Beam measurement method and apparatus, device, storage medium and program product

Abstract

The present disclosure relates to a beam measurement method and apparatus, a device, a storage medium and a program product. An AI model is configured in a terminal device, and prediction is performed on beams on the basis of AI, so as to obtain a prediction result corresponding to a measurement task, thus improving the efficiency of acquiring results of measurement tasks; moreover, performing prediction by means of an AI model can also ensure the accuracy of prediction results and ensure the communication performance of communication links; in addition, on the basis of an AI model, a plurality of different measurement tasks can be executed simultaneously, such that the technical problem that a plurality of measurement tasks cannot be executed simultaneously can be solved, thereby further improving the efficiency of measurement task execution.

Description

Beam measurement methods, apparatus, equipment, storage media, and software products Technical Field

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

[0002] In wireless communication systems, in order to monitor the quality of the communication link and maintain good data transmission, terminal devices need to perform beam measurement tasks. Based on the measurement results, they can judge the health status of the communication link, effectively allocate resources, maintain high-quality connections, reduce overhead, and improve network performance. Summary of the Invention

[0003] This disclosure provides a beam measurement method, apparatus, device, storage medium, and program product to improve the execution efficiency of measurement tasks while ensuring the accuracy of prediction results, thereby improving communication performance.

[0004] According to a first aspect of the present disclosure, a beam measurement method is provided, the method comprising:

[0005] Obtain the first measurement result of the beam set;

[0006] The first measurement result is input into the AI ​​model to predict the beam set based on the AI ​​model, thereby obtaining the prediction results for at least one measurement task.

[0007] In this embodiment of the disclosure, an AI model is set in the terminal device to predict the beam based on the AI, so as to obtain the prediction result corresponding to the measurement task. This can improve the efficiency of obtaining the measurement task result. Moreover, the prediction by the AI ​​model can also ensure the accuracy of the prediction result and ensure the communication performance of the communication link.

[0008] Furthermore, based on AI models, multiple different measurement tasks can be executed simultaneously, which can solve the technical problem that multiple measurement tasks cannot be executed at the same time, thereby further improving the execution efficiency of measurement tasks.

[0009] According to a second aspect of the present disclosure, a beam measurement apparatus is provided, comprising:

[0010] The processing module is used to acquire the first measurement results of the beam set;

[0011] Furthermore, the first measurement result is input into an artificial intelligence (AI) model to predict the beam set based on the AI ​​model, thereby obtaining prediction results for at least one measurement task.

[0012] According to a third aspect of the present disclosure, a communication device is provided, comprising:

[0013] One or more processors;

[0014] The processor is used to execute the beam measurement method of any one of the first aspects.

[0015] According to a fourth aspect of the present disclosure, a storage medium is provided that stores instructions which, when executed on a communication device, implement a beam measurement method as described in any of the first aspects.

[0016] According to a fifth aspect of the present disclosure, a computer program product is provided, including a program and / or instructions, which, when executed by a communication device, cause the communication device to perform a beam measurement method as described in the first aspect. Attached Figure Description

[0017] 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.

[0018] Figure 1 is a schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure.

[0019] Figure 2a is an exemplary flowchart illustrating a beam measurement method according to an embodiment of the present disclosure.

[0020] Figure 2b is a schematic diagram illustrating the principle of a terminal device performing a measurement task according to an embodiment of the present disclosure.

[0021] Figure 2c is an exemplary schematic diagram of an AI model according to an embodiment of the present disclosure.

[0022] Figure 2d is an exemplary schematic diagram illustrating the principle of a first capability according to an embodiment of the present disclosure.

[0023] Figure 2e is an exemplary schematic diagram illustrating the principle of a second capability according to an embodiment of the present disclosure.

[0024] Figure 2f is an exemplary schematic diagram illustrating the principle of a third capability according to an embodiment of the present disclosure.

[0025] Figure 2g is an exemplary schematic diagram illustrating the principle of a fourth capability according to an embodiment of the present disclosure.

[0026] Figure 2h is a schematic diagram illustrating the exemplary principle of an AI model according to an embodiment of the present disclosure.

[0027] Figure 3 is a second exemplary flowchart illustrating a beam measurement method according to an embodiment of the present disclosure.

[0028] Figure 4 is an exemplary structural schematic diagram of a beam measurement device provided according to an embodiment of the present disclosure.

[0029] Figure 5a is an exemplary structural diagram of a communication device provided according to an embodiment of the present disclosure.

[0030] Figure 5b is an exemplary structural diagram of a chip provided according to an embodiment of the present disclosure. Detailed Implementation

[0031] This disclosure provides a beam measurement method, apparatus, device, storage medium, and program product. These are used to improve the execution efficiency of measurement tasks while ensuring the accuracy of prediction results, thereby enhancing communication performance.

[0032] In a first aspect, embodiments of this disclosure provide a beam measurement method, which includes:

[0033] Obtain the first measurement result of the beam set;

[0034] The first measurement result is input into an artificial intelligence (AI) model to predict the beam set based on the AI ​​model, thereby obtaining the prediction results for at least one measurement task.

[0035] In this embodiment of the disclosure, an AI model is set in the terminal device to predict the beam based on the AI, so as to obtain the prediction result corresponding to the measurement task. This can improve the efficiency of obtaining the measurement task result. Moreover, the prediction by the AI ​​model can also ensure the accuracy of the prediction result and ensure the communication performance of the communication link.

[0036] Furthermore, based on AI models, multiple different measurement tasks can be executed simultaneously, which can solve the technical problem that multiple measurement tasks cannot be executed at the same time, thereby further improving the execution efficiency of measurement tasks.

[0037] In conjunction with some embodiments of the first aspect, in some embodiments, the measurement task includes at least one of the following:

[0038] Predict and obtain beam reports corresponding to the beam set;

[0039] Predicting synchronization or loss of synchronization of wireless links for beam sets;

[0040] Predict failed beams in the beam set;

[0041] Alternatively, predict the beams in the beam set used to recover failed beams.

[0042] In this embodiment of the disclosure, by predicting and obtaining the beam report corresponding to the beam set, the signal strength and quality can be maximized, while resources can be allocated better, thereby improving data transmission efficiency and communication reliability, and improving the overall user experience;

[0043] By predicting the synchronization or loss of synchronization of wireless links in the beam set, measures can be taken in advance to maintain or restore the stability of the link, reducing dropped calls and communication interruptions.

[0044] By predicting failed beams in the beam set, potentially failing beams can be identified, and recovery can be triggered when beam failure is detected, ensuring seamless communication and maintaining link stability, thus maintaining high-quality communication and improving user experience.

