Systems, methods, devices, and programs for interface management of AI / ML applications via wireless access parameters

Abstract

A method for adaptive machine learning related action signaling in a telecommunications network may be provided. The method may be executed by at least one processor and may include detecting, by a user device, a transition of the user device from a first state associated with a first 5G New Radio protocol to a second state associated with the first 5G New Radio protocol, transmitting, by the user device, first information related to the transition in response to detecting the transition, receiving, by the user device, change information related to a target action based on the first information transmitted by the user device, and performing, by the user device, the target action based on the change information.

Description

Technical Field

[0001] The present disclosure relates to management of Artificial Intelligence and Machine-Learning (AI / ML) models using wireless access parameters and interfaces. In particular, the present disclosure relates to methods, apparatuses and systems for adaptive machine learning-related operation(s) signaling in telecommunication networks and / or wireless networks.

Background Art

[0002] In a telecommunication network, AI / ML modeling can be used to optimize a plurality of functions performed by components of the telecommunication network. While there are some discussions relating to optimizing several functions of a telecommunication network using AI / ML models, these discussions may focus on application frameworks, model evaluation, and the like. However, there are no existing standards or discussions relating to the use of specific wireless access parameters, New Radio protocols, and / or interfaces for managing AI / ML models. For example, there are no discussions relating to specific wireless access parameters, New Radio protocols, and / or interfaces that can be used for signaling coordination information, user device capability information, or other parameters that affect real-time applications of optimized AI / ML models in a telecommunication network.

[0003] In other words, there is no discussion and / or consensus regarding the management of AI / ML models and their applications in telecommunications networks. More specifically, there is no discussion and / or standards related to signaling the dynamic application of AI / ML functionality using radio access parameters. This lack of signaling guidance can lead to the ineffective use of AI / ML modeling in telecommunications networks and increase latency and inefficiencies in those networks.

[0004] Therefore, in order to effectively leverage and dynamically manage the use of AI / ML in telecommunications networks, it is necessary to define which radio access parameters, New Radio protocols, and / or New Radio interfaces can be used. [Overview of the project]

[0005] According to the embodiment, a method for adaptive machine learning-related operation signaling in a telecommunications network is provided. The method may be performed by at least one processor and may include: detecting a transition of a user device from a first state related to a first 5G New Radio protocol to a second state related to a first 5G New Radio protocol; transmitting first information related to the transition by the user device in response to the detection of the transition; receiving change information related to a target operation based on the first information transmitted by the user device; and performing the target operation based on the change information by the user device.

[0006] According to one embodiment, the first information may include a request to change the machine learning collaboration level associated with a user device, and the change information may include at least one of the following: an acknowledgment of the request to change the machine learning collaboration level or a new machine learning collaboration level based on the request to change the machine learning collaboration level.

[0007] In one embodiment, the change information may be further based on at least one of the type of target action, the time sensitivity of the target action, and the importance associated with the target action.

[0008] In one embodiment, executing a target operation may include executing the target operation based on received support information related to the target operation, on the basis that the new machine learning collaboration level is a first level, where the received support information may be based on one or more machine learning models related to the target operation; executing the target operation without received support information related to the target operation, on the basis that the new machine learning collaboration level is a second level; and terminating the target operation on the user device, on the basis that the new machine learning collaboration level is a third level.

[0009] In one embodiment, the first state may be either an RRC idle state or an RRC inactive state, the second state may be an RRC connected state, and the change information may include instructions for the machine learning collaboration level based on the capabilities of one or more user devices.

[0010] In one embodiment, the first state may be a Radio Resource Control (RRC) connected state, the second state may be either an RRC idle state or an RRC inactive state, and the change information may include a new machine learning collaboration level based on the transition.

[0011] According to one embodiment, the first 5G New Radio protocol may be associated with a radio resource control layer.

[0012] In one embodiment, based on the fact that the target operation is associated with a real-time application, information exchange may be signaled using one or more physical layer protocols. Information exchange may include transmitting first information and receiving change information.

[0013] According to one embodiment, based on the fact that the target operation is associated with a near real-time application, information exchange may be signaled using one or more physical layer protocols, non-access layer protocols, and radio resource control layer protocols.

[0014] According to one embodiment, based on the fact that the target operation is associated with a non-real-time application, information exchange may be signaled using one or more non-access layer protocols and radio resource control layer protocols.

[0015] According to one embodiment, a device for adaptive machine learning-related operation signaling in a telecommunications network may be provided. The device may include at least one memory configured to store computer program code, and at least one processor configured to access the computer program code and to operate as instructed by the computer program code. The program code may include detection code configured to cause at least one processor to detect a transition of a user device from a first state related to a first 5G New Radio protocol to a second state related to the first 5G New Radio protocol, transmission code configured to cause at least one processor to transmit first information related to the transition in response to the detection of the transition, reception code configured to cause at least one processor to receive change information related to a target operation based on the first information transmitted by the user device, and execution code configured to cause at least one processor to execute a target operation based on the change information.

[0016] According to one embodiment, a non-temporary computer-readable medium storing instructions for adaptive machine learning-related operation signaling in a telecommunications network may be provided. The instructions include one or more instructions that, when executed by one or more processors, cause one or more processors to cause a user device to detect a transition of the user device from a first state related to a first 5G New Radio protocol to a second state related to the first 5G New Radio protocol; in response to the detection of the transition, cause the user device to transmit first information related to the transition; cause the user device to receive change information related to a target operation based on the first information transmitted by the user device; and cause the user device to perform a target operation based on the change information.

[0017] The features, advantages, and importance of exemplary embodiments of this disclosure will be described below with reference to the attached drawings, where similar reference numerals indicate similar elements. [Brief explanation of the drawing]

[0018] [Figure 1] This is an illustrative schematic diagram of a network architecture in which the system and / or method described in this disclosure may be implemented.

