[go: up one dir, main page]

WO2025023106A1 - Première unité et procédé mis en œuvre par une première unité - Google Patents

Première unité et procédé mis en œuvre par une première unité Download PDF

Info

Publication number
WO2025023106A1
WO2025023106A1 PCT/JP2024/025576 JP2024025576W WO2025023106A1 WO 2025023106 A1 WO2025023106 A1 WO 2025023106A1 JP 2024025576 W JP2024025576 W JP 2024025576W WO 2025023106 A1 WO2025023106 A1 WO 2025023106A1
Authority
WO
WIPO (PCT)
Prior art keywords
model
unit
monitoring
base station
feature
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/JP2024/025576
Other languages
English (en)
Inventor
Pravjyot Deogun
Neeraj Gupta
Sadafuku Hayashi
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
NEC Corp
Original Assignee
NEC Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by NEC Corp filed Critical NEC Corp
Publication of WO2025023106A1 publication Critical patent/WO2025023106A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition

Definitions

  • the present disclosure relates to a communication system.
  • the disclosure has particular but not exclusive relevance to wireless communication systems and devices thereof operating according to the 3rd Generation Partnership Project (3GPP) standards or equivalents or derivatives thereof (including LTE-Advanced, Next Generation or 5G networks, future generations, and beyond).
  • 3GPP 3rd Generation Partnership Project
  • the disclosure has particular, although not necessarily exclusive, relevance to providing improved signalling for leveraging artificial intelligence and machine learning (AI/ML) models in 'New Radio' systems (also referred to as 'Next Generation' systems), and similar systems.
  • 3GPP 3rd Generation Partnership Project
  • AI/ML artificial intelligence and machine learning
  • LTE Long-Term Evolution
  • EPC Evolved Packet Core
  • E-UTRAN Evolved UMTS Terrestrial Radio Access Network
  • NR Evolved UMTS Terrestrial Radio Access Network
  • NPL 2 Next Generation Mobile Networks
  • a NodeB (or an eNB in LTE, and gNB in 5G) is the radio access network (RAN) node (or simply 'access node', 'access network node' or 'base station') via which communication devices (user equipments or 'UEs') connect to a core network and communicate with other communication devices or remote servers.
  • RAN radio access network
  • the present application will use the term access network node, RAN node or base station to refer to any such access nodes.
  • the present application will use the term mobile device, user device, or UE to refer to any communication device that is able to connect to the core network via one or more base stations.
  • the present application may refer to mobile devices in the description, it will be appreciated that the technology described can be implemented on any communication devices (mobile and/or generally stationary) that can connect to a communications network for sending/receiving data, regardless of whether such communication devices are controlled by human input or software instructions stored in memory.
  • the gNB structure may be split into two or more parts.
  • the Central Unit (CU or gNB-CU) - sometimes referred to as a 'control unit' - and the Distributed Unit (DU or gNB-DU), connected by an F1 interface.
  • CU Central Unit
  • DU Distributed Unit
  • a 'split' architecture in which the typically 'higher' CU layers (for example, but not necessarily or exclusively, Packet Data Convergence Protocol (PDCP) and Radio Resource Control (RRC) layers) and the, 'lower' DU layers (for example, but not necessarily or exclusively, Radio Link Control (RLC), Media (sometimes referred to as 'Medium') Access Control (MAC), and Physical (PHY) layers) are separated between a particular CU, and one or more DUs that are connected to and controlled by that CU via the F1 interface.
  • PDCP Packet Data Convergence Protocol
  • RRC Radio Resource Control
  • RLC Radio Link Control
  • MAC Media
  • PHY Physical
  • the higher layer CU functionality for a number of gNBs may be implemented centrally (for example, by a single processing unit, or in a cloud-based or virtualised system), whilst retaining the lower layer DU functionality locally separately for each gNB.
  • RU Radio Unit
  • the concept of a Radio Unit (RU) - sometimes referred to as a 'remote unit' - has been introduced.
  • the RU is responsible for handling the digital front end (DFE), digital beamforming functionality and, typically, the functionality of the lower parts of the PHY layer, whilst the DU typically handles the higher parts of the PHY layer and the RLC and MAC layers.
  • the CU in this architecture continues to be responsible for controlling one or more DUs (each DU corresponding to a different respective gNB) and to handle higher layer signalling (typically RRC and PDCP layers).
  • the actual functional split between the CU and DUs (and potentially RUs where applicable) of these distributed architectures is flexible allowing the functionality to be optimised for different use cases. Effectively, the split architecture enables a 5G network to use a different distribution of protocol stacks between CU and DUs (and potentially RUs) depending on, for example, midhaul availability and network design.
  • the choice of how to split functions in the architecture depends on, among other things, factors related to radio network deployment scenarios, constraints and intended supported use cases. Key considerations include: the need to support a specific quality of service for each service offered and for real/non-real time applications; support of specific user density and load demand in a given geographical area; and available transport networks with different performance levels.
  • AI/ML artificial intelligence
  • CSI channel state information
  • Beam management e.g., beam prediction in time, and/or spatial domain for overhead and latency reduction, beam selection accuracy improvement
  • Positioning accuracy enhancements for different scenarios including, e.g., those with heavy non-line of sight (NLOS) conditions.
  • NLOS non-line of sight
  • AI/ML models can be used to predict the path of a UE based on previous mobility of the UE, used for beam management (as mentioned above), or used in methods of encoding and transmitting information.
  • An AI/ML model may be hosted at a base station (or any other suitable network node), and the base station may perform control of communication resources for UEs it serves, and/or perform control related to the status of a UE (e.g. control of UE mobility, or control of a radio resource control, RRC, state of the UE) based on an inference (e.g. determination or prediction) generated using the AI/ML model.
  • RRC radio resource control
  • the base station may also transmit an inference generated using the model to another node in the network, for use at the other node.
  • an AI/ML model may be hosted at two nodes of the network, for example at a base station and at a UE.
  • the base station and the UE may both make determinations and/or predictions using the model.
  • the UE may use the model as part of an encoding process for encoding (and/or compressing) CSI for transmission to the base station as mentioned above, and the base station may use the same model as part of a corresponding decoding (and/or decompression) process for decoding the CSI received from the UE.
  • Level x No collaboration between the network and the UE.
  • level x is an implementation-based AI/ML operation without any dedicated AI/ML-specific enhancement.
  • Level y Signalling-based collaboration without model transfer. For example, this level is applicable when model training is performed offline, and models are registered to a base station and the UE is offline. Here, the base station and the UE are aware of available models (before operation), and the base station is only required to activate/deactivate UE residing models.
  • Level z Signalling-based collaboration with model transfer.
  • a single-sided AI/ML model is deployed (hosted) only at the UE side or at the network side.
  • This type of model is beam prediction in time, which can be deployed at the UE side.
  • the model need not necessarily be trained at e.g. UE 3 or a base station 5.
  • the model could be trained at the base station 5 or at another node in the network (e.g. core network node/function), and then transmitted to the UE 3 for use at the UE 3.
  • the AI/ML model may be trained at another network node, and then transferred/deployed to the UE 3.
  • the AI/ML model may be a 'two-sided' model, in which an AI/ML model is hosted at the UE 3, and a corresponding AI/ML model is hosted at the base station 5 (however, the models need not necessarily be hosted at a UE 3 and a base station 5 - any other suitable two network nodes could alternatively be used).
  • Such a model may be referred to as a 'paired' AI/ML model, over which joint inference is performed (the AI/ML model hosted at the UE 3 and the AI/ML model hosted at the base station 5 may be the same AI/ML model, but need not necessarily be the same model).
  • the joint inference comprises an AI/ML inference whose inference is performed jointly across the UE 3 and the network.
  • the first part of the inference may be performed by UE 3, and then the remaining part may be performed by the base station 5 (or at another network-side node). These roles may be reversed, such that the first part of the inference is performed at the network side and the second part at the UE side.
  • One example of this type of model could be channel state information (CSI) compression, where the UE performs CSI compression and network performs CSI decompression.
  • CSI channel state information
  • the two-sided model (or models) may be trained at any suitable network node, and then transmitted to the UE 3 and the base station 5 (or other respective node or nodes).
  • Model training This includes the process of compiling a trained AI/ML model and packaging it into an executable format and delivering to a target device.
  • Model inference operation Model selection, activation, deactivation, switching, and fallback operation: Model selection refers to the selection of an AI/ML model among models for the same functionality.
  • Model switching i.e., switching among a group of models where each model is for a particular scenario/configuration/site; Fallback: Switching off AI/ML operation to operate with legacy RAN procedure (i.e., non-AI/ML based procedure); Model monitoring; Study AI/ML model monitoring for at least the following purposes: model activation, deactivation, selection, switching, fallback, and update (including re-training).
  • Model update This includes model fine-tuning, retraining, and re-development via online/offline training. Model update, i.e., using one model whose parameters are flexibly updated as the scenario/configuration/site that the device experiences changes over time. Fine-tuning is one example. Model transfer; UE capability.
  • an AI/ML model has a model ID with associated information and/or model functionality for at least some AI/ML operations.
  • Additional monitoring under study relates to monitoring based on data distribution, which may be input-based, e.g., monitoring: the validity of the AI/ML input, out-of-distribution detection, drift detection of input data, or checking SNR, delay spread, etc., or output-based: e.g., drift detection of output data.
  • Monitoring may also be based on applicable conditions in the communication system (e.g. related to load, path loss, etc). The above noted model monitoring, and any associated calculations, may be performed on the network-side or at the UE-side.
  • a distributed base station such as a gNB
  • CU central unit
  • DUs distributed units
  • NPL 1 3GPP TS 38.331
  • NPL 2 NGMN 5G White Paper' V1.0
  • NPL 3 3GPP specification TS 38.473
  • the disclosure aims to provide one or more apparatus and/or one or more associated methods that contributes to meeting the above needs.
  • a method performed by a first unit of a distributed base station comprising: transmitting, to a second unit of the distributed base station, a request for monitoring Artificial Intelligence, AI,/Machine Learning, ML, models, AI/ML functionalities or AI/ML features; receiving, from the second unit, reporting information including at least one of inference results for AI/ML models at the second unit and measurements at the second unit which can be used for the monitoring; and transmitting, to the second unit, performance results based on the reporting information.
  • a first unit of a distributed base station comprising: means for transmitting, to a second unit of the distributed base station, a request for monitoring Artificial Intelligence, AI,/Machine Learning, ML, models, AI/ML functionalities or AI/ML features; means for receiving, from the second unit, reporting information including at least one of inference results for AI/ML models at the second unit and measurements at the second unit which can be used for the monitoring; and means for transmitting, to the second unit, performance results based on the reporting information.
  • an access network node a method performed by a user equipment, a method performed by a core network node, an access network node, a user equipment, and a core network node.
  • Fig. 1 schematically illustrates a mobile ('cellular' or 'wireless') communication system 1;
  • Fig 2 illustrates a typical frame structure that may be used in the communication system 1 of Fig. 1;
  • Fig. 3 illustrates a framework in respect of an AI/ML model;
  • Fig. 4 shows an illustration of a method of training an AI/ML model, and of monitoring the performance of the AI/ML model;
  • Fig. 5 illustrates the relationship between AI/ML features, functionalities and models;
  • Fig. 6 illustrates a method of CU based model monitoring;
  • Fig. 7 illustrates a method of DU based model monitoring;
  • Fig. 8 is a schematic block diagram illustrating the main components of a UE 3 for the communication system 1 of Fig. 1;
  • Fig. 9 is a schematic block diagram illustrating the main components of a base station 5 of a distributed type for the communication system 1 of Fig. 1; and Fig. 10 is a schematic block diagram illustrating the main components of a core network node or function for the communication system 1 of Fig. 1.
  • Fig. 1 schematically illustrates a mobile ('cellular' or 'wireless') communication system 1 to which examples of the present disclosure are applicable.
