Methods and apparatus for handling data over control plane

Abstract

The present invention relates to methods, apparatus and / or systems for providing a framework relating to prioritising data content. A terminal of the invention comprises at least one processor configured to: receive, from a network entity, configuration information for use in identifying at least one data feature, and identification information for the at least one data feature; identify the at least one data feature based on the configuration information and the identification information; and transmit the identified at least one data feature or information on the identified at least one data feature to the network entity over a control plane.

Description

METHODS AND APPARATUS FOR HANDLING DATA OVER CONTROL PLANE

[0001] Certain examples of the present disclosure relate to methods, apparatus and / or systems for providing a framework relating to prioritising data content. In particular, in various examples the data is being, or to be, sent via RRC and / or NAS messages or signalling. According to various examples, the prioritising is on the basis of one or more features of the data content, where further examples define such data content. In an example, a different priority level may be applied to AI / ML related data than to other data to be sent by RRC or NAS message / signalling.

[0002] 5G mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in "Sub 6GHz" bands such as 3.5GHz, but also in "Above 6GHz" bands referred to as mmWave including 28GHz and 39GHz. In addition, it has been considered to implement 6G mobile communication technologies (referred to as Beyond 5G systems) in terahertz bands (for example, 95GHz to 3THz bands) in order to accomplish transmission rates fifty times faster than 5G mobile communication technologies and ultra-low latencies one-tenth of 5G mobile communication technologies.

[0003] At the beginning of the development of 5G mobile communication technologies, in order to support services and to satisfy performance requirements in connection with enhanced Mobile BroadBand (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine-Type Communications (mMTC), there has been ongoing standardization regarding beamforming and massive MIMO for mitigating radio-wave path loss and increasing radio-wave transmission distances in mmWave, supporting numerologies (for example, operating multiple subcarrier spacings) for efficiently utilizing mmWave resources and dynamic operation of slot formats, initial access technologies for supporting multi-beam transmission and broadbands, definition and operation of BWP (BandWidth Part), new channel coding methods such as a LDPC (Low Density Parity Check) code for large amount of data transmission and a polar code for highly reliable transmission of control information, L2 pre-processing, and network slicing for providing a dedicated network specialized to a specific service.

[0004] Currently, there are ongoing discussions regarding improvement and performance enhancement of initial 5G mobile communication technologies in view of services to be supported by 5G mobile communication technologies, and there has been physical layer standardization regarding technologies such as V2X (Vehicle-to-everything) for aiding driving determination by autonomous vehicles based on information regarding positions and states of vehicles transmitted by the vehicles and for enhancing user convenience, NR-U (New Radio Unlicensed) aimed at system operations conforming to various regulation-related requirements in unlicensed bands, NR UE Power Saving, Non-Terrestrial Network (NTN) which is UE-satellite direct communication for providing coverage in an area in which communication with terrestrial networks is unavailable, and positioning.

[0005] Moreover, there has been ongoing standardization in air interface architecture / protocol regarding technologies such as Industrial Internet of Things (IIoT) for supporting new services through interworking and convergence with other industries, IAB (Integrated Access and Backhaul) for providing a node for network service area expansion by supporting a wireless backhaul link and an access link in an integrated manner, mobility enhancement including conditional handover and DAPS (Dual Active Protocol Stack) handover, and two-step random access for simplifying random access procedures (2-step RACH for NR). There also has been ongoing standardization in system architecture / service regarding a 5G baseline architecture (for example, service based architecture or service based interface) for combining Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies, and Mobile Edge Computing (MEC) for receiving services based on UE positions.

[0006] As 5G mobile communication systems are commercialized, connected devices that have been exponentially increasing will be connected to communication networks, and it is accordingly expected that enhanced functions and performances of 5G mobile communication systems and integrated operations of connected devices will be necessary. To this end, new research is scheduled in connection with eXtended Reality (XR) for efficiently supporting AR (Augmented Reality), VR (Virtual Reality), MR (Mixed Reality) and the like, 5G performance improvement and complexity reduction by utilizing Artificial Intelligence (AI) and Machine Learning (ML), AI service support, metaverse service support, and drone communication.

[0007] Furthermore, such development of 5G mobile communication systems will serve as a basis for developing not only new waveforms for providing coverage in terahertz bands of 6G mobile communication technologies, multi-antenna transmission technologies such as Full Dimensional MIMO (FD-MIMO), array antennas and large-scale antennas, metamaterial-based lenses and antennas for improving coverage of terahertz band signals, high-dimensional space multiplexing technology using OAM (Orbital Angular Momentum), and RIS (Reconfigurable Intelligent Surface), but also full-duplex technology for increasing frequency efficiency of 6G mobile communication technologies and improving system networks, AI-based communication technology for implementing system optimization by utilizing satellites and AI (Artificial Intelligence) from the design stage and internalizing end-to-end AI support functions, and next-generation distributed computing technology for implementing services at levels of complexity exceeding the limit of UE operation capability by utilizing ultra-high-performance communication and computing resources.

[0008] It is an aim of certain examples of the present disclosure to address, solve and / or mitigate, at least partly, at least one of the problems and / or disadvantages associated with the related art, for example at least one of the problems and / or disadvantages described herein. It is an aim of certain examples of the present disclosure to provide at least one advantage over the related art, for example at least one of the advantages described herein.

[0009] According to an aspect of the present disclosure, there is provided a UE comprising: a transmitter; a receiver; at least one processor configured to: receive, from a network entity, configuration information for use in identifying at least one data feature, and identification information for the at least one data feature; identify the at least one data feature based on the configuration information and the identification information; and transmit the identified at least one data feature or information on the identified at least one data feature to the network entity over Control Plane (CP).

[0010] According to various examples, the configuration information and the identification information are received via Radio Resource Control (RRC) signalling or messaging or via Non-Access Stratum (NAS) signalling or messaging.

[0011] According to various examples, the identified at least one data feature or the information on the identified at least one data feature is transmitted via RRC signalling or messaging or via NAS signalling or messaging.

[0012] According to various examples, the identified at least one data feature or the information on the identified at least one data feature is transmitted via one or more RRC message or NAS message transmitted over one or more signalling radio bearer (SRB).

[0013] According to various examples, the at least one processor is further configured to: control the transmission of the identified at least one data feature according to a priority configured for the identified at least one data feature.

[0014] According to various examples, the priority is configured or the data feature is prioritised based on the identified at least one data feature being artificial intelligence / machine learning (AI / ML) data for a life-cycle management (LCM) purpose, data content, data size, data type, data latency, data validity, or data generation and / or collection side.

[0015] According to various examples, the AI / ML data for a LCM purpose includes one or more of: AI / ML data for model training, AI / ML data for model functionality training, AI / ML data for model monitoring AI / ML data for model functionality monitoring, AI / ML data for model inference, or AI / ML data for model functionality inference.

[0016] According to various examples, the priority configured for the identified at least one data feature is different to a priority configured for other data to be transmitted to the network entity.

[0017] According to various examples, when the identified at least one data feature is to be transmitted via RRC signalling or messaging, the priority configured for the at least one data feature is lower than a priority configured for RRC radio related signalling messages.

[0018] According to various examples, when the identified at least one data feature is to be transmitted via NAS signalling or messaging, the priority configured for the at least one data feature is lower than a priority configured for a NAS 5G system session management (5GSM) signalling message and / or higher than a priority configured for a consumer internet of things (CIoT) message.

[0019] According to various examples, the priority configured for the identified at least one data feature is pre-configured in the UE by the network entity.

[0020] According to various examples, the at least one processor is further configured to: transmit, to the network entity, a first indication that the UE supports transmitting AI / ML data over RRC and / or a second indication that the UE supports prioritizing the transmission of data sent over RRC; or receive, from the network entity, a third indication that the network entity supports transmitting / receiving AI / ML data over RRC and / or a fourth indication that the network entity supports using priority information for the transmission of data over RRC.

