Configuration of mobility events and measurement reporting in wireless communication systems

Abstract

A UE receives several configuration sets. Each configuration set links first and second measurement reporting events. The first measurement reporting event is either an actual or a predicted measurement reporting event. The second measurement reporting event is the other one of the actual or predicted measurement reporting event. Each configuration set includes one or more criteria for triggering the corresponding first and second reporting events. The UE determines that one or more criteria for triggering a first measurement reporting event corresponding to a first configuration set are satisfied. In a case that one or more criteria for triggering a second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are satisfied, the UE generates a measurement report based on measurements associated with the first and second measurement reporting events corresponding to the first configuration set. Otherwise, the UE foregoes generating the measurement report.

Description

CONFIGURATION OF MOBILITY EVENTS AND MEASUREMENT REPORTING IN WIRELESS COMMUNICATION SYSTEMS

[0001] The technology generally relates to wireless communications, and more particularly, to predictive beam configuration.

[0002] Because of the tremendous growth in the number of connected devices and the rapid increase in the user / network (NW) traffic volume, various efforts have been made to improve different aspects of the wireless communications in the next-generation radio communication systems, such as the 5th generation (5G) New Radio (NR). Such improvements include improving data rate, latency, reliability, mobility, etc.

[0003] The 5G NR system is designed to provide flexibility and configurability to optimize NW services and types, thus accommodating various use cases, such as enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC), and Ultra-Reliable and Low-Latency Communication (URLLC).

[0004] As the demand for radio access continues to grow, however, there is a need for further improvements in wireless communications in the next-generation radio communication systems, such as improvements in the network mobility management.

[0005] In a first aspect of the present application, a user equipment (UE) is provided. The UE includes one or more non-transitory computer-readable media storing one or more computer-executable instructions for configuring inter-dependence between predicted and actual measurement reporting events, and at least one processor coupled to the one or more non-transitory computer-readable media. The at least one processor is configured to receive several configuration sets. Each configuration set links a first measurement reporting event with a second measurement reporting event. The first measurement reporting event includes one of an actual measurement reporting event or a predicted future measurement reporting event. The second measurement reporting event includes the other one of the actual measurement reporting event or the predicted future measurement reporting event. Each configuration set includes one or more criteria for triggering the corresponding first and second reporting events. The at least one processor is configured to determine that one or more criteria for triggering a first measurement reporting event corresponding to a first configuration set in the configuration sets are satisfied. The processor is configured to, in a case that one or more criteria for triggering a second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are satisfied, generate a measurement report based on measurements associated with the first and second measurement reporting events corresponding to the first configuration set. The processor is configured to, in a case that the one or more criteria for triggering the second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are not satisfied, forego generating the measurement report.

[0006] In an implementation of the first aspect, the UE is configured to prioritize the actual measurement reporting events over the predicted future measurement reporting events. The first measurement reporting event corresponding to the first configuration is an actual measurement reporting event. The second measurement reporting event corresponding to the first configuration is a predicted future measurement reporting event. The at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to generate actual measurement results at one or more time instances prior to determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied; and determine that one or more criteria for triggering the first measurement reporting event corresponding to the first configuration set are satisfied based on the generated actual measurement results.

[0007] In another implementation of the first aspect, the at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to, after determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied, generate inference results for the second measurement reporting event corresponding to the first configuration set; and after determining that the one or more criteria for triggering the second measurement reporting event corresponding to the first configuration are satisfied, generate predicted future measurement results associated with the second measurement reporting event using the generated inference results.

[0008] In another implementation of the first aspect, the actual measurement results includes one or more of a set of layer 1 reference signal reception power (L1-RSRP) measurements, a set of layer 1 reference signal received quality (RSRQ) measurements, a set of signal to interference and noise ratio (SINR) measurements, a set of layer 3 RSRP (L3- RSRP), and a set of L3- RSRQ.

[0009] In another implementation of the first aspect, the UE is configured to prioritize the predicted future measurement reporting events over the actual measurement reporting events. The first measurement reporting event corresponding to the first configuration is a predicted future measurement reporting event. The second measurement reporting event corresponding to the first configuration is an actual measurement reporting event. The at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to, prior to determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied, generate actual measurement results at several time instances; and calculate one or more predicted future measurement results associated with the first measurement reporting event based on the actual measurement results.

[0010] In another implementation of the first aspect, at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to, after determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied, and prior to determining that the one or more criteria for triggering the second measurement reporting event corresponding to the first configuration are satisfied: reduce or increase a number of actual measurements performed; and transmit the one or more predicted future measurement results associated with the first measurement reporting event to a network node.

[0011] In another implementation of the first aspect, calculating the one or more predicted future measurement results associated with the first measurement reporting event includes using an artificial intelligence / machine learning (AI / ML) model.

[0012] In another implementation of the first aspect, calculating the one or more predicted future measurement results associated with the first measurement reporting event includes calculating the one or more predicted future measurement results for one or more network entities including a serving cell, a serving base station (BS), a serving relay, a neighboring cell, a neighboring BS, or a neighboring relay.

[0013] In another implementation of the first aspect, the at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to receive the configuration sets at a time of manufacturing the UE.

[0014] In another implementation of the first aspect, the at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to receive the configuration sets in one or more messages from a network node, the one or more messages include one or more radio resource control (RRC) messages, one or more non-access-stratum (NAS) messages, or one or more system information blocks (SIBs).

[0015] In another implementation of the first aspect, each configuration set is included in an information element (IE) that includes one or more parameters for the first measurement reporting event of the configuration set.

[0016] In another implementation of the first aspect, the at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to transmit the measurement report generated based on measurements associated with the first and second measurement reporting events to a network node.

[0017] In another implementation of the first aspect, the measurement report is for use by the network node to perform proactive steps related to mobility of the UE.

[0018] In another implementation of the first aspect, wherein the network node is one of a BS, a location management function (LMF) server, a UE acting as a sidelink relay, a core network (CN) node, or a radio access network (RAN) node.

[0019] In a second aspect of the present application, a method of configuring inter-dependence between predicted and actual measurement reporting events to a UE is provided. The method includes receiving several configuration sets. Each configuration set links a first measurement reporting event with a second measurement reporting event. The first measurement reporting event includes one of an actual measurement reporting event or a predicted future measurement reporting event. The second measurement reporting event includes the other one of the actual measurement reporting event or the predicted future measurement reporting event. Each configuration set includes one or more criteria for triggering the corresponding first and second reporting events. The method includes determining that one or more criteria for triggering a first measurement reporting event corresponding to a first configuration set in the configuration sets are satisfied. The method, in a case that one or more criteria for triggering a second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are satisfied, generates a measurement report based on measurements associated with the first and second measurement reporting events corresponding to the first configuration set. The method, in a case that the one or more criteria for triggering the second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are not satisfied, foregoes generating the measurement report.

[0020] The foregoing and other objects, features, and advantages of the technology disclosed herein will be apparent from the following more particular description of preferred embodiments as illustrated in the accompanying drawings in which reference characters refer to the same parts throughout the various views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the technology disclosed herein.Figure 1 is a schematic diagram illustrating a radio communication system, according to an example implementation of the present disclosure.Figure 2 is a functional diagram illustrating an example timeline of measurements performed by the UE, according to an example implementation of the present disclosure.Figure 3 is a functional diagram illustrating an example timeline showing predictions data for a future measurement instance already available at the current measurement instance, according to an example implementation of the present disclosureFigure 4 is a sequence diagram illustrating an example message flow for configuring the UE with report triggering criteria to handle prediction-based measurements, according to an example implementation of the present disclosure.Figure 5 is a sequence diagram illustrating an example message flow that shows the UE actions related to the configured AI / ML-based mobility events, according to an example implementation of the present disclosure.Figure 6 is a sequence diagram illustrating an example message flow between the network node and the UE to link the legacy measurement reporting events and the AI / ML-based measurement reporting events, according to an example implementation of the present disclosure.Figure 7A illustrates an example table that links legacy measurement events to the AI / ML events, according to an example implementation of the present disclosure.Figure 7B illustrates an example table that links AI / ML events to the legacy measurement events, according to an example implementation of the present disclosure.Figure 8A is a flowchart illustrating a method / process performed by a UE for prioritizing actual measurement reporting events over the predicted future measurement reporting events, according to an example implementation of the present disclosure.Figure 8B is a flowchart illustrating a method / process performed by a UE for prioritizing predicted future measurement reporting events over the actual measurement reporting events, according to an example implementation of the present disclosure.Figure 9 is a flowchart illustrating an example method / process performed by a UE for configuring measurement reporting to the UE, according to an example implementation of the present disclosure.Figure 10 is a flowchart illustrating an example method / process performed by a UE for configuring inter-dependence between predicted and actual measurement reporting events to the UE, according to an example implementation of the present disclosure.Figure 11 is a block diagram illustrating a node for wireless communication, according to an example implementation of the present disclosure.

[0021] The following description contains specific information pertaining to example implementations in the present disclosure. The drawings in the present disclosure and their accompanying detailed description are directed to merely example implementations. However, the present disclosure is not limited to merely these example implementations. Other variations and implementations of the present disclosure will occur to those skilled in the art. Unless noted otherwise, like or corresponding elements among the figures may be indicated by like or corresponding reference numerals. Moreover, the drawings and illustrations in the present disclosure are generally not to scale and are not intended to correspond to actual relative dimensions.

[0022] For the purposes of consistency and ease of understanding, like features may be identified (although, in some examples, not shown) by the same numerals in the example figures. However, the features in different implementations may differ in other respects, and thus may not be narrowly confined to what is shown in the figures.

[0023] The description uses the phrases “in one implementation,” or “in some implementations,” which may each refer to one or more of the same or different implementations. The term “coupled” is defined as connected, whether directly or indirectly through intervening components, and is not necessarily limited to physical connections. The term “comprising,” when utilized, means “including, but not necessarily limited to”; it specifically indicates open-ended inclusion or membership in the so-described combination, group, series, and the equivalent. In addition, the terms “system” and “network” herein may be used interchangeably.

[0024] As used herein, the term “and / or” should be interpreted to mean one or more items. For example, the phrase “A, B, and / or C” should be interpreted to mean any of: only A, only B, only C, A and B (but not C), B and C (but not A), A and C (but not B), or all of A, B, and C. As used herein, the phrase “at least one of” should be interpreted to mean one or more items. For example, the phrase “at least one of A, B, and C” or the phrase “at least one of A, B, or C” should be interpreted to mean any of: only A, only B, only C, A and B (but not C), B and C (but not A), A and C (but not B), or all of A, B, and C. As used herein, the phrase “one or more of” should be interpreted to mean one or more items. For example, the phrase “one or more of A, B and C” or the phrase “one or more of A, B or C” should be interpreted to mean any of: only A, only B, only C, A and B (but not C), B and C (but not A), A and C (but not B), or all of A, B, and C.

[0025] Any two or more of the following paragraphs, (sub)-bullets, points, actions, behaviors, terms, or claims described in the present disclosure may be combined logically, reasonably, and properly to form a specific method.

