Managing NG-RAN node interoperability during UE handover

Abstract

An embodiment of the present disclosure discloses managing NG-RAN node interoperability during UE handover. The method includes transmitting an Xn handover request for handing over a UE from a source NG-RAN node (204) to a target NG-RAN node (206), the Xn handover request including a list of AI / ML use cases configured in the source NG-RAN node (204). An Xn handover response is then received from the target NG-RAN node. The method further includes determining, by the source NG-RAN node, measurement reconfiguration data (319) for the UE based on the active list of AI / ML use cases until the UE is handed over to the target NG-RAN node. Thereafter, the method includes transmitting an RRC reconfiguration message including the measurement reconfiguration data to the UE (202) to dynamically update the list of AI / ML use cases. The present disclosure can reduce unnecessary overhead in the NG-RAN node by eliminating the receipt of measurements from the UE.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to Indian Provisional Application No. 202341025338, filed on April 3, 2023, and Indian Patent Application No. 202341025338, filed on November 30, 2023, the disclosures of which are incorporated herein by reference in their entireties.

[0002] The present disclosure relates generally to managing NG-RAN node interoperability during UE handover. [Background technology]

[0003] Third Generation Partnership Project (3 rd The 3GPP (Radio Generation Partnership Project) Release 18 specifications include the use of artificial intelligence / machine learning (AI / ML) models to optimize the Radio Access Network (RAN) and air interface. The AI / ML models focus on improving energy consumption, signal support, and user behavior prediction. Due to network complexity, 3GPP Release 18 focuses on establishing a framework by specifying AI / ML-based energy saving, load balancing, mobility management, channel state information feedback, beam management, and data collection enhancements and signal support for location accuracy cases.

[0004] AI / ML model generation requires a lot of data sourced from entities in the network or from User Equipment (UE). With each 3GPP release, there may be new AI / ML use cases that may or may not require UE involvement. Considering that training the AI / ML model for such a new AI / ML use case may require UE involvement, for example, where training the AI / ML model for such a new AI / ML use case is performed by the network, it is essential that measurements at the UE or some assistance information from the UE may be required by the network, either periodically or based on the occurrence of an event. Since AI / ML models are data-driven, measurements at the UE or assistance information from the UE form an important part of AI / ML model generation for AI / ML use cases requiring UE involvement.

[0005] When a UE experiences inter-base station mobility (movement between base stations), the following mobility scenario combinations are possible: a. The source and target Next Generation (NG)-RAN nodes belong to the same vendor and are interoperable with each other. b. The source and target NG-RAN nodes belong to the same vendor and are not interoperable due to different software releases in use. c. The source and target NG-RAN nodes belong to different vendors and are still interoperable, e.g., two NG-RAN nodes supplied by different ORAN vendors. d. The source and target NG-RAN nodes belong to different vendors and are not interoperable due to different AI / ML models supported by them, or one of the NG-RAN nodes not supporting the AI-ML use case in question.

[0006] Based on the aforementioned scenarios, since the AI / ML use cases, corresponding AI / ML models, and corresponding measurements supported by each NG-RAN node, such as the source NG-RAN node and the target NG-RAN node, may differ, various cases may occur between the source NG-RAN node and the target NG-RAN node, including, but not limited to, interoperability, partial interoperability, or non-interoperability, etc. Furthermore, since these AI / ML models are applicable to a large number of UEs, partial interoperability or non-interoperability between NG-RAN nodes will cause a lot of unnecessary overhead due to the huge amount of data sent over the air interface.

[0007] Therefore, there is a need for improvised interoperability management of NG-RAN nodes during UE handover.

[0008] The information disclosed in the Background section of this disclosure (herein) is intended only to enhance understanding of the general background of the present invention and should not be construed as an admission or any form of suggestion that this information forms prior art already known to those skilled in the art. Summary of the Invention [Problem to be solved by the invention]

[0009] AI / ML model generation requires a lot of data sourced from entities in the network or user equipment (UE). With each 3GPP release, there may be new use cases that may or may not require UE involvement. Based on the aforementioned scenarios, the AI / ML use cases, corresponding AI / ML models, and corresponding measurements supported by each NG-RAN node, such as the source NG-RAN node and the target NG-RAN node, may differ. This may result in various cases, including, but not limited to, interoperability, partial interoperability, or non-interoperability between the source and target NG-RAN nodes. Furthermore, because these AI / ML models are applicable to a large number of UEs, partial interoperability or non-interoperability between NG-RAN nodes results in a lot of unnecessary overhead due to the huge amount of data sent over the air interface. [Means for solving the problem]

[0010] In one embodiment, a method for managing NG-RAN node interoperability during UE handover is disclosed. A source NG-RAN node sends an Xn handover request to a target NG-RAN node via an NG-RAN node-to-NG-RAN node interface to hand over a user equipment (UE) from the source NG-RAN node to the target NG-RAN node. The Xn handover request includes a list of artificial intelligence or machine learning (AI / ML) use cases configured for the UE at the source NG-RAN node and corresponding first measurement information associated with the list of AI / ML use cases. In response to the Xn handover request, the source NG-RAN node receives an Xn handover response from the target NG-RAN node that includes an active list of AI / ML use cases to be configured for the UE at the target NG-RAN node based on the list of AI / ML use cases received by the target NG-RAN node from the source NG-RAN node and corresponding second measurement information associated with the active list of AI / ML use cases. Furthermore, the source NG-RAN node determines measurement reconfiguration data for the UE based on the active list of AI / ML use cases received from the target NG-RAN node until the UE is handed over to the target NG-RAN node, and then transmits the measurement reconfiguration data to the UE in a Radio Resource Control (RRC) reconfiguration message to dynamically update the measurements according to the list of AI / ML use cases supported for the UE by the target NG-RAN node based on the measurement reconfiguration data.

[0011] In another embodiment, a method for a source Next Generation-Radio Access Network (NG-RAN) node is disclosed. The method includes transmitting, by the source NG-RAN node, an Xn handover request for handing over a user equipment (UE) from the source NG-RAN node to a target NG-RAN node. The Xn handover request includes a list of artificial intelligence or machine learning (AI / ML) use cases configured for the UE at the source NG-RAN node and corresponding first measurement information associated with the list of AI / ML use cases. In response to the Xn handover request, the method includes receiving, by the source NG-RAN node from the target NG-RAN node, an Xn handover response including an active list of AI / ML use cases to be configured for the UE at the target NG-RAN node based on the list of AI / ML use cases sent from the source NG-RAN node to the target NG-RAN node and corresponding second measurement information associated with the active list of AI / ML use cases. The method further includes determining, by the source NG-RAN node, measurement reconfiguration data for the UE based on an active list of AI / ML use cases received from the target NG-RAN node until the UE is handed over to the target NG-RAN node, and then transmitting, by the source NG-RAN node, a radio resource control (RRC) reconfiguration message including the measurement reconfiguration data to the UE to dynamically update measurements in accordance with the list of AI / ML use cases supported for the UE by the target NG-RAN node based on the measurement reconfiguration data.

