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). The disclosure has particular, although not necessarily exclusive, relevance to data collection for artificial intelligence and machine learning (AI/ML) models used in 'New Radio' systems (also referred to as 'Next Generation' systems), and similar systems.
(Related Arts)
Recent developments of the 3GPP standards are referred to as the Long-Term Evolution (LTE) of Evolved Packet Core (EPC) network and Evolved UMTS Terrestrial Radio Access Network (E-UTRAN), also commonly referred as '4G'. In addition, the term '5G' and 'new radio' (NR) refer to an evolving communication technology that is expected to support a variety of applications and services. Various details of 5G networks are described in, for example, the 'NGMN 5G White Paper' V1.0 by the Next Generation Mobile Networks (NGMN) Alliance, which document is available from https://www.ngmn.org/5g-white-paper.html. 3GPP intends to support 5G by way of the so-called 3GPP Next Generation (NextGen) radio access network (RAN) and the 3GPP NextGen core network.
Under the 3GPP standards, a NodeB (or an eNB in LTE, gNB in 5G) is the RAN node (or simply 'access node', 'access network node' or 'base station') via which communication devices (user equipment or 'UE') connect to a core network and communicate with other communication devices or remote servers. For simplicity, the present application will use the term RAN node, base station, or access network node to refer to any such access nodes.
Some of the additional developments in 3GPP relate to the use of artificial intelligence (AI) and machine learning (ML), often abbreviated to AI/ML. Predictions or inferences generated using an AI/ML model can be used as part of various methods for improving the reliability or efficiency of communications in the network. For example, AI/ML models can be used to predict the path of a UE based on previous mobility of the UE, used for beam management, 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 or 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. The base station may also transmit an inference generated using the model to another node in the network, for use at the other node. Alternatively, an AI/ML model may be hosted at two nodes of the network, for example at a base station and at a UE. In this case, the base station and the UE may both make determinations or predictions using the model. For example, the UE may use the model as part of an encoding process for encoding (and/or compressing) channel state information (CSI) for transmission to the base station, 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.
(Problem of Related Arts)
AI/ML data may be collected by nodes in a communication system for training the AI/ML model, for data analytics (e.g. model performance monitoring), and for generating the inferences using the AI/ML model. However, improved methods for obtaining and transmitting such AI/ML data are needed. Whilst in some scenarios the AI/ML data may be collected by the same node that hosts the AI/ML model (e.g. a UE), in other scenarios the AI/ML data may need to be transmitted from the node that collects the AI/ML data to a node that hosts the AI/ML model. The data volume of the AI/ML data is often large, and improved methods for controlling the collection of the AI/ML data and the transmission of the AI/ML data to the node that hosts the AI/ML model are needed, for improving the efficiency and reliability of the data collection and transmission. For example, there is a need for improved methods for managing UE consent for AI/ML data collection, and for configuring the data collection at the UEs. There is also a need for more reliable methods for transmitting AI/ML data from the UE to the node that hosts the AI/ML model, for example to handle radio link failure, handover of the UE, or changes in the radio resource control (RRC) state of the UE.
More generally, there is a need for improved apparatus and methods for collection and transmission of data for use with AI/ML models.
The disclosure aims to provide apparatus and methods that at least partially address the above needs and/or issues.
(Description of Aspects)
The present disclosure describes multiple aspects and variants for each instance. These aspects and variants can be arbitrarily combined with each other.
In one aspect there is provided a method performed by an access network node, the method comprising: receiving, from a core network node, information indicating whether a logged measurement is permitted to be performed by a user equipment, UE, or a user, wherein the logged measurement is for use with a model, and wherein the model is for generating a determination, prediction, or output parameter corresponding to a user privacy for a user related to the UE; in a case where the information indicates that the logged measurement is permitted to be performed by the UE or the user, transmitting, to the UE, measurement configuration information for use by the UE to perform the logged measurement; and in a case where the information indicates that the logged measurement is not permitted to be performed by the UE, not transmitting the measurement configuration information to the UE.
The method may further comprise: transmitting, to the core network node, a request for the information indicating whether the logged measurement is permitted to be performed by the UE, wherein the receiving is performed in response to the transmitting the request.
The receiving may be performed in a case where the UE accesses to the core network node.
The request may be a request for information indicating whether the logged measurement is permitted to be performed by each of a plurality of UEs, and the method may further comprise: selecting, for the logged measurement, UEs from the plurality of the UEs based on the information indicating whether the logged measurement is permitted to be performed by the each of the plurality of the UEs.
The method may further comprise: receiving, from a data collection entity, a request for data for use with the model; and the transmitting the request for information indicating whether the measurement is permitted to be performed by the user equipment or the user may be responsive to the request received from the data collection entity.
The method may further comprise receiving, from a data collection entity, at least one of: an indication of one or more AI/ML model identities for which data is to be obtained for use with the one or more AI/ML models; an indication of one or more AI/ML function identities for which data is to be obtained for use with a corresponding one or more AI/ML models; or measurement configuration information for obtaining data for use with one or more AI/ML models.
The information indicating whether the logged measurement is permitted to be performed by the UE may be determined in a registration procedure of the UE.
The measurement configuration information transmitted to the UE by the access network node may comprise at least one of: an indication of one or more AI/ML model identities for which data, for use with the one or more AI/ML models, is to be obtained by performing the measurement; an indication of one or more AI/ML function identities for which data, for use with a corresponding one or more AI/ML models, is to be obtained by performing the measurement; or an indication of a type of data to be obtained by the UE, by performing the measurement, for a particular AI/ML model or AI/ML model function.
The measurement configuration information transmitted to the UE by the access network node may comprise an indication of one or more beams to be measured by the UE to obtain the data.
The measurement configuration information transmitted to the UE by the access network node may comprise an indication of a Synchronization Signal Block, SSB, or channel state information reference signal, CSI-RS, to be measured by the UE to obtain the data.
The method may further comprise transmitting, to the UE, an indication that the measurement is to be activated or deactivated.
The model may be an artificial intelligence or machine learning, AI/ML, model.
The data may comprise: inference data for generating an inference using the AI/ML model; training data for training the AI/ML model; or monitoring data for monitoring a performance of the AI/ML model.
The method may further comprise receiving, from the core network node, capability information that indicates whether the model is supported for use at the core network node.
The method may comprise: receiving, from the UE when the UE is in a radio resource control, RRC, connected state, data obtained by the UE performing the logged measurement for use with the model, receiving, from the UE, an indication that additional data for use with the model is available for transmission from the UE to the access network node; and receiving, from the UE, the additional data.
The method may further comprise transmitting, to the UE, a request for the additional data.
The method may further comprise: receiving, from the UE, when the UE is in a radio resource control, RRC, connected state, data for use with the model; and transmitting, to the UE, an RRC release message comprising an indication of a measurement configuration for use by the UE, when the UE is in an RRC idle or RRC inactive state, to perform a measurement for obtaining additional data for use with the model.
In one aspect there is provided a method performed by a core network node, the method comprising: receiving, from an access network node, a request for information indicating whether a logged measurement is permitted to be performed by a user equipment, UE, wherein the logged measurement is for use with a model, and wherein the model is for generating a determination, prediction, or output parameter corresponding to a user privacy for a user related to the UE; and transmitting, to the access network node, the information indicating whether the logged measurement is permitted to be performed by the UE.
The core network node may comprise an Access and Mobility Management Function, AMF.
The method may further comprise transmitting, to the access network node, capability information that indicates whether the model is supported for use at the core network node.
In one aspect there is provided a method performed by a user equipment, UE, the method comprising: receiving, from an access network node, measurement configuration information for use by the UE to perform a logged measurement for use with a model, wherein the model is for generating a determination, prediction, or output parameter corresponding to a user privacy for a user related to the UE; wherein the measurement configuration information comprises at least one of: an indication of one or more AI/ML model identities for which data, for use with the one or more AI/ML models, is to be obtained by performing the measurement; an indication of one or more AI/ML function identities for which data, for use with a corresponding one or more AI/ML models, is to be obtained by performing the measurement; or an indication of a type of data to be obtained by the UE, by performing the measurement, for a particular AI/ML model or AI/ML model function; and wherein the method further comprises performing the logged measurement.
The measurement configuration information may comprise an indication of one or more beams to be measured by the UE, and the method may further comprise performing the measurement of the one or more beams.
The measurement configuration information may comprise an indication of a Synchronization Signal Block, SSB, or channel state information reference signal, CSI-RS, to be measured by the UE, and the method may further comprise performing the measurement of the SSB or CSI-RS.
The method may further comprise: receiving, from the access network node, an indication that the measurement is to be activated or deactivated; and activating or deactivating the measurement based on the indication that the measurement is to be activated or deactivated.
The method may further comprise transmitting, to the access network node, when the UE is in a radio resource control, RRC, idle or RRC inactive state, an indication that a buffer used to store the logged measurement has insufficient space for storing additional data.
The method may further comprise: transmitting, to the access network node, when the UE is in a RRC connected state, a logged measurement for use with the model; transmitting, to the access network node, an indication that an additional logged measurement for use with the model is available for transmission from the UE to the access network node; and transmitting the additional logged measurement to the access network node.
The method may further comprise receiving, from the access network node, a request for the additional logged measurement.
In one aspect there is provided a method performed by an access network node, the method comprising: receiving, from a user equipment, UE, when the UE is in a radio resource control, RRC, connected state, data for use with a model, wherein the model is for generating a determination, prediction, or output parameter; receiving, from the UE, an indication that additional data for use with the model is available for transmission from the UE to the access network node; and receiving, from the UE, the additional data.
The method may further comprise transmitting, to the UE, a request for the additional data.
In one aspect there is provided a method performed by user equipment, UE, the method comprising: transmitting, to an access network node, when the UE is in a radio resource control, RRC, connected state, data for use with a model, wherein the model is for generating a determination, prediction, or output parameter; and transmitting, to the access network node, an indication that additional data for use with the model is available for transmission from the UE to the access network node; and transmitting, to the access network node, the additional data.
The method may further comprise receiving, from the access network node, a request for the additional data.
In one aspect there is provided a method performed by a first access network node, the method comprising: receiving, from a user equipment, UE, when the UE is in a radio resource control, RRC, connected state, a first portion of data for use with a model, wherein the model is for generating a determination, prediction, or output parameter; maintaining, at the access network node, when the UE experiences radio link failure or the UE is handed over to a second access network node, information indicating a second portion of the data to be transmitted by the UE; receiving, from the second access network node, after the UE has connected to the second access network node, a request for the information indicating the second portion of the data; and transmitting the information indicating the second portion of the data to the second access network node.
The information indicating the second portion of the data may comprise RRC context information that is associated with the UE.
The information indicating the second portion of the data may comprise an RRC segment number.
The method may further comprise: receiving the second portion of the data from the second access network node; and transmitting the first portion of the data and the second portion of the data to a data collection entity.
The method may further comprise transmitting the first portion of the data to the second access network node.
The first portion of the data may be received from the UE using a dedicated radio bearer that is terminated at the first access network node.
The information indicating the second portion of the data to be transmitted by the UE may comprise a Packet Data Convergence Protocol, PDCP, sequence number.
In one aspect there is provided a method performed by a second access network node, the method comprising: performing communication with a user equipment, UE, following radio link failure between the UE and a first access network node, or following handover of the UE from the first access network node to the second access network node, wherein the UE has transmitted a first portion of data for use with a model to the first access network node, and wherein the model is for generating a determination, prediction, or output parameter; transmitting, to the first access network node, a request for information indicating a second portion of the data to be transmitted by the UE; receiving, from the first access network node, the information indicating the second portion of the data; transmitting the information indicating the second portion of the data to the UE; and receiving the second portion of the data from the UE.
The method may further comprise transmitting the second portion of the data to the first access network node.
The method may further comprise: receiving the first portion of the data from the first access network node; and transmitting the first portion of the data and the second portion of the data to a data collection entity.
The information indicating the second portion of the data to be transmitted by the UE may comprise a Packet Data Convergence Protocol, PDCP, sequence number.
The method may further comprise: performing a radio resource control, RRC, establishment procedure with the UE; transmitting, to the UE, an indication of a measurement configuration for use by the UE, when the UE is in an RRC idle or RRC inactive state; receiving the second portion of the data from the UE; and transmitting the second portion of the data to the first access network node.
