Wireless communication method and apparatus, and device

Abstract

The present application belongs to the field of communications. Disclosed are a wireless communication method and apparatus, and a device. The wireless communication method in the embodiments of the present application comprises: a terminal sending a random access report to a network-side device, wherein the random access report comprises at least one of the following: first information for indicating whether random access is related to a prediction by an AI unit; KPI information predicted by the AI unit and related to the random access; and event information predicted by the AI unit and related to the random access.

Description

Wireless communication methods, apparatus and equipment

[0001] Cross-reference to related applications

[0002] This application claims priority to Chinese Patent Application No. 202411603316.X, filed on November 11, 2024, entitled "Wireless Communication Method, Apparatus and Device", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application belongs to the field of communication technology, specifically relating to a wireless communication method, apparatus, and device. Background Technology

[0004] In related technologies, when the beam period is lengthened for purposes such as energy saving, and beam quality is predicted using an artificial intelligence (AI) unit instead of obtaining beam identification based on measured beams, existing random access reports cannot be used for model monitoring of the AI ​​unit. Summary of the Invention

[0005] This application provides a wireless communication method, apparatus, and device that can solve the problem that random access reports cannot enable model monitoring of AI units related to random access.

[0006] Firstly, a wireless communication method is provided, comprising:

[0007] The terminal sends a random access report to the network-side device;

[0008] The random access report includes at least one of the following:

[0009] The first piece of information is used to indicate whether the random access is related to the AI ​​unit's prediction;

[0010] KPI information related to random access predicted by the AI ​​unit;

[0011] AI unit predicts event information related to random access.

[0012] Secondly, another wireless communication method is provided, including:

[0013] Network-side devices receive random access reports from terminals;

[0014] The random access report includes at least one of the following:

[0015] The first piece of information is used to indicate whether the random access is related to the AI ​​unit's prediction;

[0016] KPI information related to random access predicted by the AI ​​unit;

[0017] AI unit predicts event information related to random access.

[0018] Thirdly, a wireless communication device is provided, comprising:

[0019] The sending module is used to send random access reports to network-side devices;

[0020] The random access report includes at least one of the following:

[0021] The first piece of information is used to indicate whether the random access is related to the AI ​​unit's prediction;

[0022] Key performance indicators (KPIs) related to random access predicted by the AI ​​unit;

[0023] AI unit predicts event information related to random access.

[0024] Fourthly, another wireless communication device is provided, including:

[0025] The receiving module is used to receive random access reports from the terminal;

[0026] The random access report includes at least one of the following:

[0027] The first piece of information is used to indicate whether the random access is related to the AI ​​unit's prediction;

[0028] KPI information related to random access predicted by the AI ​​unit;

[0029] AI unit predicts event information related to random access.

[0030] Fifthly, a wireless communication device is provided, the device being configured to perform the steps of the method described in the first aspect, or to implement the steps of the method described in the second aspect.

[0031] In a sixth aspect, a terminal is provided, the terminal including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method as described in the first aspect.

[0032] Seventhly, a terminal is provided, including a processor and a communication interface;

[0033] The communication interface is used to send random access reports to network-side devices.

[0034] The random access report includes at least one of the following:

[0035] The first piece of information is used to indicate whether the random access is related to the AI ​​unit's prediction;

[0036] KPI information related to random access predicted by the AI ​​unit;

[0037] AI unit predicts event information related to random access.

[0038] Eighthly, a network-side device is provided, the network-side device including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method as described in the second aspect.

[0039] Ninthly, a network-side device is provided, including a processor and a communication interface;

[0040] The communication interface is used to receive random access reports from the terminal;

[0041] The random access report includes at least one of the following:

[0042] The first piece of information is used to indicate whether the random access is related to the AI ​​unit's prediction;

[0043] KPI information related to random access predicted by the AI ​​unit;

[0044] AI unit predicts event information related to random access.

[0045] In a tenth aspect, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect, or implement the steps of the method described in the second aspect.

[0046] Eleventhly, a wireless communication system is provided, comprising: a terminal and a network-side device, wherein the terminal can be used to perform the steps of the method as described in the first aspect, and the network-side device can be used to perform the steps of the method as described in the second aspect.

[0047] In a twelfth aspect, a chip is provided, the chip including a processor and a communication interface coupled to the processor, the processor being configured to run programs or instructions to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0048] In a thirteenth aspect, a computer program / program product is provided, which is stored in a storage medium and is executed by at least one processor to implement the steps of the wireless communication method as described in the first aspect, or to implement the steps of the wireless communication method as described in the second aspect.

[0049] In this embodiment, the terminal sends a random access report to the network-side device. The random access report includes at least one of the following: first information indicating whether the random access is related to the prediction of the AI ​​unit; KPI information related to random access predicted by the AI ​​unit; and event information related to random access predicted by the AI ​​unit. Specifically, the random access report carries relevant information from the AI ​​unit's predictions, making it applicable to different types of beam prediction scenarios or energy-saving beam prediction scenarios. The network-side device can monitor the AI ​​unit on the terminal side based on the random access report, determining whether to enable or disable the corresponding AI unit, thus avoiding communication performance degradation caused by AI unit performance degradation. Attached Figure Description

[0050] Figure 1 is a schematic diagram of a communication system architecture provided in an embodiment of this application.

[0051] Figure 2 is a schematic diagram of an energy-saving beam and a normal beam provided in this application.

[0052] Figure 3 is a schematic flowchart of a wireless communication method provided according to an embodiment of this application.

[0053] Figure 4 is one of the schematic diagrams of predicting a second type of beam based on a first type of beam according to an embodiment of this application.

[0054] Figure 5 is one of the schematic diagrams of predicting a second type of beam based on a first type of beam and a third type of beam according to an embodiment of this application.

[0055] Figure 6 is one of the schematic flowcharts of beam prediction provided according to an embodiment of this application.

[0056] Figure 7 is a second schematic diagram of predicting a second type of beam based on a first type of beam and a third type of beam, according to an embodiment of this application.

[0057] Figure 8 is a third schematic diagram of predicting a second type of beam based on a first type of beam and a third type of beam, according to an embodiment of this application.

[0058] Figure 9 is a second schematic flowchart of beam prediction provided according to an embodiment of this application.

[0059] Figure 10 is one of the schematic flowcharts of a transmission random access report provided according to an embodiment of this application.

[0060] Figure 11 is one of the schematic diagrams of a frequency sweep beam not measured during prediction-based random access according to an embodiment of this application.

[0061] Figure 12 is a second schematic diagram of a frequency sweep beam not measured during prediction-based random access according to an embodiment of this application.

[0062] Figure 13 is a second schematic flowchart of a transmission random access report provided according to an embodiment of this application.

[0063] Figure 14 is one of the schematic diagrams of a frequency sweep beam measured during prediction-based random access according to an embodiment of this application.

[0064] Figure 15 is a second schematic diagram of a frequency sweep beam measured during prediction-based random access according to an embodiment of this application.

[0065] Figure 16 is a third schematic diagram of a frequency sweep beam measured during prediction-based random access according to an embodiment of this application.

[0066] Figure 17 is a schematic flowchart of the transmission random access report provided according to an embodiment of this application.

[0067] Figure 18 is a schematic block diagram of a wireless communication device according to an embodiment of this application.

[0068] Figure 19 is a schematic block diagram of another wireless communication device provided according to an embodiment of this application.

[0069] Figure 20 is a schematic block diagram of a communication device provided according to an embodiment of this application.

[0070] Figure 21 is a schematic diagram of the hardware structure of a terminal according to an embodiment of this application.

[0071] Figure 22 is a schematic block diagram of a network-side device provided according to an embodiment of this application. Detailed Implementation

[0072] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0073] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, the first object can be one or more. Furthermore, "or" in this application indicates at least one of the connected objects. For example, the scope of protection for "A or B" covers at least three scenarios: Scenario 1: including A but not B; Scenario 2: including B but not A; Scenario 3: including both A and B. In addition, the terms "A and / or B," "at least one of A and B," and "at least one of A or B" also cover at least the above three scenarios. The character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0074] The term "instruction" in this application can be either a direct instruction (or explicit instruction) or an indirect instruction (or implicit instruction). A direct instruction can be understood as one in which the sender explicitly informs the receiver of specific information, the operation to be performed, or the requested result, etc., in the instruction sent. An indirect instruction can be understood as one in which the receiver determines the corresponding information based on the instruction sent by the sender, or makes a judgment and determines the operation to be performed or the requested result, etc., based on the judgment result.

[0075] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA), or other systems. The terms "system" and "network" in this application are often used interchangeably, and the described technologies can be used with the systems and radio technologies mentioned above, as well as with other systems and radio technologies. The following description describes New Radio (NR) systems for illustrative purposes, and the term NR is used in most of the following description; however, these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) radio systems. th Generation 6G communication system.

[0076] Figure 1 shows a block diagram of a wireless communication system applicable to an embodiment of this application. Specifically, the wireless communication system includes a terminal 11 and a network-side device 12.

[0077] Terminal 11 can be a mobile phone, tablet computer, laptop computer, notebook computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile internet device (MID), augmented reality (AR), virtual reality (VR) device, robot, wearable device, flight vehicle, vehicle user equipment (VUE), shipborne equipment, pedestrian user equipment (PUE), smart home device (home device with wireless communication function, such as refrigerator, television, washing machine or furniture), game console, personal computer (PC), ATM or self-service machine, etc. Wearable devices include: smartwatches, smart bracelets, smart earphones, smart glasses, smart jewelry (smart bracelets, smart chains, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among these, in-vehicle devices can also be referred to as in-vehicle terminals, in-vehicle controllers, in-vehicle modules, in-vehicle components, in-vehicle chips, or in-vehicle units, etc. It should be noted that the specific type of terminal 11 is not limited in the embodiments of this application.

[0078] Among them, network-side equipment 12 may include access network equipment or core network equipment.

[0079] Alternatively, access network equipment may also be referred to as Radio Access Network (RAN) equipment, radio access network function, or radio access network unit. Access network equipment may include base stations, wireless local area network (WLAN) access points (APs), or wireless Fidelity (WiFi) nodes, etc. The term "base station" can be referred to as Node B (NB), Evolved Node B (eNB), Next Generation Node B (gNB), New Radio Node B (NR Node B), Access Point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), Radio Base Station, Radio Transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home Evolved Node B, Transmit / Receive Point (TRP), or any other suitable term in the relevant field, as long as the same technical effect is achieved. The term "base station" is not limited to any specific technical terminology. It should be noted that this application embodiment only uses a base station in an NR system as an example for description and does not limit the specific type of base station.

[0080] Optionally, core network equipment may also be referred to as core network nodes, core network functions, or core network elements, and includes, but is not limited to, at least one of the following: Mobility Management Entity (MME), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Server Discovery Function (EASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized network configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (or L-NEF), and Binding Support. The core network functions include: BSF (Block Network Function), Application Function (AF), Location Management Function (LMF), Gateway Mobile Location Centre (GMLC), and Network Data Analytics Function (NWDAF). It should be noted that this application embodiment only uses core network equipment in the NR system as an example and does not limit the specific type of core network equipment. If the name of the core network equipment mentioned in this application embodiment changes in subsequent protocol versions (e.g., 6G), it will still be within the scope of protection of this application.

[0081] Optionally, the core network equipment can be implemented by one or more functional modules in a single device, or by multiple devices working together; this application does not specifically limit this. It is understood that the aforementioned functional modules can be network elements in hardware devices, software functional modules running on dedicated hardware, or virtualized functional modules instantiated on a platform (e.g., a cloud platform).