[0012] In yet another embodiment, a non-transitory computer-readable medium is disclosed that, when processed by at least one processor, causes a source NG-RAN node to perform operations including transmitting, by the source NG-RAN node to a target NG-RAN node via an NG-RAN node-to-node interface, an Xn handover request for handing over user equipment (UE) from the source NG-RAN node to the target NG-RAN node. The Xn handover request includes a list of artificial intelligence or machine learning (AI / ML) use cases configured for the UE at the source NG-RAN node and corresponding first measurement information associated with the list of AI / ML use cases. The instructions further cause the source NG-RAN node to perform operations including receiving, in response to the Xn handover request, from the target NG-RAN node an Xn handover response including an active list of AI / ML use cases to be configured for the UE at the target NG-RAN node based on the list of AI / ML use cases received by the target NG-RAN node from the source NG-RAN node and corresponding second measurement information associated with the active list of AI / ML use cases. Further, the instructions cause the source NG-RAN node to perform operations including determining reconfiguration measurement data for the UE based on an active list of AI / ML use cases received from the target NG-RAN node until the UE is handed over to the target NG-RAN node, after which the instructions cause the source NG-RAN node to perform operations including transmitting the measurement reconfiguration data to the UE in a radio resource control (RRC) reconfiguration message based on the measurement reconfiguration data to dynamically update the measurements in accordance with the list of AI / ML use cases supported for the UE by the target NG-RAN node.

[0013] The foregoing summary of the invention is illustrative only and is not intended to be in any way limiting. In addition to the exemplary aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following Detailed Description of the Invention.

[0014] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, explain the disclosed principles. In the drawings, the leftmost digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and components. Some embodiments of systems and / or methods according to embodiments of the present subject matter will now be described, by way of example only, with reference to the accompanying drawings, in which: [Brief explanation of the drawings]

[0015] [Figure 1] 1 shows an overview of communication between different base stations over the Xn interface during handover according to an exemplary problem scenario.

[0016] [Figure 2A] 1 illustrates an Xn setup procedure according to an example scenario, in accordance with some embodiments of the present disclosure.

[0017] [Figure 2B] 1 illustrates an example system for managing NG-RAN node interoperability in a UE handover, according to some embodiments of the present disclosure.

[0018] [Figure 3] FIG. 1 illustrates a detailed block diagram of a source NG-RAN node, in accordance with some embodiments of the present disclosure.

[0019] [Figure 4] FIG. 1 shows a sequence diagram illustrating an example embodiment for managing NG-RAN node interoperability in a UE handover, according to some embodiments of the present disclosure.

[0020] [Figure 5] 1 shows a flowchart illustrating a method for a source NG-RAN node, according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0021] Those skilled in the art should appreciate that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, any flowcharts, flow diagrams, state transition diagrams, pseudocode, and the like, may be substantially represented on a computer-readable medium and will be understood to represent various processes that may be performed by such a computer or processor, whether or not such a computer or processor is explicitly shown.

[0022] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

[0023] While the present disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and are described in detail below. It is to be understood, however, that the specific embodiments are not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is intended to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[0024] The terms "comprises," "comprising," "includes," or any other variation thereof, are intended to cover non-exclusive inclusions, so that a setup, device, or method that includes a list of components or steps does not include only those components or steps, but may include other components or steps that are not expressly listed or inherent in such setup or device or method. In other words, one or more elements in a system or apparatus that end with "comprises..." does not, absent further constraints, exclude the presence of other or additional elements in the system or method.

[0025] In the following "Detailed Description" of embodiments of the present disclosure, reference is made to the accompanying drawings which form a part hereof, and which show, by way of illustration, specific embodiments in which the present disclosure may be practiced. These embodiments have been described in sufficient detail to enable those skilled in the art to practice the disclosure, but it should be understood that other embodiments may be utilized and changes may be made without departing from the scope of the present disclosure. Accordingly, the following description is not to be construed in a limiting sense.

[0026] When a UE is subjected to mobility between base stations (when the UE experiences inter-base station mobility), a combination of scenarios is possible with respect to mobility. The combination of scenarios may result in various cases, such as, but not limited to, interoperability, partial interoperability, or no interoperability, between the source NG-RAN node and the target NG-RAN node. An exemplary scenario of partial interoperability between NG-RAN nodes is shown in Figure 1.

[0027] FIG. 1 illustrates an overview of communication between different base stations over the Xn interface during handover according to an exemplary challenge scenario. In some cases, NG-RAN node 20, NG-RAN node 21, NG-RAN node 22, and NG-RAN node 23 may belong to one vendor, and NG-RAN node 30, NG-RAN node 31, NG-RAN node 32, and NG-RAN node 33 may belong to another vendor. There may be cases where some nodes are interoperable and some nodes are not. Based on the aforementioned case, when a UE moves from NG-RAN node 23 to NG-RAN node 31, which is partially interoperable for one of a subset of AI / ML use cases, there may be relevant measurements between the serving NG-RAN node and the target NG-RAN node due to the partial interoperability between NG-RAN node 23 and NG-RAN node 31.

[0028] It should be noted that for ease of explanation, this disclosure uses terms and names defined in 3GPP RAN. More specifically, terms such as Radio Access Network (RAN), Radio Resource Configuration (RRC), User Equipment (UE), Artificial Intelligence (AI) or Machine Learning (ML) mode, Next Generation Radio Access Network (NG-RAN), Key Performance Indicators (KPIs), etc., should be interpreted as defined in the 3GPP RAN standards.

[0029] FIG. 2A illustrates an Xn setup procedure according to an example scenario, in accordance with some embodiments of the present disclosure.

[0030] In some embodiments, prior to handover, an Xn setup procedure is performed between NG-RAN nodes to exchange configuration data necessary for the NG-RAN nodes to properly interoperate over the Xn-C interface. The Xn setup procedure is described with the help of an example scenario in FIG. 2A . In FIG. 2A , there is a first NG-RAN node A and peer NG-RAN nodes B, C, D, and E. The Xn setup procedure may be initiated by sending an Xn setup request by any of the NG-RAN nodes communicating between the first NG-RAN node A and each of the peer NG-RAN nodes B, C, D, and E. In the example scenario, it is assumed that the first NG-RAN node A is sending an Xn setup request to each of the peer NG-RAN nodes B, C, D, and E. The Xn setup request of the first NG-RAN node may include information indicating a list of AI / ML use cases supported by the first NG-RAN node and interoperability information of AI / ML models for the AI / ML use cases supported by the first NG-RAN node. In some embodiments, the interoperability information may indicate, for example, versions of interoperable AI / ML models, vendor software implemented for the interoperable AI / ML models, etc. In response to the Xn setup request, each of the peer NG-RAN nodes B, C, D, and E may send an Xn setup response to the first NG-RAN node. The Xn setup response may include information indicating a list of AI / ML use cases supported by the corresponding peer NG-RAN node and interoperability information of the AI / ML models for the AI / ML use cases supported by the corresponding peer NG-RAN node. In some embodiments, interoperability between any two NG-RAN nodes may be an operator-configured parameter. In some embodiments, the information exchanged between the NG-RAN nodes via the Xn setup procedure enables the first NG-RAN node A and the peer NG-RAN nodes B, C, D, and E to filter only interoperable peer NG-RAN nodes for future actions, such as UE handover.For example, consider the case where, based on information exchanged between a first NG-RAN node A and peer NG-RAN nodes B, C, D, and E during the Xn setup procedure, the first NG-RAN node A identifies that it is not interoperable with peer NG-RAN nodes C and E. Therefore, when a UE needs to be handed over from the first NG-RAN node A, the peer NG-RAN nodes C and E may not be considered as eligible target NG-RAN nodes for handing over the UE because non-interoperability was inferred during the Xn setup procedure.