[0021] According to various examples, the first indication, the second indication, the third indication and / or the fourth indication are exchanged in an RRC message(s) or in a NAS message(s).

[0022] According to various examples, the at least one data feature includes data content, data size, data type, data latency, data collection side and / or data life-cycle-management purpose.

[0023] According to another aspect of the present disclosure, there is provided a network entity comprising: a transmitter, a receiver; and at least one processor configured to: identify at least one data feature of data available at a user equipment (UE) or at the network entity; transmit configuration information and identification information for the at least data feature to the UE over Control Plane (CP); and receive, from the UE, the at least one data feature or information on the at least one data feature.

[0024] According to various examples, the at least one data feature includes one or more of data content, data size, data type, data latency, data collection side, data life-cycle management (LCM) purpose, artificial intelligence / machine learning (AI / ML) data for model training, AI / ML data for model functionality training, AI / ML data for model monitoring AI / ML data for model functionality monitoring, AI / ML data for model inference, or AI / ML data for model functionality inference.

[0025] According to various examples, the at least one processor is configured to: prioritize the identified at least one data feature relative to other data to be transmitted via the CP, based on the identified at least one data feature being transmitted over Radio Resource Control (RRC) or non-access stratum (NAS).

[0026] According to various examples, the configuration information and the identification information are transmitted via Radio Resource Control (RRC) signalling or messaging or via Non-Access Stratum (NAS) signalling or messaging.

[0027] According to various examples, the at least one data feature or the information on the at least one data feature is received via RRC signalling or messaging or via NAS signalling or messaging.

[0028] According to various examples, the at least one data feature or the information on the at least one data feature is received via one or more RRC message or NAS message transmitted over one or more signalling radio bearer (SRB).

[0029] According to various examples, the at least one processor is further configured to: receive, from the UE, a first indication that the UE supports transmitting AI / ML data over RRC and / or a second indication that the UE supports prioritizing the transmission of data sent over RRC; or transmit, to the UE, a third indication that the network entity supports transmitting / receiving AI / ML data over RRC and / or a fourth indication that the network entity supports using priority information for the transmission of data over RRC.

[0030] According to various examples, the first indication, the second indication, the third indication and / or the fourth indication are exchanged in an RRC message(s) or in a NAS message(s).

[0031] According to various examples, the at least one processor is further configured to: transmit, to the UE, information on a priority to be applied to the at least one data feature for transmission by the UE, or receive, from the UE, an indication of a priority applied to the at least one data feature for transmission by the UE.

[0032] According to another aspect of the present disclosure, there is provided a method of a UE, the method comprising: receiving, from a network entity, configuration information for use in identifying at least one data feature and identification information for the at least one data feature; identifying the at least one data feature based on the configuration information and the identification information; and transmitting the identified at least one data feature or information on the identified at least one data feature to the network entity over Control Plane (CP).

[0033] According to another aspect of the present disclosure, there is provided a method of a network entity, the method comprising: identifying at least one data feature of data available at a user equipment (UE); transmitting configuration information and identification information for the at least data features to the UE over Control Plane (CP); and receiving, from the UE, the at least one data feature or information on the identified at least one data feature.

[0034] According to other aspects of the present disclosure, there is provided a machine-readable storage comprising instructions or code which, when executed, implement a method in accordance with any of the aspects described above.

[0035] Other aspects, advantages, and salient features of the disclosure will become apparent to those skilled in the art from the following detailed description taken in conjunction with the accompanying drawings.

[0036] The disclosure provides a method of controlling energy use per network slice in a wireless communication system.

[0037] Embodiments / examples of the present disclosure are further described hereinafter with reference to the accompanying drawings, in which:

[0038] Figure 1 illustrates a block diagram illustrating an example structure of an entity in accordance with certain examples of the present disclosure.

[0039] Figure 2 illustrates a flow diagram illustrating a method in accordance with various examples of the present disclosure.

[0040] Figure 3 illustrates a flow diagram illustrating a method in accordance with various examples of the present disclosure.

[0041] The content of the following documents is referred to below and / or their content provides background information that the following disclosure should be considered in the context of:

[0042] [1] 3GPP TR 38.300 - 5G; NR; NR and NG-RAN Overall Description; Stage-2,

[0043] (e.g. v17.6.0).

[0044] [2] 3GPP TS 38.331 - NR; Radio Resource Control (RRC); Protocol specification,

[0045] (e.g. v17.6.0).

[0046] [3] TS 3GPP TS 24.501 - Non-Access-Stratum (NAS) protocol for 5G System (5GS); Stage 3

[0047] (e.g. V18.4.0)

[0048] Note: the indicated version numbers are provided for illustrative purposes, other (including future) versions of these documents are considered also.

[0049] For example, any acronyms or abbreviations not defined in this document may be interpreted in the context of [1], [2] or an appropriate document referenced therein.

[0050] Wireless or mobile (cellular) communications networks in which a mobile terminal (e.g., user equipment (UE), such as a mobile handset) communicates via a radio link with a network of base stations, or other wireless access points or nodes, have undergone rapid development through a number of generations. The 3rdGeneration Partnership Project (3GPP) design, specify and standardise technologies for mobile wireless communication networks. Fourth (4th) Generation (4G) and Fifth Generation (5G) systems (5GS) are now widely deployed, while beyond 5G (B5G) and 6G systems are being considered.

[0051] 3GPP standards for 4G systems include an Evolved Packet Core (EPC) and an Enhanced-UTRAN (E-UTRAN: an Enhanced Universal Terrestrial Radio Access Network). The E-UTRAN uses Long Term Evolution (LTE) radio technology. LTE is commonly used to refer to the whole system including both the EPC and the E-UTRAN, and LTE is used in this sense in the remainder of this document. LTE should also be taken to include LTE enhancements such as LTE Advanced and LTE Pro, which offer enhanced data rates compared to LTE.

[0052] In 5G systems a new air interface has been developed, which may be referred to as 5G New Radio (5G NR) or simply NR. NR is designed to support the wide variety of services and use case scenarios envisaged for 5G networks, though builds upon established LTE technologies. B5G systems, such as 6G, are currently being considered and developed, and are expected to at least partly build on 5G systems.

[0053] New frameworks and architectures are being developed as part of 5G network (and beyond, such as 6G networks) in order to increase the range of functionality and use cases available through 5G networks.

[0054] A new framework developed as part of 5G networks (and beyond) is the use of artificial intelligence / machine learning (AI / ML), which may be used for the optimisation of the operation of 5G networks. In AI / ML operation, AI / ML models and / or data might be transferred across the AI / ML applications (e.g., application functions (AFs)), 5GC (5G core), UEs (user equipments) etc.). Without limitation, the AI / ML works could be divided into two main phases: model training and inference. During model training and inference, multiple rounds of interaction may be required.

[0055] In Section 6.40 ('AI / ML model transfer in 5GS') in TS 22.261 [1], three types of AI / ML operations to be supported in Release 18 and in Release 19 are described as follows:

[0056] a)AI / ML operation splitting between AI / ML endpoints

[0057] The AI / ML operation / model is split into multiple parts according to the current task and environment. The intention is to offload the computation-intensive, energy-intensive parts to network endpoints, whereas leave the privacy-sensitive and delay-sensitive parts at the end device. The device executes the operation / model up to a specific part / layer and then sends the intermediate data to the network endpoint. The network endpoint executes the remaining parts / layers and feeds the inference results back to the device.

[0058] b)AI / ML model / data distribution and sharing over 5G system

[0059] Multi-functional mobile terminals might need to switch the AI / ML model in response to task and environment variations. The condition of adaptive model selection is that the models to be selected are available for the mobile device. However, given the fact that the AI / ML models are becoming increasingly diverse, and with the limited storage resource in a UE, it can be determined to not pre-load all candidate AI / ML models on-board. Online model distribution (i.e., new model downloading) is needed, in which an AI / ML model can be distributed from a NW (network) endpoint to the devices when they need it to adapt to the changed AI / ML tasks and environments. For this purpose, the model performance at the UE needs to be monitored constantly.

