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 claims priority based on 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 generally relates to communication systems, and more particularly, to methods and apparatuses for beam management using artificial intelligence and machine learning (AI / ML).

Background Art

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

[0004] The CSI resource configuration includes a list configuration of CSI resource sets with S ≥ 1, where this list is composed of references to either or both of the NZP CSI-RS resource set(s) and the SS / PBCH block set(s), or this list is composed of references to the CSI-IM resource set(s). Each CSI-RS resource set includes one or more CSI-RS resources. The behavior of CSI-RS resources in the time domain can be set periodically, aperiodically, or semi-persistently. The CSI-RS resources within a set may be transmitted with various transmission beams (e.g., beam sweeping), and the UE can identify the index of the resource with the maximum RSRP. The CSI-RS resources can also be transmitted with the same beam so that the UE can perform beam sweeping. The beam used for transmission in the downlink can be indicated to the UE using the TCI state parameters. The TCI state parameters indicate to the UE that the receiving channel (e.g., PDCCH or PDSCH) is transmitted with the same beam as the reference signal included in the TCI state parameters.

[0005] Technologies belonging to the prior art may be used for beam management. Specifically, technologies belonging to the prior art may be used by the UE to monitor and measure CSI-RS resources (e.g., periodically), identify the best beam, and transmit information in CSI reports. However, the overhead for transmitting and measuring CSI-RS resources is large and is expected to increase further as the number of transmission beams and reception beams increases. Therefore, there is a need to provide more efficient beam management.

[0006] Improvements are presented in this specification. These improvements may also be applicable to other multi-access technologies and telecommunication standards using these technologies.

Summary of the Invention

Problems to be Solved by the Invention

[0007] The following presents a simplified overview of one or more embodiments of the present disclosure to provide a basic understanding of such embodiments. This overview is not an extensive overview of all possible embodiments, nor is it 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 follows.

[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, from a base station via a channel, a plurality of reference signals corresponding to a state of the channel. The method includes measuring a subset of the received plurality of reference signals. The method further includes transmitting, to the base station, a channel state report corresponding to the measurement of the subset of the received plurality of reference signals and the prediction of another reference signal received by the UE after a first time interval, within the first time interval.

[0010] According to an 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 reception code configured to cause at least one of the at least one processor to receive, via a channel, a plurality of reference signals corresponding to a state of the channel from a base station. 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 plurality of received reference signals. The computer program code further includes transmission code configured to cause at least one of the at least one processor to transmit, to the base station, a channel state report corresponding to measurement of a subset of the plurality of received reference signals within a first time interval and prediction of another reference signal received by the UE after the first time interval.

[0011] According to an exemplary embodiment, a non-transitory computer-readable medium stores instructions that, when executed by a processor in a user equipment (UE), cause the UE to perform a method including receiving, via a channel, a plurality of reference signals corresponding to a state of the channel from a base station. The method further includes measuring a subset of the plurality of received reference signals. The method further includes transmitting, to the base station, a channel state report corresponding to measurement of a subset of the plurality of received reference signals within a first time interval and prediction of another reference signal received by the UE after the first time interval.

[0012] Further embodiments are described below, some of which will be apparent from the description and / or may be learned by practice of the presented embodiments of the disclosure.

[0013] The above and other aspects, features, and aspects of the embodiments of the disclosure will become apparent from the following description when taken in conjunction with the accompanying drawings.

Brief Description of the Drawings

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

[0026] The above disclosure provides examples and explanations, but is not intended to be exhaustive or to limit the disclosed implementation forms to the exact form. Changes and modifications are possible in light of the above disclosure, or the changes and modifications can be obtained from the implementation of the implementation forms. Furthermore, one or more features or components of one embodiment may be incorporated into another embodiment (or one or more features of another embodiment), or may be combined with another embodiment (or one or more features of another embodiment). In addition, it is understood that in the flowcharts and descriptions of operations provided below, one or more operations may be omitted, one or more operations may be added, one or more operations may be (at least partially) executed simultaneously, and the order of one or more operations may be interchanged.