[0082] To better understand the technical solution of this application, the following explains the random access report related to this application.

[0083] When a terminal (UE) switches from a connected state to a connected state, the network-side device can obtain a random access report or a random access report list from the UE. Specifically, for example, the information element (IE) in which the UE reports a random access report includes a random access report list (ra-ReportList-r16), as shown in ra-ReportList-r16 within the terminal information response element (UEInformationResponse-r16-Ies).

[0084] The Random Access Report List (ra-ReportList-r16) contains multiple Random Access Reports (RA-Report-r16).

[0085] Each random access report (RA-Report-r16) includes information such as cell identifier (cellId-r16), reason for random access (raPurpose-r16), and random access related information (ra-InformationCommon).

[0086] Random access related information (ra-InformationCommon) includes a list of random access information for a single instance (e.g., perRAInfoList-r16).

[0087] A single random access information list (e.g., perRAInfoList-r16) includes multiple single random access information entries.

[0088] The single random access information includes the SSB information list (perRASSBInfoList-r16) and the CSI-RS information list (perRACSI-RSInfoList-r16) for a single random access.

[0089] The SSB information list (perRASSBInfoList-r16) includes the SSB ID (ssb-Index-r16), the number of preambles sent by the UE on this SSB for this random access (numberOfPreamblesSentOnSSB-r16), and the random access attempt information (perRAAttemptInfoList-r16).

[0090] The information on this random access attempt (perRAAttemptInfoList-r16) includes information on multiple single random access attempts.

[0091] Each random access attempt includes information such as whether the UE detected contention (contentionDetected-r16) and whether the SSB signal quality measured by the UE is higher than a preset threshold (dlRSRPAboveThreshold-r16).

[0092] To better understand the technical solution of this application, the following explains the use of the random access report related to this application.

[0093] Network-side devices can obtain the distribution of the number of Random Access Channel (RACH) preambles for each cell based on the random access report, as well as the distribution of random access delay, and calculate the cumulative distribution function (CDF) curve of random access probability and the CDF curve of random access delay, as shown in Table 1.

[0094] Table 1

[0095] To better understand the technical solution of this application, the problems solved by this application are explained below.

[0096] In network energy saving, base stations are divided into two states: energy-saving state and normal state. For base stations in energy-saving state, one method is to turn off all or part of the beams, and another method is to lengthen the beam period, so that the base station can enter deep sleep, thereby achieving the purpose of energy saving, as shown in Figure 2.

[0097] It should be noted that the beam in Figure 2 can also be called a Synchronization Signal Block (SSB) or an SSB beam. An SSB can also be called a Synchronization Signal / Physical Broadcast Channel Block (SS / PBCH block).

[0098] Assuming the UE is in a disconnected state, it first camps on an energy-saving cell, using a long-period beam for paging, receiving system messages, and performing cell measurements. When the UE needs to initiate random access, it needs to predict the beam quality under normal conditions based on the long-period beam measurement results, and then select a suitable beam for initiating random access.

[0099] Alternatively, suppose that when the UE is in a disconnected state, it first camps on cell 1, where it uses the normal beam to listen for paging, receive system messages, and perform cell measurements. When the UE needs to initiate random access, it initiates random access to cell 2, which is a hotspot and can provide the UE with a higher transmission rate. The UE needs to predict the beam quality of cell 2 in its normal state based on the normal beam measurement results of cell 1 and the energy-saving beam measurement results of cell 2, and then select a suitable beam for initiating random access to cell 2.

[0100] From the perspective of model monitoring, network-side devices need to determine whether the UE should activate the AI ​​unit or continue to use related AI functions based on the UE's inference performance.

[0101] In related technologies, information related to random access is included in the random access report. In energy-saving scenarios, the UE determines the beam identifier with the strongest channel quality based on AI unit prediction, rather than on actual measurements. Current random access reports are based only on information from actual UE measurements and do not include UE predictions, making them unsuitable for energy-saving beam prediction scenarios.

[0102] Therefore, this application designs an enhanced random access report, which carries relevant information about AI unit predictions. This makes it applicable to prediction scenarios for different types of beams or to energy-saving beam prediction scenarios. Network-side devices can monitor the AI ​​units on the terminal side based on the random access report and determine whether to turn the corresponding AI units on or off, thus avoiding the problem of communication performance degradation caused by AI unit performance degradation.

[0103] The wireless communication method provided in this application will be described in detail below with reference to the accompanying drawings and through some embodiments and application scenarios.

[0104] Figure 3 is a schematic flowchart of a wireless communication method 200 according to an embodiment of this application. As shown in Figure 3, the wireless communication method 200 may include at least some of the following:

[0105] S210, the terminal sends a random access report to the network-side device;

[0106] The random access report includes at least one of the following:

[0107] The first piece of information is used to indicate whether the random access is related to the AI ​​unit's prediction;

[0108] KPI information related to random access predicted by the AI ​​unit;

[0109] AI unit predicts event information related to random access;

[0110] S220, the network-side device receives the random access report from the terminal.

[0111] It should be understood that Figure 3 illustrates the steps or operations of the wireless communication method 200, but these steps or operations are merely examples, and other operations or variations of the operations shown in Figure 3 may also be performed in this application.

[0112] In this embodiment, the terminal sends a random access report to the network-side device. The random access report includes at least one of the following: first information indicating whether the random access is related to the prediction of the AI ​​unit; KPI information related to random access predicted by the AI ​​unit; and event information related to random access predicted by the AI ​​unit. Specifically, the random access report carries relevant information from the AI ​​unit's predictions, making it applicable to different types of beam prediction scenarios or energy-saving beam prediction scenarios. The network-side device can monitor the AI ​​unit on the terminal side based on the random access report, determining whether to enable or disable the corresponding AI unit, thus avoiding communication performance degradation caused by AI unit performance degradation.

[0113] In some embodiments, the information carried in the random access report may be related to the terminal's random access over a period of time, including related to the terminal's random access within the cell corresponding to the network-side device, or related to the terminal's random access within one or more other cells.

[0114] The AI ​​unit described in this application embodiment may also be referred to as an AI model, AI model / AI unit, machine learning (ML) model, ML unit, AI structure, AI function, AI characteristic, neural network, neural network function, neural network functionality, etc. Alternatively, the AI ​​unit described in this application embodiment may refer to a processing unit capable of implementing specific algorithms, formulas, processing flows, capabilities, etc., related to AI. Or, the AI ​​unit described in this application embodiment may be a processing method, algorithm, function, module, or unit for a specific dataset. Alternatively, the AI ​​unit described in this application embodiment may be a processing method, algorithm, function, module, or unit running on AI / ML related hardware such as a graphics processing unit (GPU), neural processing unit (NPU), tensor processing unit (TPU), or application-specific integrated circuit (ASIC). This application embodiment does not specifically limit these aspects. Optionally, the specific dataset includes the input or output of the AI ​​unit.

[0115] For example, AI features combined with specific configurations can yield AI functions. For instance, an AI feature might be beam management, and an AI function might be time-domain beam prediction configured for a base station with 32 transmit beams.

[0116] Optionally, the identifier of the AI ​​unit described in the embodiments of this application may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific dataset associated with the AI ​​unit, or an identifier of a specific scenario, environment, channel characteristics, or device related to AI / ML, or an identifier of a function, feature, capability, or module related to AI / ML. The embodiments of this application do not specifically limit this.

[0117] The random access report described in this application embodiment can be, for example, the random access report (such as RA-Report-r16) in the terminal information response information element (UEInformationResponse-r16-IEs).

[0118] In some embodiments, prior to S210 described above, the wireless communication method 200 further includes:

[0119] The terminal receives request information from the network-side device;

[0120] The request information is used to request the random access report.

[0121] In this embodiment, after the terminal enters the connected state from the disconnected state, the network-side device needs to obtain the random access report from the terminal by sending a request message in order to know the relevant information predicted by the terminal-side AI unit. In this way, the network-side device can monitor the terminal-side AI unit based on the random access report and determine whether to turn the corresponding AI unit on or off, thus avoiding the problem of communication performance degradation caused by AI unit performance degradation.

[0122] Optionally, the request information can be carried by Radio Resource Control (RRC) signaling. It should be noted that the request information can also be carried by other signaling, such as Downlink Control Information (DCI) or Media Access Control Control Element (MAC CE).

[0123] In some implementations, the first information may occupy one bit; wherein, a value of 0 indicates that random access is related to the prediction of the AI ​​unit, and a value of 1 indicates that random access is not related to the prediction of the AI ​​unit; or, a value of 1 indicates that random access is related to the prediction of the AI ​​unit, and a value of 0 indicates that random access is not related to the prediction of the AI ​​unit.

[0124] In some implementations, the first information may also occupy more bits. For example, in subsequent schemes, if the first information includes at least one of the first indication information, the second indication information, and the third indication information, the first information may occupy more bits as needed.

[0125] In some implementations, when the first information indicates that random access is unrelated to the predictions of the AI ​​unit, the random access report does not include KPI information related to random access predicted by the AI ​​unit, nor does it include event information related to random access predicted by the AI ​​unit.

[0126] In some implementations, when the first information indicates that random access is related to the prediction of the AI ​​unit, the random access report may optionally include KPI information related to random access predicted by the AI ​​unit and / or event information related to random access predicted by the AI ​​unit.

[0127] In some implementations, random access is associated with the predictions of the AI ​​unit by default. In this case, the random access report includes KPI information related to random access predicted by the AI ​​unit and / or event information related to random access predicted by the AI ​​unit.

[0128] In some embodiments, the first information includes first indication information;

[0129] The first indication information is used to indicate whether the target beam identifier is obtained based on AI unit prediction;

[0130] The target beam identifier is the beam identifier corresponding to the message in the random access sent by the terminal.

[0131] In this embodiment, the terminal reports first indication information to the network-side device through a random access report. Thus, the network-side device can know whether the beam identifier corresponding to the message in the random access sent by the terminal is based on the prediction of the AI ​​unit, so that the network-side device can determine whether to monitor the relevant AI unit, and / or, so that the network-side device can determine whether to activate or deactivate the relevant AI unit, and / or, so that the network-side device can determine the reliability of the prediction result of the relevant AI unit.

[0132] The beam identifier described in the embodiments of this application can be equivalent to or replaced by beam index, SSB index, SSB beam index, etc., and the beam described in the embodiments of this application can be equivalent to or replaced by SSB, SSB beam, etc.

[0133] Specifically, the beam identifier corresponding to the message sent by the terminal in random access can be understood as:

[0134] The terminal sends the random access message on the beam identified by the beam identifier corresponding to the message in the random access.

[0135] In some embodiments, the messages in the random access include, but are not limited to, at least one of the following: message 1 (message1, Msg1) in four-step random access, and message A (messageA, MsgA) in two-step random access.

[0136] In some embodiments, the first information includes, but is not limited to, at least one of the following:

[0137] The second indication information is used to indicate whether the beam quality being greater than the first threshold is based on predictions from the AI ​​unit.

[0138] The third indication information is used to indicate whether the synchronization of random access is based on AI unit prediction.

[0139] In some implementations, the second indication information can also be used to indicate whether the beam quality equals the first threshold based on AI unit prediction.

[0140] In this embodiment, the terminal reports second indication information to the network-side device through a random access report. As a result, the network-side device can know whether the beam quality is greater than the first threshold based on the prediction of the AI ​​unit, so that the network-side device can determine whether to monitor the relevant AI unit, and / or, so that the network-side device can determine whether to activate or deactivate the relevant AI unit, and / or, so that the network-side device can determine the reliability of the prediction results of the relevant AI unit.