[0026] Any sentence, paragraph, (sub)-bullet, point, action, behaviors, terms, or claims described in the present disclosure may be implemented independently and separately to form a specific method.

[0027] Dependency, e.g., “based on”, “more specifically”, “preferably”, “in one embodiment”, “in some implementations”, etc., in the present disclosure is just one possible example which would not restrict the specific method.

[0028] Additionally, for the purposes of explanation and non-limitation, specific details, such as functional entities, techniques, protocols, standard, and the like are set forth for providing an understanding of the described technology. In other examples, detailed descriptions of well-known methods, technologies, systems, architectures, and the like are omitted so as not to obscure the description with unnecessary details.

[0029] Persons skilled in the art will immediately recognize that any network function(s) or algorithm(s) described in the present disclosure may be implemented by hardware, software, or a combination of software and hardware. Described functions or algorithms may correspond to modules which may be software, hardware, firmware, or any combination thereof. The software implementation may include computer executable instructions stored on a computer-readable medium, such as a memory or other types of storage devices. For example, one or more microprocessors or general-purpose computers with communication processing capability may be programmed with corresponding executable instructions and carry out the described network function(s) or algorithm(s). The microprocessors or general-purpose computers may include of one or more Application-Specific Integrated Circuits (ASICs), programmable logic arrays, and / or one or more Digital Signal Processor (DSPs). Although some of the example implementations described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative example implementations implemented as firmware, as hardware, or as a combination of hardware and software are well within the scope of the present disclosure.

[0030] The computer-readable medium includes, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory, Compact Disc Read-Only Memory (CD-ROM), magnetic cassettes, magnetic tape, magnetic disk storage, or any other equivalent medium capable of storing computer-readable instructions.

[0031] A radio communication network architecture (e.g., a Long-Term Evolution (LTE) system, an LTE-Advanced (LTE-A) system, an LTE-Advanced Pro system, or a 5G NR Radio Access Network (RAN)) typically includes at least one base station (BS), at least one UE, and one or more optional network elements that provide connection towards a network. The UE communicates with the network (e.g., a Core Network (CN), an Evolved Packet Core (EPC) network, an Evolved Universal Terrestrial Radio Access network (E-UTRAN), a 5G Core (5GC), or an internet), through a radio communication network established by one or more BSs.

[0032] It should be noted that, in the present disclosure, a UE (or a terminal device) may include, but is not limited to, a mobile station, a mobile terminal or device, a user communication radio terminal. For example, a UE may be a portable radio equipment, which includes, but is not limited to, a mobile phone, a tablet, a wearable device, a sensor, a vehicle, or a Personal Digital Assistant (PDA) with wireless communication capability. The UE is configured to receive and transmit signals over an air interface to one or more cells in a radio access network.

[0033] A BS may be configured to provide communication services according to at least one of the following Radio Access Technologies (RATs): Worldwide Interoperability for Microwave Access (WiMAX), Global System for Mobile communications (GSM, often referred to as 2G), GSM Enhanced Data rates for GSM Evolution (EDGE) Radio Access Network (GERAN), General Packet Radio Service (GPRS), Universal Mobile Telecommunication System (UMTS, often referred to as 3G) based on basic wideband-code division multiple access (W-CDMA), high-speed packet access (HSPA), LTE, LTE-A, evolved LTE (eLTE), for example, LTE connected to 5GC, NR (often referred to as 5G), and / or LTE-A Pro. However, the scope of the present disclosure should not be limited to the above-mentioned protocols.

[0034] A BS may include, but is not limited to, a node B (NB) as in the UMTS, an evolved node B (eNB) as in the LTE or LTE-A, a radio network controller (RNC) as in the UMTS, a base station controller (BSC) as in the GSM / GSM Enhanced Data rates for GSM Evolution (EDGE) Radio Access Network (GERAN), a next-generation eNB (ng-eNB) as in an Evolved Universal Terrestrial Radio Access (E-UTRA) BS in connection with the 5GC, a next-generation Node B (gNB) as in the 5G Access Network (5G-AN), and any other apparatus capable of controlling radio communication and managing radio resources within a cell. The BS may connect to serve the one or more UEs through a radio interface to the network.

[0035] The BS may be operable to provide radio coverage to a specific geographical area using several cells included in the radio communication network. The BS may support the operations of the cells. Each cell may be operable to provide services to at least one UE within its radio coverage. Specifically, each cell (often referred to as a serving cell) may provide services to serve one or more UEs within its radio coverage (e.g., each cell may correspond to the Downlink (DL) and optionally Uplink (UL) resources to at least one UE within its radio coverage for DL and optionally UL packet transmission). The BS may communicate with one or more UEs in the radio communication system through the cells.

[0036] A cell may correspond to sidelink (SL) resources for supporting Proximity Service (ProSe) or Vehicle to Everything (V2X) services. Each cell may have overlapped coverage areas with other cells.

[0037] As discussed above, the frame structure for NR is to support flexible configurations for accommodating various next generation (e.g., 5G) communication requirements, such as Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC), Ultra-Reliable and Low-Latency Communication (URLLC), while fulfilling high reliability, high data rate and low latency requirements. The Orthogonal Frequency-Division Multiplexing (OFDM) technology as agreed in the 3rd Generation Partnership Project (3GPP) may serve as a baseline for NR waveform. The scalable OFDM numerology, such as the adaptive sub-carrier spacing, the channel bandwidth, and the Cyclic Prefix (CP) may also be used. Additionally, two coding schemes are considered for NR: (1) Low-Density Parity-Check (LDPC) code and (2) Polar Code. The coding scheme adaption may be configured based on the channel conditions and / or the service applications.

[0038] Moreover, it should also be noted that in a transmission time interval (TTI) of a single NR frame, DL transmission period, a guard period, and UL transmission data may at least be included, where the respective portions of the DL transmission data, the guard period, and the UL transmission data should also be configurable, for example, based on the network dynamics of NR. In addition, sidelink resources may also be provided in an NR frame to support ProSe services, (E-UTRA / NR) sidelink services, or (E-UTRA / NR) V2X services.

[0039] A UE configured with multi-connectivity may connect to a Master Node (MN) as an anchor and one or more Secondary Nodes (SNs) for data delivery. Each one of these nodes may be formed by a cell group that includes one or more cells. For example, a Master Cell Group (MCG) may be formed by an MN, and a Secondary Cell Group (SCG) may be formed by an SN. In other words, for a UE configured with dual connectivity (DC), the MCG may be a set of one or more serving cells including the PCell and zero or more secondary cells. Conversely, the SCG may be a set of one or more serving cells including the PSCell and zero or more secondary cells.

[0040] As also described above, the Primary Cell (PCell) may be an MCG cell that operates on the primary frequency, in which the UE either performs the initial connection establishment procedure or initiates the connection reestablishment procedure. In the DC mode, the PCell may belong to the MN. The Primary SCG Cell (PSCell) may be an SCG cell in which the UE performs random access (e.g., when performing the reconfiguration with a sync procedure). In Multi-RAT Dual Connectivity (MR-DC), the PSCell may belong to the SN. A Special Cell (SpCell) may be referred to a PCell of the MCG, or a PSCell of the SCG, depending on whether the Medium Access Control (MAC) entity is associated with the MCG or the SCG. Otherwise, the term Special Cell may refer to the PCell. A Special Cell may support a Physical Uplink Control Channel (PUCCH) transmission and contention-based Random Access, and may always be activated. Additionally, for a UE in a radio resource control connected (RRC_CONNECTED) state that is not configured with the carrier aggregation / dual connectivity (CA / DC), may communicate with only one serving cell (SCell) which may be the primary cell. Conversely, for a UE in the RRC_CONNECTED state that is configured with the CA / DC a set of serving cells including the special cell(s) and all of the secondary cells may communicate with the UE.

[0041] According to one aspect of the present disclosure, a waveform formed based on the OFDM may be used in a radio communication system. An OFDM symbol defines a unit in the time domain of the waveform. Each OFDM symbol is converted to a time-continuous signal during a baseband signal generation. For example, the cyclic prefix-OFDM (CP-OFDM) may be used in the downlink transmission of the radio communication system. For example, either CP-OFDM or Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplex (DFT-s-OFDM) may be used in the uplink transmission of the radio communication system.

[0042] It should be noted that the term transmission reception point (TRP) in the present disclosure may be replaced by ‘beam’ or ‘panel’. It should also be noted that the term ‘overlap’ may refer to time domain overlapping or frequency domain overlapping.

[0043] Examples of some selected terms in the present disclosure are provided as follows.

[0044] Antenna Panel: It may be assumed that an antenna panel is an operational unit for controlling a transmit spatial filter / beam. An antenna panel typically includes several antenna elements. A beam can be formed by an antenna panel and in order to form two beams simultaneously, two antenna panels are needed. Such simultaneous beamforming from multiple antenna panels is subject to the UE capability. A similar definition for “antenna panel” may be possible by applying spatial receiving filtering characteristics.

[0045] BWP: A subset of the total cell bandwidth of a cell is referred to as a bandwidth part (BWP), and bandwidth adaptation (BA) is achieved by configuring the UE with BWP(s) and telling the UE which of the configured BWPs is currently the active one. To enable BA on the PCell, the gNB configures the UE with UL and DL BWP(s). To enable BA on the SCells in case of the CA, the gNB configures the UE at least with the DL BWP(s) (e.g., there may be no BWP in the UL). For the PCell, the initial BWP is the BWP used for an initial access. For the SCell(s), the initial BWP is the BWP configured for the UE to first operate at the SCell activation. The UE may be configured with a first active uplink BWP, for example, by a firstActiveUplinkBWP IE. If the first active uplink BWP is configured for an SpCell, the firstActiveUplinkBWP information element (IE) field may contain the ID of the UL BWP to be activated upon performing the RRC (re-)configuration. If the firstActiveUplinkBWP IE field is absent, the RRC (re-)configuration may not impose a BWP switch. If the first active uplink BWP is configured for an SCell, the firstActiveUplinkBWP IE field may contain the ID of the UL BWP to be used upon the MAC-activation of an SCell.

[0046] TCI state: A transmission configuration indication (TCI) state may contain parameters for configuring a Quasi-CoLocation (QCL) relationship between one or more reference signals and a target reference signal set. For example, a target reference signal set may be the Demodulation Reference Signal (DM-RS) ports of the Physical Downlink Shared Channel (PDSCH), Physical Downlink Control Channel (PDCCH), PUCCH or Physical Uplink Shared Channel (PUSCH). The one or more reference signals may include UL or DL reference signals. In NR Rel-15 / 16, the TCI state is used for DL QCL indication whereas spatial relation information is used for providing UL spatial transmission filter information for UL signal(s) or UL channel(s). Here, a TCI state may refer to information provided similar to spatial relation information, which could be used for UL transmission. In other words, from the UL perspective, a TCI state provides a UL beam information which may provide the information for a relationship between a UL transmission and a DL (or a UL) reference signal (e.g., Channel State Information Reference Signal (CSI-RS), Synchronization Signal Block (SSB), Sounding Reference Signal (SRS), Phase Tracking Reference signal (PTRS)).

