Method and apparatus for using AI / ML for beam management

Abstract

A method performed by at least one processor in a user equipment (UE) includes receiving a plurality of reference signals corresponding to a state of a channel from a base station over the channel. The method also includes measuring a subset of the received plurality of reference signals. The method also includes transmitting a channel state report to the base station within a first time interval, the channel state report corresponding to a measurement of a subset of the received plurality of reference signals and a prediction of one or more reference signals received by the UE after the first time interval.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on and claims priority from Indian Patent Application No. 202241045765 filed on August 10, 2022, the entire disclosure of which is incorporated herein by reference. Technical Field

[0003] The present disclosure relates generally to communication systems, and more particularly to methods and apparatus using artificial intelligence and machine learning (AI / ML) for beam management. Background Art

[0004] The time and frequency resources that can be used by the UE to report CSI are controlled by the gNB. The CSI may contain channel quality indicator (CQI), precoding matrix indicator (PMI), CSI-RS resource indicator (CRI), SS / PBCH block resource indicator (SSBRI), layer indicator (LI), rank indicator (RI), L1-RSRP, L1-SINR. The CSI reporting configuration contains the following parameters: codebook configuration, including codebook subset restrictions, time domain behavior, frequency granularity for CQI and PMI, measurement restriction configuration, and CSI related quantities to be reported by the UE, such as layer indicator (LI), L1-RSRP, L1-SINR, CRI and SSBRI (SSB resource indicator). CSI reporting can be periodic, aperiodic or semi-static. A UE can be configured with multiple CSI reporting configurations.

[0005] The CSI resource configuration contains a configuration of a list of S≥1 CSI resource sets, wherein the list consists of references to one or both of (multiple) NZP CSI-RS resource sets and (multiple) SS / PBCH block sets, or the list consists of references to (multiple) CSI-IM resource sets. Each CSI-RS resource set contains one or more CSI-RS resources. The time domain behavior of the CSI-RS resources can be set to be periodic, aperiodic, or semi-persistent. CSI-RS resources within a group can be sent with different transmit beams (e.g., beam scanning), and the UE can identify the index of the resource with the maximum RSRP. CSI-RS resources can also be sent using the same beam so that the UE can perform beam scanning. The beam used for transmission in the downlink can be indicated to the UE using a TCI state parameter. The TCI state parameter indicates to the UE that the received channel (e.g., PDCCH or PDSCH) is sent using the same beam of the reference signal included in the TCI state parameter.

[0006] Existing techniques can be used for beam management. Specifically, existing techniques can be used by the UE to monitor and measure (e.g., periodically) CSI-RS resources, identify the best beam, and send information in a CSI report. However, the overhead of sending and measuring CSI-RS resources is expected to be large and further increases as the number of transmit and receive beams increases. Therefore, it is necessary to provide more efficient beam management.

[0007] Improvements are presented herein that may also be applicable to other multi-access technologies and the telecommunication standards that employ them. Summary of the invention

[0008] A simplified overview of one or more embodiments of the present disclosure is presented below in order to provide a basic understanding of such embodiments. This overview is not an extensive overview of all contemplated embodiments, and is neither intended to identify key or important elements of all embodiments nor to define the scope of any or all embodiments. Its sole purpose is to present some concepts of one or more embodiments of the present disclosure in a simplified form as a prelude to a more detailed description presented later.

[0009] The present disclosure discloses methods, apparatus, and non-transitory computer-readable media using AI / ML for beam management.

[0010] According to an exemplary embodiment, a method performed by at least one processor in a user equipment (UE) includes receiving a plurality of reference signals corresponding to a state of the channel from a base station over a channel. The method includes measuring a subset of the received plurality of reference signals. The method also includes sending a channel state report to the base station within a first time interval, the channel state report corresponding to the measurement of the subset of the received plurality of reference signals and a prediction of one or more reference signals received by the UE after the first time interval.

[0011] According to an exemplary embodiment, a user equipment (UE) includes at least one memory configured to store a computer program code and at least one processor configured to access the at least one memory and operate according to the instructions of the computer program code. The computer program code includes a receiving code, which is configured to cause at least one of the at least one processors to receive a plurality of reference signals corresponding to the state of the channel from a base station through a channel. The computer program code also includes a first measurement code, which is configured to cause at least one of the at least one processors to measure a subset of the received plurality of reference signals. The computer program code also includes a sending code, which is configured to cause at least one of the at least one processors to send a channel state report to the base station within a first time interval, the channel state report corresponding to the measurement of the subset of the received plurality of reference signals and the prediction of one or more reference signals received by the UE after the first time interval.

[0012] According to an exemplary embodiment, a non-transitory computer-readable medium having instructions stored therein, the instructions, when executed by a processor in a user equipment (UE), causes the UE to perform a method, the method comprising receiving a plurality of reference signals corresponding to a state of the channel from a base station over a channel. The method also includes measuring a subset of the received plurality of reference signals. The method also includes sending a channel state report to the base station within a first time interval, the channel state report corresponding to a measurement of a subset of the received plurality of reference signals and a prediction of one or more reference signals received by the UE after the first time interval.

[0013] Additional embodiments will be set forth in the description which follows and, in part, will be apparent from the description, and / or may be learned by practice of the presented embodiments of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other aspects, features and aspects of embodiments of the present disclosure will be apparent from the following description in conjunction with the accompanying drawings, in which:

[0015] Figure 1 is a diagram of an example network device according to various embodiments of the present disclosure.

[0016] Figure 2 is a schematic diagram of an example wireless communication system according to various embodiments of the present disclosure.

[0017] Figure 3 Example time slots for receiving CSI resources and sending CSI reports according to various embodiments of the present disclosure are illustrated.

[0018] Figure 4 Example time slots for receiving CSI resources and sending CSI reports according to various embodiments of the present disclosure are illustrated.