[0045] By predicting the beams in the beam set used to recover failed beams, the system can quickly switch to backup beams when beam failure is detected, ensuring the continuity and stability of communication. The system can effectively allocate resources and maintain high-quality connections, reduce overhead and improve network performance.

[0046] In conjunction with some embodiments of the first aspect, in some embodiments, beam reporting is used to indicate the optimal beam in the beam set at a second time.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, the first measurement result is obtained by performing L1 measurements on the beams in the beam set.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the first measurement result includes at least one of the following:

[0049] The L1 reference signal received power RSRP of the beam in the beam set at the first moment;

[0050] The L1 signal and interference-plus-noise ratio (SINR) of the beams in the beam set at the first moment;

[0051] The L1 reference signal reception quality (RSRQ) of the beams in the beam set at the first moment;

[0052] Or, the error rate (BLER) of the beams in the beam set at the first moment.

[0053] In this embodiment of the disclosure, beam prediction and selection are performed using the first measurement results described above, resulting in more accurate prediction results. Furthermore, the AI ​​model can make predictions based on various types of first measurement results, exhibiting high flexibility and applicability to a variety of prediction scenarios.

[0054] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​model possesses at least one of a first capability, a second capability, a third capability, or a fourth capability;

[0055] The first capability is used to indicate that the input of the AI ​​model is the first measurement result and the output of the AI ​​model is the first prediction result, which includes beam reporting.

[0056] The second capability is used to indicate that the input of the AI ​​model is the first measurement result, and the output of the AI ​​model is the second prediction result, which is used to identify the failed beams in the beam set.

[0057] The third capability is used to indicate that the input of the AI ​​model is the first measurement result, and the output of the AI ​​model is the third prediction result. The third prediction result is used to determine whether the wireless link is synchronized or out of sync.

[0058] Alternatively, the fourth capability is used to indicate that the input to the AI ​​model is the first measurement result, and the output of the AI ​​model is the fourth prediction result, which is used to determine the beam used to recover the failed beam.

[0059] In this embodiment, the AI ​​model possesses the aforementioned multiple capabilities. The AI ​​model with the corresponding capability can be selected according to the prediction requirements. For example, when predicting the obtained beam report, the AI ​​model with the first capability is selected; when predicting the failed beam in the beam set, the AI ​​model with the second capability is selected. The solution of this embodiment can be flexibly applied to various prediction scenarios.

[0060] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​model possesses a fifth capability;

[0061] The fifth capability is used to indicate that the input to the AI ​​model is the first measurement result, and the output of the AI ​​model includes at least one of the following:

[0062] The first prediction result includes the beam report;

[0063] The second prediction result is used to identify the failed beams in the beam set.

[0064] The third prediction result is used to determine whether the wireless link is synchronized or out of sync.

[0065] Alternatively, the fourth prediction result is used to determine the beam used to recover the failed beam.

[0066] In this embodiment of the disclosure, by predicting and obtaining the beam report corresponding to the beam set, the signal strength and quality can be maximized, while resources can be allocated better, thereby improving data transmission efficiency and communication reliability, and improving the overall user experience;

[0067] By predicting the synchronization or loss of synchronization of wireless links in the beam set, measures can be taken in advance to maintain or restore the stability of the link, reducing dropped calls and communication interruptions.

[0068] By predicting failed beams in the beam set, potentially failing beams can be identified, and recovery can be triggered when beam failure is detected, ensuring seamless communication and maintaining link stability, thus maintaining high-quality communication and improving user experience.

[0069] By predicting the beams in the beam set used to recover failed beams, the system can quickly switch to backup beams when beam failure is detected, ensuring the continuity and stability of communication. The system can effectively allocate resources and maintain high-quality connections, reduce overhead and improve network performance.

[0070] In conjunction with some embodiments of the first aspect, in some embodiments, the second prediction result includes at least one of the following:

[0071] Failed beams in the second-time beamset;

[0072] Alternatively, the first quality information of the beams in the beam set, which is used to determine the failed beams of the beam set at the second time.

[0073] In this embodiment of the disclosure, by predicting the failed beams in the beam set, it is possible to identify the beams that may fail in the beam set, and trigger recovery when beam failure is detected, so as to ensure seamless communication and maintain link stability, maintain high-quality communication, and improve user experience.

[0074] Furthermore, it provides multiple types of secondary prediction results, which can be flexibly applied to various prediction scenarios.

[0075] In cases where the AI ​​model outputs a failed beam, the terminal device does not need to further confirm the failed beam, which can reduce the data processing pressure on the terminal device. Furthermore, the AI ​​model determines the failed beam, which is more efficient and the prediction results are more accurate.

[0076] When the AI ​​model outputs the first quality information, the data processing pressure on the AI ​​model can be reduced, saving prediction resources and enabling more predictions to be made.

[0077] In conjunction with some embodiments of the first aspect, in some embodiments, the first quality information includes at least one of the following:

[0078] RSRP

[0079] SINR;

[0080] RSRQ;

[0081] BLER.

[0082] In this embodiment of the disclosure, by predicting and obtaining various first quality information, the failure beam can be evaluated and determined from multiple dimensions, thereby improving the accuracy of the determined failure beam.

[0083] In conjunction with some embodiments of the first aspect, in some embodiments, the beam is a failed beam when the first quality information of the beam is greater than or equal to a first threshold.

[0084] In conjunction with some embodiments of the first aspect, in some embodiments, the third prediction result includes at least one of the following:

[0085] Whether the wireless link corresponding to the beam set is synchronized or out of sync at the second moment;

[0086] The failed beam in the second-time beam set is used to determine whether the wireless link is synchronized or out of sync at the second time.

[0087] Alternatively, the second quality information of the beams in the beam set, which is used to determine the failed beams in the beam set at the second time, and the failed beams are used to determine whether the wireless link is synchronized or out of sync at the second time.

[0088] In this embodiment of the disclosure, by predicting the synchronization or loss of synchronization of the wireless link by the beam set, measures can be taken in advance to maintain or restore the stability of the link, thereby reducing dropped connections and communication interruptions.

[0089] Furthermore, it provides various types of third-party prediction results, which can be flexibly applied to a variety of prediction scenarios.

[0090] In this case, when the AI ​​model outputs whether the wireless link is synchronized or out of sync, the terminal device does not need to further confirm whether the wireless link is synchronized or out of sync, which can reduce the data processing pressure of the terminal device. Moreover, the AI ​​model determines whether the wireless link is synchronized or out of sync, which is more efficient and the prediction results are more accurate.