[0141] In this embodiment, the terminal reports third indication information to the network-side device through a random access report. Thus, the network-side device can know whether the synchronization of random access is based on AI unit prediction, so that the network-side device can determine whether to monitor the relevant AI unit, and / or, so that the network-side device can determine whether to activate or deactivate the relevant AI unit, and / or, so that the network-side device can determine the reliability of the prediction results of the relevant AI unit.

[0142] Optionally, the first threshold may be agreed upon by the protocol, or the first threshold may be configured by the network side.

[0143] It should be noted that random access synchronization can be uplink and downlink synchronization in random access, in order to ensure uplink and downlink synchronization and random access performance.

[0144] In some implementations, the first information includes the first indication information (mandatory), the second indication information (optional), and the third indication information (optional).

[0145] In some embodiments, the beam quality can be understood as the signal quality corresponding to spatial filtering. Specifically, the beam quality may include, but is not limited to, at least one of the following: Reference Signal Receiving Power (RSRP), Reference Signal Receiving Quality (RSRQ), and Signal to Interference plus Noise Ratio (SINR).

[0146] In some embodiments, the terminal can obtain the beam quality by measuring the SSB or a reference signal.

[0147] In some embodiments, the Key Performance Indicator (KPI) information predicted by the AI ​​unit related to random access includes, but is not limited to, at least one of the following:

[0148] Prediction accuracy;

[0149] The difference between the predicted value and the measured value (also known as the prediction error).

[0150] In this embodiment, the terminal reports the KPI information related to random access predicted by the AI ​​unit to the network-side device through the random access report. As a result, the network-side device can know the prediction accuracy and / or the difference between the predicted value and the measured value, so that the network-side device can determine the prediction accuracy of the relevant AI unit.

[0151] In some embodiments, the prediction accuracy includes, but is not limited to, at least one of the following:

[0152] The prediction accuracy of the beam identifier for the top-ranked beam quality;

[0153] The prediction accuracy of beam identifiers for the top K beam quality, where K is a positive integer and K > 1;

[0154] Predictive accuracy of synchronization for random access.

[0155] It should be noted that the beam identifier with the top beam quality can also be called the beam identifier with the strongest beam quality. The prediction accuracy of the beam identifier with the top beam quality can be understood as the probability that the predicted beam identifier with the top beam quality has the same beam quality as the measured beam identifier with the top beam quality.

[0156] It should be noted that the beam identifiers of the top K beam qualities can also be referred to as the K strongest beam identifiers. The prediction accuracy of the top K beam identifiers can be understood as: whether the predicted strongest beam identifier belongs to the top K strongest beam identifiers in the actual measurement, or whether the strongest beam identifier in the actual measurement belongs to the top K strongest beam identifiers in the predicted measurement.

[0157] In this embodiment, after obtaining the prediction accuracy of the beam identifier with the top beam quality, the network-side device can determine the accuracy of the prediction of the beam identifier with the top beam quality by the relevant AI unit, so that the network-side device can determine the prediction accuracy of the relevant AI unit.

[0158] In this embodiment, after obtaining the prediction accuracy of the beam identifiers of the top K beam qualities, the network-side device can determine the accuracy of the prediction of the beam identifiers of the top K beam qualities by the relevant AI unit, so that the network-side device can determine the prediction accuracy of the relevant AI unit.

[0159] In this embodiment, after obtaining the prediction accuracy of the synchronization of random access, the network-side device can determine the prediction accuracy of the relevant AI unit for the synchronization of random access, so that the network-side device can determine the prediction accuracy of the relevant AI unit.

[0160] In some embodiments, the difference between the predicted value and the measured value includes, but is not limited to, at least one of the following:

[0161] The difference between the measured beam quality of the predicted top-1 beam identifier and the measured beam quality of the top-1 beam identifier.

[0162] The deviation between the predicted downlink synchronization and the measured downlink synchronization;

[0163] Downlink synchronization difference between non-anchor cells and anchor cells.

[0164] In this embodiment, after obtaining the difference between the predicted value and the measured value, the network-side device can determine the prediction accuracy of the relevant AI unit, or the network-side device can determine whether the predicted value and the measured value of the relevant AI unit are the same, so that the network-side device can determine the prediction error of the relevant AI unit.

[0165] For example, the difference between the predicted top-1 beam quality of the beam identifier in the measured beam quality and the measured top-1 beam quality of the beam identifier includes:

[0166] The difference between the beam quality of the first beam identifier and the beam quality of the first beam identifier measured in Msg2 in four-step random access or MsgB in two-step random access, wherein the first beam identifier is the beam identifier with the top 1 predicted beam quality.

[0167] In some embodiments, the event information related to random access predicted by the AI ​​unit includes at least one of the following:

[0168] Whether the predicted value is greater than the second threshold in the actual measurement;

[0169] Are the predicted values ​​the same as the measured values?

[0170] In some embodiments, the event information related to random access predicted by the AI ​​unit includes at least one of the following:

[0171] Whether the predicted value is greater than or equal to the second threshold in the actual measurement;

[0172] Are the predicted values ​​the same as the measured values?

[0173] In this embodiment, after obtaining whether the predicted value is greater than or equal to the second threshold in the actual measurement, and / or obtaining whether the predicted value is the same as the actual measurement value, the network-side device can determine the prediction error of the relevant AI unit.

[0174] Optionally, the second threshold may be agreed upon by the protocol, or the second threshold may be configured by the network side.

[0175] In some embodiments, whether the predicted value is greater than the second threshold in actual measurement includes at least one of the following:

[0176] Does the beam quality of the predicted top-1 beam identifier exceed the second threshold in the actual measured beam quality?

[0177] The predicted beam quality top K beam identifiers are determined to have a measured beam quality greater than a second threshold, where K is a positive integer and K > 1;

[0178] The beam identifier of the predicted beam quality top 1 is determined by whether the beam quality measured in message 2 (message 2, Msg 2) in four-step random access or message B (message B, Msg B) in two-step random access is greater than a second threshold.

[0179] In some embodiments, whether the predicted value is greater than or equal to the second threshold in actual measurement includes at least one of the following:

[0180] Whether the beam quality of the predicted top-1 beam identifier is greater than or equal to the measured beam quality of the second threshold.

[0181] The predicted beam quality top K beam identifiers are determined to have a measured beam quality greater than or equal to a second threshold, where K is a positive integer and K > 1;

[0182] The predicted beam quality top 1 is determined by whether the beam quality measured in Msg2 in four-step random access or MsgB in two-step random access is greater than or equal to a second threshold.

[0183] In this embodiment, the network-side device can determine the accuracy of the beam identifier of the top-1 beam quality predicted by the relevant AI unit, thereby determining the prediction accuracy of the relevant AI unit.

[0184] In this embodiment, the network-side device can determine the accuracy of the beam identifier of the top K beam quality predicted by the relevant AI unit, thereby the network-side device can determine the prediction accuracy of the relevant AI unit.

[0185] In some embodiments, whether the predicted value is greater than the second threshold in actual measurement includes:

[0186] Whether the beam quality of the measured top 1 beam identifier obtained during random access via beam scanning (such as on-demand SSB scanning based on a request) is greater than a second threshold, or whether the beam quality of the measured top 1 beam identifier obtained in Msg2 in four-step random access or MsgB in two-step random access is greater than a second threshold.

[0187] In some embodiments, whether the predicted value is greater than or equal to a second threshold in actual measurement includes:

[0188] Whether the beam quality of the measured top 1 beam identifier obtained during random access via beam scanning (such as on-demand SSB scanning based on a request) is greater than or equal to a second threshold, or whether the beam quality of the measured top 1 beam identifier obtained in Msg2 in four-step random access or MsgB in two-step random access is greater than or equal to a second threshold.

[0189] In some embodiments, whether the predicted value is the same as the measured value includes at least one of the following:

[0190] Is the beam identifier of the predicted top-1 beam quality the same as the beam identifier of the actual measured top-1 beam quality?

[0191] Does the predicted beam identifier with the strongest beam quality belong to the top K beam identifiers with the strongest measured beam quality?

[0192] Does the measured beam identifier with the strongest beam quality belong to the predicted top K beam identifiers with the strongest beam quality?

[0193] For example, whether the predicted beam identifier of the top 1 beam quality is the same as the actual measured beam identifier of the top 1 beam quality includes:

[0194] As shown in Figures 14-16, the measured top 1 beam identifier obtained by beam scanning during random access is the same as the predicted top 1 (strongest) beam identifier, or the same as the transmit beam identifier of Msg2 in four-step random access or MsgB in two-step random access.

[0195] It should be noted that Msg2 in the four-step random access procedure can also be called the Random Access Response (RAR) in the four-step random access procedure.

[0196] The beam quality measurement described in this embodiment includes, but is not limited to, beam quality measurement in Msg2 of four-step random access or MsgB of two-step random access. For example, it may also include beam quality measurement in on-demand SSB scanning, or beam transmission and measurement of the original period of the first or third type of beam, as shown in Figure 14 or Figure 15.

[0197] In this embodiment, the network-side device can determine the accuracy of the AI ​​unit's prediction of the top-1 beam identifier based on whether the predicted top-1 beam identifier is the same as the actual measured top-1 beam identifier, thereby allowing the network-side device to determine the prediction accuracy of the relevant AI unit.

[0198] In this embodiment, the network-side device can determine the accuracy of the AI ​​unit's prediction of the top K beam identifiers based on whether the beam identifier with the strongest predicted beam quality belongs to the top K beam identifiers with the strongest measured beam quality, and / or whether the beam identifier with the strongest measured beam quality belongs to the top K beam identifiers with the strongest predicted beam quality. Thus, the network-side device can determine the prediction accuracy of the relevant AI unit.

[0199] In this embodiment, as shown in Figures 14-16, during random access, the measured top 1 beam identifier is obtained through beam scanning. The accuracy of the AI ​​unit's prediction of the top 1 beam identifier is determined by comparing it with the predicted top 1 (strongest) beam identifier, or with the transmitted beam identifier of Msg2 in four-step random access or MsgB in two-step random access. This allows the network-side device to determine the prediction accuracy of the relevant AI unit.

[0200] In some embodiments, the random access report includes, but is not limited to, at least one of the following: measurement information corresponding to AI unit prediction, and reporting information corresponding to AI unit prediction;

[0201] The measurement information predicted by the AI ​​unit includes at least one of the following: the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, the historical beam quality of the third type of beam, and the measurement timestamp.

[0202] The AI ​​unit predicts and reports information including at least one of the following: the beam identifier of the top 1 beam quality of the second type of beam, the beam identifier of the top K beam quality of the second type of beam, the beam quality of the second type of beam, and the prediction timestamp of the AI ​​unit.

[0203] Wherein, the period of the first type of beam is greater than the period of the second type of beam, the period of the first type of beam is greater than the period of the third type of beam, and the cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam; or, the period of the first type of beam is less than the period of the third type of beam, the period of the second type of beam is less than the period of the third type of beam, and the cell corresponding to the first type of beam is different from the cell corresponding to the second type of beam.

[0204] In this embodiment, the terminal reports the measurement information and / or the reporting information corresponding to the AI ​​unit prediction to the network-side device through a random access report. Thus, the network-side device can obtain the measurement information and / or the reporting information corresponding to the relevant AI unit.

[0205] It should be noted that the measurement information corresponding to the AI ​​unit prediction can also be referred to as or replaced by the AI ​​unit's input information or the prediction's input information or measurement information or historical measurement information, and the reporting information corresponding to the AI ​​unit prediction can also be referred to as or replaced by the AI ​​unit's output information or the AI ​​unit's prediction's output information.

