Resource configuration method and apparatus

Abstract

Embodiments of the present application provide a resource configuration method and device. The resource configuration method comprises: a terminal device receiving configuration information from a network device; wherein the configuration information comprises a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input to an AI / ML functionality / model for time beam prediction.

Description

Resource configuration method and device Technical Field

[0001] The embodiments of the present application relate to the field of communication technologies. Background Art

[0002] NR Release 18 investigates artificial intelligence / machine learning (AI / ML) over the air interface. AI / ML can be used for the following use cases: channel state information (CSI) feedback enhancement, beam management, and positioning enhancement. CSI feedback enhancement can include CSI prediction and CSI compression; beam management can include spatial beam prediction and temporal beam prediction; and positioning enhancement can include direct positioning and AI / ML-assisted positioning.

[0003] In some sub-use cases, a bilateral model can be used, with the AI / ML model located on both the terminal device side and the network device side. For example, CSI compression can be a representative use case for a bilateral model. In other sub-use cases, a unilateral model can be used, with the AI / ML model located either on the terminal device side or the network device side.

[0004] It should be noted that the above introduction to the technical background is merely intended to provide a clear and complete description of the technical solutions of this application and facilitate understanding by those skilled in the art. Simply because these solutions are described in the background technology section of this application, it should not be assumed that the above technical solutions are well known to those skilled in the art.

[0005] Summary of the Invention

[0006] The inventors discovered that terminal devices and / or network devices can utilize AI / ML functionality / models to predict future beams based on beam measurement results. However, there is currently no clear solution for configuring beam measurement and / or beam prediction based on the AI / ML functionality / model.

[0007] To address at least one of the above problems, embodiments of the present application provide a resource configuration method and apparatus.

[0008] According to one aspect of an embodiment of the present application, a resource configuration method is provided, including:

[0009] The terminal device receives configuration information from the network device;

[0010] The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML functionality / model for time beam prediction.

[0011] According to another aspect of an embodiment of the present application, a resource configuration device is provided, including:

[0012] a receiving unit, configured to receive configuration information from a network device;

[0013] The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML functionality / model for time beam prediction.

[0014] According to another aspect of an embodiment of the present application, a resource configuration method is provided, including:

[0015] The network device sends configuration information to the terminal device;

[0016] The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML functionality / model for time beam prediction.

[0017] According to another aspect of an embodiment of the present application, a resource configuration device is provided, including:

[0018] a sending unit, configured to send configuration information to a terminal device;

[0019] The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML functionality / model for time beam prediction.

[0020] According to another aspect of an embodiment of the present application, a communication system is provided, including:

[0021] Network devices send configuration information to terminal devices;

[0022] A terminal device that receives the configuration information; wherein the configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into an AI / ML functionality / model for time beam prediction.

[0023] One of the beneficial effects of the embodiments of the present application is that the AI / ML model can be used to perform time beam prediction based on the results of beam measurement, thereby improving the accuracy and reliability of AI / ML for beam prediction.

[0024] With reference to the following description and accompanying drawings, specific embodiments of the present application are disclosed in detail, indicating the manner in which the principles of the present application can be employed. It should be understood that the embodiments of the present application are not limited in scope. Within the spirit and scope of the appended claims, the embodiments of the present application include many variations, modifications and equivalents.

[0025] Features described and / or illustrated with respect to one embodiment may be used in the same or similar manner in one or more other embodiments, combined with features in other embodiments, or substituted for features in other embodiments.

[0026] It should be emphasized that the term "include / comprising" when used herein refers to the presence of features, integers, steps or components, but does not exclude the presence or addition of one or more other features, integers, steps or components. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The elements and features described in one figure or one embodiment of the present application can be combined with the elements and features shown in one or more other figures or embodiments. In addition, in the accompanying drawings, similar reference numerals represent corresponding parts in several figures and can be used to indicate corresponding parts used in more than one embodiment.

[0028] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application;

[0029] FIG2 is a schematic diagram of a resource configuration method according to an embodiment of the present application;

[0030] FIG3 is another schematic diagram of the resource configuration method according to an embodiment of the present application;

[0031] FIG4 is a schematic diagram of reporting beam prediction results according to an embodiment of the present application;

[0032] FIG5 is another schematic diagram of reporting beam prediction results according to an embodiment of the present application;

[0033] FIG6 is another schematic diagram of reporting beam prediction results according to an embodiment of the present application;

[0034] FIG7 is another schematic diagram of reporting beam prediction results according to an embodiment of the present application;

[0035] FIG8 is a schematic diagram of beam optimization resources according to an embodiment of the present application;

[0036] FIG9 is another schematic diagram of the resource configuration method according to an embodiment of the present application;

[0037] FIG10 is another schematic diagram of the resource configuration method according to an embodiment of the present application;

[0038] FIG11 is a schematic diagram of a resource configuration device according to an embodiment of the present application;

[0039] FIG12 is another schematic diagram of the resource configuration device according to an embodiment of the present application;

[0040] FIG13 is a schematic diagram of a terminal device according to an embodiment of the present application;

[0041] FIG14 is a schematic diagram of a network device according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] The above and other features of the present application will become apparent through the following description with reference to the accompanying drawings. In the description and the accompanying drawings, specific embodiments of the present application are disclosed in detail, which illustrate some embodiments in which the principles of the present application can be adopted. It should be understood that the present application is not limited to the described embodiments. On the contrary, the present application includes all modifications, variations and equivalents that fall within the scope of the appended claims.

[0043] In the embodiments of the present application, the terms "first", "second", etc. are used to distinguish different elements from the name, but do not indicate the spatial arrangement or temporal order of these elements, and these elements should not be limited by these terms. The term "and / or" includes any one and all combinations of one or more of the associated listed terms. The terms "comprising", "including", "having", etc. refer to the presence of the stated features, elements, components or components, but do not exclude the presence or addition of one or more other features, elements, components or components.

[0044] In the embodiments of this application, the singular forms "a," "the," etc. include plural forms and should be broadly understood to mean "a" or "a type" rather than being limited to "one." Furthermore, the term "said" should be understood to include both singular and plural forms, unless the context clearly indicates otherwise. Furthermore, the term "according to" should be understood to mean "at least in part based on...", and the term "based on" should be understood to mean "at least in part based on...", unless the context clearly indicates otherwise.

