Communication method, communication device, communication system, storage medium, and program product

Abstract

Embodiments of the present disclosure relate to a communication method, a communication device, a communication system, a storage medium, and a program product. The communication method comprises: obtaining first information, wherein the first information comprises a plurality of measurement results, and each measurement result corresponds to a unique beam in a first set; obtaining a measurement threshold; and predicting a target beam on the basis of the measurement threshold and the first information, or determining, on the basis of the measurement threshold and the first information, that a target beam is predicted not to meet requirements, wherein the target beam is a beam, which meets a preset condition, in a second set, and the first set is a proper subset of the second set. The embodiments of the present disclosure can avoid the problems of inaccurate prediction and affected efficiency caused by directly predicting the optimal beam index in the second set on the basis of first information, and also lay a foundation for subsequently adjusting input information of an AI model and ensuring prediction accuracy.

Description

Communication methods, communication equipment, communication systems, storage media and software products Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a communication method, communication device, communication system, storage medium, and program product. Background Technology

[0002] For a base station (BS), it transmits signals through multiple transmit beams (TX beams), and the user equipment (UE) measures the signal power received by different transmit beams using Layer 1 Reference Signal Receiving Power (L1-RSRP) measurement.

[0003] In artificial intelligence-based beam management, AI models will be used to predict the optimal beam index based on some measurements.

[0004] Summary of the Invention

[0005] This disclosure provides a communication method, communication device, and communication system to further enhance the accuracy of predicting target beams.

[0006] In a first aspect, embodiments of this disclosure provide a communication method, including:

[0007] Obtain first information, which includes multiple measurement results, each of which corresponds to a unique beam in the first set;

[0008] Obtain the measurement threshold;

[0009] Predict the target beam based on the measurement threshold and the first piece of information, or

[0010] Based on the measurement threshold and the first information, it was determined that the predicted target beam did not meet the requirements.

[0011] The target beam is the beam in the second set that meets the preset conditions, and the first set is a proper subset of the second set.

[0012] Secondly, embodiments of this disclosure also provide a communication method, including:

[0013] Configure measurement thresholds;

[0014] Send the measurement threshold to the terminal;

[0015] Configure a first set, which includes at least one beam;

[0016] The first set is sent to the terminal.

[0017] Thirdly, embodiments of this disclosure also provide a communication device for performing the communication method of the first aspect or the second aspect.

[0018] Fourthly, embodiments of this disclosure also provide a communication device, including:

[0019] One or more processors;

[0020] The communication device is used to execute the communication method that implements the first or second aspect of the embodiments of this disclosure.

[0021] Fifthly, embodiments of this disclosure also provide a communication system, including a terminal and a network device;

[0022] The terminal is configured to implement the first aspect of the communication method, and the network device is configured to implement the second aspect of the communication method.

[0023] Sixthly, embodiments of this disclosure also provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the communication method as described in the first aspect of this disclosure, or to perform the communication method as described in the second aspect of this disclosure.

[0024] In a seventh aspect, embodiments of this disclosure also provide a program product, including at least one of a program and instructions, wherein when the program and instructions are executed by a communication device, they implement the communication method of the first aspect or the communication method of the second aspect.

[0025] In this embodiment, by obtaining first information, which includes multiple measurement results, each measurement result corresponding to a unique beam in a first set; obtaining a measurement threshold; predicting a target beam based on the measurement threshold and the first information, or determining that the predicted target beam does not meet the requirements based on the measurement threshold and the first information; wherein, the target beam is a beam in a second set that meets preset conditions, and the first set is a proper subset of the second set, this avoids the problem of inaccurate prediction and reduced efficiency caused by directly predicting the optimal beam index in the second set based on the first information, and also lays the foundation for subsequent adjustment of the input information of the AI ​​model and ensuring prediction accuracy.

[0026] Additional aspects and advantages of embodiments of this disclosure will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this disclosure. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are only some embodiments of this disclosure and do not impose specific limitations on the protection scope of this disclosure.

[0028] Figure 1A is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure;

[0029] Figure 1B is a schematic diagram of the communication method provided in an embodiment of this disclosure;

[0030] Figure 1C is a schematic diagram of CDF curves for different transmit beams according to embodiments of the present disclosure;

[0031] Figure 2 is a flowchart illustrating the communication method provided according to an embodiment of the present disclosure;

[0032] Figure 3 is a flowchart illustrating the communication method proposed according to an embodiment of the present disclosure;

[0033] Figure 4A is a schematic diagram of the structure of a terminal according to an embodiment of the present disclosure;

[0034] Figure 4B is a schematic diagram of the structure of a network device according to an embodiment of the present disclosure;

[0035] Figure 5A is a schematic diagram of the structure of a communication device according to an embodiment of the present disclosure;

[0036] Figure 5B is a schematic diagram of the structure of a chip according to an embodiment of the present disclosure. Detailed Implementation

[0037] This disclosure presents a communication method, communication device, and communication system.

[0038] In a first aspect, embodiments of this disclosure provide a communication method, including:

[0039] Obtain first information, which includes multiple measurement results, each of which corresponds to a unique beam in the first set;

[0040] Obtain the measurement threshold;

[0041] Predict the target beam based on the measurement threshold and the first piece of information, or

[0042] Based on the measurement threshold and the first information, it was determined that the predicted target beam did not meet the requirements.

[0043] The target beam is the beam in the second set that meets the preset conditions, and the first set is a proper subset of the second set.

[0044] In the above embodiments, by obtaining first information, which includes multiple measurement results, each measurement result corresponding to a unique beam in a first set; obtaining a measurement threshold; predicting a target beam based on the measurement threshold and the first information, or determining that the predicted target beam does not meet the requirements based on the measurement threshold and the first information; wherein, the target beam is a beam in a second set that meets preset conditions, and the first set is a proper subset of the second set, this avoids the problem of inaccurate prediction and reduced efficiency caused by directly predicting the optimal beam index in the second set based on the first information, and also lays the foundation for subsequent adjustment of the input information of the AI ​​model and ensuring prediction accuracy.