[0206] It should be noted that the energy-saving beam can also be called or replaced by a long-period beam or a beam of the enhancement layer, and the normal-period beam can also be called or replaced by a short-period beam or a beam of the cover layer.

[0207] The embodiments of this application can be applied to scenarios where a cell randomly accesses a cell from an energy-saving state with a sparse beam period to a normal state with a dense beam period, or to a cell randomly accessing a cell from a normal state cell 1 to the current energy-saving state, but then the cell becomes a normal state cell after the random access.

[0208] In one implementation, the terminal measures the beam quality of the first type of beam and inputs the beam identifier and / or beam quality of the first type of beam into the AI ​​unit to predict: the beam identifier of the top 1 beam quality of the second type of beam, and / or the beam identifier of the top K beam quality of the second type of beam, and / or the beam quality of the second type of beam.

[0209] In one implementation, the terminal measures the beam quality of the first type of beam and inputs the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, and the historical beam quality of the third type of beam into the AI ​​unit to predict: the beam identifier of the top 1 beam quality of the second type of beam, and / or, the beam identifier of the top K beam quality of the second type of beam, and / or, the beam quality of the second type of beam.

[0210] In some embodiments, the period of the first type of beam is greater than the period of the second type of beam, the period of the first type of beam is greater than the period of the third type of beam, and the cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam. Optionally, the period of the second type of beam and the period of the third type of beam may be the same, or the period of the second type of beam and the period of the third type of beam may be different.

[0211] In some implementations, the period of the first type of beam is 800ms and the period of the second type of beam is 100ms. The cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam. Specifically, the terminal inputs the beam identifier and / or the beam quality of the first type of beam into the AI ​​unit to predict the beam identifier of the top 1 beam quality of the second type of beam and / or the beam identifier of the top K beam quality of the second type of beam, as shown in Figure 4. The period of the first type of beam is 800ms and the period of the second type of beam is 100ms. Both the first type of beam and the second type of beam correspond to cell 1.

[0212] In some implementations, the period of the first type of beam is 800ms, the period of the second type of beam is 100ms, and the period of the third type of beam is 100ms. The cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam, while the cell corresponding to the third type of beam is different from the cell corresponding to the first type of beam. Specifically, the terminal measures the beam quality of the first type of beam and inputs the beam identifier, beam quality, beam identifier, and historical beam quality of the third type of beam into the AI ​​unit to predict: the beam identifier of the top 1 beam quality of the second type of beam and / or the beam identifier of the top K beam quality of the second type of beam, as shown in Figure 5. The period of the first type of beam is 800ms, the period of the second type of beam is 100ms, and the period of the third type of beam is 100ms. The first type of beam and the second type of beam both correspond to cell 1, and the third type of beam corresponds to cell 2.

[0213] For example, the period of the first type of beam is 800ms, the period of the second type of beam is 100ms, and the period of the third type of beam is 100ms. The cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam, and the cell corresponding to the first type of beam is different from the cell corresponding to the third type of beam. The terminal initiates random access to the second type of beam in the camped cell, as shown in Figure 5. Based on the beam quality of the first type of beam, or the historical beam quality of the first type of beam and the third type of beam, the terminal predicts the identifier of the beam with the strongest beam quality in the second type of beam. Thus, while the first type of beam is maintained in energy-saving mode, Msg1 or MsgA is sent with the RO corresponding to the beam with higher channel quality, improving the uplink reception quality of the second type of beam and thus improving the random access performance. Specifically, the cells corresponding to the first type of beam and the second type of beam are the same, and the network devices (such as base stations) in the cells corresponding to the first type of beam and the second type of beam are third network devices, as shown in Figure 6. The specific process of random access may include some or all of the steps in S11 to S15.

[0214] S11. The terminal enters the cell corresponding to the first type of beam (the cell where the third network device is located).

[0215] S12. The terminal receives system messages in the cell corresponding to the first type of beam and obtains the beam configuration of the second type of beam, including information such as period and time offset.

[0216] S13. The beam quality of the first type of beam is obtained by terminal measurement.

[0217] Optionally, the terminal can also obtain the beam quality of the third type beam of the neighboring cell.

[0218] It should be noted that the order of S12 and S13 is not limited in this embodiment.

[0219] S14. The terminal prepares to initiate a service to the second type of beam in the cell where it is camped. Based on the beam quality of the first type of beam, and optionally, the historical beam quality of the third type of beam, it predicts (based on AI unit inference) to obtain the identifier of the beam with the strongest beam quality of the second type of beam.

[0220] Optionally, the input information for the AI ​​unit includes at least one of the following:

[0221] Beam identifier for Category 1 beams;

[0222] Beam quality of Type I beams;

[0223] Beam identifier for Category 3 beams;

[0224] Historical beam quality of Category 3 beams;

[0225] Measure the timestamp.

[0226] Optionally, the output information of the AI ​​unit includes at least one of the following:

[0227] The beam identifier for the best beam quality in the second type of beam;

[0228] Beam quality of type II beams;

[0229] The prediction timestamp of the AI ​​unit.

[0230] Optionally, the terminal determines the RO and the random access preamble based on the beam identifier with the strongest beam quality of the second type of beam predicted by the AI ​​unit and the beam configuration of the second type of beam obtained in S12.

[0231] S15. The terminal sends Msg1 or MsgA to the third network device based on the RO determined in S14.

[0232] In some embodiments, the period of the first type of beam is less than the period of the third type of beam, the period of the second type of beam is less than the period of the third type of beam, and the cell corresponding to the first type of beam is different from the cell corresponding to the second type of beam. Optionally, the period of the first type of beam and the period of the second type of beam can be the same, or the period of the first type of beam and the period of the second type of beam can be different.

[0233] In some implementations, the terminal's camped cell is the cell corresponding to the first type of beam. For example, the period of the first type of beam is 100ms, and the period of the second type of beam is 100ms. The cells corresponding to the first type of beam and the cells corresponding to the second type of beam are different. Specifically, the terminal inputs the beam identifier and / or the beam quality of the first type of beam into the AI ​​unit to predict: the beam identifier of the top 1 beam quality of the second type of beam, and / or, the beam identifiers of the top K beam quality of the second type of beam, and / or, the beam quality of all second type of beams, as shown in Figure 7. The period of the first type of beam is 100ms, the period of the second type of beam is 100ms, the first type of beam corresponds to cell 1, and the second type of beam corresponds to cell 2.

[0234] In some implementations, the terminal's camp cell is the cell corresponding to the first type of beam. For example, the period of the first type of beam is 100ms, the period of the second type of beam is 100ms, and the period of the third type of beam is 800ms. The cells corresponding to the first type of beam are different from those corresponding to the second type of beam, while the cells corresponding to the second type of beam are the same as those corresponding to the third type of beam. Specifically, the terminal measures the beam quality of the first type of beam and the third type of beam, and inputs the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, and the historical beam quality of the third type of beam into the AI ​​unit to predict: the beam identifier of the top 1 beam quality of the second type of beam, and / or, the beam identifier of the top K beam quality of the second type of beam, and / or, the beam quality of all second type of beams, as shown in Figure 8. The period of the first type of beam is 100ms, the period of the second type of beam is 100ms, and the period of the third type of beam is 800ms. The first type of beam corresponds to cell 1, and the second type of beam corresponds to cell 2.

[0235] For example, the period of the first type of beam is 40ms, the period of the second type of beam is 40ms, and the period of the third type of beam is 1s. The cell corresponding to the first type of beam is different from the cell corresponding to the second type of beam. The terminal initiates random access from the cell corresponding to the first type of beam to the cell corresponding to the second type of beam, as shown in Figure 8. Based on the beam quality of the first type of beam and the historical beam quality of the third type of beam, the terminal predicts the beam identifier with the best beam quality of the second type of beam. Thus, while the third type of beam is kept in power-saving mode, Msg1 or MsgA is sent at the RACH Occasion (RO) corresponding to the beam with higher channel quality, improving the uplink reception quality of the second type of beam and thus improving the random access performance. Specifically, the network device (such as a base station) in the cell corresponding to the first type of beam is the first network device, and the network devices (such as base stations) in the cells corresponding to the second and third types of beams are the second network devices, as shown in Figure 9. The specific random access process may include some or all of the steps in S21 to S25.

[0236] S21. The terminal enters the cell corresponding to the first type of beam (the cell where the first network device is located).

[0237] S22. The terminal receives system messages in the cell corresponding to the first type of beam and obtains the beam configuration of the second type of beam, including information such as period and time domain offset.

[0238] S23. The beam quality of the first type of beam is obtained by terminal measurement.

[0239] It should be noted that the order of S22 and S23 is not limited in this embodiment.

[0240] S24. The terminal prepares to initiate a service to the cell corresponding to the second type of beam. Based on the beam quality of the first type of beam, and optionally, the historical beam quality of the third type of beam, it predicts (based on AI unit reasoning) to obtain the beam identifier with the best beam quality of the second type of beam.

[0241] Optionally, the input information for the AI ​​unit includes at least one of the following:

[0242] Beam identifier for Category 1 beams;

[0243] Beam quality of Type I beams;

[0244] Beam identifier for Category 3 beams;

[0245] Historical beam quality of Category 3 beams;

[0246] Measure the timestamp.

[0247] Optionally, the output information of the AI ​​unit includes at least one of the following:

[0248] The identifier for the beam with the strongest beam quality in the second type of beam;

[0249] Beam quality of type II beams;

[0250] The prediction timestamp of the AI ​​unit.

[0251] Optionally, the terminal determines the RO (RACH Occasion) and the random access preamble based on the beam identifier of the second type of beam with the strongest beam quality predicted by the AI ​​unit and the beam configuration of the second type of beam obtained in S22.

[0252] S25. The terminal sends Msg1 or MsgA to the second network device based on the RO determined in S24.

[0253] In some embodiments, the wireless communication method 200 further includes:

[0254] The terminal receives second information from the network-side device;

[0255] The second information includes, but is not limited to, at least one of the following:

[0256] The relevant configuration of the target AI unit;

[0257] The fourth instruction is used to instruct the activation of the target AI unit;

[0258] The fifth instruction is used to instruct the deactivation of the target AI unit;

[0259] The target AI unit is an AI unit related to random access.

[0260] In this embodiment, the terminal can obtain the relevant configuration of the target AI unit based on the second information and activate or deactivate the target AI unit, thereby improving the random access performance.

[0261] In some embodiments, the target AI unit may be determined based on the random access report.

[0262] Therefore, in this embodiment, the terminal sends a random access report to the network-side device. The random access report includes at least one of the following: first information indicating whether the random access is related to the prediction of the AI ​​unit; KPI information related to random access predicted by the AI ​​unit; and event information related to random access predicted by the AI ​​unit. Specifically, the random access report carries relevant information predicted by the AI ​​unit, thus making it applicable to different types of beam prediction scenarios or energy-saving beam prediction scenarios. The network-side device can monitor the AI ​​unit on the terminal side based on the random access report, determining whether to turn the corresponding AI unit on or off, avoiding communication performance degradation caused by AI unit performance degradation.

[0263] The technical solution of this application is described below through specific embodiments.

[0264] In Example 1, after the terminal enters the cell where the network-side device is located, after the terminal enters the connected state, the network-side device requests a random access report from the terminal. Based on the terminal's historical prediction performance, the network-side device determines whether to activate, maintain, or deactivate the AI ​​prediction function when initiating random access.

[0265] As shown in Figure 10, the specific process of Embodiment 1 may include some or all of the steps in S1-1 to S1-3.

[0266] S1-1. The network-side device requests a random access report from the terminal.

