Methods and apparatus for AI / ML traffic detection

Abstract

A first network entity, in a core of a communications network, monitoring traffic from a second network entity within the communications network; and, if the traffic is associated with an artificial i

Description

BACKGROUND Field

[0001] Certain examples of the present disclosure relate to methods, apparatus and / or systems for detecting artificial intelligence I machine learning (AI / ML) traffic. In particular, certain examples of the present disclosure provide methods, apparatus and systems for determining, by a user plane function (UPF) or any 5GS network function (NF), that traffic from a user equipment (UE) or application will be or is associated with an AI / ML operation. Further, certain examples of the present disclosure provide different methods for making this determination and / or performing one or more operations to assist the AI / ML operation. Further, in certain examples of the present disclosure, information regarding the result of the determination is transmitted to a session management function (SMF) or any 5GS NF. Further, in certain examples of the present disclosure, the NFs (or network entities) are included in a 3rd Generation Partnership Project (3GPP) 5th Generation (5G) New Radio (NR) communications network. Description of Related Art

[0002] Herein, the following documents are referenced: [1] 3GPP TS 22.261 - Service requirements for the 5G system, SA1, Release 18 (e.g., V18.7.0); [2] 3GPP TS 23.502 - 5G; Procedures for the 5G System (5GS), Release 17 (e.g., V17.6.0); [3] 3GPP TS 23.501 - 5G; System architecture for the 5G System (5GS), Release 17 (e.g., v17.6.0).

[0003] In AI / ML (artificial intelligence I machine learning) operation, AI / ML models and / or data might be transferred across the AI / ML applications (application functions (AFs)), 5GC (5G core) and UEs (user equipments). The AI / ML works could be divided into two main phases: model training and inference. During model training and inference, multiple rounds of interaction may be required. The high volume and frequent transmitted AI / ML traffic will increase the challenges for the 5GC to handle the traffic (including both AI / ML and other existing traffic).

[0004] In Section 6.40 AI / ML model transfer in 5GS TS 22.261 [1], three types of AI / ML operations to be supported in Release 18 are described as follows: a) AI / ML operation splitting between AI / ML endpoints The AI / ML operation / model is split into multiple parts according to the current task and environment. The intention is to offload the computation-intensive, energy-intensive parts to network endpoints, whereas leave the privacy-sensitive and delay-sensitive parts at the end device. The device executes the operation / model up to a specific part / layer and then sends the intermediate data to the network endpoint. The network endpoint executes the remaining parts / layers and feeds the inference results back to the device. b) AI / ML model / data distribution and sharing over 5G system Multi-functional mobile terminals might need to switch the AI / ML model in response to task and environment variations. The condition of adaptive model selection is that the models to be selected are available for the mobile device. However, given the fact that the AI / ML models are becoming increasingly diverse, and with the limited storage resource in a UE, it can be determined to not pre-load all candidate AI / ML models on-board. Online model distribution (i.e. new model downloading) is needed, in which an AI / ML model can be distributed from a NW (network) endpoint to the devices when they need it to adapt to the changed AI / ML tasks and environments. For this purpose, the model performance at the UE needs to be monitored constantly. c) Distributed / Federated Learning over 5G system The cloud server trains a global model by aggregating local models partially-trained by each end devices. Within each training iteration, a UE performs the training based on the model downloaded from the Al server using the local training data. Then the UE reports the interim training results to the cloud server via 5G UL channels. The server aggregates the interim training results from the UEs and updates the global model. The updated global model is then distributed back to the UEs and the UEs can perform the training for the next iteration. SUMMARY

[0005] It is an aim of certain examples of the present disclosure to address, solve and / or mitigate, at least partly, at least one of the problems and / or disadvantages associated with the related art, for example at least one of the problems and / or disadvantages described herein. It is an aim of certain examples of the present disclosure to provide at least one advantage over the related art, for example at least one of the advantages described herein.

[0006] In accordance with an aspect of the present disclosure, there is provided a first network entity included in a core of a communication network, the first network entity comprising: a transmitter; a receiver; and a controller configured to: monitor traffic from a second network entity included in the communications network; and based on the traffic being associated with an artificial intelligence I machine learning (AI / ML) operation or with a type or phase of an AI / ML operation, perform one or more operations to assist performance of the AI / ML operation.

[0007] In various embodiments, the AI / ML operation or the type or phase of the AI / ML operation corresponds to one of: an AI / ML operation phase relating to model training including model downloading; an AI / ML operation phase relating to inference; an AI / ML operation type relating to operation splitting between AI / ML endpoints; an AI / ML operation type relating to split AI / ML image recognition; an AI / ML operation type relating to model or data distribution and sharing in the communications network; an AI / ML operation type relating to distributed or federated learning in the communications network; an AI / ML operation type relating to uncompressed federated learning in the communication network; an AI / ML operation type relating to compressed federated learning in the communication network; or an AI / ML operation type relating to data transfer disturbance in multi-agent multi-device ML operations.

[0008] In various embodiments, the controller is configured to: perform the one or more operations to assist performance of the AI / ML operation based on monitoring session inactivity relating to the traffic, monitoring traffic volume relating to the traffic, and / or reserved or predefined information associated with the AI / ML operation.

[0009] In various embodiments, the one or more operations comprise: applying a charging rate to traffic associated with the AI / ML operation based on a rate or policy set by an operator.

[0010] In various embodiments, a different charging rate is applied based on the type or phase of the AI / ML operation.

[0011] In various embodiments, the one or more operations comprise: determining a 5G quality of service (QoS) identifier (5QI) corresponding to the AI / ML operation, the 5QI representing one or more QoS requirements of traffic associated with the AI / ML operation, and processing the traffic according to the one or more QoS requirements.

[0012] In various embodiments, one or more of: the one or more QoS requirements comprise one or more of: a resource type including one of guaranteed bit rate (GBR), non-GBR or delay-critical GBR, a packet delay budget, a packet error rate, a default maximum data burst volume, or a default averaging window; the one or more QoS requirements are preset based on a type or phase of the AI / ML operation; and / or the determined 5QI corresponds to a type or phase of the AI / ML operation.

[0013] In various embodiments: the AI / ML operation is AI / ML model downloading, the determined 5QI is a first 5QI corresponding to the AI / ML model downloading, and the one or more QoS requirements comprise: a resource type of non-GBR, a default maximum data burst volume of N / A, and a default averaging window of N / A; the AI / ML operation is downlink (DL) split AI / ML image recognition, the determined 5QI is a second 5QI corresponding to the DL split AI / ML image recognition, and the one or more QoS requirements comprise: a resource type of delay-critical GBR, a default maximum data burst volume of N / A, and a default averaging window of 2000ms; or the AI / ML operation of uplink (UL) split AI / ML image recognition, the determined 5QI is a third 5QI corresponding to the UL split AI / ML image recognition, and the one or more QoS requirements comprise, a resource type of delay-critical GBR, a default maximum data burst volume of N / A, and a default averaging window of 2000ms.

[0014] In various embodiments, the controller is configured to: report an event to a third network entity, included in the core, based on the monitoring of the session inactivity or the monitoring of the traffic volume.

[0015] In various embodiments, the controller is configured to: transmit an N4 session report message to the third network entity to report the event.

[0016] In various embodiments, the event indicates the session inactivity, the traffic volume, the AI / ML operation and / or the type or phase of the AI / ML operation.

[0017] In various embodiments, the reserved or predefined information comprises one or more of: core network (CN) tunnel information, a network instance or an application identifier.

[0018] In various embodiments, one or more of: the first network entity is one of a user plane function (UPF), a user equipment (UE), a session management function (SMF), an application function (AF), an application, a network data analytics function (NWDAF), or a network function (NF) configured to support the AI / ML operation; the second network entity is one of a UE, an application, a UPF, a SMF, an AF, a NWDAF or a NF configured to support the AI / ML operation; the third network entity is one of a SMF, a UPF, a UE, an application, an AF, a NWDAF, or a NF configured to support the AI / ML operation; the communications network is a 5G network; and / or wherein the first network entity and the third network entity are included in a 5G core (5GC).

[0019] In accordance with another aspect of the present disclosure, there is provided a method of a first network entity included in a core of a communications network, the method comprising: monitoring traffic from a second network entity included in the communications network; and based on the data being associated with an artificial intelligence / machine learning (AI / ML) operation or with a type or phase of an AI / ML operation, performing one or more operations to assist performance of the AI / ML operation.

[0020] In various embodiments, the AI / ML operation or the type or phase of the AI / ML operation corresponds to one of: an AI / ML operation phase relating to model training including model downloading; an AI / ML operation phase relating to inference; an AI / ML operation type relating to operation splitting between AI / ML endpoints; an AI / ML operation type relating to split AI / ML image recognition; an AI / ML operation type relating to model or data distribution and sharing in the communications network; an AI / ML operation type relating to distributed or federated learning in the communications network; an AI / ML operation type relating to uncompressed federated learning in the communication network; an AI / ML operation type relating to compressed federated learning in the communication network; or an AI / ML operation type relating to data transfer disturbance in multi-agent multi-device ML operations.

[0021] In various embodiments, performing the one or more operations comprises: performing the one or more operations to assist performance of the AI / ML operation based on monitoring session inactivity relating to the traffic, monitoring traffic volume relating to the traffic, and / or reserved or predefined information associated with the AI / ML operation.

[0022] In various embodiments, the one or more operations comprise: applying a charging rate to traffic associated with the AI / ML operation based on a rate or policy set by an operator.

[0023] In various embodiments, a different charging rate is applied based on the type or phase of the AI / ML operation.

[0024] In various embodiments, the one or more operations comprise: determining a 5G quality of service (QoS) identifier (5QI) corresponding to the AI / ML operation, the 5QI representing one or more QoS requirements of traffic associated with the AI / ML operation, and processing the traffic according to the one or more QoS requirements.