[0045] In conjunction with some embodiments of the first aspect, in some embodiments, predicting the target beam based on a measurement threshold and first information includes:

[0046] Based on the measurement threshold and the first information, determine the second information;

[0047] Predict the target beam based on the second information;

[0048] The second information is the information after adjusting the first information based on the measurement threshold.

[0049] In the above embodiments, by determining the second information based on the measurement threshold and the first information, the second information is the information after adjusting the first information according to the measurement threshold. The target beam is then predicted using the second information, thereby achieving the purpose of processing the first information using the measurement threshold so that the predicted target beam meets the requirements.

[0050] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement threshold is used for any of the following:

[0051] Determine whether the measurement results meet the requirements of the predicted target beam;

[0052] Determine the measurement results in the first information that meet the requirements of the predicted target beam;

[0053] Identify measurement results in the first information that do not meet the requirements of the predicted target beam.

[0054] In the above embodiments, the measurement threshold can be used to determine whether the measurement result meets the requirements, or it can be used to filter out the measurement results that meet or do not meet the requirements in the first information, thus providing a basis for obtaining the second information.

[0055] In conjunction with some embodiments of the first aspect, in some embodiments, the second information is:

[0056] The first measurement result, or,

[0057] First measurement result and second measurement result;

[0058] The first measurement result is the measurement result in the first information;

[0059] The second measurement result is the result after adjusting the third test result;

[0060] The third test result is the test result other than the first measurement result in the first information.

[0061] In the above embodiments, a portion of the measurement results in the first information can be used as the first measurement result to predict the target beam. Alternatively, the measurement results in the first information other than the first measurement result, i.e., the third measurement result, can be adjusted and used as the second measurement result. The first measurement result and the second measurement result constitute the second information, realizing diversified representation of the second information and improving its practicality.

[0062] In conjunction with some embodiments of the first aspect, in some embodiments, the value of the second measurement result is a measurement threshold, a null value, or 0.

[0063] In the above embodiments, when the second measurement result is the measurement threshold, the prediction accuracy will not be affected since the requirement has been met. When the second measurement result is 0 or null, the actual number of beam indices in the first information can be obtained during prediction, and the optimal beam index will be predicted based on the obtained number, ignoring the influence of the second measurement result.

[0064] In conjunction with some embodiments of the first aspect, in some embodiments, the first measurement result refers to a measurement result that is not less than the measurement threshold.

[0065] In the above embodiments, the measurement result that is not less than the measurement threshold in the first information is taken as the first measurement result, which provides a specific way to determine the first measurement result.

[0066] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement threshold is used to determine whether the predicted target beam meets the requirements based on the relationship between at least one measurement result in the first information and the measurement threshold.

[0067] In the above embodiments, the measurement threshold, from the perspective of the whole (i.e. the first information), determines whether the target beam predicted by the first information meets the requirements. This avoids the problem of inaccurate prediction and reduced efficiency caused by directly predicting the best beam index in the second set based on the first information.

[0068] In conjunction with some embodiments of the first aspect, in some embodiments, predicting the target beam based on a measurement threshold and first information includes:

[0069] Based on the measurement threshold and the first information, it is determined that the predicted target beam meets the requirements, and the target beam is predicted based on the first information.

[0070] In the above embodiments, since the target beam is determined to meet the requirements based on the measurement threshold and the first information, the target beam can be predicted directly based on the first information without adjusting the first information, thus ensuring the accuracy of the prediction.

[0071] In conjunction with some embodiments of the first aspect, in some embodiments, the predicted target beam meets the requirements, including any one of the following:

[0072] The first piece of information contains at least one measurement result that satisfies:

[0073] At least one of the measurement results has a maximum or minimum value that is not less than the measurement threshold.

[0074] In the above embodiments, by comparing several measurement results in the first information with the measurement threshold, if the maximum or minimum value of any at least one measurement result is not less than the measurement threshold, it is determined that the predicted target beam meets the requirements. This realizes a scheme for judging whether the predicted beam meets the requirements, avoiding the problem of inaccurate prediction and reduced efficiency caused by directly predicting the best beam index in the second set based on the first information.

[0075] In conjunction with some embodiments of the first aspect, in some embodiments, the predicted target beam does not meet the requirements, including any of the following:

[0076] The first piece of information contains at least one measurement result that satisfies:

[0077] At least one of the measurement results has a maximum or minimum value that is less than the measurement threshold.

[0078] In the above embodiments, by comparing several measurement results in the first information with the measurement threshold, if the maximum or minimum value of any at least one measurement result is less than the measurement threshold, it is determined that the predicted target beam meets the requirements. This realizes a scheme for judging whether the predicted beam meets the requirements, avoiding the problem of inaccurate prediction and reduced efficiency caused by directly predicting the best beam index in the second set based on the first information.

[0079] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement threshold is used to determine the second information based on the relationship between each measurement result in the first information and the measurement threshold.

[0080] In the above embodiments, the measurement threshold can be used to adjust the input of the predicted target beam, thereby avoiding the problem of inaccurate prediction and reduced efficiency caused by directly predicting the best beam index in the second set based on the first information.

[0081] In conjunction with some embodiments of the first aspect, in some embodiments, the second information is:

[0082] The fourth test result; or,

[0083] The results of the fourth and fifth tests;

[0084] Among them, the fourth test result is the measurement result in the first information that is not less than the measurement threshold;

[0085] The fifth test result is the adjusted result of the sixth test.

[0086] The sixth test result is the test result in the first information excluding the fourth test result.

[0087] In the above embodiments, this disclosure determines how to adjust the first information to obtain the second information by measuring a threshold, avoiding the problem of inaccurate prediction and reduced efficiency caused by directly predicting the optimal beam index in the second set based on the first information.

[0088] In conjunction with some embodiments of the first aspect, in some embodiments, the value of the fifth measurement result is a measurement threshold, a null value, or 0.

[0089] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement threshold is configured by the network device.

[0090] In the above embodiments, the measurement threshold is configured by the network device, which sends the configured measurement threshold to the terminal, enabling the terminal to judge and adjust the AI ​​model input based on the received measurement threshold.

[0091] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement results include at least one of Layer 1 Reference Signal Receiving Power (L1-RSRP) and Signal-to-Noise Ratio (SNR).