[0091] When the AI ​​model outputs a failed beam, the terminal device does not need to further confirm the failed beam, which reduces the data processing burden on the terminal device. Moreover, the AI ​​model's determination of the failed beam is more efficient and the prediction results are more accurate. Furthermore, the AI ​​model does not need to further predict whether the wireless link is synchronized or out of sync, which reduces the data processing burden on the AI ​​model and saves prediction resources for making more predictions.

[0092] With the AI ​​model outputting second quality information, there is no need for the AI ​​model to further predict failed beams or determine whether the wireless link is synchronized or out of sync. This reduces the data processing pressure on the AI ​​model, saves prediction resources, and allows for more predictions.

[0093] In conjunction with some embodiments of the first aspect, in some embodiments, the second quality information includes at least one of the following:

[0094] RSRP;

[0095] SINR;

[0096] RSRQ;

[0097] BLER.

[0098] In this embodiment of the disclosure, by predicting and obtaining various second quality information, the failure beam can be evaluated and determined from multiple dimensions, thereby improving the accuracy of the results of determining whether the wireless link is synchronized or out of sync.

[0099] In conjunction with some embodiments of the first aspect, in some embodiments, the beam is a failed beam when the second quality information of the beam is greater than or equal to a second threshold.

[0100] In conjunction with some embodiments of the first aspect, in some embodiments, when all beams in the beam set fail, the wireless link becomes out of sync or unsynchronized;

[0101] The wireless link is synchronized and no loss of synchronization occurs, provided that at least one beam in the beam set has not failed.

[0102] In conjunction with some embodiments of the first aspect, in some embodiments, the fourth prediction result includes:

[0103] The beam used to recover the failed beam in the second-time beam set.

[0104] In this embodiment of the disclosure, by predicting the beams in the beam set used to recover failed beams, the system can quickly switch to backup beams when beam failure is detected, ensuring the continuity and stability of communication. The system can effectively allocate resources and maintain high-quality connections, reduce overhead and improve network performance.

[0105] Furthermore, the AI ​​model can directly output the beam used to recover the failed beam without requiring the terminal device to confirm it again, which can reduce the data processing pressure on the terminal device. Moreover, the AI ​​model is more efficient and the prediction results are more accurate when making predictions.

[0106] Secondly, embodiments of this disclosure provide a beam measurement device, comprising:

[0107] The processing module is used to acquire the first measurement results of the beam set;

[0108] Furthermore, the first measurement result is input into an artificial intelligence (AI) model to predict the beam set based on the AI ​​model, thereby obtaining prediction results for at least one measurement task.

[0109] Thirdly, embodiments of this disclosure provide a communication device, including:

[0110] One or more processors;

[0111] The processor is used to execute the beam measurement method of any one of the first aspects.

[0112] Fourthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the method described in the optional implementation of the first aspect.

[0113] Fifthly, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the method described in the optional implementation of the first aspect.

[0114] In a sixth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the method as described in an alternative implementation of the first aspect.

[0115] In a seventh aspect, embodiments of this disclosure provide a chip. The chip includes processing circuitry configured to perform the methods described in the first aspect and its optional implementations.

[0116] Understandably, the aforementioned beam measurement device, terminal equipment, storage medium, program product, computer program, and chip 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.

[0117] This disclosure provides beam measurement methods, apparatus, devices, storage media, and program products.

[0118] In some embodiments, the terms beam measurement method, beam processing method, beam prediction method, information processing method, measurement method, prediction method, data processing method, and communication method can be used interchangeably, as can the terms beam measurement device, beam processing device, beam prediction device, information processing device, measurement device, prediction device, data processing device, and communication device.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the aforementioned," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0123] In the embodiments disclosed herein, "multiple" refers to two or more.

[0124] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0125] 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 B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0126] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); 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, C, etc.

[0127] 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.

[0128] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0129] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0130] 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”.

[0131] In some embodiments, the apparatus and device may be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they may also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "body", etc.

[0132] In some embodiments, "network" can be interpreted as devices included in the network, such as access network devices, core network devices, etc.

[0133] In some embodiments, "device," "terminal," "terminal equipment (TE)," or "terminal device" may be referred to as "user equipment (UE)," "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.

[0134] In some embodiments, the terminal device 101 described above may be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. The name of the terminal device may differ in different systems; for example, in 5G or 6G systems, the terminal device may be called User Equipment (UE). Terminal devices include, but are not limited to, at least one of the following: mobile phone, wearable device, IoT device, car with communication capabilities, smart car, tablet computer, computer with wireless transceiver capabilities, 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.

[0135] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0136] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0137] 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.

[0138] Figure 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in Figure 1, the communication system 100 includes a terminal device 101, which is equipped with an artificial intelligence (AI) model. It should be understood that the number and form of the devices shown in Figure 1 are for illustrative purposes only and do not constitute a limitation on the embodiments of the present disclosure. In practical applications, two or more terminal devices may be included, and each terminal device 101 may be equipped with two or more AI models. The communication system shown in Figure 1 is only illustrated by example, including one terminal device 101 and one AI model.

[0139] 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.

[0140] The following embodiments of this disclosure can be applied to the terminal device 101 in the communication system 100 shown in FIG1, but are not limited thereto. The entities shown in FIG1 are illustrative. The communication system may include all or some of the entities in FIG1, or may include other entities other than those in FIG1. ​​The number and form of each entity are arbitrary. Each entity may be physical or virtual. The connection relationship between the entities is illustrative. The entities may not be connected or may be connected. The connection may be in any way, such as direct connection or indirect connection, wired connection or wireless connection.

[0141] 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), 6th generation mobile communication system (6G), 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), Machine-to-Machine (M2M), Internet of Things (IoT), Vehicle-to-Everything (V2X), and next-generation systems built upon these and utilizing other communication methods. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0142] In the above communication system, the terminal device 101 and the network device can communicate based on beams. During the communication process, in order to ensure communication quality, the terminal device 101 can measure the beam set between the terminal device 101 and the network device, thereby judging the health status of the communication link based on the measurement results, allocating resources and maintaining a high-quality connection, reducing overhead and improving network performance.

[0143] However, in related technologies, when terminal devices perform measurement tasks, the efficiency of acquiring measurement results is low, which leads to the inability to judge the health status of the communication link in a timely manner, thus affecting communication performance.

[0144] In addition, when performing measurement tasks, there may be issues where multiple tasks cannot be performed simultaneously.

[0145] In view of this, this disclosure proposes a beam measurement method, apparatus, device, storage medium, and program product. An AI model is set in the terminal device, and the beam is predicted based on the AI ​​to obtain the prediction results corresponding to the measurement task, thereby improving the efficiency of obtaining the measurement task results. Moreover, the prediction by the AI ​​model can also ensure the accuracy of the prediction results and ensure the communication performance of the communication link.