[0025] In various embodiments, one or more of: the one or more QoS requirements comprise one or more of: a resource type including one of guaranteed bit rate (GBR), non-GBR or delay-critical GBR, a packet delay budget, a packet error rate, a default maximum data burst volume, or a default averaging window; the one or more QoS requirements are preset based on a type or phase of the AI / ML operation; and / or the determined 5QI corresponds to a type or phase of the AI / ML operation.

[0026] In various embodiments: the AI / ML operation is AI / ML model downloading, the determined 5QI is a first 5QI corresponding to the AI / ML model downloading, and the one or more QoS requirements comprise: a resource type of non-GBR, a default maximum data burst volume of N / A, and a default averaging window of N / A; the AI / ML operation is downlink (DL) split AI / ML image recognition, the determined 5QI is a second 5QI corresponding to the DL split AI / ML image recognition, and the one or more QoS requirements comprise: a resource type of delay-critical GBR, a default maximum data burst volume of N / A, and a default averaging window of 2000ms; or the AI / ML operation of uplink (UL) split AI / ML image recognition, the determined 5QI is a third 5QI corresponding to the UL split AI / ML image recognition, and the one or more QoS requirements comprise, a resource type of delay-critical GBR, a default maximum data burst volume of N / A, and a default averaging window of 2000ms.

[0027] In various embodiments, the method further comprises: reporting an event to a third network entity, included in the core, based on the monitoring of the session inactivity or the monitoring of the traffic volume.

[0028] In various embodiments, reporting the event comprises transmitting an N4 session report message to the third network entity to report the event; and / or wherein the event indicates the session inactivity, the traffic volume, the AI / ML operation and / or the type or phase of the AI / ML operation.

[0029] In various embodiments, one or more of: the first network entity is one of a user plane function (UPF), a user equipment (UE), a session management function (SMF), an application function (AF), an application, a network data analytics function (NWDAF), or a network function (NF) configured to support the AI / ML operation; the second network entity is one of a UE, an application, a UPF, a SMF, an AF, a NWDAF or a NF configured to support the AI / ML operation; and the third network entity is one of a SMF, a UPF, a UE, an application, an AF, a NWDAF, or a NF configured to support the AI / ML operation; the communications network is a 5G network; and / or wherein the first network entity and the third network entity are included in a 5G core (5GC).

[0030] In accordance with a first example of the present disclosure, there is provided a first network entity included in a communication network, the first network entity comprising: a transmitter; a receiver; and a controller configured to: receive data from a second network entity included in the communications network; and determine, based on one or more characteristics of the data, that traffic from the second network entity will be or is associated with an artificial intelligence I machine learning (AI / ML) operation (or will be or is associated with AI / ML traffic).

[0031] In a second example, there is provided the first network entity of the first example, where the controller is configured to determine that the traffic from the second network entity will be or is associated with an AI / ML operation based on one or more of the data, data volume, a time pattern, and control / configuration information; wherein the one or more characteristics of the data include the one or more of the data, the data volume, time pattern, and the control / configuration information.

[0032] In a third example, there is provided the first network entity of the first example or the second example, where the controller is configured to determine that the traffic from the second network entity is associated with an AI / ML operation based on identifying first information included in the data, wherein the first information included in the data indicates that the traffic is associated with the AI / ML operation.

[0033] In a fourth example, there is provided the first network entity of the third example, where the first information indicates one or more of AI / ML traffic, the AI / ML operation, and a type or a phase of the AI / ML operation.

[0034] In a fifth example, there is provided the first network entity of the fourth example, where the controller determines, based on the first information, the type or phase of the AI / ML operation.

[0035] In a sixth example, there is provided the first network entity of any of the third example to the fifth example, where the first information is a 5G quality of service (QoS) identifier (5QI) corresponding to the AI / ML operation or to the type or phase of the AI / ML operation.

[0036] In a seventh example, there is provided the first network entity of the first example or the second example, where the one or more characteristics of the data include or indicate second information which is reserved or predefined for use by the AI / ML operation.

[0037] In an eighth example, there is provided the first network entity of the seventh example, where the controller is configured to determine, based on identifying the second information, that the traffic from the second network is or will be associated with an AI / ML operation and / or a type or a phase of the AI / ML operation.

[0038] In a ninth example, there is provided the first network entity of the seventh example or the eighth example, where the second information includes one or more of core network (CN) tunnel information, network instance, and application identifier.

[0039] In a tenth example, there is provided the first network entity of the first example, where the data includes third information indicating that a protocol data unit (PDU) session for the second network entity is or will be associated with the AI / ML operation.

[0040] In an eleventh example, there is provided the first network entity of the tenth example, where the controller is configured to determined, based on identifying the third information, that the traffic from the second network is or will be associated with the AI / ML operation.

[0041] In a twelfth example, there is provided the first network entity of the tenth example or the eleventh example, where third information indicates that the PDU session will be used only for traffic associated with the AI / ML operation.

[0042] In a thirteenth example, there is provided the first network entity of any of the tenth example to the twelfth example, where the third information is one of: an information element (IE) in a PDU session establishment request message or session modification request message; a bit in a 5G system session management (5GSM) capability IE; a bit in a 5G system mobility management (5GMM) capability IE; a message type indicating the PDU session is or will be used for the AI / ML operation; or a reserved or predefined PDU session ID(s) indicating a AI / ML PDU session carrying traffic associated with the AI / ML operation.

[0043] In a fourteenth example, there is provided the first network entity of any of the tenth example to the thirteenth example, where the third information indicates a type or a phase of the AI / ML operation.

[0044] In a fifteenth example, there is provided the first network entity of the first example, where the controller is configured to: determine, based on the one or more characteristics of the data, packet data unit (PDU) session activity or inactivity, and determine, based on the PDU session activity or inactivity, that the traffic from the second network is associated with the AI / ML operation and / or a type or a phase of the AI / ML operation; determine, based on the one or more characteristics of the data, timedependent quality of service (QoS) for a PDU session associated with the second network entity, and determine, based on the time-dependent QoS, that the traffic from the second network is associated with an AI / ML operation and / or the type or phase of the AI / ML operation; and / or determine, based on the one or more characteristics of the data, traffic or data volume within a period and / or a characteristic of a data packet included in the data, and determine, based on the traffic or data volume and / or the characteristic of the data packet, that the traffic from the second network is associated with an AI / ML operation or the type and / or phase of the AI / ML operation.

[0045] In a sixteenth example, there is provided the first network entity of any of the first example to the fifteenth example, where the controller is configured to determine, based on a packet detection rule (PDR), that the traffic from the second network is associated with the AI / ML operation, and / or a type or a phase of the AI / ML operation.

[0046] In a seventeenth example, there is provided the first network entity of any of the fourth example, the fifth example, the eighth example or the fourteenth to sixteenth example, where the type or phase of the AI / ML operation is one of: an AI / ML operation phase relating to model training; an AI / ML operation phase relating to inference; an AI / ML operation type relating to operation splitting between endpoints in the communications network; an AI / ML operation type relating to model or data distribution and sharing in the communications network; an AI / ML operation type relating to distributed or federated learning in the communications network; an AI / ML operation type relating to uncompressed federated learning in the communication network; an AI / ML operation type relating to compressed federated learning in the communication network; or an AI / ML operation type relating to data transfer disturbance in multi-agent multi-device ML operations.

[0047] In an eighteenth example, there is provided the first network entity of any of the first example to the seventeenth example, where the controller is configured to: based on determining that the traffic will be or is associated with the AI / ML operation, transmitting, to a third network entity, fourth information indicating the traffic will be or is associated with the AI / ML operation.

[0048] In a nineteenth example, there is provided the first network entity of the eighteenth example, where the fourth information indicates the type or phase of the AI / ML operation.

[0049] In a twentieth example, there is provided the first network entity of any of the first example to the nineteenth example, where the fourth information is transmitted in an N4 session report message.

[0050] In a twenty-first example, there is provided the first network entity of the twentieth example, where the controller is configured to receive an N4 session report acknowledgement (ACK) message in response to transmitting the N4 session report message.

[0051] In accordance with a twenty-second example of the present disclosure, there is provided a second network entity included in a communications network, the second network entity comprising: a transmitter; a receiver; and a controller configured to: transmit, to a first network entity included in the communications network, data, wherein the data is associated with one or more characteristics indicating that traffic from the second network entity will be or is associated with an AI / ML operation.

[0052] In a twenty-third example, there is provided the second network entity of the twenty-second example, where the one or more characteristics include the one or more of the data, data volume, time pattern, and control / configuration information.

[0053] In a twenty-fourth example, there is provided the second network entity of the twenty-second example or the twenty-third example, where the data includes first information indicating that the traffic is associated with the AI / ML operation.

[0054] In a twenty-fifth example, there is provided the second network entity of the twenty-fourth example, where the first information indicates one or more of AI / ML traffic, the AI / ML operation, and a type or a phase of the AI / ML operation.

[0055] In a twenty-sixth example, there is provided the second network entity of the twenty-fourth example or the twenty-fifth example, where the first information is a 5G quality of service (QoS) identifier (5QI) corresponding to the AI / ML operation or to the type or phase of the AI / ML operation.

[0056] In a twenty-seventh example, there is provided the second network entity of the twenty-second example or the twenty-third example, where the one or more characteristics of the data include or indicate second information which is reserved or predefined for use by the AI / ML operation; and, optionally, wherein the second information indicates a type or a phase of the AI / ML operation.

[0057] In a twenty-eighth example, there is provided the second network entity of the twenty-seventh example, where the second information includes one or more of core network (CN) tunnel information, network instance, and application identifier.

