Apparatuses and wireless communication methods for data plane service

Abstract

A wireless communication method for data plane service, performed by a network node, includes receiving, from a data plane service supplicant (DPSS), a service request, wherein the service request comprises a data plane network data service descriptor identifier indicating a type of a data-driven network operation, a data plane service supplicant correlation identifier to correlate the service request between the DPSS and the data plane node, a DPSS identifier (DPSSID) identifying the DPSS, a digital signature of the DPSS, and / or a public key associated with the DPSS, verifying the DPSS, and initiating metadata collection to support the data-driven network operation based on the service request.

Description

APPARATUSES AND WIRELESS COMMUNICATION METHODS FOR DATA PLANE SERVICECROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 648,081, entitled “APPROACH FOR DADTA COLLECTION TO SUPPORT INTRA AND INTER DOMAINS DATA PLANE SERVICES”, filed on May 15, 2024, and U.S. Provisional Application No. 63 / 653,136, entitled “DATA COLLECTION TO SUPPORT INTRA AND INTER DOMAINS DATA PLANE SERVICES”, filed on May 29, 2024, both of which are hereby incorporated by reference in their entirety.TECHNICAL FIELD

[0002] The present disclosure relates to the field of communication systems, and more particularly, to apparatuses and wireless communication methods for data plane service.BACKGROUND

[0003] Conventional wireless communication networks are designed to support data transmission between user equipment and core network components, focusing on throughput, latency, and connectivity. However, they are not equipped to meet the emerging demands of autonomous intelligent systems, which require real-time ingestion and processing of metadata from diverse and distributed data pipelines. These systems, such as artificial intelligence / machine learning (AI / ML)- based inference engines, sensing platforms, and digital twin models, depend on synchronized, high-fidelitay, and context-aware data to operate effectively. The lack of native support for data- driven service orchestration in existing networks presents a limitation for enabling such advanced functionalities. aaSUMMARY

[0004] An object of the present disclosure is to propose apparatuses and wireless communication methods for data plane service, which can solve these issues in the prior art and other issues, enable efficient, scalable, and context-aware data integration across distributed system domains, and / orempower autonomous intelligent systems with timely and reliable metadata for enhanced artificial intelligence / machine learning (AI / ML), sensing, and digital twin operations.

[0005] In a first aspect of the present disclosure, a wireless communication method for data plane service, performed by a network node, includes: receiving, from a data plane service supplicant (DPSS), a service request, wherein the service request includes a data plane network data service descriptor identifier indicating a type of a data-driven network operation, a data plane service supplicant correlation identifier to correlate the service request between the DPSS and the data plane node, a DPSS identifier (DPSSID) identifying the DPSS, a digital signature of the DPSS, and / or a public key associated with the DPSS; verifying the DPSS; and initiating metadata collection to support the data-driven network operation based on the service request.

[0006] In a second aspect of the present disclosure, a network node, includes a transceiver configured to receive, from a data plane service supplicant (DPSS), a service request, wherein the service request includes a data plane network data service descriptor identifier indicating a type of a data-driven network operation, a data plane service supplicant correlation identifier to correlate the service request between the DPSS and the data plane node, a DPSS identifier (DPSSID) identifying the DPSS, a digital signature of the DPSS, and / or a public key associated with the DPSS; and a determiner configured to verify the DPSS and initiate metadata collection to support the data-driven network operation based on the service request.

[0007] In a third aspect of the present disclosure, a network device includes a memory, a transceiver, and a processor coupled to the memory and the transceiver. The network device is configured to perform the above method.

[0008] In a fourth aspect of the present disclosure, a non-transitory machine-readable storage medium has stored thereon instructions that, when executed by a computer, cause the computer to perform the above method.

[0009] In a fifth aspect of the present disclosure, a chip includes a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute the above method.

[0010] In a sixth aspect of the present disclosure, a computer readable storage medium, in which a computer program is stored, causes a computer to execute the above method.

[0011] In a seventh aspect of the present disclosure, a computer program product includes a computer program, and the computer program causes a computer to execute the above method.

[0012] In an eighth aspect of the present disclosure, a computer program causes a computer to execute the above method.BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to illustrate the embodiments of the present disclosure or related art more clearly, the following figures will be described in the embodiments are briefly introduced. It is obvious that the drawings are merely some embodiments of the present disclosure, a person having ordinary skill in this field can obtain other figures according to these figures without paying the premise.

[0014] FIG. l is a block diagram of a network node according to an embodiment of the present disclosure.

[0015] FIG. 2 is a flowchart illustrating a wireless communication method for data plane service by a network node according to an embodiment of the present disclosure.

[0016] FIG. 3 is a block diagram of a network device according to an embodiment of the present disclosure.

[0017] FIG. 4 is a schematic diagram illustrating a reference architecture for a data plane in a next generation mobile system according to an embodiment of the present disclosure.

[0018] FIG. 5 is a schematic diagram illustrating a data pipeline architecture concept supported by data plane in a next generation mobile system according to an embodiment of the present disclosure.

[0019] FIG. 6 is a schematic diagram illustrating data pipeline contributor functionalities and their respective data flows according to an embodiment of the present disclosure.

[0020] FIG. 7 is a schematic diagram illustrating a use case for cross domains single data pipeline operation according to an embodiment of the present disclosure.

[0021] FIG. 8 is a flowchart illustrating data plane service request procedures to initiate and to trigger local domains data pipeline operation configured to implement some embodiments presented herein.

[0022] FIG. 9 is a flowchart illustrating data plane service request procedures to initiate and to trigger cross domains data pipeline operation configured to implement some embodiments presented herein.

[0023] FIG. 10 is a block diagram of an example of a computing device according to an embodiment of the present disclosure.

[0024] FIG. 11 is a block diagram of a communication system according to an embodiment of the present disclosure.DETAILED DESCRIPTION OF EMBODIMENTS

[0025] Embodiments of the present disclosure are described in detail with the technical matters, structural features, achieved objects, and effects with reference to the accompanying drawings asfollows. Specifically, the terminologies in the embodiments of the present disclosure are merely for describing the purpose of the certain embodiment, but not to limit the disclosure.

[0026] As 3rd generation partnership project (3GPP) system advances toward international mobile telecommunications 2030 (IMT-2030) and a next-generation such as sixth generation (6G), which is envisioned to provide data-driven network services supporting native artificial intelligence (Al), integrated sensing and communication (ISAC), and digital twin technologies, the limitations of the current fifth generation (5G) architecture become apparent. Specifically, 5G lacks: (1) a flexible architecture to enable data collaboration among various network entities acting as data sources, and (2) efficient and reliable mechanisms for network-based data processing and management. One potential approach to evolving 5G for data services is to extend a user plane function (UPF) architecture. However, in its current form, user traffic in the user plane is transmitted through a closed session between a user equipment (UE) and the UPF, following a point-to-point communication model. This model is not feasible for supporting any-to-any communication among network entities across arbitrary topologies, a requirement for enabling metadata collection and processing / pre-processing among network elements to support native Al, sensing, and digital twin operations. Additionally, UPF does not offer any mechanism for data privacy protection to comply with regional regulations or operator policies. It is designed to control data forwarding between a data source and an external data destination outside the mobile network. Therefore, the UPF is not equipped to manage data interactions among internal network entities or to provide intermediate data processing or pre-processing functionalities within the mobile system.

[0027] When considering the extension of existing 3GPP specifications, such as TS 23.288 for network data analytics function (NWDAF), data collection coordination function (DCCF), and messaging framework adaptor function (MFAF), as well as TS 28.104, TS 28.537, and TS 28.622, which define data service capabilities and management data analytics framework, including management data analytics management function (MDAMF), it becomes evident that functionalities are focused on collecting data and generating analytics reports for consumers, specifically for management data analytics purposes. However, these features are not designed to utilize the extensive computing resources available across network entities and devices, nor do they consider various application scenarios that require orchestration of different functional chains to support collaborative data collection, artificial intelligence / machine learning (AI / ML), sensing, or digital twin operations.

[0028] To support data-driven network services, current technology trends indicate a clear paradigm shift, from a session-based information transmission model to a platform-based data management and orchestration model. Therefore, it is appropriate to introduce a new data serviceframework in 6G, aimed at leveraging collaborative data collection and enabling dynamic data pipeline operations to support advanced data-driven services such as artificial intelligence / machine learning (AI / ML), integrated sensing, and digital twin functionalities.

[0029] Some embodiments of the present disclosure are to define a data plane architecture designed to enable data pipeline operations that support data-driven network services.

[0030] FIG. 1 illustrates an example of a network node 100 according to an embodiment of the present disclosure. The network node 100 is configured to implement some embodiments of the disclosure. Some embodiments of the disclosure may be implemented into the network node 100 using any suitably configured hardware and / or software. The network node 100 may include a memory 101, a transceiver 102, and a processor 103 coupled to the memory 101 and the transceiver102. The processor 103 may be configured to implement proposed functions, procedures and / or methods described in this description. Layers of radio interface protocol may be implemented in the processor 103. The memory 101 is operatively coupled with the processor 103 and stores a variety of information to operate the processor 103. The transceiver 102 is operatively coupled with the processor 103, and the transceiver 102 transmits and / or receives a radio signal. The processor 103 may include application-specific integrated circuit (ASIC), other chipset, logic circuit and / or data processing device. The memory 101 may include read-only memory (ROM), random access memory (RAM), flash memory, memory card, storage medium and / or other storage device. The transceiver 102 may include baseband circuitry to process radio frequency signals. When the embodiments are implemented in software, the techniques described herein can be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The modules can be stored in the memory 101 and executed by the processor103. The memory 101 can be implemented within the processor 103 or external to the processor 103 in which case those can be communicatively coupled to the processor 103 via various means as is known in the art.

[0031] In some embodiments, the memory 101 stores executable instructions that when executed by the processor 103 cause the processor 103 to effectuate operations including: receiving, from a data plane service supplicant (DPSS), a service request, wherein the service request includes a data plane network data service descriptor identifier indicating a type of a data-driven network operation, a data plane service supplicant correlation identifier to correlate the service request between the DPSS and the data plane node, a DPSS identifier (DPSSID) identifying the DPSS, a digital signature of the DPSS, and / or a public key associated with the DPSS; verifying the DPSS; and initiating metadata collection to support the data-driven network operation based on the service request. This can solve these issues in the prior art and other issues, enable efficient, scalable, and context-aware data integration across distributed system domains, and / or empowerautonomous intelligent systems with timely and reliable metadata for enhanced AI / ML, sensing, and digital twin operations.

