Method and apparatus used in uplink transmission node for wireless communications

Abstract

Disclosed in the present application are a method and apparatus used in an uplink transmission node for wireless communications. The method comprises: a node first receiving first signaling, which indicates K1 resource sets, wherein the K1 resource sets correspond to the same inference dataset, and K1 is a positive integer greater than 1; subsequently, transmitting K1 power indicators, and respectively transmitting K1 reference signals in the K1 resource sets by using K1 transmit power values, wherein the K1 transmit power values are respectively dependent on the K1 power indicators, and a power difference between at least two of the K1 transmit power values is configurable or predefined. The present application optimizes the compensation performance of a power amplifier of a receiver by means of transmitting K1 reference signals, thereby improving the overall uplink reception performance.

Description

A method and apparatus for uplink transmission in a node used in wireless communication Technical Field

[0001] This application relates to signal transmission methods and apparatus in wireless communication systems, and more particularly to uplink transmission methods and apparatus. Background Technology

[0002] In traditional wireless communication systems, power amplifiers (PAs) are most efficient when operating near saturation, but this introduces non-linear distortion. Non-linear compensation for PAs is a key technology in wireless communication systems, aiming to solve the signal distortion problem caused by amplifiers at high power output. The core of PA non-linear compensation is to cancel the distortion through predistortion, feedforward, or feedback techniques, among which digital predistortion (DPD) has become the mainstream solution due to its flexibility and high precision.

[0003] Currently, the development of AI (Artificial Intelligence) / ML (Machine Learning) has entered the large-scale model stage. Large-scale communication models can achieve autonomous networks and intelligent services, supporting network operation optimization and improving network efficiency. Deep integration of communication and AI is a crucial direction for the future evolution of communication. AI will empower the development and upgrade of 5G, 5.5G, and 6G, bringing new management models such as automated frequency band and traffic management, real-time analysis of user data and network load, and prediction of network status. Regarding the nonlinear compensation of power amplifiers (PAs), one approach focuses on AI algorithms, using AI / ML applications to achieve PA nonlinear compensation, thereby meeting the high standards required by 5G / 6G. Summary of the Invention

[0004] Currently, academic and industrial researchers have demonstrated through research and simulation algorithms that by introducing an AI model into the receiver, and through training and updating the AI ​​model, it is possible to achieve relatively ideal compensation for the nonlinear distortion of the PA (Power Amplifier), thereby reducing the receiver's EVM (Error Vector Magnitude) and improving overall performance. However, achieving the aforementioned performance gains requires training the receiver with training sequences to enable the application of the AI ​​model.

[0005] To address the aforementioned scenarios, this application discloses a solution. It should be noted that while this application is initially intended for power control scenarios, it can also be applied to other non-power control scenarios. Furthermore, adopting a unified design scheme for different scenarios (such as other non-uplink transmission scenarios, including but not limited to sensing integration, artificial intelligence, capacity enhancement systems, short-range communication systems, NTN (Non-Terrestrial Network), IoT (Internet of Things), and URLLC (Ultra-Reliable Low-Latency Communication) networks) helps reduce hardware complexity and cost. Where there is no conflict, embodiments and features in any node of this application can be applied to any other node. Where there is no conflict, embodiments and features in any embodiment of this application can be arbitrarily combined with each other.

[0006] Specifically, the interpretation of terms, nouns, functions, and variables in this application (unless otherwise specified) can be found in the definitions of the TS38 and TS37 series of 3GPP (3rd Generation Partnership Project) Technical Specifications (TS). Where necessary, reference can be made to TS23.273, TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.300, TS38.304, TS38.305, TS38.321, TS38.331, TS37.355, TS38.355, TS38.423, and TS38.455 in the 3GPP technical specifications to aid in understanding this application.

[0007] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS38 series.

[0008] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS37 series.

[0009] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS23 series.

[0010] As an example, the interpretation of the terms used in this application is based on the definitions in 3GPP specification protocol Rel-17.

[0011] As an example, the interpretation of the terms used in this application is based on the definitions in 3GPP specification protocol Rel-18.

[0012] This application discloses a method in a first node used for uplink transmission in wireless communication, comprising:

[0013] A first receiver receives a first signaling instruction, which indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, and K1 being a positive integer greater than 1.

[0014] The first transmitter transmits K1 power indications and K1 reference signals with K1 transmission power values ​​in the K1 resource sets respectively;

[0015] The K1 transmission power values ​​are each dependent on the K1 power indicators; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

[0016] As an example, the problem to be solved by this application includes: how to train the receiver of the sender of the first signaling using the K1 reference signals.

[0017] As an example, the problem to be solved by this application includes: how the first node transmits the K1 reference signals according to the instructions of the sender of the first signaling.

[0018] As an example, the features of the above method include: the K1 transmit power values ​​are respectively dependent on the K1 power indications, thereby ensuring that the sender of the first signaling has knowledge of the power values ​​of the K1 reference signals.

[0019] As an example, the features of the above method include: the sender of the first signaling reasonably configures the K1 transmission power values ​​of the K1 reference signals through the K1 power indicators, thereby ensuring that the power corresponding to the K1 transmission power values ​​has a wide range of applicability and good flexibility and adaptability.