In one aspect there is provided a method performed by a user equipment, UE, the method comprising: transmitting, to a first access network node, when the UE is in a radio resource control, RRC, connected state, a first portion of data for use with a model, wherein the model is for generating a determination, prediction, or output parameter; performing communication with a second access network node following radio link failure between the UE and the first access network node, or following handover of the UE from the first access network node to the second access network node; receiving, from the second access network node, information indicating a second portion of the data to be transmitted by the UE; and transmitting the second portion of the data to the second access network node.
The method may further comprise receiving, from the first access network node, an RRC release message comprising an indication of a first measurement configuration for use by the UE, when the UE is in an RRC idle or RRC inactive state, to perform a measurement for obtaining additional data for use with the model; and performing the measurement, when the UE is in the RRC idle or RRC inactive state, using the first measurement configuration.
The method may further comprise: performing an RRC establishment procedure with the second access network node; and receiving, from the second access network node, an indication of a second measurement configuration for use by the UE, when the UE is in an RRC idle or RRC inactive state.
The method may further comprise transmitting, to the second access network node, at least one of an indication of the first measurement configuration or an indication of an identity of the first access network node.
In one aspect there is provided a method performed by a first access network node, the method comprising: receiving, from a user equipment, UE, when the UE is in a radio resource control, RRC, connected state, data for use with a model, wherein the model is for generating a determination, prediction, or output parameter; and transmitting, to the UE, an RRC release message comprising an indication of a measurement configuration for use by the UE, when the UE is in an RRC idle or RRC inactive state, to perform a measurement for obtaining additional data for use with the model.
In one aspect there is provided a method performed by a second access network node, the method comprising: performing a radio resource control, RRC, establishment procedure with a user equipment, UE, that has transmitted a first portion of data for use with a model to a first access network node, wherein the model is for generating a determination, prediction, or output parameter; and transmitting, to the UE, an indication of a measurement configuration for use by the UE, when the UE is in an RRC idle or RRC inactive state; receiving a second portion of the data from the UE; and transmitting the second portion of the data to the first access network node.
In one aspect there is provided a method performed by a user equipment, UE, the method comprising: transmitting, to a first access network node, when the UE is in a radio resource control, RRC, connected state, data for use with a model, wherein the model is for generating a determination, prediction, or output parameter; and receiving, from the first access network node, an RRC release message comprising an indication of a first measurement configuration for use by the UE, when the UE is in an RRC idle or RRC inactive state, to perform a measurement for obtaining additional data for use with the model; and performing the measurement, when the UE is in the RRC idle or RRC inactive state, using the first measurement configuration.
The method may further comprise: performing an RRC establishment procedure with a second access network node; and receiving, from the second access network node, an indication of a second measurement configuration for use by the UE, when the UE is in an RRC idle or RRC inactive state.
The method may further comprise transmitting, to the second access network node, at least one of an indication of the first measurement configuration or an indication of an identity of the first access network node.
In one aspect there is provided an access network node comprising: means for receiving, from a core network node, information indicating whether a logged measurement is permitted to be performed by a user equipment, UE, wherein the logged measurement is for use with a model, and wherein the model is for generating a determination, prediction, or output parameter corresponding to a user privacy for a user related to the UE; and means for: in a case where the information indicates that the logged measurement is permitted to be performed by the UE or the user, transmitting, to the UE, measurement configuration information for use by the UE to perform the logged measurement; and in a case where the information indicates that the logged measurement is not permitted to be performed by the UE, not transmitting the measurement configuration information to the UE.
In one aspect there is provided a core network node comprising: means for receiving, from an access network node, a request for information indicating whether a measurement is permitted to be performed by a user equipment, UE, wherein the measurement is for obtaining data for use with a model, and wherein the model is for generating a determination, prediction, or output parameter; and means for transmitting, to the access network node, the information indicating whether the measurement is permitted to be performed by the UE.
In one aspect there is provided a user equipment, UE, comprising: means for receiving, from an access network node, measurement configuration information for use by the UE to perform a measurement for obtaining data for use with a model, wherein the model is for generating a determination, prediction, or output parameter; wherein the measurement configuration information comprises at least one of: an indication of one or more AI/ML model identities for which data, for use with the one or more AI/ML models, is to be obtained by performing the measurement; an indication of one or more AI/ML function identities for which data, for use with a corresponding one or more AI/ML models, is to be obtained by performing the measurement; or an indication of a type of data to be obtained by the UE, by performing the measurement, for a particular AI/ML model or AI/ML model function; and wherein the UE further comprises means for performing the measurement.
In one aspect there is provided a first access network node comprising: means for receiving, from a user equipment, UE, when the UE is in a radio resource control, RRC, connected state, a first portion of data for use with a model, wherein the model is for generating a determination, prediction, or output parameter; means for maintaining, at the access network node, when the UE experiences radio link failure or the UE is handed over to a second access network node, information indicating a second portion of the data to be transmitted by the UE; means for receiving, from the second access network node, after the UE has connected to the second access network node, a request for the information indicating the second portion of the data; and means for transmitting the information indicating the second portion of the data to the second access network node.
In one aspect there is provided a second access network node comprising: means for performing communication with a user equipment, UE, following radio link failure between the UE and a first access network node, or following handover of the UE from the first access network node to the second access network node, wherein the UE has transmitted a first portion of data for use with a model to the first access network node, and wherein the model is for generating a determination, prediction, or output parameter; means for transmitting, to the first access network node, a request for information indicating a second portion of the data to be transmitted by the UE; means for receiving, from the first access network node, the information indicating the second portion of the data; means for transmitting the information indicating the second portion of the data to the UE; and means for receiving the second portion of the data from the UE.
In one aspect there is provided a user equipment, UE, comprising: means for transmitting, to a first access network node, when the UE is in a radio resource control, RRC, connected state, a first portion of data for use with a model, wherein the model is for generating a determination, prediction, or output parameter; means for performing communication with a second access network node following radio link failure between the UE and the first access network node, or following handover of the UE from the first access network node to the second access network node; means for receiving, from the second access network node, information indicating a second portion of the data to be transmitted by the UE; and means for transmitting the second portion of the data to the second access network node.
In one aspect there is provided a method performed by an access network node, the method comprising: transmitting, to a core network node, a request for information indicating whether a measurement is permitted to be performed by a user equipment, UE, wherein the measurement is for obtaining data for use with a model, and wherein the model is for generating a determination, prediction, or output parameter; receiving, from the core network node, the information indicating whether the measurement is permitted to be performed by the UE; in a case where the information indicates that the measurement is permitted to be performed by the UE, transmitting, to the UE, measurement configuration information for use by the UE to perform the measurement; and in a case where the information indicates that the measurement is not permitted to be performed by the UE, not transmitting the measurement configuration information to the UE.
The model may be an artificial intelligence or machine learning, AI/ML, model.
The data may comprise: inference data for generating an inference using the AI/ML model; training data for training the AI/ML model; or monitoring data for monitoring a performance of the AI/ML model.
The request may be a request for information indicating whether a measurement is permitted to be performed by each of a plurality of UEs.
The method may further comprise receiving, from the core network node, capability information that indicates whether the model is supported for use at the core network node.
The method may further comprise: receiving, from a data collection entity, a request for data for use with the model; and wherein the transmitting the request for information indicating whether the measurement is permitted to be performed by the user equipment is responsive to the request received from the data collection entity.
The method may further comprise receiving, from a data collection entity, at least one of: an indication of one or more AI/ML model identities for which data is to be obtained for use with the one or more AI/ML models; an indication of one or more AI/ML function identities for which data is to be obtained for use with a corresponding one or more AI/ML models; or measurement configuration information for obtaining data for use with one or more AI/ML models.
The measurement configuration information transmitted to the UE by the access network node may comprise at least one of: an indication of one or more AI/ML model identities for which data, for use with the one or more AI/ML models, is to be obtained by performing the measurement; an indication of one or more AI/ML function identities for which data, for use with a corresponding one or more AI/ML models, is to be obtained by performing the measurement; or an indication of a type of data to be obtained by the UE, by performing the measurement, for a particular AI/ML model or AI/ML model function.
The measurement configuration information transmitted to the UE by the access network node may comprise an indication of one or more beams to be measured by the UE to obtain the data.
The measurement configuration information transmitted to the UE by the access network node may comprise an indication of a Synchronization Signal Block, SSB, or channel state information reference signal, CSI-RS, to be measured by the UE to obtain the data.
The method may further comprise transmitting, to the UE, an indication that the measurement is to be activated or deactivated.
In another aspect there is provided a method performed by a core network node, the method comprising: receiving, from an access network node, a request for information indicating whether a measurement is permitted to be performed by a user equipment, UE, wherein the measurement is for obtaining data for use with a model, and wherein the model is for generating a determination, prediction, or output parameter; and transmitting, to the access network node, the information indicating whether the measurement is permitted to be performed by the UE.
In another aspect there is provided a method performed by a user equipment, UE, the method comprising: receiving, from an access network node, measurement configuration information for use by the UE to perform a measurement for obtaining data for use with a model, wherein the model is for generating a determination, prediction, or output parameter; wherein the measurement configuration information comprises at least one of: an indication of one or more AI/ML model identities for which data, for use with the one or more AI/ML models, is to be obtained by performing the measurement; an indication of one or more AI/ML function identities for which data, for use with a corresponding one or more AI/ML models, is to be obtained by performing the measurement; or an indication of a type of data to be obtained by the UE, by performing the measurement, for a particular AI/ML model or AI/ML model function; and the method further comprises performing the measurement.
(Overview)
An exemplary communication system will now be described in general terms, by way of example only, with reference to Figs. 1 and 2.
Fig. 1 schematically illustrates a mobile ('cellular' or 'wireless') communication system 1 to which example embodiments of the present disclosure are applicable.
In the communication system 1, user equipment (UEs) 3-1, 3-2, 3-3 (e.g. mobile telephones and/or other mobile devices) 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). In the illustrated example, the RAN node 5 comprises a base station 5 or 'gNB' 5 operating one or more associated cells 9. Communication via the base station 5 is typically routed through a core network 7 (e.g. a 5G/6G or later generation core network or evolved packet core network (EPC)).
As those skilled in the art will appreciate, whilst three 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 generations, and/or any other 3GPP or non-3GPP communication protocols.
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. In this example, 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 (SMFs)) 10-2 and a number of other functions 10-n.
The base station 5 is connected to the core network nodes via appropriate interfaces (or 'reference points') such as an N2 reference point between the base station 5 and the AMF 10-1 for the communication of control signalling, and an N3 reference point between the base station 5 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 (or 'reference point') such as an N1 reference point (analogous to the S1 reference point in LTE)). It will be appreciated, that N1 communications are routed transparently via the base station 5.
One or more UPFs 11 are connected to an external data network (e.g. an IP network such as the internet) via an appropriate reference point (e.g. an N6 reference point) for communication of the user data.
The AMF 10-1 performs mobility management related functions, maintains the NAS 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 is configured to operate at least one cell 9 on an associated 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 FDD carrier that operates in paired spectrum.
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 originated from a higher layer, and the DL physical signals are used in the physical layer and correspond to REs which do not carry information originated from a higher layer.
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). The 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. 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). The PBCH provides UEs 3 with the Master Information Block (MIB). It also, in conjunction with the PDCCH, supports the synchronisation of time and frequency, which aids cell acquisition, selection and re-selection. 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 using any suitable signalling (e.g. per serving cell using ssb-periodicityServingCell). The periodicity value for the SSB may be, for example, greater than or equal to 20 ms. For initial cell selection, 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).
Similarly, 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 originated from a higher layer, and UL physical signals which are used in the physical layer and correspond to REs which do not carry information originated 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.
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, 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).
The base station 5 may be a base station 5 that is split between one or more distributed units (DUs) 50 and a central unit (CU) 60, with a CU 60 typically performing higher level functions and communication with the next generation core, and with the DU 50 performing lower level functions and communication over an air interface with UEs 3 in the vicinity (i.e. in a cell operated by the base station 5). This type of base station 5 may be referred to as a 'distributed' base station 5 or gNB 5. A distributed base station 5 includes the following functional units:
Central Unit (CU): a logical node hosting Radio Resource Control (RRC), Service Data Adaptation Protocol (SDAP) and Packet Data Convergence Protocol (PDCP) layers of the base station that controls the operation of one or more DUs. The CU terminates an appropriate interface (e.g. F1 interface) connected with the DU.
Distributed Unit (DU): a logical node hosting Radio Link Control (RLC), Medium Access Control (MAC) and Physical (PHY) layers of the gNB or en-gNB, and its operation is partly controlled by the CU. One DU supports one or multiple cells. One cell is supported by only one DU. The DU terminates an appropriate interface (e.g. F1 interface) connected with the CU.