[0047] A UE may be configured with a list including up to M TCI state configurations, where each TCI state may contain parameters for configuring at least one QCL relationship between one or more downlink reference signals and the DM-RS ports of the PDSCH, the DM-RS port of PDCCH, or the CSI-RS port(s) of a CSI-RS resource. The QCL types corresponding to each DL RS may be given, for example, by the higher layer (e.g., RRC layer), parameters for the at least one RS and may take one of the following values: Furthermore, a UE may be configured with a TCI state configuration that contains parameters for determining a UL transmission (TX) spatial filter for the UL transmissions. More specifically, when signals transmitted from different antenna ports share channels with similar properties, the antenna ports are said to be QCL signals. Basically, the QCL concept is introduced to help the UE with a precise channel estimation, frequency offset error estimation, and synchronization procedures.

[0048] Panel: The UE panel information may be derived from the TCI state / UL beam indication information or from the network signaling.

[0049] Beam: The term “beam” may be replaced with spatial filter. For example, when a UE reports a preferred gNB TX beam, the UE is essentially selecting a spatial filter used by the gNB. The term “beam information” may be used to provide information about which beam / spatial filter has been used / selected.

[0050] Multi-TRP: Multi-TRP is a feature that enables a BS (e.g., a gNB) to communicate with a UE using more than one TRP, for example, to ensure reliability. Moreover, NR supports same data stream(s) received from multiple TRPs at least with an ideal backhaul, and different NR-PDSCH data streams received from multiple TRPs with both ideal and non-ideal backhauls. An ideal backhaul may allow single Downlink Control Information (DCI) to be transmitted via a PDCCH from one TRP to schedule data transmission (or information) to / from multiple TRPs (may also be referred to as single-DCI based multi-TRP / panel transmission). On the other hand, a non-ideal backhaul may require multiple DCIs to be carried in the PDCCH(s) to schedule data transmission (or information) corresponding to each TRP (may also be referred to as multi-DCI based multi-TRP / panel transmission). To enhance reliability for the system, at least one multi-TRP scheme may be applied to at least one channel / reference signal, for example, a multi-TRP based PDSCH operation, a multi-TRP based PDCCH operation, a multi-TRP based PUCCH operation, and / or a multi-TRP based PUSCH operation.

[0051] TDM based PDCCH repetition: For example, two PDCCHs may be linked together for the repetition of the same DCI format, the same DCI payload, the same number of CCEs, and / or the same number of candidates for each AL. The two PDCCHs may be in two search spaces associated with two Control Resource Sets (CORESETs).

[0052] TDM based PDSCH repetition: PDSCH repetition refers to multiple PDSCHs that have the same TB and are associated with different TRPs. Slot-based PDSCH repetition corresponds to scheduling each repetitive PDSCH in individual slots. Non-slot-based PDSCH repetition corresponds to scheduling multiple repetitive PDSCHs within the same slot.

[0053] TDM based PUCCH repetition: PUCCH repetition refers to multiple PUCCHs with the same Uplink Control Information (UCI) content but corresponding to different beams. There are two types of PUCCH repetitions: inter-slot based PUCCH repetition and intra-slot based PUCCH repetition, which are categorized according to their timing and relate to all PUCCH formats. Inter-slot based PUCCH transmission corresponds to transmitting each repetitive PUCCH in individual slots. Intra-slot based PUCCH transmission corresponds to transmitting each repetitive PUCCH in individual slots and transmitting multiple repetitive PDSCHs within the same slot.

[0054] TDM based PUSCH repetition: PUSCH repetition refers to multiple PUSCHs with the same TB but corresponding to different TRPs. Slot-based PUSCH repetition corresponds to scheduling each repetitive PUSCH in an individual slot. Non-slot-based PUSCH repetition corresponds to scheduling multiple repetitive PUSCHs within the same slot.

[0055] Frequency Division Multiplexing (FDM) based PDSCH repetition: Multiple PDSCHs with the same TB but corresponding to two TCI states. These PDSCHs are allocated to non-overlapping frequency resources within a slot.

[0056] Multi-DCI based PDSCH scheme: Two PDCCHs from separate search spaces associated with different CORESET pool indexes that schedule the corresponding PDSCHs.

[0057] Single Frequency Network (SFN) based PDCCH scheme: A CORESET is associated with two different beams.

[0058] SFN based PDSCH scheme: A PDSCH is associated with two different beams.

[0059] Measurement objects: A list of objects on which the UE shall perform the measurements. For intra-frequency and inter-frequency measurements, a measurement object indicates the frequency / time location and subcarrier spacing of the reference signals to be measured. Associated with this measurement object, the network may configure a list of cell specific offsets, a list of exclude-listed cells and a list of allow-listed cells. The exclude-listed cells are not applicable in event evaluation or measurement reporting. The allow-listed cells are the only cells that are applicable in event evaluation or measurement reporting.

[0060] Unified TCI framework: To facilitate more efficient (lower latency and overhead) DL / UL beam management to support a larger number of configured TCI states, a unified TCI framework for beam indication may result in some benefits of low complexity and simplified controlling mechanisms. More specifically, through the unified indication, the DL or UL channels / signals may share the same indicated TCI state to reduce the signaling overhead, and different channels and / or reference signals may share similar channel properties. The unified indication may be used to indicate a common TCI state for the DL channels (e.g., including a PDCCH, PDSCH, and / or DL reference signal), a common TCI state for the UL channels (e.g., including a PUCCH, PUSCH, and / or UL reference signal), and / or a common TCI state for both DL and UL channels. The unified indication for a common TCI state for the DL channels may be referred to as a “DL TCI state” or a “DL only”. The unified indication for a common TCI state for the UL channels may be referred to as a “UL TCI state” or a “UL only”. The unified indication for a common TCI state for both DL and UL channels may be referred to as a “joint TCI state” or a “joint indication”. The “DL only” and “UL only” may also be referred to as a “separate TCI state,” as opposed to the “joint TCI state”.

[0061] Unified TCI states may be indicated through an RRC message, a Medium Access Control Element (MAC CE), and / or the DCI. For example, the RRC message may indicate whether the unified framework is enabled. The MAC CE may further indicate where to apply the unified TCI framework. In addition, the DCI may also include information for the unified TCI states to explicitly indicate the TCI states to the UE. In particular, the information contained in the MAC CE may refer to a serving cell index, a DL BWP index, a UL BWP index, the number of TCI states included in each TCI codepoint, transmission direction, and / or a TCI state index. However, when the unified TCI framework is applied to multiple TRPs, there is no further information to link the specific TCI states to the specific TRPs. Consequently, since multiple TRPs may correspond to different schemes, such as a TDM scheme, an FDM scheme, a multi-DCI scheme, and an SFN scheme, some potential impact may need to be considered when applying the unified TCI framework (e.g., including the DL only, UL only, and / or joint indication) to different schemes for multiple TRPs. The following cases are listed as possible scenarios where the unified TCI framework may be applied. Furthermore, the listed scenarios may correspond to an intra-cell or an inter-cell multi-TRP scheme. It should be noted that the disclosed implementations may include one or more of the following scenarios: When the unified TCI framework is applied to at least one multi-TRP scheme, some changes may be needed. The changes may include the association between the unified indication and at least one TRP, the mapping order of the indicated TCI states, the association between the unified indication and the respective channel, and / or the method of signaling for each channel. In the present disclosure, implementations for applying the unified TCI framework to the multi-TRP scheme are disclosed hereinafter.

[0062] The 3GPP (e.g., as indicated in Release 18, study item (SI) on artificial intelligence / machine learning (AI / ML) for air interface) has identified the following scopes: (i) identify use cases and scenarios where the AI / ML may be effectively applied within the 3GPP-defined network architectures and protocols, (ii) study the integration of the AI / ML algorithms into the network functions, protocols, and management systems to enable intelligent decision-making and automation, and (iii) evaluate the impact of the AI / ML on the network scalability, reliability, energy efficiency, spectral efficiency, and quality of service.

[0063] For an AI / ML based beam management (BM) use case, the following two use cases may be selected, as the representative AI / ML sub-use cases. The first use case (BM-Case1) may include spatial-domain downlink beam prediction for a first set of beams (e.g., Set A of beams) based on measurement results of a second set of beams (e.g., Set B of beams).

[0064] For the BM-Case1, the following alternatives may be considered. The AI / ML model training and inference may be done either at the NW side or at the UE side. Set A and Set B may be different (e.g., Set B may not be a subset of Set A) or Set B may be a subset of Set A. It should be noted that Set A is for DL beam prediction. The codebook construction of Set A and Set B may be later defined.

[0065] The AI / ML model input may consider the following alternatives: (1) The layer 1 reference signal reception power (L1-RSRP) measurement based on Set B, the L1-RSRP measurement based on Set B and assistance information, the channel impulse response (CIR) based on Set B, or the L1-RSRP measurement based on Set B and the corresponding DL Tx and / or Rx beam ID.

[0066] The second use case (BM-Case2) may include temporal downlink beam prediction for Set A of beams based on the historic measurement results of Set B of beams. For the BM-Case2, the following alternatives may be considered. The AI / ML model training and inference may be done either at the NW side or at the UE side. Set A and Set B of beams may be different (e.g., Set B may not be a subset of Set A), Set B may be a subset of Set A (e.g., Set A and Set B may not be the same), or Set A and Set B are the same.

[0067] The AI / ML model input may consider measurement results of K (K is greater than or equal to 1) latest measurement instances with the following alternatives: (1) Only the L1-RSRP measurements based on Set B, (2) The L1-RSRP measurements based on Set B and assistance information, or (3) The L1-RSRP measurements based on Set B and the corresponding DL Tx and / or Rx beam identification (ID). F predictions for F future time instances may be obtained based on the output of the AI / ML model, where each prediction is for each time instance. F may, at least be equal to 1.

[0068] Based on the parameters like report of the predicted top-K beam IDs, report of the predicted and / or actual / measured L1-RSRPs associated with the predicted top-K beams, report of the quantities indicating the confidence level of predictions for the top-K beams (e.g., the standard deviation of the predicted L1-RSRPs or statistics of the past RSRP measurements as a proxy for the confidence level of the predictions) and other related parameters like key performance indicators (KPIs), the AI / ML model may provide output in the form of F (f1,f2 … fn) predictions for T(t1,t2, … tn) future time instances. The prediction may reflect predicted beams and their corresponding configurations.

[0069] BEAM PAIR SELECTION PROCESS There may be three distinct DL processes of operations to obtain the best beam pair selection. These processes are colloquially referred to as P1, P2 and P3 in technical discussions and reports.