[0267] S1-2. The terminal sends a random access report to the network-side device;

[0268] The random access report includes at least one of the following:

[0269] The first piece of information is used to indicate whether the random access is related to the AI ​​unit's prediction;

[0270] KPI information related to random access predicted by the AI ​​unit;

[0271] AI unit predicts event information related to random access;

[0272] The AI ​​unit predicts the corresponding measurement information;

[0273] The AI ​​unit predicts the corresponding reporting information.

[0274] Optionally, the first information includes at least one of the following:

[0275] The first indication information is used to indicate whether the target beam identifier is obtained based on the prediction of the AI ​​unit, wherein the target beam identifier is the beam identifier corresponding to the message in the random access sent by the terminal;

[0276] The second indication information is used to indicate whether the beam quality is greater than or equal to the first threshold based on AI unit prediction.

[0277] The third indication information is used to indicate whether the synchronization of random access is based on AI unit prediction.

[0278] Optionally, the KPI information related to random access predicted by the AI ​​unit includes, but is not limited to, at least one of the following:

[0279] Prediction accuracy;

[0280] The difference between the predicted value and the measured value (also known as the prediction error).

[0281] Optionally, the prediction accuracy includes, but is not limited to, at least one of the following:

[0282] The prediction accuracy of the beam identifier for the top-ranked beam quality;

[0283] The prediction accuracy of beam identifiers for the top K beam quality, where K is a positive integer and K > 1;

[0284] Predictive accuracy of synchronization for random access.

[0285] Optionally, the difference between the predicted value and the measured value includes, but is not limited to, at least one of the following:

[0286] The difference between the measured beam quality of the predicted top-1 beam identifier and the measured beam quality of the top-1 beam identifier.

[0287] The deviation between the predicted downlink synchronization and the measured downlink synchronization;

[0288] Downlink synchronization difference between non-anchor cells and anchor cells.

[0289] For example, the difference between the predicted top-1 beam quality of the beam identifier in the measured beam quality and the measured top-1 beam quality of the beam identifier includes:

[0290] The difference between the beam quality of the first beam identifier and the beam quality of the first beam identifier measured in Msg2 in four-step random access or MsgB in two-step random access, wherein the first beam identifier is the beam identifier with the top 1 predicted beam quality.

[0291] Optionally, the event information related to random access predicted by the AI ​​unit includes at least one of the following:

[0292] Whether the predicted value is greater than or equal to the second threshold in the actual measurement;

[0293] Are the predicted values ​​the same as the measured values?

[0294] Optionally, whether the predicted value is greater than or equal to the second threshold in the actual measurement includes at least one of the following:

[0295] Whether the beam quality of the predicted top-1 beam identifier is greater than or equal to the measured beam quality of the second threshold.

[0296] The predicted beam quality top K beam identifiers are determined to have a measured beam quality greater than or equal to a second threshold, where K is a positive integer and K > 1;

[0297] The predicted beam quality top 1 is determined by whether the beam quality measured in Msg2 in four-step random access or MsgB in two-step random access is greater than or equal to a second threshold.

[0298] Optionally, whether the predicted value is the same as the measured value includes at least one of the following:

[0299] Is the beam identifier of the predicted top-1 beam quality the same as the beam identifier of the actual measured top-1 beam quality?

[0300] Does the predicted beam identifier with the strongest beam quality belong to the top K beam identifiers with the strongest measured beam quality?

[0301] Does the measured beam identifier with the strongest beam quality belong to the predicted top K beam identifiers with the strongest beam quality?

[0302] For example, whether the predicted beam identifier of the top 1 beam quality is the same as the actual measured beam identifier of the top 1 beam quality includes:

[0303] As shown in Figures 14-16, the measured top 1 beam identifier obtained by beam scanning during random access is the same as the predicted top 1 (strongest) beam identifier, or the same as the transmit beam identifier of Msg2 in four-step random access or MsgB in two-step random access.

[0304] Optionally, the measurement information predicted by the AI ​​unit includes at least one of the following: the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, the historical beam quality of the third type of beam, and the measurement timestamp.

[0305] Optionally, the reporting information corresponding to the AI ​​unit prediction includes at least one of the following: the beam identifier of the top 1 beam quality of the second type of beam, the beam identifier of the top K beam quality of the second type of beam, the beam quality of the second type of beam, and the prediction timestamp of the AI ​​unit.

[0306] It should be noted that the measured values ​​described in this embodiment can be obtained by measuring a first-type beam, a second-type beam, or a third-type beam.

[0307] S1-3. The network-side device sends the second information to the terminal;

[0308] The second information includes, but is not limited to, at least one of the following:

[0309] The relevant configuration of the target AI unit;

[0310] The fourth instruction is used to instruct the activation of the target AI unit;

[0311] The fifth instruction is used to instruct the deactivation of the target AI unit;

[0312] The target AI unit is an AI unit related to random access.

[0313] In Example 1, the network-side device monitors the AI ​​inference process performed on the terminal side, thereby enabling performance monitoring of the AI ​​unit on the terminal side based on the AI ​​inference performance. This allows the device to determine whether to activate, maintain, or deactivate the AI ​​prediction function when initiating random access, thus avoiding communication performance degradation caused by AI unit performance deterioration.

[0314] In Example 2, after the terminal enters the cell where the network-side device is located, and after the terminal enters the connected state, the network-side device requests a random access report from the terminal. Based on the terminal's historical prediction performance, the network-side device determines whether to activate, maintain, or deactivate the AI ​​prediction function when initiating random access. In Example 2, the first type of beam and the second type of beam correspond to the same cell (as shown in Figure 11, both the first type of beam and the second type of beam correspond to cell 1). The terminal predicts the beam identifier of the second type of beam (such as a beam with a period of 100ms) and determines the RO for sending Msg1 or MsgA. After the prediction is completed and before the random access is completed, no swept beam is measured, as shown in Figure 11. Alternatively, the first type of beam and the second type of beam may correspond to different cells (as shown in Figure 12, the first type of beam corresponds to cell 1 and the second type of beam corresponds to cell 2). The terminal predicts the beam identifier of the second type of beam (such as a beam with a period of 100ms) and determines the RO for transmitting Msg1 or MsgA. After the prediction is completed and before the random access is completed, the frequency sweep beam is not measured, as shown in Figure 12.

[0315] As shown in Figure 13, the specific process of Embodiment 2 may include some or all of the steps in S2-1 to S2-3.

[0316] S2-1. The network-side device requests a random access report from the terminal.

[0317] S2-2. The terminal sends a random access report to the network-side equipment;

[0318] The random access report includes at least one of the following:

[0319] The first piece of information is used to indicate whether the random access is related to the AI ​​unit's prediction;

[0320] KPI information related to random access predicted by the AI ​​unit;

[0321] AI unit predicts event information related to random access;

[0322] The AI ​​unit predicts the corresponding measurement information;

[0323] The AI ​​unit predicts the corresponding reporting information.

[0324] Optionally, the first information includes at least one of the following:

[0325] The first indication information is used to indicate whether the target beam identifier is obtained based on the prediction of the AI ​​unit, wherein the target beam identifier is the beam identifier corresponding to the message in the random access sent by the terminal;

[0326] The second indication information is used to indicate whether the beam quality is greater than or equal to the first threshold based on AI unit prediction.

[0327] The third indication information is used to indicate whether the synchronization of random access is based on AI unit prediction.

[0328] Optionally, the KPI information related to random access predicted by the AI ​​unit includes, but is not limited to, at least one of the following:

[0329] Prediction accuracy;

[0330] The difference between the predicted value and the measured value (also known as the prediction error).

[0331] Optionally, the prediction accuracy includes, but is not limited to, at least one of the following:

[0332] The prediction accuracy of the beam identifier for the top-ranked beam quality;

[0333] The prediction accuracy of beam identifiers for the top K beam quality, where K is a positive integer and K > 1;

[0334] Predictive accuracy of synchronization for random access.

[0335] Optionally, the difference between the predicted value and the measured value includes, but is not limited to, at least one of the following:

[0336] The difference between the measured beam quality of the predicted top-1 beam identifier and the measured beam quality of the top-1 beam identifier.

[0337] The deviation between the predicted downlink synchronization and the measured downlink synchronization;

[0338] Downlink synchronization difference between non-anchor cells and anchor cells.

[0339] For example, the difference between the predicted top-1 beam quality of the beam identifier in the measured beam quality and the measured top-1 beam quality of the beam identifier includes:

[0340] The difference between the beam quality of the first beam identifier and the beam quality of the first beam identifier measured in Msg2 in four-step random access or MsgB in two-step random access, wherein the first beam identifier is the beam identifier with the top 1 predicted beam quality.

[0341] Optionally, the event information related to random access predicted by the AI ​​unit includes at least one of the following:

[0342] Whether the predicted value is greater than or equal to the second threshold in the actual measurement;

[0343] Are the predicted values ​​the same as the measured values?

[0344] Optionally, whether the predicted value is greater than or equal to the second threshold in the actual measurement includes at least one of the following:

[0345] Whether the beam quality of the predicted top-1 beam identifier is greater than or equal to the measured beam quality of the second threshold.

[0346] The predicted beam quality top K beam identifiers are determined to have a measured beam quality greater than or equal to a second threshold, where K is a positive integer and K > 1;

[0347] The predicted beam quality top 1 is determined by whether the beam quality measured in Msg2 in four-step random access or MsgB in two-step random access is greater than or equal to a second threshold.

[0348] Optionally, whether the predicted value is the same as the measured value includes at least one of the following:

[0349] Is the beam identifier of the predicted top-1 beam quality the same as the beam identifier of the actual measured top-1 beam quality?

[0350] Does the predicted beam identifier with the strongest beam quality belong to the top K beam identifiers with the strongest measured beam quality?

[0351] Does the measured beam identifier with the strongest beam quality belong to the predicted top K beam identifiers with the strongest beam quality?

[0352] For example, whether the predicted beam identifier of the top 1 beam quality is the same as the actual measured beam identifier of the top 1 beam quality includes:

[0353] As shown in Figures 14-16, the measured top 1 beam identifier obtained by beam scanning during random access is the same as the predicted top 1 (strongest) beam identifier, or the same as the transmit beam identifier of Msg2 in four-step random access or MsgB in two-step random access.

[0354] Optionally, the measurement information predicted by the AI ​​unit includes at least one of the following: the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, the historical beam quality of the third type of beam, and the measurement timestamp.

[0355] Optionally, the reporting information corresponding to the AI ​​unit prediction includes at least one of the following: the beam identifier of the top 1 beam quality of the second type of beam, the beam identifier of the top K beam quality of the second type of beam, the beam quality of the second type of beam, and the prediction timestamp of the AI ​​unit.

[0356] It should be noted that the measured values ​​described in this embodiment can be obtained by measuring a first-type beam, a second-type beam, or a third-type beam.

[0357] S2-3. The network-side device sends the second information to the terminal;

[0358] The second information includes, but is not limited to, at least one of the following:

[0359] The relevant configuration of the target AI unit;

[0360] The fourth instruction is used to instruct the activation of the target AI unit;

[0361] The fifth instruction is used to instruct the deactivation of the target AI unit;

[0362] The target AI unit is an AI unit related to random access.

[0363] In Example 2, for the UE to predict the beam identifier of the second type of beam (such as the 800ms periodic beam in energy-saving mode) and determine the RO for transmitting Msg1 or MsgA, in a specific scenario where no other energy-saving beams are measured after prediction and before random access is completed, the content to be recorded in the random access report is designed. This allows the network-side device to monitor the model inference of the AI ​​unit on the terminal side based on the recorded information, and to determine whether the AI ​​prediction function should be activated, maintained, or deactivated when initiating random access based on the inference performance. This avoids the problem of communication performance degradation caused by the deterioration of AI unit performance.