[0019] Figure 5 Example time slots for receiving CSI resources and sending CSI reports according to various embodiments of the present disclosure are illustrated.

[0020] Figure 6 Example CSI reporting according to various embodiments of the present disclosure is illustrated.

[0021] Figure 7 An example of receiving CSI resources in multiple stages according to various embodiments of the present disclosure is illustrated.

[0022] Figure 8 An example of receiving CSI resources in multiple stages according to various embodiments of the present disclosure is illustrated.

[0023] Fig. 9An example neural network according to various embodiments of the present disclosure is illustrated.

[0024] Fig.10 An example diagram of beam prediction results according to various embodiments of the present disclosure is illustrated.

[0025] Fig.11 A flow chart of an embodiment of a process for performing beam management is illustrated. DETAILED DESCRIPTION

[0026] The following detailed description of example embodiments refers to the accompanying drawings.The same reference numbers in different drawings may identify the same or similar elements.

[0027] The above disclosure provides illustration and description, but is not intended to be exhaustive or to limit implementation to the disclosed precise form. According to the above disclosure, modification and variation are possible, or can be obtained from the practice of implementation. In addition, one or more features or parts of an embodiment can be incorporated into another embodiment (or one or more features of another embodiment) or combined with it. In addition, in the flow chart and operation description provided below, it is understood that one or more operations can be omitted, one or more operations can be added, one or more operations can be performed simultaneously (at least in part), and the order of one or more operations can be switched.

[0028] It will be apparent that the systems and / or methods described herein can be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit these implementations. Therefore, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it should be understood that software and hardware can be designed to implement these systems and / or methods based on the description herein.

[0029] Even though particular combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and / or disclosed in the specification. Although each of the dependent claims listed below may be directly dependent on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set.

[0030] Unless explicitly stated, any element, behavior or instruction used in this article should not be interpreted as critical or necessary. In addition, as used in this article, the terms "one" and "an" are intended to include one or more projects and can be used interchangeably with "one or more". If only one project is intended, the term "one" or similar language is used. In addition, as used in this article, the terms "having", "containing", "having", "including", "comprising", etc. are intended to be open terms. In addition, unless explicitly stated otherwise, the word "based on" means "based at least in part". In addition, statements such as "at least one of [A] and [B]" or "at least one of [A] or [B]" should be understood to include only A, only B, or both A and B.

[0031] In this specification, reference to "one embodiment," "an embodiment," or similar language indicates that a particular feature, structure, or characteristic described in conjunction with the illustrated embodiment is included in at least one embodiment of the present solution. Thus, the phrases "in one embodiment," "in an embodiment," and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0032] In addition, the features, advantages and characteristics of the present disclosure may be combined in any suitable manner in one or more embodiments. Based on the description herein, those skilled in the relevant art will recognize that the present disclosure may be practiced without one or more of the specific features or advantages of a particular embodiment. In other examples, additional features and advantages that may not be present in all embodiments of the present disclosure may be recognized in certain embodiments.

[0033] Embodiments of the present disclosure relate to an AI / ML-based prediction mechanism for reducing beam management overhead. Embodiments of the present disclosure increase the robustness of AI / ML-based solutions. In addition, several embodiments of the present disclosure include timing information in CSI reports so that time domain prediction for beam management can be performed using AI / ML.

[0034] In the following embodiments, an AI / ML-based beam management method is disclosed. For example, the gNB may send CSI-RS on a first set of spatial, temporal, and frequency resources. The UE may perform measurements of the CSI-RS and feed back CSI derived from the measurements. Using the AI / ML engine, the gNB may predict CSI for a second set of resources, where the second set of resources may be different from the first set of resources. The AI / ML engine may reside on the UE side, and the UE may also perform CSI prediction.

[0035] Figure 11 is a diagram of an example device for performing an embodiment of the present disclosure. 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 a computing device, a minicomputer, a mainframe computer, a microcomputer, a telephone computing device, a wired / wireless computing device (e.g., a smartphone, a personal digital assistant (PDA)), a laptop, a tablet computer, a smart device, or any other similarly functional device.

[0036] In some embodiments, Figure 1 As shown, 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 .

[0037] The bus 110 may include one or more components that allow communication between the set of components of the device 100. For example, the bus 110 may be a communication bus, a crossbar, a network, etc. Although the bus 110 may be Figure 1 Although depicted as a single line in FIG. 1 , bus 110 may be implemented using multiple (two or more) connections between the set of components of device 100. The present disclosure is not limited in this respect.

[0038] 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 include a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a general purpose single-chip or multi-chip processor, or other programmable logic devices, discrete gates or transistor logic, 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, a plurality of microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. In some embodiments, specific processes and methods may be performed by circuit systems specific to a given function.

[0039] Processor 120 may control the overall operation of device 100 and / or the set of components of device 100 (eg, memory 130 , storage component 140 , input component 150 , output component 160 , communication interface 170 ).

[0040] Device 100 may also include memory 130. In some embodiments, memory 130 may include 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.

[0041] The storage component 140 of the device 100 may store information and / or computer-readable instructions and / or code related to the operation and use of the device 100. For example, the 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 cassette, a magnetic tape, and / or another type of non-transitory computer-readable medium, and a corresponding drive.

[0042] Device 100 may also include input component 150. Input component 150 may include one or more components that allow device 100 to receive information, such as via user input (e.g., a touch screen, keyboard, keypad, mouse, stylus, buttons, switches, microphone, camera, etc.). Alternatively or additionally, input component 150 may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, etc.).

[0043] Output components 160 of device 100 may include one or more components (e.g., a display, a liquid crystal display (LCD), a light emitting diode (LED), an organic light emitting diode (OLED), a tactile feedback device, a speaker, etc.) that can provide output information from device 100.