[0146] Furthermore, through the methods of this disclosure embodiment, the results of multiple measurement tasks can be predicted simultaneously using an AI model, thereby solving the technical problem that multiple tasks cannot be executed at the same time and further improving the execution efficiency of measurement tasks.

[0147] In some embodiments, the aforementioned "network device (AN device)" may also be referred to as "radio access network device (RAN device)," "network (NW)," "base station (BS)," "radio base station," or "fixed station." In some embodiments, it 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," or "bandwidth part (BWP)," etc.

[0148] It is understood that the communication system described in the embodiments of this disclosure is for the purpose of more clearly illustrating the technical solutions of the embodiments of this disclosure, and does not constitute a limitation on the technical solutions provided in the embodiments of 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 provided in the embodiments of this disclosure are also applicable to similar technical problems.

[0149] The beam measurement methods, apparatus, equipment, storage media, and program products provided in this disclosure will now be described in detail with reference to the accompanying drawings.

[0150] Referring to Figure 2a, which is an exemplary flowchart illustrating a beam measurement method according to an embodiment of the present disclosure. As shown in Figure 2a, the beam measurement method includes the following steps:

[0151] Step S2101: Obtain the first measurement result of the beam set.

[0152] In some embodiments, the beam set includes at least one beam.

[0153] In some embodiments, the first measurement result is obtained by performing Layer 1 (L1) measurements on the beams in the beam set.

[0154] In some embodiments, the first measurement result may be obtained by performing L1 measurements on multiple different cells.

[0155] In some embodiments, the first measurement result includes, but is not limited to, at least one of the following:

[0156] 1. The L1 reference signal received power (RSRP) of the beams in the beam set at the first moment;

[0157] 2. The L1 signal-to-interference-plus-noise ratio (SINR) of a beam in a beam set at the first moment; in some scenarios, SINR can also be called "signal-to-noise ratio", "signal-to-noise-interference ratio", or "signal-noise-interference ratio", etc.

[0158] 3. The L1 reference signal received quality (RSRQ) of the beams in the beam set at the first moment;

[0159] 4. The Block Error Rate (BLER) of the beams in the beam set at the first moment.

[0160] In some embodiments, the first moment can be any moment.

[0161] In some embodiments, the network can configure L1 measurements for multiple beams. Accordingly, the terminal device can obtain a first measurement result by measuring at least one of RSRP, SINR, RSRQ, or BLER of different beams in the beam set at a first moment, based on the network configuration.

[0162] In some embodiments, the network device can be configured to allow the terminal device to perform multiple measurement tasks on multiple cells. Furthermore, the terminal device may need to perform measurements on multiple component carriers (CCs). On each CC, there are also multiple neighboring cells and transmission reception points (TRPs). Please refer to FIG2b, which is a schematic diagram illustrating the principle of a terminal device performing measurement tasks according to an embodiment of the present disclosure. As shown in FIG2b, the cells on which the terminal device performs measurement tasks include, but are not limited to, at least one of the following:

[0163] 1. The serving cell of the terminal device, for example, the terminal device serving cell 1 in CC1, and the terminal device serving cell 2 in CC2, etc.

[0164] 2. Cells or TRPs used for Multiple Input Multiple Output (MIMO);

[0165] 3. Neighbor Cell for Mobility.

[0166] In some embodiments, the measurement task may include a Layer 1 (L1) measurement task and a Layer 3 (L3) measurement task. As shown in FIG2b, the measurement task of the terminal device includes, but is not limited to, at least one of the following:

[0167] L1 Reference Signal Received Power (RSRP) measurement task;

[0168] L3-RSRP measurement task;

[0169] Radio Link Monitoring (RLM);

[0170] Beam failure detection (BFD) task;

[0171] Candidate beam detection (CBD) task;

[0172] Or, tasks such as L1 signal-to-interference-plus-noise ratio (SINR) measurement.

[0173] In some embodiments, the measurement task includes, but is not limited to, at least one of the following:

[0174] Predict and obtain beam reports corresponding to the beam set;

[0175] Predicting synchronization or loss of synchronization of wireless links for beam sets;

[0176] Predict failed beams in the beam set;

[0177] Alternatively, predict the beams in the beam set used to recover failed beams, i.e., candidate beams.

[0178] Step S2102: Input the first measurement result into the AI ​​model to predict the beam set based on the AI ​​model and obtain the prediction result of at least one measurement task.

[0179] In some embodiments, one or more AI models may be set in the same terminal device; or, the same terminal device may be set in one combined model, which includes multiple sub-models.

[0180] In some embodiments, the AI ​​model can be a model with AI capabilities.

[0181] In some embodiments, the AI ​​model can be any independent AI model, or the AI ​​model can be a combined model with AI functions obtained by combining multiple AI models.

[0182] In some embodiments, the AI ​​model may include, but is not limited to, at least one of the following: a Convolutional Neural Network (CNN) model, a Recurrent Neural Network (RNN) model, or an attention-based model, etc.

[0183] In some embodiments, the AI ​​model possesses at least one of a first capability, a second capability, a third capability, or a fourth capability. Please refer to Figure 2c, which is an exemplary schematic diagram illustrating the principle of an AI model according to an embodiment of this disclosure. As shown in Figure 2c, the inputs and outputs of different capabilities of the AI ​​model include the following:

[0184] 1. First capability; where first capability is used to indicate that the input of the AI ​​model is the first measurement result and the output of the AI ​​model is the first prediction result.

[0185] Please refer to Figure 2d, which is an exemplary schematic diagram illustrating the principle of a first capability according to an embodiment of the present disclosure. As shown in Figure 2d, when the capability of the AI ​​model is the first capability, its output first prediction result includes a beam report.

[0186] In some embodiments, beam reporting is used to indicate the best beam in the beam set at a second time.

[0187] In some embodiments, the beam report includes the beam index of the best beam in the beam set at the second time.

[0188] In some embodiments of this disclosure, the second time point may be the same as the first time point, or the second time point may be any time point after the first time point. For example, the first time point is denoted as T, and the second time point may be T or T+n. Here, n can be any value.

[0189] For example, if the AI ​​model's capability is the first capability, taking a beam set including beam 1, beam 2... beam s as an example, the input of the AI ​​model is the first measurement result of beam 1, beam 2... beam s, and the output is the beam index of the best beam among beam 1, beam 2... beam s, for example, the beam index of beam 2.

