Content for  TS 38.300  Word version:  18.4.0

In Air-to-ground (ATG) network, an NG-RAN consisting of ground-based gNBs, which provide cell towers that send signals up to an aircraft's antenna(s) of onboard ATG terminal, with typical vertical altitude of around 10,000m and take-off/landing altitudes down to 3000m. CA and DC are not supported for ATG in this release of the specification.

Support of AI/ML for NG-RAN, as a RAN function, is used to facilitate Artificial Intelligence (AI) and Machine Learning (ML) techniques in NG-RAN. The objective of AI/ML for NG-RAN is to improve network performance and user experience, through analysing the data collected and autonomously processed by the NG-RAN, which can yield further insights, e.g., for Network Energy Saving, Load Balancing, Mobility Optimization.

Support of AI/ML for NG-RAN requires inputs from neighbour NG-RAN nodes (e.g., predicted information, feedback information, measurements) and/or UEs (e.g., measurement results). Signalling procedures used for the exchange of information to support AI/ML for NG-RAN, are use case and data type agnostic, which means that the intended usage (e.g., input, output, feedback) of the data exchanged via these procedures is not indicated. AI/ML algorithms and models are out of 3GPP scope. Model-specific performance information, e.g. model performance indicators specified in clause 6 of TS 28.105, is not exchanged over NG-RAN interfaces in TS 38.401. Support of AI/ML for NG-RAN does not apply to ng-eNB. For the deployment of AI/ML for NG-RAN the following scenarios may be supported:

• AI/ML Model Training is located in the OAM and AI/ML Model Inference is located in the NG-RAN node;
• AI/ML Model Training and AI/ML Model Inference are both located in the NG-RAN node.

AI/ML Model Training follows the definition of the "ML model training" as specified in clause 3.1 of TS 28.105. An AI/ML Model needs to be trained, validated and tested before deployment for AI/ML Model Inference. AI/ML Model Inference follows the definition of the "AI/ML inference" as defined in clause 3.1 of TS 28.105.

The following information can be configured to be reported by an NG-RAN node:

• Predicted resource status information;
• UE performance feedback;
• Measured UE trajectory;
• Energy Cost (EC).

The collection and reporting are configured through the Data Collection Reporting Initiation procedure, while the actual reporting is performed through the Data Collection Reporting procedure. The collection of measured UE trajectory and UE performance feedback is triggered at successful Handover. Cell-based UE trajectory prediction, which can be used, e.g., for the Mobility Optimization use case, is transferred to the target NG-RAN node via the Handover Preparation procedure to provide information for, e.g., subsequent mobility decisions. Cell-based UE trajectory prediction is limited to the first-hop target NG-RAN node.

OAM configures the following:

• The minimum and maximum energy consumption values corresponding to the minimum and maximum EC index values respectively, based on an implementation-specific mapping rule, which is unified within a defined area; and
• The recommended time interval within which an NG-RAN node selects an implementation-specific time window for averaging of the measurements of the NG-RAN node's consumed energy.