Prediction method and apparatus, and storage medium

Abstract

The present disclosure relates to a prediction method and apparatus, and a storage medium. The method comprises: measuring a first number of first received beams to obtain first measurement results; and predicting, on the basis of the first measurement results, a second number of second received beams to obtain second measurement results, wherein the second received beams comprise the first received beams. In the embodiment, the problem of large resource consumption caused by a terminal measuring each beam is solved; and by means of measuring a certain number of beams to obtain measurement results, and then predicting measurement results of a larger number of beams on the basis of the measurement results obtained by means of measurement, measurement resources are saved, and the measurement efficiency is improved.

Description

Prediction method, device and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular to a prediction method, device, and storage medium. Background Art

[0002] With the rapid development of mobile communication technology, terminals can receive beams sent by network devices and then measure the received beams to obtain measurement results. However, the energy consumption of the terminals for measuring the beams is high and the resources are wasted.

[0003] Summary of the Invention

[0004] The solution provided by the present disclosure solves the problem that the terminal consumes a lot of resources to measure each beam. After measuring a certain number of beams to obtain measurement results, the measurement results of a larger number of beams are predicted based on the measured results, thereby saving measurement resources and improving measurement efficiency.

[0005] The embodiments of the present disclosure provide a prediction method, device, and storage medium.

[0006] According to a first aspect of an embodiment of the present disclosure, a prediction method is proposed, which is executed by a terminal and includes:

[0007] Measuring a first number of first receive beams to obtain a first measurement result;

[0008] Predicting a second number of second receive beams based on the first measurement result to obtain a second measurement result;

[0009] The second receiving beam includes the first receiving beam.

[0010] According to a second aspect of an embodiment of the present disclosure, a prediction device is provided, comprising:

[0011] a processing module, configured to measure a first number of first receiving beams to obtain a first measurement result;

[0012] The processing module is further configured to predict a second number of second receive beams based on the first measurement result to obtain a second measurement result;

[0013] The second receiving beam includes the first receiving beam.

[0014] According to a third aspect of an embodiment of the present disclosure, a terminal is provided, including:

[0015] one or more processors;

[0016] The terminal is used to execute any one of the methods described in the first aspect.

[0017] According to a fourth aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method as described in any one of the first aspect or the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the present disclosure. The illustrative embodiments of the embodiments of the present disclosure and their descriptions are used to explain the embodiments of the present disclosure and do not constitute an improper limitation on the embodiments of the present disclosure. In the drawings:

[0019] FIG1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure;

[0020] FIG2 is an interactive schematic diagram of a prediction method according to an embodiment of the present disclosure;

[0021] FIG3A is a flow chart of a prediction method according to an embodiment of the present disclosure;

[0022] FIG3B is a flow chart of a prediction method according to an embodiment of the present disclosure;

[0023] FIG4 is a flow chart of a prediction method according to an embodiment of the present disclosure;

[0024] FIG5 is a schematic diagram of the structure of a prediction device proposed in an embodiment of the present disclosure;

[0025] FIG6A is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure;

[0026] FIG6B is a schematic diagram of the structure of a chip proposed in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] The present disclosure provides a prediction method, device, and storage medium.

[0028] According to a first aspect of an embodiment of the present disclosure, a prediction method is proposed, which is executed by a terminal and includes:

[0029] Measuring a first number of first receive beams to obtain a first measurement result;

[0030] Predicting a second number of second receive beams based on the first measurement result to obtain a second measurement result;

[0031] The second receiving beam includes the first receiving beam.

[0032] In the above embodiment, the problem of high resource consumption of the terminal for measuring each beam is solved. After measuring a certain number of beams to obtain measurement results, the measurement results of a larger number of beams are predicted based on the measured results, thereby saving measurement resources and improving measurement efficiency.

[0033] In conjunction with some embodiments of the first aspect, in some embodiments, predicting a second number of second receive beams based on the first measurement result to obtain a second measurement result includes:

[0034] An AI (Artificial Intelligence) model is called to predict the second number of the second receiving beams based on the first measurement result to obtain the second measurement result.

[0035] In the above embodiment, the receiving beam is predicted by the AI ​​model to obtain the measurement result, thereby ensuring the accuracy of the prediction of the receiving beam.

[0036] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0037] The first number is determined based on a prediction accuracy of the terminal, where the prediction accuracy is used to indicate an accuracy of the terminal predicting a second number of second receive beams based on the first measurement result.

[0038] In the above embodiment, there is a problem of prediction accuracy when the terminal predicts the receiving beam. Therefore, the first number is determined according to the change of the prediction accuracy to ensure the accuracy of predicting the second number of beams based on the first number of receiving beams, thereby saving measurement resources and improving measurement efficiency.

[0039] With reference to some embodiments of the first aspect, in some embodiments, the prediction accuracy corresponds to the first quantity one-to-one; or,

[0040] The prediction accuracy is negatively correlated with the first quantity.

[0041] In the above embodiment, the method of determining the first number based on prediction accuracy is expanded, thereby ensuring the accuracy of predicting the second number of beams based on the first number of receiving beams, thereby saving measurement resources and improving measurement efficiency.