[0032] FIG. 2 illustrates a wireless communication method for data plane service by a network node according to an embodiment of the present disclosure. FIG. 2 is an example of a communication method 200 for data plane service according to an embodiment of the present disclosure. The communication method 200 for data plane service is configured to implement some embodiments of the disclosure. Some embodiments of the disclosure may be implemented into the communication method 200 for data plane service using any suitably configured hardware and / or software. In some embodiments, the communication method 200 for data plane service includes: an operation 202, receiving, from a data plane service supplicant (DPSS), a service request, wherein the service request includes a data plane network data service descriptor identifier indicating a type of a data-driven network operation, a data plane service supplicant correlation identifier to correlate the service request between the DPSS and the data plane node, a DPSS identifier (DPSSID) identifying the DPSS, a digital signature of the DPSS, and / or a public key associated with the DPSS; an operation 204, verifying the DPSS; and an operation 206, initiating metadata collection to support the data-driven network operation based on the service request. This can solve these issues in the prior art and other issues, enable efficient, scalable, and context-aware data integration across distributed system domains, and / or empower autonomous intelligent systems with timely and reliable metadata for enhanced AI / ML, sensing, and digital twin operations.

[0033] In some embodiments, the network node includes a data plane access controller (DP AC). In some embodiments, the DPSS includes a user equipment (UE), a radio access network (RAN) function node, a core network function (core NF) node, an operation, administration and maintenance (0AM) function node, or an application function (AF) node. In some embodiments, the method further includes performing at least one of following operations including: upon successful verification of the DPSS, performing, by the network node, one or more following operations: validating requested data plane network data service descriptors based on at least one of: network capabilities and policies of a serving mobile network operator (MNO); or local or regional data protection regulations applicable to the DPSS’s location or environment; upon successful validation, assigning a data pipeline identifier to establish a data pipeline supporting a requested data plane service operation; determining, based on the requested data plane network data service descriptors, that one or more functional entities in a local domain are required to support a data pipeline operation; selecting, by the network node, required functional entities in the local domain that host data pipeline functions according to descriptor criteria; or transmitting data plane service parameters to selected functional entities to enable the requested data plane service operation.

[0034] In some embodiments, the method further includes transmitting, by the network node, a data pipeline service subscribe request to a first functional entity, a second functional entity, and a third functional entity, wherein the data pipeline service subscribe request includes a data pipeline identifier, a network node identifier, a digital signature of the network node, a public key of the network node, and / or a tuple of data plane service parameters associated with the requested data-driven operation; wherein the first functional entity operates as a data sink that receives metadata from the second functional entity and the third functional entity and performs data analytics to generate aggregated outputs to support the requested data plane service operation. In some embodiments, the method further includes transmitting, by the network node to the DPSS, a service notification including the data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates that the requested data plane service operation has been initiated. In some embodiments, the method further includes receiving, by the network node from a data pipeline contributor located in a first functional entity operating as a data consumer, a metadata transaction subscribe request including a data consumer endpoint identifier, a data consumer correlation identifier, a data consumer identifier, a digital signature of the data consumer, a public key associated with the data consumer, and / or a metadata profile set including a first metadata profile and a second metadata profile, wherein the metadata transaction subscribe request is initiated by the data consumer to solicit metadata required to support the requested data plane service operation.

[0035] In some embodiments, the method further includes initiating, by the network node on behalf of a first functional entity operating as a data consumer, a metadata subscribe request toward a second functional entity and a third functional entity, wherein the metadata subscribe request is configured to collect metadata corresponding to the first metadata profile and the second metadata profile. In some embodiments, the method further includes performing at least one of following operations including consolidating and processing, by the network node, metadata responses collected from a second functional entity and a third functional entity in response to a metadata subscribe request; or transmitting, by the network node to a first functional entity operating as a data consumer, a metadata transaction subscribe response including a data consumer correlation identifier, one or more aggregated metadata data sets, and / or a transaction status indicator. In some embodiments, the method further includes performing at least one of following operations including transmitting, by the network node, aggregated metadata collected from a second functional entity and a third functional entity to a first functional entity operating as a data consumer; receiving, by the network node, an unsolicited metadata transaction request from the first functional entity, the unsolicited metadata transaction request including analytic resultsgenerated from data analytics performed on an aggregated metadata; or receiving, by the network node, a completion status notification from the first functional entity indicating that the requested data plane service operation has been fulfilled.

[0036] In some embodiments, the method further includes receiving, by the network node from a first functional entity operating as a data source, a metadata transaction create or update request including a data source endpoint identifier, a data source correlation identifier, a data source identifier, a digital signature of the data source, a public key associated with the data source, a metadata profile indicating a structure or type of an analytic output, and / or an encrypted metadata using a public key of the network node, wherein the metadata transaction create or update request is used to store the analytic output in a storage system associated with the network node. In some embodiments, the method further includes receiving, by the network node from a first functional entity, a data pipeline service subscribe response including a data pipeline identifier and / or an operation status indicator, wherein the data pipeline service subscribe response indicates that a data pipeline operation associated with requested data plane service has been completed. In some embodiments, the method further includes transmitting, by the network node to the DPSS, a service notification including a data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates whether a data plane service request has been successfully completed or has failed. In some embodiments, the method further includes performing at least one of following operations including determining, by the network node based on requested data plane network data service descriptors, that one or more functional entities in another domain are required to support a requested data plane service operation; involving, by the network node, a first data plane orchestration controller (DPOC) to communicate with one or more DPOCs associated with another domain; or coordinating, via the first DPOC, selection and orchestration of required functional entities from the another domain to support the requested data plane service operation.

[0037] In some embodiments, the method further includes relaying, by the network node via the first DPOC, the requested data plane service operation to the another domain for collaboration, by transmitting a DPOC service request to a second DPOC in the another domain, the DPOC service request including a data plane network data service descriptor identifier indicating a target data- driven network operation, a correlation identifier assigned by the first DPOC to correlate the DPOC service request between the first DPOC and the second DPOC, an identifier of the first DPOC, a digital signature and a public key of the first DPOC to enable identity verification by the second DPOC, and / or a data pipeline identifier used to coordinate a data pipeline operation across domains. In some embodiments, the method further includes performing at least one of followingoperations including receiving, by a second DPOC in the another domain, a data plane service request from a first DPOC, the data plane service request including network data service descriptors; coordinating, via the second DPOC, with a second network node to identify and select one or more functional entities that support data pipeline contributor functions, and / or meet functional and performance criteria in the network data service descriptors; or providing, via the second DPOC to the first DPOC, information identifying the selected functional entities that host the data pipeline contributor. In some embodiments, the method further includes performing at least one of following operations including receiving, by the network node via a first DPOC, a service response from a second DPOC, wherein the service response includes a correlation identifier and / or a data pipeline contributor information including one or more functional entity tuples, each tuple including a functional type and an identifier of a functional entity selected to support a data pipeline operation; determining, by the network node, that if the data pipeline contributor information is empty, the requested data plane service operation cannot be fulfilled; or transmitting, by the network node to the DPSS, a service notification including a data plane service supplicant correlation identifier and / or an operation status indicating failure of the requested data plane service operation.

[0038] In some embodiments, the method further includes performing at least one of following operations including selecting, by the network node, one or more functional entities within a local domain that host data pipeline contributor functions in accordance with criteria in data plane network data service descriptors; or upon identifying all functional entities required to support the data pipeline operation for the requested data plane service, transmitting, by the network node, data plane service parameters to respective selected functional entities to enable the requested data plane service operation. In some embodiments, the method further includes transmitting, by the network node to each selected functional entity that hosts a data pipeline contributor in a local domain, a data pipeline service subscribe request including a data pipeline identifier, an identifier of the network node, a digital signature of the network node, a public key of the network node, and / or data plane service parameters tuple in the data plane network data service descriptors, wherein the data pipeline service subscribe request is transmitted separately to a respective functional entity to enable the requested data plane service operation. In some embodiments, the method further includes transmitting, by the network node via a first DPOC, a data pipeline service subscribe request to a second DPOC in the another domain, wherein the data pipeline service subscribe request includes a data pipeline identifier, a first DPOC identifier, a digital signature of the first DPOC, a public key of the first DPOC, an identifier of a selected functional entity in the another domain, and / or a data plane service parameter tuple in data plane network data servicedescriptors; wherein the data pipeline service subscribe request is configured for the second DPOC to relay data pipeline service parameters to a corresponding selected functional entity in the another domain that hosts a data pipeline contributor.

[0039] In some embodiments, the method further includes performing at least one of following operations including transposing, by a second DPOC, a data pipeline service subscribe request received from a first DPOC into a local data pipeline service subscribe request; or transmitting, via the second DPOC or a second network node, a local data pipeline service subscribe request to a target functional entity in a domain, wherein the local data pipeline service subscribe request includes a data pipeline identifier, a second network node identifier, a digital signature of the second network node, a public key of the second network node, a functional entity identifier, and / or a data plane service parameter tuple in the data plane network data service descriptors. In some embodiments, the method further includes transmitting, by the network node to the DPSS, a service notification including a data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates that the requested data plane service operation has been initiated.

[0040] In some embodiments, the method further includes performing at least one of following operations including enabling, by the network node, each selected functional entity hosting a data pipeline contributor to perform data plane operation based on received data plane service parameters, wherein the data plane operation includes subscribing to one or more data sources, collecting and optionally pre-processing or labeling data, exposing raw or processed data to a subsequent functional entity identified within the data plane service parameters, and / or forwarding data for further analysis, storage, model training, or inference; initiating, by the network node, a data pipeline operation monitoring procedure within a local domain and across domains; storing monitoring status locally within each respective domain; receiving, by the network node, notifications of any failure, interruption, or completion of a portion of a data pipeline operation from a local network node, or in a cross-domain deployment, receiving an operation status via a DPOC relaying report from a remote domain’s DP AC. In some embodiments, the method further includes receiving, by the network node and / or a first DPOC, a completion notification from a first functional entity in a local domain and a second functional entity in a partner domain, wherein the completion notification indicates that a data pipeline operation associated with a requested data plane service has been completed. In some embodiments, the method further includes upon receiving a notification indicating completion of a data pipeline service operation, transmitting, by the network node to the DPSS, a service notification including a data plane service supplicantcorrelation identifier and / or an operation status indicator, wherein the service notification indicates a final status of requested data plane service operation.

[0041] FIG. 3 illustrates a communication device according to an embodiment of the present disclosure. FIG. 3 illustrates that, in some embodiments, a network device 300 includes a transceiver 301 configured to receive, from a data plane service supplicant (DPSS), a service request, wherein the service request includes a data plane network data service descriptor identifier indicating a type of a data-driven network operation, a data plane service supplicant correlation identifier to correlate the service request between the DPSS and the data plane node, a DPSS identifier (DPSSID) identifying the DPSS, a digital signature of the DPSS, and / or a public key associated with the DPSS; and a determiner 302 configured to verify the DPSS and initiate metadata collection to support the data-driven network operation based on the service request. This can solve these issues in the prior art and other issues, enable efficient, scalable, and context-aware data integration across distributed system domains, and / or empower autonomous intelligent systems with timely and reliable metadata for enhanced AI / ML, sensing, and digital twin operations.