[0045] In the embodiments of the present application, the term "communication network" or "wireless communication network" may refer to a network that complies with any of the following communication standards, such as Long Term Evolution (LTE), enhanced Long Term Evolution (LTE-A, LTE-Advanced), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), etc.

[0046] Furthermore, communication between devices in the communication system may be carried out according to communication protocols of any stage, for example, including but not limited to the following communication protocols: 1G (generation), 2G, 2.5G, 2.75G, 3G, 4G, 4.5G and 5G, New Radio (NR), future 6G, etc., and / or other communication protocols currently known or to be developed in the future.

[0047] In the embodiments of the present application, the term "network device" refers to, for example, a device in a communication system that connects a terminal device to the communication network and provides services to the terminal device. Network devices may include, but are not limited to, the following devices: base station (BS), access point (AP), transmission reception point (TRP), broadcast transmitter, mobile management entity (MME), gateway, server, radio network controller (RNC), base station controller (BSC), etc.

[0048] Among them, base stations may include but are not limited to: NodeB (NodeB or NB), evolved NodeB (eNodeB or eNB) and 5G base station (gNB), IAB host, etc., and may also include remote radio head (RRH, Remote Radio Head), remote radio unit (RRU, Remote Radio Unit), relay (relay) or low-power node (such as femeto, pico, etc.). The term "base station" can include some or all of their functions. Each base station can provide communication coverage for a specific geographical area. The term "cell" can refer to a base station and / or its coverage area, depending on the context in which the term is used.

[0049] In the embodiments of the present application, the term "user equipment" (UE) or "terminal equipment" (TE) refers to, for example, a device that accesses a communication network through a network device and receives network services. A terminal device can be fixed or mobile and may also be referred to as a mobile station (MS), a terminal, a subscriber station (SS), an access terminal (AT), a station, and so on.

[0050] Among them, terminal devices may include but are not limited to the following devices: cellular phones, personal digital assistants (PDAs), wireless modems, wireless communication devices, handheld devices, machine-type communication devices, laptop computers, cordless phones, smart phones, smart watches, digital cameras, etc.

[0051] For another example, in scenarios such as the Internet of Things (IoT), the terminal device can also be a machine or device for monitoring or measurement, including but not limited to: machine type communication (MTC) terminal, vehicle-mounted communication terminal, device-to-device (D2D) terminal, machine-to-machine (M2M) terminal, and so on.

[0052] In addition, the term "network side" or "network device side" refers to one side of the network, which can be a base station or one or more network devices as described above. The term "user side" or "terminal side" or "terminal device side" refers to the user or terminal side, which can be a UE or one or more terminal devices as described above. Unless otherwise specified herein, "device" can refer to either network equipment or terminal equipment.

[0053] The following describes the scenarios of the embodiments of the present application through examples, but the present application is not limited thereto.

[0054] FIG1 is a schematic diagram of a communication system according to an embodiment of the present application, schematically illustrating a situation using a terminal device and a network device as an example. As shown in FIG1 , a communication system 100 may include a network device 101 and terminal devices 102 and 103. For simplicity, FIG1 illustrates only two terminal devices and one network device as an example, but the embodiments of the present application are not limited thereto.

[0055] In the embodiment of the present application, existing services or future services can be transmitted between the network device 101 and the terminal devices 102 and 103. For example, these services may include but are not limited to: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low-latency communication (URLLC), etc.

[0056] It is worth noting that FIG1 shows that both terminal devices 102 and 103 are within the coverage range of network device 101, but the present application is not limited thereto. Both terminal devices 102 and 103 may not be within the coverage range of network device 101, or one terminal device 102 may be within the coverage range of network device 101 while the other terminal device 103 is outside the coverage range of network device 101.

[0057] In the embodiments of the present application, the high-layer signaling may be, for example, radio resource control (RRC) signaling; for example, an RRC message, including, for example, an MIB, system information, or a dedicated RRC message; or an RRC information element (RRC IE). The high-layer signaling may also be, for example, MAC (Medium Access Control) signaling; or a MAC control element (MAC CE). However, the present application is not limited thereto.

[0058] In embodiments of the present application, one or more AI / ML models may be configured and run in a network device and / or a terminal device. The AI / ML models may be used for various signal processing functions in wireless communications, such as CSI prediction, CSI compression, beamforming, positioning management, and the like; however, the present application is not limited thereto.

[0059] Embodiments of the first aspect

[0060] An embodiment of the present application provides a resource configuration method, which is described from the perspective of a terminal device.

[0061] FIG2 is a schematic diagram of a resource configuration method according to an embodiment of the present application. As shown in FIG2 , the method includes:

[0062] 201. A terminal device receives configuration information from a network device; wherein the configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein a result of the beam measurement is input into an AI / ML functionality / model for time beam prediction.

[0063] It is worth noting that FIG2 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG2 above.

[0064] In some embodiments, functionality refers to an AI / ML feature / feature group enabled by a configuration, where the configuration is supported based on conditions indicated by UE capabilities.

[0065] For example, the AL / ML function may be one or more functions, or one or more logical models, or one or more sub-functions, or one or more features, or one or more feature groups.

[0066] For another example, the function can be to use AI / ML for spatial beam prediction, or to use AI / ML for time beam prediction, or to use AI / ML for CSI prediction, or to use AI / ML for direct positioning, or to use AI / ML for assisted positioning, and so on.

[0067] In some embodiments, the AI / ML model is located on the terminal device side.

[0068] FIG3 is another schematic diagram of the resource configuration method according to an embodiment of the present application. As shown in FIG3 , the method includes:

[0069] 301. A terminal device receives configuration information from a network device; wherein the configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein a result of the beam measurement is input into an AI / ML functionality / model for time beam prediction; and

[0070] 302. The terminal device sends beam prediction results of multiple time instances to the network device.

[0071] For example, the AI / ML function is located on the terminal device side. After the AI / ML function is enabled or activated, the terminal device performs beam measurement based on the reference signal from the network side, uses AI / ML to perform time beam prediction based on the beam measurement results, and sends the beam prediction results at multiple time points to the network device.

[0072] It is worth noting that FIG3 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG3 above.

