Method and apparatus for beam management using AI / ML

Abstract

A method executed by at least one processor in a user equipment (UE), the method comprising receiving, from a base station via a channel, a plurality of reference signals corresponding to a state of the channel. The method further comprises measuring a subset of the received plurality of reference signals. The method further comprises transmitting, to the base station, a channel state report corresponding to the measurement of the subset of the received plurality of reference signals within a first time interval and the prediction of another reference signal received by the UE after the first time interval.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is based on and claims priority to Indian Patent Application No. 202241045765, filed on August 10, 2022, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to communication systems, and more particularly to a method and apparatus for beam management using artificial intelligence and machine learning (AI / ML). [Background technology]

[0003] The time and frequency resources that a UE may use to report CSI are controlled by the gNB. The CSI may include a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), a SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), a L1-RSRP, and a L1-SINR. The CSI reporting configuration includes the following parameters: codebook configuration including codebook subset restriction, time domain behavior, frequency granularity of CQI and PMI, measurement restriction configuration, and CSI-related quantities that the UE reports, such as a layer indicator (LI), a L1-RSRP, a L1-SINR, a CRI, and a SSB resource indicator (SSBRI). CSI reporting may be periodic, aperiodic, or semi-static. A UE may be configured with multiple CSI reporting configurations.

[0004] The CSI resource configuration includes a list of S≧1 CSI resource sets, where the list consists of references to either or both of the CSI-RS resource set(s) and SS / PBCH block set(s) of the NZP, or the list consists of references to the CSI-IM resource set(s). Each CSI-RS resource set includes one or more CSI-RS resources. The time-domain behavior of the CSI-RS resources can be configured to be periodic, aperiodic, or semi-persistent. The CSI-RS resources in the set may be transmitted in different transmit beams (e.g., beam sweeping), and the UE can identify the index of the resource with the highest RSRP. The CSI-RS resources can also be transmitted in the same beam so that the UE can perform beam sweeping. The beam used for downlink transmission can be indicated to the UE using the TCI state parameter. The TCI state parameter indicates to the UE that the receiving channel (e.g., PDCCH or PDSCH) is transmitted in the same beam of the reference signal included in the TCI state parameter.

[0005] Conventional techniques may be used for beam management. Specifically, conventional techniques may be used by a UE to monitor and measure CSI-RS resources (e.g., periodically), identify the best beam, and transmit the information in a CSI report. However, the overhead of transmitting and measuring CSI-RS resources is significant and is expected to increase further as the number of transmit and receive beams increases. Therefore, there is a need to provide more efficient beam management.

[0006] Improvements are presented herein that may also be applicable to other multi-access technologies and telecommunications standards that use these technologies. Summary of the Invention [Problem to be solved by the invention]

[0007] The following presents a simplified summary of one or more embodiments of the present disclosure in order to provide a basic understanding of such embodiments. This summary is not an extensive overview of all possible embodiments, and is not intended to identify key or critical elements of all embodiments or to delineate 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 the more detailed description that is presented later.

[0008] A method, apparatus, and non-transitory computer-readable medium for beam management using AI / ML are disclosed by the present disclosure. [Means for solving the problem]

[0009] According to an exemplary embodiment, a method executed by at least one processor in a user equipment (UE) includes receiving a plurality of reference signals corresponding to channel conditions from a base station over a channel. The method includes measuring a subset of the received plurality of reference signals. The method further includes measuring the subset of the received plurality of reference signals within a first time interval and measuring a subset of the received plurality of reference signals received by the UE after the first time interval. 1 t End The method includes transmitting a prediction of the reference signal of and a corresponding channel condition report to the base station.

[0010] According to one exemplary embodiment, a user equipment (UE) 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 receiving code configured to cause at least one of the at least one processor to receive, from a base station over a channel, a plurality of reference signals corresponding to channel conditions. The computer program code further includes 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. The computer program code causes the at least one of the at least one processor to measure, within a first time interval, the subset of the received plurality of reference signals and a subset of the received plurality of reference signals received by the UE after the first time interval. 1 t End and a transmit code configured to cause a base station to transmit a prediction for the reference signal of and a corresponding channel condition report.