[0042] In some embodiments, the network node includes a data plane access controller (DP AC). In some embodiments, the DPSS includes a user equipment (UE), a radio access network (RAN) function node, a core network function (core NF) node, an operation, administration and maintenance (0AM) function node, or an application function (AF) node. In some embodiments, the determiner 302 is further configured to perform at least one of following operations including: upon successful verification of the DPSS, performing one or more following operations: validating requested data plane network data service descriptors based on at least one of: network capabilities and policies of a serving mobile network operator (MNO); or local or regional data protection regulations applicable to the DPSS’s location or environment; upon successful validation, assigning a data pipeline identifier to establish a data pipeline supporting a requested data plane service operation; determining, based on the requested data plane network data service descriptors, that one or more functional entities in a local domain are required to support a data pipeline operation; or selecting required functional entities in the local domain that host data pipeline functions according to descriptor criteria; or the transceiver 301 is further configured to transmit data plane service parameters to selected functional entities to enable the requested data plane service operation. In some embodiments, the transceiver 301 is further configured to transmit a data pipeline service subscribe request to a first functional entity, a second functional entity, and a third functional entity, wherein the data pipeline service subscribe request includes a data pipeline identifier, a network node identifier, a digital signature of the network node, a public keyof the network node, and / or a tuple of data plane service parameters associated with the requested data-driven operation; the first functional entity operates as a data sink that receives metadata from the second functional entity and the third functional entity and performs data analytics to generate aggregated outputs to support the requested data plane service operation.

[0043] In some embodiments, the transceiver 301 is further configured to transmit, to the DPSS, a service notification including the data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates that the requested data plane service operation has been initiated. In some embodiments, the transceiver 301 is further configured to receive from a data pipeline contributor located in a first functional entity operating as a data consumer, a metadata transaction subscribe request including a data consumer endpoint identifier, a data consumer correlation identifier, a data consumer identifier, a digital signature of the data consumer, a public key associated with the data consumer, and / or a metadata profile set including a first metadata profile and a second metadata profile, wherein the metadata transaction subscribe request is initiated by the data consumer to solicit metadata required to support the requested data plane service operation. In some embodiments, the determiner 302 is configured to initiate, on behalf of a first functional entity operating as a data consumer, a metadata subscribe request toward a second functional entity and a third functional entity, wherein the metadata subscribe request is configured to collect metadata corresponding to the first metadata profile and the second metadata profile.

[0044] In some embodiments, the determiner 302 is further configured to perform consolidating and processing metadata responses collected from a second functional entity and a third functional entity in response to a metadata subscribe request; or the transceiver 301 is further configured to transmit to a first functional entity operating as a data consumer, a metadata transaction subscribe response including a data consumer correlation identifier, one or more aggregated metadata data sets, and / or a transaction status indicator. In some embodiments, the determiner 302 is further configured to perform at least one of following operations including transmitting aggregated metadata collected from a second functional entity and a third functional entity to a first functional entity operating as a data consumer; receiving an unsolicited metadata transaction request from the first functional entity, the unsolicited metadata transaction request including analytic results generated from data analytics performed on an aggregated metadata; or receiving a completion status notification from the first functional entity indicating that the requested data plane service operation has been fulfilled.

[0045] In some embodiments, the transceiver 301 is further configured to receive from a first functional entity operating as a data source, a metadata transaction create or update requestincluding a data source endpoint identifier, a data source correlation identifier, a data source identifier, a digital signature of the data source, a public key associated with the data source, a metadata profile indicating a structure or type of an analytic output, and / or an encrypted metadata using a public key of the network node, wherein the metadata transaction create or update request is used to store the analytic output in a storage system associated with the network node. In some embodiments, the transceiver 301 is further configured to receive from a first functional entity, a data pipeline service subscribe response including a data pipeline identifier and / or an operation status indicator, wherein the data pipeline service subscribe response indicates that a data pipeline operation associated with requested data plane service has been completed. In some embodiments, the transceiver 301 is further configured to transmit, to the DPSS, a service notification including a data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates whether a data plane service request has been successfully completed or has failed. In some embodiments, the determiner 302 is further configured to perform at least one of following operations including: determining, based on requested data plane network data service descriptors, that one or more functional entities in another domain are required to support a requested data plane service operation; involving a first data plane orchestration controller (DPOC) to communicate with one or more DPOCs associated with another domain; or coordinating, via the first DPOC, selection and orchestration of required functional entities from the another domain to support the requested data plane service operation.

[0046] In some embodiments, the determiner 302 is further configured to relay via the first DPOC the requested data plane service operation to the another domain for collaboration, by transmitting a DPOC service request to a second DPOC in the another domain, the DPOC service request including a data plane network data service descriptor identifier indicating a target data-driven network operation, a correlation identifier assigned via the first DPOC to correlate the DPOC service request between the first DPOC and the second DPOC, an identifier of the first DPOC, a digital signature and a public key of the first DPOC to enable identity verification via the second DPOC, and / or a data pipeline identifier used to coordinate a data pipeline operation across domains. In some embodiments, the transceiver 301 is further configured to perform at least one of following operations including receiving, by a second DPOC in the another domain, a data plane service request from a first DPOC, the data plane service request including network data service descriptors; coordinating, via the second DPOC, with a second network node to identify and select one or more functional entities that support data pipeline contributor functions, and / or meet functional and performance criteria in the network data service descriptors; or providing, via the second DPOC to the first DPOC, information identifying the selected functional entities that hostthe data pipeline contributor. In some embodiments, the transceiver 301 is further configured to receive via a first DPOC, a service response from a second DPOC, wherein the service response includes a correlation identifier and / or a data pipeline contributor information including one or more functional entity tuples, each tuple including a functional type and an identifier of a functional entity selected to support a data pipeline operation; or the determiner 302 is further configured to determine that if the data pipeline contributor information is empty, the requested data plane service operation cannot be fulfilled; or the transceiver 301 is further configured to transmit to the DPSS, a service notification including a data plane service supplicant correlation identifier and / or an operation status indicating failure of the requested data plane service operation.

[0047] In some embodiments, the determiner 302 is further configured to perform at least one of following operations including selecting, by the network node, one or more functional entities within a local domain that host data pipeline contributor functions in accordance with criteria in data plane network data service descriptors; or upon identifying all functional entities required to support the data pipeline operation for the requested data plane service, transmitting, by the network node, data plane service parameters to respective selected functional entities to enable the requested data plane service operation. In some embodiments, the transceiver 301 is further configured to transmit to each selected functional entity that hosts a data pipeline contributor in a local domain, a data pipeline service subscribe request including a data pipeline identifier, an identifier of the network node, a digital signature of the network node, a public key of the network node, and / or data plane service parameters tuple in the data plane network data service descriptors, wherein the data pipeline service subscribe request is transmitted separately to a respective functional entity to enable the requested data plane service operation. In some embodiments, the transceiver 301 is further configured to transmit via a first DPOC, a data pipeline service subscribe request to a second DPOC in the another domain, wherein the data pipeline service subscribe request includes a data pipeline identifier, a first DPOC identifier, a digital signature of the first DPOC, a public key of the first DPOC, an identifier of a selected functional entity in the another domain, and / or a data plane service parameter tuple in data plane network data service descriptors; wherein the data pipeline service subscribe request is configured for the second DPOC to relay data pipeline service parameters to a corresponding selected functional entity in the another domain that hosts a data pipeline contributor.

[0048] In some embodiments, the determiner 402 is further configured to perform at least one of following operations including: transposing, via a second DPOC, a data pipeline service subscribe request received from a first DPOC into a local data pipeline service subscribe request; or the transceiver 301 is further configured to transmit, via the second DPOC or a second network node,a local data pipeline service subscribe request to a target functional entity in a domain, wherein the local data pipeline service subscribe request includes a data pipeline identifier, a second network node identifier, a digital signature of the second network node, a public key of the second network node, a functional entity identifier, and / or a data plane service parameter tuple in the data plane network data service descriptors. In some embodiments, the transceiver 301 is further configured to transmit to the DPSS, a service notification including a data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates that the requested data plane service operation has been initiated. In some embodiments, the determiner 302 is further configured to perform at least one of following operations including: enabling each selected functional entity hosting a data pipeline contributor to perform data plane operation based on received data plane service parameters, wherein the data plane operation includes subscribing to one or more data sources, collecting and optionally pre-processing or labeling data, exposing raw or processed data to a subsequent functional entity identified within the data plane service parameters, and / or forwarding data for further analysis, storage, model training, or inference; initiating a data pipeline operation monitoring procedure within a local domain and across domains; or storing monitoring status locally within each respective domain; the transceiver 301 is further configured to receive notifications of any failure, interruption, or completion of a portion of a data pipeline operation from a local network node, or in a crossdomain deployment, receive an operation status via a DPOC relaying report from a remote domain’s DP AC. In some embodiments, the transceiver 301 is further configured to receive or receive via a first DPOC, a completion notification from a first functional entity in a local domain and a second functional entity in a partner domain, wherein the completion notification indicates that a data pipeline operation associated with a requested data plane service has been completed. In some embodiments, the transceiver 301 is further configured to upon receiving a notification indicating completion of a data pipeline service operation, transmit, to the DPSS, a service notification including a data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates a final status of requested data plane service operation.

[0049] FIG. 4 illustrates a reference architecture for a data plane in a next generation mobile system according to an embodiment of the present disclosure. FIG. 4 illustrates two high-level mobile system reference architecture options designed to support the new data plane functionalities referenced in some embodiments of the present disclosure for implementing the data pipeline. The data plane is responsible for the transport and routing of data flows, which may include raw or processed data originating from one or more data sources (e.g., user data, network data, applicationdata, subscription data, etc.). These data flows are directed to authorized internal or external network components across any topology, enabling flexible, reliable, and secure data services, as well as on-path data processing for improved efficiency. Along the data pipeline, several data processing functions are supported at different network entities, including data collection, preprocessing, analytics, and more, which are orchestrated and coordinated by a data plane access controller. This controller ensures cooperative operation among entities to transfer, pre-process, and / or analyze data outputs required by various data plane services.

[0050] In addition to enabling data flow transport and routing among network entities, and supporting data processing within these entities, the Data Plane also provides storage management services. These services manage the collected and processed data for immediate or future use and facilitate data tracking and auditing. This is essential for meeting data trustworthiness standards and fulfilling requirements for explainable Al.

[0051] There are two options for the mobile system architecture to enable data plane support for the UE.

[0052] Option 1 : Evolving N1 to leverage a "distributed NAS" approach: In this option, each mobile network-related function within the UE communicates directly with the 5G Core network functions, bypassing the Access and Mobility Management Function (AMF). This differs from the architecture used in the current 5G system.

[0053] Option 2: Evolving N1 to leverage a service-based architecture (SB A) approach: In this option, each mobile network-related function within the UE communicates with the 5G Core network functions using direct IP -based communication over the HTTP / 2 Service-Based Interface (SBI) protocol, also bypassing the AMF, which differs from the current 5G architecture.

[0054] It should be noted that the detailed designs of Option 1 and Option 2 will not be discussed further. The mobile network architecture options shown in FIG. 4 are provided as reference architectures to support the data pipeline design described in some embodiments of the present disclosure.