[0042] In conjunction with some embodiments of the first aspect, in some embodiments, the prediction accuracy refers to an accuracy determined based on the third measurement result and the fourth measurement result;

[0043] Among them, the third measurement result refers to the measurement result predicted based on the fifth measurement result, the fourth measurement result refers to the actual measurement result of the second number of the second receiving beams, and the fifth measurement result refers to the measurement result obtained by measuring the first number of the first receiving beams.

[0044] In the above embodiment, the prediction accuracy is determined by whether the predicted measurement result matches the actual measurement result, thereby ensuring the accuracy of predicting the second number of beams based on the first number of receiving beams, thereby saving measurement resources and improving measurement efficiency.

[0045] In combination with some embodiments of the first aspect, in some embodiments, the third measurement results include multiple, the fourth measurement results include multiple, and the prediction accuracy is determined based on whether the measurement result with the highest quality in each of the third measurement results matches the sixth measurement result in the fourth measurement results, and the sixth measurement result is the measurement result whose quality ranks among the top N in each of the fourth measurement results, where N is a positive integer.

[0046] In the above embodiment, the prediction accuracy is determined by whether the predicted measurement result matches the actual measurement result, thereby ensuring the accuracy of predicting the second number of beams based on the first number of receiving beams, thereby saving measurement resources and improving measurement efficiency.

[0047] In combination with some embodiments of the first aspect, in some embodiments, the third measurement results include multiple, the fourth measurement results include multiple, and the prediction accuracy is determined based on whether the seventh measurement result in each of the third measurement results matches the measurement result with the highest quality in each of the fourth measurement results, and the seventh measurement result is the measurement result whose quality ranks among the top M in the third measurement results, where M is a positive integer.

[0048] In the above embodiment, the prediction accuracy is determined by whether the predicted measurement result matches the actual measurement result, thereby ensuring the accuracy of predicting the second number of beams based on the first number of receiving beams, thereby saving measurement resources and improving measurement efficiency.

[0049] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0050] The first number is determined based on a terminal capability supported by the terminal, where the terminal capability refers to a capability of the terminal to support a first number of first receive beams.

[0051] In the above embodiment, the first number is determined based on the capability of the terminal, ensuring the accuracy of predicting the second number of beams based on the first number of receive beams, thereby saving measurement resources and improving measurement efficiency.

[0052] In combination with some embodiments of the first aspect, in some embodiments, the first measurement result is L1 (Layer 1) RSRP (Reference Signal Receiving Power).

[0053] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0054] The measurement period for measuring the first number of the first receiving beams is determined based on the first number, the number of receiving beams, the reference signal period, the DRX (Discontinuous Reception) period, and the configuration reporting period.

[0055] In the above embodiment, the measurement period can be determined by the first number, ensuring the accuracy of the determined measurement period, thereby ensuring that adjusting the first number of first receiving beams can predict the accuracy of the second receiving beam, thereby ensuring saving measurement resources and improving measurement efficiency.

[0056] In combination with some embodiments of the first aspect, in some embodiments, the first measurement result is L3 (Layer 3) RSRP.

[0057] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0058] The measurement period for measuring the first number of the first receiving beams is determined based on the measurement period in the absence of a gap, the scaling factor when performing the measurement, the adjustment factor of layer 1, the measurement resources, and the time adjustment factor.

[0059] In the above embodiment, the measurement period can be determined by multiple parameters to ensure the accuracy of the determined measurement period, thereby ensuring that adjusting the first number of first receiving beams can predict the accuracy of the second receiving beam, thereby ensuring saving measurement resources and improving measurement efficiency.

[0060] In combination with some embodiments of the first aspect, in some embodiments, the reference signal of the first receiving beam includes an SSB (Synchronization Signal Block) or a CSI-RS (Channel State Information-Reference Signal).

[0061] In a third aspect, an embodiment of the present disclosure provides a prediction device, which includes at least one of a transceiver module and a processing module; wherein the terminal is used to execute the optional implementation method of the first aspect.

[0062] In a fourth aspect, an embodiment of the present disclosure provides a terminal, including:

[0063] one or more processors;

[0064] The terminal is used to execute any one of the methods in the first aspect.

[0065] In a fifth aspect, an embodiment of the present disclosure provides a storage medium storing first information. When the first information is run on a communication device, the communication device executes a method as described in any one of the first aspect or the second aspect.

[0066] In a sixth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method as described in any one of the first aspect or the second aspect.

[0067] In a seventh aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a communication device, enables the communication device to execute the method described in any one of the first aspect or the second aspect.

[0068] In an eighth aspect, an embodiment of the present disclosure provides a chip or a chip system, wherein the chip or chip system includes a processing circuit configured to execute any one of the methods described in the first aspect or the second aspect.

[0069] It is understandable that the above-mentioned terminals, storage media, program products, computer programs, chips or chip systems are all used to execute the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0070] The present disclosure provides a prediction method, device, and storage medium. In some embodiments, the terms prediction method, information prediction method, and prediction method are interchangeable, the terms prediction device, information prediction device, and prediction device are interchangeable, and the terms information processing system, communication system, and so on are interchangeable.

[0071] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0072] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0073] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0074] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0075] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0076] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0077] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0078] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0079] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0080] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0081] In some embodiments, terms such as "time / frequency" and "time / frequency domain" refer to the time domain and / or the frequency domain.