  • UEs 3-1, 3-2, 3-3 can communicate with each other via a radio access network (RAN) node 5 that operates according to one or more compatible radio access technologies (RATs).
  • RAN radio access network
  • the RAN node 5 comprises a distributed base station 5 or 'gNB' operating one or more associated cells 9.
  • Communication via the RAN node 5 is typically routed through an associated core network 7 (e.g. a 5G/6G or later generation core network or evolved packet core network (EPC)).
  • core network 7 e.g. a 5G/6G or later generation core network or evolved packet core network (EPC)
  • UEs 3 and one base station 5 are shown in Fig. 1 for illustration purposes, the system, when implemented, will typically include other base stations 5 and UEs 3.
  • Each base station 5 controls one or more associated cells 9 either directly, or indirectly via one or more other nodes (such as home base stations, relays, remote radio heads, distributed units, and/or the like). It will be appreciated that the base stations 5 may be configured to support 4G, 5G, 6G, and/or later generation and/or any other 3GPP or non-3GPP communication protocols.
  • the illustrated RAN node 5 comprises a distributed base station comprising at least one distributed unit (DU) 5b (e.g., a gNB-DU or the like), and a central unit (CU) 5c (e.g., a gNB-CU or the like).
  • the CU 5c employs a separated control plane and user plane and so is, itself, split between a control plane function (CU-CP) and a user plane function (CU-UP) which respectively communicate, with the DU 5b via an appropriate interface (e.g. an F1-C interface) and an appropriate interface (e.g. F1-U interface) (together forming an F1 interface (or 'reference point')), and with one another via an appropriate interface (e.g.
  • the RAN node 5 may alternatively (or additionally) include one or more separate radio units (RUs) (e.g., providing this functionality of the lower parts of the PHY layer).
  • RUs radio units
  • the UEs 3 and their serving base station 5 are connected via an appropriate air interface (for example the so-called 'Uu' interface and/or the like).
  • Neighbouring base stations 5 may be connected to each other via an appropriate base station to base station interface (such as the so-called 'X2' interface, 'Xn' interface and/or the like).
  • the core network 7 includes a number of logical nodes (or 'functions') for supporting communication in the communication system 1.
  • the core network 7 comprises control plane functions (CPFs) 10 and one or more network node entities for the communication of user data (e.g. user plane functions (UPFs) 11).
  • the CPFs 10 include one or more network node entities for the communication of control signalling (e.g. Access and Mobility Management Functions (AMFs) 10-1), one or more network node entities for session management (e.g.
  • Session Management Functions 10-2
  • AUSF Authentication Server Function
  • UDM Unified Data Management
  • PCF Policy Control Function
  • AF Application Function
  • the RAN node 5 is connected to the core network nodes via appropriate interfaces (or 'reference points') such as an N2 reference point between the CU 5c (CU-CP) of the RAN and the AMF 10-1 for the communication of control signalling, and an N3 reference point between the CU 5c (CU-UP) of the RAN and each UPF 11 for the communication of user data.
  • the UEs 3 are each connected to the AMF 10-1 via a non-access stratum (NAS) connection over an appropriate interface (e.g. an N1 reference point (analogous to the S1 reference point in LTE)). It will be appreciated, that N1 communications are routed transparently via the RAN.
  • NAS non-access stratum
  • One or more UPFs 11 are connected to an external data network (e.g., an IP network such as the Internet) via an appropriate interface (e.g. an N6 reference point) for communication of the user data.
  • an external data network e.g., an IP network such as the Internet
  • an appropriate interface e.g. an N6 reference point
  • the AMF 10-1 performs mobility management related functions, maintains the NAS signalling connection with each UE 3 and manages UE registration.
  • the AMF 10-1 is also responsible for managing paging.
  • the SMF 10-2 provides session management functionality (that formed part of MME functionality in LTE) and additionally combines some control plane functions (provided by the serving gateway and packet data network gateway in LTE).
  • the SMF 10-2 also allocates IP addresses to each UE 3.
  • the base station 5 of the communication system 1 may be configured to operate at least one cell 9 on an associated time-division duplex (TDD) carrier that operates in unpaired spectrum. It will be appreciated that the base station 5 may also operate at least one cell 9 on an associated frequency-division duplex (FDD) carrier that operates in paired spectrum.
  • TDD time-division duplex
  • FDD frequency-division duplex
  • the base station 5 is also configured for transmission of, and the UEs 3 are configured for the reception of, control information and user data via a number of downlink (DL) physical channels and for transmission of a number of physical signals.
  • the DL physical channels correspond to resource elements (REs) carrying information originating from a higher layer, and the DL physical signals are used in the physical layer and correspond to REs which do not carry information originating from a higher layer.
  • REs resource elements
  • the physical channels may include, for example, a physical downlink shared channel (PDSCH), a physical broadcast channel (PBCH), and a physical downlink control channel (PDCCH).
  • PDSCH carries data sharing the PDSCH's capacity on a time and frequency basis.
  • the PDSCH can carry a variety of items of data including, for example, user data, UE-specific higher layer control messages mapped down from higher channels, system information blocks (SIBs), and paging.
  • SIBs system information blocks
  • the PDCCH carries downlink control information (DCI) for supporting a number of functions including, for example, scheduling the downlink transmissions on the PDSCH and also the uplink data transmissions on a physical uplink shared channel (PUSCH).
  • DCI downlink control information
  • the PBCH provides UEs 3 with the Master Information Block (MIB).
  • MIB Master Information Block
  • the UE 3 may receive a Synchronization Signal Block (SSB), and the UE 3 may assume that reception occasions of a PBCH, primary synchronization signal (PSS) and secondary synchronization signal (SSS) are in consecutive symbols and form a SS/PBCH block.
  • the base station 5 may transmit a number of synchronization signal (SS) blocks corresponding to different DL beams. The total number of SS blocks may be confined, for example, within a 5 ms duration as an SS burst.
  • the periodicity of the SSB transmissions may be indicated to the UE 3 using any suitable signalling (e.g.
  • the periodicity value for the SSB may be, for example, greater than or equal to 20 ms.
  • the UE 3 may be configured to assume that an SS burst occurs with a periodicity of 2 frames.
  • the UE 3 may also be provided with an indication of which SSBs within a 5 ms duration are transmitted (e.g. using ssb-PositionsInBurst).
  • the DL physical signals may include, for example, reference signals (RSs) and synchronization signals (SSs).
  • a reference signal (sometimes known as a pilot signal) is a signal with a predefined special waveform known to both the UE 3 and the base station 5.
  • the reference signals may include, for example, cell specific reference signals, UE-specific reference signal (UE-RS), downlink demodulation signals (DMRS), and channel state information reference signal (CSI-RS).
  • UE-RS UE-specific reference signal
  • DMRS downlink demodulation signals
  • CSI-RS channel state information reference signal
  • the UEs 3 are configured for transmission of, and the base station 5 is configured for the reception of, control information and user data via a number of uplink (UL) physical channels corresponding to REs carrying information originating from a higher layer, and UL physical signals which are used in the physical layer and correspond to REs which do not carry information originating from a higher layer.
  • the physical channels may include, for example, the PUSCH, a physical uplink control channel (PUCCH), and/or a physical random-access channel (PRACH).
  • the UL physical signals may include, for example, demodulation reference signals (DMRS) for a UL control/data signal, and/or sounding reference signals (SRS) used for UL channel measurement.
  • DMRS demodulation reference signals
  • SRS sounding reference signals
  • the UE 3 When the UE 3 initially establishes a radio resource control (RRC) connection with a base station 5 via a cell 9 it registers with an appropriate core network node (e.g., AMF 10-1, MME). The UE 3 is in the so-called RRC connected state and an associated UE context is maintained by the network. When the UE 3 is in the so-called RRC idle state, or is in the RRC inactive state, it selects an appropriate cell for camping so that the network is aware of the approximate location of the UE 3 (although not necessarily on a cell level).
  • RRC radio resource control
  • the base station 5 in this example is a 'distributed' base station 5 that is split between one or more distributed units (DUs) 5b and a central unit (CU) 5c, with a CU 5c typically performing higher level functions and communication with the next generation core, and with the DU 5b performing lower level functions and communication over an air interface with UEs 3 in the vicinity (i.e. in a cell 9 operated by the base station 5).
  • DUs distributed units
  • CU central unit
  • a distributed base station 5 may, for example, include the following functional units hosting the following functions: Central Unit (CU) 5c: a logical node hosting Radio Resource Control (RRC), Service Data Adaptation Protocol (SDAP) and Packet Data Convergence Protocol (PDCP) layers of the base station 5 that controls the operation of one or more DUs 5b.
  • the CU 5c terminates an appropriate interface (e.g. the so-called F1 interface) connected with the DU 5b.
  • DU 5b a Distributed Unit (DU) 5b: a logical node hosting Radio Link Control (RLC), Medium Access Control (MAC) and Physical (PHY) layers of the base station 5, and its operation is partly controlled by the CU 5c.
  • RLC Radio Link Control
  • MAC Medium Access Control
  • PHY Physical
  • One DU 5b supports one or multiple cells 9.
  • One cell 9 is supported by only one DU 5b.
  • the DU 5b terminates an appropriate interface (e.g. the F1 interface) connected with the CU 5c.
  • CU-CP CU-Control Plane: a logical node hosting the RRC and the control plane part of the PDCP protocol of the CU 5c for the base station 5.
  • the CU-CP terminates an appropriate interface (e.g. the so-called E1 interface) connected with the CU-UP and an appropriated interface (e.g. the F1-C (F1 control plane) interface) connected with the DU 5b.
  • E1 interface e.g. the so-called E1 interface
  • F1-C F1 control plane
  • CU-User Plane a logical node hosting the user plane part of the PDCP protocol and the SDAP protocol of the CU 5c for the base station 5.
  • the CU-UP terminates an appropriate interface (e.g. the E1 interface) connected with the CU-CP and an appropriate interface (e.g. the F1-U (F1 user plane) interface) connected with the DU 5b.
  • Fig. 2 which illustrates a typical frame structure that may be used in the communication system 1
  • the base station 5 and UEs 3 of the communication system 1 communicate with one another using resources that are organised, in the time domain, into frames of length 10 ms.
  • Each frame comprises ten equally sized subframes of 1 ms length.
  • Each subframe is divided into one or more slots comprising 14 orthogonal frequency-division multiplexing (OFDM) symbols of equal length.
  • OFDM orthogonal frequency-division multiplexing
  • the communication system 1 supports multiple different numerologies (subcarrier spacing (SCS), slot lengths and hence OFDM symbol lengths).
  • SCS subcarrier spacing
  • SCS subcarrier spacing
  • CU-DU Information Exchange In the communication system 1, various messages may be exchanged between the CU 5c and the DU 5b, for instance during a setup procedure for configuring communication between the DU 5b and CU 5c (e.g., an 'F1 setup procedure').
  • the purpose of the setup procedure is to exchange application level data needed for the DU 5b and the CU 5c to interoperate correctly (e.g., on the F1 interface).
  • This procedure is the initial procedure triggered for control plane communication (e.g., over the F1-C interface) after a transport network layer (TNL) association has become operational. Typically, this procedure will use non-UE associated signalling.
  • TNL transport network layer
  • the DU 5b may transmit a setup request message (e.g., an F1 Setup Request) to the CU 5c.
  • a setup request message e.g., an F1 Setup Request
  • the setup request message may, for example, include the following information: DU Served Cells List IE: Information about the cells supported by the DU and associated features (e.g., NR-U); DU System Information IE.
  • the CU 5c typically transmits an F1 Setup Response message to the DU 5b.
  • the following information may be included in the F1 Setup Response message: Cells to be Activated List IE: a list of cells that the CU 5c requests the DU 5b to activate.
  • PLMN Public Land Mobile Network
  • served cell information For example, GNB-DU Configuration Update or GNB-CU Configuration Update messages may be used to exchange information between the CU 5c and the DU 5b.
  • GNB-DU Configuration Update For example, GNB-DU Configuration Update or GNB-CU Configuration Update messages may be used to exchange information between the CU 5c and the DU 5b.
  • Additional messaging may be used between the CU 5c and the DU 5b, for instance when establishing a UE's context during a UE Context Setup procedure.
  • the purpose of the UE Context Setup procedure is to establish the UE Context including, signalling radio bearer (SRB), and data radio bearer (DRB). This procedure uses UE associated signalling.
  • SRB signalling radio bearer
  • DRB data radio bearer
  • the CU 5c may transmit a UE Context Setup Request message to the DU 5b (this message may comprise an RRC container used to carry additional information, e.g. in an RRC information IE).
  • This message may comprise an RRC container used to carry additional information, e.g. in an RRC information IE).