[0044] The device 100 may also include a communication interface 170. The communication interface 170 may include a receiver component, a transmitter component and / or a transceiver component. The communication interface 170 may enable the device 100 to establish a connection and / or transmit communication with other devices (e.g., a server, another device). Communication may be achieved via a wired connection, a wireless connection, or a combination of a wired and wireless connection. The communication interface 170 may allow the device 100 to receive information from another device and / or provide information to another device. In some embodiments, the communication interface 170 may provide communication with another device via a network, such as a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a private network, a self-organizing network, an intranet, the Internet, a 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, the communication interface 170 may provide communication with another device via a device-to-device (D2D) communication link, such as FlashLinQ, WiMedia, Bluetooth, ZigBee, Wi-Fi, LTE, 5G, etc. In other embodiments, the communication interface 170 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, etc.

[0045] Device 100 may perform one or more processes described herein. Device 100 may perform operations based on processor 120 executing computer-readable instructions and / or code that may be stored by a non-transitory computer-readable medium, such as memory 130 and / or storage component 140. The computer-readable medium may refer to a non-transitory memory device. The memory device may include memory space within a single physical storage device and / or memory space distributed across multiple physical storage devices.

[0046] The computer-readable instructions and / or code may be read into the memory 130 and / or storage component 140 from another computer-readable medium or from another device via the communication interface 170. The computer-readable instructions and / or code stored in the memory 130 and / or storage component 140, if or when executed by the processor 120, may cause the device 100 to perform one or more processes described herein.

[0047] Alternatively or additionally, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein.Thus, the embodiments described herein are not limited to any specific combination of hardware circuitry and software.

[0048] Figure 1 The number and arrangement of components shown are provided as examples. Figure 1 There may be more components, fewer components, different components, or components arranged differently than shown. Figure 1 Two or more components shown may be implemented in a single component, or Figure 1 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Figure 1 The illustrated set (one or more) of components may perform the operations described as being performed by Figure 1 Another group of components shown performs one or more functions.

[0049] Figure 2 2 is a diagram illustrating an example of a wireless communication system according to 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 equipments (UEs) 210, one or more base stations 220, at least one transmission network 230, and at least one core network 240. Device 100 ( Figure 1 ) can be combined in UE 210 or base station 220.

[0050] One or more UEs 210 may access at least one core network 240 and / or IP services 250 via a connection to one or more base stations 220 through a RAN domain 224 and at least one transport network 230. Examples of UE 210 may include a cellular phone, a smart phone, 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 functional device. Some of the one or more UEs 210 may be referred to as Internet of Things (IoT) devices (e.g., a parking meter, a gas pump, a toaster, a vehicle, a heart monitor, 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.

[0051] The one or more base stations 220 may wirelessly communicate with the one or more UEs 210 via the RAN domain 224. Each 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. Figure 2 As shown, the base station 220 may transmit one or more beamformed signals in one or more transmit directions to the one or more UEs 210. The one or more UEs 210 may receive the beamformed signals in one or more receive directions from the base station 220. Alternatively or additionally, the one or more UEs 210 may transmit the beamformed signals in one or more transmit directions to the base station 220. The base station 220 may receive the beamformed signals in one or more receive directions from the one or more UEs 210.

[0052] One or more base stations 220 may include a macro cell (e.g., a high-power cellular base station) and / or a small cell (e.g., a low-power cellular base station. Small cells may include femto cells, pico cells, and micro cells. Base station 220 (whether a macro cell or a large cell) may include and / or 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 of ordinary skill in the art.

[0053] The one or more base stations 220 may be configured to interface (e.g., establish a connection, transfer data, etc.) with at least one core network 240 via at least one transport network 230. In addition to other functions, the one or more base stations 220 may also perform one or more of the following functions: transfer data (e.g., uplink data) received from one or more UEs 210 to at least one core network 240 via at least one transport network 230, transfer data (e.g., downlink data) received from at least one core network 240 to one or more UEs 210 via at least one transport network 230.

[0054] The transport network 230 may transfer 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 link may be wired or wireless.

[0055] 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), and massive machine type communications (mMTC), etc.) to one or more UEs 210 connected to the RAN domain 224 via the TN domain 234. Alternatively or additionally, the core network 240 may be used 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.

[0056] In some embodiments, a UE may be configured with a first set of CSI-RS resources and a first subset of the first set of CSI-RS resources. The UE may be instructed to measure CSI-RS transmitted on a second subset of the first subset of CSI-RS resources. The first subset and the second subset may be the same resources (e.g., the subsets may contain the same CSI-RS resources). CSI-RS and CSI association may refer to deriving CSI using at least part or all of the CSI-RS.

[0057] The UE may measure the signal quality of the CSI-RS (e.g., the RSRP of the CSI-RS). The measurement may be performed based on an indication from a base station. The UE may estimate at least one or more of the RSRP of the CSI-RS, the angle of arrival of the CSI-RS, and the angle of departure of the CSI-RS. The UE may send one or more measurement results (e.g., RSRP values) and an index of a CSI-RS resource associated with the measurement result to the gNB. In this context, association may mean deriving the measurement result by measuring the CSI-RS on a specific CSI-RS resource. The CSI-RS resource index may be based on the full set of CSI-RS resources or a first subset of the CSI-RS resources.

[0058] The UE may determine a second subset of CSI-RS resources using one or more of the following embodiments. The UE may be configured and / or indicated to feedback the index of the measured CSI-RS resources. In some embodiments, the second subset of CSI-RS resources may be fixed and configured by the gNB. The gNB may configure the UE with full CSI-RS resource measurement. In some embodiments, the second subset of CSI-RS resources may be fixed and may be determined by the UE. The CSI-RS resources determined by the UE may be reported to the gNB by, for example, indicating a set of (multiple) indexes corresponding to the configuration. In some embodiments, the second subset of CSI-RS resources may be fixed within a validity period and updated when the validity period expires. In some embodiments, the second subset of CSI-RS resources may be determined using a predetermined rule.

