Apparatus and method for mobility using AI / ML channel prediction

Abstract

A method performed by at least one processor of a user equipment (UE) having a connection with a serving cell in a wireless communication network includes receiving a set of resources for slot n from a target cell in the wireless communication network. The method includes measuring a network quality parameter for the target cell based on the set of resources. The method includes reporting results corresponding to the measurement of the network quality parameter to the serving cell. The method includes receiving a command to switch from the serving cell to the target cell at a time corresponding to slot n+k based on (i) a prediction from an artificial intelligence / machine learning (AI / ML) model that uses the measurement of the network quality parameter as an input to the AI / ML model and (ii) a determination that a cell switch condition is met.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63 / 421,731, filed November 2, 2022, the disclosure of which is incorporated herein by reference in its entirety.

[0002] Apparatus and methods consistent with example embodiments of the present disclosure relate to reducing mobility latency in a Radio Access Network (RAN) using artificial intelligence / machine learning (AI / ML) model-based channel prediction. [Background technology]

[0003] Currently, a 3GPP® framework for AI / ML models of the air-interface is being considered, addressing each target use case with respect to aspects such as performance, complexity, and potential specification impacts. Some of these use cases focus on channel state information (CSI) feedback enhancements (e.g., reduced overhead, improved accuracy, prediction), beam management (e.g., beam prediction in the time and / or spatial domain for reduced overhead and latency, improved beam selection accuracy), and positioning accuracy enhancements for different scenarios, including, for example, those with heavy non-line-of-sight (NLOS) conditions.

[0004] These use cases can be categorized into sub-use cases for characterization and baseline performance evaluation. The AI / ML model approaches for the selected sub-use cases should be diverse enough to support various requirements for gNB-UE cooperation levels. The selection of use cases can be targeted at the formulation of a framework for applying AI / ML models to the air interface for these and other use cases.

[0005] AI / ML model, terminology, and descriptions are developed to identify common and specific characteristics for a framework investigation, including characterizing the definition stages and associated complexity of AI / ML model-related algorithms, such as model generation (e.g., model training (including input / output, pre / post-processing, online / offline, as appropriate), model validation, model testing), and inference operations (e.g., input / output, pre / post-processing, as appropriate).

[0006] The framework study can identify various levels of collaboration between the UE and gNB relevant to the selected use case, including, for example, (1) no collaboration: only implementation-based AI / ML model algorithms without information exchange for comparison purposes, and (2) various levels of UE / gNB collaboration covering separate or joint machine learning operations. The framework study can further characterize the lifecycle management of the AI / ML model (e.g., model training, model deployment, model inference, model monitoring, model update).

[0007] The framework study can utilize datasets for training, validation, testing, and inference, and can identify common notation and terminology for functions, procedures, and interfaces related to AI / ML models.

[0008] In 5G NR connected mode mobility, the UE periodically evaluates the link quality between the serving cell and neighbor cells. To evaluate the link quality, the UE performs measurements. These measurements may include the reference signal received power (RSRP) and / or reference signal received quality (RSRQ) of the synchronization signal blocks (SSBs) of the serving cell and neighbor cells. The measurements may be processed by L3 filtering and reported to the serving cell based on a reporting configuration. If one of the neighbor cells meets the cell switch (e.g., handover) criteria, the serving cell indicates to the UE to handover to the neighbor cell. After the cell switch, the UE can perform random access in the new cell.

[0009] 5G NR also introduces beam-level mobility, which may not require explicit radio resource control (RRC) signaling to be triggered. Beam-level mobility can be intra-cell or inter-cell (e.g., inter-cell beam management (ICBM)). In the case of ICBM, the UE may receive or transmit UE-dedicated channels / signals via a total radiated power (TRP) associated with a PCI different from that of the serving cell, while non-UE-dedicated channels / signals can only be received via a TRP associated with the physical cell ID (PCI) of the serving cell. The gNB provides the UE with a measurement configuration via RRC signaling, including the configuration of SSB / CSI resources and resource sets, reports, and trigger conditions for triggering channel and interference measurements and reporting. In the case of ICBM, the measurement configuration may include SSB resources associated with a PCI different from that of the serving cell. Beam-level mobility may be handled at lower layers by physical layer and medium access control (MAC) layer control signaling. The RRC hosted in the control unit-control plane (CU-CP) may not need to know which beam is being used by the UE at a given time.

[0010] SSB-based beam-level mobility is based on the SSB associated with the initial downlink (DL) bandwidth part (BWP) and can only be configured for the initial DL BWP and the DL BWP that includes the SSB associated with the initial DL BWP. For other DL BWPs, beam-level mobility can only be performed based on channel state information reference signal (CSI-RS) resources.