[0070] P1 is the initial process dedicated to the BS (e.g., gNB) beam selection. In P1, broad beams are typically used to sweep the angular space and a coarse serving direction may be chosen based on measurements from a broad-beam UE. P1 may be used to enable the UE measurement on different TRP Tx beams to support selection of the TRP Tx beams / UE Rx beam(s). Beamforming at the TRP, may typically include an intra / inter-TRP Tx beam sweep from a set of different beams. Beamforming at the UE may typically include a UE Rx beam sweep from a set of different beams. Before a data flow is enabled in the scheduler, periodic SSB beam scanning may be implemented on the BS side in a certain intervals (the SSB periodicity). At the same time, wide beam scanning may be implemented on the UE side to determine the optimal receive wide beam (the Optimal SSB / Physical Random Access Channel (SSB / PRACH) beams).

[0071] P2 is the second process to refine P1’s beam selection using narrower BS beams. P2 may still employ a broad beam at the UE. P2 may be used to enable the UE measurements on different TRP Tx beams to possibly change the inter / intra-TRP Tx beam(s). P2 may use a possibly smaller set of beams for beam refinement than P1. It should be noted that that P2 may be a special case of P1, for example, by performing a beam sweep in a narrower angular sector than in P1. The narrow beams closest to the wide beam in the beam grid may be selected to be examined using CSI-RS (followed by CSI-report).

[0072] P3 is the final process of beam alignment for the UEs equipped to support beamforming. After beam selection at the BS side, the transmit beam may be fixed so the UE may refine its broad beam by sweeping through its own narrow beams. P3 may be used to enable the UE measurements on the same TRP Tx beam to change the UE Rx beam in the case the UE uses beamforming.

[0073] The optimal narrow beam may be selected from P2, and the CSI-RSs may be transmitted to the UE. The UE may update its Rx beam. In the data transmission, the BS may use the best BS Tx beam found during P2 and the UE may use the best UE Rx beam found during P3.

[0074] It should be noted that, while data transmission is being performed on an active beam pair link, the UE may monitor the PDCCH on another beam pair as a backup link for swift fallback if there is a sudden blockage of the active link.

[0075] SYNCHRONIZATION SIGNAL BLOCKS The Synchronization Signal / Physical Broadcast Channel (SS / PBCH) Blocks, typically shortened to SSBs, are a pivotal part of the NR. The SSBs may be broadcast periodically for the UE’s measurement purposes. A single SSB, spanning 4 OFDM symbols in time and 240 subcarriers in frequency, may include both synchronization signals and broadcast channels. The Primary Synchronization Signal (PSS) and the Secondary Synchronization Signal (SSS) may be carried in the SSB as two 127-long pseudo random binary m-sequences employed for initial synchronization and cell identification. The PBCH associated with the Demodulation Reference Signal (DMRS) may contain system control information that the UE may require to communicate with the network.

[0076] During the beam sweeping procedure, the SSBs may be transmitted in groups, known as SSBursts, according to a numerology-dependent transmission pattern. In Frequency Range 2 (FR2), an SSBurst may contain up to 64 SSBs. Each SSB may be mapped to a unique BS beam so that the UE may decode it, measure that beam’s power level, and report the beam’s L1-RSRP value back to the BS for beam determination. This may be done through SS-RSRP, which may be defined as the linear average over the power contributions in Watt of the resource elements that carry an SSS. For beam acquisition, SSBs are usually employed during P1, where broader beams are considered.

[0077] The CSI-RSs are UE-specific signals transmitted by the BS to monitor the DL radio channel conditions. These NR signals are extremely flexible, allowing for 18 different time-frequency allocation configurations tailored to a multitude of applications, such as, Channel State Information (CSI) acquisition, radio resource management (RRM), or beam management. For beam management, the CSI-RS may only be configured through three distinct configurations to be used, similarly to SSBs, in L1-RSRP measurements for beam candidate selection. This may be achieved using the CSI-RSRP, which is the linear average over the power contributions in Watt of the resource elements of the antenna port(s) that carry CSI-RS configured for RSRP measurements within the considered measurement frequency bandwidth in the configured CSI-RS occasions.

[0078] In the context of beam acquisition, the CSI-RSs are associated with narrower beams and, therefore, are employed in both P2 and P3, as described above. However, their configurations differ in a higher layer parameter named “repetition,” which displays a binary “on” or “off” state. The repetition parameter may only be set for the CSI-RSs that are configured for the L1-RSRP and it may let the UE make a determination regarding the DL beamforming configuration on the BS side. In P2, the repetition parameter may be set to “off,” entailing that the beamforming applied to each CSI-RS resource at the BS may vary. Therefore, the UE may take that information as an indication to maintain the same spatial filtering until P2 is complete. In P3, however, the repetition parameter may be set to “on,” which means that the UE may assume that no beam sweeping is performed on the BS side and, therefore, the UE is free to sweep through its own beams for the purpose of beam refinement.

[0079] RAN work group 2 (RAN WG2 or RAN2), during phase 1 discussions, has defined different functionality types for AI / ML functionalities. A functionality may refer to an AI / ML-enabled feature, or feature group, facilitated by a configuration. A functionality, in the context of AI / ML-enabled 5G NR and beyond communication systems, may refer to a specific feature, or a collection of related features, that is enabled by artificial intelligence or machine learning capabilities. These functionalities are supported and managed through configurations, which are sets of parameters or instructions that dictate how the AI / ML enabled 5G NR or beyond system should operate. Essentially, a configuration ensures that the functionality works correctly by providing the necessary settings and data for the AI / ML processes including the life cycle Management (LCM) of the AI / ML model / functionality to work effectively.

[0080] USE OF AI / ML PREDICTION-BASED MEASUREMENT DATA IN UE MOBILITY MECHANISMS RAN2, during FS_NR_AIML_Mob study discussions (reported in document RP-234055), has considered possible benefits of AI / ML models in the UE mobility processes, such as handovers (HOs). Some objectives of the study were AI / ML based RRM measurement and event prediction, which may include the HO failure / Radio Link Failure (RLF) prediction, and measurement events prediction as a part of the UE sided model. Other objectives of the study included studying the need and benefits of any other UE assistance information for the network side model and the potential impacts of the AI / ML aided mobility.

[0081] In RAN2 meeting #125, the following agreements were made for the AI / ML-based measurement event predictions: at least measurement event evaluation based on RRM measurement prediction result will be studied, direct measurement event prediction is allowed, and the A3 event (neighbor cell becomes offset better than the SpCell) is used as the baseline.

[0082] Further, the RAN2 meeting #126 considered indirect and direct approaches to utilizing predictions. The indirect approach refers to the case where the RLF prediction based on the temporal domain serving cell measurement predictions (e.g. SINR) is studied. The direct approach considers that mobility events, such as the RLF, are directly predicted, for example, as the RLF probability within a time window or at a time instance. The expected RLF time is for further study (FFS).

[0083] The current mobility mechanisms (technical specifications TS 38.331 and TS 38.300) are aided by UE measurement reports, as configured by the network using RRC signaling. However, based on the current implementation of the 3GPP, the mobility events take into consideration only the available (legacy) measurement results based on SS / PBCH block or CSI-RS (e.g., RSRP, etc.). The AI / ML prediction-based measurement data is not considered for UE mobility in the existing 3GPP specifications.

[0084] The existing mechanisms for the UE mobility in 3GPP networks (e.g., HO) consider several measurement reporting events, as specified in the technical specification TS 38.331, with some examples listed below.

[0085] The measurement reporting events may be configured through RRC signaling using a specific information element (IE), for example, the ReportConfigNR IE (e.g., as described in Sec. 6.3.2 in technical specification TS 38.331), with various sub-elements, such as respective thresholds, hysteresis, etc.

[0086] Some of the present embodiments provide methods that utilize the AI / ML-based predictions for UE mobility alongside actual measurements which are available to the UE. Some of the present embodiments enhance the UE capabilities to detect measurement reporting events by considering prediction or inference data available to supplement the actual measurement results used by the existing measurement reporting events.

[0087] The measurement data may be utilized by the UE to predict the future radio conditions. It is expected that the UE mobility performance may be enhanced using the AI / ML mobility functionalities. It should be noted that the AI / ML functionality may be controlled by the network, or the over-the-top AI / ML models may also be utilized when allowed by the network. The current implementation of the 3GPP does not specify how the network or the UE may recognize the predictions or inferences from the AI / ML models for measurement report triggering events.

[0088] Some embodiments provide a method of configuring the UEs with new measurement events and the corresponding measurement reporting criteria which allow the UE to detect the AI / ML mobility events and inform the network accordingly. The RRC signaling between the network and the UE may include new RRC IEs for the disclosed AI / ML mobility events (e.g., referred to herein as Events PX).

[0089] The Events PX may consider sub-parameters, such as thresholds, confidence levels (e.g., accuracy) of prediction results, hysteresis, etc. The disclosed AI / ML mobility events provide mechanisms for the UE to compare the actual measurements, available to the UE, to the predictions or inferences derived from the AI / ML Mobility models configured to the UE.

[0090] Once the AI / ML mobility events are configured to the UE by the network, the UE may utilize the available measurement results and predictions / inferences from the AI / ML mobility models to determine whether the configured criteria for the AI / ML mobility Events PX is / are met. The UE may then generate an appropriate measurement report, referred to, for example, as UE_AI_measurement report, and may transmit the measurement report to the network. The network may take proactive actions related to the UE mobility based on the AI / ML mobility Events PX information received from the UE.

[0091] Figure 1 is a schematic diagram illustrating a radio communication system, according to an example implementation of the present disclosure. In Figure 1, the radio communication system 100 includes the terminal devices 101A to 101C and the base station device 103 (BS 103). The terms base station device, base station, and BS herein may be used interchangeably. The terms terminal device, user equipment, and UE herein may be used interchangeably.

[0092] BS 103 may include one or more transmission / reception devices. When BS 103 is configured with multiple transmission / reception devices, each of the multiple transmission / reception devices may be arranged at a different position. A transmission / reception device may include a transmission device and / or a reception device.

[0093] BS 103 may serve radio communication and provide one or more cells. A cell is defined in this disclosure, as a set of resources used for a wireless communication. A cell may include one or both of a downlink component carrier and an uplink component carrier. A serving cell may include a downlink component carrier and two or more uplink component carriers.

[0094] The BS 103, or another network entity, such as a location management function (LMF) server, in some embodiments, may provide multiple sets of configurations to the UE 101A-101C for a given AI / ML functionality. The BS 103, or the other network node, may provide a mechanism to change the configuration sets based on changes in the UE’s environment and / or additional conditions.

[0095] In a wireless communication system, the RRC configuration process is necessary for setting up, maintaining, and modifying the radio connection between the UE and the BS (e.g., a gNB) in the 5G / 5G-Advanced (5G-A) networks. The BS 103 or the network entity, may send an RRC message to a UE 101A-101C to configure at least one of the configuration parameters or features of a configuration set. This RRC message may be, for example, RRCSetup, RRCReconfiguration, RRCResume, RRCRelease, or other downlink messages generated by the BS 103 or another network entity. The BS 103 and / or the other network entities are considered as components of the network. In the following discussions, the term network, or network node, refers to any network entity, such as, BS (e.g., gNB), LMF server, etc., and the BS 103 may be used as an example of such network node.