CU-Control Plane (CU-CP): a logical node hosting the RRC and the control plane part of the PDCP protocol of the CU. The CU-CP terminates an appropriate interface (e.g. E1 interface) connected with the CU-UP and an appropriate interface (e.g. F1-C (F1 control plane) interface) connected with the DU.
CU-User Plane (CU-UP): a logical node hosting the user plane part of the PDCP protocol and the SDAP protocol of the CU. The CU-UP terminates an appropriate interface (e.g. E1 interface) connected with the CU-CP and an appropriate interface (e.g. F1-U (F1 user plane) interface) connected with the DU.
It will be appreciated that when a distributed base station or a similar control plane - user plane (CP-UP) split is employed, the control-plane and user-plane entities may each include an associated transceiver circuit, antenna, network interface, controller, memory, operating system, and communications control module. When the base station 5 comprises a distributed base station, the network interface also includes appropriate interfaces (e.g. an E1 interface and an F1 interface (F1-C for the control plane and F1-U for the user plane)) to communicate signals between respective functions of the distributed base station.
<Frame Structure>
Referring to 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 10ms. 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.
As seen in Fig. 2, the communication system 1 supports multiple different numerologies (subcarrier spacing (SCS), slot lengths and hence OFDM symbol lengths). Specifically, each numerology is identified by a parameter, μ, where μ=0 represents 15 kHz (corresponding to the LTE SCS). Currently, the SCS for other values of μ can, in effect, be derived from μ=0 by scaling up in powers of 2 (i.e. SCS = 15 x 2μ kHz). The relationship between the parameter, μ, and SCS (Δf) is as shown in Table 1:
Table 1 above shows one example of 5G Numerology.
<RAN Node>
(DU)
Fig. 3 is a schematic block diagram illustrating the main components of a DU 50 that may be used as part of the RAN node 5 for the communication system 1 shown in Fig. 1. As shown, the DU 50 has a transceiver circuit 451 for: transmitting signals to, and for receiving signals from, the communication devices (such as UEs 3) via the radio unit (RU) and the associated DU-RU interface 453; and for transmitting signals to, and for receiving signals from, the CU 60 of the RAN node 5 via a CU interface 454 (e.g. comprising an F1 interface which may be split into an F1-U and an F1-C interface for user plane and control plane signalling respectively).
The DU 50 has a controller 457 for controlling the operation of the DU 50. The controller 457 is associated with a memory 459. Software may be pre-installed in the memory 459 and/or may be downloaded via the communication system 1 or from a removable data storage device (RMD) for example. The controller 457 is configured to control the overall operation of the DU 50 by, in this example, program instructions or software instructions stored within memory 459.
As shown, these software instructions include, among other things, an operating system 461, a communications control module 463, an F1 module 465, a DU-RU module 468, a DU management module 472, a UE profile management module 473 and a mobility module 475.
The communications control module 463 is operable to control the communication between the DU 50 and one or more RUs (and hence between the DU 50 and the UE 3), and between the DU 50 and the CU 60. The communications control module 463 is configured for the overall control of the reception of signals corresponding to uplink communications from the UE 3 and for handling the transmission of downlink communications to the UE 3.
The F1 module 465 is responsible for the appropriate processing of signals received from, or transmitted to, the CU 60 via one or more CU (e.g. F1) interfaces 454. These signals may be separated into: user plane signals received from, or transmitted to, the CU-UP part of the CU 60 via the F1-U interface; and control plane signals received from, or transmitted to, the CU-CP part of the CU 60 via the F1-C interface.
The DU-RU module 468 is responsible for the appropriate processing of signals received from, or transmitted to, the RU via one or more RU (e.g. DU-RU) interfaces 453.
The DU management module 472 is responsible for managing the overall operation of the DU 50 and the overall performance of the tasks required of the DU 50. These tasks include, among other things, the generation and transmission of appropriate messages using appropriate signalling application protocols, depending on the functional split between the RU, DU 50 and CU 60, such as interpretation of received MAC signalling and the generation of MAC signalling for transmission. The DU management module 472 may control the overall operation of the DU 50 in accordance with any of the methods describe below, where appropriate.
The UE profile management module 473 is responsible for carrying out functions related to the UE profile including (where applicable): the reception and storage of the UE profile or related assistance/preference information from the UE 3 or from elsewhere in the network; the determination (where applicable) of appropriate mobility specific configurations, based on the UE profile / assistance information / preference information, for implementation at the UE 3 and/or RAN node 5; and/or the provision of configuration information (where applicable) for configuring the UE appropriately with mobility based configurations. The UE profile management module 473 may also store, for example, previous mobility information for a UE 3 (e.g. previous movements of the UE 3 between different communication cells of the network). It will be appreciated that, depending on implementation, the DU 50 may not implement at least some of these features.
The mobility module 475 is responsible for controlling mobility procedures for one or more UEs 3. For example, the mobility module 475 may be configured to perform one or more measurements for UE 3 mobility, or to select a candidate cell for handover.
(CU)
Fig. 4 is a schematic block diagram illustrating the main components of the CU 60 of the RAN node 5 for the communication system 1 shown in Fig. 1. As shown, the CU 60 has a transceiver circuit 551 for: transmitting signals to, and for receiving signals from, the DU 50 via one or more DU interfaces 554 (e.g. comprising an F1 interface which may be split into appropriate interfaces (e.g. an F1-U and an F1-C interface for user plane and control plane signalling respectively)); and for transmitting signals to, and for receiving signals from, the functions of the core network 7 via one or more core network interfaces 555 (e.g. comprising the N2 and N3 interfaces or the like).
The CU 60 has a controller 557 to control the operation of the CU 60. The controller 557 is associated with a memory 559. Software may be pre-installed in the memory 559 and/or may be downloaded via the communication system 1 or from a removable data storage device (RMD) for example. The controller 557 is configured to control the overall operation of the CU 60 by, in this example, program instructions or software instructions stored within memory 559.
As shown, these software instructions include, among other things, an operating system 561, a communications control module 563, an F1 module 565, an E1 module 566, an N2 module 568, an N3 module 569, a CU-UP management module 571, a CU-CP management module 572, a UE profile management module 573, and a mobility module 575. The functions of the mobility module 575 are the same as described above with reference to Fig. 3.
The communications control module 563 is operable to control the communication between the CU 60 and one or more DUs 50 (and hence between the CU 60 and the UE 3), and between the CU 60 and the core network 7. The communications control module 563 is configured for the overall control of the reception of signals corresponding to uplink communications from the UE 3 and for controlling the transmission of downlink communications.
The F1 module 565 is responsible for the appropriate processing of signals received from, or transmitted to, the DU 50 via one or more DU (e.g. F1) interfaces 554. These signals include: user plane signals received at, or transmitted by, the CU-UP part of the CU 60 via the F1-U interface; and control plane signals received at, or transmitted by, the CU-CP part of the CU 60 via the F1-C interface.
The E1 module 566 is responsible for the appropriate processing of signals transmitted between the CU-UP part of the CU 60 and the CU-CP part of the CU 60 via the corresponding internal CU interface (e.g. E1).
The N2 module 568 is responsible for the appropriate processing of signals received from, or transmitted to, the AMF 10-1 via the corresponding one or more core network interfaces (e.g. N2) 555.
The N3 module 569 is responsible for the appropriate processing of signals received from, or transmitted to, one or more core network user plane functions via one or more corresponding core network interfaces (e.g. N3) 555.
The CU-UP management module 571 is responsible for managing the overall operation of the CU-UP part of the CU 60 and the overall performance of the tasks required of the CU-UP.
The CU-CP management module 572 is responsible for managing the overall operation of the CU-CP part of the CU 60 and the overall performance of the tasks required of the CU-CP. These tasks include, among other things, the generation and transmission of appropriate messages using appropriate signalling application protocols, depending on the functional split between the RU, DU 50 and CU 60, such as interpretation of received RRC signalling and the generation of RRC signalling for transmission.
The UE profile management module 573 is responsible for carrying out functions related to the UE (mobility) profile including (where applicable): the reception and storage of the UE profile or related assistance/preference information from the UE 3 or from elsewhere in the network; the determination of appropriate mobility specific configurations, based on the UE profile / assistance information / preference information, for implementation at the UE 3 and/or RAN node 5; and/or the provision of configuration information for configuring the UE appropriately with mobility based configurations. The UE profile management module 573 may also store previous mobility information for a UE 3 (e.g. previous movements of the UE 3 between different communication cells of the network). It will be appreciated that, depending on implementation, the CU 60 may not implement at least some of these features.
<System information and SIB>
It will be appreciated that 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. 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).
The MSI comprises the MIB and system information block 1 (SIB1). The MIB 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 DL-SCH in SI messages. 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 Technical Specification (TS) 38.331 V17.4.0. 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.
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. For example, MIB may be transmitted with a periodicity of 80 ms and repetitions made within 80 ms, and SIB1 may be transmitted with a periodicity of 160 ms and a variable transmission repetition periodicity within 160 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.
A physical broadcast channel (PBCH) 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. 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). In the frequency domain, an SS/PBCH block comprises 240 contiguous subcarriers. When the UE 3 is in an RRC connected state, 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). The OSI may be similarly transmitted, for example, using a PDSCH. When one or more beamformed transmissions are transmitted in a cell provided by the base station 5, some of the SI (e.g. some of the SIB) may only be transmitted using particular beams, or using a particular transmission/reception point (TRP).
<UE Mobility>
Fig. 5 shows an overview of a mobility procedure that may be performed in a communication system 1 of the type illustrated in Fig. 1. In this example, a handover of a UE 3 from a source (R)AN node 5 to a target (R)AN node 5 is performed.
In optional step S501 the UE 3 performs a measurement. The measurement may be a measurement of a signal transmitted by the source (R)AN node 5 or a measurement of a signal transmitted by the target (R)AN node 5. The measurement may be a measurement of a signal strength, that can be used as part of a determination that the UE 3 is to be handed over from the source (R)AN node 5 to the target (R)AN node 5. In optional step S502 the UE 3 transmits a measurement report to the source (R)AN node 5 that provides an indication of the result of the measurement. The measurement report may be transmitted from the UE 3 to the source (R)AN node 5 in an RRC message. In this example the source (R)AN node 5 uses the information provided in the measurement report to determine that the UE 3 is to be handed over to the target (R)AN node 5. However, it will be appreciated that a determination that handover to the target (R)AN node 5 is to be performed may alternatively (or additionally) be based on a measurement performed at the source (R)AN node 5 or at the target (R)AN node 5. Alternatively, a determination that handover of the UE 3 is to be performed may be based on a factor other than a signal measurement, such as a level of congestion in a cell operated by the source (R)AN node 5, or an inference (e.g. determination or prediction) generated using an AI/ML model.
In Step S503 the source (R)AN node 5 transmits a handover request to the target (R)AN node 5, requesting handover of the UE 3 from the source (R)AN node 5 to the target (R)AN node 5. The handover request may include an indication of, for example, an identity of the source (R)AN node 5, a cause value for the handover, an identity of the target cell, UE 3 context information (e.g. a maximum bit rate of the UE 3, or security capabilities of the UE 3), and UE history information. If the handover has been triggered by the measurement report received by the source (R)AN node 5 in step S502, then the cause value may indicate, for example, that the handover is desirable for radio reasons. Alternatively, if the handover has been triggered to reduce the load at the source (R)AN node 5, the cause value may indicate that the handover is for reducing load in the serving cell. The handover request message may also include an indication of the AMF 10-1 that is serving the UE 3.
In step S504, the target (R)AN node 5 transmits an acknowledgement of the handover request (which may be referred to as a "handover request acknowledgement" message). The handover request acknowledgement message includes an indication of handover configuration information for the handover that is to be forwarded to the UE 3. The handover request acknowledgement message may also include configuration information that enables the source (R)AN node 5 to begin forwarding user plane data for the UE 3 to the target (R)AN node 5.
The transmissions of steps S503 and S504 may be performed over an Xn interface between the source (R)AN node 5 and the target (R)AN node 5 (and therefore the handover procedure in this example may be referred to as an Xn-based handover procedure). Steps S501 to S504 may be referred to as a 'handover preparation phase'.
In step S505, the source (R)AN node 5 transmits the handover configuration information to the UE 3. The configuration information for the handover may be, for example, an RRC configuration transmitted in an RRC configuration message or an RRC reconfiguration message. In step S506, the UE 3 applies the received configuration for handover and transmits an indication to the target (R)AN node 5 that configuration for the handover is complete. The message transmitted in step S505 may be, for example, an RRC Reconfiguration Complete message. Steps S505 and S506 may be referred to as a 'handover execution phase'.