[0059] In some embodiments, the UE may be configured to feed back all measured RSRPs, N best RSRPs, the best RSRP, and the associated CSI-RS resource index. For example, the UE may be configured with 64 CSI-RS resources. The first subset may include 16 CSI-RS resources, and the second subset may include 8 CSI-RS resources (e.g., measuring 8 of the 16). The UE may feed back one RSRP (e.g., the best RSRP of 8 RSRPs), N best RSRPs (e.g., the N best RSRPs of 8 RSRPs), and all best RSRPs (e.g., all 8 RSRPs). The index of the CSI-RS resource associated with the corresponding RSRP may also be fed back. However, when all best RSRPs are fed back, the index may not be required.

[0060] In some embodiments, the CSI report may include a CSI-related quantity (e.g., RSRP) and an associated timing information component. For example, the timing information may be the time at which the CSI-RS from which the CSI is derived is transmitted and / or received. The timing information may be the time interval at which the CSI-RS associated with the CSI is transmitted and / or received. The timing information may be based on at least one of a frame index, a slot index, a symbol index, etc.

[0061] The timing information component of the CSI report may be defined using one or more of the following embodiments. In some embodiments, a reference resource may be defined for a CSI reporting opportunity. The reference resource may compromise between time resources (e.g., slot index, symbol index) and frequency resources (e.g., RB index). The CSI report may include CSI associated with a CSI-RS resource set defined with respect to a reference resource. One or more CSI-RS resource sets may be defined with respect to a reference resource. For example, the last CSI-RS resource set before the reference resource, the last two CSI-RS resource sets before the reference resource, the last k (e.g., k is an integer) CSI-RS resource sets before the reference resource, etc.

[0062] In some embodiments, the CSI report may contain CSI associated with a CSI-RS resource set defined with respect to a reference resource. Figure 3An example time sequence 300 is illustrated in FIG. 4 . In the example time sequence, the CSI reporting opportunity is configured in time slot n, and the associated reference resource is in time slot k. The associated CSI-RS resource sets (e.g., CSI-RS resource sets 0, 1, and 2) are in time slots k-t0, k-t1, and k-t2. In this example, the CSI-RS resource set includes one time slot. In other examples, the CSI-RS resource set may include multiple time slots. For example, one CSI-RS resource set of 16 resources may be used to measure the RSRP of 16 transmit beams. Two CSI-RS resource sets of 16 resources may each be used to measure the RSRP of 16 transmit beams and 2 receive beams. In this example, the CSI reported in the CSI report is derived from the last three CSI-RS resource sets before the CSI reference resource. For example, if the CSI is RSRP, the report may contain RSRP0, RSRP1, and RSRP2 corresponding to the three CSI-RS resource sets defined with respect to the reference resource.

[0063] In some embodiments, for CSI reporting opportunities, reference resources may be defined. For example, at least one time window may be defined with respect to the reference resources. The time window may be defined using the length of the window (e.g., in time slots, in milliseconds) and a start or end time point. The UE may be configured to find a CSI-RS resource set defined with respect to the reference resources within the time window. Figure 4 An example time sequence 400 is illustrated with three windows (402A, 402B, 402C) of length L time slots (e.g., L is an integer). Time windows 402A, 402B, and 402C end in time slots k-t0, k-t1, and k-t2, respectively. The CSI report configured in time slot n may contain CSI derived from CSI-RS resource sets within these three windows. In a window, for all disclosed embodiments, there may be more than one CSI-RS resource set, for example, to find a preferred transmit / receive beam set (e.g., for improving signal-to-noise ratio) by performing beam scanning and / or repeated accumulation measurements.

[0064] In some embodiments, for a CSI reporting opportunity, a reference resource may be defined, wherein at least one time difference may be configured. The reference resource and the time difference may be used together to determine an associated CSI-RS resource set. For example, assuming t=k-t0, the associated CSI-RS resource set may be the first CSI-RS resource set before t, or the first CSI-RS resource set after t.

[0065] In some embodiments, multiple reference resources may be defined for CSI reporting opportunities. For each reference resource, at least one CSI-RS resource set may be defined using one of the above disclosed techniques. Figure 5 In FIG. 5 , three reference resources are associated with a CSI report configured in time slot n. For each reference resource, a CSI-RS resource set (502A, 502B, 502C) is also defined within the window. The CSI report may contain CSI derived from the CSI-RS resources defined with respect to the three reference resources.

[0066] In some embodiments, for each associated CSI-RS resource set, the CSI report may include CSI-related quantities, such as the N maximum RSRPs and the index of the transmit and / or receive beams for which the RSRPs are reported. In other embodiments, the RSRPs for all transmit and receive beam pairs may be reported together with the beam index. The transmit beam index may be indicated using the index of the CSI-RS resource on which the beam is transmitted (e.g., CRI). CSI-RS resource sets may be linked, for example, by using common parameters. CSI derived from linked CSI-RS resource sets may be sent in the same CSI report. Timing information of the CSI-RS resource set from which the CSI is derived may be explicitly reported in the CSI report. For example, a CSI report may include the start and / or end points (e.g., in time slots or ms) of the CSI-RS resource set used to derive the CSI. The start / end point may be defined with respect to the CSI reporting timing or associated reference resources. Figure 6 An example CSI report is illustrated.

[0067] In some embodiments, beam management may include at least two stages. In the first stage, the UE may measure the CSI-RS according to the configuration of the CSI report and the associated CSI-RS resource set. As described above, the measurement may be performed on more than one CSI-RS resource set. For each CSI-RS resource set, the UE may feedback the N best RSRP values ​​and the corresponding transmit and / or receive beam index of the maximum RSRP value. If the measured quantity is not an RSRP value, the following embodiments also apply.