[0058] In a twenty-ninth example, there is provided the second network entity of the twenty-second example or the twenty-third example, where the data includes third information indicating that a protocol data unit (PDU) session for the second network entity is or will be associated with the AI / ML operation; and, optionally, wherein the third information indicates a type or a phase of the AI / ML operation.

[0059] In a thirtieth example, there is provided the second network entity of the twentyninth example, where the third information indicates that the PDU session will be used only for traffic associated with the AI / ML operation; and, optionally, wherein the third information is one of: an information element (IE) in a PDU session establishment request message or session modification request message; a bit in a 5G system session management (5GSM) capability IE; a bit in a 5G system mobility management (5GMM) capability IE; a message type indicating the PDU session is or will be used for the AI / ML operation; or a reserved or predefined PDU session ID(s) indicating a AI / ML PDU session carrying traffic associated with the AI / ML operation.

[0060] In a thirty-first example, there is provided the second network entity of any of the twenty-fifth example to the thirtieth example, where the type or phase of the AI / ML operation is one of: an AI / ML operation phase relating to model training; an AI / ML operation phase relating to inference; an AI / ML operation type relating to operation splitting between endpoints in the communications network; an AI / ML operation type relating to model or data distribution and sharing in the communications network; an AI / ML operation type relating to distributed or federated learning in the communications network; an AI / ML operation type relating to uncompressed federated learning in the communication network; an AI / ML operation type relating to compressed federated learning in the communication network; or an AI / ML operation type relating to data transfer disturbance in multi-agent multi-device ML operations.

[0061] In accordance with a thirty-second example of the present disclosure, there is provided a third network entity included in a communications network, the third network entity comprising: a transmitter; a receiver; and a controller configured to: receive, from a first network entity included in the communications network, information indicating traffic, from a second network entity included in the communication network to the first network entity, will be or is associated with an AI / ML operation.

[0062] In a thirty-third example, there is provided the third network entity of the thirty-second example, where the information indicates a type or a phase of the AI / ML operation.

[0063] In a thirty-fourth example, there is provided the third network entity of the thirty-second example or the thirty-third example, where the fourth information is received in an N4 session report message.

[0064] In a thirty-fifth example, there is provided the third network entity of any of the thirty-second example to the thirty-fourth example, where the controller is configured to transmit an N4 session report acknowledgement (ACK) message in response to receiving the N4 session report message.

[0065] In a thirty-sixth example, there is provided the third network entity of any of the thirty-second example to the thirty-fifth example, where the controller is configured to determine one or more QoS characteristics corresponding to the AI / ML operation based on the information.

[0066] In a thirty-seventh example, there is provided the third network entity of any of the thirty-third example to the thirty-fifth example, where the controller is configured to determine a set of one or more QoS characteristics corresponding to the type or phase of the AI / ML operation from among a plurality of sets of QoS characteristics each corresponding to one of a plurality of different types of AI / ML operation.

[0067] In certain examples e.g. further to any one of the first example to the thirtyseventh example, one or more of: the first network entity is one of a user plane function (UPF), a user equipment (UE), a session management function (SMF), an application function (AF), an application, a network data analytics function (NWDAF), or a network function configured to support the AI / ML operation; the second network entity is one of a UE, an application, a UPF, a SMF, a AF, a NWDAF or a network function configured to support the AI / ML operation; and the third network entity is one of a SMF, a UPF, a UE, an application, an AF, a NWDAF, or a network function configured to support the AI / ML operation; and / or wherein the first network entity and the third network entity are included in a 5G core (5GC).

[0068] In accordance with a thirty-eighth example of the present disclosure, there is provided a method of a first network entity included in a communications network, the method comprising: receiving data from a second network entity included in the communications network; and determining, based on one or more characteristics of the data, that traffic from the second network entity will be or is associated with an artificial intelligence / machine learning (AI / ML) operation.

[0069] In a thirty-ninth example, there is provided the method of the thirty-eighth example, where the method further comprises one or more operations (or steps) according to any of the above aspects, examples and / or embodiments relating to the first network entity.

[0070] In accordance with a fortieth example of the present disclosure, there is provided a method of a second network entity included in a communications network, the method comprising: transmitting, to a first network entity included in the communications network, data, where the data is associated with one or more characteristics indicating that traffic from the second network entity will be or is associated with an artificial intelligence I machine learning (AI / ML) operation.

[0071] In a forty-first example, there is provided the method of the fortieth example, where the method further comprises one or more operations (or steps) according to any of the above aspects, examples and / or embodiments relating to the second network entity.

[0072] In accordance with a forty-second aspect of the present disclosure, there is provided a method of a third network entity included in a communications network, the method comprising: receiving, from a first network entity included in the communications network, information indicating traffic, from a second network entity included in the communication network to the first network entity, will be or is associated with an artificial intelligence / machine learning (AI / ML) operation.

[0073] In a forty-third example, there is provided the method of the forty-second example, where the method further comprises one or more operations (or steps) according to any of the above aspects, examples and / or embodiments relating to the third network entity.

[0074] In certain examples, for any of the methods disclosed above, one or more of: the first network entity is one of a user plane function (UPF), a user equipment (UE), a session management function (SMF), an application function (AF), an application, a network data analytics function (NWDAF), or a network function configured to support the AI / ML operation; the second network entity is one of a UE, an application, a UPF, a SMF, a AF, a NWDAF or a network function configured to support the AI / ML operation; and the third network entity is one of a SMF, a UPF, a UE, an application, an AF, a NWDAF, or a network function configured to support the AI / ML operation; and / or wherein the first network entity and the third network entity are included in a 5G core (5GC).

[0075] In accordance with another aspect of the present disclosure, there is provided a system comprising any combination of a first network entity, a second network entity and a third network entity according to any of the above aspects, examples and / or embodiments.

[0076] In accordance with another aspect of the present disclosure, there is provided a computer-readable [storage] medium comprising instructions which, when executed by at least one processor of a network entity, cause the network entity to carry out the operations of any one of the methods according to any of the above aspects, examples and / or embodiments.

[0077] Other aspects, advantages, and salient features of the invention will become apparent to those skilled in the art from the following detailed description taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Embodiments of the present disclosure are further described hereinafter with reference to the accompanying drawings, in which: Figure 1 shows a representation of a call flow according certain examples of the present disclosure; Figure 2 shows a representation of a call flow according to certain examples of the present disclosure; Figure 3 is a block illustrating an example structure of a network entity in accordance with certain examples of the present disclosure; Figure 4 shows a flow diagram of a method according to certain examples of the present disclosure; Figure 5 shows a flow diagram of a method according to certain examples of the present disclosure; and Figure 6 shows a flow diagram of a method according to certain examples of the present disclosure. DETAILED DESCRIPTION

[0079] The following description of examples of the present disclosure, with reference to the accompanying drawings, is provided to assist in a comprehensive understanding of the present invention, as defined by the claims. The description includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the examples described herein can be made without departing from the scope of the invention or disclosure.

[0080] The same or similar components may be designated by the same or similar reference numerals, although they may be illustrated in different drawings.

[0081] Detailed descriptions of techniques, structures, constructions, functions or processes known in the art may be omitted for clarity and conciseness, and to avoid obscuring the subject matter of the present invention.

[0082] The terms and words used herein are not limited to the bibliographical or standard meanings, but are merely used to enable a clear and consistent understanding of the invention.

[0083] Throughout the description and claims of this specification, the words “comprise”, “include” and “contain” and variations of the words, for example “comprising” and “comprises”, means “including but not limited to”, and is not intended to (and does not) exclude other features, elements, components, integers, steps, processes, operations, functions, characteristics, properties and / or groups thereof.

[0084] Throughout the description and claims of this specification, the singular form, for example “a”, “an” and “the”, encompasses the plural unless the context otherwise requires. For example, reference to “an object” includes reference to one or more of such objects.

[0085] Throughout the description, the expression “at least one of A, B and / or C” (or the like) and the expression “one or more of A, B and / or C” (or the like) should be seen to separately include all possible combinations, for example: A, B, C, A and B, A and C, A and B and C.

[0086] Throughout the description and claims of this specification, language in the general form of “X for Y” (where Y is some action, process, operation, function, activity or step and X is some means for carrying out that action, process, operation, function, activity or step) encompasses means X adapted, configured or arranged specifically, but not necessarily exclusively, to do Y.

[0087] Features, elements, components, integers, steps, processes, operations, functions, characteristics, properties and / or groups thereof described or disclosed in conjunction with a particular aspect, embodiment, example or claim are to be understood to be applicable to any other aspect, embodiment, example or claim described herein unless incompatible therewith..

[0088] Certain examples of the present disclosure provide methods, apparatus and / or systems for artificial intelligence I machine learning (AI / ML) traffic detection, or for the determination that traffic will be or is associated with AI / ML operation. The following examples are applicable to, and use terminology associated with, 3GPP 5G. However, the skilled person will appreciate that the techniques disclosed herein are not limited to these examples or to 3GPP 5G, and may be applied in any suitable system or standard, for example one or more existing and / or future generation wireless communication systems or standards. The skilled person will appreciate that the techniques disclosed herein may be applied in any existing or future releases of 3GPP 5G NR or any other relevant standard. For example, the functionality of the various network entities and other features disclosed herein may be applied to corresponding or equivalent entities or features in other communication systems or standards. Corresponding or equivalent entities or features may be regarded as entities or features that perform the same or similar role, function, operation or purpose within the network.

[0089] A particular network entity may be implemented as a network element on a dedicated hardware, as a software instance running on a dedicated hardware, and / or as a virtualised function instantiated on an appropriate platform, e.g. on a cloud infrastructure.