[0011] To further improve beam-level mobility, L1 / L2-based inter-cell mobility mechanisms and procedures for mobility latency reduction have been specified. However, the current mechanisms and procedures do not adequately handle mobility latency reduction. Summary of the Invention [Problem to be solved by the invention]

[0012] According to embodiments, systems and methods are provided for implementing mechanisms for reducing mobility (e.g., handover) latency using AI / ML model-based channel prediction. [Means for solving the problem]

[0013] According to an example embodiment, a method performed by at least one processor of a user equipment (UE) having a connection with a serving cell in a wireless communication network includes receiving a set of resources for slot n from a target cell in the wireless communication network. The method includes measuring a network quality parameter for the target cell based on the set of resources. The method includes reporting results corresponding to the measurement of the network quality parameter to the serving cell. The method includes receiving a command to switch from the serving cell to the target cell at a time corresponding to slot n+k based on (i) a prediction from an artificial intelligence / machine learning (AI / ML) model that uses the measurement of the network quality parameter as an input to the AI / ML model, and (ii) a determination that a cell switch condition is met. The parameters n and k are integers greater than 0.

[0014] According to an example embodiment, a user equipment (UE) having a connection with a serving cell in a wireless communication network includes at least one memory configured to store computer program code and at least one processor configured to access the at least one memory and operate as instructed by the computer program code. The computer program code includes first receiving code configured to cause at least one of the at least one processor to receive a set of resources for slot n from a target cell in the wireless communication network. The computer program code further includes measurement code configured to cause at least one of the at least one processor to measure a network quality parameter for the target cell based on the set of resources. The computer program code further includes reporting code configured to cause at least one of the at least one processor to report a result corresponding to the measurement of the network quality parameter to the serving cell. The computer program code further includes second receiving code configured to cause at least one of the at least one processor to receive a command to switch from the serving cell to the target cell at a time corresponding to slot n+k based on (i) a prediction from an artificial intelligence / machine learning (AI / ML) model that uses the measurement of the network quality parameter as an input to the AI / ML model, and (ii) a determination that a cell switch condition is met, where the parameters n and k are integers greater than 0.

[0015] According to an exemplary embodiment, a non-transitory computer-readable medium having instructions stored therein is proposed. The instructions, when executed by a processor in a user equipment (UE) having a connection with a serving cell in a wireless communication network, cause the processor to perform a method. The method includes receiving, by the processor, a set of resources for slot n from a target cell in the wireless communication network. The method includes measuring, for the target cell, a network quality parameter based on the set of resources. The method includes reporting to the serving cell a result corresponding to the measurement of the network quality parameter. The method includes receiving, based on (i) a prediction from an artificial intelligence / machine learning (AI / ML) model that uses the measurement of the network quality parameter as an input to the AI / ML model, and (ii) a determination that a cell switch condition is met, a command to switch from the serving cell to the target cell at a time corresponding to slot n+k. The parameters n and k are integers greater than 0.

[0016] Additional aspects will be set forth in part in the description that follows, and in part will be apparent from the description, or may be learned by practice of the presented embodiments of the present disclosure.

[0017] Features, aspects, and advantages of certain exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which like reference numerals refer to like elements. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 2 is a diagram of an example network device according to various embodiments of the present disclosure.

[0019] [Figure 2] 1 is a schematic diagram of an exemplary wireless communication system in accordance with various embodiments of the present disclosure.

[0020] [Figure 3]1 illustrates agreed on components and times that affect a UE's connection interruption when switching to a new cell in the related art, according to various embodiments of the present disclosure.

[0021] [Figure 4] 1 illustrates an inter-cell mobility scenario, in accordance with various embodiments of the present disclosure.

[0022] [Figure 5] 1 illustrates a flowchart of one embodiment of performing an AI / ML model-based cell switching process, in accordance with various embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0023] The following detailed description of exemplary embodiments refers to the accompanying drawings. The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of implementations. Moreover, one or more features or components of one embodiment may be incorporated into or combined with another embodiment (or one or more features of another embodiment). Furthermore, in the flowcharts and descriptions of operations provided below, it is understood that one or more operations may be omitted, one or more operations may be added, one or more operations may be performed concurrently (at least in part), or the order of one or more operations may be interchanged.

[0024] It will be apparent that the systems and / or methods described herein may be implemented in various forms, including hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement the systems and / or methods is not intended to limit the implementation. Accordingly, the operation and behavior of the systems and / or methods are described herein without reference to specific software code. It will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0025] Although particular combinations of features are recited in the claims and / or disclosed herein, these combinations are not intended to limit the disclosure of possible implementations. Indeed, many of these features can be combined in ways not specifically recited in the claims and / or disclosed herein. Although each dependent claim listed below may depend directly on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim within its scope.

[0026] No element, operation (process), or instruction used herein should be construed as critical or essential unless expressly stated as such. Also, as used herein, the articles "a" and "an" are intended to include one or more items and may be used interchangeably with "one or more." Where only one item is intended, the term "one" or similar language is used. Also, as used herein, terms such as "has," "have," "having," "include," and "including" are intended to be open-ended terms. Furthermore, the phrase "based on" is intended to mean "based at least in part on," unless expressly stated otherwise. Furthermore, phrases such as "at least one of A and B" or "at least one of A or B" should be understood to include A only, B only, or both A and B.