[0027] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or a combination of hardware and software. It is understood that the actual dedicated control hardware or software code used to implement these systems and / or methods does not limit the implementation forms. Therefore, the operations and behaviors of the systems and / or methods are described herein without reference to specific software code, and it is understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0028] Even if certain combinations of features are recited in the claims and / or disclosed herein, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways that are not specifically recited in the claims and / or not disclosed herein. Each dependent claim listed below may depend directly on only one claim, but the disclosure of possible implementations includes each dependent claim in combination with all other claims in the claim set.

[0029] Elements, acts, or instructions used herein should not be construed as important or essential unless so explicitly described. 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." When only one item is intended, the term "one" or similar wording is used. Also, as used herein, terms such as "has," "have," "having," "include," "including," etc. are intended to be non-limiting terms. Further, the phrase "based on" is intended to mean "at least partially based on" unless otherwise specified. Further, expressions 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.

[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 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.

[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 may 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 are not necessarily present in all embodiments of the present disclosure.

[0032] Embodiments of the present disclosure relate to an AI / ML-based prediction mechanism that reduces the overhead of beam management. Embodiments of the present disclosure enhance the robustness of AI / ML-based solutions. Additionally, in some embodiments of the present disclosure, timing information is included in the CSI report so that time-domain prediction of beam management can be performed using AI / ML.

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

[0034] Figure 1 is a diagram of an exemplary device that executes 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) 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 device with similar functionality.

[0035] In some embodiments, as shown in Figure 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 include one or more components that enable communication between the 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 shown as a single line in Figure 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 regard.

[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), 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 device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The 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 cooperating with a DSP core, or any other such configuration. In some embodiments, certain processes and methods may be performed by circuitry specific to a given function.

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

[0039] Device 100 may further include a memory 130. In some embodiments, the memory 130 may include a random access memory (RAM), a read only memory (ROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a magnetic memory, an optical memory, and / or another type of dynamic or static storage device. The memory 130 can store information and / or instructions for use (e.g., execution) by the processor 120.

[0040] The storage component 140 of the device 100 can 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, together with a corresponding drive, can include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a 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.

[0041] Device 100 may further include an input component 150. The input component 150 may include one or more components that enable Device 100 to receive information via user input or the like (e.g., touch screen, keyboard, keypad, mouse, stylus, button, switch, microphone, camera, etc.). Alternatively or additionally, the input component 150 may include sensors for sensing information (e.g., Global Positioning System (GPS) component, accelerometer, gyroscope, actuator, etc.).

[0042] The output component 160 of Device 100 may include one or more components (e.g., display, liquid crystal display (LCD), light-emitting diodes (LEDs), organic light-emitting diodes (OLEDs), tactile feedback device, speaker, etc.) that can provide output information from Device 100.

[0043] Device 100 may further 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 Device 100 to establish a connection with another device (e.g., a server, another device) and / or transfer communications. The communication may be performed via a wired connection, a wireless connection, or a combination of a wired connection and a wireless connection. The communication interface 170 may enable 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, 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 types 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, and the like.

[0044] Device 100 may execute one or more processes described herein. Device 100 may perform operations based on the execution of 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 by processor 120. The computer-readable medium may refer to a non-transitory memory device. The memory device may include a memory space within a single physical memory device and / or a memory space distributed across multiple physical memory devices.

[0045] The 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 or upon execution by processor 120, may cause device 100 to perform one or more processes described herein.

[0046] 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. Accordingly, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0047] The number and arrangement of components shown in FIG. 1 are provided as an example. In practice, there may be additional components, fewer components, different components, or components with different arrangements compared to the components shown in FIG. 1. Further, two or more components shown in FIG. 1 may be implemented within a single component, or a single component shown in FIG. 1 may be implemented as a plurality of distributed components. Additionally or alternatively, a set of (one or more) components shown in FIG. 1 can perform one or more functions described as being performed by another set of components shown in FIG. 1.