[0364] In Example 3, after the terminal enters the cell where the network-side device is located, and after the terminal enters the connected state, the network-side device requests a random access report from the terminal. Based on the terminal's historical prediction performance, the network-side device determines whether to activate, maintain, or deactivate the AI ​​prediction function when initiating random access. In Example 3, the terminal predicts the beam identifier of the second type of beam (such as a beam with a 100ms period) and determines the RO for sending Msg1 or MsgA. After the prediction is completed and before the random access is completed, the terminal measures the first type of beam (Figure 14), or measures the third type of beam (Figure 15), or measures the second type of beam (Figure 16). Specifically, for example, after receiving Msg1, the terminal switches to the normal state of the second type of beam, or after receiving Msg1, the terminal sends the second type of beam on demand and performs a second type of beam scan.

[0365] As shown in Figure 17, the specific process of Embodiment 3 may include some or all of the steps in S3-1 to S3-3.

[0366] S3-1. The network-side device requests a random access report from the terminal.

[0367] S3-2. The terminal sends a random access report to the network-side equipment;

[0368] The random access report includes at least one of the following:

[0369] The first piece of information is used to indicate whether the random access is related to the AI ​​unit's prediction;

[0370] KPI information related to random access predicted by the AI ​​unit;

[0371] AI unit predicts event information related to random access;

[0372] The AI ​​unit predicts the corresponding measurement information;

[0373] The AI ​​unit predicts the corresponding reporting information.

[0374] Optionally, the first information includes at least one of the following:

[0375] The first indication information is used to indicate whether the target beam identifier is obtained based on the prediction of the AI ​​unit, wherein the target beam identifier is the beam identifier corresponding to the message in the random access sent by the terminal;

[0376] The second indication information is used to indicate whether the beam quality is greater than or equal to the first threshold based on AI unit prediction.

[0377] The third indication information is used to indicate whether the synchronization of random access is based on AI unit prediction.

[0378] Optionally, the KPI information related to random access predicted by the AI ​​unit includes, but is not limited to, at least one of the following:

[0379] Prediction accuracy;

[0380] The difference between the predicted value and the measured value (also known as the prediction error).

[0381] Optionally, the prediction accuracy includes, but is not limited to, at least one of the following:

[0382] The prediction accuracy of the beam identifier for the top-ranked beam quality;

[0383] The prediction accuracy of beam identifiers for the top K beam quality, where K is a positive integer and K > 1;

[0384] Predictive accuracy of synchronization for random access.

[0385] It should be noted that the measured values ​​described in this embodiment can be obtained by measuring a first-type beam, a second-type beam, or a third-type beam.

[0386] Optionally, the difference between the predicted value and the measured value includes, but is not limited to, at least one of the following:

[0387] The difference between the measured beam quality of the predicted top-1 beam identifier and the measured beam quality of the top-1 beam identifier.

[0388] The deviation between the predicted downlink synchronization and the measured downlink synchronization;

[0389] Downlink synchronization difference between non-anchor cells and anchor cells.

[0390] For example, the difference between the predicted top-1 beam quality of the beam identifier in the measured beam quality and the measured top-1 beam quality of the beam identifier includes:

[0391] The difference between the beam quality of the first beam identifier and the beam quality of the first beam identifier measured in Msg2 in four-step random access or MsgB in two-step random access, wherein the first beam identifier is the beam identifier with the top 1 predicted beam quality.

[0392] Optionally, the event information related to random access predicted by the AI ​​unit includes at least one of the following:

[0393] Whether the predicted value is greater than or equal to the second threshold in the actual measurement;

[0394] Are the predicted values ​​the same as the measured values?

[0395] Optionally, whether the predicted value is greater than or equal to the second threshold in the actual measurement includes at least one of the following:

[0396] Whether the beam quality of the predicted top-1 beam identifier is greater than or equal to the measured beam quality of the second threshold.

[0397] The predicted beam quality top K beam identifiers are determined to have a measured beam quality greater than or equal to a second threshold, where K is a positive integer and K > 1;

[0398] The predicted beam quality top 1 is determined by whether the beam quality measured in Msg2 in four-step random access or MsgB in two-step random access is greater than or equal to a second threshold.

[0399] Optionally, whether the predicted value is the same as the measured value includes at least one of the following:

[0400] Is the beam identifier of the predicted top-1 beam quality the same as the beam identifier of the actual measured top-1 beam quality?

[0401] Does the predicted beam identifier with the strongest beam quality belong to the top K beam identifiers with the strongest measured beam quality?

[0402] Does the measured beam identifier with the strongest beam quality belong to the predicted top K beam identifiers with the strongest beam quality?

[0403] For example, whether the predicted beam identifier of the top 1 beam quality is the same as the actual measured beam identifier of the top 1 beam quality includes:

[0404] As shown in Figures 14-16, the measured top 1 beam identifier obtained by beam scanning during random access is the same as the predicted top 1 (strongest) beam identifier, or the same as the transmit beam identifier of Msg2 in four-step random access or MsgB in two-step random access.

[0405] Optionally, the measurement information predicted by the AI ​​unit includes at least one of the following: the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, the historical beam quality of the third type of beam, and the measurement timestamp.

[0406] Optionally, the reporting information corresponding to the AI ​​unit prediction includes at least one of the following: the beam identifier of the top 1 beam quality of the second type of beam, the beam identifier of the top K beam quality of the second type of beam, the beam quality of the second type of beam, and the prediction timestamp of the AI ​​unit.

[0407] S3-3. The network-side device sends the second information to the terminal;

[0408] The second information includes, but is not limited to, at least one of the following:

[0409] The relevant configuration of the target AI unit;

[0410] The fourth instruction is used to instruct the activation of the target AI unit;

[0411] The fifth instruction is used to instruct the deactivation of the target AI unit;

[0412] The target AI unit is an AI unit related to random access.

[0413] In Example 3, for the UE to predict the beam identifier of the second type of beam (such as the 800ms periodic beam in energy-saving mode) and determine the RO of transmitting Msg1 or MsgA, after prediction and before completing random access, the random access report is designed to record the content of other long energy-saving beams in specific scenarios. This allows the network-side equipment to monitor the model inference of the AI ​​unit on the terminal side based on the recorded information, and to determine whether the AI ​​prediction function should be activated, maintained or deactivated when initiating random access based on the inference performance. This avoids the problem of communication performance degradation caused by the deterioration of AI unit performance.

[0414] The wireless communication method provided in this application can be executed by a wireless communication device. This application uses an example of a wireless communication device executing the wireless communication method to illustrate the wireless communication device provided in this application.

[0415] This application provides a wireless communication device. As an example, the wireless communication device may be a communication equipment or a component within a communication equipment, such as a chip. The communication equipment may be a terminal, a network-side device, or a server, etc. Exemplarily, the terminal may include, but is not limited to, the type of terminal 11 listed above, and the network-side device may include, but is not limited to, the type of network-side device 12 listed above. This application does not impose specific limitations.

[0416] The wireless communication device includes a receiving module, a transmitting module, and a processing module. These modules can be implemented in software or hardware. When implemented in hardware, the processing module can be implemented by a processor. For example, the processor can include general-purpose processors, special-purpose processors, such as a Central Processing Unit (CPU), microprocessor, Digital Signal Processor (DSP), Artificial Intelligence (AI) processor, Graphics Processing Unit (GPU), Application Specific Integrated Circuit (ASIC), Network Processor (NP), Field Programmable Gate Array (FPGA), or other programmable logic devices, gate circuits, transistors, discrete hardware components, etc. The receiving and transmitting modules can be implemented by a communication interface, which can include one or more of the following: transceiver, pins, circuits, bus, radio frequency unit, etc.

[0417] Referring to Figure 18, when the wireless communication device is a terminal or a component within a terminal, the wireless communication device 300 includes:

[0418] Sending module 301 is used to send random access reports to network-side devices;

[0419] The random access report includes at least one of the following:

[0420] The first piece of information is used to indicate whether random access is related to the predictions of the artificial intelligence (AI) unit;

[0421] Key performance indicators (KPIs) related to random access predicted by the AI ​​unit;

[0422] AI unit predicts event information related to random access.

[0423] In some embodiments, the first information includes first indication information;

[0424] The first indication information is used to indicate whether the target beam identifier is obtained based on AI unit prediction;

[0425] The target beam identifier is the beam identifier corresponding to the message in the random access sent by the wireless communication device 300.

[0426] In some embodiments, the messages in the random access include at least one of the following:

[0427] Message 1 Msg1 in four-step random access, and message A MsgA in two-step random access.

[0428] In some embodiments, the first information includes at least one of the following:

[0429] The second indication information is used to indicate whether the beam quality being greater than the first threshold is based on predictions from the AI ​​unit.

[0430] The third indication information is used to indicate whether the synchronization of random access is based on AI unit prediction.

[0431] In some embodiments, the KPI information related to random access predicted by the AI ​​unit includes at least one of the following:

[0432] Prediction accuracy;

[0433] The difference between the predicted value and the measured value.

[0434] In some embodiments, the prediction accuracy includes at least one of the following:

[0435] The prediction accuracy of the beam identifier for the top-ranked beam quality;

[0436] The prediction accuracy of beam identifiers for the top K beam quality, where K is a positive integer and K > 1;

[0437] Predictive accuracy of synchronization for random access.

[0438] In some embodiments, the event information related to random access predicted by the AI ​​unit includes at least one of the following:

[0439] Whether the predicted value is greater than the second threshold in the actual measurement;

[0440] Are the predicted values ​​the same as the measured values?

[0441] In some embodiments, whether the predicted value is greater than the second threshold in actual measurement includes at least one of the following:

[0442] Whether the beam quality of the predicted top-1 beam identifier is greater than or equal to the measured beam quality of the second threshold.

[0443] The predicted beam quality top K beam identifiers are determined to have a measured beam quality greater than or equal to a second threshold, where K is a positive integer and K > 1;

[0444] The beam identifier of the predicted beam quality top 1 is determined by whether the beam quality measured in message 2Msg2 in four-step random access or message BMsgB in two-step random access is greater than or equal to a second threshold.

[0445] In some embodiments, whether the predicted value is the same as the measured value includes at least one of the following:

[0446] Is the beam identifier of the predicted top-1 beam quality the same as the beam identifier of the actual measured top-1 beam quality?

[0447] Whether the predicted beam identifier with the strongest beam quality belongs to the top K beam identifiers with the strongest measured beam quality, where K is a positive integer and K>1;

[0448] Whether the measured beam identifier with the strongest beam quality belongs to the predicted top K beam identifiers with the strongest beam quality, where K is a positive integer and K>1.

[0449] In some embodiments, the random access report includes at least one of the following: measurement information corresponding to AI unit prediction, and reporting information corresponding to AI unit prediction;

[0450] The measurement information predicted by the AI ​​unit includes at least one of the following: the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, the historical beam quality of the third type of beam, and the measurement timestamp.

[0451] The AI ​​unit predicts and reports information including at least one of the following: the beam identifier of the top 1 beam quality of the second type of beam, the beam identifier of the top K beam quality of the second type of beam, the beam quality of the second type of beam, and the prediction timestamp of the AI ​​unit.