[0019] [Figure 2] This is an exemplary flowchart illustrating an exemplary process for adaptive machine learning-related behavioral signaling in a telecommunications network according to an embodiment of the present disclosure.

[0020] [Figure 3] This is an exemplary workflow diagram illustrating an exemplary process for adaptive machine learning-related behavioral signaling in a telecommunications network according to an embodiment of the present disclosure.

[0021] [Figure 4] This is a schematic diagram illustrating the components of the network architecture shown in Figure 1 according to an embodiment of this disclosure.

[0022] [Figure 5] It is an exemplary schematic diagram of components of the network architecture of FIG. 1 according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS

[0023] The following detailed description of exemplary embodiments refers to the accompanying drawings. The same reference numerals in different drawings may identify the same or similar elements.

[0024] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementation to the precise form disclosed. Modifications and variations are possible in light of the above disclosure, or may be obtained from practicing the implementation.

[0025] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit the implementation. Accordingly, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it is understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0026] As is traditional in this art, embodiments may be described and illustrated in terms of blocks that perform one or more of the functions described herein. These blocks, which may be referred to herein as units or modules, may be physically implemented by analog or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, and hardwired circuits, and may be driven by firmware and software. The circuits may be embodied, for example, in one or more semiconductor chips or on a substrate support such as a printed circuit board. The circuits included in a block may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuits), or by a combination of dedicated hardware for performing some of the functions of the block and a processor for performing other functions of the block. Each block of an embodiment may be physically separated into two or more interacting individual blocks. Similarly, blocks of an embodiment may be physically coupled into more complex blocks.

[0027] Even if specific combinations of features are described in the claims and / or disclosed herein, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways not specifically described in the claims and / or disclosed herein. Each dependent claim listed below may directly depend on only one claim, but the disclosure of possible implementations includes each dependent claim in combination with all other claims in the set of claims.

[0028] Any elements, actions, or commands used herein should not be construed as important or essential unless expressly stated otherwise. Furthermore, as used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more.” When only one item is intended, the term “one” or similar phrases should be used. Additionally, as used herein, terms such as “has,” “have,” “having,” “include,” and “including” are intended to be non-restrictive. Furthermore, as used herein, the phrase “based on” is intended to mean “at least partially based on” unless otherwise specified.

[0029] As stated above, there are no existing standards or even discussions regarding the use of specific radio access parameters, New Radio protocols, and / or interfaces for managing AI / ML models. For example, there are no discussions regarding specific radio access parameters, New Radio protocols, and / or interfaces that could be used to signal collaborative information, user device capability information, or other parameters affecting the real-time application of optimized AI / ML models in telecommunications networks.

[0030] In other words, there is no discussion and / or consensus regarding the management of AI / ML models and their applications in telecommunications networks. More specifically, there is no discussion and / or standard relating to signaling the dynamic application of AI / ML functionality using radio access parameters. This lack of signaling guidance can lead to the ineffective utilization of AI / ML modeling in telecommunications networks and increase latency and inefficiency in those networks. Therefore, it is necessary to define which radio access parameters, New Radio protocols, and / or New Radio interfaces can be used to effectively leverage and dynamically manage the use of AI / ML in telecommunications networks.

[0031] According to aspects of this disclosure, an AI / ML model may be dynamically managed and applied to perform a target operation using appropriate radio access parameters, a New Radio protocol, and / or an interface. The appropriate radio access parameters, a New Radio protocol, and / or an interface may be based on the capabilities of one or more user devices, the type and properties of the target operation, the time sensitivity and priority of the target operation, and / or changes in the state of the user device's connection to the telecommunications network.

[0032] According to aspects of this disclosure, a user device can communicate with a telecommunications network via a network element and dynamically manage an AI / ML model related to a target operation being performed by the user device. Depending on changes in one or more capabilities of the user device, the type and properties of the target operation, the time sensitivity and priority of the target operation, and / or the state of the user device's connection to the telecommunications network, an AI / ML application function may be defined that allows the user device to adaptively and in real time control an AI / ML model deployed on the user device using radio access parameters and interfaces.

[0033] In some embodiments, the AI / ML application function can interface with multiple layers within the 5G New Radio protocol stack. For example, the AI / ML application function can interface with the Radio Resource Control (RRC) layer, the Non-Access Stratum (NAS) layer, and / or the Physical (PHY) layer of the 5G New Radio protocol stack.

[0034] According to aspects of this disclosure, the AI / ML application function may be used to determine the level of machine learning collaboration associated with a user device. The level of machine learning collaboration associated with a user device may indicate whether the AI / ML model related to the operation may require additional information from the telecommunications network or can be implemented on the user device without additional information from the telecommunications network. The level of machine learning collaboration associated with a user device may be changed in response to a change in the connection state of the user device. For example, when a user device is in "sleep mode," the connection between the user device and the network element may be inactive or idle. In some embodiments, the level of machine learning collaboration associated with the user device may be changed based on a change in the connection state to idle or inactive, and one or more of the AI / ML models may be deployed to the network element or core network. Based on the change in the connection state and the resumption of the connection between the user device and the network element, the level of machine learning collaboration associated with the user device may be changed, and one or more of the AI / ML models may be deployed to the user device. The machine learning collaboration level associated with a user device, or a modified machine learning collaboration level associated with a user device, may be signaled using the RRCResumeRequest1, RRCResumeRequest, RRCReconfiguration, and / or RRCRelease messages.