[0048] FIG. 2 is a diagram showing an example of a wireless communication system according to various embodiments of the present disclosure. The wireless communication system 200 (which can also be referred to as a wireless wide area network (WWAN)) can include one or more user equipment (UE) 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 cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptops, personal digital assistants (PDAs), satellite radios, global positioning systems (GPS), multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, tablets, smart devices, wearable devices, vehicles, electric meters, gas pumps, large or small kitchen appliances, healthcare devices, implants, sensors / actuators, displays, or any other similarly functioning devices. Some of the one or more UEs 210 may be referred to as Internet-of-Things (IoT) devices (e.g., parking meters, gas pumps, toasters, vehicles, heart monitors, etc.). The one or more UEs 210 may also be referred to as stations, mobile stations, subscriber stations, mobile units, subscriber units, radio units, remote units, mobile devices, wireless devices, wireless communication devices, remote devices, mobile subscriber stations, access terminals, mobile terminals, wireless terminals, remote terminals, handsets, user agents, mobile agents, clients, or some other suitable terms.

[0050] One or more base stations 220 can communicate wirelessly with one or more UEs 210 through the RAN domain 224. Each base station of the one or more base stations 220 can provide communication coverage to one or more UEs 210 located within the geographical coverage area of that base station 220. In some embodiments, as shown in FIG. 2, the base station 220 can transmit one or more beamformed signals to one or more UEs 210 in one or more transmission directions. One or more UEs 210 can receive the beamformed signals from the base station 220 in one or more reception directions. Alternatively or additionally, one or more UEs 210 can transmit beamformed signals to the base station 220 in one or more transmission directions. The base station 220 can receive the beamformed signals from one or more UEs 210 in one or more reception directions.

[0051] One or more base stations 220 can include macrocells (e.g., high-power cellular base stations) and / or small cells (e.g., low-power cellular base stations). Small cells can include femtocells, picocells, and microcells. The base station 220, whether it is a macrocell or a large cell, can include 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 and / or can be referred to as such.

[0052] One or more base stations 220 may be configured to interface with (e.g., establish a connection with, transfer data, etc.) at least one core network 240 through at least one transmission network 230. In addition to other functions, one or more base stations 220 may perform one or more of the following functions: namely, transfer of data (e.g., uplink data) received from one or more UEs 210 to at least one core network 240 via at least one transmission network 230, transfer of data (e.g., downlink data) received from at least one core network 240 to one or more UEs 210 via at least one transmission network 230.

[0053] The transmission 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 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 link 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), 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 serve as an entry point to the IP service 250. The IP service 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, the UE may be composed of 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 the CSI-RS transmitted in a second subset of the first subset of the CSI-RS resources. The first subset and the second subset may be the same resources (e.g., the subsets may include the same CSI-RS resources). The association of CSI-RS and CSI may refer to deriving CSI using at least some or all of the CSI-RS.

[0056] The UE can measure the signal quality of CSI-RS (e.g., the RSRP of CSI-RS). This measurement may be performed based on an instruction from the base station. The UE can estimate at least one or more of the RSRP of CSI-RS, the angle of arrival of CSI-RS, and the angle of departure of CSI-RS. The UE can send one or more measurement results (e.g., RSRP values) and the index of the CSI-RS resource related to the measurement results to the gNB. The association in this context can refer to deriving the measurement results by measuring 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 CSI-RS resources.

[0057] The UE can determine a second subset of CSI-RS resources using one or more of the following embodiments. The UE may be configured and / or instructed to feedback the index 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 can configure the UE to perform complete CSI-RS resource measurements. In some embodiments, the UE may fix and determine the second subset of CSI-RS resources. The CSI-RS resources determined by the UE can be reported to the gNB, for example, by indicating a set of corresponding index(es) in the configuration. In some embodiments, the second subset of CSI-RS resources may be fixed over a period of validity and may be updated when the period of validity expires. In some embodiments, the second subset of CSI-RS resources may be determined using a predetermined rule.

[0058] In some embodiments, the UE can be configured to feedback all measured RSRPs, the N best RSRPs, the best RSRP, and the indexes of the associated CSI-RS resources. For example, the UE can 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., 8 out of 16 are measured). The UE can feedback one RSRP (e.g., the best RSRP out of 8), the N best RSRPs (e.g., the N best ones out of 8), all best RSRPs (e.g., all 8 RSRPs). The indexes of the CSI-RS resources associated with the corresponding RSRPs may also be feedback. However, when all best RSRPs are feedback, the index may not be required.