[0096] The term “configuration,” herein, may refer to the arrangement and specification of components, settings, or parameters within a system or device, as defined by the applicable agreements, standards, or specifications. The term configuration may encompass the established setup and customization of elements necessary to ensure compliance with contractual obligations, operational requirements, and performance criteria.

[0097] The network (e.g., the BS 103), in some embodiments, may configure measurement report triggering criteria and corresponding reporting to enable the UEs (e.g., the UES 101A-101C) to consider predictions and inferences which may primarily be derived based on the measurement results (e.g. SINR, RSRP, Reference Signal Receiving Quality (RSRQ), etc.) for the purposes of UE mobility in the cellular networks. It is considered that the UE may have the relevant inference data / information available through AI / ML functionalities / models, as configured by the network (either the UE-sided model or the network-sided model), at the time of evaluation of the reporting criteria. In the examples discussed below, the UE may not provide the inference-related information to the network beforehand as the additional triggering conditions may not yet be satisfied (e.g., as described below with reference to Figures 8A-8B). However, the provided solutions do not prevent the UE from doing so.

[0098] The disclosed configuration may allow the UE to compare the prediction or inference-based data relevant for mobility, as configured by the network, which may be derived by the processing of actual measurement results using AI / ML models / functionalities, as allowed by the network, with the actual (or legacy) measurement results obtained by receiving reference signals on the physical layer. The UE may also compare one set of prediction or inference-based data with another set of prediction or inference-based data. The prediction or inferences of measurements may also consider other parameters available to the UE (telemetric data, battery status etc.), either independently or jointly with the other relevant parameters.

[0099] The aforementioned comparison may consider measurements and relevant predictions / inferences from the currently serving network entity (e.g., BS, cell, relay, etc.), as well as the detected neighboring entity (e.g., BS, cell, relay, etc.), or a combination thereof.

[0100] The configuration of the report triggering criteria may take place utilizing the RRC signaling from the BS to the UE, for example, using the disclosed sub-elements within the RRC IE EventTriggerConfig, or as a new RRC IE, that may be introduced in the 3GPP specification, for the purpose of AI / ML-based UE mobility.

[0101] The RRC signaling received from the network entity may include the RRC Setup, RRC Reconfiguration, RRC Resume, RRC Release, etc. The UE may confirm the activation of the relevant measurement reporting configurations using the corresponding RRC message, such as RRC Setup Complete, RRC Reconfiguration Complete, etc. Upon the detection of the measurement reporting events based on the configured report triggering criteria, the UE may generate an appropriate report and may transmit the generated report to the network using, e.g., the RRC signaling.

[0102] Figure 2 is a functional diagram illustrating an example timeline 200 of measurements performed by the UE, according to an example implementation of the present disclosure. In some embodiments, the UE’s measurement results, such as, m_i 240 and m_(i+x) 250, may be obtained for each corresponding measurement instance, such as, t_i 250 and t_(i+x) 260, as shown in Figure 2. The UE may evaluate the measurement report triggering criteria based on these measurements. The measurement results may consider additional processing (Layer 3 (L3) filtering, etc.) before the measurement report triggering criteria are applied, as configured by the network.

[0103] Figure 3 is a functional diagram illustrating an example timeline 300 showing predictions data for a future measurement instance already available at the current measurement instance, according to an example implementation of the present disclosure. As shown in Figure 3, in addition to the actual measurement result m_i 240 (as discussed above, with reference to Figure 1), the UE may also have prediction data P(m_(i+x)) 340 for a future measurement instance (or time) t_(i+x) 250, already available at the current measurement instance t_i 210.

[0104] The future measurement instance (or time) t_i 220 for which the prediction data P(m_(i+x)) 340 is calculated at the current measurement instance t_i 210 may be determined by a prediction window 310. The future measurement instance t_i 220 may be x measurement instances (or timeslots) in the future. The prediction window 310 may be configured to the UE by the network.

[0105] The network, in some embodiments, may be able to configure the UEs to compare at time t_i 210, the actual measurement results m_i 240 and the predicted measurements P(m_(i+x)) 340, in the form of a measurement report triggering criteria. The predicted measurements P(m_(i+x)) 340 may be obtained as the result of inferences from the AI / ML models, as configured or permitted by the network. Some embodiments may utilize the latest available inference results. Other embodiments may also use previously obtained inference data.

[0106] For example, in some embodiments, the measurement report triggering criteria may include one or more of the following exemplary events, with m_i^source representing the measurement object for the source cell, and m_i^neighbour representing the measurement object of the target cell. However, in some embodiments, the following AI / ML-based events may also be an extension of the corresponding legacy measurement events. For example, Event P1 may be an enhancement of the legacy Event A1, as described above, Event P2 maybe an enhancement of the Event A2, and so on.

[0107] Event P5a: Prediction of the SpCell becomes worse than threshold1 and actual measurement of the neighbor cell becomes better than threshold2. The entering conditions are shown in Equations 8 and 9. The leaving conditions are shown in Equations 10 and 11. The UE, in some embodiments, may generate one measurement report after the entry condition(s) is / are satisfied and one measurement report after the leaving condition(s) is / are satisfied.

[0108] Event P5b: Actual measurement of the SpCell becomes worse than threshold1 and prediction of the neighbor cell becomes better than threshold2. The entering conditions are shown in Equations 12 and 13. The leaving conditions are shown in Equations 14 and 15.

[0109] Event P5c: Prediction of the SpCell becomes worse than threshold1 and prediction of the neighbor cell becomes better than threshold2. The entering conditions are shown in Equations 16 and 17. The leaving conditions are shown in Equations 18 and 19.

[0110] Event P6a: Prediction of the neighbor cell becomes offset better than actual measurement of the SCell. The entering condition is shown in Equation 20. The leaving condition is shown in Equation 21.

[0111] Event P6b: Actual measurement of neighbor becomes offset better than Prediction of SCell. The entering condition is shown in Equation 22. The leaving condition is shown in Equation 23.

[0112] Event P6c: Prediction of neighbor becomes offset better than Prediction of SCell. The entering condition is shown in Equation 24. The leaving condition is shown in Equation 25.

[0113] As described with the above example Events P1 to P6c, in general, a new set (referred to herein as Events PX) of measurement reporting events may be defined, which may be based on the comparisons between the currently available (e.g., the actual) measurement results and the prediction results available for the source and neighboring cells. The considered offsets and hysteresis parameters may be cell specific, measurement object specific, or combinations thereof. Events PX may be triggered when the comparison between the predicted and / or actual measurements of the serving and / or the neighboring cells, as described above, results in the fulfilment of measurement configurations.

[0114] In some embodiments, the configuration for the Events PX may be provided by the network to the UE. Figure 4 is a sequence diagram 400 illustrating an example message flow for configuring the UE with report triggering criteria to handle prediction-based measurements, according to an example implementation of the present disclosure.

[0115] The UE 101 may be any of the UEs 101A-101C and the network node 490 may be the BS 103, as shown in Figure 1, a location management function (LMF) server, or any other network entity, for example, a network node on the RAN side, a network node on the CN side, or another one of the UEs shown in Figure 1. The other UE may, for example, act as a sidelink relay that transmits sidelink RRC signaling. The wireless communications system may be, for example, a 3GPP network, such as, the 5G / 5G-A or the 6th generation (6G) NR system.

[0116] In step 405, the UE 101 and the network node 490 may exchange the UE’s AI / ML related capabilities using the UE capability exchange procedure, for example, as described in the technical specification 3GPP TS 38.331 for 5G NR.

[0117] After exchanging the UE capabilities, the network node 490, in step 410, may provide the measurement reporting configurations to the UE 101. The measurement reporting configurations may include, for example, the Events PX, as described above, and the sub-configurations, as required by each type of event. The network node 490, in some embodiments, may send the measurement reporting configurations by using RRC signaling (RRC Reconfiguration / Setup / etc.), non-access-stratum (NAS) messages, system information blocks (SIBs), etc. In other embodiments, the measurement reporting configurations may be configured to the UE at the time of manufacturing the UE.

[0118] The network node 490 may provide reporting configurations using different options, such as (1) the Events PX may be RRC IEs within the ReportConfigNR or (2) the Events PX may be a new RRC IE dedicated to the AI / ML-related reporting configurations. For example, the new RRC IE may be Report_AI_ConfigNR with an associated report_AI_ConfigId.

[0119] The Events PX may each be provided with a new eventID, which may be called AIeventID RRC IE, to be used for performing measurement reporting. For example, the above-mentioned Report_AI_ConfigNR may include IEs as shown below. It should be noted that the configurations may also be provided via new or enhanced RRC messages as described with the following examples.

[0120] In the above IE, reportOnLeave indicates whether or not the UE may initiate the measurement reporting procedure when the leaving condition is met (e.g. the leaving conditions are shown in Equations 10 and 11, described above), and timeToTrigger is the time during which specific criteria for the event needs to be met in order to trigger a measurement report. The network node 490 may configure the Event P1 to the UE 101 by providing the corresponding RRC IE in the RRC signal, and the corresponding parameters as necessary for the Event P1 (threshold, hysteresis, other related filtering, etc.). Upon the reception of the RRC message (the RRC signaling from the network node may be RRC Setup, RRC Reconfiguration, RRC Resume, RRC Release, etc.), the UE may apply the new configuration as follows: Further, the network node 490 may also indicate the necessary measurement objects measObject or meas_AI_object to be used for the comparison equations for measurement report triggering as follows: Upon the successful application of the provided measurement reporting criteria, the UE 101may respond to the network in step 415 with an acknowledge message, such as an RRC message (e.g., RRC Setup complete, RRC Resume complete, etc.) Figure 5 is a sequence diagram 500 illustrating an example message flow that shows the UE actions related to the configured AI / ML-based mobility events, according to an example implementation of the present disclosure. The UE 101 may be any of the UEs 101A-101C and the network node 490 may be the BS 103 shown in Figure 1 or any other network entity, for example, an LMF server.

[0121] In step 505, the UE 101 may be configured with the AI / ML-related mobility events and their associated sub-parameters using RRC signaling, as described above with reference to Figure 4. In block 510, the UE may perform the necessary computations based on its measurement configurations (ReportConfigNR or Report_AI_ConfigNR) and may determine that the conditions required for a certain AI / ML Mobility Events PX (e.g. Event P5a) are satisfied.

[0122] When the required configurations have been applied, the UE, in block 510, may check whether the measurement reporting criteria, for an event, such as Event P5a are satisfied, for example as follows: When the entering conditions shown above are satisfied, the measurement results and / or the predicted measurement results may be used in the entry condition equations to generate a measurement report as explained in the next step. Similarly, the leaving conditions (e.g., the equations 10 and 11, as described above) may be applied to generate another measurement report.

[0123] In step 515, once the AI / ML Mobility Events PX is detected, the UE 101 may generate the measurement report (e.g., the UE_AI_measurement report), along with the necessary sub-parameters for the corresponding Events PX. The UE_AI_measurement report may be transmitted to the network node 490 during the next available measurement reporting instance as configured by the network. The measurement instance, in some embodiments, may be configured in the ReportConfigNR or in the Report_AI_ConfigNR during step 505.