[0090] The skilled person will appreciate that the present invention is not limited to the specific examples disclosed herein. For example: • The techniques disclosed herein are not limited to 3GPP 5G. • One or more entities in the examples disclosed herein may be replaced with one or more alternative entities performing equivalent or corresponding functions, processes or operations. • One or more of the messages in the examples disclosed herein may be replaced with one or more alternative messages, signals or other type of information carriers that communicate equivalent or corresponding information. • One or more further elements, entities and / or messages may be added to the examples disclosed herein. • One or more non-essential elements, entities and / or messages may be omitted in certain examples. • The functions, processes or operations of a particular entity in one example may be divided between two or more separate entities in an alternative example. • The functions, processes or operations of two or more separate entities in one example may be performed by a single entity in an alternative example. • Information carried by a particular message in one example may be carried by two or more separate messages in an alternative example. • Information carried by two or more separate messages in one example may be carried by a single message in an alternative example. • The order in which operations are performed may be modified, if possible, in alternative examples. • The transmission of information between network entities is not limited to the specific form, type and / or order of messages described in relation to the examples disclosed herein.

[0091] Certain examples of the present disclosure may be provided in the form of an apparatus / device / network entity configured to perform one or more defined network functions and / or a method therefor. Such an apparatus / device / network entity may comprise one or more elements, for example one or more of receivers, transmitters, transceivers, processors, controllers, modules, units, and the like, each element configured to perform one or more corresponding processes, operations and / or method steps for implementing the techniques described herein. For example, an operation / function of X may be performed by a module configured to perform X (or an X-module). Certain examples of the present disclosure may be provided in the form of a system (e.g., a network) comprising one or more such apparatuses / devices / network entities, and / or a method therefor.

[0092] It will be appreciated that examples of the present disclosure may be realized in the form of hardware, software or a combination of hardware and software. Certain examples of the present disclosure may provide a computer program comprising instructions or code which, when executed, implement a method, system and / or apparatus in accordance with any aspect, claim, example and / or embodiment disclosed herein. Certain embodiments of the present disclosure provide a machine-readable storage storing such a program.

[0093] As described in TS 22.261 [1], the AI / ML operation types may be categorised into three types: model splitting, model sharing, and distributed / federated learning. The requirements, frequency and volume of data transmission may differ for different AI / ML processing phrases and / or operation types. Furthermore, operators may also apply various charging rules for different AI / ML traffic. For example, operators may deploy different charging rates or policies for AI / ML traffic data compared to other traffic / data, and even different charging rates for different types of AI / ML traffic (i.e., different AI / ML operations, such as AI / ML model training and AI / ML inference). Currently, the 5G Core (5GC) is not aware of the AI / ML traffic / operation.

[0094] In Clause 7.10 ( KPIs for AI / ML model transfer in 5GS ) of TS 22.261 [1], different KPIs are identified for AI / ML operations, the details of which are illustrated in tables annexed at the end of the description.

[0095] Considering the above issues, certain embodiments of the present disclosure provide apparatus, system(s) and method(s) to notify the 5GC (or a network entity) about the AI / ML operation (or AI / ML traffic), and, in certain examples, notify the 5GC of the type or (processing) phase of the AI / ML operation. The 5CG (e.g. UPF) may then take the AI / ML operation or traffic information into account for handling issues such as data congestion, traffic routing, charging issues, traffic steering, etc.

[0096] According to certain embodiments of the present disclosure, any message and / or data packets associated with the AI / ML operation are defined as the AI / ML traffic. The 5GC distinguishes the AI / ML traffic and other types of traffic.

[0097] According to certain embodiments of the present disclosure, considering the operation of AI / ML, the AI / ML processing may include two phases: model training and inference (it is not excluded that the AI / ML work may include other phases, but for certain examples herein the model training phase and the inference phase of AI / ML work are considered as examples). Between the model training stage and the inference stage, the data volume, the packet error rate, the delay tolerance etc., might be significantly different. For example, during the model training phase, transmission of the AI / ML model may result in high data volume; however, the end-to-end delay is more tolerable. Different rules or policies might be deployed to these two phases by the 5GC.

[0098] Therefore, according to certain embodiments of the present disclosure, examples of which will be described above, AI / ML traffic (or (data) packets associated with AI / ML) may be defined based on the nature of AI / ML processing phases, that is, data for model training and inference traffic.

[0099] As described in TS 22.261 [1], it is expected that the 5GC will at least support the following three types of AI / ML operations in Release 18: a) AI / ML operation splitting between AI / ML endpoints b) AI / ML model / data distribution and sharing over 5G system c) Distributed / Federated Learning (FL) over 5G system

[00100] The characteristics of each AI / ML operation type may be different. For example, in operation type a), the privacy-sensitive and delay-sensitive parts are at the end device (e.g., a UE); therefore, the AI / ML traffic in this mode Packet Delay Budget is relatively high. For the operation type b), the AI / ML models do not pre-load all candidate AI / ML models on board; and the model can be distributed from an NW endpoint and downloaded by the end devices when they need it to adapt to the changed AI / ML tasks and environments. Therefore, the data volume operation type b) might be high. In operation type c), the AI / ML model training (and inference) is carried out by multiple end users / devices and the cloud server jointly; therefore, the data transmission may not require high reliability but a large payload size.

[00101] Therefore, according to certain embodiments of the present disclosure, the AI / ML traffic (or (data) packets associated with AI / ML) may be categorised based on the AI / ML operation types. Although operation types a), b) and c) are given above, embodiments of the present disclosure are not limited to such and other AI / ML operation types may be taken into account, as desired.

[00102] Above, model training and inference are indicated as a (processing) phase of AI / ML, while a), b) and c) are indicated as types of AI / ML operation. For ease of reference, the phases of AI / ML may also be regarded as a type of AI / ML operation, such that the term “type of AI / ML operation”, or the like, may refer to (or include) model training, inference, type a), type b) and / or type c). For example, the skilled person would understand how inference may be regarded as a type of AI / ML operation. The present disclosure will refer to phases (e.g., processing phases) of an AI / ML operation and to types of an AI / ML operation separately, and it will also be intended (unless explained otherwise) that a processing phase of an AI / ML operation may be regarded as a type of an AI / ML operation.

[00103] Herein, in certain examples traffic associated with an AI / ML operation may be AI / ML traffic.

[00104] Figure 1 shows a representation of a call flow according to an example of the present disclosure.

[00105] Figure 1 shows interaction between a first network entity 11 and a second network entity 12.

[00106] In certain examples, the first network entity 11 is a user plane function (UPF) and / or the second network entity 12 is a UE or an application (e.g., an application executed at a network entity or node). However, the first network entity 11 and the second network entity 12 are not limited to this. The first network entity 11 may be any 5GC network function (NF), i.e. UPF, session management function (SMF), network data analytics function (NWDAF), application function (AF), application, user equipment (UE), new NFs to support AI / ML operation etc. The second network entity 12 may also be any 5GC NF, i.e. UPF, AF, application, SMF, NWDAF, new NFs to support AI / ML operation etc. In certain examples, the first network entity 11 and the second network entity 12 may be included in a communication network, e.g., a 5G NR communications network.

[00107] In operation S110, the second network entity 12 transmits data (or a signal, or data which is a signal) to the first network entity. In certain examples, the data may relate to an AI / ML operation, may indicate a future AI / ML operation, may request establishment or modification of a protocol data unit (PDU) session for AI / ML operation, may implicitly relate to an AI / ML operation, etc. The data is not limited to being packet data, but may be control information, signalling data etc.

[00108] In operation S120, after receiving the data (or the signal) from the second network entity 12, the first network entity 11 determines, based on one or more characteristics of the received data, whether traffic from the second network entity is or will be associated with an AI / ML operation (e.g., is AI / ML traffic). In certain embodiments, the one or more characteristics of the received data includes one or more of: the data itself (or information included within the data), data volume, a time pattern (of the data), and control / configuration information (for example, a 5G quality of service (QoS) identifier (5QI)). For example, via or based on the one or more characteristics, the first network entity 11 may detect that the data (or the signal) is associated with AI / ML operation or AI / ML traffic, in which case the first network entity 11 may determine that the traffic from the second network entity 12 is associated with an AI / ML operation. In another example, via or based on the one or more characteristics, the first network entity 11 may identify information, e.g., in the data, which indicates that traffic from the second network entity 12 is, or may later be, associated with an AI / ML operation. In yet another example, via or based on the one or more characteristics, the first network entity 11 may determine that it is implicit that traffic from the second network entity 12 is or will be associated with an AI / ML operation. In other words, by various methods in accordance with certain examples described herein, the first network entity 11, which may be a 5G NF, may determine that traffic from (or to) the second network entity 12 is, or will be (for example, in the sense of traffic in a PDU session which is to be established), traffic associated with an AI / ML operation (i.e., AI / ML traffic).

[00109] Further, in certain examples the first network entity 11 may determine a phase (e.g., a processing phase) and / or a type (e.g., an operation type) of the AI / ML operation or the AI / ML traffic.

[00110] In various examples, the first network entity 11 may perform one or more operations to assist performance of the AI / ML operation, based on the traffic being associated with the AI / ML operation or with a type of a phase of the AI / ML operation. That is, by monitoring the traffic from the second network entity 12 and determining that the traffic is associated with the AI / ML operation (or with a type or phase of the AI / ML operation), the first network entity 11 may perform the one or more operations based on the traffic or the monitoring of the traffic, such as based on the traffic being associated with the AI / ML operation etc.

[00111] Figure 2 shows a representation of a call flow according to an example of the present disclosure.

[00112] Figure 2 shows interaction between a first network entity 21 and a third network entity 23. In certain examples, the first network entity 21 is a user plane function (UPF) and / or the third network entity 23 is a session management function (SMF). However, the first network entity 21 and the third network entity 23 are not limited to this. The first network entity 21 may be any 5GC network function (NF), i.e. UPF, session management function (SMF), network data analytics function (NWDAF), application function (AF), application, user equipment (UE), new NFs to support AI / ML operation etc. The third network entity 23 may also be any 5GC NF, i.e. UPF, UE, AF, application, NWDAF, new NFs to support AI / ML operation etc. The first network entity 21 and the third network entity 23 may be included in a communication network, e.g., a 5G NR communications network. In certain examples, the first network entity 21 is the first network entity 11 of Fig. 1.