[0452] Where K is a positive integer, and K > 1;

[0453] Wherein, the period of the first type of beam is greater than the period of the second type of beam, the period of the first type of beam is greater than the period of the third type of beam, and the cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam; or, the period of the first type of beam is less than the period of the third type of beam, the period of the second type of beam is less than the period of the third type of beam, and the cell corresponding to the first type of beam is different from the cell corresponding to the second type of beam.

[0454] In some embodiments, the wireless communication device 300 further includes:

[0455] The receiving module 302 is used to receive second information from the network-side device;

[0456] The second information includes at least one of the following:

[0457] The relevant configuration of the target AI unit;

[0458] The fourth instruction is used to instruct the activation of the target AI unit;

[0459] The fifth instruction is used to instruct the deactivation of the target AI unit;

[0460] The target AI unit is an AI unit related to random access.

[0461] Referring to Figure 19, when the wireless communication device is a network-side device or a component within a network-side device, the wireless communication device 400 includes:

[0462] Receiver module 401 is used to receive random access reports from the terminal;

[0463] The random access report includes at least one of the following:

[0464] The first piece of information is used to indicate whether random access is related to the predictions of the artificial intelligence (AI) unit;

[0465] Key performance indicators (KPIs) related to random access predicted by the AI ​​unit;

[0466] AI unit predicts event information related to random access.

[0467] In some embodiments, the first information includes first indication information;

[0468] The first indication information is used to indicate whether the target beam identifier is obtained based on AI unit prediction;

[0469] The target beam identifier is the beam identifier corresponding to the message in the random access sent by the terminal.

[0470] In some embodiments, the messages in the random access include at least one of the following:

[0471] Message 1 Msg1 in four-step random access, and message A MsgA in two-step random access.

[0472] In some embodiments, the first information includes at least one of the following:

[0473] The second indication information is used to indicate whether the beam quality being greater than the first threshold is based on predictions from the AI ​​unit.

[0474] The third indication information is used to indicate whether the synchronization of random access is based on AI unit prediction.

[0475] In some embodiments, the KPI information related to random access predicted by the AI ​​unit includes at least one of the following:

[0476] Prediction accuracy;

[0477] The difference between the predicted value and the measured value.

[0478] In some embodiments, the event information related to random access predicted by the AI ​​unit includes at least one of the following:

[0479] Whether the predicted value is greater than the second threshold in the actual measurement;

[0480] Are the predicted values ​​the same as the measured values?

[0481] In some embodiments, the random access report includes at least one of the following: measurement information corresponding to AI unit prediction, and reporting information corresponding to AI unit prediction;

[0482] The measurement information predicted by the AI ​​unit includes at least one of the following: the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, the historical beam quality of the third type of beam, and the measurement timestamp.

[0483] The AI ​​unit predicts and reports information including at least one of the following: the beam identifier of the top 1 beam quality of the second type of beam, the beam identifier of the top K beam quality of the second type of beam, the beam quality of the second type of beam, and the prediction timestamp of the AI ​​unit.

[0484] Where K is a positive integer, and K > 1;

[0485] Wherein, the period of the first type of beam is greater than the period of the second type of beam, the period of the first type of beam is greater than the period of the third type of beam, and the cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam; or, the period of the first type of beam is less than the period of the third type of beam, the period of the second type of beam is less than the period of the third type of beam, and the cell corresponding to the first type of beam is different from the cell corresponding to the second type of beam.

[0486] In some embodiments, the wireless communication device 400 further includes:

[0487] The sending module 402 is used to send second information to the terminal;

[0488] The second information includes at least one of the following:

[0489] The relevant configuration of the target AI unit;

[0490] The fourth instruction is used to instruct the activation of the target AI unit;

[0491] The fifth instruction is used to instruct the deactivation of the target AI unit;

[0492] The target AI unit is an AI unit related to random access.

[0493] Therefore, in this embodiment, the terminal sends a random access report to the network-side device. The random access report includes at least one of the following: first information indicating whether the random access is related to the prediction of the AI ​​unit; KPI information related to random access predicted by the AI ​​unit; and event information related to random access predicted by the AI ​​unit. Specifically, the random access report carries relevant information predicted by the AI ​​unit, thus making it applicable to different types of beam prediction scenarios or energy-saving beam prediction scenarios. The network-side device can monitor the AI ​​unit on the terminal side based on the random access report, determining whether to turn the corresponding AI unit on or off, avoiding communication performance degradation caused by AI unit performance degradation.

[0494] The wireless communication device provided in this application embodiment can implement the various processes implemented in the method embodiment of FIG3 and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0495] As shown in Figure 20, this application embodiment also provides a communication device 500, including a processor 501 and a memory 502, wherein the memory 502 stores a program or instructions that can be run on the processor 501.

[0496] For example, when the communication device 500 is a terminal, the program or instruction executed by the processor 501 implements the various steps executed by the terminal in the above wireless communication method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0497] For example, when the communication device 500 is a network-side device, the program or instruction executed by the processor 501 implements the various steps executed by the network-side device in the above wireless communication method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0498] This application also provides a terminal, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps in the method embodiment shown in FIG3. This terminal embodiment corresponds to the above-described terminal-side method embodiment, and all implementation processes and methods of the above-described method embodiments can be applied to this terminal embodiment and can achieve the same technical effect. The terminal may be the wireless communication device 300 shown in FIG18.

[0499] Specifically, Figure 21 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of this application.

[0500] The terminal 600 includes, but is not limited to, at least some of the following components: radio frequency unit 601, network module 602, audio output unit 603, input unit 604, sensor 605, display unit 606, user input unit 607, interface unit 608, memory 609, and processor 610.

[0501] Those skilled in the art will understand that terminal 600 may also include a power supply (such as a battery) for powering various components. The power supply can be logically connected to processor 610 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. The terminal structure shown in Figure 21 does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0502] It should be understood that, in this embodiment, the input unit 604 may include a graphics processor 6041 and a microphone 6042. The graphics processor 6041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 606 may include a display panel 6061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 607 includes at least one of a touch panel 6071 and other input devices 6072. The touch panel 6071 is also called a touch screen. The touch panel 6071 may include two parts: a touch detection device and a touch controller. Other input devices 6072 may include, but are not limited to, a physical keyboard, function keys (such as volume control buttons, power buttons, etc.), a trackball, a mouse, and a joystick, which will not be described in detail here.

[0503] In this embodiment, after receiving downlink data from the network-side device, the radio frequency unit 601 can transmit it to the processor 610 for processing; in addition, the radio frequency unit 601 can send uplink data to the network-side device. Typically, the radio frequency unit 601 includes, but is not limited to, antennas, amplifiers, transceivers, couplers, low-noise amplifiers, duplexers, etc.

[0504] The memory 609 can be used to store software programs or instructions, as well as various data. The memory 609 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 609 may include volatile memory or non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 609 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0505] Processor 610 may include one or more processing units; optionally, processor 610 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 610.

[0506] In some embodiments, the radio frequency unit 601 is used to send a random access report to a network-side device;

[0507] The random access report includes at least one of the following:

[0508] The first piece of information is used to indicate whether random access is related to the predictions of the artificial intelligence (AI) unit;

[0509] Key performance indicators (KPIs) related to random access predicted by the AI ​​unit;

[0510] AI unit predicts event information related to random access.

[0511] Therefore, in this embodiment, the terminal sends a random access report to the network-side device. The random access report includes at least one of the following: first information indicating whether the random access is related to the prediction of the AI ​​unit; KPI information related to random access predicted by the AI ​​unit; and event information related to random access predicted by the AI ​​unit. Specifically, the random access report carries relevant information predicted by the AI ​​unit, thus making it applicable to different types of beam prediction scenarios or energy-saving beam prediction scenarios. The network-side device can monitor the AI ​​unit on the terminal side based on the random access report, determining whether to turn the corresponding AI unit on or off, avoiding communication performance degradation caused by AI unit performance degradation.

[0512] It is understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the method embodiment and achieve the same or corresponding technical effect. To avoid repetition, it will not be described again here.

[0513] This application also provides a network-side device, including a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps of the method embodiment shown in FIG3. This network-side device embodiment corresponds to the method embodiment executed by the above-described network-side device. All implementation processes and methods of the above-described method embodiments can be applied to this network-side device embodiment and can achieve the same technical effect.

[0514] This application also provides a network-side device, which may be the wireless communication device 400 shown in FIG19.

[0515] Specifically, as shown in Figure 22, the network-side device 700 includes: an antenna 71, a radio frequency (RF) device 72, a baseband device 73, a processor 74, and a memory 75. The antenna 71 is connected to the RF device 72. In the uplink direction, the RF device 72 receives information through the antenna 71 and transmits the received information to the baseband device 73 for processing. In the downlink direction, the baseband device 73 processes the information to be transmitted and sends it to the RF device 72. The RF device 72 processes the received information and transmits it through the antenna 71.

[0516] The method executed by the network-side device in the above embodiments can be implemented in the baseband device 73, which includes a baseband processor.

[0517] The baseband device 73 may include at least one baseband board, on which multiple chips are disposed, as shown in FIG22. One of the chips is, for example, a baseband processor, which is connected to the memory 75 via a bus interface to call the program in the memory 75 and execute the operation of the network-side device shown in the above method embodiment.

[0518] The network-side device may also include a network interface 76, such as a Common Public Radio Interface (CPRI).

[0519] Specifically, the network-side device 700 in this application embodiment further includes: instructions or programs stored in memory 75 and executable on processor 74. Processor 74 calls the instructions or programs in memory 75 to execute the methods executed by each module shown in FIG19 and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

[0520] In some embodiments, the radio frequency device 72 is configured to receive a random access report from a terminal;

[0521] The random access report includes at least one of the following:

[0522] The first piece of information is used to indicate whether random access is related to the predictions of the artificial intelligence (AI) unit;

[0523] Key performance indicators (KPIs) related to random access predicted by the AI ​​unit;

[0524] AI unit predicts event information related to random access.

[0525] Therefore, in this embodiment, the terminal sends a random access report to the network-side device. The random access report includes at least one of the following: first information indicating whether the random access is related to the prediction of the AI ​​unit; KPI information related to random access predicted by the AI ​​unit; and event information related to random access predicted by the AI ​​unit. Specifically, the random access report carries relevant information predicted by the AI ​​unit, thus making it applicable to different types of beam prediction scenarios or energy-saving beam prediction scenarios. The network-side device can monitor the AI ​​unit on the terminal side based on the random access report, determining whether to turn the corresponding AI unit on or off, avoiding communication performance degradation caused by AI unit performance degradation.

[0526] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described wireless communication method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0527] The processor mentioned above is the processor in the terminal or network-side device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk. In some examples, the readable storage medium may be a non-transient readable storage medium.

[0528] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described wireless communication method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0529] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0530] This application also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described wireless communication method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0531] This application also provides a wireless communication system, including: a terminal and a network-side device. The terminal can be used to perform the steps performed by the terminal in the wireless communication method described above, and the network-side device can be used to perform the steps performed by the network-side device in the wireless communication method described above.

[0532] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0533] From the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of computer software products plus necessary general-purpose hardware platforms, and of course, they can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes several instructions to cause the terminal or network-side device to execute the methods described in the various embodiments of this application.

[0534] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other implementations under the guidance of this application without departing from the spirit and scope of the claims. All of these implementations are within the protection scope of this application.