[0055] The data plane includes functional components: Data plane orchestration coordinator (DPOC), data plane access controller (DP AC), data plane repository (DPR), and data pipeline contributor (DPC). The functional components collectively enable efficient, secure, and scalable support for data-driven services across various domains of a mobile system.

[0056] The DP AC plays a central role in managing data flow operations. Its responsibilities include data acquisition, identity validation for data sources and consumers, data processing, data exposure, and data distribution or sharing. It also interacts with data plane repository function (DPRF) for data storage management and associated services. Additionally, the DP AC supportsauditing and traceability, maintaining the membership information of data producers and consumers and the corresponding domains associated with each metadata profile. To ensure reliability and scalability, DPACs can be grouped into DP AC sets, similar to AMF sets in existing 5G systems. DPACs from different domains collaborate under the coordination of the DPOC to support inter-domain data plane functionalities.

[0057] The DPOC is a distributed logical function responsible for orchestrating cross-domain data plane communication. This includes ensuring security, managing access control between domains, and supporting topology hiding, similar in concept to the Signaling Communication Proxy (SCP) in today's 5G architecture. The DPOC may be co-located with the DP AC and is designed to work in coordination with other DPOC instances across domains. Within each mobile system functional domain, one or more network entities may be selected to serve as Data Pipeline Contributors (DPCs), depending on the specific service requirements.

[0058] The DPR provides data storage for scenarios that require handling of large data volumes or long-term data retention. Examples include storage of network logs, Al model training datasets, and snapshots taken during iterative Al model optimization processes. Given increasing regulatory demands for data privacy and the critical need for reliability in Al-govemed network operations, data stored in the DPR must be immutable and traceable.

[0059] The data pipeline is defined as a composite chain of data-related activities, potentially spanning multiple functional domains, to manipulate metadata collected from one or more sources in support of a particular data plane service. The Data Pipeline Contributor (DPC) is the network entity component that executes specific data plane functions — such as data collection, preprocessing, labeling, analytics, and routing. In the context of a data pipeline, the output from one DPC becomes the input for the next, facilitating a continuous and automated data flow from source to destination. A data pipeline starts at one or more pipeline data sources and ends at pipeline data sinks that receive the final, processed outputs. DPCs are capable of automating data operations such as extraction, transformation, integration, validation, and loading. They can handle various data types, including continuous, intermittent, and batch data.

[0060] DPC functionalities are embedded within network entities involved in the mobile system, such as UE, radio access network (RAN) functions, core network functions, operations and management (0AM) functions, or application functions. When a data pipeline is initiated to support a data plane service, appropriate network entities with DPC capabilities are selected to participate and perform the necessary data operations.

[0061] FIG. 5 illustrates a data pipeline architecture concept supported by data plane in a next generation mobile system according to an embodiment of the present disclosure. FIG. 5 illustratesa high-level data pipeline architecture concept, which forms part of the data plane functionalities designed to support various data plane services. A data pipeline contributor can also serve as a pipeline data source or a pipeline data sink, depending on its role within the pipeline. The pipeline data source represents the starting point of a data pipeline, where metadata begins its journey. A data pipeline can include multiple pipeline data sources, such as applications, cloud storage, streaming data from sensors or loT devices, and APIs from external services. These sources ingest raw data and inject it into the pipeline for further processing. The pipeline data sink is the endpoint of the data pipeline, where clean, processed, and integrated data becomes available for downstream actions — such as data analysis, machine learning model training, or real-time decision-making. For example, in a vehicle-to-everything (V2X) scenario, the processed data may be used for machine learning operations to support autonomous driving. Each data pipeline is dedicated to a specific data service operation, which may be initiated by an entity that is either part of or external to the pipeline components. In some cases, the data service request may originate from an authorized external entity outside the mobile system, indicating the flexibility and extensibility of the data plane architecture.

[0062] FIG. 6 illustrates data pipeline contributor functionalities and their respective data flows according to an embodiment of the present disclosure. FIG. 6 illustrates an example of data flows within a data pipeline operation designed to support AI / ML model training. This figure demonstrates how data pipeline contributor function in various roles, such as pipeline data source, data controller, or other contributor components within the pipeline. In the role of a pipeline data source, data, whether structured or unstructured, can be introduced into the pipeline in batch, intermittent, or continuous modes. The data may be pushed to the DP AC, which acts as a data collector, or pulled by the DP AC from the source. Export of data can occur through real-time events, batch records, or a combination of both. Once collected, the data is exposed to the DP AC, which may have subscribed to a corresponding metadata profile, thereby enabling authorized data consumers to access and utilize it.

[0063] Raw data can be processed before use. This involves converting the data into a standardized format defined by the metadata profile to ensure compatibility with downstream functions. Prior to participating in pipeline operations, network entities are authenticated and authorized to guarantee secure and policy-compliant access. Only verified contributors are permitted to interact with the pipeline, maintaining system integrity.

[0064] As part of data acquisition and processing, information is ingested from various sources and stored in a Data Repository for future use. The ingested data is logged and archived, potentially in compressed formats that can later be retrieved and uncompressed. Handling varies by data type:real-time streaming data is processed sequentially, while continuous data is validated immediately. The processing stage may include aggregation, parsing, and transformation to convert raw inputs into structured or semi -structured formats suitable for analytics and model training.

[0065] The system also features robust data pipeline monitoring, which includes three types. Pipeline Performance Monitoring tracks metrics such as data availability and latency. Data Profile Monitoring ensures data conforms to expected metadata profiles. Condition Monitoring detects anomalies or faults, triggers event logging or alarms, and supports diagnostic and mitigation processes, thereby enhancing traceability and explainability.

[0066] To support ongoing data operations, the pipeline provides permanent, reliable, and immutable storage for raw, processed, and pre-processed data. This ensures long-term data availability for auditing, reuse, or additional analysis. After storage, data can be exposed again, as the DP AC enables registered and subscribed consumers to retrieve archived data according to their access configurations — whether via instant notifications, periodic access, or on-demand downloads.

[0067] As pipelines are constructed, the DP AC uses pre-configured Data Plane Network Data Service Descriptors to identify and select appropriate Data Pipeline Contributors and determine routing paths. Upon establishing the pipeline, a unique Data Pipeline ID is assigned to track data flows and contributor associations.

[0068] The DP AC also orchestrates the data pipeline service by identifying participating network entities, assigning their processing roles, and determining the execution order necessary to support the intended data service operation. This orchestration ensures that the entire pipeline operates in a coordinated and efficient manner.

[0069] To improve data quality and ML performance, data pre-processing is performed before the data reaches the target ML models. This step includes data cleaning, normalization, labelling, quality assessment, imputation, encoding, and sampling. The pre-processed output is then fed into the AI / ML model for training, retraining, testing, and validation.

[0070] Throughout the operation, data pipeline contributors can communicate with one another, subject to operator-defined policies and mutual authorization. The pipeline is continuously monitored, enabling proactive issue detection, event logging, and alarm generation, as well as execution of mitigation actions when needed. At any stage, contributors may store processed outputs in the data repository for reuse by other applications or for future data services, supporting long-term auditing and data traceability.

[0071] As illustrated in FIG. 6, when a data pipeline contributor operates as a pipeline data source, it performs several key functions that initiate and enable the flow of data through the pipeline.First, it handles data collection, where structured or unstructured data may be introduced into the data pipeline in batch, intermittent, or continuous modes. This data can be either pushed from the source to the DP AC, acting as a data collector, or pulled by the DP AC from the respective data sources. Data export may occur through real-time events, batch records, or a combination of both.

[0072] Once collected, the data undergoes data exposure, where it is made available to the DP AC. The DP AC may have subscribed to a specific metadata profile associated with the data source, enabling selective and authorized access to the data. Following this, the data processing step involves converting the raw data into a predefined format based on the metadata profile. This ensures the data is compatible with downstream processing components and systems.

[0073] When a data pipeline contributor functions as a pipeline data sink, it receives processed and integrated data and uses it to support the target data service operation, such as data analytics or AI / ML model training. For instance, in V2X machine learning scenarios, the data sink may utilize the incoming data to train models that enhance autonomous driving capabilities.

[0074] In some cases, before the pipeline data sink can begin its designated operation, it may require assistance from other data pipeline contributors to conduct preliminary steps such as data pre-processing. This process optimizes the data for use in machine learning algorithms and may include data quality assessment, imputation, encoding, sampling, and labeling. The output from pre-processing is then forwarded to the Pipeline Data Sink, which hosts the target service (e.g., model training, retraining, testing, or validation) and utilizes the refined data accordingly.

[0075] To orchestrate, organize, and direct data flows and processing tasks across the various contributors in support of a specific data service operation, the DPOC and the DPACs within the involved domains work collaboratively. Their actions are guided by the data plane network data service descriptors, which define the necessary processing logic and control policies.

[0076] Among the core responsibilities of the DPOC and DPAC is data pipeline access authentication and authorization, which ensures that each participating network entity is properly validated and permitted to interact with the pipeline. This enforces secure, policy-compliant access across the system.

[0077] The data acquisition and processing function involves collecting data from various sources and ingesting it into a data repository for long-term use. All ingested data is logged and archived, potentially in a compressed format, and can be retrieved and uncompressed later as needed. Different data types are processed in distinct ways — for example, real-time streaming data is handled sequentially, while continuous data is validated immediately. Processing steps may include aggregation, parsing, and transformation, converting raw inputs into structured or semistructured formats suitable for statistical analysis and machine learning applications.

[0078] Throughout the pipeline, monitoring is carried out across dimensions: Pipeline performance monitoring, which tracks metrics like data availability and latency; pipeline QoS monitoring, which verifies that data quality meets the requirements defined in the descriptors; and pipeline condition monitoring, which detects faults, triggers logs and alarms, and facilitates traceability and explainability in case of data-related issues. The system also provides robust data storage capabilities, ensuring permanent, reliable, and immutable storage for raw, processed, and pre-processed data. This stored data can be revisited for future auditing, analysis, or data exposure. Regarding data exposure, the DP AC enables authorized and registered consumers to retrieve archived data using metadata profiles. Consumers may access this data via on-demand queries, periodic updates, or event-driven notifications, depending on their subscription configurations. In terms of contributor selection, the DP AC uses the Data Plane Network Data Service Descriptors to determine the appropriate Data Pipeline Contributors for a given operation. Once the participants are identified, the system assigns a unique Data Pipeline Identifier (DPID) to facilitate flow classification and association of contributors within that specific pipeline. Finally, the DP AC oversees data pipeline service orchestration. It determines the list of required network entities, their roles, responsibilities, and the execution sequence needed to fulfill the service operation. In cross-domain scenarios, the DP AC that receives the Data Plane Service Request (from the supplicant) assumes responsibility for orchestrating the entire cross-domain data pipeline, ensuring cohesive operation across different system domains.

[0079] The data plane network data service descriptors define the characteristics of the data workflow within the data pipeline for a specific type of network data service (e.g., Autonomous Driving, Smart Metering, Network Congestion Control, etc.). These descriptors are detailed in Table 1 below.