[0082] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0083] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0084] In some embodiments, devices and equipment can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they can also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc.

[0085] In some embodiments, "network" can be interpreted as devices included in the network, such as access network equipment, core network equipment, etc.

[0086] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)", "base station (BS)", "radio base station", "fixed station", and in some embodiments may also be understood as "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission and / or reception point (TRP)" "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)", etc.

[0087] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (terminal)", "user terminal" "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc.

[0088] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0089] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0090] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0091] FIG1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG1 , the method provided in the embodiment of the present disclosure can be applied to a communication system 100, which may include a terminal 101 and a network device 102. It should be noted that the communication system 100 may also include other devices, and the present disclosure does not limit the devices included in the communication system 100.

[0092] In some embodiments, the terminal 101 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0093] In some embodiments, the network device 102 may include at least one of an access network device and a core network device.

[0094] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0095] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0096] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0097] In some embodiments, a core network device may be a device including one or more network elements, or may be multiple devices or device groups, each including all or part of the one or more network elements. The network element may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0098] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0099] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or a portion thereof, but are not limited thereto. The entities shown in FIG1 are illustrative only. The communication system may include all or part of the entities shown in FIG1 , or may include other entities outside of FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationships between the entities are illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0100] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems using other prediction methods, and next-generation systems based on and extending these systems. Furthermore, a combination of multiple systems (e.g., a combination of LTE or LTE-A with 5G) may also be used.

[0101] FIG2 is an interactive diagram of a prediction method according to an embodiment of the present disclosure. As shown in FIG2 , the embodiment of the present disclosure relates to a prediction method, which includes:

[0102] Step S2101: The network device sends first information to the terminal.

[0103] In some embodiments, the terminal receives the first information sent by the network device. In some embodiments, the above step S2101 can be replaced by: the network device sends the first information. Correspondingly, the terminal receives the first information.

[0104] In some embodiments, the first information is used to configure channel measurement for the terminal. In some embodiments, the first information is used by a network device to configure channel measurement for the terminal. In some embodiments, the first information is used to configure a measurement type for the channel measurement for the terminal. Optionally, the measurement type includes layer 1 measurement or layer 3 measurement.

[0105] In some embodiments, the present disclosure does not limit the name of the first information, which may be, for example, configuration information, indication information, configuration signaling, indication signaling, etc.

[0106] Step S2102: The terminal determines a first quantity.

[0107] In some embodiments, the first number is the number of receive beams that the terminal measures. In some embodiments, the terminal supports a maximum of K receive beams. Optionally, K is a positive integer. For example, K is 4, 8, or other values, which are not limited in the embodiments of the present disclosure. In some embodiments, the terminal supports a maximum of 8 receive beams.

[0108] In some embodiments, different receiving beams have different directions, or it can be understood that the beam has a direction, and different beams are used to receive data in different directions.

[0109] In some embodiments, the receive beam includes a reference signal. In some embodiments, the receive beam is used to receive the reference signal. Optionally, the reference signal includes an SSB or a CSI-RS.

[0110] In some embodiments, the terminal determines the first number based on its prediction accuracy. In some embodiments, the prediction accuracy indicates the accuracy with which the terminal predicts the second number of second receive beams based on the first measurement result. In some embodiments, the prediction accuracy refers to the accuracy with which the terminal predicts a number of receive beams greater than a certain number of receive beams based on a certain number of receive beams. Alternatively, the prediction accuracy may refer to the accuracy with which the terminal predicts the measurement result.

[0111] In some embodiments, the name of the prediction accuracy is not limited, and it can be, for example, accuracy, prediction accuracy, etc.

[0112] In some embodiments, the prediction accuracy corresponds to the first quantity one-to-one. In the embodiments of the present disclosure, there is a corresponding relationship between the prediction accuracy and the first quantity, or it can also be understood that each prediction accuracy corresponds to a first quantity. For example, prediction accuracy 1 corresponds to first quantity 1, prediction accuracy 2 corresponds to first quantity 2, and prediction accuracy 3 corresponds to first quantity 3. In some embodiments, if the prediction accuracy of the terminal is prediction accuracy 1, the first quantity is determined to be first quantity 1, that is, the terminal can measure the first receiving beam of the first quantity being the first quantity 1, and then predict the second receiving beam of the second quantity based on the first measurement result of the first receiving beam to obtain the second measurement result.

[0113] In some embodiments, the prediction accuracy is negatively correlated with the first quantity. In some embodiments, the negative correlation means that the greater the prediction accuracy, the smaller the first quantity. For example, the prediction accuracy is 90%, the first quantity is 2, if the prediction accuracy is 70%, the first quantity is 3, if the prediction accuracy is 60%, the first quantity is 4. For another example, the prediction accuracy is 90%, the first quantity is 1, if the prediction accuracy is 80%, the first quantity is 2, if the prediction accuracy is 70%, the first quantity is 3. It should be noted that the above is an example and does not limit the present disclosure. In some embodiments, the negative correlation means that the prediction accuracy is inversely proportional to the first quantity.