Following the handover execution phase, the UE 3 is operable to transmit uplink transmissions to the target (R)AN node 5 (e.g uplink data) and receive downlink transmissions from the target (R)AN node 5 (e.g. downlink data).
It will be appreciated that mobility methods and handover procedures for the UE 3 are not restricted to the example illustrated in Fig. 5. For example, the UE 3 may be configured to perform a conditional handover (CHO) in which the UE 3 determines whether handover of the UE 3 to a candidate cell is to be performed based on one or more execution conditions. It will also be appreciated that handover may be performed in which the DU 50 changes but the CU 60 remains the same (inter-DU intra-CU handover), in which both the DU 50 and CU 60 change (inter-DU inter-CU handover), or between two cells operated by the same DU 50.
<Logical Channels and Logical Channel Priority>
A logical channel (LCH) may be a control channel for the transmission of control and/or configuration information (control plane information), or may be a channel used for transmission of user data (user plane information). Logical channels that may be used in the system illustrated in Fig. 1 include the broadcast control channel (BCCH), the paging control channel (PCCH), the common control channel (CCCH) used by the UE 3 during initial access, the dedicated control channel (DCCH) and the dedicated traffic channel (DTCH). One or more transport channels may also be used in the system of Fig. 1. Transport channels include the broadcast channel (BCH), paging channel (PCH), downlink shared channel (DLSCH), uplink shared channel (ULSCH) and random access channel (RACH). Mapping between the logical channels and the transport channels may be performed at the medium access control (MAC) layer, and multiple logical channels may be multiplexed for transmission using a transport channel (e.g. based on the priority of each logical channel, as described later). For example, the BCCH may be mapped to the BCH or the DLSCH, and the PCCH may be mapped to the PCH. The transport channels are mapped to corresponding physical channels (e.g: PDCCH, PDSCH or PBCH for downlink transmissions; or PUSCH, PUCCH or PUSCH for uplink transmissions).
A logical channel may be identified using a corresponding logical channel ID (LCID). A set of logical channels may be grouped into a logical channel group (LCG), which can be identified using a corresponding index (e.g. an index between 0 and 7).
A logical channel can be assigned a priority by the network (e.g. a transmission priority). For example, a logical channel being used for part of a handover procedure may be assigned a relatively high priority for transmission, since transmission delays in the handover procedure increase the likelihood of handover failure. The base station 5 may determine to preferentially include data (or other information) corresponding to a higher priority logical channel in a medium access control (MAC) protocol data unit (PDU) for transmission to the UE 3, rather than including data or other information corresponding to a lower priority logical channel. The base station 5 may also control the scheduling of uplink transmissions by the UE 3 based on the logical channel priorities.
A prioritised bit rate (PBR) may be defined for a logical channel. The prioritised bit rate may be configured by the base station 5. The prioritised bit rate is a bit rate configured for use for a higher priority logical channel, and the remaining available bit rate (or a portion of the remaining available bit rate) is configured for transmission of the lower priority logical channels. Use of the PBR beneficially helps to avoid a situation in which only the highest priority logical channels are transmitted.
<Artificial Intelligence (AI)/Machine Learning (ML)>
Fig. 6 illustrates a framework in respect of an AI/ML model, and how various entities of the framework may interact with one another.
The entities include a data collection function 41, a model training function 43, an 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 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 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.
The 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 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 inference function 45 may receive an AI/ML model from the model storage entity 51, and inference data from the data collection 41 for use with the AI/ML model. The 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 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 inference function 45. The management function 49 may also transmit, to the inference function 45, performance feedback or a retraining request for the AI/ML model.
The functions illustrated in Fig. 6 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).
Terms referred to by 3GPP in the context of this framework include:
AI/ML Training: An online or offline process for training an AI/ML model.
AI/ML Validation: A method for evaluating the quality (e.g. prediction accuracy) of an AI/ML model using a dataset that is different from the one used for the training of the model.
AI/ML Model Testing: A method for evaluating the performance of a final AI/ML model, using a dataset different from the ones used for training and validation.
AI/ML Data Collection: A method of collecting data by network nodes, a management entity, and/or a UE 3, for training the AI/ML model, for data analytics (e.g. model performance monitoring), and/or for generating an inference using the AI/ML model.
Model Monitoring: A method of monitoring the inference performance (e.g. prediction accuracy) of the AI/ML model.
Training Data: Data for input to the AI/ML Model Training function.
Supervised Learning: A method of training an AI/ML model using labelled data.
Unsupervised Leaning: A method of training an AI/ML model using unlabelled data.
Semi-supervised Learning: A method of training an AI/ML model using both labelled and unlabelled data.
Inference Data: Data for input to the AI/ML Model Inference function, for generating an inference.
Model Deployment/Update: A method of deploying (e.g. transmitting to a network node) an AI/ML model to the Model Inference function, or of delivering an updated model to the Model Inference function.
The data collection function 41 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. 7 shows an illustration of a method of training an AI/ML model, and of monitoring the performance of the AI/ML model. As illustrated in Fig. 7, stored data/features may first be extracted in a data extraction step. In the data validation step, a determination of whether to proceed with training or retaining the AI/ML model is made (e.g based on the extracted data). In 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.
In the model training step, the AI/ML model is trained (or retrained) using training data prepared in the data preparation step. It will be appreciated that any suitable training method can be used to train the AI/ML model (e.g a method that comprises supervised learning or unsupervised learning). In the model evaluation step, 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). In the model validation 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).
In the model serving 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. For example, the AI/ML model may be transmitted to the base station 5 and/or the UE 3, for use at the base station and/or the UE to generate predictions or determinations using the AI/ML model as part of a prediction service step, as illustrated in the figure. In 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. For example, when the AI/ML model is used to predict a location of a UE 3, the prediction accuracy of the AI/ML model may be assessed using a measurement of an actual location of the UE 3. Alternatively, if the AI/ML model is used for determining parameters for use in encoding and decoding data transmitted between a base station 5 and a UE 3, the model may be assessed based on the performance of the encoding and/or decoding processes. In the 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.
As described above with reference to Fig. 6, each step of the method of Fig. 7 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.
As discussed above with reference to Figs. 6 and 7, information collected by nodes/functions in the communication system 1 (e.g. at a UE 3) 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'. Methods of requesting and transmitting AI/ML information will now be described.
Fig. 8 shows an example of an AI/ML information request and an AI/ML response. In Step S1501, the first base station 5-1 transmits an AI/ML information request to the second base station 5-2. The AI/ML request is a request for AI/ML information (e.g. information regarding an actual mobility of a UE 3) from the second base station 5-2.
After receiving the AI/ML information request in step S1501, the second base station 5-2 transmits an AI/ML Information Response to the first base station 5-1 that includes the AI/ML information. The second base station 5-2 may also begin periodic reporting of the AI/ML information to the first base station 5-1 in response to receiving the AI/ML information request. The periodic reporting may be configured using a corresponding AI/ML information reporting configuration indicated by the AI/ML information request (e.g. including a periodicity of the reporting, number of reports, or reporting duration/time period). The AI/ML information request may include an information element (IE) that indicates that the second base station 5-2 is to start or stop periodic reporting of the AI/ML information to the first base station 5-1. The AI/ML information request may alternatively be a request for a single report of AI/ML information from the second base station 5-2, rather than for periodic reporting. Whilst in the example of Fig. 8 the AI/ML information request is transmitted from a first base station 5-1 to a second base station 5-2, the AI/ML information request could alternatively be transmitted from the first base station 5-1 to any other suitable node in the communication system 1, for example a UE 3. Similarly, the AI/ML information response could be transmitted from the UE 3 to the first base station 5-1.
If the second base station 5-2 is unable to transmit the requested AI/ML information to the first base station 5-1 (e.g. because the requested information is not available at the second base station 5-2), then the second base station 5-2 may transmit a corresponding indication to the first base station 5-1 that the second base station 5-2 is unable to provide the requested information, for example an AI/ML information failure message. The AI/ML information failure message may include an indication of why the second base station 5-2 is unable to provide the requested AI/ML information (e.g. a cause value).
Upon receipt of the AI/ML information, the first base station 5-1 may use the AI/ML information to train (or update) a corresponding AI/ML model (e.g. for UE mobility), or to generate a prediction (e.g. a prediction of UE mobility). Alternatively, the first base station 5-1 may forward the AI/ML information to another network node, for use with an AI/ML model at that network node.
Whilst in the example shown in Fig. 8 the AI/ML information response may include the requested AI/ML information, alternatively the AI/ML information response may be an indication that the second base station 5-2 will transmit the AI/ML information in a subsequent AI/ML information update (e.g. an acknowledgement of the AI/ML information request). Fig. 9 shows an example of an AI/ML information update. In step S1601 the second base station 5-2 (or, for example, a UE 3) determines to transmit an AI/ML information update to the first base station 5. For example, the second base station 5-2 may determine to transmit the AI/ML information update to the first base station 5-1 based on a reporting periodicity received by the second base station 5-2 in step S1501 of Fig. 8, or may determine to transmit the AI/ML information update based on a change in AI/ML information stored at the second base station 5-2 (or based on new AI/ML information obtained at the second base station 5-2). In step S1602 the second base station 5-2 transmits the AI/ML information to the first base station 5-1 in the AI/ML information update.
(Distributed AI/ML Architecture)
Whilst the network may include a primary node/function that hosts the AI/ML model and generates the AI/ML model inferences, alternatively the AI/ML model may be distributed amongst various nodes in the network. For example, a plurality of base stations 5 may host the AI/ML model and generate inferences. Whilst this may increase the processing required at some network nodes, when the AI/ML model is distributed amongst the network nodes there is a reduction in the number of inferences that are transmitted between the nodes.
When the AI/ML model (or a plurality of AI/ML models - the same model need not necessarily be used at each node) is provided at a plurality of base stations 5, feedback information (monitoring data) can be provided to each of the base stations that generates inferences using the AI/ML model (for example, to verify the accuracy of the model, as described above).
(Configuration information for AI/ML)
Configuration information for an AI/ML model (which may be referred to as "AI/ML configuration information") may be exchanged between nodes in the communication system 1. For example, a core network node may transmit AI/ML configuration information to a base station 5 that hosts an AI/ML model. The AI/ML configuration information may include a list of supported use cases for the AI/ML model (the AI/ML model need not necessarily be for predicting UE mobility). The supported use cases may be, for example: energy saving; traffic steering; anomaly detection; quality of experience (QoE) optimisation; mobility robustness optimisation (MRO); RAN slice service level agreement (SLA) assurance; massive multiple-input multiple-output (MIMO) beamforming optimisation; network slice subnet instance (NSSI) resource allocation; optimisation coverage and capacity optimisation (CCO); mobility load balancing (MLB); RACH optimisation; or UE transmission power optimisation. The AI/ML configuration information may include an indication of a particular AI/ML model to use for a particular use case. The AI/ML configuration information may also include an indication of whether feedback is required (e.g. from another network node). The feedback may include, for example, communication performance feedback (e.g. indicating a communication performance for communication between a UE 3 and a base station 5).
When a plurality of AI/ML models are stored at a UE 3 (or base station 5, or other network node), the UE 3 may receive an indication of which of the AI/ML models to use. The UE 3 may receive (e.g. from a base station 5) an indication that use of a particular model is to be activated or deactivated (e.g. in response to a determination in the performance monitoring step of Fig. 7 - a particular AI/ML model may be deactivated if the prediction accuracy has fallen below an acceptable accuracy threshold). The UE 3 may be provided with a plurality of AI/ML models, wherein each model is for use in a particular scenario or configuration.
<Single-sided and two-sided models>
An AI/ML model may be hosted (stored, for generating inferences) at both a base station 5 and a UE 3, may be hosted at only the base station 5, or may be hosted at only the UE 3. When the AI/ML model is used at the UE 3 only, the AI/ML model may be referred to as a 'single-sided' model. For example, the UE 3 may host an AI/ML model for generating a time (e.g. time resource) for communication using a particular beam transmitted by the base station 5. However, even when the model is a single-sided model, it will be appreciated that the model need not necessarily be trained at the UE 3. For example, 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. In other words, the AI/ML model may be trained at another network node, and then transferred/deployed to the UE 3.