[0080] Tablet : Data plane network data service descriptors.

[0081] FIG. 7 illustrates a use case for a cross-domain single data pipeline operation, in accordance with an embodiment of the present disclosure. This example demonstrates how a single data pipeline can support operations across multiple domains. Use Case: ML data collection is requested by the V2X application server (V2X-AS) to enable AI / ML-assisted sensing operations for estimating vehicle location, which contributes to building an autonomous driving map. In this scenario, the RAN and the Core Network are configured as separate domains. The DPOC works in coordination with the DPACs to determine which network entities from each domain are required to act as Data Pipeline Contributors in order to support the requested operation. Once thecontributors are selected based on the Data Plane Network Data Service Descriptors, the DPOC assigns a unique Data Pipeline Identifier (DP ID) to the established pipeline. This identifier enables tracking and orchestration of the pipeline throughout its lifecycle.

[0082] Table 2 below lists the data plane service request parameters that may be included in a service request initiated by a service request supplicant (e.g., UE, RAN functions, core network functions, 0AM, AF, etc.), or in a data pipeline service request used to trigger a data pipeline operation.

[0083] Table 2: Data plane service parameter descriptions.

[0084] FIG. 8 illustrates data plane service request procedures to initiate and to trigger local domains data pipeline operation configured to implement some embodiments presented herein. FIG. 8 illustrates that, in some embodiments, data plane service request procedures to initiate and to trigger local domains data pipeline operation includes at least one of following operations. This process may involve a sequence of coordinated operations between the data plane service supplicant, the DP AC, and relevant functional entities within the domain.

[0085] Operation 1 : Data Plane Service Supplicant — which may be a User Equipment (UE), RAN function, Core Network Function (NF), Operations and Management (0AM) system, or Application Function (AF) — initiates a Data Plane Service Request to the Data Plane Access Controller (DPACa). This request is made to collect appropriate metadata required to support a specific type of data-driven network operation, such as Al-assisted integrated sensing model training. The service request is sent using the message format DPAC_Service_Request(DataPlaneNetworkDataServiceDescriptorsID, DPSShcorrlD, DPSSID, DPS SDigital Signature, DPSSPublicKey). This message includes several critical parameters: the DataPlaneNetworkDataServiceDescriptorsID, which indicates the type of data-driven service operation being requested; the DPSShcorrlD, which is a correlation identifier used to match the service request between the supplicant and the DPACa; the DPSSID, which uniquely identifies the supplicant; and the DPS SDigital Signature along with the DPSSPublicKey, which are used by the DPACa to verify the identity of the supplicant. It is assumed that, prior to initiating this request, the supplicant has already been registered with the DP AC and that mutual authentication between the supplicant and the DP AC has already been successfully completed.

[0086] Operation 2: Data Plane Access Controller (DPACa) verifies the identity of the Data Plane Service Supplicant (DPSS). Upon successful verification, DPACa proceeds to validate the Data Plane Network Data Service Descriptors requested by the DPSS. This validation is performed against the serving Mobile Network Operator's (MNO's) network capabilities and policy framework, as well as applicable local or regional data protection regulations relevant to the DPSS’s geographical location or operational environment. If all validations are successfully passed, DPACa assigns a unique Data Pipeline Identifier, which serves as a reference for establishing a data pipeline to support the requested data plane service operation. Based on the service descriptors, DPACa determines whether the requested operation requires support from functional entities within the local domain. It then proceeds to select the appropriate functional entities that are capable of hosting the required Data Pipeline Contributor functions, in accordance with the criteria defined in the Data Plane Network Data Service Descriptors. Once all relevant functional entities are identified to support the pipeline operation, DPACa or DPOCa sends the corresponding Data Plane Service Parameters to each of the selected functional entities, initiating their participation in the data pipeline.

[0087] Operation 3: Data Plane Access Controller (DPACa) sends a Data Pipeline Service Subscribe Request to the selected functional entities — Entity-X, Entity-Y, and Entity -Z — to notify them of their respective roles and associated Data Plane Service Request parameters. This request is delivered in the format: DPAC_DataPipelineServiceSubscribe_Request(DataPipelineIdentifier, DPACalD, DP ACaDigital Signature, DPACaPublicKey, DataPlaneServiceParametersTuple). In this use case, Function Entity-X acts as the data sink, which requires metadata to be collected from Entity-Y and Entity-Z. The purpose of this collaboration is to perform data analytics that will produce aggregated analytical outputs in support of the data plane service operation initially requested by the Data Plane Service Supplicant.

[0088] Operation 4: Data Plane Access Controller (DPACa) sends a notification to the Data Plane Service Supplicant to inform them that the requested data service operation has been successfully initiated. This notification is sent in the form of DPAC_Service_Notify(DPSShconTD, Operation Status), where DPSShcorrlD is the correlation identifier used to track the original request, and Operation Status indicates the current status of the operation — such as initiation success or failure.

[0089] Operation 5: Data Pipeline Contributor within Function Entity-X, acting as the data consumer, initiates a solicited metadata transaction request to the DP AC for two specific metadata profiles, identified as Profile M and Profile N. This request is sent in the format: DPAC_MetaTrans_Subscribe_Request(DataConsumerDPID, DataConsumerhconTD,DataConsumerlD, DataConsumerDigi tai Signature, DataConsumerPublicKey, Metadata Profile{M, N}). The message includes the Data Pipeline Identifier associated with the data consumer, a correlation ID, the data consumer’s identity, digital signature, and public key for verification purposes. The request explicitly specifies that Metadata Profiles M and N are required to support the ongoing data pipeline operation.

[0090] Operations 6 and 7: Data Plane Access Controller (DPACa), acting on behalf of Function Entity-X, initiates Solicited Metadata Subscribe Requests to Functional Entity-Y and Entity-Z. These requests are issued to collect the required metadata corresponding to Metadata Profiles M and N, as previously specified by Function Entity-X. This step ensures that the necessary data sources are engaged to provide the relevant metadata for downstream processing and analytics within the data pipeline.

[0091] Operations 8 and 9: DPACa consolidates and processes all the metadata responses received from Functional Entity-Y and Entity-Z as a result of the subscription requests initiated in the previous steps. After processing, DPACa sends the aggregated metadata along with the transaction status to Function Entity-X using the message: DPAC_MetaTrans_Subscribe_Response(DataConsumerhcorrID, Metadata, Transaction Status). This response ensures that Function Entity-X receives the complete and validated set of metadata required to proceed with the data analytics task defined within the data pipeline operation.

[0092] Operation 10: Function Entity-X receives the metadata collected from Entity-Y and Entity-Z and performs further data pre-processing to prepare the aggregated metadata for analysis. Once the pre-processing is completed, Function Entity-X proceeds to trigger the data analytics operation. After generating the analytical results, it initiates an unsolicited metadata transaction request to DPACa to store the analytic output, and subsequently notifies DPACa that the Data Plane Service Request has been completed.

[0093] Operation 11 : Function Entity-X initiates a DPAC MetaTrans Create / Update Request to DPACa in order to store the analytic output in the designated storage. The request is formatted as: DPAC_MetaTrans_Create / Update_Request(DataSourceDPID, DataSourcehcorrlD,DataSourcelD, DataSourceDigi tai Signature, DataSourcePublicKey, Metadata Profile, DP AC pub key (Metadata)). This message includes the Data Pipeline Identifier, a correlation ID, the identity and credentials of the data source (Function Entity-X), the associated Metadata Profile, and the encrypted analytic data payload, secured using DPACa’ s public key.

[0094] Operation 12: Function Entity-X sends aDPAC DataPipelineServiceSubscribe Response message to DPACa to formally indicate the completion of the Data Pipeline operation. The message includes the Data Pipeline Identifier and the Operation Status, confirming that all required tasks, such as metadata collection, pre-processing, analytics, and data storage, have been successfully executed within the established data pipeline.

[0095] Operation 13: Data Plane Access Controller (DPACa) sends a final notification to the Data Plane Service Supplicant to inform them of the completion status of the requested data service operation. This notification is delivered using the message: DPAC_Service_Notify(DPSShconTD, Operation Status), where DPSShcorrlD is the correlation identifier associated with the original request, and Operation Status indicates whether the operation is successful or failed. This marks the conclusion of the data pipeline service workflow.

[0096] FIG. 9 illustrates the data plane service request procedures used to initiate and trigger a cross-domain data pipeline operation, as configured in accordance with some embodiments of the present disclosure. As shown in FIG. 9, in some embodiments, the procedure may include at least one of the following operations, which collectively enable the coordination and execution of data pipeline activities across multiple administrative or functional domains.

[0097] Operation 1 : Data Plane Service Supplicant, such as a UE, RAN function, Core Network Function (NF), Operations and Maintenance (0AM) system, or Application Function (AF), initiates a Data Plane Service Request to the Data Plane Access Controller (DPACa) in order to collect the necessary metadata required to support a specific type of data-driven network operation, such as Al-assisted integrated sensing model training. The request is sent in the form: DPAC_Service_Request(DataPlaneNetworkDataServiceDescriptorsID, DPSShcorrlD, DPSSID, DP SSDigi tai Signature, DPSSPublicKey). This message includes the Data Plane Network Data Service Descriptors Identifier, which specifies the targeted data-driven operation; the DPSS Correlation Identifier, which allows the request to be correlated between the supplicant and the DPACa; the supplicant’s identifier; and the digital signature and public key, which enable the DPACa to verify the identity of the supplicant. Note: It is assumed that the supplicant has already been registered with the DP AC and that mutual authentication has previously been completed.

[0098] Operation 2: Data Plane Access Controller (DPACa) verifies the identity of the Data Plane Service Supplicant (DPSS). Upon successful verification, DPACa validates the requested Data Plane Network Data Service Descriptors against the serving Mobile Network Operator’s (MNO’s) network capabilities and policies, as well as applicable local or regional data protection regulations, considering the DPSS’s physical location and operational environment. If all validations are successful, DPACa assigns a unique Data Pipeline Identifier in preparation for establishing a data pipeline that may support the requested data plane service operation. Based on the content of the requested service descriptors, DPACa may determine that the requested operation requires additional support from functional entities located in another domain, such as Domain-B. In suchcases, DPACa engages its associated Data Plane Orchestration Coordinator (DPOCa) to communicate with the DPOC(s) of the other domains. Together, they select and coordinate the necessary cross-domain functional entities required to support and fulfill the data plane service request.

[0099] Operation 3: Data Plane Orchestration Coordinator (DPOCa) relays the Data Plane Service Request to Domain-B in order to initiate collaboration for the cross-domain data pipeline operation required to support the requested data plane service. This is done by sending a message in the format: DPOC_Service_Request(DataPlaneNetworkDataServiceDescriptorsID, DPOCahcorrlD, DPOCalD, DPOCaDigital Signature, DPOCaPublicKey, DataPipelineldentifier). This message includes the Data Plane Network Data Service Descriptors Identifier, which indicates the target data-driven network operation; a correlation identifier specified by DPOCa to link and track the service request between the two DPOCs; and DPOCa’ s identifier, digital signature, and public key, which allow the receiving DPOCb in Domain-B to verify DPOCa’ s identity. The message also includes the Data Pipeline Identifier, which serves as a reference for coordinating the data pipeline operation across domains. Note: It is assumed that DPOCa and DPOCb have already been mutually authenticated prior to this communication.