[0011] According to an example embodiment, a non-transitory computer-readable medium has stored thereon instructions that, when executed by a processor in a user equipment (UE), cause the UE to perform a method including receiving a plurality of reference signals from a base station over a channel corresponding to channel conditions. The method further includes measuring a subset of the received plurality of reference signals. The method further includes measuring the subset of the received plurality of reference signals within a first time interval and measuring a subset of the received plurality of reference signals received by the UE after the first time interval. 1 t End The method includes transmitting a prediction of the reference signal of and a corresponding channel condition report to the base station.

[0012] Additional embodiments are set forth in the description that follows, and in part will be apparent from the description and / or may be learned by practice of presented embodiments of the present disclosure.

[0013] These and other aspects, features, and modes of embodiments of the present disclosure will become apparent from the following description taken in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram of an exemplary network device according to various embodiments of the present disclosure.

[0015] [Figure 2] 1 is a schematic diagram of an exemplary wireless communication system in accordance with various embodiments of the present disclosure.

[0016] [Figure 3] 1 illustrates example time slots for receiving CSI resources and transmitting CSI reports in accordance with various embodiments of the present disclosure.

[0017] [Figure 4] 1 illustrates example time slots for receiving CSI resources and transmitting CSI reports in accordance with various embodiments of the present disclosure.

[0018] [Figure 5] 1 illustrates example time slots for receiving CSI resources and transmitting CSI reports in accordance with various embodiments of the present disclosure.

[0019] [Figure 6] 1 illustrates an example CSI report, according to various embodiments of the present disclosure.

[0020] [Figure 7] 1 illustrates an example of receiving CSI resources in multiple phases, according to various embodiments of the present disclosure.

[0021] [Figure 8] 1 illustrates an example of receiving CSI resources in multiple phases, according to various embodiments of the present disclosure.

[0022] [Figure 9]1 illustrates an exemplary neural network, according to various embodiments of the present disclosure.

[0023] [Figure 10] 10 illustrates an exemplary graph of beam prediction results, according to various embodiments of the present disclosure.

[0024] [Figure 11] 10 shows a flowchart of one embodiment of a process for performing beam management. DETAILED DESCRIPTION OF THE INVENTION

[0025] The following detailed description of the exemplary embodiments refers to the accompanying drawings, in which the same reference numbers in different drawings may identify the same or similar elements.

[0026] 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 practicing implementations. Furthermore, 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). Additionally, 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 (at least partially) concurrently, and the order of one or more operations may be permuted.

[0027] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or combinations of hardware and software. The actual specialized control hardware or software code used to implement these 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.

[0028] 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 may 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 all other claims in the claim set.

[0029] No element, act, or instruction used herein should be construed as critical or required unless explicitly described 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 phrases are 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 specifically 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.

[0030] Throughout this specification, references to "one embodiment," "an embodiment," or similar language mean that a particular feature, structure, or characteristic described in connection with the illustrated embodiment is included in at least one embodiment of the solution. Thus, throughout this specification, the phrases "in one embodiment," "in an embodiment," and similar language may, but do not necessarily, all refer to the same embodiment.

[0031] Furthermore, the described features, advantages, and characteristics of the present disclosure may be combined in any suitable manner in one or more embodiments. Those skilled in the art will recognize in light of the description herein that the present disclosure can be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the present disclosure.

[0032] Embodiments of the present disclosure relate to AI / ML-based prediction mechanisms that reduce beam management overhead. Embodiments of the present disclosure increase the robustness of AI / ML-based solutions. Additionally, some embodiments of the present disclosure include timing information in CSI reports so that time-domain prediction of beam management can be performed using AI / ML.

[0033] The following embodiments disclose a method for AI / ML-based beam management. For example, a gNB may transmit CSI-RS on a first set of spatial, time, and frequency resources. A UE may perform measurements on the CSI-RS and feedback CSI derived from the measurements. The gNB may use an AI / ML engine to predict CSI for a second resource set, which may be different from the first resource set. The AI / ML engine may reside on the UE side, and the UE may also perform CSI prediction.

[0034] 1 is a diagram of an exemplary device for implementing embodiments of the present disclosure. Device 100 may correspond to any type of known computer, server, or data processing device. For example, device 100 may comprise a processor, a personal computer (PC), a printed circuit board (PCB) with 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, a smart device, or any other similarly functional device.

[0035] 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.