[0190] 2. Second capability; wherein, the second capability is used to indicate that the input of the AI ​​model is the first measurement result, the output of the AI ​​model is the second prediction result, and the second prediction result is used to determine the failed beams in the beam set.

[0191] Please refer to Figure 2e, which is an exemplary schematic diagram illustrating the principle of a second capability according to an embodiment of this disclosure. As shown in Figure 2e, when the capability of the AI ​​model is the second capability, its output second prediction result includes at least one of the following:

[0192] The second prediction result is specifically the beam index of the failed beam in the beam set at the second time. For example, when the AI ​​model's capability is the second capability, taking a beam set including beam 1, beam 2...beams as an example, the input of the AI ​​model is the first measurement result of beam 1, beam 2...beams, and the output is the beam index of the failed beam in beam 1, beam 2...beams. For example, the output is the beam index of beam 1 and the beam index of beam 2, meaning that beam 1 and beam 2 are the failed beams in the beam set.

[0193] Alternatively, the first quality information of the beams in the beam set, used to determine the failed beams of the beam set at a second time. In some embodiments, the first quality information includes at least one of the following:

[0194] RSRP;

[0195] SINR;

[0196] RSRQ;

[0197] Or, BLER.

[0198] In some embodiments, when the second prediction result is the first quality information, the terminal device is further configured to perform the following steps:

[0199] Step S2103: Based on the first quality information, determine whether the beam corresponding to the first quality information is a failed beam.

[0200] In some embodiments, a beam is considered a failed beam when the first quality information of the beam is greater than or equal to a first threshold.

[0201] In this embodiment of the disclosure, the first threshold may be predetermined by the protocol or configured by the network device, and this embodiment of the disclosure does not limit this. It should be noted that the first threshold corresponds to different types of first quality information. For example, RSRP corresponds to the first RSRP threshold, SINR corresponds to the first SINR threshold, RSRQ corresponds to the first RSRQ threshold, and BLER corresponds to the first BLER threshold. For example, taking the RSRP of beam 1, beam 2... beam s as the first quality information, if the RSRP of beam 1 output by the AI ​​model is greater than or equal to the first RSRP threshold, then the terminal device determines that beam 1 is a failed beam; if the RSRP of beam 2 output by the AI ​​model is less than the first RSRP threshold, then the terminal device determines that beam 2 is not a failed beam.

[0202] 3. Third capability; where the third capability is used to indicate that the input of the AI ​​model is the first measurement result, the output of the AI ​​model is the third prediction result, and the third prediction result is used to determine whether the wireless link is synchronized or out of sync.

[0203] Please refer to Figure 2f, which is an exemplary schematic diagram illustrating the principle of a third capability according to an embodiment of this disclosure. As shown in Figure 2f, when the AI ​​model's capability is a third capability, its output third prediction result includes at least one of the following:

[0204] a. Whether the wireless link corresponding to the beam set is in sync (INS) or out of sync (OOS) at the second time. For example, when the AI ​​model has the third capability, taking a beam set including beam 1, beam 2... beam s as an example, the input of the AI ​​model is the first measurement result of beam 1, beam 2... beam s; the output of the AI ​​model is whether the wireless link corresponding to the beam set has lost sync, or whether the wireless link corresponding to the beam set remains synchronized.

[0205] b. The failed beams in the beam set at the second time moment are used to determine whether the wireless link is synchronized or out of sync at the second time moment. For example, when the AI ​​model has the third capability, taking the beam set including beam 1, beam 2... beam s as an example, the input of the AI ​​model is the first measurement result of beam 1, beam 2... beam s; the output of the AI ​​model is the beam index of the failed beam in the beam set. Accordingly, the terminal device can determine whether the beam set has lost sync, or whether the wireless link corresponding to the beam set remains synchronized, based on the beam index output by the AI ​​model.

[0206] c. Second quality information of beams in the beam set. The second quality information is used to identify failed beams in the beam set at the second time point. These failed beams are used to determine whether the wireless link is synchronized or out of sync at the second time point.

[0207] In some embodiments, the second quality information includes at least one of the following:

[0208] RSRP;

[0209] SINR;

[0210] RSRQ;

[0211] BLER.

[0212] In some embodiments, when the second prediction result is second quality information, the terminal device is further configured to perform the following steps:

[0213] Step S2104: Based on the second quality information, determine whether the beam corresponding to the second quality information is a failed beam.

[0214] In some embodiments, a beam is considered a failed beam when the second quality information of the beam is greater than or equal to a second threshold.

[0215] In this embodiment of the disclosure, the second threshold may be predetermined by the protocol or configured by the network device, and this embodiment of the disclosure does not limit it.

[0216] In some embodiments, the first threshold and the second threshold may be the same value, or the first threshold and the second threshold may be different values.

[0217] It should be noted that different types of second quality information correspond to different second thresholds. For example, RSRP corresponds to the second RSRP threshold, SINR corresponds to the second SINR threshold, RSRQ corresponds to the second RSRQ threshold, and BLER corresponds to the second BLER threshold. For instance, taking the RSRP of beam 1, beam 2…beam s as the first quality information, if the RSRP of beam 1 output by the AI ​​model is greater than or equal to the second RSRP threshold, the terminal device determines that beam 1 is a failed beam; if the RSRP of beam 2 output by the AI ​​model is less than the second RSRP threshold, the terminal device determines that beam 2 is not a failed beam.

[0218] Step S2105: Determine whether the wireless link is synchronized or out of sync at the second moment based on the failed beams in the beam set.

[0219] Specifically, in some embodiments, if it is determined that all beams in the beam set fail at the second moment, then it is determined that the wireless link has lost synchronization at the second moment. For example, taking a beam set including beam 1, beam 2, and beam 3 as an example, if the beam index of the failed beam output by the AI ​​model points to beam 1, beam 2, and beam 3, that is, all beams in the beam set fail at the second moment, then the terminal device can determine that the wireless link corresponding to the beam set has lost synchronization at the second moment (or determine that the wireless link has not maintained synchronization at the second moment).

[0220] If at least one beam in the beam set does not fail at the second time point, then the wireless link is determined to remain synchronized and not out of sync at the second time point. For example, taking a beam set including beam 1, beam 2, and beam 3 as an example, if the beam index of the failed beam output by the AI ​​model points to beam 1 and beam 2, that is, beam 1 and beam 2 in the beam set have failed, while beam 3 has not failed, then the terminal device can determine that the wireless link corresponding to this beam set has not lost sync at the second time point (or determine that the wireless link remains synchronized at the second time point).