[0100] Operation 4: DPOCb receives the Data Plane Service Request from DPOCa, which includes the relevant Network Data Service Descriptors. Upon receipt, DPOCb coordinates with the Data Plane Access Controller of Domain-B (DPACb) to identify and select the appropriate functional entity that supports the Data Pipeline Contributor function and is capable of meeting the functional and performance requirements specified in the target Data Plane Network Service Descriptors. Once the participation of the required Data Pipeline Contributor(s) is confirmed, DPOCb provides the details of the selected functional entities, the hosting the Data Pipeline Contributor functions, back to DPOCa to support coordination of the cross-domain data pipeline operation. Note: The specific process by which DP AC verifies whether a functional entity meets the resource requirements for supporting the requested Data Plane Service will not be discussed further.

[0101] Operations 5a and 5b: DPOCb responds to DPOCa with the results of the functional entity selection process for the requested Data Plane Service Request. Specifically, DPOCb sends a message in the format: DPOC_Service_Response(DPOCahconTD,DataPipelineContributorInfo(FunctionEntityTuple(FunctionalType, Identifier))), which includes one or more functional entities that have been selected to serve as Data Pipeline Contributors for the data pipeline operation. This message allows DPOCa to continue orchestrating the crossdomain coordination. Note: If DPOCb and DPACb fail to identify any suitable functional entitiescapable of supporting the requested Data Plane Service, the function entity list returned will be empty. In such a case, DPACa will notify the Data Plane Service Supplicant of the failed operation by sending DPAC_Service_Notify(DPSShcorrID, Operation Status), and the subsequent steps in the procedure will be skipped.

[0102] Operation 6: while DPOCa continues coordinating with other DPOC(s) to support the requested Data Plane Service, the relevant DPACs proceed to select the required functional entities that host the necessary Data Pipeline Contributor functions. This selection is made based on the criteria defined in the corresponding Data Plane Network Data Service Descriptors. Once all the functional entities needed to support the data pipeline operation are identified, DPACs and / or DPOCa send the appropriate Data Plane Service Parameters to each of the selected functional entities to initiate their participation in the data pipeline process. Note: The specific mechanisms by which a DP AC verifies the resource requirements and capabilities of a functional entity to fulfill the requested Data Plane Service will not be discussed further.

[0103] Operation 7a: DPACa sends a Data Pipeline Service Subscribe Request to each of the selected functional entities within its local domain that have been designated as Data Pipeline Contributors. The request is sent individually to each entity using the following format: DPAC_DataPipelineServiceSubscribe_Request(DataPipelineIdentifier, DPACalD,DP ACaDigital Signature, DPACaPublicKey, DataPlaneServiceParametersTuple). This message includes the unique Data Pipeline Identifier, the identity and credentials of DPACa, and the corresponding Data Plane Service Parameters tailored to each functional entity's role in the data pipeline operation.

[0104] Operation 7b 1 : DPOCa sends a Data Pipeline Service Subscribe Request to DPOCb for each selected functional entity in Domain-B. The request is formatted as: DPOC_DataPipelineServiceSubscribe_Request(DataPipelineIdentifier, DPOCalD,DPOCaDigital Signature, DPOCaPublicKey, FunctionalEntity Identifier,DataPlaneServiceParametersTuple). This message includes the Data Pipeline Identifier, the identity and credentials of DPOCa, the identifier of the target functional entity, and the corresponding Data Plane Service Parameters. Upon receiving the request, DPOCb is responsible for relaying the subscription message to the already selected functional entity in Domain-B, which has been designated as a Data Pipeline Contributor, to initiate its role in the pipeline operation.

[0105] Operation 7b2: Upon receiving the Data Pipeline Service Subscribe Request from DPOCa, DPOCb transposes it into a local subscription request targeted at the corresponding functional entity within Domain-B. This localized request is formatted as: DPAC_DataPipelineServiceSubscribe_Request(DataPipelineIdentifier, DPACblD,DP ACbDigi tai Signature, DPACbPublicKey, FunctionalEntity Identifier,DataPlaneServiceParametersTuple). The message includes the Data Pipeline Identifier, the identity and credentials of DPACb, the identifier of the target functional entity, and the relevant Data Plane Service Parameters, enabling the designated Data Pipeline Contributor in Domain-B to participate in the requested data pipeline operation.

[0106] Operation 8: DPACa notifies the Data Plane Service Supplicant that the requested Data Plane Service has been successfully initiated. This notification is sent using the message format: DPAC_Service_Notify(DPSShconTD, Operation Status), where DPSShcorrlD is the correlation identifier associated with the original service request, and Operation Status indicates the current status of the operation, confirming that the data pipeline setup process has begun.

[0107] Operation 9: all Data Pipeline Contributors associated with the selected functional entities have been provided with their respective Data Plane Service Parameters. Each contributor proceeds with its designated operational procedures in accordance with the specific Data Plane Network Data Service Descriptors. For example, a contributor may begin by subscribing to the pipeline data source for data collection and subsequently expose raw or processed data to the next functional entity in the pipeline. The contributor may also perform data pre-processing and / or data labeling to prepare the data for subsequent operations such as data analysis, storage, or AI / ML model training or inference. As part of the provided service parameters, each functional entity is made aware of the next entity in the pipeline. This allows the Data Pipeline Contributor to seamlessly forward the processed output to the subsequent entity, enabling the continuation or completion of the pipeline operation as required. The initial Data Plane Access Controller (DPACa) is responsible for initiating Data Pipeline Operation Monitoring, both within its local domain and across domains. Monitoring results are stored locally within each domain, and any detected abnormal behavior is reported back to DPACa for logging and traceability. The specific implementation details of the monitoring mechanisms, however, are outside the scope of this disclosure. If any part of the data pipeline operation fails, is interrupted, or completes, the respective local DP AC responsible for that segment will be notified of the operation status. In cross-domain scenarios, the local DP AC will also inform its associated DPOC, which in turn relays the status to the initial DP AC (DPACa) to ensure end-to-end visibility and coordination across the entire pipeline.

[0108] Operations 10 and 11 : Function Entity-X from the local domain and Function Entity-Y from the partner domain notify DPACa and / or DPOCa of the completion of their respective data pipeline operations in relation to the original Data Plane Service Request. This confirmationensures that the data processing tasks assigned to each contributor have been successfully carried out and that their roles in the pipeline have been fulfilled as expected.

[0109] Operation 12: Once DPACa is informed of the completion of the data pipeline service operation, it proceeds to notify the Data Plane Service Supplicant of the final status. This notification is sent using the message format: DPAC_Service_Notify(DPSShconTD, Operation Status), where DPSShcorrlD serves as the correlation identifier for the original service request, and Operation Status indicates whether the data pipeline operation was successfully completed or encountered a failure.

[0110] In summary, to support data-driven network services, technology trends indicate a clear paradigm shift from traditional session-based information transmission to a platform-oriented approach focused on data management and orchestration. In this context, it is beneficial to introduce a new Data Service Framework in 6G that enables collaborative data collection and dynamic data pipeline operations to support advanced services such as AI / ML, sensing, and digital twin functionalities. Some embodiments of the present disclosure describe the design of a 6G system architecture that establishes data pipelines to provide data-driven services supporting emerging AI / ML training and inference, sensing, and digital twin operations. These operations rely on metadata sourced from multiple pipeline data contributors, enabling autonomous intelligent system functions. The contributing system entities may be distributed across one or more system domains (e.g., operator-configured administrative domains) and interconnected through arbitrary network topologies. Some embodiments of the present disclosure further define how the data plane architecture is structured to enable such data pipeline operations and facilitate the delivery of data- driven network services in next-generation wireless communication systems.[oni] Commercial interests for some embodiments are as follows. 1. Solve issues in the prior art and other issues. 2. Enable efficient, scalable, and context-aware data integration across distributed system domains. 3. empower autonomous intelligent systems with timely and reliable metadata for enhanced AI / ML, sensing, and digital twin operations. Some embodiments of the present disclosure can be used in many applications. Some embodiments of the present disclosure are used by chipset vendors, video system development vendors, automakers including cars, trains, trucks, buses, bicycles, moto-bikes, helmets, and etc., drones (unmanned aerial vehicles), smartphone makers, communication devices for public safety use, AR / VR / MR device maker for example gaming, conference / seminar, education purposes. Some embodiments of the present disclosure are a combination of “techniques / processes” that can be adopted in video standards to create an end product. Some embodiments of the present disclosure propose technical mechanisms. The at least one proposed solution, method, system, and apparatus of some embodiments of thepresent disclosure may be used for current and / or new / future standards regarding communication systems such as a UE, a base station, a network device, and / or a communication system. Compatible products follow at least one proposed solution, method, system, and apparatus of some embodiments of the present disclosure. The proposed solution, method, system, and apparatus are widely used in a UE, a base station, a network device, and / or a communication system. With the implementation of the at least one proposed solution, method, system, and apparatus of some embodiments of the present disclosure, at least one modification / improvment to methods and apparatus for data plane service are considered for standardizing.

[0112] In some embodiments of the present disclosure, a network device is provided. The network device includes a memory, a transceiver, and a processor coupled to the memory and the transceiver. The network device is configured to perform the above-described method. In some embodiments, the network device may be implemented as or within a computing device, such as computing device 1100 illustrated in FIG. 10. As shown, computing device 1100 may include a processor 1112 communicatively coupled to a memory 1114 and configured to execute computerexecutable program code stored therein to perform the disclosed operations. The processor may include a microprocessor, an application-specific integrated circuit (ASIC), or other suitable processing units.

[0113] In some embodiments of the present disclosure, a non-transitory machine-readable storage medium is provided. The storage medium has stored thereon instructions that, when executed by a computer (e.g., the computing device 1100 of FIG. 10), cause the computer to perform the method described above. In some embodiments of the present disclosure, a chip is provided. The chip includes a processor configured to call and run a computer program stored in a memory, such that the device in which the chip is installed (e.g., as part of computing device 1100 or communication system 1200 of FIG. 11) performs the method described above. In some embodiments of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program that causes a computer (e.g., processor 1112 in FIG. 10 or components in FIG. 11 such as baseband circuitry 1220 or application circuitry 1230) to execute the method described above. In some embodiments of the present disclosure, a computer program product is provided. The computer program product includes a computer program that, when executed by a computer, causes the computer to perform the method described above. In some embodiments of the present disclosure, a computer program is provided that causes a computer to perform the method described above. The computing environment may include components such as RF circuitry 1210, application circuitry 1230, and memory / storage1240 as depicted in FIG. 11, which collectively support the execution and operation of the data- driven method described herein.