  • the following information may be included in the UE Context Setup Request message: UE-CapabilityRAT-ContainerList: the DU 5b may take this information into account for UE specific configurations; DRX Cycle IE: the DU 5b may use the provided value from the CU 5c; RRC Information IE; in the case where the CU 5c receives a UEAssistanceInformation IE from the UE 3, the UEAssistanceInformation IE may be included in the RRC Information IE.
  • the DU 5b may, if supported, take the UE's assistance information into account when configuring resources for the UE 3.
  • the DU 5b transmits a UE Context Setup Response message to the CU 5c.
  • the following information may be included in the UE Context Setup Response message: A list of DRBs (and SRBs) which are successfully established and DRBs (and SRBs) which failed to establish;
  • the DU 5b reports the unsuccessful establishment of a DRB or SRB, an associated cause value should be precise enough to enable the CU 5c to know the reason for the unsuccessful establishment.
  • CellGroupConfig As an Octet string which is included by CU 5c transparently within an RRC reconfiguration message sent to UE 3
  • DRX Config As an Octet string which is included by CU 5c transparently within the RRC reconfiguration message sent to UE 3.
  • alternative messages may be used to update the UE's configuration between the CU 5c and the DU 5b.
  • UE Context Modification Request message from CU 5c to DU 5b
  • UE Context Modification Required from DU 5b to CU 5c
  • transmissions in a cell 9 of a base station 5 may include one or more broadcast transmissions, one or more unicast transmissions for reception by a UE 3, and/or one or more multicast transmissions for reception by a group of UEs 3.
  • System information (SI) transmitted in a cell may include 'minimum SI' (MSI) and 'other SI' (OSI).
  • the OSI may be broadcast on-demand, for example using a downlink shared channel (DL-SCH).
  • the OSI may be broadcast upon request from a UE 3 that is in a radio resource control (RRC) idle or RRC inactive state.
  • RRC radio resource control
  • the OSI may also be requested by a UE 3 that is in the RRC connected state, for example via one or more dedicated RRC transmissions.
  • the SI may include information for enabling (e.g. configuring) the UE 3 to complete a cell selection, may include information for enabling the UE 3 to complete a cell reselection procedure, or for enabling the UE 3 to receive one or more paging messages transmitted in a cell.
  • SI may be broadcast using a Master Information Block (MIB) and one or more System Information Blocks (SIB).
  • MIB Master Information Block
  • SIB System Information Blocks
  • the MSI comprises the MIB and system information block 1 (SIB1).
  • SIB includes information for use by the UE 3 to receive SIB1, for example a subcarrier spacing for SIB1.
  • the MIB provides information corresponding to a Control Resource Set (CORESET) and Search Space.
  • SIB1 may be referred to as 'remaining MSI' (RMSI).
  • SIB1 may be transmitted in a dedicated RRC message, and other SIB (e.g. SIB2 to SIB9) may be transmitting using one or more other suitable RRC transmissions (e.g. another dedicated RRC message).
  • the MIB and SIB1 may provide the UE 3 with an indication of scheduling information for receiving and decoding the other SIB, such as SIB2 to SIB9, and may provide information for use by the UE 3 to receive one or more paging messages.
  • the OSI may comprise, for example, SIB2 to SIB9 transmitted using a downlink shared channel (DL-SCH) in SI messages.
  • DL-SCH downlink shared channel
  • a mapping of SIB2 to SIB9 to corresponding SI messages may be provided to the UE 3 by the base station 5.
  • MIB and SIB1 to SIB9 are described in more detail, for example, in 3GPP TS 38.331 (NPL 1).
  • SIB2 provides information for intra-frequency, inter-frequency and inter-system cell reselection.
  • SIB3 provides cell-specific information for intra-frequency cell reselection.
  • SIB4 provides information for inter-frequency cell reselection.
  • SIB5 provides information regarding inter-system cell reselection towards 4G (LTE).
  • SIB6 and SIB7 provide information for an earthquake and tsunami warning system (ETWS).
  • SIB8 provides information for a commercial mobile alert service (CMAS) notification, for example to provide warning text messages to the UE 3.
  • SIB9 includes information regarding coordinated universal time (UTC), global positioning system (GPS) time (e.g. for GPS initialisation) and local time.
  • UTC coordinated universal time
  • GPS global positioning system
  • SIB may be broadcast periodically (e.g. according to a predetermined periodic pattern), or alternatively may be provided 'on-demand', for example in response to a request from a UE 3.
  • MIB may be transmitted with a periodicity of 80 ms and repetitions made within 80 ms
  • SIB1 may be transmitted with a periodicity of 15c ms and a variable transmission repetition periodicity within 15c ms (e.g. 20 ms).
  • SIB1 can be used to indicate to a UE 3 which SIB are transmitted periodically and which SIB are available on-demand in response to a request from the UE 3.
  • a UE 3 may be configured to request on-demand SIB using message 1 (MSG1), which may be referred to as a MSG1-based on-demand SI request, or message 3 (MSG3), which may be referred to as a MSG3-based on-demand SI request.
  • MSG1 message 1
  • MSG3 message 3
  • a physical broadcast channel can be used to broadcast the MIB.
  • the base station 5 may transmit the PBCH with synchronisation signals (SS) (e.g. primary synchronisation signal (PSS) and secondary synchronisation signal (SSS)) in a SS/PBCH Block.
  • SS synchronisation signals
  • PSS primary synchronisation signal
  • SSS secondary synchronisation signal
  • the SS/PBCH block comprises four orthogonal frequency-division multiplexed (OFDM) symbols that are mapped to PSS, SSS and PBCH associated with a demodulation reference signal (DM-RS).
  • OFDM-RS demodulation reference signal
  • an SS/PBCH block comprises 240 contiguous subcarriers.
  • the base station 5 may provide the UE 3 with an indication of resources used for the SS/PBCH, for example using dedicated signalling.
  • SIB1 may be transmitted using a physical downlink shared channel (PDSCH).
  • PDSCH physical downlink shared channel
  • the OSI may be similarly transmitted, for example, using a PDSCH.
  • some of the SI e.g. some of the SIB
  • TRP transmission/reception point
  • the base station 5 and UE 3 are each configured to take advantage of AI/ML for the purposes of enhancing their respective interfaces with features enabling improved support of AI/ML based algorithms for enhanced performance and/or reduced complexity/overhead.
  • Enhanced performance could be, for example, improved throughput, robustness, accuracy or reliability, etc.
  • Fig. 3 illustrates a functional framework for AI/ML models, and how various entities of the framework may interact with one another, that may be implemented in the communication system 1.
  • the entities include a data collection function 41, a model training function 43, a model inference function 45, an actor 47, a management function 49, and a model storage entity 51.
  • the model storage entity 51 may be a reference point for protocol terminations for model transfer and delivery.
  • the AI/ML models could be stored at any suitable node in the network.
  • the data collection function 41 provides training data to the model training function 43, inference data to the model inference function 45, and monitoring data to the management function 49.
  • the collected data may be, for example, data regarding mobility (e.g. handover of a UE 3, or a location of the UE 3).
  • the data may be obtained, for example, by a UE 3 or a base station 5 (e.g. by receiving a measurement report from a UE 3, or by receiving data from another base station 5 or a core network node/function) and transmitted to another base station 5 or core network node that generates the AI/ML model inference output (or alternatively, the same base station 5 that obtains the data may generate the AI/ML model output).
  • the model training function 43 performs the ML model training, validation, and testing, and may generate model performance metrics as part of a model testing procedure.
  • the model training function 43 may output a trained AI/ML model to the model storage entity 51 (though it will be appreciated that the output model may be stored at locations other than model storage entity 51).
  • the model inference function 45 provides AI/ML model inference output (e.g., predictions or decisions), and the actor 47 is a function or node that receives the output from the model inference function 45 and triggers or performs corresponding actions (e.g. a base station 5 that increases/reduces its transmit power, or initiates a handover procedure for a UE 3).
  • the AI/ML model inference output may be, for example, a prediction of mobility (e.g. expected path, route or trajectory, inter-cell or inter-beam mobility, or expected handover) of the UE 3, or one or more parameters for use in encoding or decoding transmissions between the base station 5 and the UE 3.
  • the model inference function 45 may receive an AI/ML model from the model storage entity 51, and inference data from the data collection function 41 for use with the AI/ML model.
  • the model inference function 45 may also output monitoring data for use at the management function 49, and receive information indicating an AI/ML to activate or deactivate from the management function 49.
  • the management function 49 receives monitoring data from the data collection function 41, and may also receive monitoring data from the model inference function 45.
  • the management function 49 may transmit, to the model storage entity 51, an indication of an AI/ML model to be transmitted for use at the model inference function 45.
  • the management function 49 may also transmit, to the model training function 43, performance feedback or a retraining request for the AI/ML model.
  • Fig. 3 may be co-located at a single node of the communication system 1 (e.g. at a base station 5 or core network node/function), or may be distributed amongst a plurality of network nodes (e.g. a plurality of base stations 5).
  • AI/ML model training A process to train an AI/ML Model [by learning the input/output relationship] in a data driven manner and obtain the trained AI/ML Model for inference. Model training can be performed offline or online or combination of both.
  • AI/ML model validation A subprocess of training, to evaluate the quality of an AI/ML model using a dataset different from one used for model training, that helps selecting model parameters that generalize beyond the dataset used for model training.
  • AI/ML model testing A subprocess of training, to evaluate the performance of a final AI/ML model using a dataset different from one used for model training and validation. Differently from AI/ML model validation, testing does not assume subsequent tuning of the model.
  • AI/ML model Inference A process of using a trained AI/ML model to produce a set of outputs based on a set of inputs.
  • Data collection A process of collecting data by the network nodes, management entity, or UE 3 for the purpose of AI/ML model training, data analytics and inference.
  • Model monitoring A procedure that monitors the inference performance of the AI/ML model.
  • Model activation Enable an AI/ML model for a specific function.
  • Model deactivation Disable an AI/ML model for a specific function.
  • Model switching Deactivating a currently active AI/ML model and activating a different AI/ML model for a specific function.
  • Supervised learning A process of training a model from input and its corresponding labels.
  • Unsupervised leaning A process of training a model without labelled data.
  • Semi-supervised learning A process of training a model with a mix of labelled data and unlabelled data.
  • Reinforcement Learning A process of training an AI/ML model from input (also referred to as 'state') and a feedback signal (also referred to as 'reward') resulting from the model's output (also referred to as 'action') in an environment the model is interacting with.
  • the data collection may be performed at various nodes of the communication system 1 (e.g., at one or more base stations 5 or UEs 3). Particularly advantageous methods of obtaining, at a UE 3, data for an AI/ML model, and transmitting the AI/ML data from the UE 3 to a base station 5, will be described in more detail later.
  • Fig. 4 schematically illustrates of a method of training an AI/ML model, and of monitoring the performance of the AI/ML model.
  • stored data/features may first be extracted in a data extraction step.
  • a determination of whether to proceed with training or retaining the AI/ML model is made (e.g., based on the extracted data).
  • the data preparation stage the data is prepared for use in training the AI/ML model. For example, the data may be cleaned (e.g., filtered), subject to a transformation, or modified in any other suitable manner.
  • the data may also be divided in training data, validation data and test data sets in the data preparation stage.
  • the AI/ML model is trained (or retrained) using training data prepared in the data preparation step.
  • any suitable training method can be used to train the AI/ML model (e.g. a method that comprises supervised learning or unsupervised learning).
  • the AI/ML model is evaluated (e.g. a prediction accuracy of the AI/ML model is evaluated) using a test data set (which may be generated in the data preparation step).
  • a determination of whether the AI/ML model is suitable for deployment in the communication system 1 is made (e.g. based on the results of the model evaluation step).
  • the AI/ML model is deployed for use in the communication system 1.
  • AI/ML model deployment may comprise compiling a trained AI/ML model, packaging the model into an executable format, and delivering the AI/ML model to a target device.
  • the AI/ML model may be transmitted to the base station 5 and/or the UE 3, for use at the base station 5 and/or the UE 3 to generate predictions or determinations using the AI/ML model as part of a prediction service step, as illustrated in the figures.
  • the performance monitoring step the performance of the deployed AI/ML model is monitored.
  • the predictive performance of the AI/ML model may be monitored by comparing predictions generated using the model with one or more measurements.