[0059] In some embodiments, the CSI report may include components of CSI-related quantities (e.g., RSRP) and associated timing information. For example, the timing information may be the time when the CSI-RS from which the CSI was derived was transmitted and / or received. The timing information may be the time interval during 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 the CSI report can be defined in one or more of the following embodiments. In some embodiments, for a CSI report, a reference resource can be defined. The reference resource can include a time resource (e.g., slot index, symbol index) and a frequency resource (e.g., RB index). The CSI report can include CSI associated with a CSI-RS resource set defined with respect to the reference resource. One or more CSI-RS resource sets may be defined with respect to 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 (e.g., k is an integer) CSI-RS resource sets before the reference resource, etc.

[0061] In some embodiments, the CSI report may include CSI associated with a CSI-RS resource set defined with respect to the reference resource. An exemplary time series 300 is shown in FIG. 3. In this exemplary time series, the occasion of the CSI report 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 k-t, slot k-t 1 , and slot k-t 2 . In this example, the CSI-RS resource set includes one slot. In other examples, the CSI-RS resource set may include multiple slots. For example, one CSI-RS resource set out of 16 resources can be used to measure the RSRP of 16 transmission beams. Two CSI-RS resource sets of 16 resources can be used respectively to measure the RSRP of 16 transmission beams and 2 reception 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 is the RSRP corresponding to the three CSI-RS resource sets defined with respect to the reference resource 0 , RSRP1 and RSRP 2 can be included.

[0062] In some embodiments, for the time point of CSI reporting, a reference resource can be defined. For example, at least one time window can be defined for the reference resource. The time window can be defined using the window length (e.g., in slot units, in milliseconds), and the start time or end time. The UE can be configured to find a CSI-RS resource set defined for the reference resource within the time window. FIG. 4 shows an exemplary time series 400 having three windows (402A, 402B, 402C) of slots of length L (e.g., L is an integer). The time windows 402A, 402B, and 402C end at slot k - t 0 , k - t 1 , and k - t 2 respectively. The CSI report configured for slot n can include CSI derived from the CSI-RS resource sets within these three windows. In the window, for all disclosed embodiments, for example, two or more CSI-RS resource sets may exist to perform beam sweeping any number of times and / or find a preferred set of transmit / receive beams (e.g., to improve SNR) by accumulating measurement values.

[0063] In some embodiments, for the time point of CSI reporting, a reference resource can be defined that constitutes at least one time difference. The reference resource and the time difference can be used together to determine the associated CSI-RS resource set. For example, assuming t = k - t 0 , the associated CSI-RS resource set can be the first CSI-RS resource set before t, or the first CSI-RS resource after t.

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

[0065] In some embodiments, the CSI report can include quantities related to the CSI, such as, for each associated CSI-RS resource set, the N largest RSRPs for the reported RSRP and the indices of the transmission beam and / or reception beam. In other embodiments, the RSRP of all pairs of transmission beams and reception beams may be reported together with the beam index. The transmission beam index can be indicated using the index of the CSI-RS resource that transmitted the beam (e.g., CRI). The CSI-RS resource sets may be linked using, for example, common parameters. The CSI derived from the linked CSI-RS resource sets may be transmitted in the same CSI report. The timing information of the CSI-RS resource set from which the CSI was derived can be explicitly reported in the CSI report. For example, the CSI report may include the start point and / or end point of the CSI-RS resource set (e.g., in slot units or millisecond units) used to derive the CSI. The start point / end point can be defined with respect to the time point of the CSI report or the associated reference resource. FIG. 6 shows an exemplary CSI report.

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

[0067] In some embodiments, after the CSI report is sent, an indication regarding which beam to use for reception and / or transmission can be provided to the UE. The beam can be derived based on the CSI report using an AI / ML engine at the gNB. For this indication, a MAC CE can be used to indicate a group of beams, and a PDCCH can be used to select at least one beam from the group. Each group of beams can be valid for a time interval, and separate groups of beams can be indicated for separate time intervals.