[0060] c)Distributed / Federated Learning over 5G system

[0061] The cloud server trains a global model by aggregating local models partially-trained by each end devices. Within each training iteration, a UE performs the training based on the model downloaded from the AI server using the local training data. Then the UE reports the interim training results to the cloud server via 5G UL channels. The server aggregates the interim training results from the UEs and updates the global model. The updated global model is then distributed back to the UEs and the UEs can perform the training for the next iteration.

[0062] Radio bearers and SRB0 are described in section 6.1 and section 6.3.1 of TS 38.300 [1], respectively:

[0063] Radio bearers are categorized into two groups: data radio bearers (DRB) for user plane data and signalling radio bearers (SRB) for control plane data.

[0064] For SRB0, paging and broadcast system information, TM mode is used. For other SRBs AM mode used. For DRBs, either UM or AM mode are used.

[0065] For transport of Non-Access Stratum messages in New Radio (NR), section 7.6 of TS 38.300 [2] discloses the following:

[0066] 7.6Transport of NAS Messages

[0067] NR provides reliable in-sequence delivery of NAS messages over SRBs in RRC, except at handover where losses or duplication can occur when PDCP is re-established. In RRC, NAS messages are sent in transparent containers. Piggybacking of NAS messages can occur in the following scenarios:

[0068] -At bearer establishment / modification / release in the DL;

[0069] -For transferring the initial NAS message during connection setup and connection resume in the UL.

[0070] NOTE:In addition to the integrity protection and ciphering performed by NAS, NAS messages can also be integrity protected and ciphered by PDCP.

[0071] Multiple NAS messages can be sent in a single downlink RRC message during PDU Session Resource establishment or modification. In this case, the order of the NAS messages contained in the RRC message shall be in the same order as that in the corresponding NG-AP message in order to ensure the in-sequence delivery of NAS messages.

[0072] NG-RAN node may trigger the NAS Non Delivery Indication procedure to report the non-delivery of the non PDU Session related NAS PDU received from the AMF as specified in TS 38.413

[0026] .

[0073] In relation to signalling radio bearers (SRBs), section 4.2.2 of TS 38.331 discloses the following:

[0074] 4.2.2Signalling radio bearers

[0075] "Signalling Radio Bearers" (SRBs) are defined as Radio Bearers (RBs) that are used only for the transmission of RRC and NAS messages. More specifically, the following SRBs are defined:

[0076] -SRB0 is for RRC messages using the CCCH logical channel;

[0077] -SRB1 is for RRC messages (which may include a piggybacked NAS message) as well as for NAS messages prior to the establishment of SRB2, all using DCCH logical channel;

[0078] -SRB2 is for NAS messages and for RRC messages which include logged measurement information, all using DCCH logical channel. SRB2 has a lower priority than SRB1 and may be configured by the network after AS security activation;

[0079] -SRB3 is for specific RRC messages when UE is in (NG)EN-DC or NR-DC, all using DCCH logical channel;

[0080] -SRB4 is for RRC messages which include application layer measurement report information, all using DCCH logical channel. SRB4 has a lower priority than SRB1 and can only be configured by the network after AS security activation.

[0081] In downlink, piggybacking of NAS messages is used only for one dependant (i.e. with joint success / failure) procedure: bearer establishment / modification / release. In uplink piggybacking of NAS message is used only for transferring the initial NAS message during connection setup and connection resume.

[0082] NOTE 1:The NAS messages transferred via SRB2 are also contained in RRC messages, which however do not include any RRC protocol control information.

[0083] Once AS security is activated, all RRC messages on SRB1, SRB2, SRB3 and SRB4, including those containing NAS messages, are integrity protected and ciphered by PDCP. NAS independently applies integrity protection and ciphering to the NAS messages, see TS 24.501

[0023] .

[0084] Split SRB is supported for all the MR-DC options in both SRB1 and SRB2 (split SRB is not supported for SRB0, SRB3 and SRB4).

[0085] For operation with shared spectrum channel access in FR1, SRB0, SRB1 and SRB3 are assigned with the highest priority Channel Access Priority Class (CAPC), (i.e. CAPC = 1) while CAPC for SRB2 is configurable.

[0086] The above parts of TS 38.300 [1] and TS 38.331 [2] may be seen to provide background information to various other discussions found herein.

[0087] A 5G system (5GS) (and / or a 4G system (4GS)) supports the transport of data over NAS. A user equipment (UE) uses the Control Plane Service Request message to send data over NAS from idle mode, and a UE uses the uplink (UL) NAS TRANSPORT message to send data over NAS when in connected mode.

[0088] It should be noted that the UL NAS TRANSPORT message is also used to send NAS signaling such as session management messages (e.g. 5G system session management (5GSM) messages) or other types of data such as: SMS, location services message, LPP message, etc.

[0089] It should be noted that the UE may be faced with cases in which there is data to be sent over NAS while at the same time there is / are signaling message(s) to send. The following was specified in TS 24.501 [3] (e.g. see section 5.1.2):

[0090] NOTE 1:In NB-N1 mode, the UE NAS using 5GS services with control plane CIoT 5GS optimization can wait for the lower layers to complete the transmission of the previous UL NAS TRANSPORT messages carrying control plane user data before providing subsequent NAS messages. Other implementations are possible.

[0091] NOTE 2:When providing NAS messages to the lower layers for transmission, the UE NAS using 5GS services with control plane CIoT 5GS optimization can prioritize sending NAS signalling messages over the UL NAS TRANSPORT messages carrying control plane user data. How the UE performs this prioritization is implementation specific.

[0092] From the above, it can be seen that the UE may prioritize sending data over NAS versus NAS signaling messages, where the latter can be prioritized over control plane data. However the method of prioritization is not standardized.

[0093] As part of 3GPP work on AI / ML for NR air interface (e.g., see: document3GPP TSG RAN Meeting #96: RP-221348; Qualcomm: "Revised SID: Study on Artificial Intelligence (AI) / Machine Learning (ML) for NR Air Interface"), RAN2 working group agreed to study the following potential solutions for AI / ML model transfer / delivery over Control Plane (CP) (i.e. radio resource control (RRC) and NAS signalling), User Plane (UP), LPP signalling and via Server (e.g. Operations, Administration and Maintenance (OAM), OTT) (e.g. see document:3GPP TSG-RAN WG2 Meeting #121 (Athens, Greece, February 27 - March 3, 2023) - "Chair Notes"):

[0094] ⇒Agreed:Aim to at least analyze the feasibility and benefits of model / transfer solutions based on the following:Solution 1a: gNB can transfer / deliver AI / ML model(s) to UE via RRC signalling.Solution 2a: CN (except LMF) can transfer / deliver AI / ML model(s) to UE via NAS signalling.Solution 3a: LMF can transfer / deliver AI / ML model(s) to UE via LPP signalling.Solution 1b: gNB can transfer / deliver AI / ML model(s) to UE via UP data.Solution 2b: CN (except LMF) can transfer / deliver AI / ML model(s) to UE via UP data.Solution 3b: LMF can transfer / deliver AI / ML model(s) to UE via UP data.Solution 4: Server (e.g. OAM, OTT) can transfer / delivery AI / ML model(s) to UE (e.g. transparent to 3GPP).

[0095] ⇒The table(below)can serve as starting point for continued discussion (but contains some parts that seems non consensus, e.g. delta configuration).