[0031] FIG. 2B illustrates an example system (200B) for managing NG-RAN node interoperability in a UE (202) handover, according to some embodiments of the present disclosure.

[0032] The system 200B may be a telecommunications network including a UE 202, a source NG-RAN node 204, and a target NG-RAN node 206. In some other embodiments, the system 200B may be a radio access network. In some embodiments, the source NG-RAN node 204 is the NG-RAN node currently serving the UE 202, and the target NG-RAN node 206 is the NG-RAN node selected to hand over the UE 202 from the source NG-RAN node 204 when a handover scenario occurs.

[0033] In some embodiments, the source NG-RAN node 204 may send an Xn handover request to the target NG-RAN node 206 to hand over the UE 202 from the source NG-RAN node 204 to the target NG-RAN node 206. The Xn handover request may include a list of AI / ML use cases configured for the UE 202 at the source NG-RAN node 204 and corresponding first measurement information associated with the list of AI / ML use cases. In some embodiments, the first measurement information includes measurement parameters of the UE 202 associated with the list of AI / ML use cases configured at the source NG-RAN node 204. For example, if the AI / ML use case is cell deformation for beam management, the first measurement information may include requirements such as 30 Mbps bandwidth, 4 lambda wavelengths, etc. In some embodiments, based on the interoperability of the target NG-RAN node 206, the Xn handover request may also include key performance indicators (KPIs) associated with each AI / ML use case to ensure better Quality of Service (QoS) for the UE 202. In response to the handover request, in some embodiments, the target NG-RAN node 206 may send an Xn handover response to the source NG-RAN node 204. The Xn handover response may include an active list of AI / ML use cases to be configured in the UE 202 and corresponding second measurement information associated with the active list of AI / ML use cases. The active list of AI / ML use cases may include a list of AI / ML use cases to be deconfigured or configured from the list of AI / ML use cases received by the target NG-RAN node 206 from the source NG-RAN node 204. This active list of AI / ML use cases are use cases supported by the target NG-RAN node 206.The active list of AI / ML use cases may be selected by the target NG-RAN node (206) based on factors such as the version of the AI / ML model, its ability to support the particular AI / ML use case, etc.

[0034] In some embodiments, the source NG-RAN node 204 may determine measurement reconfiguration data for the UE 202 based on an active list of AI / ML use cases received from the target NG-RAN node 206 until the UE 202 is handed over to the target NG-RAN node 206. The measurement reconfiguration data may include sets of AI / ML measurements for use cases to be de-configured by identifying AI / ML use cases other than the one or more AI / ML use cases selected by the target NG-RAN node 206. The measurement reconfiguration data may further include additional sets of measurements for new AI / ML use cases to be configured for the UE 202 at the target NG-RAN node 206. In some embodiments, the check on whether the set of additional measurements is supported by the UE (202) may be performed by the target NG-RAN node (206) based on the capabilities of the UE (202) shared by the source NG-RAN node (204) with the target NG-RAN node (206) via the Xn handover request.

[0035] In some embodiments, the source NG-RAN node (204) may transmit measurement reconfiguration data to the UE (202) via an RRC reconfiguration message. The measurement reconfiguration data may be dynamically updated by the UE (202) according to a list of AI / ML use cases supported for the UE (202) by the target NG-RAN node (206). In some embodiments, the source NG-RAN node (204) may transmit the RRC reconfiguration message together with a handover (HO) command or separately. In some embodiments, based on the measurement reconfiguration data, the UE (202) may dynamically update measurements according to a list of AI / ML use cases supported for the UE (202) by the target NG-RAN node (206). The UE (202) may then send an uplink synchronization request to the target NG-RAN node (206) via a Random Access Channel (RACH) preamble. In response to the uplink synchronization request, a random access response may be sent to the target NG-RAN node 206 to establish a connection between the target NG-RAN node 206 and the UE 202. In some embodiments, upon establishing a connection between the UE 202 and the target NG-RAN node 206, the UE 202 may be configured to share measurement reports based on updated measurement reconfiguration data for a set of AI / ML use cases via RRC messages to the target NG-RAN node 206.

[0036] FIG. 3 shows a detailed block diagram of a source NG-RAN node (204) according to some embodiments of the present disclosure.

[0037] In some implementations, the source NG-RAN node 204 may include an I / O interface 304, a processor 302, and a memory 303. In one embodiment, the memory 303 may be communicatively coupled to the processor 302 of the source NG-RAN node 204. The processor 302 may be configured to implement one or more functions of the source NG-RAN node 204 for managing NG-RAN node interoperability in a UE 202 handover using the data 305 and one or more modules 307. In one embodiment, the memory 303 may store the data 305 of the source NG-RAN node 204. While FIG. 3 illustrates hardware components of the source NG-RAN node 204, it should be understood that other embodiments are not limited in this regard. In other embodiments, the source NG-RAN node 204 may include fewer or more components. Furthermore, component labels or names are used for illustrative purposes only and are not intended to limit the scope. One or more components may be combined together to implement the same or substantially similar technical features for managing NG-RAN node interoperability in a UE (202) handover.

[0038] In one embodiment, the data 305 stored in the memory 303 may include, but is not limited to, Xn handover request data 309, Xn handover response data 311, measurement information data 313, AI / ML use case data 315, active list data 317, measurement reconfiguration data 319, and other data 321. In some implementations, the data 305 may be stored in the memory 303 in the form of various data structures. Furthermore, the data 305 may be organized using a data model, such as a relational or hierarchical data model. The other data 321 may include various temporary data and files generated by one or more modules 307.

[0039] In one embodiment, the Xn handover request data (309) includes an Xn handover request sent by the source NG-RAN node (204) to the target NG-RAN node (206) along with a list of AI / ML use cases for managing NG-RAN node interoperability in a UE (202) handover. The Xn handover request data (309) may include a list of AI / ML use cases configured for the UE (202) at the source NG-RAN node (204) and corresponding first measurement information associated with the list of AI / ML use cases.

[0040] In one embodiment, the Xn handover response data (311) comprises an Xn handover response comprising an active list of AI / ML use cases to be configured in the UE (202) based on the list of AI / ML use cases received from the target NG-RAN node (206) and corresponding second measurement information associated with the active list of AI / ML use cases.