[0092] In the above embodiments, the AI ​​model input can be judged and adjusted based on at least one of L1-RSRP and SNR to improve applicability.

[0093] Secondly, embodiments of this disclosure also provide a communication method, including:

[0094] Configure measurement thresholds;

[0095] Send the measurement threshold to the terminal;

[0096] Configure a first set, which includes at least one beam;

[0097] The first set is sent to the terminal.

[0098] In the above embodiments, the network device configures a measurement threshold and a first set, and sends them to the terminal, so that the terminal can predict the target beam in the second set based on the measurement threshold and the measurement results of the first set.

[0099] Thirdly, embodiments of this disclosure also provide a communication device, which is used to perform optional implementations of the first aspect or the second aspect.

[0100] Fourthly, embodiments of this disclosure also provide a communication device, including:

[0101] One or more processors;

[0102] The communication device is used to execute either the optional implementation of the first aspect or the optional implementation of the second aspect.

[0103] Fifthly, embodiments of this disclosure also provide a communication system, including a terminal and a network device; wherein the terminal is configured to perform an optional implementation as described in the first aspect, and the network device is configured to perform an optional implementation as described in the second aspect.

[0104] In a sixth aspect, embodiments of this disclosure also provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform an optional implementation as described in the first or second aspect.

[0105] In a seventh aspect, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the method as described in the optional implementation of the first or second aspect.

[0106] Eighthly, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the methods described in an optional implementation of the first or second aspect.

[0107] Ninthly, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the method described according to an optional implementation of the first or second aspect above.

[0108] It is understood that the aforementioned communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0109] This disclosure provides communication methods, communication devices, communication systems, storage media, and program products. In some embodiments, the terms "communication method" and "signal transmission method," "wireless frame transmission method," etc., can be used interchangeably, as can the terms "information processing system" and "communication system."

[0110] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular 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 particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0111] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0112] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0113] In the embodiments disclosed herein, "multiple" refers to two or more.

[0114] In some embodiments, the terms “at least one of A or B, at least one of A and B”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0115] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of whether there is a branch B); in some embodiments, B (execute B regardless of whether there is a branch A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, both A and B are executed. The same applies when there are more branches such as A, B, C, etc.

[0116] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execute A regardless of whether a branch B exists); in some embodiments, B (execute B regardless of whether a branch A exists); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, and C.

[0117] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0118] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0119] In some embodiments, terms such as "time / frequency" and "time-frequency domain" refer to the time domain and / or frequency domain.

[0120] In some embodiments, terms such as “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “when…”, “if…”, etc. can be used interchangeably. These descriptions all refer to the device making a corresponding action under certain objective circumstances. They do not necessarily limit the time, nor do they require the device to make a judgment action when implementing it, nor do they mean that there must be other limitations.

[0121] In some embodiments, the terms “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 lower than,” and “above” can be used interchangeably, as can the terms “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”.

[0122] In some embodiments, devices, etc., may be interpreted as physical or virtual, and their names are not limited to those described in the embodiments. Terms such as “device,” “equipment,” “circuit,” “network element,” “network function,” “network device,” “function,” “node,” “unit,” “section,” “system,” “network,” “chip,” “chip system,” “entity,” and “subject” are interchangeable.

[0123] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0124] In addition, terms such as "uplink" and "downlink" can be replaced with terms corresponding to inter-terminal communication (e.g., "side"). For example, uplink channel and downlink channel can be replaced with side channel, and uplink link and downlink link can be replaced with side link.

[0125] In some embodiments, "link" can mean "connection" or "link"; in various embodiments, "connection" and "link" can be used interchangeably.

[0126] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0127] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0128] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0129] Figure 1A is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0130] As shown in Figure 1A, the communication system 100 includes a terminal 101 and a network device 102.

[0131] In some embodiments, terminal 101 includes, for example, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home, but is not limited thereto.

[0132] In some embodiments, network device 102 may include at least one of access network device and core network device.

[0133] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices, each comprising all or part of the aforementioned one or more network elements. Network elements 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), or a Next Generation Core (NGC).

[0134] In some embodiments, the access network device is, for example, a node or device that connects a terminal to a wireless network. The access network device may include, but is not limited to, at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation eNB (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system. In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within access network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0135] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0136] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0137] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1A, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1A are illustrative. The communication system may include all or some of the main bodies in FIG1A, or it may include other main bodies outside of FIG1A. The number and form of each main body are arbitrary. Each main body may be physical or virtual. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0138] The embodiments disclosed herein 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), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a 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, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0139] For the base station (BS) end, it will transmit signals through multiple transmit beams. The user equipment (UE) will measure the signal power received by different transmit beams through Layer 1 Reference Signal Receiving Power (L1-RSRP).

[0140] In AI-based beam management, an AI model is used to predict the optimal beam index based on measured values ​​of some signal parameters. The performance of the beam prediction accuracy needs to be evaluated. The predicted beam index is then compared to the ideal optimal beam index, which is also referred to as the true value of the optimal beam index or the Top-1 optimal beam index.

[0141] In this embodiment of the disclosure, the AI ​​model predicts the optimal beam index among all transmit beam indices based on the measurements of a subset of transmit beams. The beam indices of this subset constitute the measurement beam set, referred to as set B, while the beam indices of all transmit beams are referred to as set A. Referring to Figure 1B, which exemplarily illustrates a schematic diagram of the communication method provided in this embodiment of the disclosure, the optimal beam index in set A is obtained by inputting each beam index (not shown in the figure) in set B and the L1-RSRP measurement value of each beam index into the AI ​​model. For example, if a base station transmits signals through 32 transmit beams, set B may include the beam indices of 8 transmit beams, and set A includes the beam indices of all 32 transmit beams. The UE uses the measured signal parameters of the 8 transmit beams in set B as input to the AI ​​model, which then predicts the optimal beam among the 32 transmit beams in set A. AI beam prediction can reduce measurement time and reference signal transmission.