[0035] In some embodiments, the AI / ML application function may be used to determine the machine learning capability level associated with a user device. The machine learning capability level associated with a user device may indicate whether an AI / ML model associated with a certain operation can be trained, updated, and / or deployed on the user device. The machine learning capability level associated with a user device may be changed in response to a change in the user device's connection state. For example, when a user device is in "sleep mode," the connection between the user device and network elements may be inactive or idle. In some embodiments, when the connection state changes to idle or inactive, the machine learning capability level associated with the user device may be changed, and one or more AI / ML models may be deployed to the network elements or core network. Based on the change in connection state and the resumption of the connection between the user device and network elements, the machine learning capability level associated with the user device may be changed, and one or more AI / ML models may be deployed to the user device. The machine learning capability level associated with a user device, or a changed machine learning capability level associated with a user device, may be signaled using RRCResumeRequest1, RRCResumeRequest, RRCReconfiguration, and / or RRCRelease messages.

[0036] According to aspects of this disclosure, the AI / ML application function can interface with and / or communicate with a telecommunications network using one or more of the RRC layer, NAS layer, or PHY layer of the 5G New Radio protocol stack. In some embodiments, the NAS layer may be used to transmit and / or receive information relating to the type of user device, user device properties, slice type information, user device positioning information, time sensitivity information, etc. The NAS layer may also be used to transmit one or more of the user device capabilities to the telecommunications network. In some embodiments, the RRC layer may be used to transmit and / or receive user device connectivity to the telecommunications network. Based on the user device's connectivity status to the telecommunications network, or based on changes in the user device's connectivity status to the telecommunications network, the AI / ML model may be trained, updated, and / or deployed on the user device.

[0037] Figure 1 is an illustrative schematic diagram of an excerpt of a network architecture in which the system and / or method described herein may be implemented.

[0038] As shown in Figure 1, the network infrastructure 100 of the telecommunications network may include one or more user devices 110, one or more network elements 105, and a core network 115.

[0039] According to the embodiment, the user device 110 may be any device used by an end user to communicate using a telecommunications network. The user device 110 can communicate with the network element 105 via a wireless channel. The user device 110 may be replaced with "terminal," "user equipment (UE)," "mobile station," "subscriber station," "customer premises equipment (CPE)," "remote terminal," "wireless terminal," "user device," "device," "laptop," "computing device," or other terms with equivalent technical meaning.

[0040] In some embodiments, the user device 110 may include multiple 5G New Radio layers and / or protocol stacks. For example, the user device 110 may include a NAS layer 132, an RRC layer 134, a Service Data Adaptation Protocol (SDAP) layer 136, a Packet Data Convergence Protocol (PDCP) layer 138, a Radio Link Control (RLC) layer 140, a Medium Access Control (MAC) layer 142, and a PHY layer 144. According to one embodiment, the user device 110 may include a data management function 120 and an AI / ML application function 125.

[0041] According to aspects of this disclosure, a user device can communicate with a telecommunications network via a network element and dynamically manage an AI / ML model related to a target operation being performed by the user device. Depending on changes in the capabilities of one or more of the user device, the type and properties of the target operation, the time sensitivity and priority of the target operation, and / or the state of the user device's connection to the telecommunications network, an AI / ML application function 125 may be defined that allows the user device to adaptively and in real time control an AI / ML model deployed on the user device using radio access parameters and interfaces.

[0042] In some embodiments, the AI / ML application function 125 can interface with multiple layers within the 5G New Radio protocol stack. For example, the AI / ML application function can interface with the RRC layer 134, the NAS layer 132, and / or the PHY layer 144 of the 5G New Radio protocol stack.

[0043] According to aspects of this disclosure, the AI / ML application function 125 may be used to determine the machine learning collaboration level associated with a user device. The machine learning collaboration level associated with a user device may indicate whether the AI / ML model associated with the operation may require additional information from the telecommunications network or can be implemented on the user device without additional information from the telecommunications network. The machine learning collaboration level associated with a user device may be changed in response to a change in the connection state of the user device. For example, when the user device is in "sleep mode", the connection between the user device 110 and the network element 105 may be inactive or idle. In some embodiments, the machine learning collaboration level associated with the user device may be changed based on the connection state changing to idle or inactive, and one or more AI / ML models may be deployed to the network element 105 or the core network 115. Based on the change in the connection state and the resumption of the connection between the user device 110 and the network element 105, the machine learning collaboration level associated with the user device 110 may be changed, and one or more AI / ML models may be deployed to the user device 110.

[0044] In some embodiments, the AI / ML application function 125 may be used to determine the machine learning capability level associated with the user device 110. The machine learning capability level associated with the user device may indicate whether an AI / ML model associated with a certain operation can be trained, updated, and / or deployed on the user device 110. The machine learning capability level associated with the user device may be changed in response to a change in the connection state of the user device 110. For example, when the user device 110 is in "sleep mode", the connection between the user device 110 and the network element 105 may be inactive or idle. In some embodiments, when the connection state changes to idle or inactive, the machine learning capability level associated with the user device 110 may be changed, and one or more AI / ML models may be deployed to the network element 105 or the core network 115. Based on the change in the connection state and the resumption of the connection between the user device 110 and the network element 105, the machine learning capability level associated with the user device 110 may be changed, and one or more AI / ML models may be deployed to the user device 110.

[0045] According to one embodiment of the present disclosure, the user device 110 may include a data management function 120. The data management function 120 can interface with an AI / ML application function 125. The data management function 120 can collect and share information about one or more AI / ML models. In some embodiments, the data management function 120 can selectively share information about one or more applications, operations, and / or functionalities of a telecommunications network running using AI / ML modeling.

[0046] In some embodiments, the user device 110 can communicate with a telecommunications network using a network element 105, such as a base station. An example of a base station may be a gNodeB or an eNodeB. The network element 105 may be a node that enables communication between the user device 110 and the telecommunications system using a radio channel. The network element 105 may be a network infrastructure component that can provide wireless connectivity to the user device 110. The network element 105 may have coverage defined as a specific geographical area, also known as a sector, based on the signal transmission distance.

[0047] The network element 105 may include multiple 5G New Radio layers and / or protocol stacks. For example, the network element 105 may include an RRC layer 154, an SDAP layer 156, a PDCP layer 158, an RLC layer 160, a MAC layer 162, and a PHY layer 164.