[0027] Embodiments of the present disclosure are directed to reducing the high latency of mobility mechanisms through AI / ML model-based channel prediction. While new L1 / L2-based mobility specifies mechanisms for reducing latency, this latency can be further reduced through AI / ML model-based channel prediction. AI / ML model-based channel prediction can be performed by the UE or on the network side (e.g., a base station). Based on the prediction, the UE can perform the necessary configurations and synchronizations before the UE switches from a serving cell to a target cell, thereby reducing latency.

[0028] 1 is a diagram of an exemplary device for performing a translation service. Device 100 may correspond to any type of known computer, server, or data processing device. For example, device 100 may include a processor, a personal computer (PC), a printed circuit board (PCB) including computing device, a minicomputer, a mainframe computer, a microcomputer, a telephonic computing device, a wired / wireless computing device (e.g., a smartphone, a personal digital assistant (PDA)), a laptop, a tablet, a smart device, or any other similarly functional device.

[0029] In some embodiments, as shown in FIG. 1, device 100 may include a set of components such as a processor 120, a memory 130, a storage component 140, an input component 150, an output component 160, and a communication interface 170.

[0030] Bus 110 may comprise one or more components that enable communication within (between) the set of components of device 100. For example, bus 110 may be a communications bus, a crossover bar, a network, etc. Although bus 110 is shown in FIG. 1 as a single line, bus 110 may be implemented using (providing multiple connections to) multiple (two or more) connections within the set of components of device 100. The present disclosure is not limited in this respect.

[0031] Device 100 may include one or more processors, such as processor 120. Processor 120 may be implemented in hardware, firmware, and / or a combination of hardware and software. For example, processor 120 may comprise a central processing unit (CPU), graphics processing unit (GPU), accelerated processing unit (APU), microprocessor, microcontroller, digital signal processor (DSP), field programmable gate array (FPGA), application-specific integrated circuit (ASIC), general-purpose single-chip or multi-chip processor or other programmable logic device, discrete gate or transistor logic circuitry, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, or any conventional processor, controller, microcontroller, or state machine. Processor 120 may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In some embodiments, particular processes and methods may be performed by circuitry that is specific to a given function.

[0032] The processor 120 may control the overall operation of the device 100 and / or a set of components of the device 100 (e.g., the memory 130, the storage component 140, the input component 150, the output component 160, and the communication interface 170).

[0033] Device 100 may further comprise memory 130. In some embodiments, memory 130 may comprise random access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic memory, optical memory, and / or another type of dynamic or static storage device. Memory 130 may store information and / or instructions for use (e.g., execution) by processor 120.

[0034] Storage component 140 of device 100 may store information and / or computer-readable instructions and / or code related to the operation and use of device 100. For example, storage component 140 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid-state disk), a compact disk (CD), a digital versatile disk (DVD), a universal serial bus (USB) flash drive, a Personal Computer Memory Card International Association (PCMCIA) card, a floppy disk, a cartridge, a magnetic tape, and / or another type of non-transitory computer-readable medium, along with a corresponding drive.

[0035] Device 100 may further comprise input component 150. Input component 150 may include one or more components that enable device 100 to receive information via user input (e.g., a touchscreen, a keyboard, a keypad, a mouse, a stylus, a button, a switch, a microphone, a camera, etc.), etc. Alternatively or additionally, input component 150 may include sensors for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, etc.).

[0036] Output component 160 of device 100 may include one or more components that may provide output information from device 100 (e.g., a display, a liquid crystal display (LCD), a light emitting diode (LED), an organic light emitting diode (OLED), a haptic feedback device, a speaker, etc.).

[0037] Device 100 may further comprise a communication interface 170. Communication interface 170 may include a receiver component, a transmitter component, and / or a transceiver component. Communication interface 170 may enable device 100 to establish connections and / or transfer communications with other devices (e.g., a server, another device). Communication may be achieved via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communication interface 170 may enable device 100 to receive information from and / or provide information to another device. In some embodiments, communication interface 170 may provide for communication with another device over a network such as a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a private network, an ad hoc network, an intranet, the Internet, an optical fiber-based network, a cellular network (e.g., a fifth generation (5G) network, a long term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a telephone network (e.g., a public switched telephone network (PSTN)), etc., and / or a combination of these or other types of networks. Alternatively or additionally, communication interface 170 may provide for communication with another device over a device-to-device (D2D) communication link such as FlashLinQ, WiMedia, Bluetooth, ZigBee, Wi-Fi, LTE, 5G, etc. In other embodiments, communication interface 170 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, etc.

[0038] Device 100 may be included in core network 240 and may perform one or more processes described herein. Device 100 may perform operations based on processor 120 executing computer-readable instructions and / or code, which may be stored by a non-transitory computer-readable medium, such as memory 130 and / or storage component 140. A computer-readable medium may refer to a non-transitory memory device. A memory device may include memory space within a single physical storage device and / or memory space distributed across multiple physical storage devices.

[0039] Computer readable instructions and / or code may be loaded into memory 130 and / or storage component 140 from another computer readable medium or from another device via communication interface 170. The computer readable instructions and / or code stored in memory 130 and / or storage component 140, when executed by processor 120, may cause device 100 to perform one or more processes described herein.