[0142] Assume set B contains 8 transmit beams and set A contains 32 TX beams. The UE needs to first measure the 8 TX beams to obtain the L1-RSRP of the 8 TX beams in set B. Different TX beams will have different signal-to-noise ratios (SNR). This disclosure analyzes the SNR conditions of 8 different TX beams.

[0143] Assume there are 32 TX beams in set A, with directions as shown below. The beam directions will involve both azimuth and vertical directions:

[0144] Taking the CDL-C channel model as an example, it has 24 clusters, each with different predefined Angle of Departure (AOD) and Angle of Arrival (AOA). However, a CDL-C channel model represents only a single channel implementation. By introducing angle translation and scaling, the predefined angle values ​​in the CDL model can be generalized—translation changes the average angle to the desired direction, and scaling changes the angle spread.

[0145] The ray angle after translation and scaling can be obtained according to the following equation in the embodiments of this disclosure:

[0146] Where, φ n，model Represents the tabulated CDL ray angles;

[0147] AS model Including the root mean square angle extension of the tabulated CDL for offset ray angles;

[0148] μ φ，model Represents the average angle of the tabular CDL;

[0149] μ φ，desired This represents the expected average angle;

[0150] AS desired This represents the expected root mean square angle expansion;

[0151] φ n，scaled This indicates the angle of the ray after scaling.

[0152] This disclosure generates multiple rotation angles and plots the cumulative distribution function (CDF) curves of L1-RSRP differences between different transmit beams in set B. Please refer to Figure 1C, which exemplarily shows a schematic diagram of the CDF curves of different transmit beams in this disclosure. Analysis of the CDF curves reveals that the CDF curves of L1-RSRP differences between different transmit beams differ significantly, indicating that the cumulative difference of different L1-RSRP differences is huge. The closer the L1-RSRP excellence levels are, the smaller the cumulative L1-RSRP difference is.

[0153] Please refer to Table 1, which exemplarily illustrates the distributional differences in SNR.

[0154] Table 1. SNR Differences Between Beams

[0155] It should be understood that in Table 1, the first beam, i.e., the best beam, the second beam, i.e., the second best beam, and so on, with the eighth beam being the worst beam. Table 1 shows that the signal-to-noise ratio (SNR) difference among the eight transmit beams in set B can be greater than 20 dB, meaning that the measurement error of some transmit beams is significant. For the same user equipment (UE), the noise power of all transmit beams will be similar. Therefore, the L1-RSRP difference can be directly converted into an SNR condition.

[0156] This disclosure reveals that when considering measurement errors of different beams in set B, prediction performance suffers a significant drop. When a transmit beam with a considerably low signal-to-noise ratio is coupled with a large measurement error, the measurement prediction performance degrades by more than 30%.

[0157] Table 2 Predictive performance of different cases

[0158] Please refer to Table 2. In this embodiment, the above 32 beams are divided into 4 groups, each group is called a case. For each case, the prediction accuracy of different K values ​​is statistically analyzed under different combinations of mean and variance measurement errors. K value refers to the number of optimal beam indices predicted. It can be clearly seen from the figure that when the variance of the measurement error is large, the prediction accuracy is significantly reduced.

[0159] As the above analysis shows, the prediction results obtained by directly using the measurement results of set B as input to the AI ​​model are uncertain. It is necessary to judge or adjust the input before inputting the measurement results of set B into the AI ​​model.

[0160] Figure 2 is a flowchart illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 2, the present disclosure relates to a communication method, which includes:

[0161] Step S2101: The network device sends the measurement threshold.

[0162] In some embodiments, the network device further transmits a first set to the terminal, the first set comprising multiple beams. It should be understood that the network device may transmit the first set and the measurement threshold to the terminal simultaneously, or it may transmit the first set and the measurement threshold sequentially.

[0163] In some embodiments, the terminal receives a measurement threshold.

[0164] In some embodiments, the terminal measures a first set to obtain first information. The first information includes multiple measurement results, each corresponding to a unique beam in the first set.

[0165] In some embodiments, the terminal predicts a target beam based on a measurement threshold and first information. The target beam is a beam in a second set that meets preset conditions, and the first set is a proper subset of the second set. For example, the second set includes 32 beams (or beam indices of the beams). For ease of description, the following description assumes the set includes beam indices. The first set includes 8 beam indices out of the 32 beam indices.

[0166] In some embodiments, a measurement threshold is used to determine whether the measurement results meet the requirements for predicting the target beam. That is, the terminal can determine whether each measurement result in the first information meets the requirements based on the measurement threshold, and thus predict the target beam using only the measurement results in the first information that meet the requirements. For example, if the first information includes measurement results for 8 beam indices, and the measurement threshold determines that the measurement results for 6 beam indices meet the requirements, it means that the AI ​​model can use the measurement results for 6 beam indices to predict the target beam among 32 beam indices (assuming the second set includes 32 beam indices). For ease of description, the target beam refers to the optimal index in the above embodiments.

[0167] In some embodiments, a measurement threshold is used to determine the measurement results in the first information that meet the requirements for predicting the target beam. That is, the terminal can determine the measurement results that meet the requirements from the first information based on the measurement threshold, and use the measurement results that meet the requirements to predict the target beam.

[0168] In some embodiments, the terminal can also adjust the measurement results that do not meet the requirements, and use the adjusted measurement results and the measurement results that meet the requirements to predict the target beam. For example, if the first information includes measurement results of 8 beam indices, and it is determined that the measurement results of 6 beam indices meet the requirements and the measurement results of 2 beam indices do not meet the requirements according to the measurement threshold, then the 2 measurement results that do not meet the requirements can be adjusted, and the adjusted 2 measurement results and the measurement results of the aforementioned 6 beam indices can be used to predict the target beam.

[0169] In some embodiments, a measurement threshold is used to determine measurement results in the first information that do not meet the requirements for predicting the target beam. That is, the terminal can exclude those measurement results that do not meet the requirements from the first information based on the measurement threshold, and use the remaining measurement results to predict the target beam.