[0041] In some embodiments, the measurement information data 313 may include measurements required from the UE 202 to perform AI / ML use cases configured at an NG-RAN node, such as the source NG-RAN node 204 or the target NG-RAN node 206. In the context of the present disclosure, the measurement information data 313 may include first measurement information and second measurement information. The first measurement information includes measurement parameters of the UE 202 associated with a list of AI / ML use cases that may be configured for the UE 202 at the source NG-RAN node 204. The second measurement information includes measurement parameters of the UE 202 associated with an active list of AI / ML use cases configured for the UE 202 at the target NG-RAN node 206 during handover of the UE 202 from the source NG-RAN 204 to the target NG-RAN 206.

[0042] In one embodiment, the AI / ML use case data 315 may include a list of AI / ML use cases configured for the UE 202 at the source NG-RAN node 204. For example, the list of AI / ML use cases may include, but is not limited to, beam management and load management for network energy conservation, and UE selection 202. For example, consider a case where the AI / ML use case is beam management. The first measurement information for beam management may include bandwidth, throughput, and latency required to perform cell transformation as part of beam management for direct cell transmission or reception.

[0043] In one embodiment, the active list data 317 may include an active list of AI / ML use cases determined for the UE 202 at the target NG-RAN node 206 based on a list of AI / ML use cases configured for the UE 202 at the source NG-RAN node 204. The active list of AI / ML use cases may be AI / ML use cases supported by the target NG-RAN node 206. The active list of AI / ML use cases may include, but is not limited to, one or more AI / ML use cases selected by the target NG-RAN node 206 from the list of AI / ML use cases received from the source NG-RAN node 204 and / or one or more additional AI / ML use cases required by the target NG-RAN node 206. For example, one or more AI / ML use cases selected by the target NG-RAN node (206) based on the above example as in paragraph

[0041] include load management for network energy conservation in the active list of AI / ML use cases to be configured for the UE (202) because the target NG-RAN node (206) supports the load management use case.

[0044] In one embodiment, the measurement reconfiguration data 319 may include measurements that may be configured or deconfigured based on an active list of use cases. Measurements for a set of AI / ML use cases may be deconfigured by identifying an AI / ML use case other than one or more AI / ML use cases selected by the target NG-RAN node 206 from the list of AI / ML use cases configured in the source NG-RAN node 204. The measurement reconfiguration data 319 may further include an additional set of measurements for a new AI / ML use case to be configured for the UE 202 in the target NG-RAN node 206. For example, if the AI / ML use case selected by the target NG-RAN node 206 requires measurements such as X, Y, and Z for the use case "beam management." Consider that the previous measurements configured for the UE 202 for the use case "beam management" were X, P, Q, and R. Therefore, to comply with the selected AI / ML use case of the target NG-RAN node (206), the UE (202) may need to deconfigure measurements P, Q, and R and instead configure measurements Y and Z. This data related to deconfiguration and configuration determined by the source NG-RAN node (204) and sent to the UE (202) may be referred to as measurement reconfiguration data (319).

[0045] In one embodiment, the data 305 may be processed by one or more modules 307. In some implementations, the one or more modules 307 may be communicatively coupled to the processor 302 to perform one or more functions of the source NG-RAN node 204. In one implementation, the one or more modules 307 may include, but are not limited to, a transceiver module 323, a decision module 325, and other modules 327.

[0046] As used herein, the term module may refer to a processor (302) (shared, dedicated, or group) executing one or more software or firmware programs and memory, combinatorial logic, and / or other suitable components that provide the described functionality. In one implementation, each of the one or more modules (307) may be configured as a standalone hardware computing unit. In one embodiment, other modules (327) may be used to perform various other functionalities on the source NG-RAN node (204). It will be understood that such one or more modules (307) may be represented as a single module or a combination of different modules.

[0047] In one embodiment, the transceiver module 323 may be configured to transmit an Xn handover request to the target NG-RAN node 206 via an NG-RAN node-to-node interface to hand over the UE 202 from the source NG-RAN node 204 to the target NG-RAN node 206. In some embodiments, the Xn handover request may include a list of AI / ML use cases configured for the UE 202 at the source NG-RAN node 204 and corresponding first measurement information associated with the list of AI / ML use cases.

[0048] In response to the Xn handover request, the transceiver module (323) may be configured to receive an Xn handover response from the target NG-RAN node (206). In some embodiments, the Xn handover response may include an active list of AI / ML use cases to be configured for the UE at the target NG-RAN node based on the list of AI / ML use cases sent by the source NG-RAN node (204) to the target NG-RAN node (206), and corresponding second measurement information associated with the active list of AI / ML use cases.

[0049] Based on the active list of AI / ML use cases received from the target NG-RAN node 206 in the handover response, the determination module 325 may be configured to determine measurement reconfiguration data 319 until the UE 202 is handed over to the target NG-RAN node 206. In some embodiments, determining the measurement reconfiguration data 319 includes determining measurements that may be configured or deconfigured based on the active list of use cases. In some embodiments, the determination module 325 may determine a set of measurements to be deconfigured for an AI / ML use case configured for the UE 202 at the source NG-RAN node 204. In some other embodiments, the determination module 325 may determine an additional set of measurements for a new AI / ML use case to be configured in the UE 202.

[0050] In one embodiment, the transceiver module (323) may be configured to transmit the measurement reconfiguration data (319) to the UE (202) in the form of an RRC reconfiguration message. However, transmitting the measurement reconfiguration data (319) using an RRC reconfiguration message should not be construed as limiting, as the measurement reconfiguration data (319) may be transmitted using other message types that can support functionality similar to an RRC reconfiguration message.

[0051] In some embodiments, upon receiving the measurement reconfiguration data (319), the UE (202) may dynamically update the measurements according to a list of AI / ML use cases supported by the target NG-RAN node (206).

[0052] FIG. 4 shows a sequence diagram illustrating an example embodiment for managing NG-RAN node interoperability in a UE (401) handover, according to some embodiments of the present disclosure.

[0053] In step 1, the target NG-RAN (403) node may send an Xn setup request indicating that the UE (401) will be handed over from the source NG-RAN node (402) to the target NG-RAN node (403). In step 2, the source NG-RAN node (402) may send an Xn setup response including information indicating a list of supported AI / ML use cases, including information on whether a specific AI / ML model is interoperable with the target NG-RAN node (403). In step 3, a connection is established between the source NG-RAN node (402) and the UE (401) based on a Radio Resource Control (RRC) setup and a Data Radio Bearer (DRB) setup so that the UE (401) is served by the source NG-RAN node (402). In step 4, a handover decision is made to hand over the UE (401) to the target NG-RAN node (403).

[0054] In step 5, the source NG-RAN node (402) may send an Xn handover request to hand over the UE (401) from the source NG-RAN node (402) to the target NG-RAN node (403). The Xn handover request may include a list of AI / ML use cases configured for the UE (401) in the source NG-RAN node (402) and corresponding first measurement information associated with the list of AI / ML use cases. In response to the handover request, in step 6, the target NG-RAN node (403) may send an Xn handover response to the source NG-RAN node (402). The Xn handover response may include an active list of AI / ML use cases to be configured in the UE (401) and corresponding second measurement information associated with the active list of AI / ML use cases. The active list of AI / ML use cases is based on the list of AI / ML use cases received by the target NG-RAN node (403) from the source NG-RAN node (402).