[0114] In some embodiments, the prediction accuracy refers to the accuracy determined based on the third measurement result and the fourth measurement result, wherein the third measurement result refers to the measurement result predicted based on the fifth measurement result, the fourth measurement result refers to the actual measurement result of the second number of the second receive beams, and the fifth measurement result refers to the measurement result obtained by measuring the first number of the first receive beams. In the embodiment of the present disclosure, for each second receive beam, each receive beam corresponds to an actual measurement result, that is, the fourth measurement result, and each receive beam also corresponds to a predicted measurement result, that is, the third measurement result. The prediction accuracy can be determined based on the predicted third measurement result and the actual fourth measurement result. Alternatively, it can also be understood that the prediction accuracy refers to whether there is a large difference between the predicted third measurement result and the actual fourth measurement result. If the difference is small, the prediction accuracy is high; if the difference is large, the prediction accuracy is low. Therefore, the prediction accuracy needs to be determined based on the third measurement result and the fourth measurement result.

[0115] In some embodiments, the third measurement results include multiple results, and the fourth measurement results include multiple results, each of which includes a different prediction result for the receive beam. Furthermore, different third measurement results are generated at different times. In some embodiments, the prediction accuracy is determined based on whether the highest-quality measurement result among each of the third measurement results matches a sixth measurement result among the fourth measurement results, where the sixth measurement result is the measurement result ranked in the top N quality categories among each of the fourth measurement results, where N is a positive integer. Alternatively, if N is 1, the prediction accuracy is determined based on whether the highest-quality measurement result among each of the third measurement results matches the highest-quality measurement result among the fourth measurement results. In some embodiments, the above embodiment can also be understood as meaning that if the highest-quality measurement result among each of the third measurement results is among the top N quality categories among the fourth measurement results, the third measurement result is accurate; and if the highest-quality measurement result among each of the third measurement results is not among the top N quality categories among the fourth measurement results, the third measurement result is inaccurate. For example, if there are 100 second measurement results, 90 of which meet the above requirement, then the prediction accuracy is 90%; if 85 of which meet the above requirement, then the prediction accuracy is 85%.

[0116] In some embodiments, the third measurement results include multiple results, and the fourth measurement results include multiple results. The terminal determines the prediction accuracy based on whether the seventh measurement result in each of the third measurement results matches the highest-quality measurement result in each of the fourth measurement results, where the seventh measurement result is the measurement result ranked in the top M in quality among the third measurement results, where M is a positive integer. Optionally, if M is 1, the prediction accuracy is determined based on whether the highest-quality measurement result in each of the fourth measurement results matches the highest-quality measurement result in the third measurement results. In some embodiments, the above embodiment can also be understood as meaning that if at least one of the top M measurement results in quality among the fourth measurement results is the highest-quality measurement result, the third measurement result is accurate; if none of the top M measurement results in quality among the fourth measurement results is the highest-quality measurement result, the third measurement result is inaccurate. For example, if there are 100 second measurement results and 90 of them meet the above requirement, the prediction accuracy is 90%. If 85 of them meet the above requirement, the prediction accuracy is 85%.

[0117] It should be noted that the above embodiments involve a scheme for determining the prediction accuracy. The above scheme for determining the prediction accuracy can actually be understood as an initialization stage, which refers to the stage for determining the prediction accuracy. In some embodiments, the above steps can be based on AI model prediction, and the prediction accuracy can actually be understood as the accuracy of the AI ​​model. In some embodiments, the above scheme for determining the prediction accuracy can be understood as a process for training the AI ​​model. In some embodiments, the above stage for determining the prediction accuracy is similar to the scheme for determining the first quantity based on the prediction accuracy, and then predicting the second quantity of the second receiving beam based on the first quantity of the first receiving beam, and the principles are similar, and will not be repeated here.

[0118] In some embodiments, the first number is determined based on a terminal capability supported by the terminal, where the terminal capability refers to a capability of the terminal to support a first number of first receive beams. Optionally, if the terminal capability supported by the terminal includes the first number, it may be determined that the terminal can predict a second number of second receive beams based on the first number of first receive beams.

[0119] Step S2103: The terminal measures a first number of first receiving beams to obtain a first measurement result.

[0120] In an embodiment of the present disclosure, the terminal may receive a reference signal via a first receive beam and measure the reference signal to obtain a first measurement result. Optionally, the reference signal includes at least one of an SSB or a CSI-RS.

[0121] In some embodiments, the terminal may perform L1RSRP measurement on the reference signal. In some embodiments, the terminal may perform L3RSRP measurement on the reference signal.

[0122] In some embodiments, the terminal predicts the L3RSRPs of the second number of second receive beams based on the L1RSRPs of the first number of first receive beams to obtain the L3RSRPs of the second receive beams.

[0123] In some embodiments, the terminal predicts the L1RSRPs of the second number of second receive beams based on the L3RSRPs of the first number of first receive beams to obtain the L1RSRPs of the second receive beams.

[0124] In some embodiments, the terminal predicts the L1RSRPs of the second number of second receive beams based on the L1RSRPs of the first number of first receive beams to obtain the L1RSRPs of the second receive beams.

[0125] In some embodiments, the terminal predicts the L3RSRPs of the second number of second receive beams based on the L3RSRPs of the first number of first receive beams to obtain the L3RSRPs of the second receive beams.