[0040] Alternatively, or in addition, hardwired circuitry may be used in place of or in combination with software instructions to implement one or more processes described herein. Thus, the embodiments described herein are not limited to any specific combination of hardware circuitry and software.

[0041] The number and arrangement of components shown in Figure 1 are provided as an example. In practice, there may be additional, fewer, different, or differently arranged components than those shown in Figure 1. Furthermore, two or more components shown in Figure 1 may be implemented within a single component, or a single component shown in Figure 1 may be implemented as multiple distributed components. Additionally or alternatively, a set of components shown in Figure 1 may perform one or more functions that are described as being performed by another set of components shown in Figure 1.

[0042] 2 illustrates an example wireless communication system in accordance with various embodiments of the present disclosure. The wireless communication system 200 (which may also be referred to as a wireless wide area network (WWAN)) may include one or more user equipment (UE) 210, one or more base stations 220, at least one transport network 230, and at least one core network 240. The device 100 (FIG. 1) may be incorporated into the UE 210 or the base station 220.

[0043] One or more UEs 210 can access at least one core network 240 and / or IP services 250 via connections to one or more base stations 220 over the RAN domain 224 and through at least one transport network 230. Examples of UEs 210 may include a cellular phone, a smartphone, a Session Initiation Protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system (GPS), a multimedia device, a video device, a digital audio player (e.g., an MP3 player), a camera, a game console, a tablet, a smart device, a wearable device, a vehicle, an electric meter, a gas pump, a large or small kitchen appliance, a healthcare device, an implant, a sensor / actuator, a display, or any other similarly functioning device. Some of the one or more UEs 210 may be referred to as Internet-of-Things (IoT) devices (e.g., parking meters, gas pumps, toasters, vehicles, heart monitors, etc.). One or more UEs 210 may also be referred to as a station, a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communication device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile agent, a client, or some other suitable terminology.

[0044] One or more base stations 220 may communicate wirelessly with one or more UEs 210 via the RAN domain 224. Each base station of the one or more base stations 220 may provide communication coverage to one or more UEs 210 located within the geographic coverage area of ​​the base station 220. In some embodiments, as shown in FIG. 2, a base station 220 may transmit one or more beamformed signals to one or more UEs 210 in one or more transmit directions. One or more UEs 210 may receive the beamformed signals from the base station 220 in one or more receive directions. Alternatively or additionally, one or more UEs 210 may transmit beamformed signals to the base station 220 in one or more transmit directions. The base station 220 may receive the beamformed signals from one or more UEs 210 in one or more receive directions.

[0045] The one or more base stations 220 may include macrocells (e.g., high-power cellular base stations) and / or small cells (e.g., low-power cellular base stations). Small cells may include femtocells, picocells, and microcells. The base station 220, whether a macrocell or a large cell, may include and / or may be referred to as an access point (AP), an evolved (or evolved Universal Terrestrial Radio Access Network (E-UTRAN)) Node B (eNB), a next-generation Node B (gNB), or any other type of base station known to those skilled in the art.

[0046] One or more base stations 220 may be configured to interface (e.g., establish connections, transfer data, etc.) with at least one core network 240 through at least one transport network 230. In addition to other functions, one or more base stations 220 may perform one or more of the following functions: forwarding data received from one or more UEs 210 (e.g., uplink data) to the at least one core network 240 via the at least one transport network 230; forwarding data received from the at least one core network 240 (e.g., downlink data) to one or more UEs 210 via the at least one transport network 230.

[0047] The transport network 230 may transport data (e.g., uplink data, downlink data) and / or signaling between the RAN domain 224 and the CN domain 244. For example, the transport network 230 may provide one or more backhaul links between one or more base stations 220 and at least one core network 240. The backhaul links may be wired or wireless.

[0048] The core network 240 may be configured to provide one or more services (e.g., enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine type communications (mMTC), etc.) to one or more UEs 210 connected to the RAN domain 224 via the TN domain 234. As an example, the core network 240 performs translation services. Alternatively or additionally, the core network 240 may serve as an entry point for IP services 250. The IP services 250 may include the Internet, an intranet, an IP multimedia subsystem (IMS), streaming services (e.g., video, audio, games, etc.), and / or other IP services.

[0049] 3 shows an example timeline 300 of components and times that affect a UE's connection interruption when switching to a new cell. As shown in FIG. 3, after a cell switch command 308, the UE needs to perform DL 304 and UL 306 synchronization. In addition to this synchronization, there is a processing delay, which may include part of the UE reconfiguration 302 procedure.

[0050] Mobility (e.g., handover) latency can be advantageously reduced by using AI / ML model-based channel prediction. The UE or the network can predict a measured metric (e.g., RSRP of a reference signal), and if certain conditions are met, the UE can be instructed to switch to a target cell at a later time. In the time interval between receiving and applying a switch command, the UE can prepare for the cell switch, including performing UE reconfiguration, DL synchronization, and UL synchronization. For example, if a cell switch command is predicted for future slot k, then UE reconfiguration 302, DL synchronization 304, and UL synchronization 306 can be performed before the cell switch command at slot k.