[0096] ProsConsSolution 1a6. The existing RRC signaling solutions can be reused as baseline, at least including delta signaling and segementation9. Additional security and verification may not be necessary as the UE already established security before the transfer is initiated11. gNB can take the control of the AIML model transfer itself, which can not be achieved by traditional UP based solution1. Face challenges to convey large sizeor "no upper limit size"AI model by RRC message(e.g. >45kBytes)2. Maybe high control plane overhead, as a large model size may need segmentation / transmission / acknowledgment. This consumes critical configuration time for model transfer / delivery3. An incomplete control plane model transfer has to be restarted upon mobility, as there are no current procedures to resume transmission across gNBs. Some companies wonder whether it is critical or not as it depends on how frequent the gNB to send new / updated AI / ML to the UESolution 2aand 3a5. Service continuity on model transfer / delivery is easy to achieve compared with Solution 1a6. Impacts on RAN2 may be limited (some companies think that LPP signalling is in RAN2 scope)1. Face challenges to convey large size or"no upper limit size"AI model by RRC message(e.g. >45kBytes)3.If NAS does the segmentation, it may introduce some overhead4. (only valid for Solution 2a) CN is not a good option for later on model monitoring / activation / deactivation / fallback / update that requires less latency. The model transfer / delivery is transparent to gNB, it could be tricky to get gNB involved in the AI model LCM. It could be problematic when the network needs to be in control of what happening at the UE side and especially in two-sided models where one side of the model is intended to be located at the network sideSolution 1b1. The network can provide different 5QIs for model transfer / delivery with different QoS requirements (e.g. can support large model size)2. Compared with CP-based solutions, this Solution 1b can reduces control plane overhead, reduces overhead at gNB for model delivery / transfer5. Compared with CP-based solutions, it may not need to consider CP message segmentation, CP message blocking issue5. Not compatible with current mobility procedure. Supporting model transfer during mobility is not so straightforwardSolution 2b and 3b1. The network can provide different 5QIs for model transfer / delivery with different QoS requirements (e.g. can support large model size)5. Compared with CP-based solutions, it may not need to consider CP message segmentation, CP message blocking issue2. CP signalling is needed to configure and initiate the model transfer from the CN4. May be unable to support delta-model transfer / delivery based on current user plane frameworkSolution 42. If 3GPP network can be aware of AI / ML model in this Solution 4, the network can provide different 5QIs for model transfer / delivery with different QoS requirements (e.g. can support large model size). How to synchronize 3GPP and server so that the network can take appropriate actions is not clear, and it may not be fully under 3GPP control2. There may be inter-operability issues, such as:a) Different implementations may lead to different model performances and a huge burden of model management (e.g., frequent model activation / deactivation)b) Massive offline coordination is needed or requires lots of coordinations among vendors, especially for the CSI compression use case4. When network cannot control the model transfer / delivery, the transfer of large model may impact important and delay sensitive user data traffic

[0097] In the tables above, certain passages areunderlinedfor emphasis, these passages relating to "solution 1a" and "solution 2a".

[0098] As indicated in the table above, for potential model transfer / delivery solutions 1a (via RRC) and 2a (via NAS), it is expected that the transfer of large size models (and / or AI / ML data related the model) may introduce high control plane overhead and latency due in the case of transfer of large model size (and / or high data volume), and there could be a need for RRC and / or NAS messages segmentation.

[0099] Moreover, transferring AI / ML model(s) (and / or related data), over RRC signaling may delay the transfer of RRC messages carrying information related to the UE's connection management and mobility, as both the AI / ML data and UE connection data would be sharing same SRBs. However, it may be assumed that some AI / ML data (e.g. model and related data) may be less urgent than the UE access / connection establishment carried over RRC messages. Hence, it is possible to consider some sort of RRC messages prioritisation over different SRBs. For example, as mentioned in document3GPP TSG-RAN WG2 Meeting #123bis R2-2310880:

[0100] considering that SRB1 is used to carry important RRC messages (e.g. RRCSetupRequest, RRCSetup, RRCSetupComplete, RRCResumeRequest, RRCResume, RRCResumeComplete, RRCReconfiguration, RRCReconfigurationComplete, RRCReestablishment RRCReestablishmentRequest, etc.), sending RRC messages, carrying AI / ML related information, on SRB1 would require allocating lower priority to the AI / ML related RRC messages.

[0101] Another way to address the above is to introduce a new SRB to carry the AI / ML related data over RRC signalling. For example, as mentioned in3GPP TSG-RAN WG2 Meeting #123bis R2-2310880:

[0102] SRBx (e.g. SRB5) that can be configured to send / receive RRC messages for AI / ML related information, the network can configure LCP parameters (i.e. allocation of a priority and a prioritised bit rate (PBR)) for SRBx to control the prioritization of data transmission over SRBx.

[0103] Accordingly, 3GPP discussion on solutions for transfer / delivery of AI / ML model and / or model related data (e.g. monitoring, training, inference, other type of data), over control plane (i.e. using RRC and / or NAS signaling), has not yet concluded (or finalized) whether to use RRC (and / or NAS) segmentation and / or priority differentiation of RRC messages (and / or NAS messages) carrying AI / ML data (e.g. model and / or model data) from other RRC messages (and / or NAS messages). Therefore, there is a need for such solutions.

[0104] The following description of examples of the present disclosure, with reference to the accompanying drawings, is provided to assist in a comprehensive understanding of certain examples of the present disclosure. The description includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the examples described herein can be made without departing from the scope of the invention or disclosure.

[0105] The same or similar components may be designated by the same or similar reference numerals, although they may be illustrated in different drawings.

[0106] Detailed descriptions of techniques, structures, constructions, functions or processes known in the art may be omitted for clarity and conciseness, and to avoid obscuring the subject matter of the present disclosure.

[0107] The terms and words used herein are not limited to the bibliographical or standard meanings, but are merely used to enable a clear and consistent understanding of the disclosure.

[0108] Throughout the description of this specification, the words "comprise", "include" and "contain" and variations of the words, for example "comprising" and "comprises", means "including but not limited to", and is not intended to (and does not) exclude other features, elements, components, integers, steps, processes, operations, functions, characteristics, properties and / or groups thereof.

[0109] Throughout the description of this specification, the singular form, for example "a", "an" and "the", encompasses the plural unless the context otherwise requires. For example, reference to "an object" includes reference to one or more of such objects.

[0110] Throughout the description, the expression "at least one of A, B and / or C" (or the like), the expression "and / or" and the expression "one or more of A, B and / or C" (or the like) should be seen to separately include all possible combinations, for example: A, B, C, A and B, A and C, A and B and C.

[0111] Throughout the description of this specification, language in the general form of "X for Y" (where Y is some action, process, operation, function, activity or step and X is some means for carrying out that action, process, operation, function, activity or step) encompasses means X adapted, configured or arranged specifically, but not necessarily exclusively, to do Y.

[0112] Features, elements, components, integers, steps, processes, operations, functions, characteristics, properties and / or groups thereof described or disclosed in conjunction with a particular aspect, embodiment or example are to be understood to be applicable to any other aspect, embodiment or example described herein unless incompatible therewith.

[0113] Certain examples of the present disclosure relate to methods, apparatus and / or systems for providing a framework relating to prioritising data content. In particular, in various examples the data is being, or to be, sent via RRC and / or NAS messages or signalling. According to various examples, the prioritising is on the basis of one or more features of the data content. In an example, a different priority level may be applied to AI / ML related data than to other data to be sent by RRC or NAS message / signalling.

[0114] The following examples are applicable to, and use terminology associated with, 3GPP 4G and 5G. However, the skilled person will appreciate that the techniques disclosed herein are not limited to these examples or to 3GPP 4G or 5G, and may be applied in any suitable system or standard, for example one or more existing and / or future generation wireless communication systems or standards. The skilled person will appreciate that the techniques disclosed herein may be applied in any existing or future releases of 3GPP 4G or 5G NR or any other relevant standard. For example, the functionality of the various entities or network entities and other features disclosed herein may be applied to corresponding or equivalent entities or features in other communication systems or standards. Corresponding or equivalent entities or features may be regarded as entities or features that perform the same or similar role, function, operation or purpose within the network. In particular, the following disclosure should be considered at least in relation to 6G also, which is expected to use at least part of the 5G architecture, or equivalent, and to which the present disclosure also relates.

[0115] A particular entity may be implemented as a network element on a dedicated hardware, as a software instance running on a dedicated hardware, and / or as a virtualised function instantiated on an appropriate platform, e.g. on a cloud infrastructure.