[0221] 4. Fourth capability; wherein the fourth capability is used to indicate that the input of the AI ​​model is the first measurement result, the output of the AI ​​model is the fourth prediction result, and the fourth prediction result is used to determine the beam used to recover the failed beam. Please refer to Figure 2g, which is an exemplary schematic diagram of the fourth capability according to an embodiment of this disclosure.

[0222] As shown in Figure 2g, when the AI ​​model's capability is the fourth capability, the fourth prediction result includes:

[0223] The beam used to recover the failed beam in the second-time beam set.

[0224] In some embodiments, the beam used to recover a failed beam may be referred to as a candidate beam.

[0225] In some embodiments, the fourth prediction result is the beam index of the beam used to recover the failed beam in the beam set at the second time. For example, when the AI ​​model has the fourth capability, taking the beam set including beam 1, beam 2... beam s as an example, the input of the AI ​​model is the first measurement result of beam 1, beam 2... beam s; the output of the AI ​​model is the beam index of beam 2, then beam 2 is the beam used to recover the failed beam in the beam set at the second time.

[0226] In some embodiments, the AI ​​model possesses a fifth capability;

[0227] Please refer to Figure 2h, which is a second exemplary schematic diagram of an AI model according to an embodiment of this disclosure. As shown in Figure 2h, the fifth capability is used to indicate that the input of the AI ​​model is a first measurement result, and the output of the AI ​​model includes at least one of the following:

[0228] 1. First prediction results, including beam reports.

[0229] In some embodiments, the first prediction result includes a beam report.

[0230] In some embodiments, beam reporting is used to indicate the best beam in the beam set at a second time.

[0231] In some embodiments, the beam report includes the beam index of the best beam in the beam set at the second time.

[0232] In some embodiments of this disclosure, the second time point may be the same as the first time point, or the second time point may be any time point after the first time point. For example, the first time point is denoted as T, and the second time point may be T or T+n. Here, n can be any value.

[0233] For example, when the AI ​​model has the fifth capability, taking the beam set as including beam 1, beam 2... beam s as an example, the input of the AI ​​model is the first measurement result of beam 1, beam 2... beam s, and the output of the AI ​​model includes the beam index of the best beam among beam 1, beam 2... beam s, for example, the beam index of beam 2.

[0234] 2. Second prediction result: The second prediction result is used to identify the failed beams in the beam set.

[0235] In some embodiments, the second prediction result includes at least one of the following:

[0236] a. Failed beams in the beam set at the second time step; wherein, the second prediction result is specifically the beam index of the failed beams in the beam set at the second time step. For example, when the AI ​​model's capability is the fifth capability, taking the beam set including beam 1, beam 2... beam s as an example, the input of the AI ​​model is the first measurement result of beam 1, beam 2... beam s; the output of the AI ​​model includes the beam index of the failed beams in beam 1, beam 2... beam s, for example, the beam index of beam 1, the beam index of beam 2.

[0237] b. First quality information of the beams in the beam set, the first quality information being used to determine the failed beams of the beam set at a second time. In some embodiments, the first quality information includes at least one of the following:

[0238] RSRP;

[0239] SINR;

[0240] RSRQ;

[0241] Or, BLER.

[0242] In some embodiments, when the second prediction result is the first quality information, the terminal device is further configured to perform the above-described step S2103. For details, please refer to the above embodiments; further elaboration is not provided here.

[0243] 3. The third prediction result is used to determine whether the wireless link is synchronized or out of sync.

[0244] In some embodiments, the third prediction result includes at least one of the following:

[0245] a. Whether the wireless link corresponding to the beam set is synchronized or out of sync at the second moment. For example, with the AI ​​model having the fifth capability, taking the beam set including beam 1, beam 2... beam s as an example, the input of the AI ​​model is the first measurement result of beam 1, beam 2... beam s; the output of the AI ​​model is whether the wireless link corresponding to the beam set has lost sync, or whether the wireless link corresponding to the beam set remains synchronized.

[0246] b. Failed beams in the beam set at the second time moment are used to determine whether the wireless link is synchronized or out of sync at the second time moment. For example, with the AI ​​model having the fifth capability, taking a beam set including beams 1, 2, ..., s as an example, the input to the AI ​​model is the first measurement result of beams 1, 2, ..., s; the output of the AI ​​model includes the beam index of the failed beam in the beam set. Accordingly, the terminal device can determine whether the beam set has lost sync, or whether the wireless link corresponding to the beam set remains synchronized, based on the beam index output by the AI ​​model.

[0247] c. Second quality information of beams in the beam set. The second quality information is used to identify failed beams in the beam set at the second time point. These failed beams are used to determine whether the wireless link is synchronized or out of sync at the second time point.

[0248] In some embodiments, the second quality information includes at least one of the following:

[0249] RSRP;

[0250] SINR;

[0251] RSRQ;

[0252] BLER.

[0253] In some embodiments, when the second prediction result is second quality information, the terminal device is further configured to perform the above steps S2104 to S2105. For details, please refer to the above embodiments; further elaboration is not provided here.

[0254] 4. The fourth prediction result is used to determine the beam used to recover the failed beam.

[0255] In some embodiments, the fourth prediction result includes: the beam used to recover the failed beam in the beam set at the second time moment.

[0256] In some embodiments, the beam used to recover a failed beam may be referred to as a candidate beam.

[0257] In some embodiments, the fourth prediction result is the beam index of the beam used to recover the failed beam in the beam set at the second time. For example, when the AI ​​model has the fifth capability, taking the beam set including beam 1, beam 2... beam s as an example, the input of the AI ​​model is the first measurement result of beam 1, beam 2... beam s; the output of the AI ​​model is the beam index of beam 2, then beam 2 is the beam used to recover the failed beam in the beam set at the second time.

[0258] In some embodiments, the AI ​​model, first capability, second capability, third capability, fourth capability, and fifth capability described above may not be limited to the names recorded in the embodiments. For example, "AI model" can be used interchangeably with terms such as "model," "artificial intelligence model," "preset model," "test model," "target model," "beam prediction model," and "prediction model"; "first capability (or second capability, etc.)" can be used interchangeably with terms such as "model capability," "model parameter," and "capability type."

[0259] Referring to Figure 3, which is an exemplary flowchart illustrating a beam measurement method according to an embodiment of the present disclosure. As shown in Figure 3, the beam measurement method includes the following steps:

[0260] Step S3101: Obtain the first measurement result of the beam set.

[0261] Step S3102: Input the first measurement result into the AI ​​model to predict the beam set based on the AI ​​model and obtain the prediction result of at least one measurement task.

[0262] The optional implementations of steps S3101-S3102 can be found in the optional implementations of steps S2101-S2102 in Figure 2a, as well as other related parts in the embodiments involved in Figure 2a, which will not be repeated here.