[0048] NAS layer 132 and NAS layer 172 may be used to determine the positioning of user device 110 and user device 110 in the core network 115, respectively. In some embodiments, the AI / ML application function 125 may interface with NAS layer 132 and transmit and / or receive user device type, user device properties, slice type information, user device indoor positioning, and time sensitivity information related to one or more target operations being performed using the AI / ML model. In some embodiments, NAS layer 132 may be used to interface with the AI / ML model being used to perform target operations, which may include, but are not limited to, operations related to model transfer, control for normal time optimization, user device AI / ML capability, user device AI / ML cooperation level, user device type and properties, user device positioning, etc.

[0049] RRC layers 134 and 154 may be used to determine where the AI / ML model resides and / or where it can be executed. In some embodiments, the AI / ML application function 125 may interface with RRC layer 134 and transmit and / or receive connectivity status between the user device 110 and the network element 105. In some embodiments, RRC layer 134 may be used to interface with the AI / ML model used to perform a target operation, which may include, but is not limited to, operations related to model transfer, control for near real-time optimization, control for normal-time optimization, key performance indicator monitoring, tuning of device-related parameters, user device AI / ML capability, user device AI / ML cooperation level, user device positioning, etc.

[0050] PHY layers 144 and 164 may be used to set, exchange, and signal radio control parameters related to the configuration of wireless communication between the user device 110 and the telecommunications network (network element 105 and / or core network 115). In some embodiments, the AI / ML application function 125 may interface with PHY layer 144 and transmit and / or receive parameters related to establishing and / or coordinating the connection between the user device 110 and the network element 105 or core network 115. In some embodiments, PHY layer 144 may be used to interface with an AI / ML model used to perform a target operation, which may include, but is not limited to, operations related to model transfer, control for real-time optimization, control for normal-time optimization, user device AI / ML capability, user device AI / ML cooperation level, user device positioning, etc.

[0051] According to the embodiments, SDAP layers 136 and 156 can be used for quality of service and flow processing between the user device 110 and the network element 105. In some embodiments, SDAP layer 136 may be used to interface with an AI / ML model used to perform a target operation, which may include, but is not limited to, operations related to quality of service and flow processing.

[0052] According to the embodiments, PDCP layers 138 and 158 may be used for packet compression, encryption, and maintaining data integrity between the user device 110 and the network element 105. In some embodiments, PDCP layer 138 may be used to interface with an AI / ML model used to perform a target operation, which may include, but is not limited to, operations related to packet compression, encryption, and maintaining data integrity.

[0053] According to the embodiments, RLC layers 140 and 160 may be used for error correction, sequence numbering, segmentation, and deduplication between the user device 110 and the network element 105. In some embodiments, RLC layer 140 may be used to interface with an AI / ML model used to perform a target operation, which may include, but is not limited to, operations related to error correction, sequence numbering, segmentation, and deduplication.

[0054] According to the embodiments, MAC layers 142 and 162 may be used for mapping, multiplexing, demultiplexing, scheduling, HARQ optimization, and the like between logical channels and transport channels. In some embodiments, MAC layer 142 may be used to interface with an AI / ML model used to perform a target operation, which may include, but is not limited to, operations related to mapping, multiplexing, demultiplexing, scheduling, and HARQ optimization between logical channels and transport channels.

[0055] In some embodiments, the network infrastructure 100 may include a core network 115. The core network 115 may include an AI / ML enabler 175 and one or more databases 180. The AI / ML enabler 175 can communicate with network elements 105 using the N1 protocol and / or interface. The AI / ML enabler 175 can communicate with user devices 110 using the N2 protocol and / or interface. The AI / ML enabler 175 can store AI / ML models that can be used to perform one or more operations and / or functionalities of a telecommunications network. The databases 180 can store data related to the training, testing, and validation of AI / ML models. The databases 180 can send data to and / or receive data from the AI / ML enabler 175.

[0056] According to one embodiment of the present disclosure, the network infrastructure 100 may include one or more real-time applications 190 and one or more near-real-time applications 195. According to one aspect of the present disclosure, the AI / ML application function 125 may select one or more interfaces for exchanging information related to one or more operations running on the AI / ML model and / or user device, based on the type of application. For example, in the case of a real-time application using an AI / ML model, information exchange related to the real-time application and / or the AI / ML model may be signaled using one or more physical layer protocols. For example, in the case of a near-real-time application using an AI / ML model, information exchange related to the near-real-time application and / or the AI / ML model may be signaled using one or more physical layer protocols, non-access layer protocols, and radio resource control layer protocols. For example, in the case of a non-real-time application using an AI / ML model, information exchange related to the near-real-time application and / or the AI / ML model may be signaled using one or more non-access layer protocols and radio resource control layer protocols.

[0057] Figure 2 is an exemplary flowchart showing an exemplary process 200 for adaptive machine learning-related behavioral signaling in a telecommunications network according to an embodiment of the present disclosure.

[0058] As shown in Figure 2, one or more process blocks of process 200 may be executed by any of the components of Figures 1 and 5-6 described in this application. In Figure 2, one or more process blocks of process 200 may correspond to operations related to the user device 110.

[0059] In operation 205, the user device can detect a transition of the user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol. For example, the first and second states may include the RRC connected state, the RRC idle state, and the RRC inactive state. The first 5G New Radio protocol may include the RRC layer, the NAS layer, and / or the PHY layer.

[0060] In one embodiment of the present disclosure, in operation 210, in response to the detection of a transition, the user device may transmit first information related to the transition.

[0061] In one embodiment of the present disclosure, the first information may include a request to change the machine learning collaboration level associated with a user device or one or more capabilities of the user device.

[0062] In operation 215, the user device may receive change information related to the target operation from the network element 105 and / or the core network 115. The change information may be based on the first information transmitted by the user device.