[0051] FIG. 4 illustrates an exemplary wireless communication network 400 having cells Cell-1, Cell-2, and Cell-3. A UE 402 may be configured with a set of neighbor cells (candidate target cells) to measure. In some embodiments, the UE may be configured with a measurement configuration including, for example, configurations for SSB and / or CSI-RS resources, physical cell IDs, etc. The UE may also be configured with a measurement reporting configuration. The reporting configuration may include, for example, periodic and aperiodic reporting. For example, for the UE 402 of FIG. 4, Cell-1 may be the serving cell, and the UE may be configured to measure the signal quality of Cell-1 and the signal qualities of neighbor cells Cell-2 and Cell-3.

[0052] A UE may perform measurements while connected to a serving cell. For inter-frequency measurements, the UE may be configured with measurement gaps during which the UE can perform measurements of other cells. For example, the UE may perform measurements at periodic intervals.

[0053] The following examples refer to a serving cell and a target cell. However, these examples are not limited to this configuration and may accommodate two or more target cells. The target cell may belong to the same DU as the serving cell or a different DU. The method is applicable to two or more target cells.

[0054] In some examples, an AI / ML model can be used to predict a channel or a metric of a channel, a reference signal, or a metric of a reference signal. For example, an AI / ML model can be used to predict a channel impulse response. In another example, an AI / ML model can be used to predict a metric of a reference signal, e.g., the RSRP of a reference signal. A first set of reference signals, such as a set of SSBs or a set of CSI-RS resources, can be used as input to the AI / ML model. The reference signals can be beamformed. As an example, a gNB can transmit 64 SSBs in slot (n), where each SSB can be transmitted on a different beam (e.g., a different beamforming vector can be applied to the SSB). A UE can measure the RSRP of the SSBs and use the RSRP measurements as input to the AI / ML model to infer the RSRP of all or a subset of the SSBs in slot (n+k). Each of n and k can be a positive integer. In this manner, slot (n+k) occurs at a later time than slot n. The reference signal or channel can be associated with a Transmission Configuration Indication (TCI) state. The RS / channel associated with a particular TCI state may be assumed to be transmitted in the same beam as the reference signal (SSB or CSI-RS) associated with the TCI state.

[0055] According to some embodiments, a UE may have multiple AI / ML models. A first AI / ML model may be used to predict a metric for a serving cell, and a second AI / ML model may be used to predict a metric for a target cell. For example, an AI / ML model may be used to predict RSRP and / or RSRQ for a set of SSBs (e.g., the set may be all SSBs or SSBs with the highest RSRP and / or RSRQ). If the inference of the AI / ML model is performed in the network (e.g., gNB), the UE may report reference signal measurements (e.g., RSRP) to the network, and the inference (e.g., prediction) may be performed on the network side. In some examples, the first and second models are the same model.

[0056] According to some embodiments, the model used to infer metrics for the target cell may be transferred from the serving cell to the UE if the inference is performed in the UE, or from the target cell to the serving cell if the inference is performed on the network side.

[0057] If the inference is performed on the UE side, the UE may report the inferred value to the serving cell. Reporting configuration (e.g., reporting period, resources to use for reporting, etc.) may be performed by the network (e.g., using RRC). Furthermore, if the inference is performed on the UE side, the UE may trigger a measurement report if certain events occur. For example, if the inference is performed at time t and the metric is RSRP, some of the events include, but are not limited to, the following: (i) the target cell RSRP may be predicted to be better than a threshold at time t+T, (ii) the target cell RSRP may be predicted to be better than a threshold+offset at time t+T, (iii) the serving cell RSRP may be predicted to be worse than a first threshold and the target cell RSRP may be predicted to be better than a second threshold, etc. The events may be predicted within a time interval.

[0058] If the inference is performed on the network side, the UE can report measurements of the serving cell and the target cell (e.g., RSRP of SSBs from both cells) to the network, and the network can infer (e.g., predict) the corresponding RSRP value.

[0059] Based on the inference result, the network can instruct the UE to switch to the target cell, and the instruction may include the target cell ID and the TCI state (e.g., beam) to use after the switch.

[0060] A cell switch command may be applicable to a time later than the time the command is received. For example, if the command is received at time t, the UE may be expected to switch to the target cell at time t+T. The unit of time may be ms, slots, etc. The duration T may be included in the cell switch command.

[0061] In some examples, the UE and / or network may determine that the inferred value may not have been predicted with sufficient accuracy before the switch was scheduled to occur. If it is determined that the inferred value was not predicted with sufficient accuracy, the network may send a cancellation indication to the UE. For example, the inferred value may be compared to one or more reference values, and if the difference between the inferred value and the one or more reference values ​​is greater than a threshold, the inferred value may be determined to be inaccurate.