[0126] In some embodiments, the first measurement result is L1RSRP. Optionally, when the terminal performs L1RSRP measurement on the first receive beam, the measurement period is determined in the following manner. In some embodiments, the terminal predicts the second receive beam by performing L1RSRP measurement on the SSB. In some embodiments, the terminal determines the measurement period for measuring the first number of the first receive beams based on the first number, the number of receive beams, the reference signal period, the DRX period, and the configuration reporting period.

[0127] In some embodiments, the terminal determines the measurement period in the manner shown in Table 1, where T Report To configure the reporting period, N is the first number, M is the number of receiving beams, T SSB is the reference signal period, and DRX cycle is the DRX period:

[0128] Table 1

[0129] In some embodiments, if the higher layer parameter timeRestrictionForChannelMeasurement is configured, then M=1, otherwise M=3

[0130] -If the terminal supports [AI-based L1 RX beam prediction capability], N=M, where M is a simplification of the number of RX beam scans based on the terminal capability.

[0131] -If the terminal does not support [AI-based L1 RX beam prediction capability], N in Table 1 = 8.

[0132] (M=1 if higher layer parameter timeRestrictionForChannelMeasurement is configured, and M=3 otherwise

[0133] -if UE support[AI-based RX beam prediction capability for L1],N=M.where M is reduced RX beam sweeping number,which is based on UE capability.

[0134] -if UE doesn't support[AI-based RX beam prediction capability for L1],N=8 in Table 1. )

[0135] In some embodiments, the first measurement result is L3RSRP. Optionally, the terminal determines the measurement period for measuring the first number of the first receive beams based on the measurement period without a gap (measurement interval), the scaling factor when performing the measurement, the adjustment factor of layer 1, the measurement resource, and the time adjustment factor, wherein M meas_period_w / o_gaps It refers to the measurement period without gap, Kp is the scaling factor when measuring, K layer1_measurement It is the adjustment factor of layer 1. SMTC (SSB Measurement Timing Configuration) period is the measurement resource, CSSF intra is the time adjustment factor. Optionally, M meas_period_w / o_gaps Refers to the measurement period when there is no gap, Kp is the scaling factor when SSB measurement is performed when there is no gap, K layer1_measurement When L1 measurement resources and SMTC resources conflict, the adjustment factor of layer 1 measurement time. SMTC (SSB Measurement Timing Configuration) period: measurement resource configuration based on SSB, CSSF intra It is the measurement time adjustment factor for multi-carrier co-frequency measurement.

[0136] Table 2

[0137] In some embodiments, if the terminal does not support [L3 ai-based RX beam prediction capability], N in Table 2 = 8.

[0138] Mmeas_period_w / o_gaps: For terminals supporting FR2-1 power class 1 or 5, Mmeas_period_w / o_gaps = 40. For terminals supporting FR2-1 power class 2, Mmeas_period_w / o_gaps = 24. For terminals supporting FR2-1 power class 3, Mmeas_period_w / o_gaps = 24. For terminals supporting FR2-1 power class 4, Mmeas_period_w / o_gaps = 24. For terminals supporting FR2-2 power class 1, Mmeas_period_w / o_gaps = 60. For terminals supporting FR2-2 power class 2, Mmeas_period_w / o_gaps = 36. For terminals supporting FR2-2 power class 3, Mmeas_period_w / o_gaps = 36.

[0139] -If the terminal supports [L3 AI-based RX beam prediction capability], N = M, where M is the reduction in the number of RX beam scans based on the terminal capability.

[0140] Mmeas_period_w / o_gaps: For terminals supporting FR2-1 power levels 1 or 5, Mmeas_period_w / o_gaps = [R1] (R1 < 40). For terminals supporting FR2-1 power level 2, Mmeas_period_w / o_gaps = [R2] (R2 < 24). For terminals supporting FR2-1 power level 3, Mmeas_period_w / o_gaps = [R3] (R3 < 24). For terminals supporting FR2-1 power level 4, Mmeas_period_w / o_gaps = [R3] (R3 < 24).

[0141] (if UE doesn't support [AI-based RX beam prediction capability for L3], N=8 in Table2.

[0142] M meas_period_w / o_gaps :For a UE supporting FR2-1 power class 1 or 5,M meas_period_w / o_gaps =40.For a UE supporting FR2-1 power class 2,M meas_period_w / o_gaps =24.For a UE supporting FR2-1 power class 3,M meas_period_w / o_gaps=24.For a UE supporting power class 4,M meas_period_w / o_gaps =24.For a UE supporting FR2-2 power class 1,M meas_period_w / o_gaps =60.For a UE supporting FR2-2 power class 2,M meas_period_w / o_gaps =36.For a UE supporting FR2-2 power class 3,M meas_period_w / o_gaps =36.

[0143] -if UE support[AI-based RX beam prediction capability for L3],N=M.where M is reduced RX beam sweeping number,which is based on UE capability.

[0144] M meas_period_w / o_gaps :For a UE supporting FR2-1 power class 1 or 5,M meas_period_w / o_gaps =[R1](R1<40).For a UE supporting FR2-1 power class 2,M meas_period_w / o_gaps =[R2](R2<24).For a UE supporting FR2-1 power class 3,M meas_period_w / o_gaps =[R3](R3<24).For a UE supporting power class 4,M meas_period_w / o_gaps =[R3](R3<24).)

[0145] Step S2104: The terminal predicts a second number of second receive beams based on the first measurement result to obtain a second measurement result.