[00113] In operation S210, the first network entity 21 may detect a trigger to report an event. The event may be that traffic from a second network entity (not shown), for example the second network entity 12 of Fig. 1, is or will be associated with an AI / ML operation. The trigger may be the determining, by the first network entity 21, that the traffic from the second network entity is or will be associated with the AI / ML operation. For example, the outcome of operation S120 of Fig. 1 may be that the first network entity 21 determines that traffic from the second network entity is or will be associated with an AI / ML operation, and this result triggers the first network entity 21 to report the event to the third network entity 23.

[00114] In operation S220, the first network entity 21 may transmit information indicating the traffic from the second network entity will be or is associated with the AI / ML operation to the third network entity 23. That is, the first network entity 21 may report this result or event to the third network entity 23. In certain examples, this reporting is optional.

[00115] In certain examples, the first network entity 21 is a UPF and the third network entity 23 is a SMF, and the UPF transmits a N4 session report message to the SMF to report the event (where N4 interface connects the UPF to the SMF); for example, to report that AI / ML traffic is detected, AI / ML model training or inference data is detected, data packets for a specific AI / ML operation type are detected, the second network entity requests establishment of a PDU session to be used for AI / ML traffic etc.

[00116] In operation 230, the third network entity 23 transmits an acknowledgement (ACK) of the report from the first network entity 21.

[00117] In certain examples where the report was via or included in a N4 session report, the third network entity 23 (e.g., SMF) may identify the N4 session context based on the received N4 Session ID and apply the reported information for the corresponding PDU Session. Additionally, the SMF responds, to the UPF, with an N4 session report ACK message.

[00118] In the following, the example of the first network entity being a UPF, the second network entity being a UE and the third network entity being a SMF is used on occasion; however, the present disclosure is not limited to this - this example (i.e., reference to UPF, UE and SMF) is used by way of example only to illustrate the concepts disclosed herein. It will be appreciated that each of the first network entity, the second network entity and the third network entity may be any NF, for example: a UPF, an AF a SMF, a UE, a NWDAF, a new NF to support AI / ML operation etc. Furthermore, while parts of the following refers to the UPF (or first network entity) and the SMF (or third network entity) separately, the present disclosure also considers and includes the case where UPF and SMF are regarded together as part of the 5GC, in which case the described separate behaviours of the UPF and the SMF should be considered together as behaviours of the 5GC - in other words, certain examples consider the first network entity and the third network entity to be implemented together in a single network entity. Explicit indication to the 5GC about AI / ML traffic

[00119] According to certain embodiments of the present disclosure, the AI / ML traffic or operation might be explicitly indicated to the 5GC or any network entity or NF (i.e. the UPF, session management function (SMF), etc.). For example, the data or signal transmitted by a second network entity (such as second network entity 12 of Fig. 1) to a first network entity (such as first network entity 11 of Fig. 1) may include a specific indicator, or specific information, which indicates to the first network entity that the traffic from the second network entity will be, or is, associated with an AI / ML operation (e.g., the traffic is AI / ML traffic). In certain examples, the first network entity may report this result to a third network entity, such as the third network entity (such as the third network entity 23 of Fig. 2), e.g., via a process such as shown in Fig. 2. Accordingly, the 5GC is informed that (some) traffic from the second network entity, which may be a UE, is associated (or will be associated, in the case of future traffic) with an AI / ML operation.

[00120] The information may, in certain examples, allow the first network entity to determine (or identify, or detect) a type and / or a phase of the AI / ML operation, for example in accordance with one of the examples of types of AI / ML operation described above. For example, the first network entity may determine that the AI / ML traffic will be, or is, for a model training operation (processing phase), or for an inference operation (processing phase), or for a type of an operation being an AI / ML operation splitting between AI / ML endpoints.

[00121] The information may take the form, or include, a 5G quality of service (QoS) identifier (5QI) transmitted by the second network entity to the first network entity. That is, one or more new 5Qls may be defined for the AI / ML operation types, with a different 5QI indicating a different AI / ML operation type. Alternatively, a new 5QI may be used to indicate an AI / ML operation in general.

[00122] The first network entity or third network entity (or other NF) may determine AI / ML traffic or a type of AI / ML operation at the UE (e.g., corresponding to the traffic) by identifying a value of a received 5QI. For example, for a case of a plurality of new 5Qls, each new 5QI may have a value and corresponding QoS characteristics associated with that 5QI. These QoS characteristics may include one or more of Resource Type, Default Priority Level, Packet Delay Budget, Packet Error Rate, Default Maximum Data Burst Volume, Default Averaging Window, and Example Services. An example of QoS characteristics mapped to a 5QI which generally indicates AI / ML traffic or FL traffic is shown in Table 1:

[00123] Table 1 - Example of standardized 5QI to QoS characteristics mapping 5QI Value Resource Type Default Priority Level Packet Delay Budget Packet Error Rate Default Maximum Data Burst Volume Default Averaging Window Example Services N GBR / non-GBR / Delay-critical GBR M X ms Y Z bytes / N / A 2000 ms / N / A AI / ML service / traffic or FL traffic

[00124] In certain examples, the Example Services may be AI / ML service I traffic and may include the model training and inference data. The Example Services may also or alternatively be the federated learning traffic. The AI / ML service I traffic may also indicate the data packets for any type of AI / ML operation; that is, may indicate a type of the AI / ML operation.

[00125] To give some non-limiting examples: • for AI / ML inference: the payload may be up to 1.5 Mbyte, and Packet Delay Budget may be up to 100 milliseconds. • For AI / ML model training related data, i.e. model downloading, depending on a different purpose for the AI / ML model training - i.e. AI / ML model distribution (e.g. model downloading) for image recognition, AI / ML model distribution for speech recognition, Real-time media editing with onboard Al inference etc. - the payload could be 138Mbyte, 80Mbyte, 64Mbyte respectively; and the packet delay budget might be varied between 1 second to 3 seconds. • For Federated Learning between UE and Network Server / Application function, for different types of FL - i.e. Uncompressed Federated Learning for image recognition, Compressed Federated Learning for image / video processing, Data Transfer Disturbance in Multi-agent multi-device ML Operations - the parameters / requirements vary: e.g., the payload size for federated learning types may be 132Mbyte or 10Mbyte; delay may be 1 second.

[00126] In a case of defining or introducing a plurality of new 5Qls for indicating that traffic is or will be associated with an AI / ML operation, the new 5Qls may indicate the different QoS characteristics or requirements for AI / ML data transmission for each AI / ML operation.

[00127] In certain examples, as the data transmission requirements for the AI / ML model training and inference might be different, i.e. the packet delay budget for the modelling training process may be more relaxed than for the inference stage, different 5Qls could be identified for the two processing phrases, correspondingly. The packets could be the data and / or the messages for model training and inference. A non-limiting example is shown in Table 2:

[00128] Table 2 - Example of standardized 5QI to QoS characteristics mapping 5QI Value Resource Type Default Priority Level Packet Delay Budget Packet Error Rate Default Maximum Data Burst Volume Default Averaging Window Example Services N1 GBR / non-GBR / Delay-critical GBR M1 X1 ms Y1 Z1 bytes / N / A 2000 ms / N / A AI / ML model training N2 GBR / non-GBR / Delay-critical GBR M2 X2 ms Y2 Z2 bytes / N / A 2000 ms / N / A AI / ML inference

[00129] Similarly, in certain examples, the QoS characteristics for different AI / ML operation types may also be different. New 5Qls could be introduced to present the corresponding QoS characteristics. It will be recalled that the operation types include but are not limited to: a) AI / ML operation splitting between AI / ML endpoints (Split AI / ML operation) b) AI / ML model / data distribution and sharing over 5G system (AI / ML model distribution and sharing) c) Distributed / Federated Learning over 5G system

[00130] Additionally, in certain examples, for the operation type of Distributed / Federated Learning over 5G system, more than one new 5QI(s) might be introduced. For example, different 5Qls indicate different types of federated learning, which may include but are not limited to (this also applies to other solutions / example in this invention): i. Uncompressed Federated Learning for image recognition ii. Compressed Federated Learning for image / video processing iii. Data Transfer Disturbance in Multi-agent multi-device ML Operations

[00131] A non-limiting example of new 5Qls for these operation types is shown in Table 3:

[00132] Table 3 - Example of standardized 5QI to QoS characteristics mapping 5QI Value Resource Type Default Priority Level Packet Delay Budget Packet Error Rate Default Maximum Data Burst Volume Default Averaging Window Example Services N3 GBR / non-GBR / Delay-critical GBR M3 X3 ms Y3 Z3 bytes / N / A 2000 ms / N / A Split AI / ML operation N4 GBR / non-GBR / Delay-critical GBR M4 X4 ms Y4 Z4 bytes / N / A 2000 ms / N / A AI / ML model distribution and sharing N5 GBR / non-GBR / Delay-critical GBR M5 X5 ms Y5 Z5 bytes / N / A 2000 ms / N / A Distributed / Federa ted Learning

[00133] Of course, as mentioned earlier, AI / ML model training and inference may be considered AI / ML operation types for ease of reference, and so, in an example, Tables 2 and 3 may be combined to provide 5Qls N1 to N5, for use in indicating a type of AI / ML operation or phase to the UPF or 5GC or other NF.