[0068] In the second phase, the AI / ML engine can further refine the beam indexes selected. The UE may measure a separate set of CSI-RS resources. The CSI-RS resource set in the second phase may include a smaller number of resources than in the first phase. In the second phase, the UE can report the K best RSRP values regarding the measurement time and / or the indexes of the transmit beam / receive beam. As an example, during the first phase, the UE can measure 64 CSI-RS resources and feedback the RSRP for all the resources in the CSI report. The gNB can use the AI / ML engine to predict the four best transmit beams to be used in the next 100 milliseconds. During the 100 milliseconds which is the second phase, the gNB can configure the UE to measure four CSI-RS resources, one for each of the four indicated beams. The UE can feedback the best beam and the gNB can use that beam. The best beam can be continuously updated based on new measurement values and CSI reports. The second phase may be configured. The second phase can be activated and / or deactivated by the PDCCH (e.g., by a 1-bit indication in the PDCCH). The second phase can be deactivated when a timer expires or after a duration. In one way, the second phase may be implicitly activated. For example, the second phase may be activated when the N parameter used in the first phase is greater than or less than a certain value. The activation of the second phase may be determined by a parameter set for the UE. For example, if the UE is a high Doppler UE, the second phase can be activated.

[0069] The performance of an AI / ML engine that predicts beams within a spatial region and selects the best beam can be evaluated through simulation. In the learning 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 N best beam pairs (k = 1, ..., 8).

[0070] Figure 7 shows an example of measurements performed when beam management is executed in two phases. In the first phase, the UE receives 64 resources and can measure 8 out of the 64 received resources. Based on the measured resources during the first phase, the 4 best resources out of the 64 resources can be predicted. During the second phase, the UE can receive and measure the 4 predicted resources to determine the best resource (e.g., the resource with the highest output). The UE can then report the best resource to the base station. Figure 8 further shows the reception and measurement value reporting of resources during the first phase and the second phase.

[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 1D convolutional layers (902, 904), followed by a Flatten layer 906 and Dense layers (908, 910). The output of the last layer gives the probability of the best beam for all possible beam combinations. To obtain 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, the prediction accuracy of the best beam increases as the number of top predictions (e.g., N) increases.

[0072] In some embodiments, the two-step beam management process may also be used during the UE's initial attach process by measuring the RSRP of the SSB signal. For example, in the first phase, the UE can measure the RSRP of a subset of SSBs (e.g., 4 SSBs). Using these measurements as inputs to an AI / ML engine, the UE can predict the top k best beams from among all possible beam pairs. In the second phase, the UE can measure the RSRP of the top k best beams predicted in the first phase and subsequently select and communicate with the best beam based on the measured RSRP values.

[0073] In some embodiments, one or more TCI states of a channel can be indicated within a MAC CE. The 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 states. The TCI state can include a reference signal (e.g., CSI-RS) resource ID or the ID of an SSB that is QCLed to the channel. These features mean that the UE can assume a reference signal or that the SSB is transmitted on the same beam as the channel (e.g., the TCI can be regarded as an indication of a beam index).

[0074] In some embodiments, the duration of validity may be divided at equal intervals, and for each interval, the TCI state may be indicated by a MAC CE. This feature is shown in Table 1, where the duration of validity 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 received beam index can also be indicated as shown in Table 2.

Table 2

[0076] The received beam index may be included in the TCI state as a parameter. A new information element (e.g., received configuration index) can be defined to include the received beam index. In other embodiments, the period during 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, a plurality of TCI states and / or rx beam indices can be indicated by MAC CE. One TCI state among the plurality of TCI states and / or one rx beam index among the plurality of rx beams can be further indicated by PDCCH.

[0078] FIG. 11 shows an embodiment of a process 1100 for performing beam management. The process 1100 may be executed by a UE. The process 1100 can start with an operation S1102 in which the UE receives a plurality of reference signals corresponding to the state of a channel from a base station via the channel. This process proceeds to an operation S1104 in which the UE measures a subset of the plurality of received reference signals. For example, if the UE receives 64 reference signals, the UE may be configured to measure 8 out of the 64 reference signals. This process proceeds to an operation S1106 in which the UE transmits, to the base station, a channel state report corresponding to the measurement of a subset of the plurality of received reference signals within a first time interval and the prediction of one or more reference signals that the UE will receive after the first time interval. For example, the UE can 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 can use an AI / ML engine to predict the best RSRP based on the measured subset of the plurality of received reference signals, where the CSI report includes the predicted RSRP.