[0116] The skilled person will appreciate that the present disclosure is not limited to the specific examples disclosed herein. For example:

[0117] ● The techniques disclosed herein are not limited to 3GPP 4G, 5G, B5G or 6G.

[0118] ● One or more entities in the examples disclosed herein may be replaced with one or more alternative entities performing equivalent or corresponding functions, processes or operations.

[0119] ● One or more of the messages in the examples disclosed herein may be replaced with one or more alternative messages, signals or other type of information carriers that communicate equivalent or corresponding information.

[0120] ● One or more further elements, entities and / or messages may be added to the examples disclosed herein.

[0121] ● One or more non-essential elements, entities and / or messages may be omitted in certain examples.

[0122] ● The functions, processes or operations of a particular entity in one example may be divided between two or more separate entities in an alternative example.

[0123] ● The functions, processes or operations of two or more separate entities in one example may be performed by a single entity in an alternative example.

[0124] ● Information carried by a particular message in one example may be carried by two or more separate messages in an alternative example.

[0125] ● Information carried by two or more separate messages in one example may be carried by a single message in an alternative example.

[0126] ● The order in which operations are performed may be modified, if possible, in alternative examples.

[0127] ● The transmission of information between entities is not limited to the specific form, type and / or order of messages described in relation to the examples disclosed herein.

[0128] Certain examples of the present disclosure may be provided in the form of an apparatus / device / network entity configured to perform one or more defined network functions and / or a method therefor. Such an apparatus / device / network entity may comprise one or more elements, for example one or more of receivers, transmitters, transceivers, processors, controllers, modules, units, and the like, each element configured to perform one or more corresponding processes, operations and / or method steps for implementing the techniques described herein. For example, an operation / function of X may be performed by a module configured to perform X (or an X-module). Certain examples of the present disclosure may be provided in the form of a system (e.g., a network) comprising one or more such apparatuses / devices / network entities, and / or a method therefor.

[0129] It will be appreciated that examples of the present disclosure may be realized in the form of hardware, software or a combination of hardware and software. Certain examples of the present disclosure may provide a computer program comprising instructions or code which, when executed, implement a method, system and / or apparatus in accordance with any aspect, example and / or embodiment disclosed herein. Certain embodiments of the present disclosure provide a machine-readable storage storing such a program.

[0130] As described above, 3GPP discussion on solutions for transfer / delivery of AI / ML model and / or model related data (e.g. monitoring, training, inference, other type of data), over control plane (i.e. using RRC and / or NAS signaling), has not yet been finalized; for example, it has not been concluded whether to use RRC and / or NAS segmentation and / pr priority differentiation of RRC and / or NAS messages carrying AI / ML data compared to other RRC messages and / or NAS messages. One problem related to segmentation and / or prioritization of RRC messages sent on SRBs is related to the following description in section 7.10 of TS 38.300 [1]:

[0131] 7.10Segmentation of RRC messages

[0132] An RRC message may be segmented in case the size of the encoded RRC message PDU exceeds the maximum PDCP SDU size. Segmentation is performed in the RRC layer using a separate RRC PDU to carry each segment. The receiver reassembles the segments to form the complete RRC message. All segments of an RRC message are transmitted before sending another RRC message. Segmentation is supported in both uplink and downlink as specified in TS 38.331

[0012] .

[0133] According to the text above, even if the SRB priority is adjusted, i.e. to prioritise exchange of RRC messages for non-AI / ML data (e.g. UE connection establishment, etc.) over RRC messages carrying AI / ML data (e.g. model and / or model related data), there is still a possibility of delay (or postponing) in exchange of some RRC messages (e.g. other than RRC messages related to connection establishment management). For example, such delay may arise in the case of a gNB (or next generation radio access network, NG-RAN) needing to complete the transmission of all segments of on-going RRC signalling carrying AI / ML data (e.g. model and / or model related data) over the lower priority SRBs, before transmitting other RRC messages over the higher priority SRBs. In other words, in view of the text "All segments of an RRC message are transmitted before sending another RRC message" in the above quoted passage of TS 38.300 [1], a gNB or NG-RAN may be required to postpone transmission of other RRC message(s) (e.g. other than RRC messages related to connection establishment management) until all segments of RRC message carrying the AI / ML-related data are transmitted.

[0134] Similarly, at the NAS, there is currently no specified solution to prioritize different types of contents such as: 5GSM messages, AI / ML data that may be sent over NAS, other data types that can be sent over NAS (e.g. for IoT), etc.

[0135] According, various examples of the present disclosure provide methods and apparatus for handling priority of:

[0136] ●Data carried over RRC messages / signalling, and / or

[0137] ●Data carried over NAS messages / signalling.

[0138] Various examples provided herein aim to address the aforementioned limitation(s) by describing / providing a framework for priority (e.g., a priority concept, or the use of a prioritisation system) among AI / ML data (such as described above) and how the network and / or an entity (such as a UE, or a gNB) may behave when faced with different types of data and / or signaling to be sent. For example, in various examples this is in terms of which type of data content should be prioritized (e.g. relative to other types of data) and how. In general, various examples disclosed herein relate to a framework for prioritizing data that is sent over the RRC and / or NAS messages / signaling; however, it will be appreciated that the examples or concepts disclosed herein may be extended to other types of signalling, if desired. By a "framework", it is intended to mean a system, method, apparatus, concept, example, aspect etc, or a part thereof.

[0139] All the proposals, examples, embodiments, aspects, and disclosures herein apply to 5GS and / or any other system such as 3G, 4G, Beyond 5G (B5G), 6G, etc. Furthermore, any proposals, examples, embodiments, aspects, and disclosures herein which describe interactions between the core network (e.g. AMF, SMF, UPF, UDM, MME, etc.) and the RAN (e.g. NG-RAN, gNB, eNB) would apply to both / all cases - i.e. for 3GPP RAN and non-3GPP RAN - and also to non-terrestrial network (NTN) and / or internet of things (IoT) NTN systems. That is, for any example which describes interactions between the core network and the RAN, the present disclosure should be seen to include a version of the example applicable to 3GPP RAN, a version of the example applicable to non-3GPP RAN, a version of the example applicable to NTN(s), and a version of the example applicable to IoT NTN(s).

[0140] According to various examples of the present disclosure, data is prioritized based on data features (and / or characteristics, parameters, values etc.) such as any one or more of: data content, data size, data type, data latency, data validity, data generation (and / or collection) side, data life-cycle-management (LCM) purpose (e.g. data for monitoring, training, inference, other type of AI / ML data) and / or other data related parameters or values. This definition of prioritization and / or the definition of data features / characteristics / parameters / values may be seen to apply for all examples disclosed herein which make reference to such.

[0141] Various examples are now provided in relation to methods and / or apparatus relating to:

[0142] 1. Identification or determination of data features.

[0143] 2. Prioritization at the RRC during transport of different contents.

[0144] 3. Prioritization at the NAS during transport of different contents.

[0145] 1.Identification or determination of data features

[0146] The following examples may be provided or applied in any order or combination.

[0147] According to an example, one or more data features are identified (e.g. determined, calculated, obtained, detected etc.) based on assistance information from one or more network entity(-es) (and / or network function(s)) that are involved in this data. That is, one or more data features are identified for data based on assistance information acquired / obtained by a first entity from a second entity (or second entities), where the second entity / entities are related to or associated with the data. In various examples, the identifying of the data features is performed by the network, and / or by a UE, and / or by another entity.

[0148] Examples of data feature(s) include data content, data size, data type, data latency, data validity, data generation (and / or collection) side, data life-cycle-management (LCM) purpose (e.g. data for monitoring, training, inference, other type of AI / ML data) and / or other data related parameters or values.

[0149] The data may be data available at the first entity, at the second entity / entities or at a third entity in the network.

[0150] The data features may be identified based on pre-configurations (e.g. via OAM) provided to the network and / or the UE. For example, the entity identifying the data features may have previously been provided with configuration information for use in identifying the data features, or the data features may have been pre-configured in said entity.