[0062] The cancel message may carry (or may include) the target cell ID. The switch message and the cancel message may have the same format (e.g., they may be the same MAC CE), and a bit in the message (MAC CE) may indicate the type of message (e.g., 1 for switch, 0 for cancel switch). In some examples, after a switch command, the UE may determine that the target cell is no longer a viable candidate and send a request-cancel-switch MAC CE to the network.

[0063] In some examples, AI / ML models can be used for neighbor cell measurement relaxation. For example, the UE or the network can infer a quality metric of the serving cell. If the metric is expected to remain above a threshold for at least T seconds, the serving cell can indicate to the UE to relax neighbor cell measurements. Relaxation can refer to the UE measuring neighbor cells at a higher periodicity. In other examples, neighbor or cell measurements can be activated and / or deactivated.

[0064] When the current serving cell has a beam quality (e.g., signal strength, signal strength history, or the result of a stability estimation based on an AI / ML model calculation) that is better than a configured threshold, the UE can reduce, or the network can configure the UE to reduce, the frequency of neighbor beam measurements, resulting in reduced power consumption by the UE. This decision may be made based on the AI / ML model.

[0065] For example, an output of the estimated duration during which the signal strength of the camping beam is greater than a threshold and / or the probability of that scenario occurring, which can be derived by accuracy information calculated by model monitoring.

[0066] 5 shows a flowchart of one embodiment of a process 500 for predicting a cell switch. Process 500 may be performed by a UE, such as UE 402 (FIG. 4), in communication with serving cell Cell-1, where cells Cell-2 and Cell-3 may be target cells. Process 500 may begin with operation S502, in which the UE receives a set of resources for slot n from a target cell in a wireless communication network. For example, the UE may receive resources from Cell-2 and Cell-3.

[0067] The process continues to operation S504, where the UE measures a network quality parameter for the target cell based on a set of resources, where the set of resources may include multiple reference signals, where the network quality parameter may be an RSRP of each reference signal.

[0068] The process proceeds to operation S506, where the UE reports results corresponding to the measurements of the network quality parameters to the serving cell. In some examples, the AI / ML model may be included in the UE, and the results corresponding to the measurements of the network quality parameters include predictions of the AI / ML model. In other examples, the AI / ML model is included on the network side (e.g., a base station), and the results corresponding to the measurements of the network quality parameters include measurements performed by the UE.

[0069] The process continues at operation S508, where the UE receives a command to switch from the serving cell to the target cell at a time corresponding to slot n+k based on (i) a prediction from the AI / ML model that uses measurements of the network quality parameters as inputs to the AI / ML model, and (ii) a determination that a cell switch condition is met. Each of the parameters n and k may be a positive integer. In some examples, the cell switch condition specifies that the AI / ML model predicts that the target cell will have an RSRP greater than a threshold at a time corresponding to slot n+k. In some examples, the cell switch condition specifies that the AI / ML model predicts that the serving cell will have an RSRP less than a first threshold and the target cell will have an RSRP greater than a second threshold at a time corresponding to slot n+k.

[0070] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit implementations to the precise forms disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of implementations.

[0071] Some embodiments may relate to systems, methods, and / or computer-readable media at any possible technical detail level of integration. Additionally, one or more of the above components described above may be implemented as instructions stored on a computer-readable medium and executable by at least one processor (and / or may include at least one processor). The computer-readable medium may include computer-readable non-transitory storage medium(s) having computer-readable program instructions for causing a processor to perform operations.

[0072] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction-execution device. A computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded devices such as punch cards or ridge-in-groove structures with instructions recorded thereon, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted through wires.

[0073] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing / processing device.

[0074] The computer-readable program code / instructions for carrying out operations may be either source code or object code written in any combination of one or more programming languages, including assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuits, or object-oriented programming languages ​​such as Smalltalk, C++, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet Service Provider). In some embodiments, an electronic circuit, including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit to perform an aspect or operation.

[0075] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute on the processor of the computer or other programmable data processing apparatus, create means for performing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. These computer-readable program instructions may also be stored on a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that the computer-readable storage medium on which the instructions are stored includes a product containing instructions that implement aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0076] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause the computer, other programmable apparatus, or other device to execute a series of operational steps to create a computer-implemented process, such that the instructions executing on the computer, other programmable apparatus, or other device perform the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0077] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. In this regard, each block in the flowcharts or block diagrams may represent a portion of a microservice(s), module, segment, or instruction set, which includes one or more executable instructions for implementing the specified logical function(s). The methods, computer systems, and computer-readable media may include additional, fewer, different, or differently arranged blocks than depicted in the figures. In some alternative implementations, the functions noted in the blocks may occur out of the order depicted in the figures. For example, two blocks shown in succession may actually be executed concurrently or substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, can be implemented by a dedicated hardware-based system that performs the specified functions or operations or executes a combination of dedicated hardware and computer instructions.

[0078] It will be apparent that the systems and / or methods described herein may be implemented in various forms, including hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement the systems and / or methods is not intended to limit the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0079] The above disclosure also encompasses the embodiments listed below.