Alternatively, 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). 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 UE 3 can use the AI/ML model to generate a first inference, and the base station 5 can use the AI/ML model to generate a corresponding second inference. For example, the first inference may be an inference of a parameter to use for encoding or compressing data (e.g. channel state information (CSI)) to be transmitted from the UE 3 to the base station 5, and the second inference may be an inference of a parameter to use to decode or decompress the data at the base station 5. As with the single-sided model case, 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.
<RRC Procedures>
RRC procedures may be used for AI/ML related queries transmitted between the network and the UE 3 when the UE 3 is in the RRC connected state. For example, the network may request (e.g. via the base station 5), using an RRC message (e.g. a dedicated RRC message, or other non-legacy RRC message), information indicating an identity of one or more AI/ML models stored at the UE 3, may request AI/ML data (e.g. training or inference data) stored at the UE 3, or may transmit an indication that the UE 3 is to perform one or more measurements to obtain the AI/ML data. The network may request information indicating the identity of one or more AI/ML models, or the AI/ML data, corresponding to a particular AI/ML model function or feature. The UE 3 may transmit a corresponding RRC message to the base station 5 that includes the requested information. For example, the UE 3 may transmit an RRC message to the base station 5 that includes an indication of an AI/ML model ID of an AI/ML model stored at the UE 3.
Similarly, the UE 3 may request AI/ML related information from the network (e.g. via the base station 5) using an RRC message (e.g. a dedicated RRC message, or other non-legacy RRC message). For example, the UE 3 may request an identity of an AI/ML model supported by the base station 5 for a particular function, or may request a version number of an AI/ML model available at the base station 5 (e.g. the UE 3 may request the current version number of an AI/ML model, in order to obtain the most recent version of the model). The base station 5 may then transmit a corresponding RRC message to the UE 3 that includes the requested information (e.g. including an indication of an AI/ML model ID of an AI/ML model stored at the base station 5).
<AI/ML Data Collection>
As described above with reference to Figs. 6 and 7, various data may be collected for use with an AI/ML model The collected data may be training data for an AI/ML model, monitoring data for monitoring the performance of an AI/ML model, and/or inference data for generating an inference using an AI/ML model. The data may be collected by a UE 3.
For offline model training (e.g. for a model and the UE 3, at another node in the communication system 1, or for a two-sided model), there may be no latency requirements for the data collection. However, for model inference data, when the data is obtained at the UE 3 for use at another node that hosts the AI/ML model, there may be latency requirements for the data collection and transmission to the other network node. There may also be latency requirements for monitoring data for real-time model monitoring. It will be appreciated that the latency requirements will depend on the particular AI/ML models in use.
AI/ML data may be collected (e.g. by performing measurements) at a UE 3 using various different methods. Some exemplary methods for AI/ML data collection at a UE 3 are described below.
(Logged MDT)
The UE 3 may collect AI/ML data using a logged minimisation of drive test (MDT) method. The UE 3 may receive an indication from the network that the UE 3 is to perform measurements and logging. The UE 3 may receive an indication from the network of a configuration to use for the measurements and logging (e.g. 'loggedMeasurementConfiguration').
In this example, the UE 3 may be in the RRC idle or RRC inactive state when collecting the data. The UE logs (stores) the MDT measurements whilst the UE 3 is in the RRC idle or RRC inactive state, and stores the measurements until they are requested by the network.
The maximum payload size per report may be less than 9 kB. The collected information may comprise layer 3 cell or beam measurements, location information, sensor information and/or timing information.
The AI/ML data may be transmitted to a base station 5 (or, for example, an operations and maintenance (OAM) node) upon receiving a request for the AI/ML data from the base station 5 after the UE 3 has entered the RRC connected state.
Logged MDT measurements may be configured using an MDT measurement configuration procedure, over the air interface. The network may initiate the procedure, when the UE 3 is in the RRC connected state, by transmitting a logged measurement configuration message to the UE 3. The UE 3 then begins to measure and log (store) the data when the UE 3 is in the RRC inactive or RRC idle state. The measurement configuration for the logged MDT method may be under the control of a trace collection entity (TCE) in the OAM domain. In other words, the logged MDT (as well as immediate MDT, described below) is not terminated at the base station 5, but is controlled by the OAM and subject to signalling from the core network. The method may therefore be referred to as 'OAM-centric data collection'. The AI/ML data may be transmitted by the UE 3 to the TCE (e.g. via the base station 5).
In the logged MDT method, downlink pilot strength measurements may be measured and logged per cell.
The UE 3 may provide an indication to the network of the availability of logged MDT measurements when the UE 3 accesses the network. The network may then transmit a request for the measurement logs, to be transmitted by the UE 3 using any suitable RRC signalling.
(Immediate MDT)
The UE 3 may collect AI/ML data using an immediate MDT method. In this example the data may be collected by the UE 3 when the UE 3 is in the RRC connected state.
The maximum payload size per report may be less than 9 kB. The collected information may comprise layer 3 cell or beam measurements, location information, sensor information and/or timing information.
Transmission of the AI/ML data by the UE 3 to the node that hosts the corresponding AI/ML model function may be via an event triggered report, or the UE 3 may be configured to transmit periodic reports comprising the AI/ML data.
(L3 Measurements)
The UE 3 may collect AI/ML data using a layer 3 (L3) measurement method. In this example the data may be collected by the UE 3 when the UE 3 is in the RRC connected state.
The maximum payload size per report may be less than 9 kB. The collected information may comprise L3 cell or L3 beam measurements.
The UE 3 may transmit the collected AI/ML data as part of an event triggered report (e.g .triggered by a buffer at the UE 3 that stores the AI/ML data becoming full), or as part of a periodic report.
(L1 Measurements (CSI Reporting))
The UE 3 may collect AI/ML data using a layer 1 (L1) measurement method. In this example the data may be collected by the UE 3 when the UE 3 is in the RRC connected state.
The maximum payload size per report may be less than 1706 bits when the report is transmitted using the PUCCH, or less than 3840 bits when the report is transmitted using the PUSCH. The collected information may comprise L1 CSI measurements.
The AI/ML data may be transmitted by the UE 3 (e.g. to the base station 5) as part of an aperiodic report, a semi-persistent report, or a periodic report.
(UE Assistance Information (UAI))
The UE 3 may collect AI/ML data using a UE assistance information (UAI) method. In this example the data may be collected by the UE 3 when the UE 3 is in the RRC connected state.
The maximum payload size per report may be less than 9 kB. The collected information may comprise UE assistance information. The UE 3 may transmit the collected AI/ML data using any suitable transmission.
(Early Measurement)
The UE 3 may collect AI/ML data using an early measurement method. In this example the data may be collected by the UE 3 when the UE 3 is in the RRC idle or RRC inactive state.
The maximum payload size per report may be less than 9 kB. The collected information may comprise L3 cell or beam measurements.
The UE 3 may be configured to transmit the AI/ML data in response to a request received from a base station 5 after the UE 3 enters the RRC connected state.
(LTE positioning protocol (LPP))
The UE 3 may collect AI/ML data using an LTE positioning protocol (LPP) method. In this example the data may be collected by the UE 3 when the UE 3 is in the RRC connected state.
The maximum payload size per report may be less than 9 kB. The collected information may comprise location information.
Transmission of the AI/ML data may be UE-triggered, or may be triggered by the network (e.g. by the base station 5).
<Data Collection Entity>
As described above, in the logged MDT method, for example, the measurement configuration is controlled by the OAM, and the collected data is transmitted to the TCE. Alternatively, a new Data Collection Entity (DCE) may be defined in the OAM domain for the collection of AI/ML data, or a new logical entity at the RAN (e.g. CU) may be defined, for the data collection method (which need not necessarily be logged MDT). The DCE may control the measurements performed by a plurality of UEs 3 (e.g. by providing the UEs 3 with measurement configurations via a base station 5) to collect the AI/ML data. The DCE may receive AI/ML data from UEs 3 via a plurality of different base stations 5. The DCE may perform control of the AI/ML data collection, and control of training of one or more AI/ML models using the AI/ML data.
<UE Consent (or User Consent)>
A method for determining, at a base station 5, whether AI/ML data collection is allowed at a UE 3 will now be described with reference to Fig. 10. In the example illustrated in Fig. 10 the UE 3 is in the RRC connected state, although it will be appreciated that the UE 3 could alternatively be in the RRC idle or RRC inactive state. For example, the UE 3 may receive the configuration for AI/ML data collection from the base station 5 in step S105 when the UE 3 is in the RRC connected state, and may then transition to the RRC idle or RRC inactive state. The UE 3 may then perform the corresponding measurements for the AI/ML data collection when the UE 3 is in the RRC idle or RRC inactive state.
During registration of a UE 3 (or the user of UE 3) at the network, a consent or permission for use of the UE 3 in the collection of AI/ML data may be defined. For example, UE 3 may be configured to be permitted (or authorized) to collect (e.g. measure) AI/ML data in response to a request from the network. Alternatively, the UE 3 (or the user of UE 3) may not be permitted (or may be prohibited) to collect the AI/ML data (e.g. to reduce the power consumption at the UE 3). The UE 3 (or the user of UE 3) could be permitted to collect AI/ML data for a particular use case. For example, the UE 3 (or the user of UE 3) could be permitted to collect AI/ML data for generating an inference using an AI/ML model, but prohibited from collecting data for training an AI/ML model.
In step S101 the base station 5 receives a data collection request from the DCE. The data collection request is a request for the base station 5 to collect AI/ML data (e.g. inference or training data). In this example, the base station 5 transmits, to a UE 3, a measurement configuration for use by the UE 3 to perform one or more measurements to collect the AI/ML data. The data can then be forwarded to the DCE from the UE 3 via the base station 5.
In step S102 the base station 5 transmits, to the AMF 10-1, a request for an indication of UE 3 consent for AI/ML data collection. In other words, the base station 5 requests information indicating whether one or more UEs 3 are allowed to be configured to collect the AI/ML data. Whilst in this example the request is transmitted to the AMF 10-1, it will be appreciated that the request could alternatively be transmitted to any other suitable network node that stores the requested information (e.g. another core network node). The query transmitted from the base station 5 to the AMF 10-1 is for determining whether a UE 3 (or a group of UEs 3) has consent (or is subscribed for) AI/ML data collection. The transmission in step S102 may be UE 3 associated signalling transmitted via the NG interface between the base station 5 and the AMF 10-1. Whilst in the example of Fig. 10 the request is transmitted from the base station 5 to the AMF 10-1, the request could alternatively be transmitted from the base station 5 to another node in the network (e.g. another core network node). The request may be transmitted from the base station 5 to the AMF 10-1 per UE 3, or could be transmitted for a group of UEs 3. For example, the base station 5 may transmit a request for the AI/ML data collection consent information for all RRC connected UEs 3.
In step S103 the AMF 10-1 transmits, to the base station, the requested information regarding the UE 3 consent for AI/ML data collection. In other words, the AMF 10-1 transmits, to the base station 5, an indication of a consent status or subscription status of a UE 3 for AI/ML data collection.
In step S104, the base station performs data collection configuration for the UEs 3 that are allowed to be configured for AI/ML data collection (the UEs 3 for which there is consent for AI/ML data collection). In step S105 the base station 5 transmits a corresponding configuration for AI/ML data collection to the UE 3. The configuration for AI/ML data collection may be transmitted using dedicated RRC signalling. The UE 3 uses the received configuration to perform measurements to collect the AI/ML data, which can then be forwarded to the DCE via the base station 5 using any suitable transmissions (e.g. any suitable RRC transmissions).
Advantageously, therefore, by virtue of the base station 5 requesting the information regarding the UE 3 consent for AI/ML data collection from the AMF 10-1, the base station 5 can determine which UEs 3 to transmit a configuration for AI/ML data collection to. Beneficially, this helps to avoid a situation in which the base station 5 transmits a measurement configuration for obtaining AI/ML data to a UE 3 for which there is no consent for collecting the AI/ML data (e.g. collection of the AI/ML data is prohibited at the UE 3).
Whilst in the example shown in Fig. 10 the base station 5 transmits, in step S102, the request for the indication of UE consent for AI/ML data collection, this need not necessarily be the case. Alternatively, for example, the AMF 10-1 may be configured to provide the base station 5 with the UE 3 consent information upon access of the UE 3 to the network (for example, during the establishment of a NAS connection between the UE 3 and the base station 5).