[0263] In the embodiments disclosed herein, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations in other embodiments.

[0264] This disclosure also provides embodiments of an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the first device in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by the second device in any of the above methods.

[0265] 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 a configuration file, 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.

[0266] 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. Furthermore, 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), or a Deep Learning Processing Unit (DPU).

[0267] Figure 4 is an exemplary structural schematic diagram of a beam measurement device provided according to an embodiment of the present disclosure. As shown in Figure 4, the beam measurement device 4100 may include:

[0268] Processing module 4101 is used to acquire the first measurement result of the beam set;

[0269] Furthermore, the first measurement result is input into an artificial intelligence (AI) model to predict the beam set based on the AI ​​model, thereby obtaining prediction results for at least one measurement task.

[0270] In some embodiments, the measurement task includes at least one of the following:

[0271] Predict and obtain beam reports corresponding to the beam set;

[0272] Predicting synchronization or loss of synchronization of wireless links for beam sets;

[0273] Predict failed beams in the beam set;

[0274] Alternatively, predict the beams in the beam set used to recover failed beams.

[0275] In some embodiments, the first measurement result is obtained by performing layer 1L1 measurements on the beams in the beam set.

[0276] In some embodiments, the first measurement result includes at least one of the following:

[0277] The L1 reference signal received power RSRP of the beam in the beam set at the first moment;

[0278] The L1 signal and interference-plus-noise ratio (SINR) of the beams in the beam set at the first moment;

[0279] The L1 reference signal reception quality (RSRQ) of the beams in the beam set at the first moment;

[0280] Or, the error rate (BLER) of the beams in the beam set at the first moment.

[0281] In some embodiments, the AI ​​model possesses at least one of a first capability, a second capability, a third capability, or a fourth capability;

[0282] The first capability is used to indicate that the input of the AI ​​model is the first measurement result and the output of the AI ​​model is the first prediction result, which includes beam reporting.

[0283] The second capability is used to indicate that the input of the AI ​​model is the first measurement result, and the output of the AI ​​model is the second prediction result, which is used to identify the failed beams in the beam set.

[0284] The third capability is used to indicate that the input of the AI ​​model is the first measurement result, and the output of the AI ​​model is the third prediction result. The third prediction result is used to determine whether the wireless link is synchronized or out of sync.

[0285] Alternatively, the fourth capability is used to indicate that the input to the AI ​​model is the first measurement result, and the output of the AI ​​model is the fourth prediction result, which is used to determine the beam used to recover the failed beam.

[0286] In some embodiments, the AI ​​model possesses a fifth capability;

[0287] The fifth capability is used to indicate that the input to the AI ​​model is the first measurement result, and the output of the AI ​​model includes at least one of the following:

[0288] The first prediction result includes the beam report;

[0289] The second prediction result is used to identify the failed beams in the beam set.

[0290] The third prediction result is used to determine whether the wireless link is synchronized or out of sync.

[0291] Alternatively, the fourth prediction result is used to determine the beam used to recover the failed beam.

[0292] In some embodiments, beam reporting is used to indicate the best beam in the beam set at a second time.

[0293] In some embodiments, the second prediction result includes at least one of the following:

[0294] Failed beams in the second-time beamset;

[0295] Alternatively, the first quality information of the beams in the beam set, which is used to determine the failed beams of the beam set at the second time.

[0296] In some embodiments, the first quality information includes at least one of the following:

[0297] RSRP

[0298] SINR;

[0299] RSRQ;

[0300] BLER.

[0301] In some embodiments, a beam is considered a failed beam when the first quality information of the beam is greater than or equal to a first threshold.

[0302] In some embodiments, the third prediction result includes at least one of the following:

[0303] Whether the wireless link corresponding to the beam set is synchronized or out of sync at the second moment;

[0304] The failed beam in the second-time beam set is used to determine whether the wireless link is synchronized or out of sync at the second time.

[0305] Alternatively, the second quality information of the beams in the beam set, which is used to determine the failed beams in the beam set at the second time, and the failed beams are used to determine whether the wireless link is synchronized or out of sync at the second time.

[0306] In some embodiments, the second quality information includes at least one of the following:

[0307] RSRP;

[0308] SINR;

[0309] RSRQ;

[0310] BLER.

[0311] In some embodiments, a beam is considered a failed beam when the second quality information of the beam is greater than or equal to a second threshold.

[0312] In some embodiments, if all beams in the beam set fail, the wireless link becomes out of sync or unsynchronized.

[0313] The wireless link is synchronized and no loss of synchronization occurs, provided that at least one beam in the beam set has not failed.

[0314] In some embodiments, the fourth prediction result includes: the beam used to recover the failed beam in the beam set at the second time moment.

[0315] In some embodiments, the processing module 4101 is also used to execute at least one of the other steps executed by the terminal device in any of the above methods (e.g., steps S2101, S2102, S2103, S2104, S2105, S3101, S3102, but not limited thereto), which will not be described in detail here.

[0316] Optionally, the beam measurement device 4100 may further include a transceiver module 4102 for performing communication steps such as sending and / or receiving performed by the terminal device;

[0317] Figure 5a is an exemplary structural diagram of a communication device provided according to an embodiment of the present disclosure. The communication device may be a terminal device, or a chip, processor, etc., that supports the terminal device 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.

[0318] As shown in Figure 5a, the communication device 5100 includes one or more processors 5101. The processor 5101 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can 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. The communication device 5100 is used to execute any of the above methods.

[0319] In some embodiments, the communication device 5100 further includes one or more memories 5102 for storing instructions. Optionally, all or part of the memories 5102 may also be located outside the communication device 5100.

[0320] In some embodiments, the processor 5101 performs at least one of other steps (e.g., steps S2101, S2102, S2103, S2104, S2105, S3101, S3102, but not limited thereto).

[0321] Optionally, the communication device 5100 further includes one or more transceivers 5103. When the communication device 5100 includes one or more transceivers 5103, the transceivers 5103 perform the communication steps such as sending and / or receiving in the above method.

[0322] In some embodiments, a transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, etc., may be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., may be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., may be used interchangeably.

[0323] In some embodiments, the communication device 5100 may include one or more interface circuits 5104. Optionally, the interface circuit 5104 is connected to the memory 5102, and the interface circuit 5104 can be used to receive signals from the memory 5102 or other devices, and can be used to send signals to the memory 5102 or other devices. For example, the interface circuit 5104 can read instructions stored in the memory 5102 and send the instructions to the processor 5101.