[0151] In various examples, the data features are provided (or pre-configured) to an entity performing the identification of data features (e.g., the network and / or a UE) by the data owner (e.g. a third entity / entities, data server, operator, UE-vendor, network-vendor, sever-vendor, other data owners etc.).

[0152] In various examples, the data features are identified (e.g. as above) based on data tagging (or data marking or labelling or sorting, and / or other form of data processing or identification, etc.) provided by one or more internal and / or external network entity (and / or function) server (e.g. OTT server, UE server, etc.), application function, cloud, etc. For example, one or more data feature are identified based on data processing / tagging / identification (and, optionally, based on any other factor disclosed herein, such as configuration information) provided by another entity.

[0153] In various examples, the network identifies (e.g. determines, calculates, obtains, detects etc.) data features for data available at the network and / or the UE.

[0154] The network may provide configurations to said UE to identify (and / or determine) the data features for data available at the UE.

[0155] The network and / or the UE may identify (and / or determine) the data features.

[0156] The network and / or the UE may exchange information related to the identified data features (e.g. all or part of identified data feature(s), or information indicating all or part of the identified data feature(s)); in one example, together with the data exchanged in the same RRC messages / signalling, in another example, separately from the exchanged data, i.e. in different RRC messages / signalling. For instance: in an example, the network and / or UE exchange information related to the identified data features together with the data (e.g., from which the data features are identified) exchanged in the same RRC messages / signalling (or NAS messages / signalling); and in another example, the network and / or UE exchange information related to the identified data features in different RRC messages / signalling (or NAS messages / signalling) to those with the data (e.g., from which the data features are identified).

[0157] 2.Prioritization at the RRC during transport of different contents

[0158] The examples disclosed in this section aim to provide a framework to prioritize data that is sent over the RRC messages / signalling.

[0159] In one example, the NG-RAN (or, more generally, a first entity in the network) identifies (e.g. determines, calculates, obtains, detects etc.) one or more data feature of the data carried in / over RRC message(s) / signalling that is pending for transmission to the UE (or, more generally, a second entity in the network) in the downlink.

[0160] In another example, the NG-RAN (or, more generally, a first entity in the network) may provide one or more configurations (e.g., configuration information) to the UE (or, more generally, a second entity in the network) to perform the identification (e.g. determination, calculation, obtaining, detection etc.) of data features of the data carried in / over RRC message(s) / signalling that is pending for transmission to NG-RAN in the uplink. Optionally, the identified data features may be indicated to the NG-RAN, using same or separate RRC signaling / messages to those carrying the data.

[0161] After data feature identification (and / or determination etc.), the NG-RAN and / or the UE may apply a priority for transmission of data at the RRC (layer).

[0162] The term "data content" may refer to the content of a RRC message, where the content may be any one or more of the following (e.g. see: TS 38.331 [2]):

[0163] - AIML data,

[0164] - Radio related information transported between the UE and the NG-RAN,

[0165] - Or any other type of data that can be sent over RRC

[0166] The RRC, or entity sending the data content, may be configured to apply priority when sending data content (over RRC). For example, the following priority can be applied:

[0167] 1) RRC radio related signalling messages have a priority level of 1, where 1 may be considered as the highest priority. Note that using the value 1 as highest priority is simply an example to demonstrate the proposal, however other values may be used to infer or indicate highest priority

[0168] 1) AI / ML data has the second level of priority, where the second level of priority may be level 2;

[0169] 2) AI / ML data may have more than one priority levels, or sub-level priority, depending on the data feature, as mentioned herein (e.g. data size, latency, etc.).

[0170] 3) In one example, AI / ML data for monitoring (e.g. high latency requirements) is assigned priority level 2, and AI / ML data for training (e.g. lower or no latency requirements) is assigned priority level 3.

[0171] 3) In another example, AI / ML data has an overall priority level, and depending on AI / ML data features, another priority sublevel is assigned. For example, AI / ML data is assigned the second level, then AI / ML data for monitoring and training data are assigned the second level - sublevel-1 (e.g. priority level 2.1), and second level - sublevel-2 (e.g. priority level 2.2), for monitoring and training data, respectively, where second level sublevel-1 (monitoring data) has a higher priority than second level sublevel-2 (training data) (i.e. priority level 2.2 is lower than priority level 2.1).

[0172] 1) Other data transported over RRC may be assigned with the third level of priority may be level 3.

[0173] Note that in the examples disclosed above, the priority is considered to be in decreasing order with increase in the priority level number. As such priority level 1 is highest priority and hence priority 2 is of less priority than level 1 but is of higher priority than level 3. However, as indicated it will be appreciated that other methods of indicating priority (e.g., relative priority between different data) exist and are included within the scope of this disclosure.

[0174] It should be noted that the UE may apply the priority levels, such as in the example proposed above, when the UE has data content to send, where the priority level for the data content may be pre-configured by the NG-RAN in the UE. Optionally, the UE may indicate the applied priority levels to the NG-RAN. For example, the UE may prioritise the data content or apply priority levels to data content to send, and then indicate the method of prioritising the data content that is used by the UE to the NG-RAN.

[0175] In other examples, the NG-RAN may provide the UE with the priority level for the data content where the UE may then store the received information and apply it accordingly. For such examples, one or more of the following may apply or be provided / included:

[0176] 1) The UE may send (e.g., to NG-RAN or another entity) a capability indication for any of the following (e.g., may transmit an indication of one or more of the following), in any order or combination:

[0177] 2) The UE supports sending AI / ML data over RRC, where this capability indication may be defined in any information element (IE) which may be existing or new.

[0178] 2) The UE supports prioritizing the transmission of data content which is sent over RRC, where this capability indication may be defined in any IE which may be existing or new IE.

[0179] 1) The NG-RAN may indicate (e.g., to the UE or another entity) it supports sending / receiving AI / ML data over RRC, where this capability indication may be defined in any IE which may be existing or new.

[0180] 1) The NG-RAN may indicate (e.g., to the UE or another entity) it supports providing / using / provisioning of priority information for the transmission of data contents over RRC.

[0181] In various examples, the above capabilities (e.g., capability information, capability indication etc.) may be exchanged in a new RRC message, existing RRC message carrying UE capability (e.g. indicated in UE AS capability in RRC (i.e., UECapabilityEnquiry / UECapabilityInformation)), and / or any other RRC messages (e.g. RRCSetupRequest, RRCSetup, RRCSetupComplete, RRCResumeRequest, RRCResume, RRCResumeComplete, RRCReconfiguration, RRCReconfigurationComplete, RRCReestablishment RRCReestablishmentRequest, etc.).

[0182] The network may provide the UE with priority information for sending data contents over RRC. The network may do so using any RRC message. The network may be the NG-RAN or any other entity.

[0183] The UE may receive priority information for sending data content over RRC. The UE may store the received priority information and apply priority handling when the UE has data content. The network may send detailed priority handling information such as, but not limited to, the examples listed above.

[0184] At any time, the network may update the priority handling information for a UE, and the network may use any RRC message to do so. The UE may receive updated priority handling information for sending data content over RRC. The UE may replace the existing priority information with the new information which is received and may then use the new stored information when sending data content over RRC. For example, the network may transmit or signal updated priority information to the UE; whereby, upon reception of the updated priority information, the UE may use it in applying priority to data content in the future, such as from a specified time or within a specified time period.

[0185] The RRC, or RRC layer or an entity controlling transmission of RRC messages and / or signalling, may be configured to have the following behavior or perform one or more of the following example actions to control prioritization of data carried in RRC message / signaling on the DL and / or UL transmission direction:

[0186] - Apply priority to data pending transmission according to allocated (or assigned) data priority level. For example, transmission of said data pending transmission is then controlled or performed based on the applied priority.

[0187] - Modifying (or changing) priority assigned to data pending transmission and / or data in transmission.

[0188] - Delaying data pending transmission or in-transmission, in one example, based on assigned and / or modified data priority.