[0073] In some embodiments, the prediction results of the multiple time instances are reported in multiple beam reports, wherein one beam report corresponds to the prediction result of one time instance, and the number of the multiple time instances and / or the period of beam reporting are predefined or configured by the network device.

[0074] For example, the prediction results of multiple moments are reported at multiple moments, for example, beam reporting is performed at K moments. Assuming that the periodicity of CSI-RS transmission is T1, the periodicity of beam reporting can be T2, where T2<=T1 (or the values ​​of T1 and T2 are independent). The values ​​of K and / or T2 can be predefined or configurable, and can also depend on UE capabilities. For each report, the predicted beam is for the current moment. In this case, the traditional UCI format of beam reporting can be reused. The reporting of prediction results can be periodic / semi-persistent / aperiodic.

[0075] Figure 4 is an example diagram of reporting beam prediction results in an embodiment of the present application. As shown in Figure 4, the terminal device can periodically receive CSI-RS and perform beam measurement and beam prediction, and can report beam prediction results of multiple time instances in multiple beam reports, where one beam report corresponds to the prediction result of one time instance.

[0076] As shown in Figure 4, 401 indicates CSI-RS transmission on time domain resources, and 402 indicates reporting of beam prediction results at multiple time instances. For example, assuming that CSI-RS uses T1 as the transmission period, CSI-RS for beam measurement is transmitted at 401-1, 401-2, 401-3, etc.

[0077] As shown in Figure 4, 402-1, 402-2, 402-3, and 402-4 report the beam prediction results for the current time points K1, K2, K3, and K4, respectively. For example, at time point K1, the UE reports the prediction results for beams 3 and 5; at time point K2, the UE reports the prediction results for beams 1 and 6; at time point K3, the UE reports the prediction results for beams 2 and 7; and at time point K4, the UE reports the prediction results for beams 3 and 4. In this case, the UCI format used for beam reporting can be reused for reporting.

[0078] As shown in FIG4 , beam reporting is performed at 402-1, 402-2, 402-3, 402-4, 402-5, 402-6, 402-7, 402-8, 402-9, etc. with a period of T2, wherein T2 is less than or equal to T1, or the values ​​of T1 and T2 are independently determined. As shown in FIG4 , K=4 is used as an example. In the above embodiment, the values ​​of K and / or T2 may be predefined or configured by the network device based on the capabilities of the UE, and this application does not impose any restrictions on this.

[0079] In some embodiments, the prediction results of the multiple time instances are reported in one beam report, and the number of the multiple time instances and / or the interval between two time instances are predefined or configured by the network device.

[0080] For example, the prediction results for multiple time instants (e.g., K time instants) are reported via a single beam report. The value of K and / or the interval between two time instants may be predefined or configurable and may also depend on the capabilities of the UE. The reporting of the prediction results may be periodic, semi-persistent, or aperiodic.

[0081] FIG5 is another example diagram of reporting beam prediction results according to an embodiment of the present application. As shown in FIG5 , 501 indicates that CSI-RS is transmitted on time domain resources, and 502 indicates reporting beam prediction results at multiple time instances. For example, assuming that CSI-RS uses T1 as the transmission period, CSI-RS for beam measurement is transmitted at 501-1, 501-2, 501-3, etc.

[0082] As shown in Figure 5, for example, beam reports also use a period of T1, reporting beam prediction results at 502-1, 502-2, 502-3, and so on. Taking K = 4 as an example, each beam report corresponds to the prediction results at K moments. For example, beam report 502-1 reports the beam prediction results at moments K1 through K4. In the above embodiment, the K value can be predefined or configured by the network device based on the UE's capabilities, and this application does not impose any restrictions on this.

[0083] In some embodiments, a prediction result of one of the multiple time instances is used as reference beam information, and the difference between the one prediction result and the other prediction results is included in the beam report; wherein, the method of using the difference can be applied to all moments, or, the method of using the difference can also be applied to each one or more moments.

[0084] For example, the L1-RSRP / L1-SINR difference can be reported. When the difference method is applied to all moments, a prediction result at one moment can be selected from the prediction results of all moments as the reference beam information (for example, the beam with the highest L1-RSRP / L1-SINR is selected as the reference beam), and the L1-RSRP / L1-SINR values ​​in the beam prediction results at other moments are compared with the L1-RSRP / L1-SINR values ​​of the reference beam and the difference is calculated. Thus, in a beam report, beams other than the reference beam only need to report the difference in L1-RSRP / L1-SINR with the reference beam, thereby reducing the number of bits required for the beam report and saving signaling overhead.

[0085] In addition, the beam report includes relevant information indicating the prediction result at which moment the reference beam is selected. For example, the beam report may include an identifier for each moment so that each moment corresponds to multiple predicted beams.

[0086] For another example, when the difference method is applied to a moment, a prediction result is selected from the prediction results at a moment as the reference beam information (for example, the beam with the highest L1-RSRP / L1-SINR is selected as the reference beam), and then the L1-RSRP / L1-SINR values ​​of other prediction results at the moment are compared with the L1-RSRP / L1-SINR values ​​of the reference beam and the difference is calculated. Therefore, in the report on the beam prediction result at the moment, only the reference beam information and the difference between the other beam information and the reference beam information need to be reported. This can reduce the number of bits required for the beam report and save signaling overhead.

[0087] In some embodiments, a prediction window for reporting time beam prediction can be defined, which includes one or more moments for beam prediction, wherein the length of the prediction window and / or the number of moments within the prediction window or the intervals between moments can be predefined or can be configured by the network device according to the capabilities of the UE. In addition, the prediction window can be periodic, semi-periodic or non-periodic.

[0088] For example, the period of the prediction window may be predefined, or may be configured by the network device according to the capability of the UE.

[0089] In some embodiments, the terminal device does not change the receiving beam used for beam prediction within the prediction window. In other embodiments, the terminal device may also change the receiving beam used for beam prediction within the prediction window.

[0090] In some embodiments, a measurement window for beam measurement may also be defined, where the measurement window includes one or more transmission opportunities for reference signals used for beam measurement.

[0091] For example, a measurement window may include multiple CSI-RS transmission opportunities for beam measurement. The length of the measurement window and / or the number or interval of reference signal transmission opportunities within the prediction window may be predefined, or may be configured by a network device based on the capabilities of the UE. For example, the measurement window may be a fixed window or a sliding window.