[0036] Bus 110 may comprise one or more components that enable communication between a set of components of device 100. For example, bus 110 may be a communication bus, a crossover bar, a network, etc. Although bus 110 is depicted in FIG. 1 as a single line, bus 110 may be implemented using multiple (two or more) connections between a set of components of device 100. This disclosure is not limited in this respect.

[0037] 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), 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, 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.

[0038] The processor 120 may control the overall operation of the device 100 and / or a set of components of the device 100 (eg, memory 130, storage component 140, input component 150, output component 160, communication interface 170).

[0039] 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.

[0040] 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, optical disk, magneto-optical disk, and / or solid-state disk), a compact disc (CD), a digital versatile disc (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.

[0041] Device 100 may further include input component 150. Input component 150 may include one or more components that enable device 100 to receive information via user input or the like (e.g., a touchscreen, a keyboard, a keypad, a mouse, a stylus, a button, a switch, a microphone, a camera, 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.).

[0042] 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), Light-Emitting Diodes (LEDs), Organic Light-Emitting Diodes (OLEDs), a haptic feedback device, a speaker, etc.).

[0043] Device 100 may further comprise a communications interface 170. Communications interface 170 may include a receiver component, a transmitter component, and / or a transceiver component. Communications interface 170 may enable device 100 to establish connections and / or transfer communications with other devices (e.g., a server, another device). Communications may occur via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communications interface 170 may enable device 100 to receive information from and / or provide information to other devices. 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, 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, communication interface 170 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, or the like.

[0044] 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, 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.

[0045] 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.

[0046] Alternatively, or in addition, hardwired circuitry may be used in place of or in combination with software instructions to implement one or more of the processes described herein. Thus, the embodiments described herein are not limited to any specific combination of hardware circuitry and software.

[0047] 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.

[0048] 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 equipments (UEs) 210, one or more base stations 220, at least one transmission 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.

[0049] 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 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., 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, mobile station, subscriber station, mobile unit, subscriber unit, radio unit, remote unit, mobile device, radio device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, radio terminal, remote terminal, handset, user agent, mobile agent, client, or some other suitable terminology.

[0050] One or more base stations 220 may communicate wirelessly with one or more UEs 210 through 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 ​​that 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 beamformed signals from a base station 220 in one or more receive directions. Alternatively or additionally, one or more UEs 210 may transmit beamformed signals to a base station 220 in one or more transmit directions. A base station 220 may receive beamformed signals from one or more UEs 210 in one or more receive directions.

[0051] 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. Whether a macrocell or a large cell, base station 220 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 skilled in the art.

[0052] The 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 transmission network 230. In addition to other functions, the one or more base stations 220 may perform one or more of the following functions: forwarding data (e.g., uplink data) received from one or more UEs 210 to the at least one core network 240 via the at least one transmission network 230; forwarding data (e.g., downlink data) received from the at least one core network 240 to the one or more UEs 210 via the at least one transmission network 230.

[0053] The transmission 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 transmission 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.

[0054] 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. Alternatively or additionally, the core network 240 may serve as an entry point to 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.

[0055] 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 and second subsets may be the same resources (e.g., the subsets may include the same CSI-RS resources). Associating CSI-RS and CSI may refer to deriving CSI using at least some or all of the CSI-RS.

[0056] The UE may measure the signal quality of the CSI-RS (e.g., the RSRP of the CSI-RS). This measurement may be performed based on instructions from the 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 transmit one or more measurement results (e.g., RSRP values) and an index of a CSI-RS resource associated with the measurement results to the gNB. Association in this context may refer to deriving the measurement results by measuring the CSI-RS on specific CSI-RS resources. The CSI-RS resource index may be based on the full set of CSI-RS resources or a first subset of CSI-RS resources.

[0057] The UE may determine the second subset of CSI-RS resources using one or more of the following embodiments. The UE may be configured and / or indicated to feed back indices of the measured CSI-RS resources. In some embodiments, the second subset of CSI-RS resources may be fixed or configured by the gNB. The gNB may configure the UE with full CSI-RS resource measurements. In some embodiments, the second subset of CSI-RS resources may be fixed and determined by the UE. The UE-determined CSI-RS resources may be reported to the gNB, for example, by indicating a set of index(es) corresponding to the configuration. In some embodiments, the second subset of CSI-RS resources may be fixed for a validity period and may be updated if the validity period expires. In some embodiments, the second subset of CSI-RS resources may be determined using a predetermined rule.