[0063] In some embodiments, the change information may include at least one of the following: a machine learning collaboration level instruction based on the capabilities of one or more user devices; an acknowledgment of a machine learning collaboration level change request; or a new machine learning collaboration level based on a machine learning collaboration level change request or transition. Furthermore, in some embodiments, the change information may be further based on at least one of the target behavior type, the time sensitivity of the target behavior, and the importance associated with the target behavior.

[0064] In operation 220, the user device can perform a target operation based on change information. In one embodiment, based on change information including a machine learning collaboration level associated with the user device, the target operation may be performed using information from AI / ML models and / or network elements. In some embodiments, performing a target operation based on a new machine learning collaboration level being a first level may be based on received support information associated with the target operation, and the received support information may be based on one or more machine learning models associated with the target operation. In some embodiments, performing a target operation based on a new machine learning collaboration level being a second level may not be based on received support information associated with the target operation. In some embodiments, the target operation in the user device is terminated based on a new machine learning collaboration level being a third level.

[0065] According to one aspect of this disclosure, information exchange between a user device and a network element and / or core network, including transmitting first information and receiving change information, may be signaled using a specific 5G New Radio protocol based on the application associated with the target operation. For example, based on the target operation being associated with a real-time application, the information exchange may be signaled using one or more physical layer protocols. For example, based on the target operation being associated with a near-real-time application, the information exchange may be signaled using one or more physical layer protocols, a non-access layer protocol, and a radio resource control layer protocol. For example, based on the target operation being associated with a non-real-time application, the information exchange may be signaled using one or more non-access layer protocols and a radio resource control layer protocol.

[0066] Figure 3 is an exemplary workflow diagram showing an exemplary workflow 300 for adaptive machine learning-related behavioral signaling in a telecommunications network according to an embodiment of the present disclosure.

[0067] As shown in Figure 3, one or more operational blocks of the workflow 300 may be executed by any of the components of Figures 1 and 4-5 described herein. In Figure 3, one or more operational blocks of the workflow 300 may correspond to operations related to the user device 110, the network element 105, and / or the core network 115.

[0068] In operation 305, the user device 110 can transmit one or more user device capabilities to the network element 105 and / or the core network 115. In some embodiments, the user device 110 can signal user device capabilities using the NAS layer 132.

[0069] In operation 310, the AI / ML framework can determine the machine learning collaboration level and / or machine learning capability level associated with the user device based on the user device capabilities. In some embodiments, the AI / ML framework can determine the machine learning collaboration level and / or machine learning capability level associated with the user device in the network element 105 and / or the core network 115. In operation 315, the network element 105 and / or the core network 115 can transmit instructions to the user device 110 regarding the machine learning collaboration level and / or machine learning capability level associated with the user device. In some embodiments, instructions regarding the machine learning collaboration level and / or machine learning capability level associated with the user device can be signaled using an RRCReconfiguration message.

[0070] In operation 320, the connection state of the user device 110 can be changed to a connected state, and in operation 325, the user device 110 can be enabled to fully cooperate with the network element 105 and / or the core network 115 to perform a target operation, such as indoor positioning of the user device 110.

[0071] In operation 330, the network element 105 or the core network 115 may use the RRCRelease message to send instructions for changing the machine learning collaboration level and / or machine learning capability level associated with the user device. A change in the machine learning collaboration level and / or machine learning capability level associated with the user device can be an example of change information.

[0072] In operation 330, based on the change information received, in operation 335, the connection state of the user device 110 can be changed from connected to idle or inactive. In operation 340, the user device 110 may be made capable of performing a target operation, such as indoor positioning of the user device 110, without further cooperation with the network element 105 and / or the core network 115, i.e., in standalone mode.

[0073] Figure 4 is a diagram illustrating an exemplary environment for implementing one or more devices, operations, and / or frameworks from Figures 1 to 3.

[0074] As shown in Figure 4, the environment 400 may include a user device 110, a platform 420, and a network 430. The devices in environment 400 can be interconnected via wired connections, wireless connections, or a combination of wired and wireless connections. In embodiments, any function of an element included in the network infrastructure 100 can be performed by any combination of elements shown in Figure 4. For example, in embodiments, the user device 110 may perform one or more functions related to a personal computing device, and the platform 420 may perform one or more functions related to any of the network elements 105.

[0075] The user device 110 may include one or more devices that can receive, generate, store, process, and / or provide information related to the platform 420. For example, the user device 110 may include computing devices (e.g., desktop computers, laptop computers, tablet computers, handheld computers, smart speakers, servers, etc.), mobile phones (e.g., smartphones, wireless phones, etc.), camera devices, wearable devices (e.g., smart glasses or smartwatches), or similar devices. In some implementations, the user device 110 may receive information from and / or transmit information to the platform 420.

[0076] Platform 420 may include one or more devices capable of receiving, generating, storing, processing, and / or providing information. In some implementations, Platform 420 may include a cloud server or a group of cloud servers. In some implementations, Platform 420 may be designed to be modular so that certain software components can be swapped in or swapped out as needed. In this way, Platform 420 can be easily and / or quickly reconfigured for different uses.

[0077] In some implementations, as shown in the figure, platform 420 may be hosted in a cloud computing environment 422. It should be noted that while the implementations described herein are described with platform 420 hosted in a cloud computing environment 422, in some implementations platform 420 may not be cloud-based (i.e., may be implemented outside a cloud computing environment), or may be partially cloud-based.

[0078] The cloud computing environment 422 includes an environment that hosts platform 420. The cloud computing environment 422 can provide services such as computing, software, data access, and storage that do not require the end user's (e.g., user device 110) knowledge of the physical location and configuration of the system(s) and / or device(s) that host platform 420. As shown in the figure, the cloud computing environment 422 may include a group of computing resources 424 (collectively referred to as “computing resources(plural)424” and individually as “computing resource(single)424”).