[00134] In certain examples, upon receiving the 5QI value or identifying the 5QI value via communication with the first network entity, the third network entity may determine one or more QoS characteristics corresponding to the AI / ML operation based on the 5QI value. For example, the third network entity may determine a set of one or more QoS characteristics (such as one or more of resource type, default priority level, packet delay budget, packet error rate, default maximum data burst volume, default averaging window, and / or example services) corresponding to the type or the phase of the AI / ML operation / traffic from among a plurality of sets of QoS characteristic each corresponding to one of a plurality of different types of AI / ML operation. For example, the third network entity can check a received 5QI value against a stored table, such as one or more of Tables 1 to 3, to identify corresponding QoS characteristics.

[00135] In an example, a 5QI may represent the QoS requirements for AI / ML model training, including model downloading (e.g. such as in model distribution). Here, in one example, the resource type may be non-GBR, the default averaging window may be N / A, and a default maximum data burst volume may be N / A, and, optionally, the packet error rate (corresponding to “Reliability” in Table 7.10-2 of TS 22.621 [1])) may be 10-3 (referring to “Reliability2 value 99.9% given in Table 7.10-1 of TS 22.621 [1]). The first network entity, e.g. UPF, may determine the 5QI corresponding to AI / ML traffic that is from the second network entity (the first network entity having determined that this traffic is associated with an AI / ML operation or with a type or a phase of an AI / ML operation) as the aforementioned 5QI corresponding to the QoS requirements for AI / ML model training including model downloading. The first network entity may then process the traffic from the second network entity according to the QoS requirements corresponding to the determined 5QI value.

[00136] In another example, the first network entity may determined a 5QI representing QoS requirements for split AI / ML inference operation, such as relating to DL split AI / ML image recognition. Here, for instance, the resource type may be delay-critical GBR and the default averaging window may be 2000ms, and, optionally, the packet error rate (corresponding to “Reliability” in Table 7.10-1 of TS 22.621 [1])) may be 10'5 (referring to “Reliability” value 99.999% given in Table 7.10-1 of TS 22.621 [1]). If the first network entity determines the 5QI corresponding to traffic from the second network entity to be this 5QI, the first network entity may process the traffic according to the corresponding QoS requirements.

[00137] In another example, the first network entity may determine a 5QI representing QoS requirements for split AI / ML inference operation, such as relating to UL split AI / ML image recognition. Here, for instance, the resource type may be delay-critical GBR and the default averaging window may be 2000ms, and, optionally, the packet error rate (corresponding to “Reliability” in Table 7.10-1 of TS 22.621 [1])) may be 10'3 (referring to “Reliability” value 99.9% given in Table 7.10-1 of TS 22.621 [1]). If the first network entity determines the 5QI corresponding to traffic from the second network entity to be this 5QI, the first network entity may process the traffic according to the corresponding QoS requirements. Implicit Indication to the 5GC about the AI / ML traffic

[00138] According to certain embodiments of the present disclosure, the AI / ML traffic or operation might be implicitly indicated to the 5GC or any NF (e.g., the UPF, session management function (SMF), etc.). That is, the 5GC may determine whether the traffic (from another network entity, such as a UE) is associated with AI / ML without explicit indication. It will be appreciated that this may contrast to the embodiments disclosed above where an explicit indication is transmitted to the UPF, for example using a new 5QI.

[00139] According to certain examples, implicit indication that traffic from the second network entity is, or will be, associated with an AI / ML operation is achieved through reserving and / or predefining specific information for use by AI / ML operations. For example, operators and service providers may reserve one or more of the following information for AI / ML: • CN Tunnel Info • Network Instance • Application Identifier

[00140] Once the predefined / standardised value is detected by the 5GC (e.g., the first network entity, a UPF etc.), the 5GC is aware of the transmission of AI / ML traffic. That is, for example, the first network entity may determine that data received from the second network entity, such as a UE, includes the predefined or standardised value, thereby determining that traffic from the UE is, or will be, associated with an AI / ML operation. Following this, the first network entity may report, to the third network entity (e.g., SMF), that the traffic is associated with the AI / ML operation.

[00141] In certain embodiments, the first and third network entities (e.g., the UPF and SMF) have the same knowledge of the reserved I predefined specific information. For example, the specific information may be associated with control / configuration information (for example, 5QI) known or accessible to both the first and third network entities. For example, the specific information may be defined in a technical standard, or a SMF may transmit (or otherwise indicate) the specific information to a UPF.

[00142] For example, according to TS 23.501 [3], a SMF informs an UPF about the reserved I predefined information. TS 23.501 [3] describes that the SMF is responsible for instructing the UPF about how to detect user data traffic belonging to a Packet Detection Rule (PDR) and that the other parameters provided within a PDR describe how the UPF shall treat a packet that matches the detection information. According to TS 23.501 [3], detection information may include: CN tunnel info; Network instance; QFI; IP Packet Filter Set as defined in clause 5.7.6.2 of TS 23.501 [3] I Ethernet Packet Filter Set as defined in clause 5.7.6.3 of TS 23.501 [3]; and Application Identifier (the Application Identifier is an index to a set of application detection rules configured in UPF). According to certain embodiments of the present disclosure, the UPF (i.e., first network entity) may determine whether the information included in the data packets matches the detection information that has been indicated by the SMF (i.e., third network entity). If it matches, the UPF determines it is AI / ML traffic and may report this to the SMF. Monitoring and reporting of AI / ML traffic via N4 session

[00143] Referring to Section 4.4.2 (“N4 Reporting Procedures”) of TS 23.502 [2], it is described that the N4 reporting procedure is used by the UPF to report events to the SMF.

[00144] Accordingly, in certain embodiments of the present disclosure, the UPF is allowed to report the detection of AI / ML traffic to the SMF. Therefore, the SMF will be aware of the transmission of the AI / ML traffic. An example of this is illustrated in Fig. 2, described above.

[00145] Referring to Clause 5.8.2.4 of TS 23.501 [3], it is described that the SMF controls the traffic detection at the UPF by providing detection information for every packet detection rule (PDR). Therefore, based on the information provided in PDR - for example, this may be a new 5QI for AI / ML traffic / operation in accordance with various embodiments of the present disclosure as described above and / or other information in the PDR, the UPF can determine / monitor whether the traffic (from a UE, or second network entity) is AI / ML traffic or AI / ML operation.

[00146] Furthermore, if the AI / ML traffic-related information can indicate the AI / ML processing phases and / or AI / ML operation types (e.g., in accordance with a method described herein), the UPF may report the corresponding detection results to the SMF.

[00147] In certain examples, new reporting case(s) and / or reporting triggers are introduced for the AI / ML traffic reporting. In certain examples, existing reporting case(s) and / or reporting triggers are re-used, thereby introducing the AI / ML traffic related information / indication to the existing reporting case(s) I reporting triggers - accordingly, the UPF may detect the AI / ML traffic using implicit information (that is, through re-use of the existing reporting case(s) and / or reporting triggers, the UPF may implicitly detect the AI / ML traffic, with reference here to the discussion of the “Implicit Indication to the 5GC about the AI / ML traffic” above). For example: • The UPF detects the AI / ML traffic based on the detection of protocol data unit (PDU) Session Inactivity (for a specified period). If the AI / ML traffic is detected, UPF will report this to SMF. The detection may be a combination of the following: o The inactivity timer(s), PDU session activity / inactivity pattern for different time etc. is configured; o there is no data transferred for a period specified by the Inactivity Timer / pattern; o data transmission is resumed / available during the PDU session activity period / pattern; o if one or more of the above criteria (activity / inactivity timer I pattern) is configured for a group of UEs, or the PDU session activity / inactivity are detected (the detection may happen to one than more PDU sessions for a group of UEs ), the UPF may determine traffic is for FL. • UPF detects the AI / ML traffic based on the detection of time-dependent QoS. That is, for the PDU session, the QoS requirements I measurement varies against time. From time 1-2, the QoS parameters are set A; but from time 2-3, the QoS parameters are set B. • UPF detects the AI / ML traffic based on the detection of the traffic I data volume, the data volume within a certain period, or the characteristics of the data packets. For example, for model training, the UE may need to download the model within 1-3s, and the total packet sizes may be up to more than 536Mbyte. For example, for AI / ML inference, the end-to-end latency might be 2 ms, 12 ms, 100 ms with a high data rate. For example, for a model splitting type operation, smaller size models could be shared frequently, as it may not be convenient to share or distribute very large models frequently.

[00148] If the above detections are triggered, the UPF will report about detecting AI / ML traffic this to the SMF. The UPF may also report, to the SMF, which operation type the AI / ML traffic belongs to, i.e. federated learning, or, if considered distinct to operation type, which operation phase the AI / ML traffic belongs to (i.e. model training / downloading or inference).

[00149] In an example, a procedure is as follows:

[00150] Step 1: The UPF may detects the AI / ML traffic. The UPF may trigger the reporting of the reported event. For example, AI / ML traffic is detected, AI / ML model training or inference data is detected (i.e., AI / ML phase), or the data packets for the corresponding AI / ML operation type are detected.

[00151] Step 2: The UPF may, optionally, send / transmit an N4 session report message to the SMF. In an example, the message includes the corresponding information related to AI / ML in Step 1).

[00152] Step 3: The SMF may, optionally, identify the N4 session context based on the received N4 Session ID and may apply the reported information for the corresponding PDU Session. In a further example, the SMF may, optionally, respond with an N4 session report ACK message.

[00153] Examples above refer to a case of a UPF and a SMF. It will be appreciated that this is in view of reference to the N4 interface, and that the concepts could also be extended to cases where the UPF is replaced by another network entity (e.g., another NF, such as one of those referred to in the present disclosure) and the SMF is replaced by another network entity (e.g., another NF, such as one of those referred to in the present disclosure). Indicating AI / ML traffic during PDU session establishment / modification

[00154] In an AI / ML operation, a very large amount of data may be transmitted within a certain time for AI / ML model exchange and inference. At other times, no significant AI / ML traffic might be transmitted. And in some use cases, the AI / ML model exchange or inference may not happen freguently.