[0058] In some embodiments, the UE may be configured to feed back all measured RSRPs, the N best RSRPs, the best RSRPs, and the index of the associated CSI-RS resource. For example, the UE may be configured with 64 CSI-RS resources. A first subset may include 16 CSI-RS resources, and a second subset may include 8 CSI-RS resources (e.g., 8 of the 16 measured). The UE may feed back one RSRP (e.g., the best RSRP of the 8 RSRPs), the N best RSRPs (e.g., the N best of the 8 RSRPs), or 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, if all best RSRPs are fed back, the index may not be needed.

[0059] In some embodiments, the CSI report may include components of a CSI-related quantity (e.g., RSRP) and associated timing information. For example, the timing information may be the time at which the CSI-RS from which the CSI was derived was transmitted and / or received. The timing information may be the time interval at which the CSI-RS associated with the CSI was 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.

[0060] The timing information component of a CSI report may be defined in one or more of the following embodiments. In some embodiments, for a CSI report, a reference resource may be defined. The reference resource may comprise a time resource (e.g., a slot index, a symbol index) and a frequency resource (e.g., an RB index). The CSI report may include CSI associated with a CSI-RS resource set defined for the reference resource. One or more CSI-RS resource sets may be defined for the 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 CSI-RS resource sets (e.g., k is an integer) before the reference resource, etc.

[0061] In some embodiments, a CSI report may include CSI associated with a CSI-RS resource set defined with respect to a reference resource. An example timeline 300 is shown in FIG. 3. In this example timeline, the CSI report occasion is configured in slot n, and the associated reference resource is in slot k. The associated CSI-RS resource sets (e.g., CSI-RS resource sets 0, 1, and 2) are in slot kt, slot k-t1, and slot k-t2. In this example, a CSI-RS resource set includes one slot. In other examples, a CSI-RS resource set may comprise multiple slots. For example, one CSI-RS resource set of 16 resources can be used to measure the RSRP of 16 transmit beams. Two CSI-RS resource sets of 16 resources can be used to measure the RSRP of 16 transmit beams and two receive beams, respectively. 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 include RSRP0, RSRP1, and RSRP2 corresponding to the three CSI-RS resource sets defined for the reference resource.

[0062] In some embodiments, a reference resource may be defined for a time point for a CSI report. For example, at least one time window may be defined for the reference resource. The time window may be defined using a window length (e.g., in slots, milliseconds) and a start or end time point. The UE may be configured to find the CSI-RS resource set defined for the reference resource within the time window. FIG. 4 shows an example timeline 400 having three windows (402A, 402B, 402C) of length L slots (e.g., L is an integer). Time windows 402A, 402B, and 402C end in slots k-t0, k-t1, and k-t2, respectively. A CSI report configured for slot n may include CSI derived from the CSI-RS resource sets within these three windows. In a window, for all disclosed embodiments, for example, two or more CSI-RS resource sets may be present in order to find a preferred set of transmit / receive beams (e.g., to improve SNR) by performing beam sweeping any number of times and / or accumulating measurements.

[0063] In some embodiments, a reference resource may be defined that can configure at least one time difference for the CSI reporting time point. The reference resource and the time difference may be used together to determine the 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.

[0064] In some embodiments, multiple reference resources may be defined for a CSI report time point. For each reference resource, at least one CSI-RS resource set may be defined using one of the techniques disclosed above. For example, in FIG. 5, three reference resources are associated with a CSI report configured for slot n. For each reference resource, a CSI-RS resource set (502A, 502B, 502C) is also defined within the window. The CSI report may include CSI derived from the CSI-RS resources defined for the three reference resources.

[0065] In some embodiments, a CSI report may include CSI-related quantities, such as the N largest RSRPs and the index of the transmit beam and / or receive beam for the reported RSRP for each associated CSI-RS resource set. In other embodiments, the RSRPs of all transmit and receive beam pairs may be reported along with the beam index. The transmit beam index may be indicated using the index (e.g., CRI) of the CSI-RS resource from which the beam was transmitted. CSI-RS resource sets may be linked, for example, using a common parameter. CSI derived from linked CSI-RS resource sets may be transmitted 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, the CSI report may include the start and / or end points of the CSI-RS resource set (e.g., in slots or milliseconds) used to derive the CSI. The start / end points may be defined with respect to the time of the CSI report or the associated reference resource. Figure 6 shows an example CSI report.