[0079] A single computing resource 424 includes one or more personal computers, clusters of computing devices, workstation computers, server devices, or other types of computing and / or communication devices. In some implementations, a single computing resource 424 can host a platform 420. Cloud resources may include computing instances running within a single computing resource 424, storage devices located within a single computing resource 424, data transfer devices provided by a single computing resource 424, and so on. In some implementations, a single computing resource 424 can communicate with other computing resources 424 via wired connections, wireless connections, or a combination of wired and wireless connections.

[0080] As further shown in Figure 4, a single computing resource 424 includes a group of cloud resources such as one or more applications (APP) 424-1, one or more virtual machines (VM) 424-2, virtualized storage (VS) 424-3, and one or more hypervisors (HYP) 424-4.

[0081] Application 424-1 includes one or more software applications that may be provided to or accessed by a user device 110 or a network element 105. Application 424-1 eliminates the need to install and run software applications on the user device 110 or the network element 105. For example, Application 424-1 may include software associated with Platform 420 and / or any other software that can be provided via a cloud computing environment 422. In some implementations, one application 424-1 may send and receive information to and from one or more other applications 424-1 via a virtual machine 424-2.

[0082] A virtual machine 424-2 includes a software implementation of a machine (e.g., a computer) that runs programs like a physical machine. Depending on its use and the degree to which it corresponds to an actual machine, a virtual machine 424-2 can be either a system virtual machine or a process virtual machine. A system virtual machine can provide a complete system platform that supports the execution of a complete operating system (OS). A process virtual machine can run a single program and support a single process. In some implementations, a virtual machine 424-2 can run on behalf of a user (e.g., a user device 110) and manage the infrastructure of a cloud computing environment 422, such as data management, synchronization, or long-term data transfer.

[0083] Virtualized storage 424-3 includes one or more storage systems and / or one or more devices that use virtualization technology within a storage system or device of a single computing resource 424. In some implementations, the types of virtualization in relation to the storage system may include block virtualization and file virtualization. Block virtualization can refer to the extraction (or separation) of logical storage from physical storage, allowing access to the storage system regardless of whether it is physical storage or heterogeneous. Separation can give storage system administrators flexibility in how they manage storage for end users. File virtualization can eliminate the dependency between data accessed at the file level and the location where the file is physically stored. This can enable optimization of storage usage, server consolidation, and / or performance for non-disruptive file migration.

[0084] Hypervisor 424-4 can provide hardware virtualization technology that enables multiple operating systems (e.g., "guest operating systems") to run simultaneously on a host computer such as computing resource (singular) 424. Hypervisor 424-4 can present a virtual operating platform to guest operating systems and manage the execution of guest operating systems. Multiple instances of various operating systems can share virtualized hardware resources.

[0085] Network 430 includes one or more wired and / or wireless networks. For example, Network 430 may include cellular networks (e.g., Fifth Generation (5G) networks, Long-Term Evolution (LTE) networks, Third Generation (3G) networks, Code Division Multiple Access (CDMA) networks, etc.), Public Land Mobile Networks (PLMN), Local Area Networks (LAN), Wide Area Networks (WAN), Metropolitan Area Networks (MAN), telephone networks (e.g., Public Switched Telephone Networks (PSTN)), private networks, ad-hoc networks, intranets, the Internet, fiber optic-based networks, etc., and / or combinations of these or other types of networks.

[0086] The number and arrangement of devices and networks shown in Figure 4 are provided as examples. In practice, there may be additional devices and / or networks, fewer devices and / or networks, different devices and / or networks, or devices and / or networks in different arrangements compared to those shown in Figure 4. Furthermore, two or more devices shown in Figure 4 may be implemented within a single device, or a single device shown in Figure 4 may be implemented as multiple distributed devices. In addition, or instead, a set of devices in environment 400 (e.g., one or more devices) may perform one or more functions that are described as being performed by another set of devices in environment 400.

[0087] Figure 5 is a diagram illustrating an exemplary component of one or more of the devices shown in Figures 1 to 4, according to an embodiment of the present disclosure.

[0088] According to one embodiment, Figure 5 may be a diagram of exemplary components of a user device 110. The user device 110 may correspond to a device associated with an authorized user, cell operator, or RF engineer. The user device 110 can be used to communicate with a cloud platform 420 via a network element 105. As shown in Figure 5, the user device 110 may include a bus 510, a processor 520, memory 530, a storage component 540, an input component 550, an output component 560, and a communication interface 570.

[0089] Bus 510 may include components that enable communication between components of the user device 110. The processor 520 may be implemented in hardware, firmware, or a combination of hardware and software. The processor 520 may be a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Accelerated Processing Unit (APU), microprocessor, microcontroller, Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), Application-Specific Integrated Circuit (ASIC), or another type of processing component. In some implementations, the processor 520 includes one or more processors that can be programmed to perform functions. The memory 530 includes random access memory (RAM), read-only memory (ROM), and / or other types of dynamic or static storage devices (e.g., flash memory, magnetic memory, and / or optical memory) for storing information and / or instructions for use by the processor 520.

[0090] The storage component 540 stores information and / or software related to the operation and use of the user device 110. For example, the storage component 540 may include, along with a corresponding drive, a hard disk (e.g., magnetic disk, optical disk, magneto-optical disk, and / or solid-state disk), a Compact Disc (CD), a Digital Versatile Disc (DVD), a floppy disk, a cartridge, magnetic tape, and / or another type of non-temporary computer-readable media. The input component 550 includes components that enable the user device 110 to receive information via user input (e.g., a touchscreen display, keyboard, keypad, mouse, buttons, switches, and / or microphone). In addition, or instead, the input component 550 may include sensors for sensing information (e.g., a Global Positioning System (GPS) component, an accelerometer, a gyroscope, and / or actuators). The output component 560 includes components that provide output information from the user device 110 (e.g., a display, a speaker, and / or one or more light-emitting diodes (LEDs)).