[0068] In some embodiments, after the CSI report is issued, the UE may be provided with an indication of which beams to use for reception and / or transmission. The beams may be derived based on the CSI report using an AI / ML engine at the gNB. For this indication, a MAC CE may be used to indicate a set of beams, and a PDCCH may be used to select at least one beam from the set. Each set of beams may be valid for a time interval, and separate beam groups may be indicated for separate time intervals.

[0069] In the second phase, the beam index selected by the AI / ML engine may be further refined. The UE may measure a separate set of CSI-RS resources. The CSI-RS resource set in the second phase may contain a smaller number of resources than in the first phase. In the second phase, the UE may report the K best RSRP values ​​and / or transmit / receive beam indexes for the measurement occasion. For example, during the first phase, the UE may measure 64 CSI-RS resources and feedback the RSRP of all resources in the CSI report. Using the AI / ML engine, the gNB may predict the 4 best transmit beams to be used in the next 100ms. During the 100ms as the second phase, the gNB may configure the UE to measure 4 CSI-RS resources, each for one of the 4 indicated beams. The UE may feedback the best beam and the gNB may use the beam. The best beam may be continuously updated based on new measurements and CSI reports. The second phase may be configured. The second phase may be activated and / or deactivated with PDCCH (e.g., indicated with 1 bit in PDCCH). The second phase may be deactivated when a timer expires or after a duration. In one method, the second stage may be implicitly activated. For example, if the N parameter used in the first stage is greater than or less than a certain value, the second stage may be activated. The second stage activation may be determined by a parameter set for the UE. For example, if the UE is a high Doppler UE, the second stage may be activated.

[0070] The performance of the AI / ML engine in predicting beams in the spatial domain and selecting the best beam can be evaluated using simulation. In the training phase, 64 transmit beams and 4 receive beams can be used to train the AI / ML model. In the evaluation phase, only 4 or 8 transmit beams and 4 receive beams are used to select the N best beam pairs, where k = 1, ..., 8.

[0071] Figure 7 The diagram illustrates example measurements performed when beam management is performed in two phases. In the first phase, the UE may receive 64 resources and measure 8 of the 64 received resources. Based on the resources measured during the first phase, the 4 best resources of the 64 resources may be predicted. During the second phase, the UE may receive and measure the 4 predicted resources to determine the best resource (e.g., the resource with the highest power). The UE may then report the best resource to the base station. Figure 8 The receipt and reporting of the measurements of resources during the first and second phases is further illustrated.

[0072] Fig. 9An embodiment of the structure of a sample AI model 900 for simulation is shown in FIG. The AI ​​model 900 may include two 1D convolutional layers (902, 904), followed by a flat layer 906 and a dense layer (908, 910). The output of the last layer gives the probability of the best beam for all possible beam combinations. To obtain the N best predictions, the beam index with the N highest probabilities may be selected. Fig.10 The beam prediction results are shown in the figure. Fig.10 As shown, as the number of top predictions (eg, N) increases, the accuracy of the best beam prediction increases.

[0073] In some embodiments, a two-step beam management process may also be used during the UE's initial attach process by measuring the RSRP of SSB signals. For example, in the first phase, the UE may measure the RSRP of a subset of SSBs (e.g., 4 SSBs). Using these measurements as input to the AI / ML engine, the UE may predict the top k best beams of all possible beam pairs. In the second phase, the UE may measure the RSRP of the top k best beams predicted in the first phase, and then select the best beam to communicate based on the measured RSRP values.

[0074] In some embodiments, one or more TCI states of a channel may be indicated in a MAC CE. A TCI state may be valid for a specific duration. The TCI state may be configured, and the MAC CE may indicate a subset of the configured TCI state. The TCI state may include a reference signal (e.g., CSI-RS) resource ID, or an SSB ID with a channel QCL. These features indicate that the UE may assume that the reference signal or SSB is transmitted with the same beam as the channel (e.g., the TCI may be considered as a beam index indication).

[0075] In some embodiments, the validity period may be divided into equal intervals, and for each interval, the TCI state may be indicated in the MAC CE. This feature is shown in Table 1, where the validity period is divided into 4 intervals, each interval being T time slots.

[0076] TCI state 0 (for 0-1T) TCI state 0 (for 1T-2T) TCI state 1 (for 2T-3T) TCI state 2 (for 3T-4T)

[0077] Table 1: TCI status for each interval

[0078] The MAC CE may also include other information, such as serving cell ID, CORESET ID or BWP ID. The receive beam index may also be indicated as shown in Table 2.

[0079] TCI state 0 (for 0-1T) <![CDATA[rx_beam0]]> TCI state 0 (for 1T-2T) <![CDATA[rx_beam0]]> TCI state 1 (for 2T-3T) <![CDATA[rx_beam1]]> TCI state 2 (for 3T-4T) <![CDATA[rx_beam2]]>

[0080] Table 2: TCI status and rx beam index per interval

[0081] The receive beam index may be included as a parameter in the TCI state. A new information element (e.g., receive configuration index) may be defined to include the receive beam index. In other embodiments, the duration during which the TCI state is valid may be explicitly indicated, for example using multiples of T, as shown in Table 3.

[0082] 2T TCI Status 0 <![CDATA[rx_beam0 <!-- 9 -->]]> 1T TCI Status 1 <![CDATA[rx_beam1]]> 1T TCI Status <![CDATA[rx_beam2]]>

[0083] Table 3: TCI status and rx beam index and validity period

[0084] In other embodiments, for each interval or duration within the validity period, multiple TCI states and / or rx beam indexes may be indicated by MAC CE. One TCI state in multiple TCI states and / or one rx beam index in multiple rx beams may be further indicated in PDCCH.