[0092] For another example, the measurement window may be periodic, semi-periodic or aperiodic. In some embodiments, the period of the measurement window may be predefined, or may be configured by the network device according to the capability of the UE.

[0093] In some embodiments, the terminal device does not change the receiving beam used for beam measurement within the measurement window. In other embodiments, the terminal device may also change the receiving beam used for beam measurement within the measurement window.

[0094] In some embodiments, the beam measurement is based on a reference signal group or burst; the reference signal group or burst forms a measurement window, and the length of the measurement window and / or the number or interval of reference signal transmission opportunities in the measurement window are predefined or configured by the network device.

[0095] For example, beam measurement is based on a reference signal group or burst. That is, a group of reference signals can form a measurement window. The length of the measurement window and / or the number or interval of transmission opportunities of the reference signal within the prediction window can be predefined, or can be configured by the network device according to the capabilities of the UE. In addition, the beam measurement window can also be periodic, semi-periodic or aperiodic, and the reporting window of the beam prediction result can also be periodic, semi-periodic or aperiodic, which is not limited in this application.

[0096] In some embodiments, the prediction results of the multiple time instances are reported in multiple beam reports, wherein one beam report corresponds to the prediction result of one time instance, and the number of the multiple time instances and / or the period of beam reporting are predefined or configured by the network device.

[0097] Figure 6 is another example diagram of reporting beam prediction results in an embodiment of the present application; as shown in Figure 6, 601 indicates that CSI-RS is transmitted on time domain resources, and 602 indicates reporting of beam prediction results at multiple time instances.

[0098] As shown in Figure 6, for example, each set of CSI-RS transmissions 601-1, 601-2, 601-3, ... for beam measurement includes three CSI-RS transmission opportunities. The CSI-RS transmission period is, for example, T1. As shown in Figure 6, prediction results at multiple time instants can be reported in multiple beam prediction result reports. For example, one beam prediction result report corresponds to a prediction result at one time instant.

[0099] As shown in Figure 6, the first to third groups of CSI-RS are transmitted with a period of T1, and beam prediction reports 602-1 to 602-9 report the beam prediction results with a period of T2. That is, beam reports 602-1, 602-2, 602-3, and 602-4 correspond to the beam prediction results at time K1, K2, K3, and K4, respectively. In the above embodiment, the reporting period T2 and the K value can be predefined or configured by the network device based on the UE capabilities; the beam measurement window can be periodic, and its period T1 can be predefined or configured by the network device based on the UE capabilities; this application does not impose any restrictions on this.

[0100] In some embodiments, the prediction results of the multiple time instances are reported in one beam report, and the number of the multiple time instances and / or the interval between two time instances are predefined or configured by the network device.

[0101] FIG7 is another schematic diagram of reporting beam prediction results according to an embodiment of the present application; as shown in FIG7 , 701 indicates that CSI-RS is transmitted on time domain resources, and 702 indicates reporting of beam prediction results.

[0102] As shown in Figure 7 , for example, each set of CSI-RS transmissions 701-1, 701-2, 701-3, ... for beam measurement includes three CSI-RS transmission opportunities. The CSI-RS transmission period is, for example, T1. As shown in Figure 7 , prediction results at multiple time points can be reported in a single beam prediction result report.

[0103] As shown in FIG7 , for example, a set of reference signals is transmitted in the time domain with a period of T1, and the UE measures and predicts them, and also reports the beam prediction results at 702 - 1 , 702 - 2 , 702 - 3 . . . with a period of T1.

[0104] As shown in Figure 7, using K=4 as an example, each beam report corresponds to prediction results at multiple time instants. For example, beam report 702-1 includes beam prediction results for time instants K1 to K4 for the first set of CSI-RS transmissions (701-1). In the above embodiment, the K value can be predefined or configured by the network device based on the UE's capabilities, and this application does not impose any restrictions on this.

[0105] In some embodiments, a prediction result of one of the multiple time instances is used as reference beam information, and the difference between the one prediction result and the other prediction results is included in the beam report; wherein, the method of using the difference can be applied to all moments, or, the method of using the difference can also be applied to each one or more moments.

[0106] For example, reporting the L1-RSRP / L1-SINR difference. For example, when the difference method is applied to all moments, a prediction result at one moment is selected from the prediction results at multiple moments as the reference beam information (for example, the beam with the highest L1-RSRP / L1-SINR is selected as the reference beam), and the L1-RSRP / L1-SINR values ​​in the beam prediction results at other moments are compared with the L1-RSRP / L1-SINR values ​​of the reference beam and the difference is calculated. Thus, in a beam report, beams other than the reference beam only need to report the difference in L1-RSRP / L1-SINR with the reference beam, thereby reducing the number of bits required for the beam report and saving signaling overhead.

[0107] In addition, the beam report includes relevant information indicating the prediction result at which time the reference beam is selected. For example, the beam report may include an identifier for each time so that each time corresponds to multiple predicted beams.

[0108] For another example, when the difference method is applied to a moment, a prediction result is selected from the prediction results at a moment as the reference beam information (for example, the beam with the highest L1-RSRP / L1-SINR is selected as the reference beam), and then the L1-RSRP / L1-SINR values ​​in other prediction results at the moment are compared with the L1-RSRP / L1-SINR values ​​of the reference beam and the difference is calculated. Therefore, in the report on the beam prediction result at the moment, only the reference beam information and the difference between the other beam information and the reference beam information need to be reported. This can reduce the number of bits required for the beam report and save signaling overhead.

[0109] In the above embodiment, a prediction window for reporting time beam prediction can be defined, and the prediction window includes one or more moments for beam prediction, wherein the length of the prediction window, and / or the number of moments within the prediction window or the interval between moments can be predefined, or can be configured by the network device according to the capabilities of the UE. In addition, the prediction window can be periodic, semi-periodic or non-periodic.

[0110] For example, the period of the prediction window may be predefined, or may be configured by the network device according to the capability of the UE. For another example, the prediction window and the measurement window do not overlap.

[0111] In some embodiments, the UE does not change the receive beam used for beam prediction within the prediction window. In another embodiment, the UE may also change the receive beam used for beam prediction within the prediction window.

[0112] In some embodiments, within a measurement window, at least one reference signal in the reference signal group or burst is used for refinement of a receive beam during the beam measurement.