[0146] In some embodiments, the second receive beam includes the first receive beam. In some embodiments, the second number is greater than the first number. In some embodiments, the second receive beam includes at least the first receive beam.

[0147] In some embodiments, an AI model is invoked to predict the second number of second receive beams based on the first measurement result to obtain the second measurement result. In some embodiments, the AI ​​model is used to predict the measurement result of the second beam. Optionally, the AI ​​model is a neural network model, a convolutional model, etc., which is not limited in the present embodiment.

[0148] Step S2105: The terminal reports the receiving beam with the highest quality in the second measurement result to the network device.

[0149] In the embodiment of the present disclosure, after the terminal determines the receiving beam with the highest quality in the second measurement result, it can report the receiving beam to the network device.

[0150] The prediction method involved in the embodiments of the present disclosure may include at least one of steps S2101 to S2105. For example, step S2101 can be implemented as an independent embodiment, step S2102 can be implemented as an independent embodiment, step S2103 can be implemented as an independent embodiment, step S2104 can be implemented as an independent embodiment, and step S2105 can be implemented as an independent embodiment. Steps S2101 and S2102 can be implemented as independent embodiments, steps S2101 and S2103 can be implemented as independent embodiments, steps S2101 and S2104 can be implemented as independent embodiments, steps S2102 and S2103 can be implemented as independent embodiments, steps S2102 and S2104 can be implemented as independent embodiments, steps S2103 and S2104 can be implemented as independent embodiments, and steps S2104 and S2105 can be implemented as independent embodiments, but the present invention is not limited thereto.

[0151] In some embodiments, step S2101 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0152] In some embodiments, step S2102 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0153] In some embodiments, step S2103 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0154] In some embodiments, step S2104 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0155] In some embodiments, step S2105 is optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0156] In some embodiments, step S2101 and step S2102 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0157] In some embodiments, step S2101 and step S2103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0158] In some embodiments, step S2101 and step S2104 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0159] In some embodiments, step S2102 and step S2103 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0160] In some embodiments, step S2102 and step S2104 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0161] In some embodiments, step S2103 and step S2104 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0162] In some embodiments, step S2104 and step S2105 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0163] In some embodiments, reference may be made to other optional implementations described before or after the description corresponding to FIG. 2 .

[0164] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0165] In some embodiments, terms such as "uplink", "uplink", "physical uplink" can be interchangeable, and terms such as "downlink", "downlink", "physical downlink" can be interchangeable, and terms such as "side", "sidelink", "side communication", "sidelink communication", "direct connection", "direct link", "direct communication", and "direct link communication" can be interchangeable.

[0166] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0167] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0168] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0169] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0170] FIG3A is a flow chart of a prediction method according to an embodiment of the present disclosure, which is applied to a terminal. As shown in FIG3A , the embodiment of the present disclosure relates to a prediction method, which includes:

[0171] Step S3101: The terminal determines a first quantity.

[0172] The optional implementation of step S3101 can refer to the optional implementation of step S2102 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0173] Step S3102: The terminal measures a first number of first receiving beams to obtain a first measurement result.

[0174] The optional implementation of step S3102 can refer to the optional implementation of step S2103 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0175] Step S3103: The terminal predicts a second number of second receive beams based on the first measurement result to obtain a second measurement result.

[0176] The optional implementation of step S3103 can refer to the optional implementation of step S2104 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0177] Step S3104: The terminal reports the receiving beam with the highest quality in the second measurement result to the network device.

[0178] The optional implementation of step S3104 can refer to the optional implementation of step S2105 in Figure 2 and other related parts in the embodiment involved in Figure 2, which will not be repeated here.

[0179] The prediction method involved in the embodiments of the present disclosure may include at least one of steps S3101 to S3103. For example, step S3101 may be implemented as an independent embodiment, step S3102 may be implemented as an independent embodiment, step S3103 may be implemented as an independent embodiment, and step S3104 may be implemented as an independent embodiment.

[0180] FIG3B is a flow chart of a prediction method according to an embodiment of the present disclosure, which is applied to a terminal. As shown in FIG3B , the embodiment of the present disclosure relates to a prediction method, which includes:

[0181] Step S3201: The terminal predicts a second number of second receive beams based on the first measurement result to obtain a second measurement result.

[0182] The optional implementation of step S3201 can refer to the optional implementation of step S2104 in Figure 2, step S3102 in Figure 3A, and other related parts in the embodiments involved in Figures 2 and 3A, which will not be repeated here.

[0183] FIG4 is a flow chart of a prediction method according to an embodiment of the present disclosure. As shown in FIG4 , the embodiment of the present disclosure relates to a prediction method, which includes:

[0184] Step S4101: The terminal supports predicting the best beam based on partial beams.

[0185] In some embodiments, the number of RX beam scans for L1-RSRP measurement is reduced.

[0186] In some embodiments, for L1 measurement, the UE needs to perform RX beam scanning based on the refined beams, find the optimal RX beam, and then calculate the RSRP. For example, the terminal will measure the RSRPs of all 8 RX beams. However, due to a certain relationship between the different RSRPs from different RX beams, the terminal can only measure some RX beams and then predict the remaining beams using AI methods or other advanced methods. For example, the terminal can only measure 3 beams and predict all 8 beams. Based on the 8 beams, the terminal can select the optimal beam refractive index. Then, the terminal only needs to measure L1-RSRP based on the best predicted RX beam. In this way, the terminal can perform simplified RX beam scanning based on the terminal's capabilities.