[0079] The above disclosure provides illustrations and descriptions, but is not intended to be exhaustive or to limit the disclosed embodiments to the exact forms disclosed. Changes and modifications are possible in light of the above disclosure, or the changes and modifications may be obtained from the practice of the embodiments.

[0080] It is understood that the specific order or hierarchy of blocks in the processes / flowcharts disclosed herein is illustrative. It is understood that based on design preferences, the specific order or hierarchy of blocks in a process / flowchart can be reconfigured. Additionally, some blocks may be combined, omitted, or the like. The appended method claims present various block elements in a sample order and are not meant to be limited to the specific order or hierarchy presented.

[0081] Some embodiments may relate to a system, a method, and / or a computer-readable medium at any possible technical detail level of integration. Further, one or more of the above-described 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 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 can be a tangible device that can hold and store instructions for use by an instruction execution device. The computer-readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium includes a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punch card or raised structure in a groove having instructions recorded thereon, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be construed to be a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission media (e.g., an optical pulse passing through an optical fiber cable), or an electrical signal transmitted through a wire.

[0083] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical transmission fibers, wireless transmission, 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 transfers the computer-readable program instructions for storage on a computer-readable storage medium within each respective computing / processing device.

[0084] The computer-readable program code / instructions for performing the operations may be in any combination of one or more programming languages, including assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuits, or source code or object code written in an object-oriented programming language such as Smalltalk or C++, and a procedural programming language such as the "C" programming language or a similar programming language. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, for example, an electronic circuit including a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may execute the computer-readable program instructions by utilizing the state information of the computer-readable program instructions for personalizing the electronic circuit in order to perform the aspects or operations.

[0085] These computer-readable program instructions may be provided to the 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 via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in one or more blocks of 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 apparatus, and / or other devices to function in a particular manner, such that the computer-readable storage medium storing the instructions comprises an article of manufacture including instructions for implementing the aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0086] The computer-readable program instructions may also be loaded onto a computer, other programmable apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[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 flowchart or block diagram can represent a module, segment, or portion of one or more executable instructions for implementing a particular logical function. The methods, computer systems, and computer-readable media can include additional blocks, fewer blocks, different blocks, or differently arranged blocks compared to those shown in the figures. In some alternative implementations, the functions described in the blocks may be performed in a different order than those described in the figures. For example, two blocks shown in succession may actually be performed simultaneously, substantially simultaneously, or the blocks may sometimes be performed in the reverse order depending on the related functions. It should also be noted that each block in 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 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 a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not limiting of the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it is understood that software and hardware can be designed based on the description herein to implement the systems and / or methods.

[0089] Abbreviations: BWP Bandwidth Part CE Control Element 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 Received Quality CSI-SINR CSI Signal-to-Interference-plus-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 Quasi-Co-Location 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 Synchronization Signal SSB Synchronization Signal Block SSS Secondary Synchronization Signal SS-RSRP SS Reference Signal Received Power SS-RSRQ SS Reference Signal Received Quality SS-SINR SS Signal-to-Interference-plus-Noise Ratio TCI Transmission 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 via a channel, a plurality of reference signals corresponding to a state of the channel; measuring a subset of the received plurality of reference signals; and transmitting, to the base station, a channel state report corresponding to the measurement 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 within the first time interval.

[0092] (2) The method according to feature (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 measurement of the subset of the received plurality of reference signals.

[0093] (3) The method according to feature (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 state report includes the prediction of the one or more reference signals.

[0094] (4) The method according to any one of features (1) to (3), wherein the predicted one or more reference signals include at least one reference signal not included in the measured subset of the received plurality of reference signals.

[0095] (5) The method according to 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 specifies the last K of the previously received z root reference resource signals 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 designates one or more reference signals at one or more intervals with respect to slot K as the subset of the plurality of reference signals to be measured.