[0189] - Starting handling or controlling the transmission of data over RRC based on the assigned data priority.

[0190] - Pausing handling or controlling the transmission of data over RRC based on the assigned data priority.

[0191] - Stopping handling or controlling of the transmission of data over RRC based on the assigned data priority.

[0192] - Stopping an ongoing data transmission to accommodate the transmission of another data of a higher priority. Optionally, discarding any data related to the stopped data transmission.

[0193] - Releasing the RRC messages carrying data of lower priority and re-establishing SRB to carry the RRC messages carrying a higher priority data.

[0194] - Removing RRC segments related to a lower priority (or in-completed transmission of RRC messages) data. Additionally, to accommodate or insert RRC messages of higher priority data.

[0195] - Stop transmission of RRC segments related to a lower priority data and deleting those segments. Optionally, indicating to the entities (e.g. NG-RAN and / or UE) the case of RRC segments deletion and the optionally the reason (Cause value: Release to transmit higher priority data.

[0196] - Releasing the SRBs carrying lower priority data

[0197] - Addition or modification of SRBs according to the data priority carried by the RRC messages.

[0198] - Other actions related to the handling of RRC messages (and / or RRC segments) order over SRBs.

[0199] 3.Prioritization at the NAS during transport of different contents.

[0200] The examples disclosed in this section aim to provide a framework to prioritize data that is sent over the NAS.

[0201] According to various examples, the NAS (e.g.an entity controlling transmission at / in NAS) is configured to (e.g., should) identify or determine the type of content(s) of a NAS message that is pending for transmission, and after which the NAS should apply a priority for transmission - based on which a prioritized transmission should be performed by the NAS. For example, the NAS should be configured to identify the type of contents of a transmission-pending NAS message, apply a priority for the transmission of this NAS message based on the identified contents, and then perform a prioritised transmission based on the applied priority (it will be appreciated that this may be relative to other NAS messages to which a priority has been applied).

[0202] The term "data content" may refer to the content of a NAS message, where the content may be any one or more of the following:

[0203] - AIML data,

[0204] - 5GSM message,

[0205] - CIoT data,

[0206] - Location services message,

[0207] - LPP,

[0208] - SMS,

[0209] - or any other type of data that can be sent over NAS.

[0210] The NAS (or network entity, NAS entity etc.) may be configured to apply priority when sending data content (over NAS). For example, the priority can be applied as in the following example:

[0211] - NAS 5GSM signalling messages have a priority level of 1, where 1 may be considered as the highest priority. Note that using the value 1 as highest priority is simply an example to demonstrate the proposal, however other values may be used to infer highest priority.

[0212] - AI / ML data has the second level of priority, where the second level of priority may be level 2

[0213] - CIoT data (which is not AI / ML data) has the third level of priority, where the third level of priority may be level 3.

[0214] Note that in the examples disclosed above, the priority is considered to be in decreasing order with increase in the priority level number. As such priority level 1 is highest priority and hence priority 2 is of less priority than level 1 but is of higher priority than level 3. However, as indicated it will be appreciated that other methods of indicating priority (e.g., relative priority between different data) exist and are included within the scope of this disclosure.

[0215] It should be noted that the UE may apply the priority levels proposed above when the UE has data content to send, where the priority level for the data content may be pre-configured in the UE.

[0216] Regarding the description of AI / ML data priority level in the above example, it will be appreciated that, in various examples, the AI / ML data may have more than one priority level or be provided with sub-level priority such as described above in relation to RRC signalling / messages.

[0217] In other examples, the network may provide the UE with the priority level for the data content where the UE may then store the received information and apply it accordingly. For such examples, one or more of the following may apply or be provided / included:

[0218] 1) The UE may send (e.g. to the network or another entity) a capability indication (e.g., information indicating capability) for any of the following, in any order or combination:

[0219] 2) The UE supports sending AIML data over NAS, where this capability indication may be defined in any IE which may be existing or new.

[0220] 2) The UE supports prioritizing the transmission of data content which is sent over NAS, where this capability indication may be defined in any IE which may be existing or new IE.

[0221] 1) The network may indicate (e.g. to the UE or other entity) it supports sending / receiving AIML data over NAS, where this capability indication may be defined in any IE which may be existing or new.

[0222] 1) The network may indicate (e.g. to the UE or other entity) it supports providing / using / provisioning of priority information for the transmission of data contents over NAS.

[0223] The above capabilities may be exchanged in any NAS message e.g. Registration Request and / or Registration Accept, etc. Other NAS messages may also be used.

[0224] In various examples, the network may provide the UE with priority information for sending data contents over NAS. The network may do so using any NAS message. The network may be the AMF or any other entity.

[0225] In various examples, the UE may receive priority information for sending data content over NAS. The UE may store the received priority information and apply priority handling when the UE has data content. The network may send detailed priority handling information such as, but not limited to, the examples listed above.

[0226] At any time the network may update the priority handling information for a UE, and the network may use any NAS message to do so. The UE may receive updated priority handling information for sending data content over NAS. The UE may replace the existing priority information with the new information which is received and may then use the new stored information when sending data content over NAS. For example, the network may transmit or signal updated priority information to the UE; whereby, upon reception of the updated priority information, the UE may use it in applying priority to data content in the future, such as from a specified time or within a specified time period.

[0227] Above are provided three sets of examples relating to: 1. Identification or determination of data features; 2. Prioritization at the RRC during transport of different contents; and 3. Prioritization at the NAS during transport of different contents. It will be appreciated that the present disclosure includes each and every combination of the examples, both within each set and between the different sets.

[0228] According to an example of the present disclosure, there is provided a first network entity (e.g. UE, network, CN, 5GC, NG-RAN, RRC entity, NAS entity etc.) comprising a transceiver and at least one processor, the at least one processor configured to: identify one or more data feature (e.g. characteristic, parameter or value) of data to be sent via control plane or NAS or RRC message(s) / signalling, wherein transmission of the data is pending; and prioritise at least part of the data according to the identified one or more data feature.

[0229] According to another example, prioritising at least part of the data comprises applying a priority level (amongst a plurality of different priority levels) to the data.

[0230] According to another example, the one or more data feature is identified based on assistance information obtained from a second network entity (which may be the first network entity or a different network entity) associated with the data. For example, if the data is AI / ML data, the assistance information may be obtained from a NF or application function, or the like, associated with the AI / ML data.

[0231] According to another example, the one or more data feature comprises one or more of data content, data size, data type, data latency, data validity, data generation (and / or collection) side, data life-cycle-management (LCM) purpose (e.g. data for monitoring, training, inference, other type of AI / ML data) and / or other data related parameters or values.

[0232] According to another example, the data is available at the first network entity.

[0233] According to another example, the data is to be transmitted to a third network entity (e.g. UE, network, CN, 5GC, NG-RAN, RRC entity, NAS entity etc.) by the first network entity, or received from the third network entity by the first network entity. For example, if the first network entity is a UE, the third network entity may be the network, NG-RAN, RRC or NAS. Here, the UE may have the data and may identify data feature(s) for use in prioritising the data so as to transmit the data according to priority (e.g. according to any example disclosed herein). The assistance information may be provided by the third network entity, e.g. NG-RAN etc., and / or the third network entity may provide other information for use in identifying the data feature(s) at UE (such as pre-configuration information as disclosed in various examples herein). In another example, the first network entity is the network or NG-RAN, and the third network entity may be a UE; where the data is available at the network for transmitting to the UE via control plane (e.g., NAS message / signalling or RRC message / signalling). The network may obtain the assistance information from within the network itself (e.g. one or more NF in the CN).

[0234] According to another example, the first network entity prioritises the data relative to other data to be transmitted via control plane (e.g. other data pending transmission via RRC message / signalling or NAS message / signalling). The other data may differ to the data, in which case the priority applied to the data may differ to that applied to the other data. For example, the data may be determined to be higher priority than the other data based on the data features of the data (e.g., compared to the priority of the other data as determined based on data feature(s) identified for the other data).