[0080] (1) A method executed by at least one processor of a user equipment (UE) having a connection with a serving cell in a wireless communication network, the method including: receiving a set of resources for slot n from a target cell in the wireless communication network; measuring a network quality parameter for the target cell based on the set of resources; reporting a result corresponding to the measurement of the network quality parameter to the serving cell; and receiving a command to switch from the serving cell to the target cell at a time corresponding to slot n+k based on (i) a prediction from an artificial intelligence / machine learning (AI / ML) model that uses the measurement of the network quality parameter as an input to the AI / ML model, and (ii) a determination that a cell switch condition is met, where n and k are integers greater than 0.

[0081] (2) The method described in (1) above, wherein the set of resources includes a plurality of reference signals, and the network quality parameter is a reference signal resource power (RSRP) of each reference signal.

[0082] (3) The method described in (2) above, wherein the cell switching condition specifies that the AI / ML model predicts that the target cell will have an RSRP greater than a threshold at a time corresponding to slot n+k.

[0083] (4) The method described in (2) above, wherein the cell switching condition specifies that the AI / ML model predicts that at a time corresponding to slot n+k, the serving cell has an RSRP less than a first threshold and the target cell has an RSRP greater than a second threshold.

[0084] (5) The method according to (1) above, wherein the network quality parameter is a channel impulse response (CIR).

[0085] (6) The method described in (1) above, wherein the AI / ML model is located in the UE and reporting results corresponding to measurements of network quality parameters includes prediction.

[0086] (7) The method described in (6) above, in which the UE receives the AI / ML model from the serving cell.

[0087] (8) The method described in (1) above, wherein the AI / ML model is located in a base station of a serving cell, and the base station of the serving cell receives the AI / ML model from the target cell.

[0088] (9) The method described in (1) above, further comprising, after receiving a command to switch from the serving cell to the target cell, performing downlink synchronization and uplink synchronization with the target cell before a time corresponding to slot n+k has elapsed.

[0089] (10) The method of (1) above, further comprising, after receiving a command for switching from the serving cell to the target cell, sending a request to the serving cell to cancel the switch based on a determination that the conditions for switching from the serving cell to the target cell are no longer valid.

[0090] (11) The method described in (1) above, further comprising increasing the period for measuring one or more neighboring cells based on a determination that the AI / ML model predicts that the network quality parameter for the serving cell will remain above the threshold for a predetermined period of time.

[0091] (12) A user equipment (UE) having a connection with a serving cell in a wireless communication network, the UE including: at least one memory configured to store computer program code; and at least one processor configured to access the at least one memory and operate as instructed by the computer program code, the computer program code including: first receiving code configured to cause at least one of the at least one processor to receive a set of resources for slot n from a target cell in the wireless communication network; measurement code configured to cause the at least one of the at least one processor to measure, for the target cell, a network quality parameter based on the set of resources; reporting code configured to cause the at least one of the at least one processor to report a result corresponding to the measurement of the network quality parameter to the serving cell; and second receiving code configured to cause the at least one of the at least one processor to receive a command to switch from the serving cell to the target cell at a time corresponding to slot n+k based on (i) a prediction from an artificial intelligence / machine learning (AI / ML) model that uses the measurement of the network quality parameter as an input to the AI / ML model, and (ii) a determination that a cell switch condition is met, wherein n and k are integers greater than 0.

[0092] (13) The UE described in (12) above, wherein the set of resources includes a plurality of reference signals, and the network quality parameter is a reference signal resource power (RSRP) of each reference signal.

[0093] (14) The UE of (13) above, wherein the cell switching condition specifies that the AI / ML model predicts that the target cell has an RSRP greater than a threshold at a time corresponding to slot n+k.

[0094] (15) The UE described in (13) above, wherein the cell switching condition specifies that the AI / ML model predicts that at a time corresponding to slot n+k, the serving cell has an RSRP less than a first threshold and the target cell has an RSRP greater than a second threshold.

[0095] (16) The UE according to (12) above, wherein the network quality parameter is a channel impulse response (CIR).

[0096] (17) The UE described in (12) above, wherein the AI / ML model is located in the UE and the reported results corresponding to measurements of network quality parameters include predictions.

[0097] (18) The UE described in (17) above, wherein the UE receives an AI / ML model from a serving cell.

[0098] (19) The UE described in (12) above, wherein the AI / ML model is located in a base station of a serving cell, and the base station of the serving cell receives the AI / ML model from the target cell.

[0099] (20) A non-transitory computer-readable medium having instructions stored therein, the instructions, when executed by a processor in a user equipment (UE) having a connection with a serving cell in a wireless communication network, causing the processor to perform a method, the method including receiving a set of resources for slot n from a target cell in the wireless communication network; measuring, for the target cell, a network quality parameter based on the set of resources; reporting a result corresponding to the measurement of the network quality parameter to the serving cell; and receiving a command to switch from the serving cell to the target cell at a time corresponding to slot n+k based on (i) a prediction from an artificial intelligence / machine learning (AI / ML) model that uses the measurement of the network quality parameter as an input to the AI / ML model, and (ii) a determination that a cell switch condition is met, where n and k are integers greater than 0.