[0124] The following is an example of a measurement report that the UE 101 may generate: The generated measurement report may consider legacy measurements, as well as the AI / ML-based inference data / information used for the measurement reporting criteria.

[0125] In block 520, after the network node 490 may receive the UE_AI_measurement report and may perform proactive steps related UE mobility. For example, the network node 490 may delay any pending downlink transmissions to the UE until the radio conditions are improved.

[0126] LINKED MEASUREMENT REPORTING EVENTS Each AI / ML-based event may be utilized by the system to provide the network with early indications for UE mobility-related procedures (e.g., handover, radio link failure, etc.), which may require legacy measurement reporting to be triggered. In some embodiments, the UE actions based on the legacy measurement event configurations may also consider the observed states of the newly proposed AI / ML-based mobility events (EventPX). To realize this, when the UE is configured with AI / ML-based mobility events, as described above, the network node (e.g., the BS) may configure the UE with linked measurement reporting events as follows.

[0127] Figure 6 is a sequence diagram 600 illustrating an example message flow between the network node and the UE to link the legacy measurement reporting events and the AI / ML-based events (Events PX), according to an example implementation of the present disclosure. The UE 101 may be any of the UEs 101A-101C and the network node 490 may be the BS 103 shown in Figure 1 or any other network entity, for example, an LMF server.

[0128] In step 605, the UE 101 and the network node 490 may exchange the UE’s AI / ML related capabilities using the UE capability exchange procedure, for example, as described in the technical specification 3GPP TS 38.331 for 5G NR.

[0129] In step 610, the network node 490 may send a message, such as RRC Setup / Reconfiguration reportConfig to the UE 101. In some embodiments, to map the legacy measurement events and the disclosed AI / ML events (Events PX), a new (sub-) IE for the event trigger configuration may be used as shown in Figure 6. For example, the inter-dependence between the required events may be configured by the network node 490 using RRC signaling through the linkedEvent (sub-)IE within the EventTriggerConfig as shown below by eventPX and linkedEvent.

[0130] In the above example, Event P1 is linked to event A3. The “linkedEvent” may be referred to as, for example, linkedLegacyEvent. The eventID may be referred to as, for example, legacyEventID.

[0131] Similarly, the legacy measurement events may have corresponding linked AI / ML events (Events PX) as described below for event A3 linked to events P2 and P3.

[0132] In the above example, the “linkedEvent” may be referred to as, for example, linkedLegacyEvent.

[0133] If no explicit IE is configured to map the legacy measurement events and the AI / ML events (Events PX) exist, the UEs may be pre-configured to consider the AI / ML-based events and legacy measurement events together with a table, such as the eventMappingTable shown in Figures 7A and 7B.

[0134] Figure 7A illustrates an example table 710 that links legacy measurement events to the AI / ML events, according to an example implementation of the present disclosure. As shown in Figure 7A, each legacy measurement events 720 may be linked to one or more AI / ML events 730. Figure 7B illustrates an example table 750 that links AI / ML events to the legacy measurement events, according to an example implementation of the present disclosure. As shown in Figure 7B, each AI / ML event 760 may be linked to one or more legacy measurement events 770.

[0135] In step 615 shown in Figure 6, the UE may send an acknowledge message, such as, RRC Setup / reconfiguration complete (AI / ML mobility events configured) to the network node 490.

[0136] Figure 8A is a flowchart illustrating a method / process 801 performed by a UE for prioritizing actual measurement reporting events over the predicted future measurement reporting events, according to an example implementation of the present disclosure. Figure 8B is a flowchart illustrating a method / process 802 performed by a UE for prioritizing predicted future measurement reporting events over the actual measurement reporting events, according to an example implementation of the present disclosure. The processes 801 and 802, in some embodiments, may be performed by one or more processors of a UE, such as the UEs 101A-101C, shown in Figure 1.

[0137] As shown in the exemplary flowcharts in Figures 8A and 8B, the hierarchy between the linked legacy measurement events and the AI / ML events (Events PX) may be configured by the network. The priority order in which the legacy events and AI / ML-based events are considered may indicate either a reactive approach (as shown in Figure 8A) or proactive approach (as shown in Figure 8B). As described below, the UE may be configured to perform certain actions based on whether the legacy measurement reports and / or AI / ML-based events are triggered as configured by the network.

[0138] With reference to Figure 8A, the process 801 may first check, as shown in block 805, for the fulfilment of the triggering criteria for legacy measurement events, which are only based on the actual measurement results. The process 801 may end if the legacy measurement events are not triggered. In other words, the process 801 may only check (at block 815) for the fulfillment criteria for the AI / ML-based events, which are based on the prediction / inference data and / or actual measurement results, if the corresponding / appropriate legacy measurement events are also triggered.

[0139] In a case that the triggering criteria for legacy measurement events are satisfied, the process 801 may perform (at block 810) intermediate actions / procedures. For example, at block 810, the process 801 may generate new inference results required for the corresponding AI / ML-based events.

[0140] At block 815, the process 801 may determine as to whether the linked AI / MI-based measurement event(s) (Events PX) is / are triggered. If the linked AI / MI-based measurement event(s) is / are not triggered, the process 801 may end. Otherwise, the process 801 may perform actions on the measurement reporting configurations such as generating appropriate measurement report and / or sending the report to the network. The process 801 may then end. The process 801 may be useful to save the UE’s battery by only performing inferences when necessary for the configured procedure.

[0141] With reference to Figure 8B, the process 802 may first check, as shown in block 825, the fulfillment criteria for the AI / ML-based Events PX. The process 802 may end if the AI / ML-based Events PX is not triggered. In other words, the process 802 may check (at block 835) for the fulfilment criteria for the legacy measurement events only if the required AI / ML-based measurement event(s) is / are satisfied.

[0142] In a case that the triggering criteria for legacy measurement events are satisfied, the process 802 may perform (at block 830) intermediate actions / procedures. For example, at block 830, the process 802 may provide the latest available inference results when the AI / ML-based Events have been triggered.

[0143] At block 835, the process 802 may determine as to whether the linked legacy measurement event(s) is / are triggered. If the linked legacy measurement event(s) is / are not triggered, the process 802 may end. Otherwise, the process 802 may perform actions on the measurement reporting configurations such as generating appropriate measurement report and / or sending the report to the network. The process 802 may then end. The process 802 may be useful to provide the network with the latest available inference results before the legacy measurement events are triggered to initiate mobility-related procedures.

[0144] In some embodiments, priority to the AI / ML events (Events PX) may be assigned through a new IE, referred to herein as, AIeventPriority IE in the ReportConfigNR as described below. In some embodiments, if the AI / ML events (events PX) are configured, the priority to either the AI / ML events (events PX) or the legacy events may be configured by default to either sets of measurement events.

[0145] In the case where the AI / ML-based events are prioritized over the legacy events, the UE behavior may be configured, for example, by setting the AIeventPriority to true. In this case, the UE may first check whether the linked AI / ML event is triggered before checking whether the configured conditions for the legacy measurement event are fulfilled.

[0146] In the case where the AI / ML-based events are configured to be of lower priority than the legacy events, the UE behavior may be configured, for example, by setting the AIeventPriority to false. In this case, the UE may check the conditions for the linkedAIEvent only when the conditions for the corresponding legacy events are already fulfilled. This is described in the following example pseudo-code.

[0147] For example, if the required conditions for EventA3 are observed indicating that a handover may be imminent, but the AI / ML-based Event P4b (the prediction of the neighbor cell becomes worse than the absolute threshold) is also triggered, it might suggest that the UE may experience handover failure if a handover is triggered by the network. To address this case, the prioritization may be configured by the network in such a way that the legacy A3 event is only triggered by the UE, if the linked AI / ML Event P4b is not triggered, which may be used as an indication by the network that the chosen target cell may continue to be an appropriate choice for the UE handover.

[0148] In the example of UE handovers, the disclosed enhancements may supplement the legacy procedure based on Event A3 by providing the following configurations to the UE to utilize the AI / ML-related inferences / prediction data appropriately. For example, if the required conditions for the legacy EventA3 are observed indicating that a handover may be imminent, but the AI / ML Event P4b (the prediction of neighbor becomes worse than the absolute threshold) is also (currently / simultaneously) triggered, it might suggest that the UE may experience handover failure if a handover is triggered by the network. When the network is informed by the UE of the aforementioned linked events (EventA3 and Event P4b), the network may, for example, delay the handover for the UE or may handover the UE to another base station.

[0149] In some embodiments, the prioritization may be configured by default to either the AI / ML-based events (Events PX) or the legacy measurement events. In other embodiments, to assign equal priority to the legacy and AI / ML-based events, the fulfilment of the configured conditions for all linked events may be considered together (instead of one after the other). In some embodiments, the UE may be configured to perform one or more intermediate actions based on the fulfilment of the measurement reporting criteria of all or a subset of the linked events.

[0150] Figure 9 is a flowchart illustrating an example method / process 900 performed by a UE for configuring measurement reporting to the UE, according to an example implementation of the present disclosure. The process 900, in some embodiments, may be performed by one or more processors of a UE, such as such as the UEs 101A-101C, shown in Figure 1.

[0151] The process 900 may receive (at block 905), several configuration sets from a network node for several measurement reporting events. Each configuration set may include one or more triggering criteria that are satisfied, at least partially, based on one or more predicted future measurement results.

[0152] The network node, for example, may be the BS 103 shown in Figure 1, an LMF server, or any other network entity, for example, a network node on the RAN side, a network node on the CN side, or one of the UEs shown in Figure 1 that may be acting as a sidelink relay.

[0153] The configuration sets, in some embodiments, may be received from the network node in one or more messages. The message, for example, may be an RRC message, a NAS message, a SIB, etc. The messages, in some embodiments, may include an IE that includes one or more parameters for each measurement reporting event. The parameters may include, for example, one or more of a threshold, a confidence level for an accuracy of prediction results, and hysteresis value.

[0154] The process 900 may calculate (at block 910) predicted future measurement results associated with a first measurement reporting event. For example, the network node, in some embodiments, may generate actual measurement results that are associated with the first measurement reporting event. The actual measurement results may include one or more of a set of L1-RSRP measurements, a set of RSRQ measurements, a set of SINR measurements, a set of L3-RSRP measurements, and a set of L3-RSRQ measurements. The network node may than then calculate the predicted future measurement results using the actual measurement results.

[0155] Calculating the predicted future measurement results associated with the first measurement reporting event, in some embodiments, may include using an AI / ML model. associated with the first measurement reporting event, in some embodiments, may include calculating the predicted future measurement results for one or more network entities such as a serving cell, a serving BS, a serving relay UE, a neighboring cell, a neighboring BS, or a neighboring relay UE.

[0156] The process 900 may determine (at block 915) that one or more criteria associated with triggering the first measurement reporting event are satisfied based on the calculated predicted future measurement results. The one or more criteria associated with triggering the first measurement reporting event may include comparing the predicted future measurement results with the actual measurement results, comparing the predicted future measurement results with previously predicted results, and / or comparing the predicted future measurement results with a threshold.