[0114] FIG. 10 is an example of a computing device 1100 according to an embodiment of the present disclosure. Any suitable computing device can be used for performing the operations described herein. For example, FIG. 10 illustrates an example of the computing device 1100 that can implement apparautes and / or methods illustrated in FIG. 2 to FIG. 9 using any suitably configured hardware and / or software. In some embodiments, the computing device 1100 can include a processor 1112 that is communicatively coupled to a memory 1114 and that executes computer-executable program code and / or accesses information stored in the memory 1114. The processor 1112 may include a microprocessor, an application-specific integrated circuit (“ASIC”), a state machine, or other processing device. The processor 1112 can include any of a number of processing devices, including one. Such a processor can include or may be in communication with a computer-readable medium storing instructions that, when executed by the processor 1112, cause the processor to perform the operations described herein.

[0115] The memory 1114 can include any suitable non-transitory computer-readable medium. The computer-readable medium can include any electronic, optical, magnetic, or other storage device capable of providing a processor with computer-readable instructions or other program code. Non-limiting examples of a computer-readable medium include a magnetic disk, a memory chip, a read-only memory (ROM), a random access memory (RAM), an application specific integrated circuit (ASIC), a configured processor, optical storage, magnetic tape or other magnetic storage, or any other medium from which a computer processor can read instructions. The instructions may include processor-specific instructions generated by a compiler and / or an interpreter from code written in any suitable computer-programming language, including, for example, C, C++, C#, visual basic, java, python, perl, javascript, and actionscript.

[0116] The computing device 1100 can also include a bus 1116. The bus 1116 can communicatively couple one or more components of the computing device 1100. The computing device 1100 can also include a number of external or internal devices such as input or output devices. For example, the computing device 1100 is illustrated with an input / output (“VO”) interface 1118 that can receive input from one or more input devices 1120 or provide output to one or more output devices 1122. The one or more input devices 1120 and one or more output devices 1122 can be communicatively coupled to the I / O interface 1118. The communicative coupling can be implemented via any suitable manner (e.g., a connection via a printed circuit board, connection via a cable, communication via wireless transmissions, etc.). Non-limiting examples of input devices 1120 include a touch screen (e g., one or more cameras for imaging a touch areaor pressure sensors for detecting pressure changes caused by a touch), a mouse, a keyboard, or any other device that can be used to generate input events in response to physical actions by a user of a computing device. Non-limiting examples of output devices 1122 include a liquid crystal display (LCD) screen, an external monitor, a speaker, or any other device that can be used to display or otherwise present outputs generated by a computing device.

[0117] The computing device 1100 can execute program code that configures the processor 1112 to perform one or more of the operations described above with respect to some embodiments illustrated in FIG. 2 to FIG. 9. The program code may be resident in the memory 1114 or any suitable computer-readable medium and may be executed by the processor 1112 or any other suitable processor.

[0118] The computing device 1100 can also include at least one network interface device 1124. The network interface device 1124 can include any device or group of devices suitable for establishing a wired or wireless data connection to one or more data networks 1128. Non limiting examples of the network interface device 1124 include an Ethernet network adapter, a modem, and / or the like. The computing device 1100 can transmit messages as electronic or optical signals via the network interface device 1124.

[0119] FIG. 11 is a block diagram of an example of a communication system 1200 according to an embodiment of the present disclosure. Embodiments described herein may be implemented into the communication system 1200 using any suitably configured hardware and / or software. FIG. 11 illustrates the communication system 1200 including a radio frequency (RF) circuitry 1210, a baseband circuitry 1220, an application circuitry 1230, a memory / storage 1240, a display 1250, a camera 1260, a sensor 1270, and an input / output (I / O) interface 1280, coupled with each other at least as illustrated.

[0120] The application circuitry 1230 may include a circuitry such as, but not limited to, one or more single-core or multi-core processors. The processors may include any combination of general -purpose processors and dedicated processors, such as graphics processors, application processors. The processors may be coupled with the memory / storage and configured to execute instructions stored in the memory / storage to enable various applications and / or operating systems running on the system. The communication system 1200 can execute program code that configures the application circuitry 1230 to perform one or more of the operations described above with respect to FIG. 2 to FIG. 9. The program code may be resident in the application circuitry 1230 or any suitable computer-readable medium and may be executed by the application circuitry 1230 or any other suitable processor.

[0121] The baseband circuitry 1220 may include circuitry such as, but not limited to, one or more single-core or multi-core processors. The processors may include a baseband processor. The baseband circuitry may handle various radio control functions that may enable communication with one or more radio networks via the RF circuitry. The radio control functions may include, but are not limited to, signal modulation, encoding, decoding, radio frequency shifting, etc. In some embodiments, the baseband circuitry may provide for communication compatible with one or more radio technologies. For example, in some embodiments, the baseband circuitry may support communication with an evolved universal terrestrial radio access network (EUTRAN) and / or other wireless metropolitan area networks (WMAN), a wireless local area network (WLAN), a wireless personal area network (WPAN). Embodiments in which the baseband circuitry is configured to support radio communications of more than one wireless protocol may be referred to as multimode baseband circuitry.

[0122] In various embodiments, the baseband circuitry 1220 may include circuitry to operate with signals that are not strictly considered as being in a baseband frequency. For example, in some embodiments, baseband circuitry may include circuitry to operate with signals having an intermediate frequency, which is between a baseband frequency and a radio frequency. The RF circuitry 1210 may enable communication with wireless networks using modulated electromagnetic radiation through a non-solid medium. In various embodiments, the RF circuitry may include switches, filters, amplifiers, etc. to facilitate the communication with the wireless network. In various embodiments, the RF circuitry 1210 may include circuitry to operate with signals that are not strictly considered as being in a radio frequency. For example, in some embodiments, RF circuitry may include circuitry to operate with signals having an intermediate frequency, which is between a baseband frequency and a radio frequency.

[0123] In various embodiments, the transmitter circuitry, control circuitry, or receiver circuitry discussed above with respect to apparatuses and / or methods illustrated in FIG. 2 to FIG. 9 may be embodied in whole or in part in one or more of the RF circuitry, the baseband circuitry, and / or the application circuitry. As used herein, “circuitry” may refer to, be part of, or include an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group), and / or a memory (shared, dedicated, or group) that execute one or more software or firmware programs, a combinational logic circuit, and / or other suitable hardware components that provide the described functionality. In some embodiments, the electronic device circuitry may be implemented in, or functions associated with the circuitry may be implemented by, one or more software or firmware modules. In some embodiments, some or all of the constituent components of the baseband circuitry, the application circuitry, and / or the memory / storage may beimplemented together on a system on a chip (SOC). The memory / storage 1240 may be used to load and store data and / or instructions, for example, for system. The memory / storage for one embodiment may include any combination of suitable volatile memory, such as dynamic random access memory (DRAM)), and / or non-volatile memory, such as flash memory.

[0124] In various embodiments, the I / O interface 1280 may include one or more user interfaces designed to enable user interaction with the system and / or peripheral component interfaces designed to enable peripheral component interaction with the system. User interfaces may include, but are not limited to a physical keyboard or keypad, a touchpad, a speaker, a microphone, etc. Peripheral component interfaces may include, but are not limited to, a non-volatile memory port, a universal serial bus (USB) port, an audio jack, and a power supply interface. In various embodiments, the sensor 1270 may include one or more sensing devices to determine environmental conditions and / or location information related to the system. In some embodiments, the sensors may include, but are not limited to, a gyro sensor, an accelerometer, a proximity sensor, an ambient light sensor, and a positioning unit. The positioning unit may also be part of, or interact with, the baseband circuitry and / or RF circuitry to communicate with components of a positioning network, e.g., a global positioning system (GPS) satellite.

[0125] In various embodiments, the display 1250 may include a display, such as a liquid crystal display and a touch screen display. In various embodiments, the communication system 1200 may be a mobile computing device such as, but not limited to, a laptop computing device, a tablet computing device, a netbook, an Ultrabook, a smartphone, an AR / VR glasses, etc. In various embodiments, system may have more or less components, and / or different architectures. Where appropriate, methods described herein may be implemented as a computer program. The computer program may be stored on a storage medium, such as a non-transitory storage medium.

[0126] A person having ordinary skill in the art understands that each of the units, algorithm, and steps described and disclosed in the embodiments of the present disclosure are realized using electronic hardware or combinations of software for computers and electronic hardware. Whether the functions run in hardware or software depends on the condition of application and design requirement for a technical plan. A person having ordinary skill in the art can use different ways to realize the function for each specific application while such realizations should not go beyond the scope of the present disclosure. It is understood by a person having ordinary skill in the art that he / she can refer to the working processes of the system, device, and unit in the above-mentioned embodiment since the working processes of the above-mentioned system, device, and unit are basically the same. For easy description and simplicity, these working processes will not be detailed.

[0127] It is understood that the disclosed system, device, and method in the embodiments of the present disclosure can be realized with other ways. The above-mentioned embodiments are exemplary only. The division of the units is merely based on logical functions while other divisions exist in realization. It is possible that a plurality of units or components are combined or integrated in another system. It is also possible that some characteristics are omitted or skipped. On the other hand, the displayed or discussed mutual coupling, direct coupling, or communicative coupling operate through some ports, devices, or units whether indirectly or communicatively by ways of electrical, mechanical, or other kinds of forms.

[0128] The units as separating components for explanation are or are not physically separated. The units for display are or are not physical units, that is, located in one place or distributed on a plurality of network units. Some or all of the units are used according to the purposes of the embodiments. Moreover, each of the functional units in each of the embodiments can be integrated in one processing unit, physically independent, or integrated in one processing unit with two or more than two units.

[0129] If the software function unit is realized and used and sold as a product, it can be stored in a readable storage medium in a computer. Based on this understanding, the technical plan proposed by the present disclosure can be essentially or partially realized as the form of a software product. Or, one part of the technical plan beneficial to the conventional technology can be realized as the form of a software product. The software product in the computer is stored in a storage medium, including a plurality of commands for a computational device (such as a personal computer, a server, or a network device) to run all or some of the steps disclosed by the embodiments of the present disclosure. The storage medium includes a USB disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a floppy disk, or other kinds of media capable of storing program codes.

[0130] While the present disclosure has been described in connection with what is considered the most practical and preferred embodiments, it is understood that the present disclosure is not limited to the disclosed embodiments but is intended to cover various arrangements made without departing from the scope of the broadest interpretation of the appended claims.

Claims

What is claimed is:

1. A wireless communication method for data plane service, performed by a network node, comprising: receiving, from a data plane service supplicant (DPSS), a service request, wherein the service request comprises a data plane network data service descriptor identifier indicating a type of a data-driven network operation, a data plane service supplicant correlation identifier to correlate the service request between the DPSS and the data plane node, a DPSS identifier (DPSSID) identifying the DPSS, a digital signature of the DPSS, and / or a public key associated with the DPSS; verifying the DPSS; and initiating metadata collection to support the data-driven network operation based on the service request.