[0113] Figure 8 is an example diagram of beam optimization resources in an embodiment of the present application. For example, at least one reference signal in a reference signal group or burst is used for receive beam optimization (refinement) during beam measurement. For example, there are three CSI-RSs in the measurement window shown in Figure 8, marked as 801, 802, and 803 respectively. Beam optimization (refine) can be performed at the beginning of the measurement window, for example, CSI-RS 801 is used for beam optimization in the measurement window. The present application is not limited to this, and can also be performed in the middle of the measurement window or at the end of the measurement window.

[0114] For example, beam optimization is used to select the UE's receive beam. The CSI-RS used for beam optimization can reuse the legacy configuration. For example, the CSI-RS configuration parameter "repetition" is set to "on." The remaining CSI-RS can be used to use the measurement results as input to the AI / ML model. For example, by optimizing using CSI-RS 801, the terminal device can select a receive beam with a better signal; then use this receive beam to measure CSI-RS 802 and CSI-RS 803.

[0115] In some embodiments, within a measurement window, a group / burst of reference signals is determined based on a resource set, wherein reference signal resources in the resource set that are located in a prediction window are muted.

[0116] For example, a CSI-RS resource set includes multiple CSI-RS resources, and a muting pattern may be introduced to form a reference signal group / burst (group or burst) based on the CSI-RS resource set. For example, the reference signal is not transmitted during the prediction window, that is, the CSI-RS is muted during the prediction window, thereby forming a reference signal group or burst (group or burst) as shown in Figure 6 or Figure 7. For another example, the muting pattern is configured and / or indicated by RRC signaling, MAC-CE or DCI. The present application is not limited to this.

[0117] In some embodiments, the grouping or bursting of reference signals is determined based on multiple resource sets, for example, CSI-RSs in at least two CSI-RS resource sets form a reference signal group or burst.

[0118] For example, three resource sets #1, #2 and #3 can be configured; resource set #1 includes CSI-RS resource #0, CSI-RS #7, CSI-RS resource #14, resource set #2 includes CSI-RS resource #1, CSI-RS #8, CSI-RS resource #15, and resource set #3 includes CSI-RS resource #2, CSI-RS #9, CSI-RS resource #16; then CSI-RS #0, CSI-RS #1, CSI-RS #2 can form a reference signal group or burst (group or burst), CSI-RS #7, CSI-RS #8, CSI-RS #9 can form another reference signal group or burst (group or burst), and CSI-RS #14, CSI-RS #15, CSI-RS #16 can form yet another reference signal group or burst (group or burst).

[0119] In some embodiments, the terminal device sends a request to the network device, wherein the request is used to change the configuration of the beam measurement and / or the time beam prediction.

[0120] For example, the request includes configuration information preferred by the terminal device. After receiving the transmission request, the network device may change the configuration of beam measurement and / or time beam prediction for the terminal device according to the configuration information preferred by the terminal device.

[0121] For example, when the speed of the terminal device increases, the terminal device expects that the interval of the CSI-RS for measurement can be reduced, and / or the number of predicted time instances can be reduced. In this case, the terminal device may include the interval of the CSI-RS for measurement and / or the number of predicted time instances preferred by the terminal device in the request.

[0122] In some embodiments, the terminal device preferred configuration information may be sent to the base station in a UEAssistanceInformation message. Alternatively, the terminal device preferred configuration information may be sent to the base station in a MAC-CE message. The present application is not limited thereto, and for example, other RRC messages may also be used.

[0123] The above describes the case where the AI / ML functionality / model is located in the terminal device. The following describes the case where the AI / ML functionality / model is located in the network device. The content that is the same as the previous embodiment is not repeated.

[0124] FIG9 is another schematic diagram of a resource configuration method according to an embodiment of the present application. As shown in FIG9 , the method includes:

[0125] 901. A terminal device receives configuration information from a network device; wherein the configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; and

[0126] 902. The terminal device sends beam measurement results of multiple time instances to the network device; wherein the beam measurement results are input into the AI / ML functionality / model for time beam prediction.

[0127] For example, the AI / ML function resides on the network device side. The terminal device performs beam measurement based on the reference signal from the network side and sends the beam measurement results to the network device. After the AI / ML function is enabled or activated, the network device uses AI / ML to perform time beam prediction based on the beam measurement results.

[0128] It is worth noting that FIG9 above is merely a schematic illustration of an embodiment of the present application, and the present application is not limited thereto. For example, the execution order of the various operations may be appropriately adjusted, and other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above description, and are not limited to the description of FIG9 above.

[0129] For example, measurement results of multiple time moments (e.g., K time moments) in the past may be reported through one beam report. The value of K and / or the interval between two time moments may be predefined or configurable, and may also depend on the capabilities of the UE.

[0130] For example, if the L1-RSRP / L1-SINR difference is applied at all moments, the beam report indicates the moment from which the strongest beam (e.g., the beam with the highest L1-RSRP / L1-SINR) is selected as the reference beam for L1-RSRP / L1-SINR. Alternatively, an identifier for each moment may be included in a report corresponding to multiple measurement beams. For another example, the L1-RSRP / L1-SINR difference is applied to the measurement beam at one or more moments, rather than to all moments.

[0131] In some embodiments, the terminal device sends a request to the network device, wherein the request is used to change the configuration of the beam measurement and / or the time beam prediction, or the request is only used to change the configuration of the beam measurement.

[0132] For example, the request includes configuration information preferred by the terminal device. After receiving the sending request, the network device may change the configuration of the beam measurement for the terminal device according to the configuration information preferred by the terminal device.

[0133] For example, when the speed of the terminal device increases, the terminal device expects that the interval of the CSI-RS for measurement can be reduced, and / or the number of predicted time instances can be reduced. In this case, the terminal device may include the interval of the CSI-RS for measurement and / or the number of predicted time instances preferred by the terminal device in the request.

[0134] In some embodiments, the terminal device preferred configuration information may be sent to the base station in a UEAssistanceInformation message. Alternatively, the terminal device preferred configuration information may be sent to the base station in a MAC-CE message. The present application is not limited thereto, and for example, other RRC messages may also be used.