[00155] According to certain embodiments of the present disclosure, PDU session(s) which are only for AI / ML traffic may be established. For example, by isolating the AI / ML traffic from other types of traffic in one or more PDU sessions, the 5GC may inactivate the one or more PDU sessions while there is no data to be transmitted, configure proper rules for the one or more PDU sessions etc.

[00156] In certain examples, the AI / ML traffic and the traffic for other types of services are transferred using the same PDU session.

[00157] Therefore, in certain embodiments of the present disclosure, the data transmitted by the second network entity (such as second network entity 12 of Fig. 1) to the first network entity (such as first network entity 11 of Fig. 1) therefore indicates, to the first network entity, that a (potentially yet to be established) PDU session will be used for AI / ML traffic (i.e., for traffic associated with an AI / ML operation), and (optionally) will only be used for AI / ML traffic. Accordingly, the first network entity may determine that the traffic from the second network entity, or at least that future traffic from the second network entity, is associated with an AI / ML operation or is AI / ML traffic.

[00158] To inform the 5GC (for example, the UPF or first network entity) of the potential traffic / usage of the corresponding PDU session, the UE (or second network entity) may send the indication to the 5GC during PDU session establishment / modification. The indication may inform the 5GC of one or more of the following • that the PDU session will be used for AI / ML traffic only, • that the PDU session will be used for AI / ML traffic (other traffic is not excluded), • whether the traffic to be transmitted is for model training or inference, and / or • which type of AI / ML operation generates the AI / ML traffic.

[00159] Some non-limiting examples of the indication are: • a new information element (IE) in a PDU session establishment / modification request message; • a bit in 5GSM capability IE (e.g., a spare bit could be used); • a bit in 5GMM capability IE (e.g., a spare bit could be used); • a new message type may be introduced to indicate that the PDU session is used for the AI / ML services I operation; • Reserved or predefined PDU session ID(s) for AI / ML PDU session may be used. The AI / ML PDU session indicates the PDU session that carries AI / ML traffic.

[00160] Fig. 3 is a block diagram illustrating an exemplary network entity 300 (or electronic device 300, or network node 300 etc.) that may be used in examples of the present disclosure.

[00161] For example, a first network entity, a second network entity, a third network entity, a UPF, a SMF, a NWDAF , a UE and / or another NF (such as a new NF introduced to support AI / ML traffic / operation) may be implemented by or comprise network entity 300 (or be in combination with a network entity 3000) such as illustrated in Fig. 3. The network entity 300 comprises a controller 305 (or at least one processor) and at least one of a transmitter 301, a receiver 303, or a transceiver (not shown).

[00162] For example, referring to Figs. 1 and / or 2 for illustrative purposes, in a case where the first network entity 11,21 is implemented using network entity 300: receiver 303 may be used in the process of receiving data or a signal from the second network entity 22; controller 305 may be used in the process of determining based on one or more characteristics of the data, that traffic from the second network entity will be or is associated with an AI / ML operation; and transmitter 301 may be used in the process of transmitting information indicating the traffic will be or is associated with the AI / ML operation to a third network entity 23. In a case where the second network entity 22 is implemented using network entity 300: transmitter 301 may be used in the process of transmitting a signal or data to the first network entity 11, where the data I signal may include or be associated with one or more characteristics indicating that traffic from the second network entity will be or is associated with an artificial intelligence I machine learning (AI / ML) operation. In a case where the third network entity 23 is implemented using network entity 300: receiver 301 may be used in the process of receiving, from the first network entity 21, information indicating traffic, from a second network entity, will be or is associated with an artificial intelligence I machine learning (AI / ML) operation.

[00163] Fig. 4 is a flow diagram of a method of a first network entity according to an example of the present disclosure.

[00164] In S410, a first network entity receives data (or a signal) from a second network entity.

[00165] In S420, the first network entity determines, based on one or more characteristics of the data, that traffic from the second network entity will be or is associated with an AI / ML operation.

[00166] Operation S430 is optional (depicted by dashed lines in the figure, in this instance). In S430, the first network entity transmits, to a third network entity, information indicating the traffic will be or is associated with the AI / ML operation. For example, the information may be transmitted in an N4 session report message.

[00167] Fig. 5 is a flow diagram of a method of a second network entity according to an example of the present disclosure.

[00168] Operation S510 is optional (depicted by dashed lines in the figure, in this instance). In S510, the second network entity executes or prepares to execute (that is, is aware that it will be executing in the future) an AI / ML operation.

[00169] In operation S520, the second network entity transmits, to a first network entity, data, wherein the data is associated with one or more characteristics indicating that traffic from the second network entity will be or is associated with an AI / ML operation.

[00170] Fig 6 is a flow diagram of a method of a third network entity according to an example of the present disclosure.

[00171] In operation S610, the third network entity receives, from a first network entity, information indicating traffic, from a second network entity to the first network entity, will be or is associated with an AI / ML operation. For example, the information may be received in a N4 session report message.

[00172] Operation S620 is optional (depicted by dashed lines in the figure, in this instance). In S620, the third network entity transmits, to the first network entity, an acknowledgement in response to receiving the information. For example, the response may be an N4 session report ACK.

[00173] It will be appreciated that, in certain examples, in the methods of Fig. 4, Fig. 5 and / or Fig. 6: the first network entity may be in accordance with any first network entity (e.g., a UPF, SMF, UE, application, NWDAF, AMF, PCF, UDM, NEF, NRF, AUSF, NSSF, UDR, AF or new NF for supporting or implementing AI / ML) described above; and / or that the second network entity may be in accordance with any second network entity (e.g., a UE, application, SMF, UPF, NWDAF, AMF, PCF, UDM, NEF, NRF, AUSF, NSSF, UDR, AF or new NF for supporting or implementing AI / ML) described above; and / or the third network entity may be in accordance with any third network entity (e.g., a SMF, UPF, UE, NWDAF, AMF, PCF, UDM, NEF, NRF, AUSF, NSSF, UDR, AF or new NF for supporting or implementing AI / ML) described above.

[00174] All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and / or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. The invention is not restricted to the details of any foregoing embodiments. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

[00175] The techniques described herein may be implemented using any suitably configured apparatus and / or system. Such an apparatus and / or system may be configured to perform a method according to any aspect, embodiment, example or claim disclosed herein. Such an apparatus may comprise one or more elements, for example one or more of receivers, transmitters, transceivers, processors, controllers, modules, units, and the like, each element configured to perform one or more corresponding processes, operations and / or method steps for implementing the techniques described herein. For example, an operation / function of X may be performed by a module configured to perform X (or an X-module). The one or more elements may be implemented in the form of hardware, software, or any combination of hardware and software.

[00176] It will be appreciated that examples of the present disclosure may be implemented in the form of hardware, software or any combination of hardware and software. Any such software may be stored in the form of volatile or non-volatile storage, for example a storage device like a ROM, whether erasable or rewritable or not, or in the form of memory such as, for example, RAM, memory chips, device or integrated circuits or on an optically or magnetically readable medium such as, for example, a CD, DVD, magnetic disk or magnetic tape or the like.

[00177] It will be appreciated that the storage devices and storage media are embodiments of machine-readable storage that are suitable for storing a program or programs comprising instructions that, when executed, implement certain examples of the present disclosure. Accordingly, certain examples provide a program comprising code for implementing a method, apparatus or system according to any example, embodiment, aspect and / or claim disclosed herein, and / or a machine-readable storage storing such a program. Still further, such programs may be conveyed electronically via any medium, for example a communication signal carried over a wired or wireless connection.

[00178] While the invention has been shown and described with reference to certain examples, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention.