During an NG interface setup procedure, the AMF 10-1 may provide an indication of a capability of the AMF in an NG setup response message. The capability may include a support for a particular AI/ML function. The indication of support for a particular AI/ML function may be provided in a suitable information element (IE), for example 'AIML Support'. The consent for AI/ML data collection at the UE 3 may also be indicated using a suitable IE, for example 'AIML Data Collection Support'.
<Configuration for Data Collection>
Configurations for data collection, for example for obtaining AI/ML model training data, will now be described.
The DCE may configure the base station to perform AI/ML data collection, by providing a corresponding data collection configuration to the base station 5. The base station 5 can then perform corresponding configuration of one or more UEs 3 in order to collect the AI/ML data (e.g. as described above with reference to Fig. 10).
The DCE may transmit, to the base station 5, an indication of one or more configuration parameters for the AI/ML data collection. The configuration parameters may comprise an indication of one or more AI/ML Model IDs (e.g. a globally unique model ID, or any suitable local ID of the AI/ML model) for which the AI/ML data is to be collected. The configuration parameters may comprise an indication of one or more AI/ML function IDs for which AI/ML data is to be collected. One AI/ML function may serve multiple AI/ML models.
The configuration parameters may comprise an indication of a location (e.g. geographical or logical area) in which the AI/ML data is to be collected. For example, the configuration parameters may comprise a list of cells.
The configuration parameters may comprise an indication of a data type, that indicates the type of AI/ML data to be collected. For example, the configuration parameters may comprise an indication of a particular radio measurement to be performed. The type of AI/ML data to be collected may be indicated per AI/ML model or per AI/ML function.
The configuration parameters may comprise a data collection interval, a data collection duration, a data reporting format and reporting configuration, and/or an indication of data reporting trigger events and/or data reporting trigger thresholds. For example, the configuration parameters may comprise an indication that a UE 3 is to transmit stored AI/ML data to the base station 5 when a buffer at the UE 3 for storing the AI/ML data is full.
The configuration parameters may comprise a data reporting interval, a data reporting amount for a one-shot report, a transaction identity for data collection, or an address of the DCE to which the AI/ML data is to be transmitted (e.g. an IP address of the DCE).
The base station 5 may be configured to transmit dedicated RRC signalling to the connected UEs 3 in order to configure the data collection for a particular AI/ML model or function. The base station 5 may configure the data collection based on the capability of the UE 3 and/or the UEs 3 (or the user's) consent for AI/ML data collection.
AI/ML data collection configuration parameters, transmitted from the base station to the UE 3, for configuring the AI/ML data collection may comprise an indication of one or more AI/ML model IDs (e.g. a globally unique ID, or any suitable local ID).
The AI/ML data collection configuration parameters may comprise an indication of one or more AI/ML function IDs (one function may serve multiple AI/ML models).
The AI/ML data collection configuration parameters may comprise an indication of a location (e.g. geographical or logical area) in which the AI/ML data is to be collected by the UE 3. For example, the configuration parameters may comprise a list of cells.
The AI/ML data collection configuration parameters may comprise an indication of a data type that indicates the type of AI/ML data to be collected by the UE 3. For example, the configuration parameters may comprise an indication of a particular radio measurement to be performed. The type of AI/ML data to be collected may be indicated per AI/ML model or per AI/ML function.
The AI/ML data collection configuration parameters may comprise an indication of a data collection interval, a data collection duration, a data reporting format and report content.
The AI/ML data collection configuration parameters may comprise an indication of data reporting trigger events and corresponding thresholds. For example, the AI/ML data collection configuration parameters may comprise an indication that the UE 3 is to transmit the AI/ML data when a buffer at the UE 3 for storing the AI/ML data becomes full.
The AI/ML data collection configuration parameters may comprise an indication of a data reporting interval, or a data reporting amount for a one-shot report.
The AI/ML data collection configuration parameters may comprise an indication of a destination to which the UE 3 is to transmit the AI/ML data (either directly or indirectly, e.g. via the base station 5). The destination to which the UE 3 is to transmit the AI/ML data may be, for example, the base station 5 that transmits the AI/ML data collection configuration parameters to the UE 3, the DCE, or a serving base station 5 to which the UE performs a handover procedure to (e.g. the handover procedure described above with reference to Fig. 5).
In a radio resource monitoring (RRM) framework, a measurement object may be associated with a logging configuration for AI/ML model training. The RRM measurement configuration and reporting may be for cell-level measurements. However, for AI/ML model training, the configuration may be per AI/ML model or AI/ML function.
Advantageously, the AI/ML data collection configuration parameters (configuration information) may comprise an indication of one or more beams that the UE 3 is to use to perform the measurements. Beneficially, therefore, the base station 5 is able to provide an indication to the UE 3 that the UE 3 is to perform measurements to collect AI/ML data using a particular beam, or a particular subset of beams. The AI/ML data collection configuration parameters may comprise an indication of a specific SSB or CSI-RS to be measured by the UE 3 to collect the AI/ML data. The indication of one or more beams, SSB or CSI-RS to be measured by the UE 3 may be an explicit or an implicit indication. For example, the indication may be implicit based on an indication of an AI/ML model ID or AI/ML function ID included in the AI/ML data collection configuration information.
The base station 5 may control the activation and deactivation of the AI/ML data collection at the UE 3. For example, the base station 5 may determine to disable (deactivate) the AI/ML data collection at the UE 3 in order to reduce the power consumption at the UE 3. For example, the base station 5 may be configured to use L1/L2/L3 signalling, or any other suitable signalling, to dynamically start, resume, stop or pause the AI/ML data collection at the UE 3.
<RRC Based Data Collection>
RRC-based AI/ML data collection will now be described with reference to Fig. 11. Advantageously, in the method of Fig. 11 the AI/ML data can be more reliably transmitted to the network even when the UE 3 experiences radio link failure (RLF) during transmission of the AI/ML data.
The UE 3 may be configured to collect the AI/ML data by recording measurements whilst the UE is in the RRC connected state. The UE 3 may collect the AI/ML data per AI/ML model or AI/ML model function based on the configuration information received from the network (e.g. from the base station 5 as described above). In this example the UE 3 is configured to report the collected data via dedicated RRC signalling. The report interval and report amount (e.g. the number of RRC segments) may be configured by the network (e.g. via a suitable transmission from the base station 5 to the UE 3).
The volume of AI/ML data collected by the UE 3 may be relatively large. The UE 3 may be configured to transmit, to the network, an indication that AI/ML data is stored at the UE 3. In response to receiving the indication from the UE 3, the network may provide an allocation of communication resources for transmitting the AI/ML data (e.g. AI/ML model training data). The UE 3 may be configured to use dedicated RRC data availability signalling, or any other suitable uplink transmission, to indicate that there is AI/ML data available at the UE 3 for transmission, for a particular AI/ML model or AI/ML function.
Advantageously, the UE 3 may be configured to transmit AI/ML data to the base station 5, and then transmit an indication that there are additional RRC segments available for transmission by the UE 3 that contain additional AI/ML data. The UE 3 may be configured to provide, in each transmitted report, an indication to the network of whether there is any remaining AI/ML data to be reported by the UE 3. Alternatively, the network may poll the UE 3 to trigger a data report based on network scheduling. In this alternative, the network may transmit, to the UE 3, requests for the UE 3 to transmit measurement reports (or parts of a measurement report) until all of the AI/ML data has been transmitted (e.g. continue to request the UE 3 to transmit information until the end of a report has been reached).
During transmission of the AI/ML data from the UE 3 to the base station 5 the UE 3 may experience radio link failure (RLF). Following the RLF, the UE 3 may reconnect to the original cell via an RRC reestablishment procedure. In this case, the UE 3 may simply continue the interrupted transmission of the AI/ML data. RRC segmentation may be used to transmit the AI/ML data. The RRC segment number may be included in the RRC context of the UE 3, and will therefore be known at both the UE 3 and the base station 5. Advantageously, in this example, when the UE 3 is transmitting AI/ML data and experiences RLF, the UE 3 and the base station 5 are configured to maintain the RRC context of the UE 3. Beneficially, therefore, when the RRC connection is resumed, transmission of the AI/ML data report can be continued (recovered) based on the stored RRC context information including the RRC segment number, for the AI/ML data transmissions. Alternatively, the base station 5 can be configured to provide the RRC segment number (and optionally the AI/ML model ID or AIML function ID associated with the AI/ML data report) to the UE 3 when the connection with the UE 3 is reestablished. As will be described in more detail with reference to Fig. 11, if the UE 3 is transmitting the AI/ML data to a first base station 5-1, and then connects to a second base station 5-2 following RLF, the second base station 5-2 may be configured to obtain the UE context from the first base station 5-1 in order to obtain the RRC segment number. The second base station 5-2 can then transmit the RRC segment number to the UE 3, for use by the UE 3 to transmit the remaining portion of the AI/ML data to the second base station 5-2. The second base station 5-2 may forward the remaining portion of the AI/ML data to a DCE to which the first base station 5-1 was forwarding the AI/ML data. Alternatively, the second base station 5-2 may transmit the remaining portion of the AI/ML data to the first base station 5-1 for forwarding to the DCE. In a further alternative, the first base station 5-1 may transmit, to the second base station 5-2, the AI/ML data received at the first base station 5-1 from the UE 3. The second base station 5-2 can then forward all of the collected AI/ML information transmitted by the UE 3 (to the first and second base stations) to the DCE. It will be appreciated that the UE 3 may connect to the second base station 5-2 following a handover procedure (e.g. the handover procedure described above with reference to Fig. 5), rather than RLF, in which case this method may also be used.
Fig. 11 illustrates an example in which the UE 3 experiences RLF during transmission of the AI/ML data.
In step S111 the UE 3 transmits AI/ML data to the first base station 5-1. However, transmission of the AI/ML data is interrupted by RLF (but could also be interrupted, for example, by handover to the second base station 5-2).
In step S112 the UE 3 connects (accesses) to a cell of the second base station 5-2.
In step S113 the second base station 5-2 requests the RRC context of the UE 3 from the first base station 5-1. In step S114 the first base station 5-1 transmits the RRC context to the second base station 5-2. The RRC context includes the RRC segment number (e.g. next RRC segment to be transmitted, or the last RRC segment that was transmitted) for the AI/ML data transmission between the UE 3 and the first base station 5-1.
In step S115 the second base station requests the AI/ML data from the UE 3. The second base station 5-2 also provides an indication of the RRC segment number to the UE 3. In step S116 the UE 3 transmits the remaining portion of the AI/ML data to the second base station 5-2. Advantageously, the remaining portion of the AI/ML data to be transmitted can be determined based on the RRC segment number.
<DRB Based Data Report>
The AI/ML data may be transmitted from the UE 3 to the base station 5 using a data radio bearer (DRB). A priority may be assigned to the DRB or logical channel (LCH). For example, a PBR may be defined for a logical channel used for transmitting the AI/ML data. Alternatively, the AI/ML data may be transmitted from the UE 3 to the base station 5 using a dedicated radio bearer (which may also be referred to as a 'specific' radio bearer). A dedicated LCH may be defined for carrying this type of radio bearer (RB). The LCH carrying the AI/ML data RBs may be subject to restrictions on multiplexing with other LCHs carrying signalling radio bearer (SRB) or DRB. Advantageously, the dedicated radio bearer can be assigned a priority based on the quality of service (QoS) requirements for transmission of the AI/ML data. For example, when the AI/ML model data has a low latency requirement, the dedicated radio bearer can be assigned a corresponding high priority. The establishment of the RB could be initiated by the base station 5 or the UE 3 (this is different from legacy RB establishment, which terminates between the UE 3 and the UPF 11). The RB established for the AI/ML data transmission is not based on the QoS mapping of the QoS flow governed by the NAS layer, and will not go across the SDAP protocol layer. Instead, the RB established for the AI/ML data transmission is terminated at the PDCP protocol layer between the UE 3 and the base station 5. The base station 5 may assemble the PDCP data received from one or more UEs 3, and forward the AI/ML data to the DCE based on a configured report interval.
Alternatively, local break out could be used for the transmission of the AI/ML data. In this case, the data stream can be terminated at the base station 5. If the DCE is provided at the base station 5, then it may be considered to be a separate logical entity.
Due to the typically relatively large volume of the AI/ML data collected by the UE 3, segmentation may be performed at the PDCP layer due to the size limits for PDCP service data units (SDUs). However, transmission of the SDUs from the UE 3 to the base station 5 may be interrupted. For example, transmission of the SDUs may be interrupted by handover of the UE 3, or by RLF. In the case of handover from a first base station 5-1 to a second base station, the remaining SDUs can advantageously be transmitted to the second base station 5-2 (target base station 5-2), improving the reliability of the transmission of the AI/ML data to the network. A method in which the remaining PDCP packets are transmitted to the target base station 5-2 when transmission of the AI/ML data is interrupted by handover will now be described with reference to Fig. 12.