[0055] Based on the active list of AI / ML use cases received from the target NG-RAN node (403), in step 7, the source NG-RAN node (402) may determine measurement reconfiguration data (319) for the UE (401) until the UE (401) is handed over to the target NG-RAN node (403). The measurement reconfiguration data (319) may include a set of AI / ML measurements for the use cases to be deconfigured and an additional set of measurements for new AI / ML use cases to be configured for the UE (401) at the target NG-RAN node (403).

[0056] In step 8, the source NG-RAN node (402) may send measurement reconfiguration data (319) to the UE (401) via an RRC reconfiguration message. The measurement reconfiguration data (319) may be dynamically updated by the UE (401) according to a list of AI / ML use cases supported for the UE (401) by the target NG-RAN node (403). Based on the measurement reconfiguration data (319), in step 9, the UE (401) may update the measurement reconfiguration data (319) for each AI / ML use case and send an uplink synchronization request with the target NG-RAN node (403) via a RACH preamble. In response to the uplink synchronization request, in step 10, a random access response may be sent to the target NG-RAN node (403) to establish a connection between the target NG-RAN node (403) and the UE (401). In step 11, a connection may be established between the target NG-RAN node (403) and the UE (401) based on the RRC reconfiguration acknowledgement shared from the UE (401) to the target NG-RAN node (403). Upon establishing the connection between the UE (401) and the target NG-RAN node (403), in step 12, the UE (401) may share measurement reports based on updated measurement reconfiguration data (319) for a set of AI / ML use cases via an RRC message to the target NG-RAN node (403).

[0057] In the following, for a better understanding of the present disclosure, the process of managing the interoperability of NG-RAN nodes in a UE (202) handover will be described with the help of one or more example use cases, however, the one or more examples should not be considered as limitations of the present disclosure.

[0058] In exemplary scenario 1, managing the interoperability of NG-RAN nodes in a UE (202) handover based on use case 1 is shown below.

[0059] In exemplary scenario 1, consider the case of a beam management use case that performs cell de-formation to predict a suitable beam for the UE (202). Consider a case where a total of 10 beams are created in network node 1 and network node 2. Based on the total number of beams, consider the case where the source NG-RAN node (204) predicts that beam 5 at time T1 is a suitable beam for the UE (202) in the spatial domain, and relevant measurement information 1 for retaining only beam 5 at time T1 uses a load of 5 packets at a bandwidth of 30 Mbps. In step 1, the source NG-RAN node (204) shares an Xn handover request including the beam management use case together with measurement information 1. However, consider a scenario where the target NG-RAN node (206) considers beam 5 and beam 4 to be suitable beams for the UE (202) in the spatial domain at time T1, and measurement information 2 for the beam management use case indicates that beam 5 and beam 4 are suitable beams at time T1, requiring a load of 7 packets at a bandwidth of 20 Mbps. In step 2, the target NG-RAN node (206) shares measurement information 2 along with the beam management use case to the source NG-RAN node (204). Based on measurement information 2, the source NG-RAN node (204) determines measurement reconfiguration data for the UE (202), which means that the configuration in the UE (202) needs to be updated from a packet load of 5 to a packet load of 7, and from 30 Mbps to 20 Mbps, so that only beams 4 and 5 out of 10 beams can be retained for the UE (202) at time T1. This updated measurement reconfiguration data is shared with the UE (202). Based on the updated measurement reconfiguration data, the UE (202) shares a measurement report for the beam management use case with the target NG-RAN node (206).

[0060] Exemplary scenario 2 illustrates managing NG-RAN node interoperability during UE (202) handover based on use case 2.

[0061] In exemplary scenario 2, consider a case where a load balancing use case for network energy conservation for the UE (202) is implemented. The source NG-RAN node (204) determines that UE2, UE3, and UE4 are not loaded by many users for 30 minutes after 12 hours. Based on this aforementioned scenario, the source NG-RAN node (204) determines, based on measurement information 1, that UE2, UE3, and UE4 can be turned off for 30 minutes after 12 hours. In step 1, the source NG-RAN node (204) shares an Xn handover request including use case 2 and measurement information 1 with the target NG-RAN node (206). Based on measurement information 1, the target NG-RAN node (206) determines that UE7, UE8, and UE9 are also not loaded by many users for 30 minutes after 12 hours, and determines measurement information 2 based on use case 2 of switching UE7, UE8, and UE9 for 30 minutes after 12 hours to conserve network energy. In step 2, the target NG-RAN node (206) shares the Xn handover response including use case 2 and related measurement information 2. Based on use case 2 and related measurement information 2, in step 3, the source NG-RAN node (204) configures measurement information 3 by checking whether measurement information 1 needs to be de-configured or configured based on measurement information 2. In step 4, the source NG-RAN node (204) sends measurement information 3 to the UE (202) to configure itself with measurement information 3 regarding the switching of UE7, UE8, and UE9, along with UE2, UE3, and UE4, for 30 minutes after 12 hours for network energy saving according to the load management use case. In step 5, based on the measurement information 3, the UE (202) generates a measurement report and sends it to the target NG-RAN node (206).

[0062] FIG. 5 shows a flowchart illustrating a method for a source NG-RAN node (204) according to some embodiments of the present disclosure.

[0063] As shown in Figure 5, the method (500) may include one or more blocks illustrating a method for managing NG-RAN node interoperability in a UE (202) handover according to some embodiments of the present disclosure, as shown in Figure 5. The method (500) may be described in the general context of computer-executable instructions. Generally, computer-executable instructions may include routines, programs, objects, components, data structures, procedures, modules, and functions that perform particular functions or implement particular abstract data types.

[0064] The order in which the method 500 is described is not intended to be construed as a limitation, as any number of the described method blocks may be combined in any order to implement the method. Additionally, individual blocks may be deleted from the method without departing from the scope of the subject matter described herein. Additionally, the method may be implemented in any suitable hardware, software, firmware, or combination thereof.

[0065] At block 502, the method 500 includes transmitting, by a source NG-RAN node 204, an Xn handover request over an NG-RAN node-to-node interface to hand over the UE 202 from the source NG-RAN node 204 to a target NG-RAN node 206. The Xn handover request includes a list of artificial intelligence or machine learning (AI / ML) use cases configured for the UE 202 at the source NG-RAN node 204 and corresponding first measurement information associated with the list of AI / ML use cases.

[0066] At block 504, the method (500) includes receiving, by the source NG-RAN node (204), from the target NG-RAN node (206) an Xn handover response including an active list of AI / ML use cases to be configured for the UE (202) at the target NG-RAN node (206) based on the list of AI / ML use cases sent from the source NG-RAN node (204) to the target NG-RAN node (206) and corresponding second measurement information associated with the active list of AI / ML use cases.