[0135] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0136] It can be seen from the above embodiments that the embodiments of the present application can use the AI / ML model to perform time beam prediction based on the results of beam measurement, thereby improving the accuracy and reliability of AI / ML for beam prediction.

[0137] Embodiments of the second aspect

[0138] The embodiment of the present application provides a resource configuration method, which is described from the perspective of a network device. The embodiment of the second aspect can be combined with the embodiment of the first aspect, and the same contents as the embodiment of the first aspect will not be repeated.

[0139] FIG10 is another schematic diagram of the resource configuration method according to an embodiment of the present application. As shown in FIG10 , the method includes:

[0140] 1001, the network device sends configuration information to the terminal device;

[0141] The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML functionality / model for time beam prediction.

[0142] As shown in FIG10 , the method may further include:

[0143] 1002. The network device receives beam prediction results and / or beam measurement results of multiple time instances sent by the terminal device.

[0144] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0145] It can be seen from the above embodiments that the embodiments of the present application can use the AI / ML model to perform time beam prediction based on the results of beam measurement, thereby improving the accuracy and reliability of AI / ML for beam prediction.

[0146] Embodiments of the third aspect

[0147] The embodiment of the present application provides a resource configuration device, which may be, for example, a terminal device, or one or more components or assemblies configured in the terminal device, and the same contents as those in the first and second aspects of the embodiment will not be repeated.

[0148] FIG11 is another schematic diagram of a resource configuration apparatus according to an embodiment of the present application. As shown in FIG11 , a resource configuration apparatus 1100 according to an embodiment of the present application includes:

[0149] A receiving unit 1101 receives configuration information from a network device;

[0150] The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML functionality / model for time beam prediction.

[0151] In some embodiments, the AI / ML functionality / model is located in the terminal device. As shown in FIG11 , the resource configuration device 1100 may further include:

[0152] The sending unit 1102 sends the beam prediction results of multiple time instances to the network device.

[0153] In some embodiments, the prediction results of the multiple time instances are reported in multiple beam reports, wherein one beam report corresponds to the prediction result of one time instance, and the number of the multiple time instances and / or the period of beam reporting are predefined or configured by the network device.

[0154] In some embodiments, the prediction results of the multiple time instances are reported in one beam report, and the number of the multiple time instances and / or the interval between two time instances are predefined or configured by the network device.

[0155] In some embodiments, a prediction result at one of the multiple time instances is used as reference beam information, and a difference between the prediction result and other prediction results is included in the beam report.

[0156] In some embodiments, the difference-based approach is applied to all moments.

[0157] In some embodiments, the difference-based approach is applied at each one or more moments.

[0158] In some embodiments, a prediction window for the time beam prediction is defined, the prediction window including one or more moments for beam prediction; the length of the prediction window and / or the number or interval of moments in the prediction window are predefined or configured by the network device.

[0159] In some embodiments, within the prediction window, the receive beam used for beam prediction is not changed.

[0160] In some embodiments, within the prediction window, the receive beam used for beam prediction is changed.

[0161] In some embodiments, a measurement window for the beam measurement is defined, and the measurement window includes one or more reference signal transmission opportunities for beam measurement; the length of the measurement window and / or the number or interval of reference signal transmission opportunities in the measurement window are predefined or configured by the network device.

[0162] In some embodiments, within the measurement window, the receive beam used for beam measurement is not changed.

[0163] In some embodiments, within the measurement window, the receive beam used for beam measurement is changed.

[0164] In some embodiments, the beam measurement is based on a reference signal group or burst;

[0165] The reference signal group or burst forms a measurement window, and the length of the measurement window and / or the number or interval of reference signal transmission opportunities in the measurement window are predefined or configured by the network device.

[0166] In some embodiments, within the measurement window, the receive beam used for beam measurement is not changed.

[0167] In some embodiments, within the measurement window, the receive beam used for beam measurement is changed.

[0168] In some embodiments, the prediction results of the multiple time instances are reported in one beam report, and the number of the multiple time instances and / or the interval between two time instances are predefined or configured by the network device.

[0169] In some embodiments, a prediction result at one of the multiple time instances is used as reference beam information, and a difference between the prediction result and other prediction results is included in the beam report.

[0170] In some embodiments, the difference-based approach is applied to all moments.

[0171] In some embodiments, the difference-based approach is applied to each one or more moments.

[0172] In some embodiments, the prediction results of the multiple time instances are reported in multiple beam reports, wherein one beam report corresponds to the prediction result of one time instance, and the number of the multiple time instances and / or the period of beam reporting are predefined or configured by the network device.

[0173] In some embodiments, a prediction window for the time beam prediction is defined, the prediction window including one or more moments for beam prediction; the length of the prediction window and / or the number or interval of moments in the prediction window are predefined or configured by the network device.

[0174] In some embodiments, within the prediction window, the receive beam used for beam prediction is not changed.

[0175] In some embodiments, within the prediction window, the receive beam used for beam prediction is changed.

[0176] In some embodiments, within the measurement window, at least one reference signal in the reference signal group or burst is used for refinement of a receive beam during the beam measurement.

[0177] In some embodiments, within the measurement window, the reference signal group or burst is determined based on a resource set, wherein reference signal resources in the resource set that are located in a prediction window are muted.

[0178] In some embodiments, within the measurement window, the reference signal group or burst is determined based on a plurality of resource sets.

[0179] In some embodiments, the sending unit 1102 sends a request to the network device, where the request is used to change the configuration of the beam measurement and / or the time beam prediction.

[0180] In some embodiments, the request includes configuration information of the terminal device preference.

[0181] In some embodiments, the AI / ML functionality / model is located on the network device.

[0182] In some embodiments, the sending unit 1102 sends beam measurement results of multiple time instances to the network device.

[0183] In some embodiments, the result of the beam measurement is used by the network device to perform the time beam prediction; wherein, the measurement results of the multiple time instances are reported in a beam report, and the number of the multiple time instances and / or the interval between two time instances are predefined or configured by the network device.

[0184] In some embodiments, the sending unit 1102 sends a request to the network device, where the request is used to change the configuration of the beam measurement and / or the time beam prediction.

[0185] In some embodiments, the request includes configuration information of the terminal device preference.

[0186] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0187] It is worth noting that the above description only describes the components or modules related to the present application, but the present application is not limited thereto. The resource configuration device 1100 may also include other components or modules. For the specific contents of these components or modules, reference may be made to the relevant art.