[0187] In some embodiments, if the UE can predict the best RX beam with high accuracy, for example, the UE can predict the best RX beam with 90% accuracy, then the UE only needs to measure the best beam. The reduced number of RX beam scans is 1. For some terminals, due to the influence of terminal capabilities, the prediction accuracy of the best RX beam is not ideal. If the predicted best RX beam is not accurate enough, for example, the best RX beam index has only 50% accuracy. Then the terminal may need to predict N (1 < N < 8) RX beams to ensure that the ideal best RX beam is within the N beams with an accuracy rate of 90%. In this case, the reduced RX beam scan coefficient is n, so the terminal will report different RX beam scan number capabilities. This capability is defined as the RX beam scan coefficient and can be [1 - 7].

[0188] In some embodiments, reduction of the number of RX beam scans for L3-RSRP measurement

[0189] For L3 measurement, the terminal needs to perform RX beam scanning based on a coarse beam to find the best RX beam, and then calculate the RSRP. Similar to the reduction of the RX beam scan factor for L1-RSRP, the terminal can also predict the best RX beam for L3-RSRP measurement. Then, the terminal only needs to measure the L3-RSRP according to the best RX beam index. The terminal also needs to feedback the terminal's capability after the reduction of the RX beam.

[0190] In some embodiments, the same method as for L1-RSRP can be used to determine the number of RX beam scans for L3-RSRP measurement.

[0191] In some embodiments, the terminal needs to perform L3 measurement on mobility and L1 measurement on beam management. For L3 measurement, the terminal needs to perform 8RX beam scanning based on a coarse beam. For L1 measurement, the terminal needs to perform 8RX beam scanning based on a fine beam. Since the beam width of the fine beam is narrower than that of the coarse beam, the granularity of the fine beam is smaller. There is a certain relationship between the coarse beam and the fine beam. Therefore, it is possible for the terminal to predict the best RX fine beam based on the measurement of the coarse beam. Or the terminal predicts the best coarse beam based on the measurement of the fine beam. In this way, the terminal can predict the best beam for L1 measurement based on L3 measurement. Or the terminal predicts the best beam for L3 measurement based on the measurement of L1 measurement.

[0192] In the embodiments of the present disclosure, some or all of the steps and their optional implementation manners can be arbitrarily combined with some or all of the steps in other embodiments, and can also be arbitrarily combined with the optional implementation manners of other embodiments.

[0193] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0194] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), and the functions of some or all of the above units or modules are realized by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the remaining part by the form of hardware circuits.

[0195] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0196] Figure 5 is a schematic diagram of the structure of the prediction device proposed in an embodiment of the present disclosure. As shown in Figure 5, the prediction device 5100 may include: at least one of a transceiver module 5101, a processing module 5102, etc. In some embodiments, the processing module 5102 is used to measure a first number of first receiving beams to obtain a first measurement result; the processing module is also used to predict a second number of second receiving beams based on the first measurement result to obtain a second measurement result; wherein the second receiving beam includes the first receiving beam. Optionally, the above-mentioned transceiver module 5101 is used to execute at least one of the communication steps such as sending and / or receiving executed by the terminal in any of the above methods (such as step S2101 but not limited thereto), which will not be repeated here. Optionally, the above-mentioned processing module is used to execute at least one of the other steps executed by the terminal in any of the above methods, which will not be repeated here.

[0197] Optionally, the processing module 5102 is used to execute at least one of the communication steps such as processing performed by the terminal in any of the above methods, which will not be repeated here.

[0198] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0199] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules respectively execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0200] Figure 6A is a schematic diagram of the structure of a communication device 6100 proposed in an embodiment of the present disclosure. Communication device 6100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal, a chip, a chip system, or a processor that supports a network device in implementing any of the above methods, or a chip, a chip system, or a processor that supports a terminal in implementing any of the above methods. Communication device 6100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0201] As shown in Figure 6A, the communication device 6100 includes one or more processors 6101. Processor 6101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the prediction device (such as a base station, baseband chip, terminal, terminal chip, DU or CU, etc.), execute programs, and process program data. The communication device 6100 is used to perform any of the above methods.

[0202] In some embodiments, the communication device 6100 further includes one or more memories 6102 for storing instructions. Optionally, all or part of the memories 6102 may be located outside the communication device 6100.

[0203] In some embodiments, the communication device 6100 further includes one or more transceivers 6103. When the communication device 6100 includes one or more transceivers 6103, the transceiver 6103 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., step S2101, step S2102, step S2103, step S2104, but not limited thereto).

[0204] In some embodiments, a transceiver may include a receiver and / or a transmitter. The receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0205] In some embodiments, the communication device 6100 may include one or more interface circuits 6104. Optionally, the interface circuit 6104 is connected to the memory 6102. The interface circuit 6104 may be configured to receive signals from the memory 6102 or other devices, and may be configured to send signals to the memory 6102 or other devices. For example, the interface circuit 6104 may read instructions stored in the memory 6102 and send the instructions to the processor 6101.