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

[0099] (9) The method according to any one of features (1) to (8), further comprising: measuring the one or more predicted reference signals; determining one reference signal from the one or more measured and predicted reference signals having the highest power level; and reporting the reference signal from the one or more measured and predicted reference signals having 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, wherein the computer program code includes: reception code configured to cause at least one of the at least one processors to receive, via a channel, a plurality of reference signals corresponding to a state of the channel from a base station; measurement code configured to cause at least one of the at least one processors to measure a subset of the received plurality of reference signals; and transmission code configured to cause at least one of the at least one processors to transmit, to a base station, a channel state report corresponding to measurement of the subset of the received plurality of reference signals within a first time interval and prediction of another reference signal received by the UE after the first time interval.

[0101] (11) The base station performs the prediction of the one or more reference signals 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 plurality of received reference signals, the UE according to feature (10).

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

[0103] (13) The predicted one or more reference signals include at least one reference signal not included in the measured subset of the plurality of received reference signals, the UE according to any one of features (10) to (12).

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

[0105] (15) The reference resource signal specifies the last K of the 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, the UE according to feature (14).

[0106] (16) 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 with respect to slot K as the subset of the plurality of reference signals to be measured, the UE according to feature (14).

[0107] (17) Each of the one or more intervals includes at least two reference signals, the UE according to feature (16).

[0108] (18) The computer program code further includes: 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; a 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 that has the highest power level; and a 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 that has the highest power level to the base station. The UE according to any one of features (10) to (17).

[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 including: receiving, from a base station via a channel, a plurality of reference signals corresponding to a state of the channel; measuring a subset of the received plurality of reference signals; and transmitting, to the base station, a channel state report corresponding to the measurement of the subset of the received plurality of reference signals and a prediction of another reference signal received by the UE after the first time interval within the first time interval.

[0110] (20) The non-transitory computer-readable medium according to feature (19), wherein the base station performs the prediction of the one or more reference signals 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.

Claims

1. 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; measuring a subset of the received plurality of reference signals; transmitting, to the base station, a channel state report corresponding to the measurement of the subset of the received plurality of reference signals and a prediction of another reference signal to be received by the UE after a first time interval, within the first time interval.

2. The method according to claim 1, wherein the base station performs the prediction of the one or more reference signals 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.

3. The method according to claim 1, wherein the UE performs the prediction of the one or more reference signals 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 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 not included in the measured subset of the received plurality of reference signals.

5. The method according to 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. The method according to claim 5, wherein the reference resource signal specifies the last K of the 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. The method according to 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 with respect to slot K as the subset of the plurality of reference signals to be measured.

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

9. measuring the predicted one or more reference signals; Determining one reference signal from the one or more measured and predicted reference signals having 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 according to claim 1, further comprising. **Claim 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 operate as instructed by the computer program code, the computer program code being Receiving code configured to cause at least one of the at least one processors to receive, via a channel, from a base station, a plurality of reference signals corresponding to a state of the channel; First measurement code configured to cause at least one of the at least one processors to measure a subset of the received plurality of reference signals; Transmission code configured to cause at least one of the at least one processors to transmit, 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 a prediction of another reference signal received by the UE after the first time interval. A user equipment (UE) comprising. **Claim 11** The UE according to claim 10, wherein the base station performs the prediction of the one or more reference signals 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. **Claim 12** The UE according to claim 10, wherein the UE performs the prediction of the one or more reference signals 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 state report includes the prediction of the one or more reference signals. **Claim 13** The UE according to claim 10, wherein the predicted one or more reference signals include at least one reference signal not included in the measured subset of the received plurality of reference signals. **Claim 14** The UE according to 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. The UE according to claim 14, wherein the reference resource signal specifies the last K of the 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. The UE according to 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 with respect to slot K as the subset of the plurality of reference signals to be measured.

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

18. The computer program code is 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 the highest power level; The UE according to claim 10, further comprising 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 the base station.

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, from a base station via a channel, a plurality of reference signals corresponding to a state of the channel; measuring a subset of the received plurality of reference signals; transmitting, to the base station, a channel state report corresponding to the measurement 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, within the first time interval.

20. The base station performs the prediction of the one or more reference signals 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 plurality of received reference signals. The non-transitory computer-readable medium according to claim 19.

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