[0085] Fig.11 An embodiment of a process 1100 for performing beam management is illustrated. Process 1100 may be performed by a UE. Process 1100 may start at operation S1102, where the UE receives a plurality of reference signals corresponding to the state of the channel from a base station through a channel. The process proceeds to operation S1104, where the UE measures a subset of the received plurality of reference signals. For example, if the UE receives 64 reference signals, the UE may be configured to measure 8 of the 64 reference signals. The process proceeds to operation S1106, where the UE sends a channel state report to the base station within a first time interval, the channel state report corresponding to the measurement of the subset of the received plurality of reference signals and the prediction of one or more reference signals received by the UE after the first time interval. For example, the UE may send a CSI report, the CSI report including the N best RSRPs measured by the UE, and the base station may use the CSI report to predict the best RSRP using an AI / ML engine. As another example, the UE may use an AI / ML engine to predict the best RSRP based on the measured subset of the received plurality of reference signals, wherein the CSI report includes the predicted RSRP.

[0086] The above disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementation to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of implementation.

[0087] It should be understood that the specific order or hierarchy of the blocks in the process / flowchart disclosed herein is an illustration of an exemplary method. Based on design preferences, it is understood that the specific order or hierarchy of the blocks in the process / flowchart can be rearranged. In addition, some blocks can be combined or omitted. The attached method claims present the current elements of various blocks in a sample order, and are not intended to be limited to the specific order or hierarchy presented.

[0088] Some embodiments may involve systems, methods, and / or computer-readable media at any possible level of integrated technical detail. In addition, one or more of the above components 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 (multiple) computer-readable non-transitory storage media having computer-readable program instructions thereon for causing the processor to perform operations.

[0089] Computer readable storage medium can be a tangible device, which can retain and store instructions for use by instruction execution devices. Computer readable storage medium can be, for example, but not limited to, electronic storage device, magnetic storage device, optical storage device, electromagnetic storage device, semiconductor storage device, or any suitable combination of the above. The non-exhaustive list of more specific examples of computer readable storage medium includes: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device (such as a punch card or a convex structure in a groove) with instructions recorded thereon, and any suitable combination of the above. Computer readable storage medium as used in this article should not be interpreted as transient signal itself, such as radio waves or other free propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (for example, light pulses by optical fiber cables), or electrical signals sent by wires.

[0090] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a corresponding computing / processing device via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network), or to an external computer or external storage device. The network may include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The 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 to be stored in a computer-readable storage medium within the corresponding computing / processing device.

[0091] The computer readable program code / instruction for performing the operation can be an assembly instruction, an instruction set architecture (ISA) instruction, a machine instruction, a machine-related instruction, a microcode, a firmware instruction, a state setting data, the configuration data of the integrated circuit system, or a source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) and process programming languages ​​(such as "C" programming languages ​​or similar programming languages). The computer readable program instruction can be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or a connection to an external computer (for example, using an Internet service provider through the Internet) can be made. In some embodiments, the electronic circuit system includes (for example, a programmable logic circuit system, a field programmable gate array (FPGA) or a programmable logic array (PLA)) can be executed by using the state information of the computer readable program instruction to personalize the electronic circuit system to execute the computer readable program instruction, thereby performing various aspects or operations.

[0092] 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 device to produce a machine, such that the instructions executed via the processor of the computer or other programmable data processing device create components for implementing the functions / actions specified in the block or blocks in the flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing device, and / or other equipment to operate in a particular manner, such that the computer-readable storage medium having the instructions stored therein includes an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in the block or blocks of the flowchart and / or block diagram.

[0093] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operating steps are performed on the computer, other programmable apparatus, or other device, thereby producing a computer-implemented process, so that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / actions specified in a block or multiple blocks in the flowchart and / or block diagram.

[0094] The flowchart and block diagram in the figure illustrate the possible implementation architecture, function and operation of the system, method and computer-readable medium according to various embodiments. In this regard, each block in the flowchart or block diagram can represent an instruction module, segment or part, which includes one or more executable instructions for implementing (multiple) specified logical functions. The method, computer system and computer-readable medium may include more blocks, fewer blocks, different blocks or blocks arranged differently than shown in the figure. In some alternative implementations, the functions described in the block may not appear in the order shown in the figure. For example, in fact, the two blocks shown in succession can be executed simultaneously or substantially simultaneously, or these blocks can sometimes be executed in the opposite order, depending on the functions involved. It will also be noted that each block illustrated in the block diagram and / or the flowchart and the combination of the blocks in the block diagram and / or the flowchart can be implemented by a system based on special hardware, which performs a specified function or action or performs a combination of special hardware and computer instructions.

[0095] It will be apparent that the systems and / or methods described herein can be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit these implementations. Therefore, the operation and behavior of the systems and / or methods are described herein without reference to specific software code - it should be understood that software and hardware can be designed to implement these systems and / or methods based on the description herein.