[0066] In some embodiments, beam management may comprise at least two phases. In the first phase, the UE may measure CSI-RS according to the CSI report and the configuration of the associated CSI-RS resource set. The measurements may be performed across two or more CSI-RS resource sets, as disclosed above. For each CSI-RS resource set, the UE may feed back the N best RSRP values ​​and the index of the transmit beam and / or receive beam corresponding to the largest RSRP value. The following embodiments are equally applicable when the measured quantities are not RSRP values.

[0067] In some embodiments, after a CSI report is transmitted, the UE may be provided with an indication as to 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, the MAC CE may be used to indicate a group of beams, and the PDCCH may be used to select at least one beam from the group. Each group of beams may be valid for a time interval, and separate groups of beams may be indicated for separate time intervals.

[0068] In the second phase, the beam index selected by the AI / ML engine can be further refined. The UE may measure a separate set of CSI-RS resources. The CSI-RS resource set in the second phase may include fewer resources than in the first phase. In the second phase, the UE may report the K best RSRP values ​​for the measurement time point and / or transmit / receive beam index. As an example, during the first phase, the UE may measure 64 CSI-RS resources and feedback the RSRP for all resources in the CSI report. The gNB may use the AI / ML engine to predict the four best transmit beams to be used in the next 100 ms. During the second 100 ms phase, the gNB may configure the UE to measure four CSI-RS resources, one for each of the four indicated beams. The UE may feedback the best beam, and the gNB may use that beam. The best beam may be continuously updated based on new measurements and CSI reports. The second phase may also be configured. The second phase can be activated and / or deactivated by the PDCCH (e.g., by a one-bit indication in the PDCCH). The second phase can be deactivated when a timer expires or after a duration. In some methods, the second phase can be activated implicitly. For example, the second phase can be activated when the N parameter used in the first phase is greater than or less than a certain value. Activation of the second phase can also be determined by a parameter set for the UE. For example, the second phase can be activated if the UE is a high-Doppler UE.

[0069] The performance of the AI / ML engine to predict beams in the spatial domain and select the best beam can be evaluated through 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 ( N =1,...,8).

[0070] FIG. 7 shows an example of measurements performed when performing beam management in two phases. In the first phase, the UE may receive 64 resources and measure 8 of the received 64 resources. Based on the measured resources during the first phase, the UE may predict the four best resources of the 64 resources. During the second phase, the UE may receive and measure the four 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. FIG. 8 further shows the reception of resources and reporting of measurements during the first and second phases.

[0071] An embodiment of the structure of a sample AI model 900 used in the simulation is shown in Figure 9. The AI ​​model 900 can include two one-dimensional convolutional layers (902, 904), followed by a flattening layer 906 and a densely connected layer (908, 910). The output of the last layer provides the probability of the best beam for all possible beam combinations. To obtain the N best predictions, the beam indices with the N highest probabilities can be selected. Figure 10 shows the results of beam prediction. As shown in Figure 10, as the number of top predictions (e.g., N) increases, the prediction accuracy of the best beam increases.

[0072] 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 a first phase, the UE may measure the RSRP of a subset of SSBs (e.g., 4 SSBs). Using these measurements as input to an AI / ML engine, the UE may predict the top k best beams from among all possible beam pairs. In a second phase, the UE may measure the RSRP of the top k best beams predicted in the first phase and subsequently select the best beam to communicate with based on the measured RSRP values.

[0073] In some embodiments, one or more TCI states of a channel can be indicated within the MAC CE. A TCI state may be valid for a specific duration. A TCI state may be configured, and the MAC CE may indicate a subset of the configured TCI states. A TCI state may include a reference signal (e.g., CSI-RS) resource ID or an ID of an SSB QCL'd to the channel. These features mean that the UE can assume the reference signal or SSB is transmitted on the same beam as the channel (e.g., the TCI can be considered an indication of the beam index).