  • the prediction accuracy of the AI/ML model may be assessed using a measurement of an actual location of the UE 3.
  • the model may be assessed based on the performance of the encoding and/or decoding processes.
  • retraining trigger step retraining of the AI/ML model is triggered (e.g. because the prediction accuracy of the AI/ML model has fallen below an acceptable threshold accuracy, or because a performance of a method that uses inferences from the AI/ML model has fallen below an acceptable threshold performance), and the method returns to the data extraction step.
  • each step of the method of Fig. 4 may be executed at a single node of the communication system 1, or alternatively steps of the method may be distributed between a plurality of different nodes (or indeed one or more of these steps may be performed online or offline).
  • information collected by nodes/functions in the communication system 1 can be used as training data for an AI/ML model, and used as inference data for use in generating one or more model inferences using the AI/ML model.
  • the information used as training data, monitoring data, and/or to generate the one or more model inferences may be referred to as 'AI/ML information' or 'AI/ML data'.
  • Fig. 5 illustrates the relationship between AI/ML features, functionalities and models which may be used in the communication system 1.
  • an AI/ML feature - such as 'Feature 1' and 'Feature 2' shown in Fig. 5 - is generally considered as an AI/ML use case (e.g., CSI compression-decompression, spatial beam prediction, etc.).
  • 'Feature 1' has two 'Functionalities'
  • each functionality may refer to one configuration of an associated AI/ML feature.
  • different functionalities can be defined in terms of the number of beams predicted by the AI/ML model.
  • a functionality is defined by configuration of input, output and other applicable conditions (e.g., UE vendor, deployment scenarios) associated with a given AI/ML feature.
  • AI/ML model (or models) can provide even finer granularity than AI/ML functionality, but any such AI/ML model may also be defined without any reference to functionality (i.e., each AI/ML feature can have multiple AI/ML models without any functionality definition).
  • An AI/ML model is associated with a configuration related to an AI/ML feature (similar to the above-described definition of AI/ML functionality) and may further include additional detailed aspects such as the version of the model. Hence, if one or more AI/ML functionalities are defined, then each functionality can be associated to multiple AI/ML models. As shown in Fig. 5, a given functionality may be discretely associated with a functionality specific model. A given functionality may, alternatively or additionally, be associated, together with one or more other functionalities, with a shared model.
  • the communication system 1 implements a general model management framework that advantageously provides for effective coordination between network nodes, such as a CU 5c and a DU 5b, to achieve the AI/ML LCM operations.
  • network nodes such as a CU 5c and a DU 5b
  • This is beneficial because, given that AI/ML applications are physical layer based, in the context of a base station 5 of the distributed type, active involvement of the DU 5b may be required. Similarly, CU 5c involvement may be required, for example in the context of handling radio resource control protocols.
  • the framework beneficially allows for variations of AI/ML model management including the use of the above-described single-sided and two-sided models, as well as alternative reporting mechanisms (e.g., RRC based, L1/L2 based, etc.).
  • Model monitoring and management decisions may be CU-based or DU-based.
  • the present disclosure provides improved apparatus and methods for model monitoring, including improved coordination between the CU 5c and the Du 5b. These methods will now be discussed in greater detail below. Improved methods of handling of model monitoring reports by the network are also disclosed.
  • the set of information exchanged between the CU 5c and the DU 5b can be provided either via an existing CU-DU message (e.g., a UE Context Setup Request and/or F1 Setup Request) or can be provided using a newly defined message (or messages).
  • an existing CU-DU message e.g., a UE Context Setup Request and/or F1 Setup Request
  • F1 Setup Request e.g., UE Context Setup Request and/or F1 Setup Request
  • the procedures can be implemented together in the same system any of the procedures may be implemented independently to provide a commensurate benefit.
  • AI/ML model performance monitoring may be initiated by a CU 5c or DU 5b, so that the performance of the AI/ML operation can be evaluated. Decisions based on the performance evaluation can be used, for example, for switching, fallback or deactivation or an AI/ML feature, functionality or model.
  • AI/ML model monitoring may be CU-based or DU-based. For a two-sided model, some of the information needed for the AI/ML model monitoring may be available at the UE 3 and the remaining information may be available at the DU 5b. For example, the AI/ML input and ground truth measurements may be available at the UE 3, whereas the inference outputs may be available at the DU 5b, whereas the node that performs the AI/ML monitoring may need both parts of this information in order to evaluate the AI/ML functionality or model performance.
  • Fig. 6 shows an example of a CU-based model monitoring method.
  • the CU 5c is the entity that collects (receives) the reports associated with the AI/ML model, and uses the reports to evaluate whether an action needs to be taken.
  • step S601 the CU 5c transmits an indication, to the DU 5b, of an intention to begin model monitoring.
  • the model monitoring request may be included, for example, in a UE Context Modification Request.
  • the transmission in step S601 includes an indication of the AI/ML features, functionalities or models for which monitoring is to be initiated.
  • the CU 5c may also transmit, to the DU 5b, an indication of model monitoring parameters.
  • the CU 5c may also transmit, to the DU 5b, a DU 5b reporting periodicity, and an indication of information to be included in the reports.
  • the indication of the model monitoring parameters could be transmitted from the CU 5c to the DU 5b using a dedicated message, or alternatively could be transmitted from the CU 5c to the DU 5b in a transmission that is part of an AI/ML model activation method.
  • Examples of the information elements (IEs) that may transmitted to the DU 5b by the CU 5c in step S601 include (but are not limited to): Selected AI/ML features: a list of AI/ML features requested for monitoring. The following IEs could be included: AI/ML Feature ID, and/or AI/ML Feature name; Selected AI/ML functionalities: list of AI/ML functionalities requested for monitoring.
  • Information indicating the selected AI/ML functionalities could include (using a suitable IE) an AI/ML Functionality ID, applicable cells and/or DU reporting parameters.
  • the applicable cells are a list of cells 9 for which the functionality can be activated, and each entry contains a cell ID value which maps to the cells 9 supported by the DU 5b.
  • the DU reporting parameters may include a reporting periodicity, and/or reporting contents.
  • An IE indicating a list of parameters to report could be used to indicate the reporting contents.
  • the parameters may be defined specific to each AI/ML application.
  • the DU reporting parameters could be provided per AI/ML feature, or per UE 3, in which case they can be common for a group of AI/ML functionalities and models.
  • the information transmitted to the DU 5b by the CU 5c in step S601 may optionally include an indication of selected AI/ML models.
  • the indication may comprise a list of AI/ML models requested for monitoring, and could be provided per selected AI/ML functionality entry.
  • AI/ML model ID Applicable Cells A list of cells 9 for which this model can be activated, each entry containing a cell ID value that maps to the cells 9 supported by the DU 5b.
  • DU reporting parameters Reporting periodicity and/or reporting contents.
  • An IE indicating a list of parameters to report could be used to indicate the reporting contents.
  • the parameters may be defined specific to each AI/ML application.
  • step S602 the DU 5b indicates an acknowledgement, ACK, or negative acknowledgement (or rejection), NACK, for the request received in step S601.
  • the DU 5b may need to make changes to the AI/ML operation configuration for model monitoring (for example, model monitoring may require a different L1/L2 reporting format)
  • the DU 5b may transmit an updated RRC configuration for AI/ML operation and/or radio operation to the CU 5c in step S602 (for use by the CU 5c when transmitting the RRC model monitoring configuration to the UE 3 in step S603).
  • the DU 5b may also provide an indication of other parameters related to model monitoring configuration that can be used by the CU 5c, such as UE report periodicity, updated DU reporting periodicity, and/or report contents.
  • the following IEs may be transmitted from the DU 5b to the CU 5c in step S602: Monitor-Success AI/ML features: a list of AI/ML features activated for monitoring. For each entry, the following IEs can be included: AI/ML Feature ID and/or AI/ML Feature name Monitor-Success AI/ML functionalities: a list of AI/ML functionalities activated for monitoring.
  • AI/ML Functionality ID Applicable Cells a list of cells 9 for which this functionality is monitored, and each entry contains a cell ID value which maps to the cells 9 supported by the DU 5b
  • DU Reporting Parameters DU reporting parameters updated by the DU 5b, which may include reporting periodicity and/or reporting contents (an IE indicating list of parameters to report; the parameters may be defined specific to each AI/ML application, or alternatively the DU reporting parameters could be provided per AI/ML feature, or per UE 3, in which case they can be common for a group of AI/ML functionalities and models)
  • Monitor-Success AI/ML models a list of AI/ML models activated for monitoring (which may be provided per Monitor-Success AI/ML functionalities entry).
  • AI/ML model ID Applicable Cells a list of cells 9 for which this model is monitored, and each entry containing a cell ID value which maps to the cells 9 supported by the DU 5b
  • DU Reporting Parameters DU reporting parameters updated by the DU 5b, which may include reporting periodicity and/or reporting contents (an IE indicating list of parameters to report; the parameters may be defined specific to each AI/ML application)
  • Monitor-Fail AI/ML features a list of AI/ML features for which monitoring cannot be performed.
  • AI/ML feature ID and/or AI/ML feature name Monitor-Fail AI/ML functionalities: a list of AI/ML functionalities for which monitoring cannot be performed.
  • AI/ML Functionality ID Applicable Cells a list of cells 9 for which this functionality cannot be monitored, each entry containing a cell ID value which maps to the cells supported by the DU 5b Failure Cause: Cause of failure for the monitoring
  • Monitor-Fail AI/ML models a list of AI/ML models for which monitoring cannot be performed (which may be provided per Monitor-Fail AI/ML functionalities entry).
  • AI/ML model ID Applicable Cells List of cells 9 for which this model cannot be monitored, and each entry contains a cells ID value which maps to the cells 9 supported by the DU 5b; Failure Cause: Cause of failure for the monitoring
  • RRC Information an IE containing RRC configuration information for forwarding by the CU 5c to the UE 3 (in step S603).
  • the CU 5c is advantageously able to construct an RRC configuration message to be transmitted to the UE 3 in step S603 (in which the CU 5c transmits an RRC model monitoring configuration to the UE 3).
  • step S604 the UE 3 generates a UE L1/L2 report (e.g. comprising inference results), and transmits the report to the DU 5b.
  • step S605 the DU 5b forwards the report to the CU 5c (as a DU performance monitoring report transmitted to the CU 5c).
  • the IEs included in the reports of steps S604 and S605 may comprise a Monitoring Results IE including: UE ID; Cell ID; and Measurement Results.
  • the Measurement Results may comprise an aggregated list of monitoring results for a single UE 3, where each entry comprises a resource ID, and values for each of a set of parameters (e.g. Param 1 Value, Param2 Value, Param3 Value, ).
  • the parameters may be measurement or inference results (obtained by the UE 3 and/or the DU 5b) for different metrics specific to each AI/ML application.
  • the UE ID and Cell ID are identities of the UE 3 and a cell 9 that the measurements are associated with.
  • the UE ID may be in form of a gNB-CU UE F1AP ID, and/or gNB-DU UE F1AP ID (described, for example, in 3GPP specification TS 38.473 (NPL 3)).
  • the Cell ID may be in the form of SpCell ID and/or ServCellIndex.
  • the Resource ID is a resource identifier corresponding to the measurements, for example a timestamp, or an index associated with the measurement instance.
  • the periodicity of the transmissions of steps S604 and S605 may be configured by the DU 5b or the CU 5c.
  • the DU 5b may provide an indication of a periodicity value for the reports to the CU 5c.
  • the CU 5c may provide an indication of a periodicity value for the reports to the DU 5b, and the DU 5b may acknowledge or reject the periodicity value.
  • the periodicity for the reports may be determined based on the UE RRC reporting interval to the CU 5c, or the UE L1/L2 reporting to the DU 5b.
  • the information included in the reports may include inference results and may also include other measurements available at the DU 5b that can be used for AI/ML monitoring at the CU 5c.
  • step S606 the UE 3 transmits a UE RRC reporting for performance monitoring to the CU 5c.
  • the UE RRC reporting for performance monitoring may comprise measurement results of measurements performed at the UE 3.
  • IEs included in the UE RRC report transmitted to the CU 5c by the UE 3 in step S606 may comprise a Monitoring Report IE that includes a list of monitoring results, where each entry contains: Measurement Results - an aggregated list of monitoring results for a single UE 3, where each entry comprises a Measurement ID, and values for each of a set of parameters (e.g. Param 1 Value, Param2 Value, Param3 Value, ).
  • the parameters may be measurements for different metrics specific to each AI/ML application.