[0096] abbreviation:

[0097] BWP: Bandwidth Part

[0098] CE: Control Element

[0099] CORESET: Control resource set

[0100] CQI: Channel Quality Indicator

[0101] CRI: CSI-RS resource indicator

[0102] CSI: Channel State Information

[0103] CSI-RS: Channel State Information Reference Signal

[0104] CSI-RSRP: CSI reference signal received power

[0105] CSI-RSRQ: CSI reference signal reception quality

[0106] CSI-SINR: CSI signal to interference and noise ratio

[0107] DCI: Downlink Control Information

[0108] DL: Downlink

[0109] DM-RS: Demodulation Reference Signal

[0110] L1-RSRP: Layer 1 reference signal received power

[0111] LI: Layer indicator

[0112] MCS: Modulation Coding Scheme

[0113] PDCCH: Physical Downlink Control Channel

[0114] PDSCH: Physical Downlink Shared Channel

[0115] PSS: Primary Synchronization Signal

[0116] PUCCH: Physical Uplink Control Channel

[0117] QCL: Quasi Collocated

[0118] PMI: Precoding Matrix Indicator

[0119] PRB: Physical Resource Block

[0120] PRG: Precoding resource block group

[0121] RB: Resource Block

[0122] RBG: Resource Block Group

[0123] RI: Rank Indicator

[0124] RS: Reference signal

[0125] SS: Synchronous signal

[0126] SSB: Synchronization Signal Block

[0127] SSS: Secondary synchronization signal

[0128] SS-RSRP: SS reference signal received power

[0129] SS-RSRQ: SS reference signal reception quality

[0130] SS-SINR: SS signal to interference and noise ratio

[0131] TCI: Transmission Configuration Indicator

[0132] TDM: Time Division Multiplexing

[0133] UE: User Equipment

[0134] UL: Uplink

[0135] The above disclosure also covers the following embodiments:

[0136] (1) A method performed by at least one processor in a user equipment (UE), the method comprising:

[0137] receiving a plurality of reference signals corresponding to the state of the channel from the base station through the channel;

[0138] measuring a subset of the received plurality of reference signals; and

[0139] A channel state report is sent to a base station in a first time interval, the channel state report corresponding to a measurement of a subset of the received plurality of reference signals and a prediction of one or more reference signals received by the UE after the first time interval.

[0140] (2). A method according to feature (1), wherein prediction of one or more reference signals is performed by a base station using an artificial intelligence model learning engine, wherein the prediction of one or more reference signals is based on measurements of a subset of multiple reference signals received.

[0141] (3). A method according to feature (1), wherein the prediction of one or more reference signals is performed by the UE using an artificial intelligence model learning engine, wherein the prediction of one or more reference signals is based on a measured subset of the received multiple reference signals, and wherein the channel state report includes the prediction of one or more reference signals.

[0142] (4) A method according to any one of features (1) to (3), wherein the predicted one or more reference signals include at least one reference signal that is not included in the measured subset of the received multiple reference signals.

[0143] (5) A method according to any one of features (1) to (4), wherein the plurality of reference signals comprises a reference resource signal that specifies a subset of the plurality of reference signals being measured.

[0144] (6) A method according to feature (5), wherein the reference resource signal specifies the last K reference signals received before the reference resource signal as a subset of the multiple reference signals measured, where K is an integer greater than zero.

[0145] (7). A method according to feature (5), wherein a reference resource signal is received at time slot K, wherein K is an integer greater than zero, and wherein the reference resource signal specifies one or more reference signals at one or more intervals with respect to time slot K as a subset of the multiple reference signals measured.

[0146] (8) A method according to feature (7), wherein each of the one or more intervals includes at least two reference signals.

[0147] (9) The method according to any one of features (1) to (8), further comprising:

[0148] measuring the predicted one or more reference signals;

[0149] determining a reference signal having a highest power level from the measured predicted one or more reference signals; and

[0150] A reference signal having a highest power level among the one or more reference signals predicted from the measurements is reported to the base station.

[0151] (10) A user equipment (UE), comprising:

[0152] at least one memory configured to store computer program code; and

[0153] At least one processor is configured to access at least one memory and operate according to the instructions of the computer program code, the computer program code comprising:

[0154] receiving code configured to cause at least one of the at least one processor to receive a plurality of reference signals corresponding to the state of the channel from the base station through the channel,

[0155] a first measurement code configured to cause at least one of the at least one processor to measure a subset of the received plurality of reference signals, and

[0156] Transmit code configured to cause at least one of the at least one processor to transmit a channel state report to a base station within a first time interval, the channel state report corresponding to a measurement of a subset of a plurality of reference signals received and a prediction of one or more reference signals received by the UE after the first time interval.

[0157] (11). A UE according to feature (10), wherein prediction of one or more reference signals is performed by a base station using an artificial intelligence model learning engine, wherein the prediction of one or more reference signals is based on measurements of a subset of multiple reference signals received.

[0158] (12). A UE according to feature (10), wherein prediction of one or more reference signals is performed by the UE using an artificial intelligence model learning engine, wherein the prediction of one or more reference signals is based on a measured subset of a plurality of received reference signals, and wherein a channel state report includes the prediction of one or more reference signals.

[0159] (13) A UE according to any one of features (10) to (12), wherein the predicted one or more reference signals include at least one reference signal that is not included in the measured subset of the received multiple reference signals.

[0160] (14) A UE according to any one of features (10) to (13), wherein the multiple reference signals include a reference resource signal that specifies a subset of the multiple reference signals being measured.

[0161] (15). A UE according to feature (14), wherein the reference resource signal specifies the last K reference signals received before the reference resource signal as a subset of the multiple reference signals measured, where K is an integer greater than zero.

[0162] (16). A UE according to feature (14), wherein a reference resource signal is received at time slot K, wherein K is an integer greater than zero, and wherein the reference resource signal specifies one or more reference signals at one or more intervals with respect to time slot K as a subset of the multiple reference signals measured.

[0163] (17) A UE according to feature (16), wherein each of the one or more intervals comprises at least two reference signals.

[0164] (18) A UE according to any one of features (10) to (17), wherein the computer program code further comprises:

[0165] a second measurement code configured to cause at least one of the at least one processor to measure the predicted one or more reference signals,

[0166] determining code configured to cause at least one of the at least one processor to determine a reference signal having a highest power level from among the measured predicted one or more reference signals, and

[0167] The reporting code is configured to cause at least one of the at least one processor to report to the base station a reference signal having a highest power level among the one or more reference signals predicted from the measurement.