[0170] In some embodiments, the terminal can also adjust the measurement results that do not meet the requirements, and use the adjusted measurement results and the measurement results that meet the requirements to predict the target beam. For example, if the first information includes measurement results of 8 beam indices, and it is determined that the measurement results of 6 beam indices meet the requirements and the measurement results of 2 beam indices do not meet the requirements according to the measurement threshold, then the 2 measurement results that do not meet the requirements can be adjusted, and the adjusted 2 measurement results and the measurement results of the aforementioned 6 beam indices can be used to predict the target beam.

[0171] In some embodiments, the measurement threshold is used to determine whether the predicted target beam meets the requirements based on the relationship between at least one measurement result in the first information and the measurement threshold. That is, before predicting the target beam based on the first information, the terminal can first use the measurement threshold to determine whether the predicted target beam meets the requirements. If it is determined that it does not meet the requirements, the terminal will not use the first information for prediction and will wait for new measurement results to be received before using the measurement threshold again for judgment. Prediction will only be performed when it is determined that the predicted target beam using the received measurement results meets the requirements. For example, after the terminal measures the measurement results of 8 out of 32 beams (beams 1-8), if the measurement threshold determines that the predicted target beam based on the measurement results of these 8 beams does not meet the requirements, the terminal continues to measure the other beams of the 32 beams. When the terminal measures the measurement results of another 8 beams (beams 9-16), if the measurement threshold determines that the predicted target beam based on the measurement results of beams 9-16 meets the requirements, the terminal predicts the target beam among the 32 beams based on the measurement results of beams 9-16.

[0172] In some embodiments, a measurement threshold is used to adjust the first information to determine the input of the predicted target beam.

[0173] In some embodiments, the terminal can compare the various measurement results in the first information with the measurement threshold. If all the measurement results in the first information are not less than the measurement threshold, the target beam is predicted based on the first information. If at least one measurement result in the first information is less than the measurement threshold, the target beam can be predicted based only on those measurement results that are not less than the measurement threshold. Alternatively, the measurement results that are less than the measurement threshold can be adjusted, and the target beam can be predicted based on the adjusted measurement results and the measurement results that are not less than the measurement threshold.

[0174] In some embodiments, the terminal can predict the target beam using an AI model.

[0175] In step S2102, the terminal predicts the target beam based on the measurement threshold and the first information, or determines that the predicted target beam does not meet the requirements based on the measurement threshold and the first information.

[0176] In some embodiments, terminal 101 receives a measurement threshold sent by network device 102, but is not limited thereto.

[0177] In some embodiments, the terminal 101 obtains the measurement threshold from the upper layer(s), in which case step S2101 can be omitted.

[0178] In some embodiments, the terminal 101 processes the information to obtain the first information, and step S2101 can be omitted.

[0179] In some embodiments, the terminal 101 autonomously implements the function indicated by the first information, or the above function is a default or default value, in which case step S2101 can be omitted.

[0180] In some embodiments, the terminal measures a first set of network devices to obtain first information.

[0181] In some embodiments, the first information includes measurement results of each beam index in the first set. It should be understood that the first set in this disclosure embodiment may be equivalent to set B in the above embodiments. Taking set B as an example, which includes 8 beam indices, the first information includes the measurement results of each of the 8 beam indices.

[0182] In some embodiments, a measurement threshold is used to determine whether the measurement results meet the requirements for predicting the target beam. That is, the terminal can determine whether each measurement result in the first information meets the requirements based on the measurement threshold, and thus predict the target beam using only the measurement results in the first information that meet the requirements. For example, if the first information includes measurement results for 8 beam indices, and the measurement threshold determines that the measurement results for 6 beam indices meet the requirements, it means that the AI ​​model can use the measurement results for 6 beam indices to predict the target beam among 32 beam indices (assuming the second set includes 32 beam indices). For ease of description, the target beam refers to the optimal index in the above embodiments.

[0183] In some embodiments, a measurement threshold is used to determine the measurement results in the first information that meet the requirements for predicting the target beam. That is, the terminal can determine the measurement results that meet the requirements from the first information based on the measurement threshold, and use the measurement results that meet the requirements to predict the target beam, thereby eliminating interference with the prediction.

[0184] In some embodiments, the terminal can also adjust the measurement results that do not meet the requirements, and use the adjusted measurement results and the measurement results that meet the requirements to predict the target beam. For example, if the first information includes measurement results of 8 beam indices, and it is determined that the measurement results of 6 beam indices meet the requirements and the measurement results of 2 beam indices do not meet the requirements according to the measurement threshold, then the 2 measurement results that do not meet the requirements can be adjusted, and the adjusted 2 measurement results and the measurement results of the aforementioned 6 beam indices can be used to predict the target beam.

[0185] In some embodiments, the terminal classifies each measurement result in the first information into at least one of a first measurement result and a third measurement result based on a measurement threshold. The first measurement result refers to a measurement result that meets the requirements as determined by the measurement threshold, and the third measurement result refers to a measurement result that does not meet the requirements as determined by the measurement threshold. In some embodiments, the first measurement result refers to a measurement result that is not less than the measurement threshold, and correspondingly, the second measurement result is a measurement result that is less than the measurement threshold.

[0186] In some embodiments, the result adjusted from the third measurement result becomes the second adjusted result. The value of the second measurement result can be a measurement threshold, a null value, or 0. This ensures that the number of measurement results sampled when predicting the target beam remains the same as the number of beams in the first set.

[0187] In some embodiments, a measurement threshold is used to determine measurement results in the first information that do not meet the requirements for predicting the target beam. That is, the terminal can exclude non-compliant measurement results from the first information based on the measurement threshold and use the remaining measurement results to predict the target beam. In some embodiments, the terminal can also adjust the non-compliant measurement results and use the adjusted measurement results along with the compliant measurement results to predict the target beam. For example, if the first information includes measurement results for 8 beam indices, and the measurement threshold determines that 6 beam indices meet the requirements while 2 beam indices do not, then the 2 non-compliant measurement results can be adjusted, and the adjusted 2 measurement results along with the aforementioned 6 beam indices can be used to predict the target beam.