[0074] 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 four intervals of T slots. [Table 1]

[0075] Other information such as a serving cell ID, a CORESET ID, or a BWP ID may also be included in the MAC CE. The receive beam index may also be indicated as shown in Table 2. [Table 2]

[0076] The receive beam index may be included in the TCI state as a parameter. A new information element (e.g., receive configuration index) can be defined to include the receive beam index. In other embodiments, the period for which the TCI state is valid can be explicitly indicated, for example, using a multiple of T, as shown in Table 3. [Table 3]

[0077] In other embodiments, for each interval or duration within the validity period, multiple TCI states and / or rx beam indices may be indicated by the MAC CE. One TCI state of the multiple TCI states and / or one rx beam index of the multiple rx beams may be further indicated on the PDCCH.

[0078] FIG. 11 illustrates one embodiment of a process 1100 for performing beam management. Process 1100 may be performed by a UE. Process 1100 may begin at operation S1102, in which the UE receives a plurality of reference signals corresponding to channel conditions from a base station over a channel. The process proceeds to operation S1104, in which 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, in which the UE transmits a channel condition report to the base station within a first time interval corresponding to measurements of the subset of the received plurality of reference signals and a prediction of one or more reference signals that the UE will receive after the first time interval. For example, the UE may transmit a CSI report including the N best RSRPs measured by the UE, which the base station may use 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, where the CSI report includes the predicted RSRP.

[0079] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise forms disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practicing the implementations.

[0080] It is understood that the particular order or hierarchy of blocks in the processes / flowcharts disclosed herein represents example approaches. It is understood that the particular order or hierarchy of blocks within the processes / flowcharts may be rearranged based on design preferences. Furthermore, some blocks may be combined or omitted. The accompanying method claims present various block elements in a sample order and are not meant to be limited to the particular order or hierarchy presented.

[0081] Some embodiments may relate to systems, methods, and / or computer-readable media at any possible level of technical detail of integration. Furthermore, 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 one or more computer-readable non-transitory storage media having computer-readable program instructions for causing a processor to perform operations.

[0082] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction-execution device. The computer-readable storage medium may be, for example, but 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 structures in grooves on which instructions are recorded, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as being, per se, transitory signals 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.

[0083] 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 may 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.

[0084] The computer-readable program code / instructions for performing 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 and 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 standalone 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.

[0085] 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 comprises a product containing instructions that implement aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0086] 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.

[0087] 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 module, segment, or portion of instructions, including one or more executable instructions for implementing a particular logical function. The methods, computer systems, and computer-readable media may include additional, fewer, different, or differently arranged blocks compared to the blocks shown in the figures. In some alternative implementations, the functions noted in the blocks may occur in a different order than that noted 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.

[0088] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or combinations of hardware and software. The actual specialized control hardware or software code used to implement these 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.

[0089] Abbreviation: BWP Bandwidth section CE Control Elements CORESET Control resource set CQI Channel Quality Indicator CRI CSI-RS Resource Indicator CSI Channel State Information CSI-RS Channel State Information Reference Signal CSI-RSRP CSI reference signal received power CSI-RSRQ CSI reference signal reception quality CSI-SINR CSI signal to interference and noise ratio DCI Downlink Control Information DL Downlink DM-RS demodulation reference signal L1-RSRP Layer 1 Reference Signal Received Power LI Layer Indicator MCS Modulation and Coding Scheme PDCCH Physical Downlink Control Channel PDSCH Physical Downlink Shared Channel PSS primary synchronization signal PUCCH Physical Uplink Control Channel QCL pseudo-collocation PMI Precoding Matrix Indicator PRB Physical Resource Block PRG Precoding Resource Block Group RB Resource Block RBG Resource Block Group RI Rank Indicator RS reference signal SS Sync Signal SSB sync signal block SSS secondary synchronization signal SS-RSRP SS reference signal received power SS-RSRQ SS Reference Signal Reception Quality SS-SINR SS signal to interference and noise ratio TCI Transmit Configuration Indicator TDM time division multiplexing UE User Equipment UL Uplink

[0090] The above disclosure also encompasses the embodiments listed below.

[0091] (1) A method executed by at least one processor in a user equipment (UE), the method comprising: receiving, from a base station over a channel, a plurality of reference signals corresponding to channel conditions; measuring a subset of the received plurality of reference signals; and, within a first time interval, measuring the subset of the received plurality of reference signals and a plurality of reference signals received by the UE after the first time interval. 1 t End transmitting a channel condition report corresponding to the prediction of the reference signal to the base station.