[0168] (19) A non-transitory computer-readable medium having instructions stored therein, which instructions, when executed by a processor in a user equipment (UE), cause the UE to perform a method, the method comprising:

[0169] receiving a plurality of reference signals corresponding to the state of the channel from the base station through the channel;

[0170] measuring a subset of the received plurality of reference signals; and

[0171] A channel state report is sent to a base station in a first time interval, the channel state report corresponding to a measurement of a subset of the received plurality of reference signals and a prediction of one or more reference signals received by the UE after the first time interval.

[0172] (20). A non-transitory computer-readable medium according to feature (19), wherein prediction of one or more reference signals is performed by a base station using an artificial intelligence model learning engine, wherein the prediction of one or more reference signals is based on measurements of a subset of the received multiple reference signals.

Claims

1. A method performed by at least one processor in a user equipment (UE), the method include: receiving a plurality of reference signals corresponding to the state of the channel from a base station through the channel; measuring a subset of the received plurality of reference signals; as well as A channel state report is sent to the base station in a first time interval, the channel state report corresponding to the measurement of the subset of the received plurality of reference signals and a prediction of one or more reference signals received by the UE after the first time interval.

2. The method of claim 1 , wherein the prediction of the one or more reference signals is performed by the base station using an artificial intelligence model learning engine, wherein the prediction of the one or more reference signals is based on the measurements of the subset of the received multiple reference signals.

3. The method of claim 1 , wherein the prediction of the one or more reference signals is performed by the UE using an artificial intelligence model learning engine, wherein the prediction of the one or more reference signals is based on the measured subset of the received multiple reference signals, and wherein the channel state report includes the prediction of the one or more reference signals.

4. The method according to claim 1, wherein the predicted one or more reference signals include: At least one reference signal that is not included in the measured subset of the received plurality of reference signals. 5 . The method of claim 1 , wherein the plurality of reference signals comprises a reference resource signal that specifies the subset of the plurality of reference signals that are measured.

6. The method of claim 5, wherein the reference resource signal specifies last K reference signals received prior to the reference resource signal as the subset of the plurality of reference signals measured, wherein K is an integer greater than zero.

7. The method of claim 5, wherein the reference resource signal is received at time slot K, wherein K is an integer greater than zero, and wherein the reference resource signal specifies one or more reference signals at one or more intervals with respect to time slot K as the subset of the plurality of reference signals measured. The method of claim 7 , wherein each of the one or more intervals comprises at least two reference signals.

9. The method according to claim 1, further comprising: include: measuring the predicted one or more reference signals; determining a reference signal having a highest power level from among the measured predicted one or more reference signals; as well as The reference signal having the highest power level determined from among the measured predicted one or more reference signals is reported to the base station.

10. A user equipment (UE), include: at least one memory configured to store computer program code; as well as At least one processor is configured to access the at least one memory and operate according to the instructions of the computer program code, wherein the computer program code comprises: receiving code configured to cause at least one of the at least one processor to receive a plurality of reference signals corresponding to the state of the channel from a base station through a channel, a first measurement code configured to cause at least one of the at least one processor to measure a subset of the received plurality of reference signals, and The sending code is configured to cause at least one of the at least one processor to send a channel state report to the base station within a first time interval, the channel state report corresponding to the measurement of the subset of the multiple reference signals received and a prediction of one or more reference signals received by the UE after the first time interval.

11. The UE of claim 10, wherein the prediction of the one or more reference signals is performed by the base station using an artificial intelligence model learning engine, wherein the prediction of the one or more reference signals is based on the measurement of the subset of the received multiple reference signals.

12. The UE of claim 10, wherein the prediction of the one or more reference signals is performed by the UE using an artificial intelligence model learning engine, wherein the prediction of the one or more reference signals is based on the measured subset of the received multiple reference signals, and wherein the channel state report includes the prediction of the one or more reference signals.

13. The UE according to claim 10, wherein the predicted one or more reference signals include: At least one reference signal that is not included in the measured subset of the received plurality of reference signals.

14. The UE of claim 10, wherein the plurality of reference signals comprises a reference resource signal that specifies the subset of the plurality of reference signals measured.

15. The UE of claim 14, wherein the reference resource signal specifies last K reference signals received before the reference resource signal as the subset of the plurality of reference signals measured, wherein K is an integer greater than zero.

16. The UE of claim 14, wherein the reference resource signal is received at time slot K, wherein K is an integer greater than zero, and wherein the reference resource signal specifies one or more reference signals at one or more intervals with respect to time slot K as the subset of the plurality of reference signals measured.

17. The UE of claim 16, wherein each of the one or more intervals comprises at least two reference signals.

18. The UE of claim 10, wherein the computer program code further include: a second measurement code configured to cause at least one of the at least one processor to measure the predicted one or more reference signals, determining code configured to cause at least one of the at least one processor to determine a reference signal having a highest power level from among the measured predicted one or more reference signals, and A reporting code is configured to cause at least one of the at least one processor to report to the base station the reference signal having the highest power level determined from among the measured predicted one or more reference signals.

19. A non-transitory computer-readable medium having stored therein instructions, which when executed by a processor in a user equipment (UE) causes the UE to perform a method, the method include: receiving a plurality of reference signals corresponding to the state of the channel from a base station through the channel; measuring a subset of the received plurality of reference signals; as well as A channel state report is sent to the base station in a first time interval, the channel state report corresponding to the measurement of the subset of the plurality of reference signals received and a prediction of one or more reference signals received by the UE after the first time interval.

20. The non-transitory computer-readable medium of claim 19, wherein the prediction of the one or more reference signals is performed by the base station using an artificial intelligence model learning engine, wherein the prediction of the one or more reference signals is based on the measurements of the subset of the received multiple reference signals.

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