[0188] In addition, for the sake of simplicity, FIG11 only illustrates the connection relationship or signal direction between various components or modules. However, it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors, memories, transmitters, and receivers; the implementation of this application is not limited to this.

[0189] It can be seen from the above embodiments that the embodiments of the present application can use the AI / ML model to perform time beam prediction based on the results of beam measurement, thereby improving the accuracy and reliability of AI / ML for beam prediction.

[0190] Embodiments of the fourth aspect

[0191] The embodiment of the present application provides a resource configuration device. The device may be, for example, a network device, or one or more components or assemblies configured in the network device, and the same contents as the first to third embodiments are not repeated here.

[0192] FIG12 is another schematic diagram of a resource configuration apparatus according to an embodiment of the present application. As shown in FIG12 , the resource configuration apparatus 1200 includes:

[0193] A sending unit 1201, which sends configuration information to a terminal device;

[0194] The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML functionality / model for time beam prediction.

[0195] In some embodiments, as shown in FIG12 , the resource configuration apparatus 1200 may further include:

[0196] The receiving unit 1202 receives beam prediction results at multiple time instances.

[0197] In some embodiments, the receiving unit 1202 may also receive a request sent by a terminal device, where the request is used to change the configuration of the beam measurement and / or the time beam prediction.

[0198] In some embodiments, the receiving unit 1202 may also receive beam measurement results at multiple time instances.

[0199] The above embodiments are merely exemplary of the present invention, but the present invention is not limited thereto. Appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0200] It is worth noting that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The resource configuration device 1200 may also include other components or modules. For the specific contents of these components or modules, reference may be made to related technologies.

[0201] In addition, for the sake of simplicity, FIG12 only illustrates the connection relationship or signal direction between various components or modules. However, it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors, memories, transmitters, and receivers; the implementation of this application is not limited to this.

[0202] It can be seen from the above embodiments that the embodiments of the present application can use the AI / ML model to perform time beam prediction based on the results of beam measurement, thereby improving the accuracy and reliability of AI / ML for beam prediction.

[0203] Embodiments of the fifth aspect

[0204] An embodiment of the present application also provides a communication system, and reference may be made to FIG1 . The contents that are the same as those in the first to fourth aspects of the embodiments will not be repeated.

[0205] In some embodiments, the communication system 100 may include at least:

[0206] a network device that sends configuration information to a terminal device; and

[0207] A terminal device, which receives configuration information from the network device;

[0208] The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML functionality / model for time beam prediction.

[0209] The embodiment of the present application also provides a terminal device, but the present application is not limited thereto and may also be other devices.

[0210] Figure 13 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in Figure 13 , terminal device 1300 may include a processor 1310 and a memory 1320. Memory 1320 stores data and programs and is coupled to processor 1310. It should be noted that this diagram is exemplary; other types of structures may be used to supplement or replace this structure to implement telecommunication or other functions.

[0211] For example, the processor 1310 may be configured to execute a program to implement the resource configuration method as described in the embodiment of the first aspect. For example, the processor 1310 may be configured to perform the following control: receiving configuration information from a network device; wherein the configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the beam measurement result is input into an AI / ML functionality / model for time beam prediction.

[0212] As shown in Figure 13 , the terminal device 1300 may further include: a communication module 1330, an input unit 1340, a display 1350, and a power supply 1360. The functions of these components are similar to those in the prior art and are not described in detail here. It is worth noting that the terminal device 1300 does not necessarily include all of the components shown in Figure 13 , and these components are not essential. Furthermore, the terminal device 1300 may also include components not shown in Figure 13 , for which reference may be made to the prior art.

[0213] An embodiment of the present application further provides a network device, which may be, for example, a base station, but the present application is not limited thereto and may also be other network devices.

[0214] Figure 14 is a schematic diagram of the structure of a network device according to an embodiment of the present application. As shown in Figure 14 , network device 1400 may include a processor 1410 (e.g., a central processing unit (CPU)) and a memory 1420. Memory 1420 is coupled to processor 1410. Memory 1420 can store various data and also stores an information processing program 1430, which is executed under the control of processor 1410.

[0215] For example, the processor 1410 may be configured to execute a program to implement the resource configuration method as described in the embodiment of the second aspect. For example, the processor 1410 may be configured to perform the following control: sending configuration information to a terminal device; wherein the configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the beam measurement result is input into an AI / ML functionality / model for time beam prediction.

[0216] In addition, as shown in FIG14 , network device 1400 may further include: a transceiver 1440 and an antenna 1450, etc.; wherein, the functions of the above components are similar to those in the prior art and are not described in detail here. It is worth noting that network device 1400 does not necessarily include all the components shown in FIG14 ; in addition, network device 1400 may also include components not shown in FIG14 , and reference may be made to the prior art for details.

[0217] An embodiment of the present application also provides a computer program, wherein when the program is executed in a terminal device, the program causes the terminal device to execute the resource configuration method described in the embodiment of the first aspect.

[0218] An embodiment of the present application also provides a storage medium storing a computer program, wherein the computer program enables a terminal device to execute the resource configuration method described in the embodiment of the first aspect.

[0219] An embodiment of the present application also provides a computer program, wherein when the program is executed in a network device, the program causes the network device to execute the resource configuration method described in the embodiment of the second aspect.

[0220] An embodiment of the present application also provides a storage medium storing a computer program, wherein the computer program enables a network device to execute the resource configuration method described in the embodiment of the second aspect.

[0221] The above devices and methods of the present application can be implemented by hardware or by a combination of hardware and software. The present application relates to such a computer-readable program that, when executed by a logic component, enables the logic component to implement the devices or components described above, or enables the logic component to implement the various methods or steps described above. The present application also relates to a storage medium for storing the above program, such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory, etc.

[0222] The method / device described in conjunction with the embodiments of the present application can be directly embodied as hardware, a software module executed by a processor, or a combination of the two. For example, one or more of the functional block diagrams shown in the figure and / or one or more combinations of functional block diagrams can correspond to various software modules of the computer program flow or to various hardware modules. These software modules can respectively correspond to the various steps shown in the figure. These hardware modules can be implemented by solidifying these software modules, for example, using a field programmable gate array (FPGA).