[0067] At block 506, the method 500 includes determining, by the source NG-RAN node 204, measurement reconfiguration data 319 for the UE 202 based on an active list of AI / ML use cases received from the target NG-RAN node 206 until the UE 202 is handed over to the target NG-RAN node 206. The active list of AI / ML use cases includes at least one of one or more AI / ML use cases selected for the UE 202 by the target NG-RAN node 206 from the list of AI / ML use cases provided to the target NG-RAN node 206 by the source NG-RAN node 204 and one or more additional AI / ML use cases required by the target NG-RAN node 206. In some embodiments, the measurement reconfiguration data 319 may include, but is not limited to, at least one of a set of AI / ML use cases to be deconfigured by identifying AI / ML use cases other than one or more AI / ML use cases to the target NG-RAN node 206 selected by the target NG-RAN node 206 from a list of AI / ML use cases received at the source NG-RAN node 204. Additionally, the measurement reconfiguration data 319 includes a set of additional AI / ML use cases to be configured, determined based on one or more additional AI / ML use cases desired by the target NG-RAN node 206.

[0068] At block 508, the method (500) includes, by the source NG-RAN node (204), transmitting a radio resource control (RRC) reconfiguration message including the measurement reconfiguration data (319) to the UE (202) to dynamically update measurements according to a list of AI / ML use cases supported for the UE (202) by the target NG-RAN node (206) based on the measurement reconfiguration data (319). Claimable aspects: Aspect 1. In one embodiment, managing NG-RAN node interoperability in a UE handover is disclosed. A source NG-RAN node is configured to send an Xn handover request to a target NG-RAN node via an NG-RAN node-to-NG-RAN node interface to hand over a user equipment (UE) from the source NG-RAN node to the target NG-RAN node. The Xn handover request includes a list of artificial intelligence or machine learning (AI / ML) use cases configured for the UE at the source NG-RAN node and corresponding first measurement information associated with the list of AI / ML use cases. In response to the Xn handover request, the source NG-RAN node receives an Xn handover response from the target NG-RAN node that includes an active list of AI / ML use cases to be configured for the UE at the target NG-RAN node based on the list of AI / ML use cases sent from the source NG-RAN node to the target NG-RAN node and corresponding second measurement information associated with the active list of AI / ML use cases. Furthermore, the source NG-RAN node determines measurement reconfiguration data for the UE based on the active list of AI / ML use cases received from the target NG-RAN node until the UE is handed over to the target NG-RAN node, after which the source NG-RAN node transmits the measurement reconfiguration data to the UE in a radio resource control (RRC) reconfiguration message to dynamically update the measurements according to the list of AI / ML use cases supported for the UE by the target NG-RAN node based on the measurement reconfiguration data. Aspect 2. The source NG-RAN node of Aspect 1 above, wherein in one embodiment, the active list of AI / ML use cases includes at least one of one or more AI / ML use cases selected for the UE by the target NG-RAN node from a list of AI / ML use cases provided to the target NG-RAN node by the source NG-RAN node, and one or more additional AI / ML use cases required by the target NG-RAN node. Aspect 3. The source NG-RAN node of Aspects 1-2 above, wherein in one embodiment, the measurement reconfiguration data includes a set of measurements for an AI / ML use case to be deconfigured from the list of AI / ML use cases configured at the source NG-RAN node and an additional set of measurements for a new AI / ML use case to be configured for the UE at the target NG-RAN node. Aspect 4. In one embodiment, the source NG-RAN node described in Aspects 1-3 above, wherein the processor determines measurements of the set of AI / ML use cases to be deconfigured by identifying AI / ML use cases from a list of AI / ML use cases configured at the source NG-RAN node other than the one or more AI / ML use cases selected by the target NG-RAN node. Aspect 5. In one embodiment, the source NG-RAN node described in Aspects 1-4 above, wherein the first measurement information includes measurement parameters of a UE associated with a list of AI / ML use cases configured at the source NG-RAN node. Aspect 6. In one embodiment, the source NG-RAN node as described in Aspects 1-5 above, wherein the second measurement information includes measurement parameters of a UE associated with the active list of the AI / ML use case received from the target NG-RAN node. Aspect 7. In an embodiment, the source NG-RAN node as described in Aspects 1-6 above, wherein prior to the handover, the processor is further configured to receive an Xn setup request from the target NG-RAN node, the Xn setup request including information indicating a list of AI / ML use cases supported by the target NG-RAN node and interoperability information of AI / ML models for the AI / ML use cases supported by the target NG-RAN node. Thereafter, the processor is configured to send an Xn setup response to the target NG-RAN node, the Xn setup response including information indicating the list of AI / ML use cases supported by the source NG-RAN node and interoperability information of AI / ML models for the AI / ML use cases supported by the source NG-RAN node. Aspect 8. In one embodiment, a method for a source Next Generation Radio Access Network (NG-RAN) node. The method includes transmitting, by the source NG-RAN node, an Xn handover request for handing over a user equipment (UE) from the source NG-RAN node to a target NG-RAN node. The Xn handover request includes a list of artificial intelligence or machine learning (AI / ML) use cases configured for the UE at the source NG-RAN node and corresponding first measurement information associated with the list of AI / ML use cases. In response to the Xn handover request, the method includes receiving, by the source NG-RAN node from the target NG-RAN node, an Xn handover response including an active list of AI / ML use cases to be configured for the UE at the target NG-RAN node based on the list of AI / ML use cases sent from the source NG-RAN node to the target NG-RAN node and corresponding second measurement information associated with the active list of AI / ML use cases. The method further includes determining, by the source NG-RAN node, measurement reconfiguration data for the UE based on an active list of AI / ML use cases received from the target NG-RAN node until the UE is handed over to the target NG-RAN node, and then transmitting, by the source NG-RAN node, a radio resource control (RRC) reconfiguration message including the measurement reconfiguration data to the UE to dynamically update measurements in accordance with the list of AI / ML use cases supported for the UE by the target NG-RAN node based on the measurement reconfiguration data. Aspect 9. The method of aspect 8 above, wherein in one embodiment, the active list of AI / ML use cases includes at least one of one or more AI / ML use cases selected for the UE by the target NG-RAN node from a list of AI / ML use cases provided to the target NG-RAN node by the source NG-RAN node, and one or more additional AI / ML use cases required by the target NG-RAN node. Aspect 10. The method of any one of Aspects 8-9, described above, wherein in an embodiment, the measurement reconfiguration data includes at least one of a set of measurements for an AI / ML use case to be deconfigured from a list of configured AI / ML use cases at the source NG-RAN node and a set of additional measurements for a new AI / ML use case to be configured for the UE at the target NG-RAN node. Aspect 11. In one embodiment, the method of any one of Aspects 8-10 described above, wherein the set of AI / ML use cases to be deconfigured is determined by identifying, from a list of AI / ML use cases configured at the source NG-RAN node, AI / ML use cases other than the one or more AI / ML use cases selected by the target NG-RAN node. Aspect 12. In an embodiment, the method of any one of Aspects 8 to 11 described above, wherein the first measurement information includes measurement parameters of the UE associated with a list of AI / ML use cases configured at the source NG-RAN node. Aspect 13. In one embodiment, the method of any one of Aspects 8 to 12 described above, wherein the second measurement information includes measurement parameters of a UE (202) associated with the active list of the AI / ML use case received from the target NG-RAN node (206). Aspect 14. In an embodiment, the method of any one of Aspects 8 to 13 above, further including, prior to handover of the UE, receiving an Xn setup request from the target NG-RAN node. The Xn setup request includes information indicating a list of AI / ML use cases supported by the target NG-RAN node and AI / ML model interoperability information for the AI / ML use cases supported by the target NG-RAN node. Thereafter, the method includes sending an Xn setup response to the target NG-RAN node, the Xn setup response including information indicating the list of AI / ML use cases supported by the source NG-RAN node and AI / ML model interoperability information for the AI / ML use cases supported by the source NG-RAN node. Aspect 15. In yet another embodiment, a non-transitory computer-readable medium storing instructions that, when processed by at least one processor, cause a source NG-RAN node to perform an operation, the operation including transmitting, by the source NG-RAN node to a target NG-RAN node via an NG-RAN node-to-node interface, an Xn handover request to hand over a user equipment (UE) from the source NG-RAN node to the target NG-RAN node, the Xn handover request including a list of artificial intelligence or machine learning (AI / ML) use cases configured for the UE at the source NG-RAN node and corresponding first measurement information associated with the list of AI / ML use cases. The instructions further cause the source NG-RAN node to perform an operation including receiving, in response to the Xn handover request, from the target NG-RAN node an Xn handover response including an active list of AI / ML use cases to be configured for the UE at the target NG-RAN node based on the list of AI / ML use cases received by the target NG-RAN node from the source NG-RAN node and corresponding second measurement information associated with the active list of AI / ML use cases. The instructions further cause the source NG-RAN node to perform an operation including determining reconfiguration measurement data for the UE based on the active list of AI / ML use cases received from the target NG-RAN node until the UE is handed over to the target NG-RAN node. Thereafter, the instructions cause the source NG-RAN node to perform an operation including transmitting the measurement reconfiguration data to the UE in a radio resource control (RRC) reconfiguration message based on the measurement reconfiguration data to dynamically update the measurements in accordance with the list of AI / ML use cases supported for the UE by the target NG-RAN node. Advantages of embodiments of the present disclosure are presented herein. In one embodiment, the proposed method allows managing the interoperability of NG-RAN nodes in a UE handover, (1) reducing unnecessary overhead in the NG-RAN node by eliminating the receipt of measurements from the UE that may be irrelevant to the AI / ML use cases supported by the NG-RAN node; (2) It reduces the unnecessary overhead of the UE measuring and transmitting large amounts of data over the network that may be irrelevant to the NG-RAN node receiving the measurements, which also increases network overhead due to the need to manage the transmission of such large amounts of data. Unlike conventional techniques that cause a lot of unnecessary overhead due to large amounts of data sent over the air interface due to partial or non-interoperability between NG-RAN nodes, the present disclosure can provide measurement reconfiguration data to a UE based on a list of AI / ML use cases and associated measurement information for each NG-RAN node, based on which the UE deconfigures one or more existing AI / ML use cases in the UE and configures new AI / ML use cases and associated measurements. Thus, the present disclosure provides the flexibility to dynamically configure the UE according to the requirements of the AI / ML use cases of the target NG-RAN node during handover. Furthermore, by dynamically configuring the UE, the present disclosure eliminates issues related to partial or non-interoperability between NG-RAN nodes.