[0188] In some embodiments, the measurement threshold is used to determine whether the predicted target beam meets the requirements based on the relationship between at least one measurement result in the first information and the measurement threshold. That is, before predicting the target beam based on the first information, the terminal can first use the measurement threshold to determine whether the predicted target beam meets the requirements. If it is determined that it does not meet the requirements, the terminal will not use the first information to make a prediction, and will wait for a new measurement result to be received before using the measurement threshold to make a judgment again, until it is determined that the predicted target beam using the received measurement result meets the requirements, and then make a prediction.

[0189] For example, after the terminal measures the results of 8 out of 32 beams (beams 1 to 8), if it is determined according to the measurement threshold that the target beam predicted based on the measurement results of these 8 beams does not meet the requirements, then the other beams of the 32 beams are measured. When the measurement results of 8 beams (beams 9 to 16) are measured again, if it is determined according to the measurement threshold that the target beam predicted based on the measurement results of beams 9 to 16 meets the requirements, then the target beam among the 32 beams is predicted based on the measurement results of beams 9 to 16.

[0190] In some embodiments, if any at least one measurement result in the first information satisfies the following condition, the target beam predicted by the first information is considered to meet the requirements:

[0191] At least one of the measurement results must have a maximum or minimum value that is not less than the measurement threshold. For example, if the first information includes measurement results for 8 beams, and if any 3 measurement results are randomly selected from these 8 results, and the maximum or minimum value of these 3 measurement results is not less than the measurement threshold, then the target beam predicted using these 8 measurement results is considered to meet the requirements.

[0192] In some embodiments, if any at least one measurement result in the first information satisfies the following condition, the target beam predicted by the first information is considered to meet the requirements:

[0193] At least one measurement result has a maximum or minimum value that is less than a measurement threshold. For example, the first information includes measurement results for eight beams. If any three measurement results are randomly selected from these eight, and the maximum or minimum value of these three measurement results is less than the measurement threshold, then predicting the target beam using these eight measurement results is considered unacceptable. The terminal may choose not to use the first information to predict the target beam.

[0194] In some embodiments, a measurement threshold is used to adjust the first information to determine the input for predicting the target beam. The terminal can compare each measurement result in the first information with the measurement threshold. If all measurement results in the first information are not less than the measurement threshold, the target beam is predicted based on the first information without adjusting the first information. If at least one measurement result in the first information is less than the measurement threshold, the target beam can be predicted either based only on those measurement results not less than the measurement threshold, or the measurement results less than the measurement threshold can be adjusted, and the target beam is predicted based on the adjusted measurement results and the measurement results not less than the measurement threshold.

[0195] In some embodiments, the information obtained by adjusting the first information through a measurement threshold becomes the second information, and the second information may be:

[0196] The fourth test result; or,

[0197] The results of the fourth and fifth tests;

[0198] Among them, the fourth test result is the measurement result in the first information that is not less than the measurement threshold;

[0199] The fifth test result is the adjusted result of the sixth test.

[0200] The sixth test result is the test result in the first information excluding the fourth test result.

[0201] In some embodiments, the terminal can predict the target beam using an AI model. For example, when it is determined that the target beam is to be predicted based on first information, the first information is input into the AI ​​model to obtain the target beam output by the AI ​​model. When it is determined that the target beam is to be predicted based on second information, the second information is input into the AI ​​model to obtain the target beam output by the AI ​​model.

[0202] In some embodiments, the measurement results of this disclosure include at least one of L1-RSRP and signal-to-noise ratio (SNR). Correspondingly, the measurement threshold includes at least one of an L1-RSRP threshold and an SNR threshold. Further, the measurement results of this application may also include Received Signal Strength Indicator (RSSI), Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), etc.

[0203] In some embodiments, the names of information, etc., are not limited to the names described in the embodiments. Terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "bit", "data", "program", and "chip" can be used interchangeably.

[0204] In some embodiments, terms such as “moment,” “point in time,” “time,” and “time location” can be used interchangeably, as can terms such as “duration,” “segment,” “time window,” “window,” and “time.”

[0205] In some embodiments, terms such as wireless access scheme and waveform can be used interchangeably.

[0206] In some embodiments, terms such as "certain," "preset," "default," "set," "indicated," "a certain," "any," and "first" can be used interchangeably. "Certain A," "preset A," "default A," "set A," "indicated A," "a certain A," "any A," and "first A" can be interpreted as A pre-defined in a protocol or the like, or as A obtained through setting, configuration, or instruction, or as specific A, a certain A, any A, or first A, but are not limited thereto.

[0207] In some embodiments, the determination or judgment can be made by a value represented by 1 bit (0 or 1), or by a true or false value (boolean), or by a comparison of numerical values ​​(e.g., a comparison with a predetermined value), but is not limited thereto.

[0208] In some embodiments, "not expecting to receive" can be interpreted as not receiving on time domain resources and / or frequency domain resources, or as not performing subsequent processing on the data after receiving it; "not expecting to send" can be interpreted as not sending, or as sending but not expecting the receiver to respond to the sent content.

[0209] The communication method involved in the embodiments of this disclosure may include step S2101. For example, step S2011 may be implemented as a standalone embodiment.

[0210] The communication method involved in the embodiments of this disclosure may include step S2102. For example, step S2012 may be implemented as a standalone embodiment.

[0211] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0212] Figure 3 is a flowchart illustrating the communication method according to an embodiment of the present disclosure. As shown in Figure 3, compared to the method shown in Figure 1B, the embodiment of the present disclosure adds a preprocessing step for each L1-RSRP measurement value before inputting each L1-RSRP measurement value of set B into the AI ​​model, thereby improving the prediction accuracy of the AI ​​model.

[0213] The embodiments of this disclosure include the following preprocessing options for L1-RSRP measurements of beams in set B:

[0214] Option 1: Set the L1-RSRP threshold to X dBm, then:

[0215] If the L1-RSRP measurement value of beam index i is less than X, then set the L1-RSRP measurement value of beam index i to X;

[0216] If the L1-RSRP measurement of beam index i is less than X, then set the L1-RSRP measurement of beam index i to 0; or

[0217] If the L1-RSRP measurement of beam index i is less than X, then the L1-RSRP measurement of beam index i will not be sent as input to the AI ​​model.