[00179] The reader's attention is directed to all papers and documents which are filed 5 concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference. Annex The >G system shall support split AI-ML inference befttven UE and Network SetveriAgplkation function. with psrfbtmaace rsquuKomls as given is Table 7.10-1. Table 7.10-1 KPI Table of split AI / ML inference between UE and Network Server / AppUcation function Uplink KPI Downlink KPI Remarks Max allowed UL end-to-end latency Experiences data rate Payload size Common ica lion service availability Reliability Max atlowed DL end to-eml latency Experienced Sata rate Payload size Reliability 2 IRS 103<3t®s 0.27 99.959 % 99.9 % 99.999 SrtSAI^L Image recoonsBon 10S Sirs 1 5 Mbitto 550 Matt 1:.5 MW frame Enhanced media reccgnsSOT 4 "iSttd 12® 320 MbiVs Spirt control for robote NOTE 1: Communication service avaHabjitty relates to toe: service interfaces, end reSabSty relates to a given system entity. One -or more rebansmissians of network layer packets can take place in order to s atisfy the reliability requirement Tables from Clause 7.10 of TS 22.261 [1]: The 5G system shall support AdiML model downloading wfrb perfi.rmatsce requirements as given in Taste 7.75-2 Table 7.10-2 KPT Table ofAI / ML model downloading Ato a!lw«' DI znd-W-latency' m Afi&feJ use Cwama»i©siw svai&shjW ^ifobs^1- drssijy Hof dswslosiai AI-MJ. mixdvis Remarks Ji- 1 -Gt H2 7 ?'5JJ3r*s S9.W^ .5.9. y?'s fcr dsizi s^'riSifnss^oYi sf 99.99'2% "<BU G.< Al2& k 64Sl2sids ms}* «.yS?% ALJLL dttrfbiaianfy?- Is ¢0 .13]1 rv. .ParaHei of as to 5£' J£A£L *i$5 e.r-bvara .550^* S-s 200$- m «r&R4 area as a Sery-ic* 2XM&J 2q'}.-Sh:te JS. w% Al 'ALL Sxjtewx ij Shared ALL.LL Is ffsaa^r sgiaiitv A^TZ / : cwum 2222L mm&s size 64 M'. forger zises. 22OTE 2: mate te the refote te sgv&i system ar mere air ri is 5£S^- The 5G system shall support Federated Learning betwees UE and Network ServehApplication function with performance requirements as given in Table 7.10-3. Table 7.10-3: KPI Table of Federated teaming between UE and Network Server / Appiication function Max allowed DL or UL ewLto-end latency OL experienced data rate UL experienced data rate DL packet size UL packet size Cemmitmcstiori service availability Remarks Is t.OGbiSs I.OGb&s 332MByls 132MBste Ortcompresssd Federated Learning for image recognition Is 8&88MWS SG.gatws iOMtwie 1 Mbyte IBS Compressed Fesfesfed Learning for image / vMao jMacessmg ":S T8D T8D W / Byfe 13MByte Data Trar>E'er Distufoance in Miiis-agent fwtMwice ML. Operations Acronyms and Definitions 3GPP 3rd Generation Partnership Project 5G 5th Generation 5GC 5G Core 5GS 5G System 5GSM 5G System Session Management 5GMM 5G System Mobility Management AF Application Function Al Artificial Intelligence AMF Access and Mobility management Function AS Application Server ASP Application Service Provider AUSF Authentication Server Function DCAF Data Collection Application Function DNAI Data Network Access Identifier DNN Data Network Name DNS Domain Name Server FQDN Fully Qualified Domain Name GBR Guaranteed Bit Rate GPSI Generic Public Subscription Identifier ID Identity / ldentifier IMEI International Mobile Equipment Identities IP Internet Protocol l-SMF Intermediate SMF ML Machine Learning MNO Mobile Network Operator MT Mobile Termination NAS Non-Access Stratum NEF Network Exposure Function NRF Network Repository Function NSSF Network Slice Selection Function NW Network NWDAF Network Data Analytics Function OS Operating System OSAPP OS Application PCF Policy Control Function PCO Protocol Configuration Options PDR Packet Detection Rule PDU Protocol Data Unit RSD Route Selection Descriptor SIM Subscriber Identity Module SLA Service Level Agreement SM Session Management SMF Session Management Function S-NSSAI Single Network Slice Selection Assistance Information SSC Session and Service Continuity SUPI Subscription Permanent Identifier TAI Tracking Area Identity TE Terminal Equipment TS Technical Specification UDM Unified Data Manager UDR Unified Data Repository UE User Equipment UL Uplink UP User Plane UPF User Plane Function URSP UE Route Selection Policy

Claims

1. A first network entity included in a core of a communication network, the first network entity comprising:a transmitter;a receiver; anda controller configured to:monitor traffic from a second network entity included in the communications network; andbased on the traffic being associated with an artificial intelligence I machine learning (AI / ML) operation or with a type or phase of an AI / ML operation, perform one or more operations to assist performance of the AI / ML operation;wherein the AI / ML operation or the type or phase of the AI / ML operation corresponds to an AI / ML operation type relating to data transfer disturbance in multi-agent multi-device ML operations.

2. The first network entity of claim 1, wherein the controller is configured to: perform the one or more operations to assist performance of the AI / ML operation based on monitoring session inactivity relating to the traffic, monitoring traffic volume relating to the traffic, and / or reserved or predefined information associated with the AI / ML operation.

3. The first network entity of any previous claim, wherein the one or more operations to assist performance of the AI / ML operation comprise:applying a charging rate to traffic associated with the AI / ML operation based on a rate or policy set by an operator.

4. The first network entity of claim 3, wherein a different charging rate is applied based on the type or phase of the AI / ML operation.

5. The first network entity of any previous claim, wherein the one or more operations to assist performance of the AI / ML operation comprise:determining a 5G quality of service (QoS) identifier (5QI) corresponding to the AI / ML operation, the 5QI representing one or more QoS requirements of traffic associated with the AI / ML operation, and processing the traffic according to the one or more QoS requirements.

6. The first network entity of claim 5, wherein one or more of:the one or more QoS requirements comprise one or more of:a resource type including one of guaranteed bit rate (GBR), non-GBR or delay-critical GBR,a packet delay budget,a packet error rate,a default maximum data burst volume, ora default averaging window;the one or more QoS requirements are preset based on a type or phase of the AI / ML operation; and / orthe determined 5QI corresponds to a type or phase of the AI / ML operation.

7. The first network entity of claim 6, wherein:the AI / ML operation is AI / ML model downloading, the determined 5QI is a first 5QI corresponding to the AI / ML model downloading, and the one or more QoS requirements comprise: a resource type of non-GBR, a default maximum data burst volume of N / A, and a default averaging window of N / A;the AI / ML operation is downlink (DL) split AI / ML image recognition, the determined 5QI is a second 5QI corresponding to the DL split AI / ML image recognition, and the one or more QoS requirements comprise: a resource type of delay-critical GBR, a default maximum data burst volume of N / A, and a default averaging window of 2000ms; orthe AI / ML operation of uplink (UL) split AI / ML image recognition, the determined 5QI is a third 5QI corresponding to the UL split AI / ML image recognition, and the one or more QoS requirements comprise, a resource type of delay-critical GBR, a default maximum data burst volume of N / A, and a default averaging window of 2000ms.

8. The first network entity of claim 2, wherein the controller is configured to: report an event to a third network entity, included in the core, based on the monitoring of the session inactivity or the monitoring of the traffic volume.

9. The first network entity of claim 8, wherein the controller is configured to: transmit an N4 session report message to the third network entity to report the event.

10. The first network entity of claim 8 or claim 9, wherein the event indicates thesession inactivity, the traffic volume, the AI / ML operation and / or the type or phase of the AI / ML operation.

11. The first network entity of any of claims 2 or 8 to 10, wherein the reserved or predefined information comprises one or more of: core network (CN) tunnel information, a network instance or an application identifier.

12. The first network entity of any previous claim, wherein one or more of:the first network entity is one of a user plane function (UPF), a user equipment (UE), a session management function (SMF), an application function (AF), an application, a network data analytics function (NWDAF), or a network function (NF) configured to support the AI / ML operation; and / orwherein the first network entity is included in a 5G core (5GC).

13. A method of a first network entity included in a core of a communications network, the method comprising:monitoring traffic from a second network entity included in the communications network; andbased on the traffic being associated with an artificial intelligence I machine learning (AI / ML) operation or with a type or phase of an AI / ML operation, performing one or more operations to assist performance of the AI / ML operation;wherein the AI / ML operation or the type or phase of the AI / ML operation corresponds to an AI / ML operation type relating to data transfer disturbance in multi-agent multi-device ML operations.

14. The method of claim 13, wherein performing the one or more operations to assist performance of the AI / ML operation comprises:performing the one or more operations to assist performance of the AI / ML operation based on monitoring session inactivity relating to the traffic, monitoring traffic volume relating to the traffic, and / or reserved or predefined information associated with the AI / ML operation.

15. The method of any of claims 13 to 14, wherein the one or more operations to assist performance of the AI / ML operation comprise:applying a charging rate to traffic associated with the AI / ML operation based on a rate or policy set by an operator.

16. The method of claim 15, wherein a different charging rate is applied based on the type or phase of the AI / ML operation.

17. The method of any of claims 13 to 16, wherein the one or more operations to assist performance of the AI / ML operation comprise:determining a 5G quality of service (QoS) identifier (5QI) corresponding to the AI / ML operation, the 5QI representing one or more QoS requirements of traffic associated with the AI / ML operation, and processing the traffic according to the one or more QoS requirements.

18. The method of claim 17, wherein one or more of:the one or more QoS requirements comprise one or more of:a resource type including one of guaranteed bit rate (GBR), non-GBR or delay-critical GBR,a packet delay budget,a packet error rate,a default maximum data burst volume, ora default averaging window;the one or more QoS requirements are preset based on a type or phase of the AI / ML operation; and / orthe determined 5QI corresponds to a type or phase of the AI / ML operation.

19. The method of claim 18, wherein:the AI / ML operation is AI / ML model downloading, the determined 5QI is a first 5QI corresponding to the AI / ML model downloading, and the one or more QoS requirements comprise: a resource type of non-GBR, a default maximum data burst volume of N / A, and a default averaging window of N / A;the AI / ML operation is downlink (DL) split AI / ML image recognition, the determined 5QI is a second 5QI corresponding to the DL split AI / ML image recognition, and the one or more QoS requirements comprise: a resource type of delay-critical GBR, a default maximum data burst volume of N / A, and a default averaging window of 2000ms; orthe AI / ML operation of uplink (UL) split AI / ML image recognition, the determined 5QI is a third 5QI corresponding to the UL split AI / ML image recognition, and the one or more QoS requirements comprise, a resource type of delay-critical GBR, a default maximum data burst volume of N / A, and a default averaging window of 2000ms.

20. The method of claim 14, further comprising:reporting an event to a third network entity, included in the core, based on the monitoring of the session inactivity or the monitoring of the traffic volume.28 11 2421. The method of claim 20, wherein reporting the event comprises transmitting an N4 session report message to the third network entity to report the event; and / orwherein the event indicates the session inactivity, the traffic volume, the AI / ML5 operation and / or the type or phase of the AI / ML operation.

22. The method of any of claims 13 to 21, wherein one or more of:the first network entity is one of a user plane function (UPF), a user equipment (UE), a session management function (SMF), an application function (AF), an application,10 a network data analytics function (NWDAF), or a network function (NF) configured to support the AI / ML operation; and / orwherein the first network entity is included in a 5G core (5GC).

23. A computer-readable [storage] medium comprising instructions which, when15 executed by at least one processor of a network entity, cause the network entity to carry out the operations of any one of the methods of claims 13 to 22.

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