  • the Measurement ID is a resource identifier corresponding to the performing of the measurements, for example a timestamp or an index associated with the measurement instance.
  • the CU 5c combines the report received from the DU 5b in step 605 with UE RRC report received in step S606, and performs the performance evaluation in step S607.
  • the report from the DU 5b received in step S605 may comprise an identifier that enables the CU 5c to identify the DU report that is associated with the corresponding UE RRC report.
  • the identifier may be in the form of an indication of a radio resource associated with the report of step S606.
  • the UE 3 may provide information indicating radio resources (e.g. cell, or time information) using which the measurements/inference was performed.
  • the DU 5b includes the same information in the transmission of step S605, enabling the CU 5c to associate the DU performance monitoring report received in step S605 with the UE RRC report received in step S606.
  • the identifier may be in the form of an index value.
  • the UE 3 may include an index value in the UE RRC report transmitted in step S606, and the same index value is included in the L1/L2 report transmitted to the CU 5c via the DU 5b in steps S604 and S605.
  • the CU 5c is therefore able to associate the L1/L2 report received from the UE 3 via the DU 5b with the UE RRC report received in step S606, in order to perform the monitoring.
  • the performance evaluation may be performed at the CU 5c in step S607 as illustrated in Fig. 6, or alternatively the CU 5c may forward the reports received in steps S605 and S606 to another node in the network, so that the performance evaluation can be performed at the other node.
  • a model management decision is taken.
  • the model management decision may be taken by either at the CU 5c or the DU 5b based on a mechanism of activation, deactivation, switch, or fallback.
  • the CU 5c provides an indication of the performance evaluation result (from step S607) to the DU 5b.
  • the CU 5c provides an indication, to the DU 5b, of whether the AI/ML algorithm is working sufficiently well, or whether the performance is insufficient (e.g. not meeting predetermined performance metrics). This information may be provided per AI/ML feature/functionality or model.
  • the CU 5c may provide a recommendation of a model management decision to the DU 5b (e.g. deactivation, switch or fallback).
  • the recommended action may be provided per AI/ML feature/functionality or model. This may also include a recommended AI/ML feature/functionality or model for the case of model switch.
  • the performance evaluation result (for the first alternative) or the suggested model management decision (for the second alternative) is transmitted from the CU 5c to the DU 5b in step S608.
  • the information may be transmitted to the DU 5b in a UE Context Modification Request (although any other suitable transmission could alternatively be used).
  • the information transmitted to the DU 5b in step S608 may be referred to as a monitoring evaluation report (and may be transmitted as a Monitoring Evaluation Report IE).
  • the Monitoring Evaluation Report IE may comprise a list of evaluation results, where each entry contains: AI/ML Feature ID and/or AI/ML Feature name AI/ML Functionalities: a list of AI/ML functionalities to which the evaluation report corresponds.
  • AI/ML Functionality ID Applicable Cells List of cells 9 for which this evaluation report is applicable for, which may be provided per AI/ML feature or per UE 3, each entry containing a cell ID value that maps to the cells 9 supported by the DU 5b Performance score (e.g.
  • Param1 Value, Param2 Value, ...) which may be provided per AI/ML feature or per UE 3 Recommended Action (e.g. Activate, Deactivate, Switch, or Fallback), which may be provided per AI/ML feature or per UE 3 - an optional Switch AI/ML Functionality ID may be provided when the Recommended action is 'Switch' AI/ML models: a list of AI/ML models to which the evaluation report corresponds, which can be provided per Selected AI/ML functionality entry. For each entry the following IEs can be included: AI/ML model ID Applicable cells: a list of cells 9 for which this evaluation report is applicable for, and each entry containing a cell ID value that maps to the cells 9 supported by the DU 5b Performance Score (e.g.
  • the Performance Score parameters indicate the performance metric values for the AI/ML application, which may or may not be specific to an AI/ML application.
  • the performance metrics may comprise a prediction accuracy of an AI/ML model or functionality, or may comprise the performance metrics reported in the UE/DU report to the CU 5c for an AI/ML application.
  • Fig. 7 shows an example of a DU-based model monitoring method.
  • the DU 5b is the entity that collects (receives) the reports associated with the AI/ML model, and uses the reports to evaluate whether an action needs to be taken.
  • the CU 5c is involved in the overall monitoring method, for example for RRC report configuration and collection.
  • step S701 the DU 5b transmits an indication, to the CU 5c, of an intention to begin model monitoring.
  • the model monitoring request may be included, for example, in a UE Context Modification Required message.
  • the transmission in step S601 includes an indication of the AI/ML features, functionalities or models for which monitoring is to be initiated.
  • the DU 5b may transmit, to the CU 5c, information indicating the intended model monitoring parameters, for example an L3 based UE report periodicity and information contents for the report, and an expected CU reporting configuration (e.g. periodicity and contents).
  • the DU 5b may need to make changes to the AI/ML operation configuration for model monitoring (for example, model monitoring may require a different L1/L2 reporting format)
  • the DU 5b may transmit an updated RRC configuration for AI/ML operation and/or radio operation to the CU 5c in step S701 (for use by the CU 5c when transmitting the RRC model monitoring configuration to the UE 3 in step S702).
  • the CU 5c is advantageously able to construct an RRC configuration message to be transmitted to the UE 3 in step S702 (in which the CU 5c transmits an RRC model monitoring configuration to the UE 3).
  • the DU 5b may include the following IEs in the Monitor Request message of step S701: Monitor-Request AI/ML features: a list of AI/ML features intended for monitoring - for each entry the following IEs can be included: AI/ML Feature ID/ AI/ML Feature name Monitor-Request AI/ML functionalities: list of AI/ML functionalities intended for monitoring. For each entry following IEs can be included: AI/ML Functionality ID Applicable Cells: a list of cells 9 for which this functionality is monitored, each entry containing a cell ID value which maps to the cells 9 supported by the DU 5b DU Reporting Parameters: DU reporting parameters updated by the DU 5b, which may comprise: Reporting periodicity Reporting contents (e.g.
  • Param1, Param2, 8) an IE indicating a list of parameters to report, where the parameters are defined specific to each AI/ML application
  • Monitor-Request AI/ML models a list of AI/ML models intended for monitoring (this information can be provided per Monitor-Request AI/ML functionalities entry or per Monitor-Request AI/ML features entry) - for each entry the following IEs can be included:
  • AI/ML model ID Applicable Cells a list of cells 9 for which this model is monitored, each entry containing a cell ID value which maps to the cells 9 supported by the DU 5b
  • DU Reporting Parameters DU reporting parameters updated by the DU 5b, which may comprise: Reporting periodicity Reporting contents (e.g.
  • Param1, Param2, 8) an IE indicating a list of parameters to report, where the parameters are defined specific to each AI/ML application
  • RRC Information an IE containing an RRC configuration which should be forwarded by the CU 5c to the UE 3
  • the CU 5c is advantageously able to construct an RRC configuration message to be transmitted to the UE 3 for model monitoring (the RRC model monitoring configuration transmitted in step S702).
  • the CU 5c may be configured to confirm to the DU 5b the reporting parameters, for example the reporting periodicity, reporting content, or any other reporting parameter.
  • the CU 5c may transmit, to the DU 5b, a confirmation of the monitoring parameters configured at the CU 5c (the transmission is not illustrated in Fig. 7 but could be transmitted at any suitable time after the CU 5c has received the model monitoring request from the DU 5b).
  • the confirmation of the monitoring parameters configured at the CU 5c that is transmitted to the DU 5b may comprise one or more of the following IEs: Monitor-Success AI/ML features: a list of AI/ML features activated for monitoring. For each entry, the following IEs can be included: AI/ML Feature ID and/or AI/ML Feature name Monitor-Success AI/ML functionalities: a list of AI/ML functionalities activated for monitoring.
  • AI/ML Functionality ID Applicable Cells a list of cells 9 for which this functionality is monitored, each entry containing a cell ID value which maps to the cells 9 supported by the DU 5b
  • CU Reporting Parameters CU reporting parameters updated by the CU 5c, which may include reporting periodicity and/or reporting contents (an IE indicating list of parameters to report; the parameters may be defined specific to each AI/ML application, or alternatively the CU reporting parameters could be provided per AI/ML feature, or per UE 3, in which case they can be common for a group of AI/ML functionalities and models)
  • Monitor-Success AI/ML models a list of AI/ML models activated for monitoring (which may be provided per Monitor-Success AI/ML functionalities entry).
  • AI/ML model ID Applicable Cells a list of cells 9 for which this model is monitored, and each entry containing a cell ID value which maps to the cells 9 supported by the DU 5b
  • CU Reporting Parameters CU reporting parameters updated by the CU 5c, which may include reporting periodicity and/or reporting contents (an IE indicating list of parameters to report; the parameters may be defined specific to each AI/ML application)
  • Monitor-Fail AI/ML features a list of AI/ML features for which monitoring cannot be performed.
  • AI/ML feature ID and/or AI/ML feature name Monitor-Fail AI/ML functionalities: a list of AI/ML functionalities for which monitoring cannot be performed.
  • AI/ML Functionality ID Applicable Cells a list of cells 9 for which this functionality cannot be monitored, each entry containing a cell ID value which maps to the cells 9 supported by the DU 5b Failure Cause: Cause of failure for the monitoring
  • Monitor-Fail AI/ML models a list of AI/ML models for which monitoring cannot be performed (which may be provided per Monitor-Fail AI/ML functionalities entry).
  • AI/ML model ID Applicable Cells List of cells 9 for which this model cannot be monitored, and each entry contains a cells ID value which maps to the cells 9 supported by the DU 5b; Failure Cause: Cause of failure for the monitoring
  • the UE 3 transmits a UE L1/L2 report (e.g. including inference results) to the DU 5b, using the monitoring configuration received in step S702.
  • step S704 the UE 3 transmits a UE RRC report for performance monitoring to the CU 5c
  • step S705 the CU 5c transmits a corresponding CU performance monitoring report to the DU 5b (forwarding the UE RRC report).
  • the CU 5c is configured to regularly (e.g. with a predetermined or agreed periodicity) transmit the report of step S705 to the DU 5b.
  • the DU 5b may transmit, to the CU 5c, an indication of the periodicity to use for the transmission of the reports from the CU 5c to the DU 5b in step S705.
  • the CU 5c may acknowledge or reject the indicated periodicity.
  • the CU 5c may provide an indication of a periodicity to be used for the reports to the DU 5b.
  • the periodicity can be determined based on the UE RRC reporting interval to the CU 5c (e.g. the CU 5c transmits one CU performance monitoring report to the DU 5b for each UE RRC report received from the UE 3 at the CU 5c), or the periodicity of the UE L1/L2 reports transmitted from the UE 3 to the DU 5b.
  • the report transmitted from the CU 5c to the DU 5b in step S705 includes the information reported to the CU 5c by the UE 3 in step S704 (e.g. radio measurements performed by the UE 3, and/or inputs of the AI/ML model/functionality), and may also include additional measurements available at the CU 5c that can be used for AI/ML monitoring.
  • the information reported to the CU 5c by the UE 3 in step S704 e.g. radio measurements performed by the UE 3, and/or inputs of the AI/ML model/functionality
  • additional measurements available at the CU 5c that can be used for AI/ML monitoring.
  • the DU 5b combines the report received from the CU 5c in step S705 with UE L1/L2 report received in step S703, and performs the performance evaluation in step S706.
  • the report from the CU 5c received in step S705 may comprise an identifier that enables the DU 5b to identify the CU report that is associated with the corresponding UE L1/L2 report.
  • the identifier may be in the form of an indication of a radio resource associated with the report of step S703.
  • the UE 3 may provide information indicating radio resources (e.g. cell, or time information) using which the measurements/inference was performed.
  • the CU 5c includes the same information in the transmission of step S705, enabling the DU 5b to associate the CU performance monitoring report received in step S705 with the UE L1/L2 report received in step S703.
  • the identifier may be in the form of an index value.
  • the UE 3 may include an index value in the UE L1/L2 report transmitted in step S703, and the same index value is included in the CU performance monitoring report transmitted in step S705.
  • the DU 5b is therefore able to associate the L1/L2 report received from the UE 3 with the CU performance monitoring report received in step S705, in order to perform the monitoring.
  • IEs included in the CU performance monitoring report transmitted in step S705 comprise a Monitoring Results IE, that is a list of monitoring results for different UEs 3, where each entry contains: UE ID Cell ID Measurement Results: Aggregated list of monitoring results for a single UE 3, where each entry contains: Resource ID UE Measurement Container Param1 Value, Param2 Value, Param3 Value, ...