In step S121 the UE 3 transmits PDCP segments comprising AI/ML data to the first base station 5-1 (source base station 5-1).
In step S122 handover of the UE 3 from the first base station 5-1 (source base station 5-1) to the second base station 5-2 (target base station 5-2) is performed, interrupting the transmission of the AI/ML data from the UE 3 to the first base station 5-1.
In step S123 the second base station 5-2 requests the PDCP context of the UE 3 from the first base station 5-1.
In step S124 the first base station 5-1 transmits the PDCP context of the UE 3 to the second base station 5-2. Advantageously, the PDCP context information includes an indication of the PDCP sequence number (SN) corresponding to the last PDCP segment received at the first base station 5-1 from the UE 3 (or could alternatively be an indication of the next PDCP segment to be received from the UE 3). An indication of the identity of the AI/ML model associated with the AI/ML data may also be included in the PDCP context information transmitted from the first base station 5-1 to the second base station 5-2 in step S124. An indication of a configuration for a RB for transmission of the AI/ML data from the UE 3 to the second base station 5-2 may also be included in the transmission of step S124.
In step S125 the second base station 5-2 requests the remaining portion of the AI/ML data from the UE 3. The request transmitted to the UE 3 in step S125 includes an indication of the PDCP SN. The UE 3 is therefore able to determine the remaining portion of the AI/ML data to be transmitted to the second base station 5-2. In step S126 the UE 3 transmits the remaining portion of the AI/ML data to the second base station 5-2.
In optional step S127 the second base station 5-2 forwards the AI/ML data received from the UE 3 to the first base station 5-1 (which may be in response to a request, not shown in Fig. 12, from the first base station 5-1). In optional step S128 the first base station 5-1 assembles the AI/ML data (e.g. collates the AI/ML data received at the first base station 5-1 from the UE 3 and the AI/ML data received at the first base station 5-1 from the second base station 5-2 for transmission in a report), and transmits the AI/ML data to the DCE (which could be provided at another node in the communication system 1, or could be provided as a logical entity at the first base station 5-1). Alternatively, the first base station 5-1 could transmit the AI/ML data received at the first base station 5-1 from the UE 3 to the second base station 5-2, and the second base station 5-2 could assemble the AI/ML data and transmit the AI/ML data to the DCE.
Whilst in the example of Fig. 12 the transmission of the AI/ML data is interrupted by handover of the UE 3 from the first base station 5-1 to the second base station 5-2, the transmission of the AI/ML data could alternatively be interrupted by RLF of the radio link between the UE 3 and the first base station 5-1, followed by establishment of a radio link between the UE 3 and the second base station 5-2. During RLF, the PDCP SN is known by both the UE 3 and the first base station 5-1, and can be used to transmit the remaining portion of the AI/ML data to the second base station 5-2. Advantageously, in this example PDCP data recovery is supported during the RRC reestablishment procedure between the UE 3 and the second base station 5-2 following the RLF. The first base station 5-1 or the second base station 5-2 can provide a PDCP status report to the UE 3, to indicate to the UE 3 that the UE 3 is to continue transmission of the AI/ML data (transmit the remaining PDCP segments) following the RLF and the RRC reestablishment procedure between the UE 3 and the second base station 5-2. The second base station 5-2 transmits the PDCP SN corresponding to the last PDCP segment received at the first base station 5-1 (or corresponding to the next PDCP segment to be transmitted by the UE 3 to the second base station 5-2) to the UE 3, as illustrated in Fig. 12, and the UE 3 is therefore able to determine the remaining portion of the AI/ML data, to be transmitted to the second base station 5-2. Following RLF between the UE 3 and the first base station 5-1, the UE 3 may perform the RRC reestablishment procedure with the first base station 5-1, rather than with the second base station 5-2. In this case, the first base station 5-1 can transmit the indication of the PDCP SN to the UE 3, and the UE 3 is therefore able to determine the remaining portion of the AI/ML data to be transmitted to the first base station 5-1.
<Data Collection and Changes in RRC State>
The UE 3 may be configured to collect the AI/ML data (e.g. by performing measurements) when the UE 3 is in the RRC connected state, the RRC idle state, or the RRC inactive state. Transmission of the AI/ML data to the base station 5 may be performed when the UE 3 is in the RRC connected state.
If the UE 3 transitions from the RRC connected state to the RRC idle or RRC inactive state, the collection of the AI/ML data can be continued using the same configuration as used by the UE 3 to collect the AI/ML data when the UE 3 was in the RRC connected state. The AI/ML data reporting configuration (configuring the transmission of the AI/ML data from the UE 3 to the base station 5) may also be maintained. The data reporting interval could be disabled, or extended, until the UE 3 re-enters the RRC connected state. Alternatively, a new AI/ML data collection configuration or AI/ML data reporting configuration can advantageously be provided to the UE 3 in the RRC release message (or in any other suitable message transmitted to the UE 3 when the UE 3 transitions from the RRC connected state to the RRC idle or RRC inactive state).
The AI/ML data collection when the UE 3 is in the RRC idle or RRC inactive state may be configured per AI/ML use case. For example, the UE 3 may be configured to perform measurements of beams for the beam management use case but, for example, the UE 3 may not be able to perform measurements of CSI when the UE 3 is in the RRC idle or RRC inactive state.
When the UE 3 is in the RRC idle or RRC inactive state, a buffer at the UE 3 for storing the collected AI/ML data may become full. Advantageously, the UE 3 may be configured to transmit, to the network, an indication that the AI/ML data is available for transmission by the UE 3, beneficially enabling the network to configure one or more communication resources for transmission of the AI/ML data. The UE 3 may transmit, to the base station 5, an RRC connection establishment request that includes a cause value indicating that AI/ML data is available for transmission by the UE 3. The UE 3 may then transmit the AI/ML data to the base station 5 using any suitable RRC transmissions (e.g. RRC segments).
In a further alternative the UE 3 may be configured to discard the oldest AI/ML data from the buffer if the buffer becomes full (or the UE 3 may allow newly obtained AI/ML data to overwrite older AI/ML data in the buffer). In this case, the UE 3 may be configured to prioritise the storage of particular AI/ML data in the buffer (e.g. preventing it from being discarded or overwritten). For example, AI/ML data may be prioritised for storage in the buffer based on the AI/ML model or function to which the AI/ML data corresponds.
An example of AI/ML data collection when the UE 3 transitions between different RRC states will now be described with reference to Fig. 13.
In step S131 the UE 3 is in the RRC connected state and collects AI/ML data (e.g. by performing measurements of signals transmitted by the first base station 5-1). Since the UE 3 is in the RRC connected state, the AI/ML data can be transmitted to the first base station 5-1 in a corresponding report.
In step S132 the first base station 5-1 transmits an RRC release message to the UE 3. Advantageously, the RRC release message includes a new AI/ML data collection configuration for the UE 3.
In step S133, the UE 3 enters the RRC idle or RRC inactive state.
In step S134, the UE 3 collects (obtains) the AI/ML data whilst the UE 3 is in the RRC idle/inactive state. The UE 3 uses the AI/ML data collection configuration received in step S132 to collect the AI/ML data.
In step S135 the UE 3 accesses a cell of the second base station 5-2, and in step S136 the second base station 5-2 transmits a request for the UE 3 to perform RRC connection establishment with the network (i.e., a cell of the second base station 5-2). In step S135, step S136 or in any other suitable transmission during the RRC establishment procedure, the UE 3 may transmit, to the second base station 5-2, an indication of the AI/ML data collection configuration received from the first base station 5-1 in step S132. The UE 3 may transmit, to the second base station 5-2, an indication of the identity of the base station 5-1 that provided the AI/ML data collection configuration to the UE 3. The UE 3 may also provide an indication, to the second base station 5-2, of an AI/ML data reporting configuration used by the UE 3.
The RRC establishment procedure of step S136 may comprise a further AI/ML data collection configuration for use by the UE 3 to obtain AI/ML data when the UE 3 is in the RRC connected state. Alternatively, the UE 3 may continue to use the AI/ML data collection configuration used by the UE 3 when the UE 3 was in the RRC idle/inactive state in step S134.
In step S137 the UE 3 is in the RRC connected state and transmits, to the second base station 5-2, the AI/ML data collected in step S134 when the UE 3 was in the RRC idle/inactive state.
In optional step S138 the second base station 5-2 forwards the AI/ML data received from the UE 3 in step S137 to the first base station 5-1 (alternatively, for example, the second base station 5-2 could forward the AI/ML data received from the UE 3 directly to the DCE). The first base station 5-1 may then assemble the AI/ML data and transmit the AI/ML date to the DCE, as described above with reference to step S128 of Fig. 12.
When the AI/ML data is transmitted by the UE 3 to the second base station 5-2 using a radio bearer, the second base station 5-2 may provide a configuration to the UE 3 for reporting the collected AI/ML data during the RRC idle/inactive state. In this case, the UE 3 may be configured to report, using RRC signalling transmitted to the second base station 5-2 during step S135, the volume (size) of the AI/ML data stored at the UE 3.
<User Equipment>
Fig. 14 is a schematic block diagram illustrating the main components of a UE 3 as shown in Fig. 1.
As shown, 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. Although not necessarily required for its operation, 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) and 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 communication system 1 or from a removable data storage device (RMD), for example.
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. 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). 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 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 (for example, to transmit a measurement report according to 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.
<Base Station>
Fig. 15 is a schematic block diagram illustrating the main components of the base station 5 for the communication system 1 shown in Fig. 1. As shown, the base station 5 has a transceiver circuit 510 for transmitting signals to and for receiving signals from the communication devices (such as UEs 3) via one or more antenna 530 (e.g. a single or multi-panel antenna array / massive antenna), and a core network interface 550 (e.g. comprising the N2, N3 and other reference points/interfaces) for transmitting signals to and for receiving signals from network nodes in the core network 7. Although not shown, the base station 5 may also be coupled to other base stations via an appropriate interface (e.g. the so-called 'Xn' interface in NR). The base station 5 has a controller 570 to control the operation of the base station 5. The controller 570 is associated with a memory 590. Software may be pre-installed in the memory 590 and/or may be downloaded via the communication system 1 or from a removable data storage device (RMD), for example. The controller 570 is configured to control the overall operation of the base station 5 by, in this example, program instructions or software instructions stored within memory 590.
As shown, these software instructions include, among other things, an operating system 610, a communications control module 630, and an AI/ML module 650.
The communications control module 630 is operable to control the communication between the base station 5 and UEs 3 and other network entities that are connected to the base station 5. The communications control module 630 is configured for the overall control of 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). The communications control module 630 is also configured for the overall handling 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). The communications control module 630 is responsible for managing full duplex (e.g., SBFD) communication including, where appropriate, the segregation of UL and DL communication via different physical antenna elements. The communications control module 630 is responsible, for example: for determining where to configure the UE 3 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 scheduled for UE transmission/reception of UL/DL communications (including interleaved resources and resources subject to frequency hopping); for managing frequency hopping at the base station side; for configuring slots/symbols appropriately (e.g., for UL, DL or SBFD communication, or the like); for configuring one or more bandwidth parts for the UE 3; for providing related configuration signalling to the UE 3; and the like. The communications control module 630 may be configured to control communications in accordance with any of the methods described above (for example, to receive or transmit UE 3 mobility information, or a handover request).
The AI/ML module 650 is configured to perform any of the AI/ML related functions of the base station 5 of any of the methods described above.
<Core Network Node/Function>
Fig. 16 is a block diagram illustrating the main components of a core network node or function, such as the AMF, CPF, the UPF, the SMF or OAM. As shown, 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.
As shown in Fig. 16, the core network node/function may also include an AI/ML module 770. If present, 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).
<Modifications and Alternatives>
As those skilled in the art will appreciate, a number of modifications and alternatives can be made to the above example embodiments whilst still benefiting from the disclosure embodied therein.
Whilst the above examples have been described with reference to an AI/ML model, it will be appreciated that the above described methods are advantageous even when the model is not an AI/ML model. Any other suitable type of model or function may be used to generate inferences (e.g. determinations or predictions).
It will be appreciated, for example, that whilst cellular communication generation (2G, 3G, 4G, 5G, 6G etc.) specific terminology may be used, in the interests of clarity, to refer to specific communication entities, the technical features described for a given entity are not limited to devices of that specific communication generation. The technical features may be implemented in any functionally equivalent communication entity regardless of any differences in the terminology used to refer to them.