[0091] The communication interface 570 includes transceiver-like components (e.g., transceivers and / or separate receivers and transmitters) that enable the user device 110 to communicate with other devices via wired connections, wireless connections, or a combination of wired and wireless connections. The communication interface 570 may also enable the user device 110 to receive information from and / or provide information to other devices. For example, the communication interface 570 may include Ethernet interfaces, optical interfaces, coaxial interfaces, infrared interfaces, radio frequency (RF) interfaces, Universal Serial Bus (USB) interfaces, Wi-Fi interfaces, cellular network interfaces, and the like.

[0092] The user device 110 can execute one or more processes described herein. The user device 110 can execute these processes in response to a processor 520 that executes software instructions stored in a non-temporary computer-readable medium, such as memory 530 and / or storage component 540. A computer-readable medium may be defined herein as a non-temporary memory device. A memory device includes a memory space within a single physical storage device or a memory space spanning multiple physical storage devices.

[0093] Software instructions may be read into memory 530 and / or storage component 540 from another computer-readable medium or from another device via the communication interface 570. When executed, the software instructions stored in memory 530 and / or storage component 540 may cause the processor 520 to execute one or more processes described herein.

[0094] The foregoing disclosures are intended to provide examples and explanations, but are not intended to be exhaustive or to limit implementations to the exact forms disclosed. Modifications and variations are possible in light of the foregoing disclosures, or such modifications and variations may be obtained from the implementations.

[0095] Some embodiments may relate to systems, methods, and / or computer-readable media in integration at any possible level of technical detail. Furthermore, one or more of the above-described components may be implemented as instructions stored in a computer-readable medium and executable by at least one processor (and / or include at least one processor). The computer-readable medium may include one or more computer-readable non-temporary storage media having computer-readable program instructions for causing a processor to perform an operation.

[0096] A computer-readable storage medium can be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer-readable storage medium may, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media includes portable computer diskettes, hard disks, random-access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random-access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multipurpose discs (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punched cards or grooved raised structures on which instructions are recorded, and any suitable combination thereof. As used herein, computer-readable storage media should not be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through optical fiber cables), or electrical signals transmitted through wires.

[0097] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives computer-readable program instructions from the network and transfers the computer-readable program instructions for storage in a computer-readable storage medium within each computing / processing device.

[0098] The computer-readable program code / instructions for performing an operation may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk and C++, and procedural programming languages ​​such as the C programming language or similar programming languages. The computer-readable program instructions may run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may connect to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it may connect to an external computer (for example, via the Internet using an Internet service provider). In some embodiments, for example, an electronic circuit including a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) can execute computer-readable program instructions by utilizing state information of computer-readable program instructions to personalize the electronic circuit in order to perform an action or operation.

[0099] These computer-readable program instructions may be provided to a general-purpose computer, a dedicated computer, or a processor of another programmable data processing device to generate a machine such that instructions executed via the processor of a computer or other programmable data processing device create means for performing functions / actions specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored on a computer-readable storage medium on which the instructions are stored, which can be instructed to function in a particular way, such as a product containing instructions that perform a mode of function / action specified in one or more blocks of a flowchart and / or block diagram.

[0100] Computer-readable program instructions may also be loaded onto a computer, other programmable device, or other device to generate a computer implementation process by causing the computer, other programmable device, or other device to execute a series of operational steps so that the instructions executed on the computer, other programmable device, or other device perform a function / action specified in one or more blocks of a flowchart and / or block diagram.

[0101] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. From this perspective, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specific logical function(s). Methods, computer systems, and computer-readable media may include additional blocks, fewer blocks, different blocks, or blocks in different arrangements compared to the blocks shown in the diagrams. In some alternative implementations, the functions described in the blocks may be performed in a different order than those shown in the diagrams. For example, two consecutively shown blocks may actually be executed simultaneously or nearly simultaneously, or blocks may sometimes be executed in reverse order depending on the associated functionality. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs a specified function or action, or a combination of dedicated hardware and computer instructions.

[0102] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or combinations of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not limited to the implementation form. Therefore, it is understood that the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and that software and hardware may be designed to implement the systems and / or methods based on the descriptions herein.

Claims

1. A method for adaptive machine learning-related behavioral signaling in telecommunications networks, The user device detects the transition of the user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol, In response to the detection of the transition, the user device transmits first information related to the transition. The user device receives change information related to the target operation based on the first information transmitted by the user device, The user device performs the target operation based on the change information, Includes, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. A method wherein the first information includes a machine learning collaboration level change request associated with the user device, and the change information includes at least one of the following: an acknowledgment of the machine learning collaboration level change request or a new machine learning collaboration level based on the machine learning collaboration level change request.

2. The method according to claim 1, wherein the change information is further based on at least one of the type of target action, the time sensitivity of the target action, and the importance associated with the target action.

3. Executing the target operation based on the aforementioned change information Based on the fact that the new machine learning collaboration level is a first level, the execution of the target action is performed based on the received support information related to the target action, wherein the received support information is based on one or more machine learning models associated with the target action. Based on the fact that the new machine learning collaboration level is the second level, the target action is performed without the received support information related to the target action, Based on the fact that the new machine learning collaboration level is the third level, the target operation is terminated. The method according to claim 1, comprising one of the following.

4. A method for adaptive machine learning-related behavioral signaling in telecommunications networks, The user device detects the transition of the user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol, In response to the detection of the transition, the user device transmits first information related to the transition. The user device receives change information related to the target operation based on the first information transmitted by the user device, The user device performs the target operation based on the change information, Includes, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. A method wherein the first state is one of the RRC idle state or the RRC inactive state, the second state is the RRC connected state, and the change information includes instructions for a machine learning collaboration level based on the capabilities of one or more of the user devices.

