wherein the first data is comprised in the first dataset.

15. The apparatus according to claim 14, wherein the at least one request comprises an identifier of the first dataset.

16. The apparatus according to claim 14 or claim 15, wherein the at least one request associated with the first dataset comprises: a request for the generation of the first dataset according to at least one capability.

17. The apparatus according to any of claims 14 to 16, wherein the means are further for the further network entity to perform:receiving, from the network entity, an identifier of the first dataset.

18. The apparatus according to any of claims 14 to 17, wherein the means are further for the further network entity to perform:providing, to the network entity, a request for information related to datasets comprising data that has been generated synthetically; andreceiving, from the network entity, information related to the first dataset, wherein the information comprises an identity of the first dataset.

19. The apparatus according to any of claims 14 to 18, wherein the means are further for the further network entity to perform:providing, to the network entity, a request for discovery of at least one capability associated with generating synthetic data; andreceiving, from the network entity, a response comprising information related to data that can be generated according to the at least one capability.

20. The apparatus according to any of claims 14 to 19, wherein the further network entity is a network function.

21. The apparatus according to any of claims 14 to 20, wherein the further network function is configured as a consumer.

22. A method comprising:receiving, from a consumer, at least one request associated with a first dataset;determining, based on the at least one request, first data that has been generated synthetically, wherein the first data is comprised in the first dataset; andproviding, to the consumer, the first data.

23. A method comprising:providing, to a network entity, at least one request associated with a first dataset; and receiving, from the network entity, first data that has been generated synthetically, wherein the first data is comprised in the first dataset.

24. A computer program comprising instructions, which when executed by an apparatus, cause the apparatus to perform at least the following:receiving, from a consumer, at least one request associated with a first dataset;determining, based on the at least one request, first data that has been generated synthetically, wherein the first data is comprised in the first dataset; andproviding, to the consumer, the first data.

25. A computer program comprising instructions, which when executed by an apparatus, cause the apparatus to perform at least the following:providing, to a network entity, at least one request associated with a first dataset; and receiving, from the network entity, first data that has been generated synthetically, wherein the first data is comprised in the first dataset.

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