  • the 'Resource ID' is a resource identifier corresponding to where the measurements were performed (e.g. a timestamp, or an index associated with the measurement instance).
  • Param1 Value, Param2 Value and Param3 Value are measurement/inference results (obtained by the UE 3 and/or the CU 5c) for different metrics specific to each AI/ML application.
  • the 'UE Measurement Container' is the RRC container received from the UE 3 in the RRC message, that is to be transparently forwarded by the CU 5c to the DU 5b.
  • the container contains the measurement/inference results obtained by the UE 3 and reported to the CU 5c via the AI/ML monitoring procedure.
  • the UE ID and the Cell ID are the identifies of the UE 3 and the cell 9, respectively, that the measurements are associated with.
  • the UE ID may be a gNB-CU UE F1AP ID and/or gNB-DU UE F1AP ID.
  • the Cell ID may be a SpCell ID and/or ServCellIndex.
  • IEs included in the UE RRC report transmitted to the CU 5c by the UE 3 in step S704 may comprise a Monitoring Report IE that includes a list of monitoring results, where each entry contains: Measurement Results - an aggregated list of monitoring results for a single UE 3, where each entry comprises a Measurement ID, and values for each of a set of parameters (e.g. Param 1 Value, Param2 Value, Param3 Value, ).
  • the parameters may be measurements for different metrics specific to each AI/ML application.
  • the Measurement ID is a resource identifier corresponding to the performing of the measurements, for example a timestamp or an index associated with the measurement instance.
  • the performance evaluation may be performed at the DU 5b in step S706 (as illustrated in Fig. 7), or alternatively the DU 5b may forward the measurements to another node in the network (e.g. external entity) where the evaluation is performed.
  • the DU 5b may forward the measurements to another node in the network (e.g. external entity) where the evaluation is performed.
  • a model management decision is taken.
  • the model management decision may be taken at either the CU 5c or the DU 5b, based on a mechanism of activation, deactivation, switch, or fallback.
  • the DU 5b provides an indication of the performance evaluation result (from step S706) to the CU 5c.
  • the DU 5b provides an indication, to the CU 5c, of whether the AI/ML algorithm is working sufficiently well, or whether the performance is insufficient (e.g. not meeting predetermined performance metrics).
  • the information can be provided per AI/ML feature/functionality/model.
  • the DU 5b may provide a recommendation of a model management decision to the CU 5c (e.g. deactivation, switch or fallback).
  • the indication can be provided per AI/ML feature/functionality/model. This may also include a recommended AI/ML feature/functionality or model for the case of model switch.
  • the performance evaluation result (for the first alternative) or the suggested model management decision (for the second alternative) is transmitted from the DU 5b to the CU 5c in optional step S707.
  • the information may be transmitted to the DU 5b in a UE Context Modification Request (although any other suitable transmission could alternatively be used).
  • the information transmitted to the CU 5c in step S707 may be referred to as a monitoring evaluation report (and may be transmitted as a Monitoring Evaluation Report IE).
  • the Monitoring Evaluation Report IE may comprise a list of evaluation results, where each entry contains: AI/ML Feature ID and/or AI/ML Feature name AI/ML Functionalities: a list of AI/ML functionalities to which the evaluation report corresponds.
  • AI/ML Functionality ID Applicable Cells List of cells 9 for which this evaluation report is applicable for, which may be provided per AI/ML feature or per UE 3, each entry containing a cell ID value that maps to the cells 9 supported by the DU 5b Performance score (e.g.
  • Param1 Value, Param2 Value, ...) which may be provided per AI/ML feature or per UE 3 Recommended Action (e.g. Activate, Deactivate, Switch, or Fallback), which may be provided per AI/ML feature or per UE 3 - an optional Switch AI/ML Functionality ID may be provided when the Recommended action is 'Switch' AI/ML models: a list of AI/ML models to which the evaluation report corresponds, which can be provided per Selected AI/ML functionality entry. For each entry the following IEs can be included: AI/ML model ID Applicable cells: a list of cells 9 for which this evaluation report is applicable for, and each entry containing a cell ID value that maps to the cells 9 supported by the DU 5b Performance Score (e.g.
  • the Performance Score parameters (e.g. Param1 Value, Param2 Value, 10) indicate the performance metric values for the AI/ML application, which may or may not be specific to an AI/ML application.
  • the performance metrics may comprise a prediction accuracy of an AI/ML model or functionality, or may comprise the performance metrics reported in the UE report for an AI/ML application.
  • Fig. 8 is a schematic block diagram illustrating the main components of a UE 3 as shown in Fig. 1.
  • the UE 3 has a transceiver circuit 310 that is operable to transmit signals to and to receive signals from a base station 5 via one or more antenna 330 (e.g., comprising one or more antenna elements).
  • the UE 3 has a controller 370 to control the operation of the UE 3.
  • the controller 370 is associated with a memory 390 and is coupled to the transceiver circuit 310.
  • the UE 3 might, of course, have all the usual functionality of a conventional UE 3 (e.g.
  • a user interface 350 such as a touch screen / keypad / microphone / speaker and/or the like for, allowing direct control by and interaction with a user
  • this may be provided by any one or any combination of hardware, software, and firmware, as appropriate.
  • Software may be pre-installed in the memory 390 and/or may be downloaded via the communications system 1 or from a removable data storage device (RMD), for example.
  • RMD removable data storage device
  • the controller 370 is configured to control overall operation of the UE 3 by, in this example, program instructions or software instructions stored within memory 390. As shown, these software instructions include, among other things, an operating system 410, a communications control module 430, and an AI/ML module 450.
  • the communications control module 430 is operable to control the communication between the UE 3 and its one or more serving base stations 5 (and other communication devices connected to the base station 5, such as further UEs and/or core network nodes).
  • the communications control module 430 is configured for the overall handling uplink communications via associated uplink channels (e.g. via a physical uplink control channel (PUCCH), random access channel (RACH), and/or a physical uplink shared channel (PUSCH)) including both dynamic and semi-static signalling (e.g., SRS).
  • the communications control module 430 is also configured for the overall handling of receipt of downlink communications via associated downlink channels (e.g.
  • the communications control module 430 is responsible, for example: for determining where to monitor for downlink control information (e.g., the location of CSSs / USSs, CORESETs, and associated PDCCH candidates to monitor); for determining the resources to be used by the UE 3 for transmission/reception of UL/DL communications (including interleaved resources and resources subject to frequency hopping); for managing frequency hopping at the UE 3 side; for determining how slots/symbols are configured (e.g., for UL, DL or SBFD communication, or the like); for determining which one or more bandwidth parts are configured for the UE 3; for determining how uplink transmissions should be encoded; for applying any SBFD specific communication configurations appropriately; and the like.
  • the communications control module 430 may be configured to control communications in accordance with any of the methods described above
  • the AI/ML module 450 is configured to perform any of the AI/ML related functions of the UE 3 of any of the methods described above.
  • Fig. 9 is a simplified block schematic illustrating the main components of a distributed RAN comprising a distributed type of base station 5 for implementation in the system of Fig. 1.
  • the RAN includes a central unit 5c and a distributed unit 5b (although it may include other DUs as described above).
  • Each unit 5c, 5b includes respective transceiver circuit 51c, 51b.
  • the distributed unit 5b transceiver circuit 51b is operable to transmit signals to and to receive signals from UEs 3 via an air interface 53b and one or more antennas and is also operable to transmit signals to and to receive signals from the central unit 5c via an interface, for example the distributed unit side of an F1 interface (which may be provided over a satellite radio interface).
  • the central unit 5c transceiver circuit 51c is operable to transmit signals to and to receive signals from functions of the core network 7 and/or other RANs via a network interface 55c.
  • the network interface typically includes an N1, N2 and/or N3 interfaces for communicating with the core network 7 and a base station to base station (e.g. Xn) interface for communicating with other RANs.
  • the central unit 5c transceiver circuit 51c is also operable to transmit signals to and to receive signals from one or more distributed units 5b, for example the central unit side of the F1 interface provided.
  • Each unit 5c, 5b includes a respective controller 57c, 57b which controls the operation of the corresponding transceiver circuit 51c, 51b in accordance with software stored in the respective memories 59c and 59b of the distributed unit 5b and the central unit 5c.
  • the software of each unit may be pre-installed in the memory 59c, 59b and/or may be downloaded via the communication system 1 or from a removable data storage device (RMD), for example.
  • the software of each unit includes, among other things, a respective operating system 61c, 61b, a respective communications control module 63c, 63b and an AI/ML module 65c, 65b.
  • Each communications control module 63c, 63b is operable to control the communication of its corresponding unit 5c, 5b including the communication from one unit to the other.
  • the communications control module 63b of the distributed unit 5b controls communication between the distributed unit 5b and the UEs 3, and the communications control module 63c of the central unit 5c controls communication between the central unit 5c and other network entities that are connected to the distributed RAN.
  • the communications control modules 63c, 63b also respectively control the part played by the distributed unit 5b and central unit 5c in the flow of uplink and downlink user traffic and control data to be transmitted to the communications devices served by the RAN including, for example, control data for managing operation of the UEs 3.
  • Each communication control module 63c, 63b is responsible, for example, for controlling the respective part played by the distributed unit 5b and central unit 5c in the reception and decoding of uplink communications, via associated uplink channels (e.g. via a physical uplink control channel (PUCCH), a random-access channel (RACH), and/or a physical uplink shared channel (PUSCH)) including both dynamic and semi-static signalling (e.g., SRS).
  • PUCCH physical uplink control channel
  • RACH random-access channel
  • PUSCH physical uplink shared channel
  • Each communication control module 63c, 63b is responsible for controlling the respective part played by the distributed unit 5b and central unit 5c in the transmission of downlink communications via associated downlink channels (e.g. via a physical downlink control channel (PDCCH) and/or a physical downlink shared channel (PDSCH)) including both dynamic and semi-static signalling (e.g., CSI-RS, SSBs etc.).
  • associated downlink channels e.g. via a physical downlink control channel (PDCCH) and/or a physical downlink shared channel (PDSCH)
  • PDSCH physical downlink shared channel
  • dynamic and semi-static signalling e.g., CSI-RS, SSBs etc.
  • the communications control modules 63c, 63b may also include a number of sub-modules (or 'layers') to support specific functionalities for the corresponding unit 5c, 5b.
  • the modules included will depend on how the corresponding unit 5c, 5b is configured (e.g., the precise CU-DU split).
  • the communications control modules 63c of the distributed unit 5b may include a PHY sub-module, a MAC sub-module, and an RLC sub-module
  • the communications control modules 63c of the central unit 5c may include a PDCP sub-module, an SDAP sub-module, an IP sub-module, an RRC sub-module, etc.
  • the AI/ML module 65c, 65b is configured to perform any of the AI/ML related functions described above.
  • Fig. 10 is a block diagram illustrating the main components of a core network 7 or function, such as the AMF, CPF, the UPF, the SMF or OAM.
  • the core network function includes a transceiver circuit 710 which is operable to transmit signals to and to receive signals from other nodes (including the UE 3, the base station 5, and other core network nodes) via a network interface 720.
  • a controller 730 controls the operation of the core network function in accordance with software stored in a memory 740.
  • the software may be pre-installed in the memory 740 and/or may be downloaded via the communication system 1 or from a removable data storage device (RMD), for example.
  • the software includes, among other things, an operating system 750, and a communications control module 760.
  • the communications control module 760 is responsible for handling (generating/sending/ receiving) signalling between the core network function and other nodes, such as the UE 3, the base station 5, and other core network nodes.
  • the core network node/function may also include an AI/ML module 770.
  • the AI/ML module 770 is operable to perform any of the AI/ML related functions of the core network node/function according to any of the methods described above.
  • the core network node/function may be configured for training or re-training the AI/ML model as described above (for example, in response to AI/ML data that is fed back to the core network node/function from another node in the network, such as the base station 5.
