AI/ML MODEL APPLICABILITY MECHANISMS

1 . A device configured to:

evaluate an applicability of an Artificial Intelligent (AI)/Machine Learning (ML) model in relation to a mobile telecommunication network;

generate a report message that includes the evaluated applicability; and

provide the report message to at least one Network Element (NE) of the mobile telecommunication network.

2 . The device according to claim 1 ,

wherein the device is configured to autonomously perform the evaluation of the AI/ML model applicability, the generation of the reporting message, and the providing of the report message, based on a triggering condition, and

wherein the triggering condition comprises a predefined activation condition associated with a functionality of at least one of: the device and the NE.

3 . The device according to claim 1 ,

wherein the device is configured to perform the evaluation of the AI/ML model applicability, the generation of the reporting message, and the providing of the report message, in response to receiving a query message from the at least one NE, and

wherein the query message comprises information associated with at least one of: a network configuration and a network condition.

4 . The device according to claim 1 , wherein the report message comprises at least one of: a Radio Resource Control (RRC) message, a User Equipment (UE) Assistance Information (UAI) message, a Medium Access Control (MAC) Control Element (CE) message, a Physical (PHY) layer message, and a Non-Access Stratum (NAS) layer message.

5 . The device according to claim 4 , wherein the report message comprises the RRC message, and the RRC message comprises at least one of: an applicability condition under which the AI/ML model is applicable, an applicability result that indicates whether a feature of the AI/ML model is applicable, an applicability reason associated with a reason of the applicability result, an action suggestion associated with an action the at least one NE should take based on the applicability result, a parameter associated with a status of a UE, and a parameter associated with a capability of the UE.

6 . The device according to claim 4 , wherein the report message comprises the MAC CE message, and the MAC CE message comprises at least one of: an identifier (ID) associated with a feature of the AI/ML model, an applicability result that indicates whether the feature of the AI/ML model is applicable, and an applicability reason associated with a reason of the applicability result.

7 . The device according to claim 4 , wherein the report message comprises the UAI message, wherein the UAI message comprises at least one of: an identifier (ID) associated with a feature of the AI/ML model, a performance metric associated with the AI/ML model, a training progress associated with the AI/ML model, and an inference accuracy associated with the AI/ML model.

8 . The device according to claim 1 , wherein the device is configured to evaluate the applicability by:

determining whether the AI/ML model is configured correctly;

based on determining that the AI/ML model is configured correctly, determining that the AI/ML model is applicable; and

based on determining that the AI/ML model is not configured correctly, determining that the AI/ML model is not applicable.

9 . The device according to claim 8 , wherein the device is configured to determine whether the AI/ML model is configured correctly by:

comparing an applicability condition with a predefined threshold, wherein the applicability condition comprises at least one of: a network condition, a mobility of a UE, and a battery level of the UE;

based on determining that the applicability condition fulfills a condition defined by the predefined threshold, determining that the AI/ML model is configured correctly; and

based on determining that the applicability condition does not fulfill the condition defined by the predefined threshold, determining that the AI/ML model is not configured correctly.

10 . The device according to claim 9 , wherein the device is further configured to:

obtain a real-time data, wherein the real-time data comprises at least one of: a current network condition, a current UE mobility, and an environmental factor;

calculate, based on the real-time data, a new threshold for the applicability condition;

generate a second report message that comprises the new threshold; and

provide, to the at least one NE, the second report message.

11 . The device according to claim 1 , wherein the device is further configured to:

monitor a performance metric of the AI/ML model;

generate a third report message that comprises an ID associated with the monitored performance metric, a metric value associated with the monitored performance metric, and a timestamp associated with a time when the performance metric is monitored; and

provide, to the at least one NE, the third report message.

12 . The device according to claim 1 , wherein the device is further configured to:

obtain a real-time data, wherein the real-time data comprises at least one of: a current network condition, a current UE capability, and an environmental factor;

select, based on the real-time data, an AI/ML model;

generate a fourth report message that comprises information associated with the selected AI/MI model; and

provide, to the at least one NE, the fourth report message.

13 . A method comprising:

evaluating an applicability of an Artificial Intelligent (AI)/Machine Learning (ML) model in relation to a mobile telecommunication network;

generating a report message that includes the evaluated applicability; and

providing the report message to at least one Network Element (NE) of the mobile telecommunication network.

14 . The method according to claim 13 , wherein the method comprises:

automatically performing the evaluation of the AI/ML model applicability, the generation of the report message, and the providing of the report message, based on a triggering condition, and

wherein the triggering condition comprises a predefined activation condition associated with a functionality of at least one of: a device and the NE.

15 . The method according to claim 13 , wherein the method comprises:

performing the evaluation of the AI/ML model applicability, the generation of the report message, and the providing of the report message, in response to receiving a query message from the at least one NE, and

wherein the query message comprises information associated with at least one of: a network configuration and a network condition.

16 . The method according to claim 13 ,

wherein the evaluating the applicability comprises:

determining whether the AI/ML model is configured correctly;

based on determining that the AI/ML model is configured correctly, determining that the AI/ML model is applicable; and

based on determining that the AI/ML model is not configured correctly, determining that the AI/ML model is not applicable,

wherein the determining whether the AI/ML model is configured correctly comprises:

comparing an applicability condition with a predefined threshold, wherein the applicability condition comprises at least one of: a network condition, a mobility of a UE, and a battery level of the UE;

based on determining that the applicability condition fulfills a condition defined by the predefined threshold, determining that the AI/ML model is configured correctly; and

based on determining that the applicability condition does not fulfill the condition defined by the predefined threshold, determining that the AI/ML model is not configured correctly.

17 . The method according to claim 16 , wherein the method further comprises:

obtaining a real-time data, wherein the real-time data comprises at least one of: a current network condition, a current UE mobility, and an environmental factor;

calculating, based on the real-time data, a new threshold for the applicability condition;

generating a second report message that comprises the new threshold; and

providing, to the at least one NE, the second report message.

18 . The method according to claim 13 , wherein the method further comprises:

monitoring a performance metric of the AI/ML model;

generating a third report message that comprises an ID associated with the monitored performance metric, a metric value associated with the monitored performance metric, and a timestamp associated with a time when the performance metric is monitored; and

providing, to the at least one NE, the third report message.

19 . The method according to claim 13 , wherein the method further comprises:

obtaining a real-time data, wherein the real-time data comprises at least one of: a current network condition, a current UE capability, and an environmental factor;

selecting, based on the real-time data, an AI/ML model;

generating a fourth report message that comprises information associated with the selected AI/MI model; and

providing, to the at least one NE, the fourth report message.

20 . A non-transitory computer-readable recording medium having recorded thereon instructions executable by a device to cause the device to perform a method comprising:

evaluating an applicability of an Artificial Intelligent (AI)/Machine Learning (ML) model in relation to a mobile telecommunication network;

generating a report message that includes the evaluated applicability; and

providing the report message to at least one Network Element (NE) of the mobile telecommunication network.