[0069] As noted above, it should be noted that the methods of the present disclosure can be used to overcome various technical challenges associated with managing NG-RAN node interoperability during UE handover by a source NG-RAN node. In other words, the methods of the present disclosure have practical applications and provide technically advanced solutions to technical challenges associated with existing approaches to managing NG-RAN node interoperability during UE handover by a source NG-RAN node. In some embodiments, the present disclosure may also be applicable to a single NG-RAN node for mobility functions such as L1 / L2 triggered mobility, and may be applicable over F1 and E1 interfaces. In some embodiments, the present disclosure may also be applicable to Load Traffic Manager (LTM), Directory Access Protocol (DAP), etc.

[0070] In light of the technical advances provided by the method of the present disclosure, the above-mentioned claimed steps are not routine, conventional, or well-known in the art, since they provide the aforementioned solutions to the technical problems existing in the prior art. Furthermore, since the claimed steps provide a technical solution to the technical problem, it is clear that the claimed steps result in an improvement in the functionality of the system itself.

[0071] The terms "an embodiment," "embodiment," "embodiments," "the embodiment," "the embodiment," "the embodiment," "one or more embodiments," "some embodiments," and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)," unless expressly stated otherwise.

[0072] The terms "including," "comprising," "having," and variations thereof mean "including, but not limited to," unless expressly stated otherwise.

[0073] Unless otherwise specified, listed items do not imply that any or all of the items are mutually exclusive. The terms "a," "an," and "the" mean "one or more" unless otherwise specified.

[0074] A description of an embodiment in which several components are in communication with each other does not imply that all such components are required. Rather, various optional components are described to illustrate the wide variety of possible embodiments of the present invention.

[0075] Where a single device or article is described herein, it will be apparent that two or more devices / articles (whether or not they cooperate) may be used in place of the single device / article. Similarly, where two or more devices / articles (whether or not they cooperate) are described herein, it will be apparent that a single device / article may be used in place of the two or more devices / articles, and that a different number of devices / articles may be used in place of the number of devices or programs shown. The functionality and / or features of a device may alternatively be embodied by one or more other devices not explicitly described as having such functionality / features. Thus, other embodiments of the present invention need not include the device itself.

[0076] Finally, the language used herein has been chosen primarily for readability and instructional purposes, and may not have been chosen to delineate or limit the inventive subject matter. Accordingly, it is intended that the scope of the invention be limited not by this Detailed Description, but by the claims filed hereunder. Accordingly, the embodiments of the present disclosure are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

[0077] While various aspects and embodiments are disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims. [Explanation of symbols]

[0078] 202, 401 User equipment 204, 402 Source NG-RAN node 206, 403 Target NG-RAN node 302 processor 303 Memory 304 I / O interface 305 Data 307 One or more modules 309 Xn Handover Request Data 311 Xn Handover Response Data 313 Measurement Information Data 315 AI / ML use case data 317 Active List Data 319 Measurement Reconstruction Data 321 Other Data 323 Transceiver Module 325 Decision Module 327 other modules

Claims

1. A system comprising a source Next Generation Radio Access Network (NG-RAN) node (204); the source NG-RAN node (204) is configured to transmit an Xn handover request to the target NG-RAN node (206) via an NG-RAN node-to-node interface for handing over a user equipment (UE) (202) from the source NG-RAN node (204) to the target NG-RAN node (206), the Xn handover request including a list of artificial intelligence or machine learning (AI / ML) use cases configured for the UE (202) at the source NG-RAN node (204) and corresponding first measurement information associated with the list of AI / ML use cases; the source NG-RAN node (204) is configured to receive an Xn handover response from the target NG-RAN node (206) in response to the Xn handover request, the Xn handover response including an active list of AI / ML use cases to be configured for the UE (202) in the target NG-RAN node (206) based on the list of AI / ML use cases received by the target NG-RAN node (206) from the source NG-RAN node (204) and corresponding second measurement information associated with the active list of AI / ML use cases; the source NG-RAN node (204) is configured to determine measurement reconfiguration data (319) for the UE (202) based on the active list of AI / ML use cases received from the target NG-RAN node (206) until the UE (202) is handed over to the target NG-RAN node (206); The source NG-RAN node (204) is configured to transmit the measurement reconfiguration data (319) to the UE (202) in a radio resource control (RRC) reconfiguration message to dynamically update measurements according to the list of AI / ML use cases supported for the UE (202) by the target NG-RAN node (206) based on the measurement reconfiguration data (319).