  • the UEs and the base station are described for ease of understanding as having a number of discrete functional components or modules. Whilst these modules may be provided in this way for certain applications, for example where an existing system has been modified to implement the disclosure, in other applications, for example in systems designed with the inventive features in mind from the outset, these modules may be built into the overall operating system or code and so these modules may not be discernible as discrete entities.
  • the software modules may be provided in compiled or un-compiled form and may be supplied as a signal over a computer network, or on a recording medium. Further, the functionality performed by part, or all of this software may be performed using one or more dedicated hardware circuits. However, the use of software modules is preferred as it facilitates the updating of the base station or the UE in order to update their functionalities.
  • Each controller may comprise any suitable form of processing circuitry including (but not limited to), for example: one or more hardware implemented computer processors; microprocessors; central processing units (CPUs); arithmetic logic units (ALUs); input/output (IO) circuits; internal memories / caches (program and/or data); processing registers; communication buses (e.g. control, data and/or address buses); direct memory access (DMA) functions; hardware or software implemented counters, pointers and/or timers; and/or the like.
  • processing circuitry including (but not limited to), for example: one or more hardware implemented computer processors; microprocessors; central processing units (CPUs); arithmetic logic units (ALUs); input/output (IO) circuits; internal memories / caches (program and/or data); processing registers; communication buses (e.g. control, data and/or address buses); direct memory access (DMA) functions; hardware or software implemented counters, pointers and/or timers; and/or the like.
  • the User Equipment (or "UE”, “mobile station”, “mobile device” or “wireless device”) in the present disclosure is an entity connected to a network via a wireless interface.
  • UE User Equipment
  • mobile station mobile device
  • wireless device wireless device
  • terminals such as terminals, cell phones, smart phones, tablets, cellular IoT devices, IoT devices, and machinery. It will be appreciated that the terms “mobile station” and “mobile device” also encompass devices that remain stationary for a long period of time.
  • a UE may, for example, be an item of equipment for production or manufacture and/or an item of energy related machinery (for example equipment or machinery such as: boilers; engines; turbines; solar panels; wind turbines; hydroelectric generators; thermal power generators; nuclear electricity generators; batteries; nuclear systems and/or associated equipment; heavy electrical machinery; pumps including vacuum pumps; compressors; fans; blowers; oil hydraulic equipment; pneumatic equipment; metal working machinery; manipulators; robots and/or their application systems; tools; molds or dies; rolls; conveying equipment; elevating equipment; materials handling equipment; textile machinery; sewing machines; printing and/or related machinery; paper converting machinery; chemical machinery; mining and/or construction machinery and/or related equipment; machinery and/or implements for agriculture, forestry and/or fisheries; safety and/or environment preservation equipment; tractors; precision bearings; chains; gears; power transmission equipment; lubricating equipment; valves; pipe fittings; and/or application systems for any of the previously mentioned equipment or machinery etc.).
  • equipment or machinery such as: boilers;
  • a UE may, for example, be an item of transport equipment (for example transport equipment such as: rolling stocks; motor vehicles; motorcycles; bicycles; trains; buses; carts; rickshaws; ships and other watercraft; aircraft; rockets; satellites; drones; balloons etc.).
  • a UE may, for example, be an item of information and communication equipment (for example information and communication equipment such as: electronic computer and related equipment; communication and related equipment; electronic components etc.).
  • a UE may, for example, be a refrigerating machine, a refrigerating machine applied product, an item of trade and/or service industry equipment, a vending machine, an automatic service machine, an office machine or equipment, a consumer electronic and electronic appliance (for example a consumer electronic appliance such as: audio equipment; video equipment; a loud speaker; a radio; a television; a microwave oven; a rice cooker; a coffee machine; a dishwasher; a washing machine; a dryer; an electronic fan or related appliance; a cleaner etc.).
  • a consumer electronic appliance such as: audio equipment; video equipment; a loud speaker; a radio; a television; a microwave oven; a rice cooker; a coffee machine; a dishwasher; a washing machine; a dryer; an electronic fan or related appliance; a cleaner etc.
  • a UE may, for example, be an electrical application system or equipment (for example an electrical application system or equipment such as: an x-ray system; a particle accelerator; radio isotope equipment; sonic equipment; electromagnetic application equipment; electronic power application equipment etc.).
  • an electrical application system or equipment such as: an x-ray system; a particle accelerator; radio isotope equipment; sonic equipment; electromagnetic application equipment; electronic power application equipment etc.
  • a UE may, for example, be an electronic lamp, a luminaire, a measuring instrument, an analyser, a tester, or a surveying or sensing instrument (for example a surveying or sensing instrument such as: a smoke alarm; a human alarm sensor; a motion sensor; a wireless tag etc.), a watch or clock, a laboratory instrument, optical apparatus, medical equipment and/or system, a weapon, an item of cutlery, a hand tool, or the like.
  • a surveying or sensing instrument such as: a smoke alarm; a human alarm sensor; a motion sensor; a wireless tag etc.
  • a UE may, for example, be a wireless-equipped personal digital assistant or related equipment (such as a wireless card or module designed for attachment to or for insertion into another electronic device (for example a personal computer, electrical measuring machine)).
  • a wireless-equipped personal digital assistant or related equipment such as a wireless card or module designed for attachment to or for insertion into another electronic device (for example a personal computer, electrical measuring machine)).
  • a UE may be a device or a part of a system that provides applications, services, and solutions described below, as to "internet of things (IoT)", using a variety of wired and/or wireless communication technologies.
  • IoT Internet of things
  • IoT devices may be equipped with appropriate electronics, software, sensors, network connectivity, and/or the like, which enable these devices to collect and exchange data with each other and with other communication devices.
  • IoT devices may comprise automated equipment that follow software instructions stored in an internal memory. IoT devices may operate without requiring human supervision or interaction. IoT devices might also remain stationary and/or inactive for a long period of time. IoT devices may be implemented as a part of a (generally) stationary apparatus. IoT devices may also be embedded in non-stationary apparatus (e.g. vehicles) or attached to animals or persons to be monitored/tracked.
  • IoT technology can be implemented on any communication devices that can connect to a communications network for sending/receiving data, regardless of whether such communication devices are controlled by human input or software instructions stored in memory.
  • IoT devices are sometimes also referred to as Machine-Type Communication (MTC) devices or Machine-to-Machine (M2M) communication devices.
  • MTC Machine-Type Communication
  • M2M Machine-to-Machine
  • a UE may support one or more IoT or MTC applications.
  • MTC applications are listed in the following table 1. This list is not exhaustive and is intended to be indicative of some examples of machine type communication applications.
  • Applications, services, and solutions may be an MVNO (Mobile Virtual Network Operator) service, an emergency radio communication system, a PBX (Private Branch eXchange) system, a PHS/Digital Cordless Telecommunications system, a POS (Point of sale) system, an advertise calling system, an MBMS (Multimedia Broadcast and Multicast Service), a V2X (Vehicle to Everything) system, a train radio system, a location related service, a Disaster/Emergency Wireless Communication Service, a community service, a video streaming service, a femto cell application service, a VoLTE (Voice over LTE) service, a charging service, a radio on demand service, a roaming service, an activity monitoring service, a telecom carrier/communication NW selection service, a functional restriction service, a PoC (Proof of Concept) service, a personal information management service, an ad-hoc network/DTN (Delay Tolerant Networking) service, etc.
  • MVNO Mobile Virtual Network Operator
  • Non-transitory computer readable media include any type of tangible storage media.
  • Examples of non-transitory computer readable media include magnetic storage media (such as floppy disks, magnetic tapes, hard disk drives, etc.), optical magnetic storage media (e.g. magneto-optical disks), CD-ROM (Read Only Memory), CD-R, CD-R/W, and semiconductor memories (such as mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (Random Access Memory), etc.).
  • the program may be provided to the computer device using any type of transitory computer readable media. Examples of transitory computer readable media include electric signals, optical signals, and electromagnetic waves. Transitory computer readable media can provide the program to the computer device via a wired communication line, such as electric wires and optical fibers, or a wireless communication line.
  • a method performed by a first unit of a distributed base station comprising: transmitting, to a second unit of the distributed base station, a request for monitoring Artificial Intelligence, AI,/Machine Learning, ML, models, AI/ML functionalities or AI/ML features; receiving, from the second unit, reporting information including at least one of inference results for AI/ML models at the second unit and measurements at the second unit which can be used for the monitoring; and transmitting, to the second unit, performance results based on the reporting information.
  • Supplementary note 2 The method according to Supplementary note 1, wherein the request includes first information indicating at least one AI/ML feature, each of the at least one AI/ML feature corresponding to at least one AI/ML model.
  • Supplementary note 3 The method according to Supplementary note 1 or 2, further comprising: receiving, from the second unit, a first message for acknowledging the request, or a second message for rejecting the request, the second message including a cause value indicating a reason for a failure.
  • the first message includes first information indicating at least one AI/ML feature, each of the at least one AI/ML feature corresponding to at least one AI/ML model.
  • the first information includes at least one of: second information indicating at least one AI/ML functionality corresponding to one of the at least one AI/ML feature, third information indicating at least one AI/ML model corresponding to one of the at least one AI/ML feature or at least one AI/ML functionality, cell information indicating at least one cell corresponding to one of the at least one AI/ML feature, at least one AI/ML functionality or at least one AI/ML model, a periodicity used for transmitting reporting information, or information indicating reporting contents of the reporting information.
  • Supplementary note 6 The method according to any one of Supplementary notes 1 to 5, wherein the performance results include a performance score indicating performance metric values for the AI/ML models, AI/ML functionalities or AI/ML features.
  • Supplementary note 7 The method according to Supplementary note 6, wherein the performance results include a recommended action for monitoring management for the AI/ML models, AI/ML functionalities or AI/ML features.
  • Supplementary note 8 The method according to Supplementary note 7, wherein the performance results are provided per cell for which the performance results are applicable.
  • Supplementary note 9 The method according to claim 7 or 8, wherein the recommended action includes at least one of: activating at least one AI/ML model, AI/ML functionality or AI/ML feature, deactivating at least one AI/ML model, AI/ML functionality or AI/ML feature, switching at least one AI/ML model, AI/ML functionality or AI/ML feature, or fallbacking at least one AI/ML model, AI/ML functionality or AI/ML feature.
  • Supplementary note 10 The method according to any one of Supplementary notes 1 to 9, further comprising: receiving further reporting information including at least one of inference results for AI/ML models at a user equipment, UE, and measurements at the UE unit which can be used for the monitoring, and wherein the performance results are based on the further reporting information.
  • a first unit of a distributed base station comprising: means for transmitting, to a second unit of the distributed base station, a request for monitoring Artificial Intelligence, AI,/Machine Learning, ML, models, AI/ML functionalities or AI/ML features; means for receiving, from the second unit, reporting information including at least one of inference results for AI/ML models at the second unit and measurements at the second unit which can be used for the monitoring; and means for transmitting, to the second unit, performance results based on the reporting information.
  • UEs 5 radio access network (RAN) node, base station 5b distributed unit (DU) 5c central unit (CU) 7 core network 9 cells 10 control plane functions (CPFs) 10-1 Access and Mobility Management Functions (AMFs) 10-2 Session Management Functions (SMFs) 11 user plane functions (UPFs) 41 data collection function 43 model training function 45 model inference function 47 actor 49 management function 51 model storage entity 310, 51b, 51c, 710 transceiver circuit 330 antenna 53b air interface 350 user interface 55c, 720 network interface 370, 57b, 57c, 730 controller 390, 59b, 59c, 740 memory 410, 61b, 61c, 750 operating system 430, 63b, 63c, 760 communications control module 450, 65b, 65c, 770 AI/ML module