[0324] The communication device 5100 described in the above embodiments may be a terminal device, 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 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 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 and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a first device, a smart first device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0325] Figure 5b is an exemplary structural diagram of a chip provided according to an embodiment of the present disclosure. For cases where the communication device can be a chip or a chip system, please refer to the structural diagram of chip 5200 shown in Figure 5b, but it is not limited thereto.

[0326] Chip 5200 includes one or more processors 5201, which are used to perform any of the above methods.

[0327] In some embodiments, chip 5200 further includes one or more interface circuits 5202. Optionally, the interface circuit 5202 is connected to memory 5203, and the interface circuit 5202 can be used to receive signals from memory 5203 or other devices, and the interface circuit 5202 can be used to send signals to memory 5203 or other devices. For example, the interface circuit 5202 can read instructions stored in memory 5203 and send the instructions to processor 5201.

[0328] In some embodiments, the interface circuit 5202 performs the communication steps such as sending and / or receiving in the above method.

[0329] The processor 5201 executes at least one of the other steps executed by the terminal device in any of the above methods (e.g., steps S2101, S2102, S2103, S2104, S2105, S3101, S3102, but not limited thereto).

[0330] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0331] In some embodiments, chip 5200 further includes one or more memories 5203 for storing instructions. Optionally, all or part of the memories 5203 may be located outside of chip 5200.

[0332] 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.

[0333] This disclosure also proposes a storage medium storing instructions that, when executed on the communication device 5100, cause the communication device 5100 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.

[0334] This disclosure also proposes a program product, including a program and / or instructions, which, when executed by the communication device 5100, cause the communication device 5100 to perform any of the above methods. Optionally, the above program product is a computer program product.

[0335] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

[0336] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0337] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0338] The above are merely specific embodiments of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A beam measurement method, characterized in that, The method includes: Obtain the first measurement result of the beam set; The first measurement result is input into an artificial intelligence (AI) model to predict the beam set based on the AI ​​model, thereby obtaining prediction results for at least one measurement task.

2. The method according to claim 1, characterized in that, The measurement task includes at least one of the following: Predict and obtain the beam report corresponding to the beam set; Predicting the synchronization or loss of synchronization of the wireless link for the beam set; Predict failed beams in the beam set; Alternatively, predict the beam in the beam set used to recover the failed beam.

3. The method according to claim 1 or 2, characterized in that, The first measurement result is obtained by performing layer 1L1 measurement on the beams in the beam set.

4. The method according to any one of claims 1-3, characterized in that, The first measurement result includes at least one of the following: The L1 reference signal received power RSRP of the beam set at the first moment; The L1 signal and interference-plus-noise ratio (SINR) of the beams in the beam set at the first moment; The L1 reference signal reception quality (RSRQ) of the beams in the beam set at the first moment; Alternatively, the error rate (BLER) of the beams in the beam set at the first moment.

5. The method according to any one of claims 1-4, characterized in that, The AI ​​model possesses at least one of the following: a first capability, a second capability, a third capability, or a fourth capability. Wherein, the first capability is used to indicate that the input of the AI ​​model is the first measurement result, and the output of the AI ​​model is the first prediction result, the first prediction result including beam reporting; The second capability is used to indicate that the input of the AI ​​model is the first measurement result, and the output of the AI ​​model is the second prediction result, which is used to determine the failed beams in the beam set; The third capability is used to indicate that the input of the AI ​​model is the first measurement result, and the output of the AI ​​model is the third prediction result, which is used to determine whether the wireless link is synchronized or out of sync. Alternatively, the fourth capability is used to indicate that the input of the AI ​​model is the first measurement result, and the output of the AI ​​model is a fourth prediction result, which is used to determine the beam for recovering the failed beam.

6. The method according to any one of claims 1-4, characterized in that, The AI ​​model possesses a fifth capability; The fifth capability is used to indicate that the input to the AI ​​model is the first measurement result, and the output of the AI ​​model includes at least one of the following: A first prediction result, which includes a beam report; A second prediction result is used to determine the failed beams in the beam set; The third prediction result is used to determine whether the wireless link is synchronized or out of sync. Alternatively, a fourth prediction result, which is used to determine the beam used to recover the failed beam.

7. The method according to claim 5 or 6, characterized in that, The beam report is used to indicate the best beam in the beam set at a second time.

8. The method according to any one of claims 5-7, characterized in that, The second prediction result includes at least one of the following: The failed beam of the beam set at the second moment; Alternatively, the first quality information of the beams in the beam set, which is used to determine the failed beams of the beam set at a second time.

9. The method according to claim 8, characterized in that, The first quality information includes at least one of the following: RSRP SINR; RSRQ; BLER.

10. The method according to claim 8 or 9, characterized in that, When the first quality information of the beam is greater than or equal to the first threshold, the beam is a failed beam.

11. The method according to any one of claims 5-10, characterized in that, The third prediction result includes at least one of the following: Whether the wireless link corresponding to the beam set at the second moment is synchronized or out of sync; The failed beam in the beam set at the second time moment is used to determine whether the wireless link is synchronized or out of sync at the second time moment; Alternatively, the second quality information of the beams in the beam set, the second quality information being used to determine the failed beams in the beam set at the second time, the failed beams being used to determine whether the wireless link is synchronized or out of sync at the second time.

12. The method according to claim 11, characterized in that, The second quality information includes at least one of the following: RSRP; SINR; RSRQ; BLER.

13. The method according to claim 11 or 12, characterized in that, When the second quality information of the beam is greater than or equal to the second threshold, the beam is a failed beam.

14. The method according to any one of claims 11-13, characterized in that, If all beams in the beam set fail, the wireless link becomes out of sync or unsynchronized. The wireless link is synchronized and no loss of synchronization occurs if at least one beam in the beam set fails.

15. The method according to any one of claims 5-14, characterized in that, The fourth prediction result includes: The beam used to recover the failed beam in the beam set at the second moment.

16. A beam measurement device, characterized in that, include: The processing module is used to acquire the first measurement results of the beam set; Furthermore, the first measurement result is input into an artificial intelligence (AI) model to predict the beam set based on the AI ​​model, thereby obtaining prediction results for at least one measurement task.

17. A communication device, characterized in that, include: One or more processors; The processor is used to execute the beam measurement method according to any one of claims 1 to 15.

18. A storage medium storing instructions, characterized in that, When the instructions are executed on a communication device, the beam measurement method as described in any one of claims 1 to 15 is implemented.

19. A computer program product, characterized in that, Includes a program and / or instructions, which, when executed by a communication device, cause the communication device to perform the beam measurement method as described in any one of claims 1 to 15.

Images