[0206] The communication device 6100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 6100 described in the present disclosure is not limited thereto, and the structure of the communication device 6100 may not be limited to FIG6A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal, an intelligent terminal, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0207] 6B is a schematic diagram of the structure of a chip 6200 according to an embodiment of the present disclosure. If the communication device 6100 can be a chip or a chip system, reference can be made to the schematic diagram of the structure of the chip 6200 shown in FIG6B , but the present disclosure is not limited thereto.

[0208] The chip 6200 includes one or more processors 6201 , and the chip 6200 is configured to execute any of the above methods.

[0209] In some embodiments, the chip 6200 further includes one or more interface circuits 6202. Optionally, the interface circuit 6202 is connected to the memory 6203. The interface circuit 6202 can be used to receive signals from the memory 6203 or other devices, and can be used to send signals to the memory 6203 or other devices. For example, the interface circuit 6202 can read instructions stored in the memory 6203 and send the instructions to the processor 6201.

[0210] In some embodiments, the interface circuit 6202 performs at least one of the communication steps such as sending and / or receiving in the above method, and the processor 6201 performs at least one of the other steps.

[0211] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0212] In some embodiments, the chip 6200 further includes one or more memories 6203 for storing instructions. Alternatively, all or part of the memories 6203 may be located outside the chip 6200.

[0213] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 6100, the communication device 6100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a transient storage medium.

[0214] The present disclosure also provides a program product, which, when executed by the communication device 6100, enables the communication device 6100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0215] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims

1. A prediction method, characterized in that: The method is executed by a terminal, and includes: Measuring a first number of first receive beams to obtain a first measurement result; Predicting a second number of second receive beams based on the first measurement result to obtain a second measurement result; The second receiving beam includes the first receiving beam.

2. The method according to claim 1, characterized in that The predicting a second number of second receive beams based on the first measurement result to obtain a second measurement result includes: Call the AI ​​model to predict the second number of the second receiving beams based on the first measurement result to obtain the second measurement result.

3. The method according to claim 1 or 2, characterized in that The method further comprises: The first number is determined based on a prediction accuracy of the terminal, where the prediction accuracy is used to indicate an accuracy of the terminal predicting a second number of second receive beams based on the first measurement result.

4. The method according to claim 3, characterized in that The prediction accuracy corresponds to the first quantity in a one-to-one manner; or The prediction accuracy is negatively correlated with the first quantity.

5. The method according to claim 3 or 4, characterized in that The prediction accuracy refers to the accuracy determined based on the third measurement result and the fourth measurement result; Among them, the third measurement result refers to the measurement result predicted based on the fifth measurement result, the fourth measurement result refers to the actual measurement result of the second number of the second receiving beams, and the fifth measurement result refers to the measurement result obtained by measuring the first number of the first receiving beams.

6. The method according to claim 5, characterized in that The third measurement results include multiple results, the fourth measurement results include multiple results, and the prediction accuracy is determined based on whether the highest-quality measurement result in each of the third measurement results matches a sixth measurement result in the fourth measurement results, where the sixth measurement result is a measurement result whose quality ranks among the top N in each of the fourth measurement results, where N is a positive integer.

7. The method according to claim 5, characterized in that The third measurement results include multiple results, the fourth measurement results include multiple results, and the prediction accuracy is determined based on whether a seventh measurement result in each of the third measurement results matches a measurement result with the highest quality in each of the fourth measurement results, where the seventh measurement result is a measurement result whose quality ranks among the top M among the third measurement results, where M is a positive integer.

8. The method according to claim 1 or 2, characterized in that The method further comprises: The first number is determined based on a terminal capability supported by the terminal, where the terminal capability refers to a capability of the terminal to support a first number of first receive beams.

9. The method according to any one of claims 1 to 8, characterized in that: The first measurement result is L1 RSRP.

10. The method according to claim 9, characterized in that The method further comprises: The measurement period for measuring the first number of the first receiving beams is determined based on the first number, the number of receiving beams, the reference signal period, the DRX period, and the configuration reporting period.

11. The method according to any one of claims 1 to 8, characterized in that: The first measurement result is L3 RSRP.

12. The method according to claim 11, characterized in that The method further comprises: The measurement period for measuring the first number of the first receiving beams is determined based on the measurement period in the absence of a gap, the scaling factor when performing the measurement, the adjustment factor of layer 1, the measurement resources, and the time adjustment factor.

13. The method according to any one of claims 1 to 12, characterized in that: The reference signal of the first receive beam includes an SSB or a CSI-RS.

14. A prediction device, characterized in that: The prediction device comprises: a processing module, configured to measure a first number of first receiving beams to obtain a first measurement result; The processing module is further configured to predict a second number of second receive beams based on the first measurement result to obtain a second measurement result; The second receiving beam includes the first receiving beam.

15. A terminal, characterized in that: The terminal includes: one or more processors; The processor is configured to execute the prediction method according to any one of claims 1 to 13.

16. A storage medium, characterized in that The storage medium stores instructions, and when the instructions are executed on a communication device, the communication device executes the prediction method according to any one of claims 1 to 13.

17. A program product, characterized in that The program product is executed by a communication device, causing the communication device to execute the prediction method according to any one of claims 1 to 13.

Images