[0092] (2) The method of (1), wherein the prediction of the one or more reference signals is performed by a base station using an artificial intelligence model learning engine, and the prediction of the one or more reference signals is based on the measurement of the subset of the received plurality of reference signals.

[0093] (3) The method of (1), wherein the prediction of the one or more reference signals is performed by the UE using an artificial intelligence model learning engine, the prediction of the one or more reference signals is based on the measured subset of the received plurality of reference signals, and the channel condition report includes the prediction of the one or more reference signals.

[0094] (4) The method of 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 plurality of reference signals.

[0095] (5) The method of any one of features (1) to (4), wherein the plurality of reference signals includes a reference resource signal that specifies the subset of the plurality of reference signals to be measured.

[0096] (6) The method according to feature (5), wherein the reference resource signal designates the last K reference signals received before the z-root tree reference resource signal as the subset of the plurality of reference signals to be measured, where K is an integer greater than 0.

[0097] (7) The method according to feature (5), wherein the reference resource signal is received in slot K, where K is an integer greater than 0, and the reference resource signal specifies one or more reference signals at one or more intervals relative to slot K as the subset of the plurality of reference signals to be measured.

[0098] (8) The method of feature (7), wherein each interval of the one or more intervals includes at least two reference signals.

[0099] (9) The method of any one of features (1) to (8), further comprising the steps of measuring the predicted one or more reference signals, determining one reference signal from the measured and predicted one or more reference signals that has the highest power level, and reporting the reference signal from the measured and predicted one or more reference signals that has the highest power level to a base station.

[0100] (10) A user equipment (UE) comprising: 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 comprising: receiving code configured to cause at least one of the at least one processor to receive, from a base station over a channel, a plurality of reference signals corresponding to the channel condition; 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 measurement code configured to cause at least one of the at least one processor to measure the subset of the received plurality of reference signals within a first time interval and a plurality of reference signals received by the UE after the first time interval. 1 t End and a transmit code configured to cause a base station to transmit a channel condition report corresponding to a prediction of the reference signal of the user equipment (UE).

[0101] (11) The UE of feature (10), wherein the prediction of the one or more reference signals is performed by a base station using an artificial intelligence model learning engine, and the prediction of the one or more reference signals is based on the measurements of the subset of the received plurality of reference signals.

[0102] (12) The UE of feature (10), wherein the prediction of the one or more reference signals is performed by the UE using an artificial intelligence model learning engine, the prediction of the one or more reference signals is based on the measured subset of the received plurality of reference signals, and the channel condition report includes the prediction of the one or more reference signals.

[0103] (13) The UE of 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 plurality of reference signals.

[0104] (14) The UE of any one of features (10) to (13), wherein the plurality of reference signals includes a reference resource signal that specifies the subset of the plurality of reference signals to be measured.

[0105] (15) The UE of feature (14), wherein the reference resource signal designates the last K reference signals received before the reference resource signal as the subset of the plurality of reference signals to be measured, where K is an integer greater than 0.

[0106] (16) The UE of feature (14), wherein the reference resource signal is received in slot K, where K is an integer greater than 0, and the reference resource signal specifies one or more reference signals at one or more intervals relative to slot K as the subset of the plurality of reference signals to be measured.

[0107] (17) The UE of feature (16), wherein each interval of the one or more intervals includes at least two reference signals.

[0108] (18) The UE of any one of features (10) to (17), wherein the computer program code further includes: second measurement code configured to cause at least one of the at least one processor to measure the predicted one or more reference signals; determination code configured to cause at least one of the at least one processor to determine one reference signal from the measured and predicted one or more reference signals having a highest power level; and reporting code configured to cause at least one of the at least one processor to report the reference signal from the measured and predicted one or more reference signals having the highest power level to a base station.

[0109] (19) A non-transitory computer-readable medium storing instructions that, when executed by a processor in a user equipment (UE), cause the UE to perform a method, the method comprising: receiving a plurality of reference signals from a base station over a channel corresponding to the channel conditions; measuring a subset of the received plurality of reference signals; and, within a first time interval, measuring the subset of the received plurality of reference signals and a plurality of reference signals received by the UE after the first time interval. 1 t End and transmitting a channel condition report corresponding to the prediction of the reference signal to the base station.