[0157] The one or more criteria associated with triggering the first measurement reporting event, in some embodiments, may also include first and second sets of predicted future measurement results. The one or more criteria may be satisfied by comparing the first set of predicted future measurement results with the second set of predicted future measurement results.

[0158] The process 900 may generate (at block 920) a measurement report for the first measurement reporting event. For example, the measurement report for the first measurement reporting event may include the actual measurement results associated with the first measurement reporting event and the predicted future measurement results associated with the first measurement reporting event.

[0159] The process 900 may transmit (at block 920) the measurement report to the network node. The measurement report for the first measurement reporting event may be used by the network node to perform proactive steps related to mobility of the UE. The process 900 may then end. The process 900, in some embodiments, may exchange one or more measurement results prediction capabilities of the UE with the network node prior to receiving the configuration sets.

[0160] Figure 10 is a flowchart illustrating an example method / process 1000 performed by a UE for configuring inter-dependence between predicted and actual measurement reporting events to the UE, according to an example implementation of the present disclosure. The process 1000, in some embodiments, may be performed by one or more processors of a UE, such as such as the UEs 101A-101C, shown in Figure 1.

[0161] The process 1000 may receive (at block 1005) several configuration sets. Each configuration set may link a first measurement reporting event with a second measurement reporting event. For example, the first measurement reporting event may include either an actual measurement reporting event or a predicted future measurement reporting event. The second measurement reporting event may include the other one of the actual measurement reporting event or the predicted future measurement reporting event. Each configuration set may include one or more criteria for triggering the corresponding first and second reporting events.

[0162] The process 1000, in some embodiments, may receive the configuration sets at the time of manufacturing the UE. The process 1000, in some embodiments, may receive the configuration sets in one or more messages from a network node. The one or more messages may include, for example, one or more RRC messages, one or more NAS messages, or one or more SIBs. Each configuration set, in some embodiments, may be included in an IE that may include one or more parameters for the first measurement reporting event of the configuration set.

[0163] The process 1000 may determine (at block 1010) that one or more criteria for triggering a first measurement reporting event corresponding to a first configuration set of the several configuration sets are satisfied.

[0164] The process 1000 may decide (at block 1015) whether one or more criteria for triggering a second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are satisfied.

[0165] The UE, in some embodiments, may be configured to prioritize the actual measurement reporting events and the predicted future measurement reporting events. Consider a first example where the actual measurement reporting events are prioritized the predicted future measurement reporting events. In this example, the first measurement reporting event that corresponds to the first configuration is an actual measurement reporting event and the second measurement reporting event that corresponds to the first configuration is a predicted future measurement reporting event.

[0166] In the first example, the process 1000 may generate actual measurement results at one or more time instances prior to determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied. The actual measurement results may include one or more of a set of L1-RSRP measurements, a set of L1-RSRQ measurements, a set of signal to interference and noise ratio (SINR) measurements, a set of L3-RSRP measurements, a set of L3-RSRQ measurements.

[0167] Consider a second example where the predicted future measurement reporting events are prioritized over the actual measurement reporting events. In this example, the first measurement reporting event that corresponds to the first configuration is a predicted future measurement reporting event, and the second measurement reporting event that corresponds to the first configuration is an actual measurement reporting event.

[0168] In the second example, prior to determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied, the process 1000 may generate actual measurement results at several time instances. The process 1000 may the calculate one or more predicted future measurement results associated with the first measurement reporting event based on the actual measurement results.

[0169] The process 1000 may then determine that one or more criteria for triggering the first measurement reporting event corresponding to the first configuration set are satisfied based on the generated actual measurement results. After determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied, the process 1000 may generate inference results for the second measurement reporting event corresponding to the first configuration set. In some embodiments, the process 1000 may reduce or may increase the number of actual measurements that are performed. For example, the criteria for reducing / increasing may be the triggering of the corresponding event. The actual measurements, in some embodiments, may be increased over a time period instead of individual time instance. In this case, the number of measurement instances may be changed over a period / duration and the baseline may be the number of actual measurements over the last time period / duration.

[0170] After determining that the one or more criteria for triggering the second measurement reporting event corresponding to the first configuration are satisfied, the process 1000 may generate predicted future measurement results associated with the second measurement reporting event using the generated inference results. In some embodiments, the process 1000 may use an AI / ML model for calculating the predicted future measurement results associated with the first measurement reporting event.

[0171] In a case that one or more criteria for triggering a second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are satisfied, the process 1000 may generate (at block 1020) a measurement report based on measurements associated with the first and second measurement reporting events corresponding to the first configuration set. The process 1000, in some embodiments, may transmit the measurement report to a network node. The network node, for example, may be the BS 103 shown in Figure 1, an LMF server, or any other network entity, for example, a network node on the RAN side, a network node on the CN side, or one of the UEs shown in Figure 1 that may be acting as a sidelink relay. The measurement report, in some embodiments, may be used by the network node to perform proactive steps related to mobility of the UE. The process 1000 may then end.

[0172] In a case that the one or more criteria for triggering the second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are not satisfied, the process 1000 may forego (at block 1025) generating the measurement report. The process 1000 may then end.

[0173] ALTERNATIVE EMBODIMENTS In some embodiments, Events PX may have several sub-parameters, such as thresholds, hysteresis, time-to-trigger, and so on. For example, the corresponding sub-parameters may be configured with a default value, which may be used in the case where no explicit configuration is provided by the network.

[0174] In some embodiments, the network node may permit the UE to self-configure the sub-parameters, which may be used, for example, when the AI / ML model used for deriving predictions is not directly configured / managed by the network. However, in some embodiment, the network may permit the UE to self-configure the sub-parameters when the AI / ML models are managed by the network. In some embodiments, each sub-configuration may individually be configured through separate RRC signals.

[0175] In some embodiments, the legacy measurement events (e.g., A1, A3, etc.) may be utilized analogously for the Events PX. For example, the legacy measurement events may be extended to also consider prediction-based measurement data. In some embodiments, the legacy measurement events may be configured to consider appropriate prediction window(s) to be utilized along with the prediction-based measurement triggering reporting criteria.

[0176] The sub-parameters of the measurement Events PX, in some embodiments, may be configured per each cell differently, both for the source gNB and neighboring target cells / gNBs. For example, the offsets for Event P3 may consider different offsets for each detected / detectable neighboring cell. Similarly, the measurement thresholds and hysteresis parameters may be different for each cell, including the primary and secondary source cells, as well as any neighboring cells.

[0177] The predicted / inferred measurement data used for the measurement Events PX (and the corresponding criteria), in some embodiments, may be based on inference using a UE-sided AI / ML model or a network-sided model. Some embodiments may also consider two-sided models and over-the-top models, if allowed by the network.

[0178] In some embodiments, the configured measurement report triggering criteria may allow comparisons between multiple predictions for a given measurement object as configured, for example, by RRC signaling. For example, the predictions may correspond to different prediction windows, which may be used to indicate the expected radio channel conditions at different instances in the future.

[0179] The prediction window(s), in some embodiments, may be pre-configured with a default value. In some embodiments, the prediction window(s) may also be independently configured based on the AI / ML model used for deriving the predicted values.

[0180] In some embodiments, the network may configure the confidence levels for the predicted values used for the measurement reporting criteria as a sub-parameter for each configured event PX. For example, the network may configure a certain event PX to only be triggered when the predicted values exceed a certain confidence threshold (e.g., 95%). It may also be self-configured or set based on default values as specified.

[0181] In some embodiment, the configured event PX and the associated measurement reporting criteria may consider and compare a previously predicted measurement value and another predicted measurement value either for the same measurement instance, or for different measurement instances. The measurement predictions may be for the same cell or for different cells as configured.

[0182] The network node, in some embodiments, may configure sidelink measurements to also consider prediction-based measurement event PX, as similar to the Uu interface (the air interface between the UE and RAN). For example, it may be utilized for sidelink relay selection. In one example, the sidelink-related event PX and the corresponding sub-parameters may be jointly configured. Some sub-parameters, such as thresholds, hysteresis, time-to-trigger, etc., may be based on the corresponding or comparable Uu-related events.

[0183] In some embodiments, the disclosed AI / ML mobility events may be utilized as triggering events for conditional handover by the UE when configured as such by the network. Some embodiments may also consider prioritization between the AI / ML-related mobility events and the actual measurement events for the purpose of appropriate generation of UE measurement reports, which may assist the network. The priorities of the generated events may be configured using the sub-parameters of the respective events. When the required conditions for an event are fulfilled, the UE may be configured by the network to transmit the corresponding actual measurements results or the predicted / inferenced data or both information to the network.

[0184] Figure 11 is a block diagram illustrating a node 1100 for wireless communication, according to an example implementation of the present disclosure. As illustrated in Figure 11, a node 1100 may include a transceiver 1120, a processor 1128, a memory1134, one or more presentation components 1129, and at least one antenna 1136. The node 1100 may also include a radio frequency (RF) spectrum band module, a BS communications module, a network communications module, and a system communications management module, Input / Output (I / O) ports, I / O components, and a power supply (not illustrated in Figure 11).

[0185] Each of the components may directly or indirectly communicate with each other over one or more buses 1140. The node 1100 may be a UE, a BS, a LMF server, or any other network node on the RAN side or CN side that performs various functions disclosed with reference to FIGS. 1 through 10.

[0186] The transceiver 1120 has a transmitter 1122 (e.g., transmitting / transmission circuitry) and a receiver 1124 (e.g., receiving / reception circuitry) and may be configured to transmit and / or receive time and / or frequency resource partitioning information. The transceiver 1120 may be configured to transmit in different types of subframes and slots including, but not limited to, usable, non-usable, and flexibly usable subframes and slot formats. The transceiver 1120 may be configured to receive data and control channels.

[0187] The node 1100 may include a variety of computer-readable media. Computer-readable media may be any available media that may be accessed by the node 1100 and include volatile (and / or non-volatile) media and removable (and / or non-removable) media.

[0188] The computer-readable media may include computer-storage media and communication media. Computer-storage media may include both volatile (and / or non-volatile media), and removable (and / or non-removable) media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or data.

[0189] Computer-storage media may include RAM, ROM, EPROM, EEPROM, flash memory (or other memory technology), CD-ROM, Digital Versatile Disks (DVD) (or other optical disk storage), magnetic cassettes, magnetic tape, magnetic disk storage (or other magnetic storage devices), etc. Computer-storage media may not include a propagated data signal. Communication media may typically embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transport mechanisms and include any information delivery media.

[0190] The term “modulated data signal” may mean a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. Communication media may include wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, RF, infrared, and other wireless media. Combinations of any of the previously listed components should also be included within the scope of computer-readable media.