Claims

A wireless communication method, comprising: The terminal sends a random access report to the network-side device; The random access report includes at least one of the following: The first piece of information is used to indicate whether random access is related to the predictions of the artificial intelligence (AI) unit; Key performance indicators (KPIs) related to random access predicted by the AI ​​unit; AI unit predicts event information related to random access. According to the method of claim 1, wherein, The first information includes first indication information; The first indication information is used to indicate whether the target beam identifier is obtained based on AI unit prediction; The target beam identifier is the beam identifier corresponding to the message in the random access sent by the terminal. The method according to claim 2, wherein, The messages in the random access include at least one of the following: Message 1 Msg1 in four-step random access, and message A MsgA in two-step random access. The method according to claim 2 or 3, wherein, The first information includes at least one of the following: The second indication information is used to indicate whether the beam quality being greater than the first threshold is based on predictions from the AI ​​unit. The third indication information is used to indicate whether the synchronization of random access is based on AI unit prediction. The method according to any one of claims 1 to 4, wherein, The KPI information related to random access predicted by the AI ​​unit includes at least one of the following: Prediction accuracy; The difference between the predicted value and the measured value. The method according to claim 5, wherein, The prediction accuracy includes at least one of the following: The prediction accuracy of the beam identifier for the top-ranked beam quality; The prediction accuracy of beam identifiers for the top K beam quality, where K is a positive integer and K > 1; Predictive accuracy of synchronization for random access. The method according to any one of claims 1 to 6, wherein, The AI ​​unit predicts at least one of the following event information related to random access: Whether the predicted value is greater than the second threshold in the actual measurement; Are the predicted values ​​the same as the measured values? The method according to claim 7, wherein, Whether the predicted value is greater than the second threshold in actual measurement includes at least one of the following: Does the beam quality of the predicted top-1 beam identifier exceed the second threshold in the actual measured beam quality? The predicted beam quality top K beam identifiers are determined to have a measured beam quality greater than a second threshold, where K is a positive integer and K > 1; The beam identifier of the predicted beam quality top 1 is determined by whether the beam quality measured in message 2Msg2 in four-step random access or message BMsgB in two-step random access is greater than a second threshold. The method according to claim 7, wherein, Whether the predicted value is the same as the measured value includes at least one of the following: Is the beam identifier of the predicted top-1 beam quality the same as the beam identifier of the actual measured top-1 beam quality? Whether the predicted beam identifier with the strongest beam quality belongs to the top K beam identifiers with the strongest measured beam quality, where K is a positive integer and K>1; Whether the measured beam identifier with the strongest beam quality belongs to the predicted top K beam identifiers with the strongest beam quality, where K is a positive integer and K>1. The method according to any one of claims 1 to 9, wherein, The random access report includes at least one of the following: measurement information corresponding to the AI ​​unit prediction, and reporting information corresponding to the AI ​​unit prediction; The measurement information predicted by the AI ​​unit includes at least one of the following: the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, the historical beam quality of the third type of beam, and the measurement timestamp. The AI ​​unit predicts and reports information including at least one of the following: the beam identifier of the top 1 beam quality of the second type of beam, the beam identifier of the top K beam quality of the second type of beam, the beam quality of the second type of beam, and the prediction timestamp of the AI ​​unit. Where K is a positive integer, and K > 1; Wherein, the period of the first type of beam is greater than the period of the second type of beam, the period of the first type of beam is greater than the period of the third type of beam, and the cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam; or, the period of the first type of beam is less than the period of the third type of beam, the period of the second type of beam is less than the period of the third type of beam, and the cell corresponding to the first type of beam is different from the cell corresponding to the second type of beam. The method according to any one of claims 1 to 10, wherein, The method further includes: The terminal receives second information from the network-side device; The second information includes at least one of the following: The relevant configuration of the target AI unit; The fourth instruction is used to instruct the activation of the target AI unit; The fifth instruction is used to instruct the deactivation of the target AI unit; The target AI unit is an AI unit related to random access. A wireless communication method, comprising: Network-side devices receive random access reports from terminals; The random access report includes at least one of the following: The first piece of information is used to indicate whether random access is related to the predictions of the artificial intelligence (AI) unit; Key performance indicators (KPIs) related to random access predicted by the AI ​​unit; AI unit predicts event information related to random access. The method according to claim 12, wherein, The first information includes first indication information; The first indication information is used to indicate whether the target beam identifier is obtained based on AI unit prediction; The target beam identifier is the beam identifier corresponding to the message in the random access sent by the terminal. The method according to claim 13, wherein, The messages in the random access include at least one of the following: Message 1 Msg1 in four-step random access, and message A MsgA in two-step random access. The method according to claim 13 or 14, wherein, The first information includes at least one of the following: The second indication information is used to indicate whether the beam quality being greater than the first threshold is based on predictions from the AI ​​unit. The third indication information is used to indicate whether the synchronization of random access is based on AI unit prediction. The method according to any one of claims 12 to 15, wherein, The KPI information related to random access predicted by the AI ​​unit includes at least one of the following: Prediction accuracy; The difference between the predicted value and the measured value. The method according to any one of claims 12 to 16, wherein, The AI ​​unit predicts at least one of the following event information related to random access: Whether the predicted value is greater than the second threshold in the actual measurement; Are the predicted values ​​the same as the measured values? The method according to any one of claims 12 to 17, wherein, The random access report includes at least one of the following: measurement information corresponding to the AI ​​unit prediction, and reporting information corresponding to the AI ​​unit prediction; The measurement information predicted by the AI ​​unit includes at least one of the following: the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, the historical beam quality of the third type of beam, and the measurement timestamp. The AI ​​unit predicts and reports information including at least one of the following: the beam identifier of the top 1 beam quality of the second type of beam, the beam identifier of the top K beam quality of the second type of beam, the beam quality of the second type of beam, and the prediction timestamp of the AI ​​unit. Where K is a positive integer, and K > 1; Wherein, the period of the first type of beam is greater than the period of the second type of beam, the period of the first type of beam is greater than the period of the third type of beam, and the cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam; or, the period of the first type of beam is less than the period of the third type of beam, the period of the second type of beam is less than the period of the third type of beam, and the cell corresponding to the first type of beam is different from the cell corresponding to the second type of beam. The method according to any one of claims 12 to 18, wherein, The method further includes: The network-side device sends the second information to the terminal; The second information includes at least one of the following: The relevant configuration of the target AI unit; The fourth instruction is used to instruct the activation of the target AI unit; The fifth instruction is used to instruct the deactivation of the target AI unit; The target AI unit is an AI unit related to random access. A wireless communication device, comprising: The sending module is used to send random access reports to network-side devices; The random access report includes at least one of the following: The first piece of information is used to indicate whether random access is related to the predictions of the artificial intelligence (AI) unit; Key performance indicators (KPIs) related to random access predicted by the AI ​​unit; AI unit predicts event information related to random access. The apparatus according to claim 20, wherein, The first information includes first indication information; The first indication information is used to indicate whether the target beam identifier is obtained based on AI unit prediction; The target beam identifier is the beam identifier corresponding to the message in the random access sent by the wireless communication device. The apparatus according to claim 21, wherein, The first information includes at least one of the following: The second indication information is used to indicate whether the beam quality being greater than the first threshold is based on predictions from the AI ​​unit. The third indication information is used to indicate whether the synchronization of random access is based on AI unit prediction. The apparatus according to any one of claims 20 to 22, wherein, The KPI information related to random access predicted by the AI ​​unit includes at least one of the following: Prediction accuracy; The difference between the predicted value and the measured value. The apparatus according to any one of claims 20 to 23, wherein, The AI ​​unit predicts at least one of the following event information related to random access: Whether the predicted value is greater than the second threshold in the actual measurement; Are the predicted values ​​the same as the measured values? The apparatus according to any one of claims 20 to 24, wherein, The random access report includes at least one of the following: measurement information corresponding to the AI ​​unit prediction, and reporting information corresponding to the AI ​​unit prediction; The measurement information predicted by the AI ​​unit includes at least one of the following: the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, the historical beam quality of the third type of beam, and the measurement timestamp. The AI ​​unit predicts and reports information including at least one of the following: the beam identifier of the top 1 beam quality of the second type of beam, the beam identifier of the top K beam quality of the second type of beam, the beam quality of the second type of beam, and the prediction timestamp of the AI ​​unit; where K is a positive integer and K > 1. Wherein, the period of the first type of beam is greater than the period of the second type of beam, the period of the first type of beam is greater than the period of the third type of beam, and the cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam; or, the period of the first type of beam is less than the period of the third type of beam, the period of the second type of beam is less than the period of the third type of beam, and the cell corresponding to the first type of beam is different from the cell corresponding to the second type of beam. The apparatus according to any one of claims 20 to 25, wherein, The wireless communication device further includes: A receiving module is configured to receive second information from the network-side device; The second information includes at least one of the following: The relevant configuration of the target AI unit; The fourth instruction is used to instruct the activation of the target AI unit; The fifth instruction is used to instruct the deactivation of the target AI unit; The target AI unit is an AI unit related to random access. A wireless communication device, comprising: The receiving module is used to receive random access reports from the terminal; The random access report includes at least one of the following: The first piece of information is used to indicate whether random access is related to the predictions of the artificial intelligence (AI) unit; Key performance indicators (KPIs) related to random access predicted by the AI ​​unit; AI unit predicts event information related to random access. The apparatus according to claim 27, wherein, The first information includes first indication information; The first indication information is used to indicate whether the target beam identifier is obtained based on AI unit prediction; The target beam identifier is the beam identifier corresponding to the message in the random access sent by the terminal. The apparatus according to claim 28, wherein, The first information includes at least one of the following: The second indication information is used to indicate whether the beam quality being greater than the first threshold is based on predictions from the AI ​​unit. The third indication information is used to indicate whether the synchronization of random access is based on AI unit prediction. The apparatus according to any one of claims 27 to 29, wherein, The KPI information related to random access predicted by the AI ​​unit includes at least one of the following: Prediction accuracy; The difference between the predicted value and the measured value. The apparatus according to any one of claims 27 to 30, wherein, The AI ​​unit predicts at least one of the following event information related to random access: Whether the predicted value is greater than the second threshold in the actual measurement; Are the predicted values ​​the same as the measured values? The apparatus according to any one of claims 27 to 31, wherein, The random access report includes at least one of the following: measurement information corresponding to the AI ​​unit prediction, and reporting information corresponding to the AI ​​unit prediction; The measurement information predicted by the AI ​​unit includes at least one of the following: the beam identifier of the first type of beam, the beam quality of the first type of beam, the beam identifier of the third type of beam, the historical beam quality of the third type of beam, and the measurement timestamp. The AI ​​unit predicts and reports information including at least one of the following: the beam identifier of the top 1 beam quality of the second type of beam, the beam identifier of the top K beam quality of the second type of beam, the beam quality of the second type of beam, and the prediction timestamp of the AI ​​unit; where K is a positive integer and K > 1. Wherein, the period of the first type of beam is greater than the period of the second type of beam, the period of the first type of beam is greater than the period of the third type of beam, and the cell corresponding to the first type of beam is the same as the cell corresponding to the second type of beam; or, the period of the first type of beam is less than the period of the third type of beam, the period of the second type of beam is less than the period of the third type of beam, and the cell corresponding to the first type of beam is different from the cell corresponding to the second type of beam. The apparatus according to any one of claims 27 to 32, wherein, The wireless communication device further includes: The sending module is used to send second information to the terminal; The second information includes at least one of the following: The relevant configuration of the target AI unit; The fourth instruction is used to instruct the activation of the target AI unit; The fifth instruction is used to instruct the deactivation of the target AI unit; The target AI unit is an AI unit related to random access. A terminal includes a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the wireless communication method as described in any one of claims 1 to 11. A network-side device includes a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the wireless communication method as described in any one of claims 12 to 19. A readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the wireless communication method as claimed in any one of claims 1 to 11, or implement the steps of the wireless communication method as claimed in any one of claims 12 to 19.

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