Claims

1. 1. A method executed by at least one processor of a user equipment (UE) having a connection with a serving cell in a wireless communication network, the method comprising: receiving a set of resources for slot n from a target cell in the wireless communication network; measuring a network quality parameter for the target cell based on the set of resources; reporting results corresponding to the measurements of the network quality parameters to the serving cell; receiving a command to switch from the serving cell to the target cell at a time corresponding to slot n+k based on (i) a prediction from an artificial intelligence and machine learning (AI / ML) model that uses the measurement of the network quality parameter as an input to the AI / ML model, and (ii) a determination that a cell switch condition is met; The method, wherein n and k are integers greater than 0.

2. The method of claim 1 , wherein the set of resources includes a plurality of reference signals, and the network quality parameter is a reference signal resource power (RSRP) of each reference signal.

3. The method of claim 2 , wherein the cell switching condition specifies that the AI / ML model predicts that the target cell has an RSRP greater than a threshold at the time corresponding to slot n+k.

4. 3. The method of claim 2, wherein the cell switching condition specifies that the AI / ML model predicts that, at the time corresponding to slot n+k, the serving cell has an RSRP less than a first threshold and the target cell has an RSRP greater than a second threshold.

5. The method of claim 1 , wherein the network quality parameter is a channel impulse response (CIR).

6. The method of claim 1 , wherein the AI / ML model is located in the UE, and the reporting of the results corresponding to the measurements of the network quality parameters includes the prediction.

7. The method of claim 6 , wherein the UE receives the AI / ML model from the serving cell.

8. The method of claim 1 , wherein the AI / ML model is located in a base station of the serving cell, and the base station of the serving cell receives the AI / ML model from the target cell.

9. 2. The method of claim 1, further comprising, after receiving the command to switch from the serving cell to the target cell, performing downlink synchronization and uplink synchronization with the target cell before the time corresponding to slot n+k elapses.

10. 2. The method of claim 1, further comprising: after receiving the command for switching from the serving cell to the target cell, sending a request to the serving cell to cancel the switch based on a determination that conditions for switching from the serving cell to the target cell are no longer valid.

11. and increasing a periodicity for measuring one or more neighboring cells based on a determination that the AI / ML model predicts that a network quality parameter for the serving cell will remain above a threshold for a predetermined period of time.

12. 1. A user equipment (UE) having a connection with a serving cell in a wireless communication network, the UE comprising: at least one memory configured to store computer program code; at least one processor configured to access said at least one memory and to operate as instructed by said computer program code; The computer program code a first receiving code configured to cause at least one of the at least one processor to receive a set of resources for slot n from a target cell in the wireless communication network; measurement code configured to cause at least one of the at least one processor to measure, for the target cell, a network quality parameter based on the set of resources; reporting code configured to cause at least one of the at least one processor to report results corresponding to the measurement of the network quality parameter to the serving cell; and second receiving code configured to cause at least one of the at least one processor to receive a command to switch from the serving cell to the target cell at a time corresponding to slot n+k based on (i) a prediction from an artificial intelligence and machine learning (AI / ML) model that uses the measurement of the network quality parameter as an input to the AI / ML model, and (ii) a determination that a cell switch condition is met; The UE, wherein n and k are integers greater than 0.

13. 13. The UE of claim 12, wherein the set of resources includes a plurality of reference signals, and the network quality parameter is a reference signal resource power (RSRP) of each reference signal.

14. The UE of claim 13 , wherein the cell switching condition specifies that the AI / ML model predicts that the target cell has an RSRP greater than a threshold at the time corresponding to slot n+k.

15. 14. The UE of claim 13, wherein the cell switching condition specifies that the AI / ML model predicts that, at the time corresponding to slot n+k, the serving cell has an RSRP less than a first threshold and the target cell has an RSRP greater than a second threshold.

16. The UE of claim 12, wherein the network quality parameter is a channel impulse response (CIR).

17. The UE of claim 12 , wherein the AI / ML model is located in the UE, and the reported results corresponding to the measurements of the network quality parameters include the predictions.

18. The UE of claim 17 , wherein the UE receives the AI / ML model from the serving cell.

19. The UE of claim 12 , wherein the AI / ML model is located in a base station of the serving cell, and the base station of the serving cell receives the AI / ML model from the target cell.

20. A non-transitory computer-readable medium having instructions stored therein, the instructions, when executed by a processor in a user equipment (UE) having a connection with a serving cell in a wireless communication network, causing the processor to perform a method, the method comprising: receiving a set of resources for slot n from a target cell in the wireless communication network; measuring a network quality parameter for the target cell based on the set of resources; reporting results corresponding to the measurements of the network quality parameters to the serving cell; receiving a command to switch from the serving cell to the target cell at a time corresponding to slot n+k based on (i) a prediction from an artificial intelligence and machine learning (AI / ML) model that uses the measurement of the network quality parameter as an input to the AI / ML model, and (ii) a determination that a cell switch condition is met; The non-transitory computer-readable medium, wherein n and k are integers greater than 0.

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