[0235] According to another example, the first network entity performs transmission of data pending transmission (e.g., pending messages / signalling) according to the applied (or assigned) priority. If the data has higher priority that the other data, for example, then the data is transmitted before the other data.

[0236] According to another example, the first network entity is configured to apply priority when sending the data over RRC or over NAS.

[0237] According to another example, the first network entity may transmit an indication of one or more defined priority levels applied to data pending transmission (or other indication of relative priority).

[0238] It will be appreciated that all combinations of the above examples are considered to be included herein.

[0239] Figure 1 illustrates a block diagram of an exemplary apparatus, or entity, that may be used in examples of the present disclosure. The skilled person will appreciate said entity may be implemented, for example, as a network element on a dedicated hardware, as a software instance running on a dedicated hardware, and / or as a virtualised function instantiated on an appropriate platform, e.g. on a cloud infrastructure.

[0240] The entity 1000 comprises a processor (or controller) 1001, a transmitter 1003 and a receiver 1005. The receiver 1005 is configured for receiving one or more messages from one or more other entities, for example as described above. The transmitter 1003 is configured for transmitting one or more messages to one or more other entities, for example as described above. The processor 1001 is configured for performing one or more operations, for example according to the operations as described above.

[0241] Figure 2 illustrates a flow diagram illustrating a method according to various examples of the present disclosure. The method may be performed by a UE.

[0242] In operation S210, the UE may receive from a network entity, configuration information for use in identifying at least one data feature, and identification information for the at least one data feature.

[0243] In operation S220, the UE may identify the at least one data feature based on the configuration information and the identification information.

[0244] In operation S230, the UE may transmit the identified at least one data feature or information on the identified at least one data feature to the network entity over Control Plane (CP).

[0245] Figure 3 illustrates a flow diagram illustrating a method according to various examples of the present disclosure. The method may be performed by a network entity.

[0246] In operation S310, the network entity may identify at least one data feature of data available at a UE.

[0247] In operation S320, the network entity may transmit configuration information and identification information for the at least data features to the UE over Control Plane (CP).

[0248] In operation S330, the network entity may receive, from the UE, the at least one data feature or information on the at least one data feature.

[0249] It will be appreciated that, in each example / embodiment / aspect etc. described above, one or more features or operations may be omitted, modified or moved (e.g., to change the order of the features or the operations), if desired and appropriate. Additionally, one or more features or operations from any example / embodiment may be combined with features or operations from any other example / embodiment. In particular, regardless of whether or not a pointer towards a combination of features / examples is found herein, the present disclosure should be considered to include all combinations of two or more of the embodiments, examples etc. disclosed herein, and all combinations of two or more of the features disclosed herein.

[0250] The techniques described herein may be implemented using any suitably configured apparatus and / or system. Such an apparatus and / or system may be configured to perform a method according to any aspect, embodiment or example disclosed herein. Such an apparatus may comprise one or more elements, for example one or more of receivers, transmitters, transceivers, processors, controllers, modules, units, and the like, each element configured to perform one or more corresponding processes, operations and / or method steps for implementing the techniques described herein. For example, an operation / function of X may be performed by a module configured to perform X (or an X-module). The one or more elements may be implemented in the form of hardware, software, or any combination of hardware and software.

[0251] It will be appreciated that examples of the present disclosure may be implemented in the form of hardware, software or any combination of hardware and software. Any such software may be stored in the form of volatile or non-volatile storage, for example a storage device like a ROM, whether erasable or rewritable or not, or in the form of memory such as, for example, RAM, memory chips, device or integrated circuits or on an optically or magnetically readable medium such as, for example, a CD, DVD, magnetic disk or magnetic tape or the like.

[0252] It will be appreciated that the storage devices and storage media are embodiments of machine-readable storage that are suitable for storing a program or programs comprising instructions that, when executed, implement certain examples of the present disclosure. Accordingly, certain examples provide a program comprising code for implementing a method, apparatus or system according to any example, embodiment and / or aspect disclosed herein, and / or a machine-readable storage storing such a program. Still further, such programs may be conveyed electronically via any medium, for example a communication signal carried over a wired or wireless connection.

[0253] While the present disclosure has been shown, illustrated and described with reference to certain examples, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the disclosure.

[0254] The reader's attention is directed to all papers and documents which are filed concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference.

Claims

1.A terminal in a wireless communication system, the terminal comprising:a transmitter;a receiver; andat least one processor configured to:receive, from a network entity, configuration information for identifying at least one data feature, and identification information for the at least one data feature;identify the at least one data feature based on the configuration information and the identification information; andtransmit, to the network entity via a control plane (CP), the identified at least one data feature or information on the identified at least one data feature.2.The terminal of claim 1, wherein the at least one processor is further configured to:transmit the identified at least one data feature according to a priority configured for the identified at least one data feature.3.The terminal of claim 2, wherein the priority is configured or the data feature is prioritised based on at least one of the identified at least one data feature being artificial intelligence / machine learning (AI / ML) data for a life-cycle management (LCM) purpose, data content, data size, data type, data latency, data validity, or data generation or collection side.4.The terminal of claim 2, wherein the priority configured for the identified at least one data feature is different to a priority configured for other data to be transmitted to the network entity.5.A network entity in a wireless communication system, the network entity comprising:a transmitter;a receiver; andat least one processor configured to:identify at least one data feature of data available at a terminal or at the network entity;transmit, to the terminal via a control plane (CP), configuration information and identification information for the at least data feature; andreceive, from the terminal, the at least one data feature or information on the at least one data feature.6.The network entity of claim 5, wherein the at least one processor is further configured to:receive, from the terminal, at least one of a first indication that the UE supports transmitting AI / ML data over RRC or a second indication that the UE supports prioritizing the transmission of data sent over RRC; andtransmit, to the terminal, , to the network entity via a control plane (CP),a third indication that the network entity supports transmitting / receiving AI / ML data over RRC or a fourth indication that the network entity supports using priority information for the transmission of data over RRC.7.The network entity of claim 5, wherein the at least one processor is further configured to:transmit, to the terminal, information on a priority to be applied to the at least one data feature for transmission by the terminal, andreceive, from the terminal, an indication of a priority applied to the at least one data feature for transmission by the terminal.8.A method performed by a terminal in a wireless communication system, the method comprising:receiving, from a network entity, configuration information for identifying at least one data feature, and identification information for the at least one data feature;identifying the at least one data feature based on the configuration information and the identification information; andtransmitting, to the network entity via a control plane (CP), the identified at least one data feature or information on the identified at least one data feature.9.The method of claim 8, further comprising:transmitting the identified at least one data feature according to a priority configured for the identified at least one data feature.10.The method of claim 9, wherein the priority is configured or the data feature is prioritised based on at least one of the identified at least one data feature being artificial intelligence / machine learning (AI / ML) data for a life-cycle management (LCM) purpose, data content, data size, data type, data latency, data validity, or data generation or collection side.11.The method of claim 9, wherein the priority configured for the identified at least one data feature is different to a priority configured for other data to be transmitted to the network entity.12.A method performed by network entity in a wireless communication system, the method comprising:identifying at least one data feature of data available at a terminal or at the network entity;transmitting, to the terminal via a control plane (CP), configuration information and identification information for the at least data feature; andreceiving, from the terminal, the at least one data feature or information on the at least one data feature.13.The method of claim 12, further comprising:receiving, from the terminal, at least one of a first indication that the UE supports transmitting AI / ML data over RRC or a second indication that the UE supports prioritizing the transmission of data sent over RRC; andtransmitting, to the terminal, , to the network entity via a control plane (CP),a third indication that the network entity supports transmitting / receiving AI / ML data over RRC or a fourth indication that the network entity supports using priority information for the transmission of data over RRC.14.The method of claim 12, further comprising:transmitting, to the terminal, information on a priority to be applied to the at least one data feature for transmission by the terminal; andreceiving, from the terminal, an indication of a priority applied to the at least one data feature for transmission by the terminal.

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