[0218] Option 2: Set the SNR threshold to YdBm, then:

[0219] If the signal-to-noise ratio of beam index i is less than Y, then set the L1-RSRP measurement of beam index i to X;

[0220] If the signal-to-noise ratio of beam index i is less than Y, then set the L1-RSRP measurement of beam index i to 0;

[0221] If the signal-to-noise ratio of beam index i is less than Y, the L1-RSRP measurement of beam index i will not be sent as input to the AI ​​model.

[0222] Option 3: Set the L1-RSRP threshold to X dBm and the SNR threshold to Y dBm, then:

[0223] If the L1-RSRP measurement of beam index i is less than X and the signal-to-noise ratio is less than Y, then the L1-RSRP measurement of beam index i is set to X, 0, or not sent as input to the AI ​​model.

[0224] If the L1-RSRP measurement of beam index i is less than X and the signal-to-noise ratio is greater than Y, then the L1-RSRP measurement of beam index i is set to X, 0, or not sent as input to the AI ​​model.

[0225] If the L1-RSRP measurement of beam index i is greater than X and the signal-to-noise ratio is less than Y, then the L1-RSRP measurement of beam index i is set to X, 0, or not sent as input to the AI ​​model.

[0226] This disclosure also adds at least one of the following applicability rules to the communication method:

[0227] If the minimum SNR of the beams in set B is higher than a certain threshold, then the prediction results of the AI ​​model are determined to meet the accuracy requirements.

[0228] If the minimum L1-RSRP measurement of the beams in set B is higher than a certain threshold, then the prediction result of the AI ​​model is determined to meet the accuracy requirements.

[0229] If the maximum SNR of the beams in set B is higher than a certain threshold, then the prediction results of the AI ​​model are determined to meet the accuracy requirements.

[0230] If the maximum L1-RSRP measurement of the beams in set B is higher than a certain threshold, then the prediction results of the AI ​​model are determined to meet the accuracy requirements.

[0231] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0232] This disclosure also proposes an apparatus (also referred to as a communication device, etc.) for implementing any of the above methods. For example, an apparatus is proposed that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Furthermore, another apparatus is proposed that includes units or modules for implementing the steps performed by a network device (e.g., an access network device, a core network functional node, a core network device, etc.) in any of the above methods.

[0233] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC). The functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD). Taking a field-programmable gate array (FPGA) as an example, it can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0234] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute 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 relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using 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 configuring the hardware circuit 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 hardware circuits designed for artificial intelligence, which can be understood as ASICs, such as Neural Network Processing Units (NPUs), Tensor Processing Units (TPUs), and Deep Learning Processing Units (DPUs).

[0235] Figure 4A is a schematic diagram of the structure of a terminal proposed in an embodiment of this disclosure. The terminal is used to execute any of the above methods. In some embodiments, as shown in Figure 4A, the terminal may include at least one of a processing module 4101, etc. In some embodiments, the processing module 4101 is used to obtain first information, the first information including multiple measurement results, each measurement result corresponding to a unique beam in a first set; obtain a measurement threshold; predict a target beam based on the measurement threshold and the first information, or determine based on the measurement threshold and the first information that the predicted target beam does not meet the requirements;

[0236] In some embodiments, the processing module may be a single module or may include multiple sub-modules. Optionally, the multiple sub-modules may each perform all or part of the steps required by the processing module.

[0237] In some embodiments, the processing module may be interchangeable with the processor.

[0238] Figure 4B is a schematic diagram of the terminal structure proposed in an embodiment of this disclosure. The network device is used to perform any of the above methods. In some embodiments, as shown in Figure 4B, the network device may include at least one of a transceiver module 4201 and a processing module 4202. In some embodiments, the processing module 4202 is used to configure a measurement threshold and a first set, and the transceiver module 4201 is used to send the measurement threshold and transmit the first set.

[0239] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, which may be separate or integrated. Optionally, the transceiver module may be interchangeable with a transceiver.

[0240] In some embodiments, the processing module may be a single module or may include multiple sub-modules. Optionally, the multiple sub-modules may each perform all or part of the steps required by the processing module. Optionally, the processing module may be interchangeable with a processor.

[0241] Figure 5A is a schematic diagram of the structure of the communication device 5100 proposed in an embodiment of this disclosure. The communication device can be a terminal (e.g., a user equipment), a chip, chip system, or processor that supports network devices in implementing any of the above methods, or a chip, chip system, or processor that supports terminals in implementing any of the above methods. The communication device 5100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0242] As shown in Figure 5A, the communication device 5100 is used to execute any of the above methods. In some embodiments, the communication device 5100 includes one or more processors 5101. The processor 5101 may be a general-purpose processor or a special-purpose processor, such as a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. Optionally, the communication device 5100 is used to execute any of the above methods. Optionally, one or more processors 5101 are used to invoke instructions to cause the communication device 5100 to execute any of the above methods.

[0243] In some embodiments, the communication device 5100 further includes one or more transceivers 5102. When the communication device 5100 includes one or more transceivers 5102, the transceiver 5102 performs at least one of the communication steps such as transmitting and / or receiving in the above method (e.g., receiving the beam corresponding to the beam index), and the processor 5101 performs at least one of the steps (e.g., step S2101, but not limited thereto). In optional embodiments, the transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, interface, etc., can be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., can be used interchangeably; the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0244] In some embodiments, the communication device 5100 further includes one or more memories 5103 for storing data and / or instructions. Optionally, one or more processors 5101 are used to invoke instructions stored in the memory 5103 to cause the communication device 5100 to perform any of the above methods. Optionally, all or part of the memory 5103 may also be located outside the communication device 5100. In an optional embodiment, the communication device 5100 may include one or more interface circuits 5104. Optionally, the interface circuit 5104 is connected to the memory 5102 and can be used to receive data and / or instructions from the memory 5102 or other devices, and can be used to send data and / or instructions to the memory 5102 or other devices. For example, the interface circuit 5104 can read data and / or instructions stored in the memory 5102 and send the data and / or instructions to the processor 5101.