2. 2. The serving NG-RAN node of claim 1, wherein the active list of AI / ML use cases includes at least one of the one or more AI / ML use cases selected for the UE (202) by the target NG-RAN node (206) from the list of AI / ML use cases provided to the target NG-RAN node (206) by the source NG-RAN node (204), and one or more additional AI / ML use cases required by the target NG-RAN node (206).

3. The measured reconstruction data (319) a set of measurements of AI / ML use cases to be deconfigured from the list of AI / ML use cases configured in the source NG-RAN node (204); and a set of additional measurements for the new AI / ML use case to be configured for the UE (202) in the target NG-RAN node (206); The source NG-RAN node (204) of claim 1, comprising at least one of:

4. 4. The source NG-RAN node (204) of claim 3, further configured to determine the measurement values ​​of the set of AI / ML use cases to be deconfigured by identifying, from the list of AI / ML use cases configured in the source NG-RAN node, an AI / ML use case other than the one or more AI / ML use cases selected by the target NG-RAN node (206).

5. The source NG-RAN node (204) of claim 1, wherein the first measurement information includes measurement parameters of the UE (202) associated with a list of the AI / ML use cases configured in the source NG-RAN node (204).

6. The source NG-RAN node (204) of claim 1, wherein the second measurement information includes measurement parameters of the UE (202) associated with an active list of the AI / ML use case received from the target NG-RAN node (206).

7. The source NG-RAN node (204) is further configured to receive an Xn setup request from the target NG-RAN node (206) before the handover of the UE (202), the Xn setup request including information indicating a list of AI / ML use cases supported by the target NG-RAN node (206) and AI / ML model interoperability information of the AI / ML use cases supported by the target NG-RAN node (206); The source NG-RAN node (204) of claim 1, further configured to send an Xn setup response to the target NG-RAN node (206), wherein the Xn setup response includes information indicating a list of AI / ML use cases supported by the source NG-RAN node (204) and AI / ML model interoperability information for the AI / ML use cases supported by the source NG-RAN node (204).

8. 1. A method, comprising: transmitting, by a source NG-RAN node (204) to a target NG-RAN node (206) via an NG-RAN node-to-node interface, an Xn handover request for handing over a user equipment (UE) (202) from the source NG-RAN node (204), wherein the Xn handover request includes a list of artificial intelligence or machine learning (AI / ML) use cases configured for the UE (202) at the source NG-RAN node (204) and corresponding first measurement information associated with the list of AI / ML use cases; The method includes receiving, by the source NG-RAN node (204), an Xn handover response from the target NG-RAN node (206) in response to the Xn handover request, the Xn handover response including an active list of AI / ML use cases to be configured for the UE (202) at the target NG-RAN node (206) based on the list of AI / ML use cases sent from the source NG-RAN node (204) to the target NG-RAN node (206) and corresponding second measurement information associated with the active list of AI / ML use cases; The method includes determining, by the source NG-RAN node (204), measurement reconfiguration data (319) for the UE (202) based on the active list of AI / ML use cases received from the target NG-RAN node (206) until the UE (202) is handed over to the target NG-RAN node (206); The method includes transmitting, by the source NG-RAN node (204), a Radio Resource Control (RRC) reconfiguration message including the measurement reconfiguration data (319) to the UE (202) to dynamically update measurements according to the list of AI / ML use cases supported for the UE (202) by the target NG-RAN node (206) based on the measurement reconfiguration data (319). method.

9. The active list of AI / ML use cases is: the one or more AI / ML use cases selected by the target NG-RAN node (206) for the UE (202) from the list of AI / ML use cases provided to the target NG-RAN node (206) by the source NG-RAN node (204); and one or more additional AI / ML use cases required by the target NG-RAN node (206); The method of claim 8 , comprising at least one of:

10. The measured reconstruction data (319) a set of measurements of AI / ML use cases to be deconfigured from the list of AI / ML use cases configured in the source NG-RAN node (204); and a set of additional measurements for the new AI / ML use case to be configured for the UE (202) in the target NG-RAN node (206); The method of claim 8 , comprising at least one of:

11. 11. The method of claim 10, wherein the set of AI / ML use cases to be deconfigured is determined by identifying, from the list of AI / ML use cases configured in the source NG-RAN node (204), the AI / ML use cases other than the one or more AI / ML use cases selected by the target NG-RAN node (206).

12. The method of claim 8, wherein the first measurement information includes measurement parameters of the UE (202) associated with the list of AI / ML use cases configured in the source NG-RAN node (204).

13. 9. The method of claim 8, wherein the second measurement information includes measurement parameters of the UE associated with an active list of the AI / ML use case received from the target NG-RAN node.

14. The method further includes receiving an Xn setup request from the target NG-RAN node (206) before the handover of the UE (202), the Xn setup request including information indicating a list of AI / ML use cases supported by the target NG-RAN node (206) and AI / ML model interoperability information of the AI / ML use cases supported by the target NG-RAN node (206); The method further includes transmitting an Xn setup response to the target NG-RAN node (206), wherein the Xn setup response includes information indicating a list of AI / ML use cases supported by the source NG-RAN node (204) and AI / ML model interoperability information for the AI / ML use cases supported by the source NG-RAN node (204). The method of claim 8.

15. A non-transitory computer-readable medium storing thereon what, when processed by at least one processor (302), causes a source NG-RAN node to perform an operation, The operations include transmitting, by the source NG-RAN node (204) to the target NG-RAN node (206) via an NG-RAN node-to-node interface, an Xn handover request for handing over a user equipment (UE) (202) from the source NG-RAN node (204) to the target NG-RAN node (206), the Xn handover request including a list of artificial intelligence or machine learning (AI / ML) use cases configured for the UE (202) at the source NG-RAN node (204) and corresponding first measurement information associated with the list of AI / ML use cases; The operations include receiving, by the source NG-RAN node (204), an Xn handover response from the target NG-RAN node (206) in response to the Xn handover request, the Xn handover response including an active list of AI / ML use cases to be configured for the UE (202) at the target NG-RAN node (206) based on the list of AI / ML use cases received by the target NG-RAN node (206) from the source NG-RAN node (204) and corresponding second measurement information associated with the active list of AI / ML use cases; The operations include determining, by the source NG-RAN node (204), reconfiguration measurement data for the UE (202) based on the active list of AI / ML use cases received from the target NG-RAN node (206) until the UE (202) is handed over to the target NG-RAN node (206); The operations include transmitting, by the source NG-RAN node (204), the measurement reconfiguration data (319) to the UE (202) in a radio resource control (RRC) reconfiguration message to dynamically update measurements according to the list of AI / ML use cases supported for the UE (202) by the target NG-RAN node (206) based on the measurement reconfiguration data (319); Non-transitory computer-readable medium.

Images