2. The method of claim 1, wherein the network node comprises a data plane access controller (DPAC).

3. The method of claim 1 or 2, wherein the DPSS comprises a user equipment (UE), a radio access network (RAN) function node, a core network function (core NF) node, an operation, administration and maintenance (OAM) function node, or an application function (AF) node.

4. The method of any one of claims 1 to 3, further comprising performing at least one of following operations comprising: upon successful verification of the DPSS, performing, by the network node, one or more following operations: validating requested data plane network data service descriptors based on at least one of: network capabilities and policies of a serving mobile network operator (MNO); or local or regional data protection regulations applicable to the DPSS’s location or environment; upon successful validation, assigning a data pipeline identifier to establish a data pipeline supporting a requested data plane service operation; determining, based on the requested data plane network data service descriptors, that one or more functional entities in a local domain are required to support a data pipeline operation; selecting, by the network node, required functional entities in the local domain that host data pipeline functions according to descriptor criteria; or transmitting data plane service parameters to selected functional entities to enable the requested data plane service operation.

5. The method of any one of claims 1 to 4, further comprising: transmitting, by the network node, a data pipeline service subscribe request to a first functionalentity, a second functional entity, and a third functional entity, wherein the data pipeline service subscribe request comprises a data pipeline identifier, a network node identifier, a digital signature of the network node, a public key of the network node, and / or a tuple of data plane service parameters associated with the requested data-driven operation; wherein the first functional entity operates as a data sink that receives metadata from the second functional entity and the third functional entity and performs data analytics to generate aggregated outputs to support the requested data plane service operation.

6. The method of any one of claims 1 to 5, further comprising: transmitting, by the network node to the DPSS, a service notification comprising the data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates that the requested data plane service operation has been initiated.

7. The method of any one of claims 1 to 6, further comprising: receiving, by the network node from a data pipeline contributor located in a first functional entity operating as a data consumer, a metadata transaction subscribe request comprising a data consumer endpoint identifier, a data consumer correlation identifier, a data consumer identifier, a digital signature of the data consumer, a public key associated with the data consumer, and / or a metadata profile set comprising a first metadata profile and a second metadata profile, wherein the metadata transaction subscribe request is initiated by the data consumer to solicit metadata required to support the requested data plane service operation.

8. The method of any one of claims 1 to 7, further comprising: initiating, by the network node on behalf of a first functional entity operating as a data consumer, a metadata subscribe request toward a second functional entity and a third functional entity, wherein the metadata subscribe request is configured to collect metadata corresponding to the first metadata profile and the second metadata profile.

9. The method of claim 8, further comprising performing at least one of following operations comprising: consolidating and processing, by the network node, metadata responses collected from a second functional entity and a third functional entity in response to a metadata subscribe request; or transmitting, by the network node to a first functional entity operating as a data consumer, a metadata transaction subscribe response comprising a data consumer correlation identifier, one or more aggregated metadata data sets, and / or a transaction status indicator.

10. The method of any one of claims 1 to 9, further comprising performing at least one of following operations comprising: transmitting, by the network node, aggregated metadata collected from a second functional entityand a third functional entity to a first functional entity operating as a data consumer; receiving, by the network node, an unsolicited metadata transaction request from the first functional entity, the unsolicited metadata transaction request comprising analytic results generated from data analytics performed on an aggregated metadata; or receiving, by the network node, a completion status notification from the first functional entity indicating that the requested data plane service operation has been fulfilled.

11. The method of any one of claims 1 to 10, further comprising: receiving, by the network node from a first functional entity operating as a data source, a metadata transaction create or update request comprising a data source endpoint identifier, a data source correlation identifier, a data source identifier, a digital signature of the data source, a public key associated with the data source, a metadata profile indicating a structure or type of an analytic output, and / or an encrypted metadata using a public key of the network node, wherein the metadata transaction create or update request is used to store the analytic output in a storage system associated with the network node.

12. The method of any one of claims 1 to 11, further comprising: receiving, by the network node from a first functional entity, a data pipeline service subscribe response comprising a data pipeline identifier and / or an operation status indicator, wherein the data pipeline service subscribe response indicates that a data pipeline operation associated with requested data plane service has been completed.

13. The method of any one of claims 1 to 12, further comprising: transmitting, by the network node to the DPSS, a service notification comprising a data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates whether a data plane service request has been successfully completed or has failed.

14. The method of any one of claims 1 to 3, further comprising performing at least one of following operations comprising: determining, by the network node based on requested data plane network data service descriptors, that one or more functional entities in another domain are required to support a requested data plane service operation; involving, by the network node, a first data plane orchestration controller (DPOC) to communicate with one or more DPOCs associated with another domain; or coordinating, via the first DPOC, selection and orchestration of required functional entities from the another domain to support the requested data plane service operation.

15. The method of claim 14, further comprising:relaying, by the network node via the first DPOC, the requested data plane service operation to the another domain for collaboration, by transmitting a DPOC service request to a second DPOC in the another domain, the DPOC service request comprising a data plane network data service descriptor identifier indicating a target data-driven network operation, a correlation identifier assigned by the first DPOC to correlate the DPOC service request between the first DPOC and the second DPOC, an identifier of the first DPOC, a digital signature and a public key of the first DPOC to enable identity verification by the second DPOC, and / or a data pipeline identifier used to coordinate a data pipeline operation across domains.

16. The method of claim 14 or 15, further comprising performing at least one of following operations comprising: receiving, by a second DPOC in the another domain, a data plane service request from a first DPOC, the data plane service request comprising network data service descriptors; coordinating, via the second DPOC, with a second network node to identify and select one or more functional entities that support data pipeline contributor functions, and / or meet functional and performance criteria in the network data service descriptors; or providing, via the second DPOC to the first DPOC, information identifying the selected functional entities that host the data pipeline contributor.

17. The method of any one of claims 14 to 16, further comprising performing at least one of following operations comprising: receiving, by the network node via a first DPOC, a service response from a second DPOC, wherein the service response comprises a correlation identifier and / or a data pipeline contributor information comprising one or more functional entity tuples, each tuple comprising a functional type and an identifier of a functional entity selected to support a data pipeline operation; determining, by the network node, that if the data pipeline contributor information is empty, the requested data plane service operation cannot be fulfilled; or transmitting, by the network node to the DPSS, a service notification comprising a data plane service supplicant correlation identifier and / or an operation status indicating failure of the requested data plane service operation.

18. The method of any one of claims 14 to 17, further comprising performing at least one of following operations comprising: selecting, by the network node, one or more functional entities within a local domain that host data pipeline contributor functions in accordance with criteria in data plane network data service descriptors; or upon identifying all functional entities required to support the data pipeline operation for therequested data plane service, transmitting, by the network node, data plane service parameters to respective selected functional entities to enable the requested data plane service operation.

19. The method of any one of claims 14 to 18, further comprising: transmitting, by the network node to each selected functional entity that hosts a data pipeline contributor in a local domain, a data pipeline service subscribe request comprising a data pipeline identifier, an identifier of the network node, a digital signature of the network node, a public key of the network node, and / or data plane service parameters tuple in the data plane network data service descriptors, wherein the data pipeline service subscribe request is transmitted separately to a respective functional entity to enable the requested data plane service operation.

20. The method of any one of claims 14 to 19, further comprising: transmitting, by the network node via a first DPOC, a data pipeline service subscribe request to a second DPOC in the another domain, wherein the data pipeline service subscribe request comprises a data pipeline identifier, a first DPOC identifier, a digital signature of the first DPOC, a public key of the first DPOC, an identifier of a selected functional entity in the another domain, and / or a data plane service parameter tuple in data plane network data service descriptors; wherein the data pipeline service subscribe request is configured for the second DPOC to relay data pipeline service parameters to a corresponding selected functional entity in the another domain that hosts a data pipeline contributor.

21. The method of any one of claims 14 to 20, further comprising performing at least one of following operations comprising: transposing, by a second DPOC, a data pipeline service subscribe request received from a first DPOC into a local data pipeline service subscribe request; or transmitting, via the second DPOC or a second network node, a local data pipeline service subscribe request to a target functional entity in a domain, wherein the local data pipeline service subscribe request comprises a data pipeline identifier, a second network node identifier, a digital signature of the second network node, a public key of the second network node, a functional entity identifier, and / or a data plane service parameter tuple in the data plane network data service descriptors.

22. The method of any one of claims 14 to 21, further comprising: transmitting, by the network node to the DPSS, a service notification comprising a data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates that the requested data plane service operation has been initiated.

23. The method of any one of claims 14 to 22, further comprising performing at least one of following operations comprising:enabling, by the network node, each selected functional entity hosting a data pipeline contributor to perform data plane operation based on received data plane service parameters, wherein the data plane operation comprises subscribing to one or more data sources, collecting and optionally preprocessing or labeling data, exposing raw or processed data to a subsequent functional entity identified within the data plane service parameters, and / or forwarding data for further analysis, storage, model training, or inference; initiating, by the network node, a data pipeline operation monitoring procedure within a local domain and across domains; storing monitoring status locally within each respective domain; receiving, by the network node, notifications of any failure, interruption, or completion of a portion of a data pipeline operation from a local network node, or in a cross-domain deployment, receiving an operation status via a DPOC relaying report from a remote domain’s DPAC.

24. The method of any one of claims 14 to 23, further comprising: receiving, by the network node and / or a first DPOC, a completion notification from a first functional entity in a local domain and a second functional entity in a partner domain, wherein the completion notification indicates that a data pipeline operation associated with a requested data plane service has been completed.

25. The method of any one of claims 14 to 24, further comprising: upon receiving a notification indicating completion of a data pipeline service operation, transmitting, by the network node to the DPSS, a service notification comprising a data plane service supplicant correlation identifier and / or an operation status indicator, wherein the service notification indicates a final status of requested data plane service operation.

26. A network node, comprising: a transceiver configured to receive, from a data plane service supplicant (DPSS), a service request, wherein the service request comprises a data plane network data service descriptor identifier indicating a type of a data-driven network operation, a data plane service supplicant correlation identifier to correlate the service request between the DPSS and the data plane node, a DPSS identifier (DPSSID) identifying the DPSS, a digital signature of the DPSS, and / or a public key associated with the DPSS; and a determiner configured to verify the DPSS and initiate metadata collection to support the data- driven network operation based on the service request.

27. A network device, comprising: a memory;a transceiver; and a processor coupled to the memory and the transceiver; wherein the network device is configured to perform the method of any one of claims 1 to 25.

28. A non-transitory machine-readable storage medium having stored thereon instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 25.

29. A chip, comprising: a processor, configured to call and run a computer program stored in a memory, to cause a device in which the chip is installed to execute the method of any one of claims 1 to 25.

30. A computer readable storage medium, in which a computer program is stored, wherein the computer program causes a computer to execute the method of any one of claims 1 to 25.

31. A computer program product, including a computer program, wherein the computer program causes a computer to execute the method of any one of claims 1 to 25.

32. A computer program, wherein the computer program causes a computer to execute the method of any one of claims 1 to 25.

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