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Un procédé mis en œuvre par une première unité d'une station de base distribuée est divulgué. Le procédé consiste à transmettre, à une seconde unité de la station de base distribuée, une demande de surveillance de modèles d'intelligence artificielle (IA)/apprentissage automatique (ML), des fonctionnalités IA/ML ou des caractéristiques IA/ML ; à recevoir, en provenance de la seconde unité, des informations de rapport comprenant des résultats d'inférence pour des modèles AI/ML au niveau de la seconde unité et/ou des mesures au niveau de la seconde unité qui peuvent être utilisées pour la surveillance ; et à transmettre, à la seconde unité, des résultats de performance sur la base des informations de rapport.
PCT/JP2024/025576 2023-07-21 2024-07-17 Première unité et procédé mis en œuvre par une première unité Pending WO2025023106A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB2311261.8 2023-07-21
GBGB2311261.8A GB202311261D0 (en) 2023-07-21 2023-07-21 Communication system

Publications (1)

Publication Number Publication Date
WO2025023106A1 true WO2025023106A1 (fr) 2025-01-30

Family

ID=87851952

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2024/025576 Pending WO2025023106A1 (fr) 2023-07-21 2024-07-17 Première unité et procédé mis en œuvre par une première unité

Country Status (2)

Country Link
GB (1) GB202311261D0 (fr)
WO (1) WO2025023106A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023012359A1 (fr) * 2021-08-05 2023-02-09 Telefonaktiebolaget Lm Ericsson (Publ) Procédé de surveillance des performances d'un modèle ou d'un algorithme d'intelligence artificielle (ai)/d'apprentissage machine (ml)
WO2024109110A1 (fr) * 2023-07-13 2024-05-30 Lenovo (Beijing) Limited Fourniture d'informations d'assistance pour gcv

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023012359A1 (fr) * 2021-08-05 2023-02-09 Telefonaktiebolaget Lm Ericsson (Publ) Procédé de surveillance des performances d'un modèle ou d'un algorithme d'intelligence artificielle (ai)/d'apprentissage machine (ml)
WO2024109110A1 (fr) * 2023-07-13 2024-05-30 Lenovo (Beijing) Limited Fourniture d'informations d'assistance pour gcv

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
KEETH JAYASINGHE ET AL: "Further discussion on the general aspects of ML for Air-interface", vol. RAN WG1, no. Toulouse, FR; 20221114 - 20221118, 7 November 2022 (2022-11-07), XP052222884, Retrieved from the Internet <URL:https://www.3gpp.org/ftp/TSG_RAN/WG1_RL1/TSGR1_111/Docs/R1-2212326.zip> [retrieved on 20221107] *
NEXT GENERATION MOBILE NETWORKS (NGMN) ALLIANCE, NGMN 5G WHITE PAPER, Retrieved from the Internet <URL:https://www.ngmn.org/5g-white-paper.html>

Also Published As

Publication number Publication date
GB202311261D0 (en) 2023-09-06

Similar Documents

Publication Publication Date Title
WO2024171940A1 (fr) Équipement utilisateur, nœud de réseau d&#39;accès et procédés associés pour mettre en œuvre des modèles ia/aa
WO2022234807A1 (fr) Système de communication
EP3834494A1 (fr) Comportement d&#39;équipement utilisateur dans une zone autorisée ou dans une zone non autorisée
WO2024176812A1 (fr) Procédé, équipement utilisateur et nœud de réseau d&#39;accès
WO2025028222A1 (fr) Procédé, unité centrale, équipement utilisateur et nœud de réseau central
WO2024214642A1 (fr) Sortie de prédiction basée sur un modèle
WO2025009372A1 (fr) Procédé, équipement utilisateur, nœud de réseau d&#39;accès et nœud de réseau central
WO2025023106A1 (fr) Première unité et procédé mis en œuvre par une première unité
WO2024034479A1 (fr) Procédé, répéteur commandé par réseau et nœud de réseau d&#39;accès
WO2024048293A1 (fr) Procédé pour un répéteur commandé par réseau, procédé pour un nœud de réseau d&#39;accès, répéteur commandé par réseau et nœud de réseau d&#39;accès
WO2025023094A1 (fr) Première unité, seconde unité, procédé mis en œuvre par une première unité, et procédé mis en œuvre par une seconde unité d&#39;une station de base distribuée
JP2025524938A (ja) 方法、モデル訓練機能およびモデル推論機能
WO2024158006A1 (fr) Nœud de réseau d&#39;accès, nœud de réseau central, équipement utilisateur et procédé
GB2629027A (en) Communication system
WO2024171894A1 (fr) Transfert de modèle ia/ml dans un réseau sans fil
WO2025033163A1 (fr) Procédé, équipement utilisateur et nœud de réseau d&#39;accès
WO2025154649A1 (fr) Procédé mis en œuvre par un équipement utilisateur, procédé mis en œuvre par un nœud de réseau d&#39;accès, équipement utilisateur et nœud de réseau d&#39;accès
WO2025173659A1 (fr) Procédé, dispositif mobile et nœud de réseau d&#39;accès
EP4666624A1 (fr) Transfert de modèle ia/ml dans un réseau sans fil
WO2025211348A1 (fr) Procédé, dispositif mobile, nœud de réseau d&#39;accès
EP4666625A1 (fr) Équipement utilisateur, noeud de réseau d&#39;accès et procédés associés pour mettre en oeuvre des modèles ia/aa
WO2025074898A1 (fr) Procédé, première unité distribuée et équipement utilisateur
US12245129B2 (en) In-vehicle machine learning service discovery in perceptive wireless communications
WO2025249257A1 (fr) Procédé mis en œuvre par un premier nœud de réseau d&#39;accès, procédé mis en œuvre par un deuxième nœud de réseau d&#39;accès, premier nœud de réseau d&#39;accès et deuxième nœud de réseau d&#39;accès
WO2024210047A1 (fr) Procédé, équipement utilisateur et nœud de réseau d&#39;accès

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24751563

Country of ref document: EP

Kind code of ref document: A1