[0245] The communication device 5100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 5100 described in this disclosure is not limited thereto, and the structure of the communication device 5100 may not be limited by FIG. 5A. The communication device may be a standalone device or may be part of a larger device. For example, the communication device may be: (1) a standalone integrated circuit IC, or chip, or chip system or subsystem; (2) a collection of one or more ICs, optionally, the IC collection may also include storage components for storing data, programs and / or instructions; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0246] Figure 5B is a schematic diagram of the structure of chip 5200 according to an embodiment of this disclosure. For cases where the communication device 5100 can be a chip or a chip system, please refer to the schematic diagram of chip 5200 shown in Figure 5B, but it is not limited thereto.

[0247] Chip 5200 includes one or more processors 5201. Chip 5200 is used to perform any of the methods described above.

[0248] In some embodiments, chip 5200 further includes one or more interface circuits 5202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 5200 further includes one or more memories 5203 for storing data and / or instructions. Optionally, all or part of the memories 5203 may be located outside of chip 5200. Optionally, the interface circuit 5202 is connected to the memories 5203, and the interface circuit 5202 can be used to receive data and / or instructions from the memories 5203 or other devices, and the interface circuit 5202 can be used to send data and / or instructions to the memories 5203 or other devices. For example, the interface circuit 5202 can read data and / or instructions stored in the memories 5203 and send the data and / or instructions to the processor 5201.

[0249] In some embodiments, the interface circuit 5202 performs at least one of the communication steps, such as sending and / or receiving, in the above-described method. For example, the interface circuit 5202 performing the communication steps, such as sending and / or receiving, in the above-described method means that the interface circuit 5202 performs data and / or instruction interaction between the processor 5201, the chip 5200, the memory 5203, or the transceiver device. In some embodiments, the processor 5201 performs at least one of other steps (e.g., step S2101, but not limited thereto).

[0250] The modules and / or devices described in the various embodiments, such as virtual devices, physical devices, and chips, can be combined or separated arbitrarily as needed. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0251] This disclosure also proposes a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform 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 not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0252] This disclosure also proposes a program product, including a program and / or instructions, which, when executed by a communication device, cause the communication device to perform any of the above methods. Optionally, the program product is a computer program product. Optionally, the program product is stored on the storage medium.

[0253] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A communication method characterized by comprising: The method comprises: obtaining first information, the first information comprising a plurality of measurement results, each measurement result corresponding to a unique beam in a first set; obtaining a measurement threshold; predicting a target beam according to the measurement threshold and the first information, or determining that the predicted target beam does not meet a requirement according to the measurement threshold and the first information; wherein the target beam is a beam in a second set that meets a preset condition, and the first set is a proper subset of the second set.

2. The method of claim 1, wherein, The prediction of the target beam according to the measurement threshold and the first information comprises: determining second information according to the measurement threshold and the first information; predicting the target beam according to the second information; the second information is information obtained by adjusting the first information according to the measurement threshold.

3. The method of claim 2, wherein, The measurement threshold is used for any one of the following: determining whether a measurement result meets a requirement for predicting the target beam; determining measurement results in the first information that meet the requirement for predicting the target beam; determining measurement results in the first information that do not meet the requirement for predicting the target beam.

4. The method of claim 3, wherein, The second information is: a first measurement result, or a first measurement result and a second measurement result; wherein the first measurement result is a measurement result in the first information; the second measurement result is a measurement result obtained by adjusting a third measurement result; the third measurement result is a measurement result in the first information other than the first measurement result.

5. The method of claim 4, wherein, The value of the second measurement result is the measurement threshold, a null value, or 0.

6. The method according to claim 4 or 5, characterized in that, The first measurement result refers to a measurement result that is not less than the measurement threshold.

7. The method according to claim 1 or 2, characterized in that, The measurement threshold is used to determine whether the predicted target beam meets the requirement according to the relationship between at least one measurement result in the first information and the measurement threshold.

8. The method of claim 7, wherein, The prediction of the target beam according to the measurement threshold and the first information comprises: determining that the predicted target beam meets the requirement according to the measurement threshold and the first information, and predicting the target beam according to the first information.

9. The method of claim 8, wherein, The predicted target beam meeting the requirement comprises any one of the following: there is any at least one measurement result in the first information that meets: a maximum value or a minimum value in the at least one measurement result is not less than the measurement threshold.

10. The method of claim 8, wherein, The predicted target beam not meeting the requirement comprises any one of the following: there is any at least one measurement result in the first information that meets: a maximum value or a minimum value in the at least one measurement result is less than the measurement threshold.

11. The method of claim 2, wherein, The measurement threshold is used to determine second information according to the relationship between each measurement result in the first information and the measurement threshold.

12. The method of claim 11, wherein, The second information is: a fourth measurement result; or a fourth measurement result and a fifth measurement result; wherein the fourth measurement result is a measurement result in the first information that is not less than the measurement threshold; the fifth measurement result is a measurement result obtained by adjusting a sixth measurement result; the sixth measurement result is a measurement result in the first information other than the fourth measurement result.

13. The method of claim 12, wherein, The value of the fifth measurement result is the measurement threshold, a null value, or 0.

14. The method according to any one of claims 1 to 13, characterized in that, The measurement threshold is configured by a network device.

15. The method according to any one of claims 1 to 14, characterized in that, The measurement results include at least one of the Layer 1 reference signal received power L1-RSRP and the signal-to-noise ratio (SNR).

16. A method of communication, comprising: include: Configure measurement thresholds; Send the measurement threshold to the terminal; Configure a first set, the first set including at least one beam; The first set is transmitted to the terminal.

17. A communication device, characterized by The communication device is used to perform the communication method according to any one of claims 1 to 15, or to perform the communication method according to any one of claims 16.

18. A communication system, characterized by Including terminals and network equipment; The terminal is configured to implement the communication method according to any one of claims 1 to 15, and the network device is configured to implement the communication method according to any one of claims 16.

19. A storage medium, the storage medium storing instructions, wherein, When the instruction is executed on the communication device, the communication device performs the communication method as described in any one of claims 1 to 15, or performs the communication method as described in any one of claims 16.

20. A program product comprising at least one of a program, instructions, characterized in that, When at least one of the programs or instructions is executed by a communication device, it implements the communication method of any one of claims 1 to 15, or implements the communication method of claim 16.

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