5. A method for adaptive machine learning-related behavioral signaling in telecommunications networks, The user device detects the transition of the user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol, In response to the detection of the transition, the user device transmits first information related to the transition. The user device receives change information related to the target operation based on the first information transmitted by the user device, The user device performs the target operation based on the change information, Includes, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. A method wherein the first state is a wireless resource control (RRC) connected state, the second state is one of an RRC idle state or an RRC inactive state, and the change information includes a new machine learning collaboration level based on the transition.

6. The method according to claim 1, wherein the first 5G New Radio protocol is associated with a radio resource control layer.

7. A method for adaptive machine learning-related behavioral signaling in telecommunications networks, The user device detects the transition of the user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol, In response to the detection of the transition, the user device transmits first information related to the transition. The user device receives change information related to the target operation based on the first information transmitted by the user device, The user device performs the target operation based on the change information, Includes, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. A method in which information exchange, including transmitting the first information and receiving the change information, is signaled using one or more physical layer protocols, based on the fact that the target operation is associated with a real-time application.

8. A method for adaptive machine learning-related behavioral signaling in telecommunications networks, The user device detects the transition of the user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol, In response to the detection of the transition, the user device transmits first information related to the transition. The user device receives change information related to the target operation based on the first information transmitted by the user device, The user device performs the target operation based on the change information, Includes, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. A method in which an information exchange, including transmitting the first information and receiving the change information, is signaled using one or more physical layer protocols, non-access layer protocols, and radio resource control layer protocols, based on the fact that the target operation is associated with a near real-time application.

9. A method for adaptive machine learning-related behavioral signaling in telecommunications networks, The user device detects the transition of the user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol, In response to the detection of the transition, the user device transmits first information related to the transition. The user device receives change information related to the target operation based on the first information transmitted by the user device, The user device performs the target operation based on the change information, Includes, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. A method in which an information exchange, including transmitting the first information and receiving the change information, is signaled using one or more non-access layer protocol and radio resource control layer protocol, based on the fact that the target operation is associated with a non-real-time application.

10. A device for adaptive machine learning-related operation signaling in telecommunications networks, The system detects the transition of a user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol. In response to the detection of the transition, first information related to the transition is transmitted. Based on the transmitted first information, change information related to the target operation is received. The system is configured to perform the target operation based on the aforementioned change information, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. A device in which the first information includes a machine learning collaboration level change request associated with the user device, and the change information includes at least one of the following: an acknowledgment of the machine learning collaboration level change request or a new machine learning collaboration level based on the machine learning collaboration level change request.

11. The apparatus according to claim 10, wherein the change information is further based on at least one of the type of target action, the time sensitivity of the target action, and the importance associated with the target action.

12. Executing the target operation based on the aforementioned change information Based on the fact that the new machine learning collaboration level is a first level, the execution of the target action is performed based on the received support information related to the target action, wherein the received support information is based on one or more machine learning models associated with the target action. Based on the fact that the new machine learning collaboration level is the second level, the target action is performed without the received support information related to the target action, Based on the fact that the new machine learning collaboration level is the third level, the target operation is terminated, The apparatus according to claim 10, including the following:

13. A device for adaptive machine learning-related operation signaling in telecommunications networks, The system detects the transition of a user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol. In response to the detection of the transition, first information related to the transition is transmitted. Based on the transmitted first information, change information related to the target operation is received. The system is configured to perform the target operation based on the aforementioned change information, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. A device wherein the first state is one of the RRC idle state or the RRC inactive state, the second state is the RRC connected state, and the change information includes instructions for a machine learning collaboration level based on the capabilities of one or more of the user devices.

14. A device for adaptive machine learning-related operation signaling in telecommunications networks, The system detects the transition of a user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol. In response to the detection of the transition, first information related to the transition is transmitted. Based on the transmitted first information, change information related to the target operation is received. The system is configured to perform the target operation based on the aforementioned change information, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. The apparatus described above, wherein the first state is a wireless resource control (RRC) connected state, the second state is one of an RRC idle state or an RRC inactive state, and the change information includes a new machine learning collaboration level based on the transition.

15. A device for adaptive machine learning-related operation signaling in telecommunications networks, The system detects the transition of a user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol. In response to the detection of the transition, first information related to the transition is transmitted. Based on the transmitted first information, change information related to the target operation is received. The system is configured to perform the target operation based on the aforementioned change information, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. The apparatus described above, wherein, based on the fact that the target operation is associated with a real-time application, an information exchange including transmitting the first information and receiving the change information is signaled using one or more physical layer protocols.

16. A device for adaptive machine learning-related operation signaling in telecommunications networks, The system detects the transition of a user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol. In response to the detection of the transition, first information related to the transition is transmitted. Based on the transmitted first information, change information related to the target operation is received. The system is configured to perform the target operation based on the aforementioned change information, The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. An apparatus in which information exchange, including transmitting the first information and receiving the change information, is signaled using one or more physical layer protocols, non-access layer protocols, and radio resource control layer protocols, based on the fact that the target operation is associated with a near real-time application.

17. For adaptive machine learning-related behavioral signaling in telecommunications networks, a computer is required to perform the following: The system detects the transition of a user device from a first state related to the first 5G New Radio protocol to a second state related to the first 5G New Radio protocol. In response to the detection of the transition, first information related to the transition is transmitted. Based on the transmitted first information, change information related to the target operation is received. Based on the aforementioned change information, the target operation is executed. The aforementioned target operation is an operation performed by the user device using artificial intelligence and machine learning (AI / ML) models. A computer program wherein the first information includes a request to change the machine learning collaboration level associated with the user device, and the change information includes at least one of the following: an acknowledgment of the request to change the machine learning collaboration level or a new machine learning collaboration level based on the request to change the machine learning collaboration level.

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