[0110] (20) The non-transitory computer-readable medium of feature (19), wherein the prediction of the one or more reference signals is performed by a base station using an artificial intelligence model learning engine, and the prediction of the one or more reference signals is based on the measurements of the subset of the received plurality of reference signals.

Claims

1. 1. A method executed by at least one processor in a user equipment (UE), the method comprising: receiving a plurality of reference signals from a base station over a channel corresponding to the channel conditions; measuring a subset of the received plurality of reference signals, wherein the subset has a number of reference signals that is less than the received plurality of reference signals; and transmitting, within a first time interval, a channel condition report to the base station corresponding to the measurements of the subset of the received plurality of reference signals and a prediction of another reference signal that the UE will receive after the first time interval.

2. 10. 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, and the prediction of the one or more reference signals is based on the measurements of the subset of the received plurality of reference signals.

3. 2. 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, the prediction of the one or more reference signals is based on the measured subset of the received plurality of reference signals, and the channel condition report includes the prediction of the one or more reference signals.

4. The method of claim 1 , wherein the predicted one or more reference signals includes 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 includes a reference resource signal that specifies the subset of the plurality of reference signals to be measured.

6. 6. The method of claim 5, wherein the reference resource signal designates the last K reference signals received before the reference resource signal as the subset of the plurality of reference signals to be measured, where K is an integer greater than 0.

7. 6. The method of claim 5, wherein the reference resource signal is received in slot K, where K is an integer greater than 0, and the reference resource signal specifies one or more reference signals at one or more intervals relative to slot K as the subset of the plurality of reference signals to be measured.

8. The method of claim 7 , wherein each interval of the one or more intervals includes at least two reference signals.

9. measuring the predicted one or more reference signals; determining a reference signal from the one or more measured and predicted reference signals that has the highest power level; reporting the reference signal from the one or more measured and predicted reference signals having the highest power level to a base station; The method of claim 1 further comprising:

10. A user equipment (UE), at least one memory configured to store computer program code; at least one processor configured to access the at least one memory and to operate as instructed by the computer program code, the computer program code comprising: receiving code configured to cause at least one of the at least one processor to receive, from a base station over a channel, a plurality of reference signals corresponding to conditions of the 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, the subset having a number of reference signals that is less than the received plurality of reference signals; and a transmit code configured to cause at least one of the at least one processor to transmit, within a first time interval, a channel condition report to the base station corresponding to the measurements of the subset of the received plurality of reference signals and a prediction of another reference signal that the UE will receive after the first time interval.

11. 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, and the prediction of the one or more reference signals is based on the measurements of the subset of the received plurality of reference signals.

12. 11. 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, the prediction of the one or more reference signals is based on the measured subset of the received plurality of reference signals, and the channel condition report includes the prediction of the one or more reference signals.

13. 11. The UE of claim 10, wherein the predicted one or more reference signals includes 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 includes a reference resource signal that specifies the subset of the plurality of reference signals to be measured.

15. 15. The UE of claim 14, wherein the reference resource signal specifies the last K reference signals received before the reference resource signal as the subset of the plurality of reference signals to be measured, where K is an integer greater than 0.

16. 15. The UE of claim 14, wherein the reference resource signal is received in slot K, where K is an integer greater than 0, and the reference resource signal specifies one or more reference signals at one or more intervals relative to slot K as the subset of the plurality of reference signals to be measured.

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

18. the computer program code comprising: second measurement code configured to cause at least one of the at least one processor to measure the predicted one or more reference signals; determination code configured to cause at least one of the at least one processor to determine a reference signal from the measured and predicted one or more reference signals that has a highest power level; 11. The UE of claim 10, further comprising: a reporting code configured to cause at least one of the at least one processor to report to the base station the reference signal from the measured predicted one or more reference signals having the highest power level.

19. 1. A non-transitory computer-readable medium storing instructions that, when executed by a processor in a user equipment (UE), cause the UE to perform a method, the method comprising: receiving a plurality of reference signals from a base station over a channel corresponding to the channel conditions; measuring a subset of the received plurality of reference signals, wherein the subset has a number of reference signals that is less than the received plurality of reference signals; and transmitting, within a first time interval, a channel condition report to the base station corresponding to the measurements of the subset of the received plurality of reference signals and a prediction of another reference signal that the UE will receive after the first time interval.

20. 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, and the prediction of the one or more reference signals is based on the measurements of the subset of the received plurality of reference signals.

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