[0223] The software module may be located in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. A storage medium may be coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium; or the storage medium may be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The software module may be stored in the memory of the mobile terminal or in a memory card that can be inserted into the mobile terminal. For example, if the device (such as a mobile terminal) uses a large-capacity MEGA-SIM card or a large-capacity flash memory device, the software module may be stored in the MEGA-SIM card or the large-capacity flash memory device.

[0224] One or more of the functional blocks and / or one or more combinations of functional blocks described in the accompanying drawings may be implemented as a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or any appropriate combination thereof for performing the functions described in this application. One or more of the functional blocks and / or one or more combinations of functional blocks described in the accompanying drawings 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 communication with a DSP, or any other such configuration.

[0225] The present application has been described above in conjunction with specific embodiments. However, those skilled in the art should understand that these descriptions are merely illustrative and are not intended to limit the scope of protection of the present application. Those skilled in the art may make various modifications and variations to the present application based on the spirit and principles of the present application, and such modifications and variations are also within the scope of the present application.

[0226] Regarding the implementation methods including the above embodiments, the following additional notes are also disclosed:

[0227] 1. A resource allocation method, comprising:

[0228] The terminal device receives configuration information from the network device;

[0229] The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML functionality / model for time beam prediction.

[0230] 2. A resource allocation method, comprising:

[0231] The network device sends configuration information to the terminal device;

[0232] The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML functionality / model for time beam prediction.

[0233] 3. A terminal device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the resource configuration method as described in Note 1.

[0234] 4. A network device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the resource configuration method as described in Note 2.

Claims

1. A resource configuration device, comprising: A receiving unit, which receives configuration information from a network device; The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML function / model for time beam prediction.

2. The device according to claim 1, wherein: The AI / ML function / model is located in the terminal device, and the apparatus further comprises: A sending unit sends the beam prediction results at multiple moments to the network device.

3. The device according to claim 2, wherein: The prediction results of the multiple moments are reported in multiple beam reports, wherein one beam report corresponds to a prediction result of the moment, and the number of the multiple moments and / or the period of beam reporting are predefined or configured by the network device.

4. The device according to claim 2, wherein: Reporting the prediction results of the multiple moments in one beam report, where the number of the multiple moments and / or the interval between two moments are predefined or configured by the network device; A prediction result at one of the multiple moments is used as reference beam information, and the difference between the prediction result and other prediction results is included in the beam report; wherein the difference is applied to all moments, or the difference is applied to each one or more moments.

5. The device according to claim 2, wherein: A prediction window for the temporal beam prediction is defined, the prediction window comprising one or more moments for beam prediction; the length of the prediction window and / or the number or interval of moments in the prediction window are predefined or configured by a network device.

6. The device according to claim 5, wherein: Within the prediction window, the receiving beam used for beam prediction is not changed; Alternatively, within the prediction window, the receiving beam used for beam prediction is changed.

7. The device according to claim 2, wherein: A measurement window for the beam measurement is defined, and the measurement window includes one or more reference signal transmission opportunities for beam measurement; the length of the measurement window and / or the number or interval of reference signal transmission opportunities in the measurement window are predefined or configured by a network device.

8. The device according to claim 7, wherein: In the measurement window, the receiving beam used for beam measurement is not changed; Alternatively, within the measurement window, the receiving beam used for beam measurement is changed.

9. The device according to claim 2, wherein: The beam measurement is based on a reference signal group or burst; The reference signal group or burst forms a measurement window, and the length of the measurement window and / or the number or interval of reference signal transmission opportunities in the measurement window are predefined or configured by the network device; In the measurement window, the receiving beam used for beam measurement is not changed; or, in the measurement window, the receiving beam used for beam measurement is changed.

10. The device according to claim 9, wherein: Reporting the prediction results of the multiple moments in one beam report, where the number of the multiple moments and / or the interval between two moments are predefined or configured by the network device; A prediction result at one of the multiple moments is used as reference beam information, and the difference between the prediction result and other prediction results is included in the beam report; wherein the difference is applied to all moments, or the difference is applied to each one or more moments.

11. The apparatus according to claim 9, reporting the prediction results at the multiple moments in multiple beam reports, wherein: One beam report corresponds to a prediction result at one moment, and the number of the multiple moments and / or the period of beam reporting are predefined or configured by the network device.

12. The apparatus according to claim 9, wherein a prediction window of the temporal beam prediction is defined, the prediction window comprising one or more moments for beam prediction; the length of the prediction window and / or the number or interval of moments in the prediction window are predefined or configured by the network device; in, Within the prediction window, the reception beam used for beam prediction is not changed; or, within the prediction window, the reception beam used for beam prediction is changed.

13. The device according to claim 9, wherein: Within the measurement window, at least one reference signal in the reference signal group or burst is used for optimization of a receive beam during the beam measurement.

14. The device according to claim 9, wherein: In the measurement window, the reference signal group or burst is determined based on a resource set, wherein reference signal resources in the resource set that are located in a prediction window are muted; Alternatively, within the measurement window, the reference signal group or burst is determined based on a plurality of resource sets.

15. The device according to claim 2, wherein: The sending unit sends a request to the network device, where the request is used to change the configuration of the beam measurement and / or the time beam prediction; The request includes configuration information preferred by the terminal device.

16. The apparatus of claim 1, wherein the AI / ML function / model is located on the network device, the apparatus further comprising: A sending unit sends beam measurement results at multiple moments to the network device.

17. The device according to claim 16, wherein: The result of the beam measurement is used by the network device to perform the time beam prediction; The measurement results of the multiple moments are reported in one beam report, and the number of the multiple moments and / or the interval between two moments are predefined or configured by the network device.

18. The device according to claim 16, wherein: The sending unit sends a request to the network device, where the request is used to change the configuration of the beam measurement and / or the time beam prediction; The request includes configuration information preferred by the terminal device.

19. A resource allocation device, comprising: A sending unit, which sends configuration information to a terminal device; The configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into the AI / ML function / model for time beam prediction.

20. A communication system comprising: A network device that sends configuration information to a terminal device; A terminal device that receives the configuration information; wherein the configuration information includes a second reference signal resource set for beam measurement and a first reference signal resource set for time beam prediction; wherein the result of the beam measurement is input into an AI / ML function / model for time beam prediction.

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