[0191] The memory 1134 may include computer-storage media in the form of volatile and / or non-volatile memory. The memory 1134 may be removable, non-removable, or a combination thereof. Example memory may include solid-state memory, hard drives, optical-disc drives, etc. As illustrated in Figure 11, the memory 1134 may store a computer-readable and / or computer-executable instructions 1132 (e.g., software codes) that are configured to, when executed, cause the processor 1128 to perform various functions disclosed herein, for example, with reference to FIGS. 1 through 10. Alternatively, the instructions 1132 may not be directly executable by the processor 1128 but may be configured to cause the node 1100 (e.g., when compiled and executed) to perform various functions disclosed herein.

[0192] The processor 1128 (e.g., having processing circuitry) may include an intelligent hardware device, e.g., a Central Processing Unit (CPU), a microcontroller, an ASIC, etc. The processor 1128 may include memory. The processor 1128 may process the data 1130 and the instructions 1132 received from the memory 1134, and information transmitted and received via the transceiver 1120, the baseband communications module, and / or the network communications module. The processor 1128 may also process information to send to the transceiver 1120 for transmission via the antenna 1136 to the network communications module for transmission to a CN.

[0193] One or more presentation components 1129 may present data indications to a person or another device. Examples of presentation components 1129 may include a display device, a speaker, a printing component, a vibrating component, etc.

[0194] In view of the present disclosure, it is obvious that various techniques may be used for implementing the disclosed concepts without departing from the scope of those concepts. Moreover, while the concepts have been disclosed with specific reference to certain implementations, a person of ordinary skill in the art may recognize that changes may be made in form and detail without departing from the scope of those concepts. As such, the disclosed implementations are to be considered in all respects as illustrative and not restrictive. It should also be understood that the present disclosure is not limited to the particular implementations disclosed and many rearrangements, modifications, and substitutions are possible without departing from the scope of the present disclosure.

[0195] The various foregoing example embodiments and modes may be utilized in conjunction with one another, e.g., in combination with one another.

[0196] Each of a program running on the BS and the terminal device according to an aspect of the present invention may be a program that controls a CPU and the like, such that the program causes a computer to operate in such a manner as to realize the functions of the above-described embodiment according to the present invention. The information handled in these devices is transitorily stored in a Random-Access-Memory (RAM) while being processed. Thereafter, the information is stored in various types of Read-Only-Memory (ROM) such as a Flash ROM and a Hard-Disk-Drive (HDD), and when necessary, is read by the CPU to be modified or rewritten.

[0197] It should be noted that the terminal device and the BS according to the above-described embodiment may be partially achieved by a computer. In this case, this configuration may be realized by recording a program for realizing such control functions on a computer-readable recording medium and causing a computer system to read the program recorded on the recording medium for execution.

[0198] It should be noted that it is assumed that the "computer system" mentioned here refers to a computer system built into the terminal device or the BS, and the computer system includes an OS and hardware components such as a peripheral device. Furthermore, the "computer-readable recording medium" refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, a CD-ROM, and the like, and a storage device built into the computer system such as a hard disk.

[0199] Moreover, the "computer-readable recording medium" may include a medium that dynamically retains a program for a short period of time, such as a communication line that is used to transmit the program over a network such as the Internet or over a communication line such as a telephone line, and may also include a medium that retains a program for a fixed period of time, such as a volatile memory within the computer system for functioning as a server or a client in such a case. Furthermore, the program may be configured to realize some of the functions described above, and also may be configured to be capable of realizing the functions described above in combination with a program already recorded in the computer system.

[0200] Furthermore, the BS according to the above-described embodiment may be achieved as an aggregation (a device group) including multiple devices. Each of the devices configuring such a device group may include some or all of the functions or the functional blocks of the BS according to the above-described embodiment. The device group may include each general function or each functional block of the BS. Furthermore, the terminal device according to the above-described embodiment may also communicate with the base station device as the aggregation.

[0201] Furthermore, the BS according to the above-described embodiment may serve as an Evolved Universal Terrestrial Radio Access Network (E-UTRAN) and / or NG-RAN (Next Gen RAN, NR-RAN). Furthermore, the BS according to the above-described embodiment may have some or all of the functions of a node higher than an eNodeB or the gNB.

[0202] Furthermore, some or all portions of each of the terminal device and the base station device according to the above-described embodiment may be typically achieved as a large-scale integration (LSI) which is an integrated circuit or may be achieved as a chip set. The functional blocks of each of the terminal device and the BS may be individually achieved as a chip, or some or all of the functional blocks may be integrated into a chip. Furthermore, a circuit integration technique is not limited to the LSI, and may be realized with a dedicated circuit or a general-purpose processor. Furthermore, in a case that with advances in semiconductor technology, a circuit integration technology with which an LSI is replaced appears, it is also possible to use an integrated circuit based on the technology.

[0203] Furthermore, according to the above-described embodiment, the terminal device has been described as an example of a communication device, but the present invention is not limited to such a terminal device, and is applicable to a terminal device or a communication device of a fixed-type or a stationary-type electronic device installed indoors or outdoors, for example, such as an Audio-Video (AV) device, a kitchen device, a cleaning or washing machine, an air-conditioning device, office equipment, a vending machine, and other household devices.

[0204] The embodiments of the present invention have been described in detail above referring to the drawings, but the specific configuration is not limited to the embodiments and includes, for example, an amendment to a design that falls within the scope that does not depart from the gist of the present invention. Furthermore, various modifications are possible within the scope of one aspect of the present invention defined by claims, and embodiments that are made by suitably combining technical means disclosed according to the different embodiments are also included in the technical scope of the present invention. Furthermore, a configuration in which constituent elements, described in the respective embodiments and having mutually the same effects, are substituted for one another is also included in the technical scope of the present invention.

[0205] <Cross Reference> This patent application claims priority on US Provisional Patent Application No. 63 / 699,647 filed on September 26, 2024, the entire contents of which are hereby incorporated by reference.

Claims

A user equipment (UE), comprising:one or more non-transitory computer-readable media storing one or more computer-executable instructions for configuring inter-dependence between predicted and actual measurement reporting events; andat least one processor coupled to the one or more non-transitory computer-readable media, and configured to execute the one or more computer-executable instructions to cause the UE to:receive a plurality of configuration sets, wherein:each configuration set links a first measurement reporting event with a second measurement reporting event,the first measurement reporting event comprises one of an actual measurement reporting event or a predicted future measurement reporting event,the second measurement reporting event comprises the other one of the actual measurement reporting event or the predicted future measurement reporting event, andeach configuration set comprises one or more criteria for triggering the corresponding first and second reporting events,determine that one or more criteria for triggering a first measurement reporting event corresponding to a first configuration set in the plurality of configuration sets are satisfied;in a case that one or more criteria for triggering a second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are satisfied:generate a measurement report based on measurements associated with the first and second measurement reporting events corresponding to the first configuration set; andin a case that the one or more criteria for triggering the second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are not satisfied:forego generating the measurement report.The UE of claim 1, wherein:the UE is configured to prioritize the actual measurement reporting events over the predicted future measurement reporting events,the first measurement reporting event corresponding to the first configuration is an actual measurement reporting event,the second measurement reporting event corresponding to the first configuration is a predicted future measurement reporting event,the at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to:generate actual measurement results at one or more time instances prior to determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied; anddetermine that one or more criteria for triggering the first measurement reporting event corresponding to the first configuration set are satisfied based on the generated actual measurement results.The UE of claim 2, wherein the at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to:after determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied, generate inference results for the second measurement reporting event corresponding to the first configuration set; andafter determining that the one or more criteria for triggering the second measurement reporting event corresponding to the first configuration are satisfied, generate predicted future measurement results associated with the second measurement reporting event using the generated inference results.The UE of claim 2, wherein the actual measurement results comprise one or more of a set of layer 1 reference signal reception power (L1-RSRP) measurements, a set of layer 1 reference signal received quality (RSRQ) measurements, a set of signal to interference and noise ratio (SINR) measurements, a set of layer 3 RSRP (L3- RSRP), and a set of L3- RSRQ.he UE of claim 1, wherein:the UE is configured to prioritize the predicted future measurement reporting events over the actual measurement reporting events,the first measurement reporting event corresponding to the first configuration is a predicted future measurement reporting event,the second measurement reporting event corresponding to the first configuration is an actual measurement reporting event, andthe at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to:prior to determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied, generate actual measurement results at a plurality of time instances; andcalculate one or more predicted future measurement results associated with the first measurement reporting event based on the actual measurement results.The UE of claim 5, the at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to:after determining that the one or more criteria for triggering the first measurement reporting event corresponding to the first configuration are satisfied, and prior to determining that the one or more criteria for triggering the second measurement reporting event corresponding to the first configuration are satisfied:reduce or increase a number of actual measurements performed; andtransmit the one or more predicted future measurement results associated with the first measurement reporting event to a network node.The UE of claim 5, wherein calculating the one or more predicted future measurement results associated with the first measurement reporting event comprises using an artificial intelligence / machine learning (AI / ML) model.The UE of claim 5, wherein calculating the one or more predicted future measurement results associated with the first measurement reporting event comprises:calculating the one or more predicted future measurement results for one or more network entities comprising a serving cell, a serving base station (BS), a serving relay, a neighboring cell, a neighboring BS, or a neighboring relay.The UE of claim 1, wherein the at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to:receive the plurality of configuration sets at a time of manufacturing the UE.The UE of claim 1, wherein the at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to:receive the plurality of configuration sets in one or more messages from a network node, the one or more messages comprising one or more radio resource control (RRC) messages, one or more non-access-stratum (NAS) messages, or one or more system information blocks (SIBs).The UE of claim 10, wherein each configuration set is included in an information element (IE) comprising one or more parameters for the first measurement reporting event of the configuration set.The UE of claim 1, wherein the at least one processor is further configured to execute the one or more computer-executable instructions to cause the UE to:transmit the measurement report generated based on measurements associated with the first and second measurement reporting events to a network node.The UE of claim 12, wherein the measurement report is for use by the network node to perform proactive steps related to mobility of the UE.he UE of claim 12, wherein the network node is one of a base station (BS), a location management function (LMF) server, a UE acting as a sidelink relay, a core network (CN) node, or a radio access network (RAN) node.A method of configuring inter-dependence between predicted and actual measurement reporting events to a user equipment (UE), the method comprising:receiving a plurality of configuration sets, wherein:each configuration set links a first measurement reporting event with a second measurement reporting event,the first measurement reporting event comprises one of an actual measurement reporting event or a predicted future measurement reporting event,the second measurement reporting event comprises the other one of the actual measurement reporting event or the predicted future measurement reporting event, andeach configuration set comprises one or more criteria for triggering the corresponding first and second reporting events,determining that one or more criteria for triggering a first measurement reporting event corresponding to a first configuration set in the plurality of configuration sets are satisfied;in a case that one or more criteria for triggering a second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are satisfied:generating a measurement report based on measurements associated with the first and second measurement reporting events corresponding to the first configuration set; andin a case that the one or more criteria for triggering the second measurement reporting event linked to the first measurement reporting event corresponding to the first configuration set are not satisfied:foregoing generating the measurement report.

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