In the above description, 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.
In the above example embodiments, a number of software modules were described. As those skilled in the art will appreciate, 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. Various other modifications will be apparent to those skilled in the art and will not be described in further detail here.
The memories shown above may be formed by a volatile memory or a nonvolatile memory, however, the memories may be formed by a combination of a volatile memory and a nonvolatile memory.
A software to configure the software modules can be stored using various types of non-transitory computer readable media to be supplied to a computer. The non-transitory computer readable media include various types of tangible storage media. Examples of the non-transitory computer readable medium include a magnetic recording medium (for example, a flexible disk, a magnetic tape, or a hard disk drive), an optical magnetic recording medium (for example, a magneto-optical disk), a CD (compact disc) -ROM (read only memory), a CD-R (recordable), a CD-R/W (rewritable), and a semiconductor memory such as a mask ROM, a programmable ROM (PROM), an erasable PROM (EPROM), a flash ROM, or a random access memory (RAM). In addition, the program may be supplied to the computer by various types of transitory computer readable media. Examples of the transitory computer readable media include electric signals, optical signals, and electromagnetic waves. The transitory computer readable medium can supply the program to the computer via a wired communication path such as an electric wire and an optical fiber, or a wireless communication path.
The base station may comprise a 'distributed' base station having a central unit 'CU' and one or more separate distributed units (DUs).
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.
It should be noted that the present disclosure is not limited to a dedicated communication device and can be applied to any device having a communication function as explained in the following paragraphs.
The terms "User Equipment" or "UE" (as the term is used by 3GPP), "mobile station", "mobile device", and "wireless device" are generally intended to be synonymous with one another, and include standalone mobile stations, 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.).
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 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.).
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 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 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.
Internet of Things devices (or "things") 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.
It will be appreciated that 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.
It will be appreciated that IoT devices are sometimes also referred to as Machine-Type Communication (MTC) devices or Machine-to-Machine (M2M) communication devices. It will be appreciated that a UE may support one or more IoT or MTC applications. Some examples of MTC applications are listed in the following table (Table 2). 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.
Further, the above-described UE categories are merely examples of applications of the technical ideas and example embodiments described in the present document. Needless to say, these technical ideas and example embodiments are not limited to the above-described UE and various modifications can be made thereto.
Various other modifications will be apparent to those skilled in the art and will not be described in further detail here.
Part of or all the foregoing aspects can be described as in the following appendixes, but the present disclosure is not limited thereto. Some or all of elements specified in any of Supplementary Notes may be applied to various types of hardware, software, and recording means for recording software, systems, and methods.
(Supplementary Note 1)
A method performed by a user equipment, UE, the method comprising:
transmitting, to a first access network node, a first part of measurement result for Artificial Intelligence / Machine Learning, AI/ML, model training; and
transmitting, to a second access network node, a second part of the measurement result for the AI/ML model training, wherein
at least one of the first part of the measurement result or the second part of the measurement result includes information indicating a model or function for the AI/ML model training.
(Supplementary Note 2)
The method according to Supplementary Note 1, further comprising:
receiving, from the first access network node, the information indicating the model or function for the AI/ML model training.
(Supplementary Note 3)
The method according to Supplementary Note 2, wherein
the information indicating the model or function for the AI/ML model training is transmitted from a data collection entity which is connected with a plurality of access network nodes to the first access network node.
(Supplementary Note 4)
The method according to Supplementary Note 2 or 3, wherein
the information indicating the model or function for the AI/ML model training is for transmitting the second part of the measurement result for the AI/ML model training.
(Supplementary Note 5)
The method according to any one of Supplementary Notes 1 to 4, further comprising:
performing measurements for the measurement result using at least one beam corresponding to the information indicating the model or function for the AI/ML model training.
(Supplementary Note 6)
The method according to Supplementary Note 5, wherein
the information indicating the model or function for the AI/ML model training indicates at least one of:
at least one Synchronization Signal/Physical Broadcast Channel, PBCH, block, SSB, or
at least one channel state information reference signal, CSI-RS.
(Supplementary Note 7)
The method according to any one of Supplementary Notes 1 to 6, wherein
at least one of the first part of the measurement result or the second part of the measurement result includes information indicating a part of the measurement result which has been transmitted to the first access network node.
(Supplementary Note 8)
The method according to Supplementary Note 7, wherein
the information indicating the part of the measurement result which has been transmitted to the first access network node is included in context information of the UE.
(Supplementary Note 9)
The method according to Supplementary Note 8, wherein
the context information includes at least one of:
a Radio Resource Control, RRC, context, or
a Protocol Data Convergence Protocol, PDCP, context.
(Supplementary Note 10)
The method according to any one of Supplementary Notes 7 to 9, further comprising:
receiving, from the first access network node or the second access network node, the information indicating the part of the measurement result which has been transmitted to the first access network node.
(Supplementary Note 11)
The method according to Supplementary Note 10, wherein
the information indicating the part of the measurement result which has been transmitted to the first access network node is transmitted from the first access network node to the second access network node.
(Supplementary Note 12)
The method according to any one of Supplementary Notes 7 to 11, wherein
the information indicating the part of the measurement result which has been transmitted to the first access network node includes at least one of:
information of a Radio Resource Control, RRC, segment, or
information of a Packet Data Convergence Protocol, PDCP, segment.
(Supplementary Note 13)
The method according to any one of Supplementary Notes 7 to 12, wherein
the transmitting the second part of the measurement result for the AI/ML model training is performed using the information indicating the part of the measurement result which has been transmitted to the first access network node.
(Supplementary Note 14)
The method according to any one of Supplementary Notes 7 to 13, wherein
the information indicating the part of the measurement result which has been transmitted to the first access network node is transmitted from the first access network node to the second access network node.
(Supplementary Note 15)
The method according to any one of Supplementary Notes 1 to 14, further comprising:
transmitting, to the first access network node or the second access network node, at least one of:
information indicating that the UE has the second part of the measurement result for the AI/ML model training,
a request for scheduling a resource for transmitting the second part of the measurement result for the AI/ML model training, or
an establishment cause indicating a purpose of transmitting a measurement result for the AI/ML model training,
after transmitting the first part of the measurement result for the AI/ML model training.
(Supplementary Note 16)
The method according to any one of Supplementary Notes 1 to 14, further comprising:
receiving, from the first access network node or the second access network node, a request for the UE to transmit the second part of the measurement result for the AI/ML model training until the UE finishes to transmit the first part of the measurement result for the AI/ML model training.
(Supplementary Note 17)
The method according to any one of Supplementary Notes 1 to 16, wherein
declaring a Radio Link Failure, RLF, after transmitting the first part of the measurement result for the AI/ML model training; and
establishing or resuming a Radio Resource Control, RRC, connection, and wherein
the transmitting the second part of the measurement result for the AI/ML model training after the declaring the RLF is performed after the establishing or resuming the RRC connection.
(Supplementary Note 18)
The method according to any one of Supplementary Notes 1 to 17, wherein
at least one of the first part of the measurement result or the second part of the measurement result is transmitted on at least one of:
a data radio bearer with a specific priority for transmitting measurement results for the AI/ML training,
a specific radio bearer with which a special type of logical channel, LCH is defined, or
a specific radio bearer which terminates between the UE and the first access network node or the second access network node.
(Supplementary Note 19)
The method according to any one of Supplementary Notes 1 to 18, wherein
the first part of the measurement result includes measurement result while the UE is in a Radio Resource Control, RRC, connected state,
the second part of the measurement result includes measurement result while the UE is in a RRC idle state or a RRC inactive state, and
the transmitting the first part of the measurement result and the transmitting the second part of the measurement result are performed while the UE is in the RRC connected state.
(Supplementary Note 20)
The method according to Supplementary Note 19, wherein
in a case where the UE moves from the RRC connected state to the RRC idle state or the RRC inactive state, receiving configuration information for transmitting the second part of the measurement result for the AI/ML model training.
(Supplementary Note 21)
The method according to Supplementary Note 19, wherein
in a case where the UE moves from the RRC idle state or the RRC inactive state to the RRC connected state, receiving configuration information for transmitting the second part of the measurement result for the AI/ML model training.
(Supplementary Note 22)
The method according to any one of Supplementary Notes 1 to 21, wherein
the second part of the measurement result for the AI/ML model training is transmitted from the second access network node to the first access network node or a data collection entity which is connected to the first access network node and the second access network node.
(Supplementary Note 23)
The method according to any one of Supplementary Notes 1 to 21, wherein
the first part of the measurement result for the AI/ML model training is transmitted from the first access network node to the second access network node or a data collection entity which is connected to the first access network node and the second access network node.
(Supplementary Note 24)
The method according to any one of Supplementary Notes 1 to 21, wherein
the first access network node and the second access network node are the same access network node.
(Supplementary Note 25)
A method performed by a first access network node, the method comprising:
receiving, from a user equipment, UE, a first part of measurement result for Artificial Intelligence / Machine Learning, AI/ML, model training, and wherein
a second part of the measurement result for the AI/ML model training is transmitted from the UE to a second access network node, and
at least one of the first part of the measurement result or the second part of the measurement result includes information indicating a model or function for the AI/ML model training.
(Supplementary Note 26)
The method according to Supplementary Note 25, wherein
the first access network node and the second access network node are the same access network node.
(Supplementary Note 27)
A method performed by an access network node, the method comprising:
receiving, from a core network node, information indicating whether data collection regarding a measurement for Artificial Intelligence / Machine Learning, AI/ML, model training is permitted by at least one user equipment, UE or at least one user.
(Supplementary Note 28)
The method according to Supplementary Note 27, wherein
the receiving is performed during an establishment of a non-access stratum, NAS, connection between the core network node and each of the at least one UE.
(Supplementary Note 29)
The method according to Supplementary Note 27 or 28, further comprising:
receiving, from the core network node, information indicating capability for the core network node regarding the data collection regarding the measurement for the AI/ML model training, during an interface setup procedure between the access network node and the core network node.
(Supplementary Note 30)
The method according to Supplementary Note 29, wherein
the capability includes at least one of:
capability of indicating subscription information regarding the data collection regarding the measurement for the AI/ML model training, or
capability of the data collection at the core network node.
(Supplementary Note 31)
The method according to any one of Supplementary Notes 27 to 30, further comprising:
transmitting, to the core network node, a request for the information indicating whether the data collection regarding the measurement for the AI/ML model training is permitted, and wherein
the receiving is performed in response to the transmitting the request.
(Supplementary Note 32)
The method according to Supplementary Note 31, further comprising:
receiving, from a data collection entity, a request for data for the AI/ML model training, and wherein
the transmitting the request is performed based on the receiving the request for the AI/ML model training.
(Supplementary Note 33)
A method performed by a core network node, the method comprising:
transmitting, to an access network node, information indicating whether data collection regarding a measurement for Artificial Intelligence / Machine Learning, AI/ML, model training is permitted by at least one user equipment, UE or at least one user.
(Supplementary Note 34)
A user equipment, UE, comprising:
means for transmitting, to a first access network node, a first part of measurement result for Artificial Intelligence / Machine Learning, AI/ML, model training; and
means for transmitting, to a second access network node, a second part of the measurement result for the AI/ML model training, wherein
at least one of the first part of the measurement result or the second part of the measurement result includes information indicating a model or function for the AI/ML model training.
(Supplementary Note 35)
A first access network node comprising:
means for receiving, from a user equipment, UE, a first part of measurement result for Artificial Intelligence / Machine Learning, AI/ML, model training, and wherein
a second part of the measurement result for the AI/ML model training is transmitted from the UE to a second access network node, and
at least one of the first part of the measurement result or the second part of the measurement result includes information indicating a model or function for the AI/ML model training.
(Supplementary Note 36)
An access network node comprising:
means for receiving, from a core network node, information indicating whether data collection regarding a measurement for Artificial Intelligence / Machine Learning, AI/ML, model training is permitted by at least one user equipment, UE or at least one user.
(Supplementary Note 37)
A core network node comprising:
means for transmitting, to an access network node, information indicating whether data collection regarding a measurement for Artificial Intelligence / Machine Learning, AI/ML, model training is permitted by at least one user equipment, UE or at least one user.
This application is based upon and claims the benefit of priority from United Kingdom Patent Application No. 2310198.3, filed on July 3, 2023, the disclosure of which is incorporated herein in its entirety by reference.