[0020] As an example, the features of the above method include: the sender of the first signaling reasonably configures the power range of the training sequence used to train the receiver through the K1 power indicators, thereby enabling the receiver to obtain a wider range of training sequences transmitted with different power for training, in order to adapt to different PA operating points, and thus ensure the performance when the AI / ML model is used for PA compensation.

[0021] As an example, the features of the above method include: the K1 resource sets correspond to the same inference dataset, thereby ensuring that when the sender of the first signaling receives data in the inference dataset, the AI / ML model trained using the K1 reference signals sent from the K1 resource sets can be used for inference of the data in the inference dataset.

[0022] As an example, the features of the above method include: the first node does not need to know that the K1 reference signals are used for training the receiver of the sender of the first signaling.

[0023] As an example, the features of the above method include: the K1 reference signals, in addition to being used for receiver training of the sender of the first signaling, can also be used for other purposes, such as channel measurement and channel estimation, to improve spectral efficiency.

[0024] As an example, the above method is characterized by the following: although the training in this application is mainly for PA compensation training of the receiver, the training in this application can also be used for training for other purposes to achieve other AI / ML-based power, non-limiting, such as training for demodulation, channel estimation or equalization.

[0025] As an example, the features of the above method include: the sender of the first signaling has a fixed power value for the K1 reference channels received in the K1 resource sets, thereby ensuring the performance of training.

[0026] According to one aspect of this application, the above method is characterized in that the first signaling indicates K1 values, and the K1 power indications are respectively dependent on the K1 values.

[0027] As an example, the features of the above method include: determining the K1 power indication through the K1 values, non-limitingly, such as K1 indices, thereby saving signaling overhead.

[0028] According to one aspect of this application, the above method is characterized in that the power difference between any two adjacent transmission power values ​​among the K1 transmission power values ​​is the same.

[0029] As an example, the features of the above method include: the K1 transmit power values ​​are linearly and uniformly distributed, thereby more effectively training the AI / ML model parameters used by the receiver at different receive power values.

[0030] According to one aspect of this application, the above method is characterized in that the transmission channel occupied by the inference dataset includes UL-SCH (Uplink Shared Channel).

[0031] As an example, the features of the above method include: using the AI / ML model trained with the K1 reference signals for inference of data in the inference dataset, establishing the relationship between training and inference, thereby ensuring reception performance.

[0032] According to one aspect of this application, the above method is characterized by comprising:

[0033] Send the first signal;

[0034] The K1 resource sets are associated with a first ID, and the first signal depends on the first ID.

[0035] As an example, the features of the above method include: establishing the relationship between the K1 resource sets and the first signal through the first ID, thereby establishing the relationship between training and inference to ensure inference-based reception performance.

[0036] According to one aspect of this application, the method is characterized in that the transmission power value of the first signal is equal to a first power value, the first power value depending on the first ID and the multi-antenna parameters of the first signal; the multi-antenna parameters include at least one of the number of layers associated with the first signal or the number of multiple users corresponding to the first signal.

[0037] As an example, the features of the above method include: when the first signal is transmitted using a multi-antenna method, such as using different layers or different numbers of users, the total power value of the first signal, i.e., the first power value, will be limited because the receiver uses an AI / ML-based PA compensation method.

[0038] According to one aspect of this application, the above method is characterized in that the minimum and maximum transmission power values ​​among the K1 transmission power values ​​are equal to a first power difference, and the first signaling indicates the first power difference.

[0039] As an example, the features of the above method include: clearly indicating the power variation range of the training sequence covered by the K1 transmit power values ​​through the first power difference, thereby flexibly and universally training the parameters of the PA compensation model corresponding to different PA operating points.

[0040] As an example, the features of the above method include: the first node is a user equipment.

[0041] As an example, the features of the above method include: the first node is a terminal.

[0042] As an example, the features of the above method include: the first node includes a Handset.

[0043] This application discloses a method in a second node used for uplink transmission in wireless communication, comprising:

[0044] Send a first signaling instruction, which indicates K1 resource sets, wherein the K1 resource sets correspond to the same inference dataset, and K1 is a positive integer greater than 1;

[0045] Receive K1 power indications and K1 reference signals in the K1 resource sets respectively;

[0046] The transmission power values ​​of the K1 reference signals are K1 transmission power values, and each of the K1 transmission power values ​​depends on the K1 power indicators; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

[0047] As an example, the features of the above method include: the second node includes a core network.

[0048] As an example, the features of the above method include: the second node includes an entity for deploying AI / ML models.

[0049] As an example, the features of the above method include: the second node includes a node for deploying AI / ML models.

[0050] As an example, the features of the above method include: the second node includes a base station.

[0051] As an example, the features of the above method include: the second node is a base station.

[0052] As an example, the features of the above method include: the second node is an eNB.

[0053] As an example, the features of the above method include: the second node is a gNB.

[0054] As an example, the features of the above method include: the second node is a network device, which includes at least one of a core network device and an access network device.

[0055] As an example, the features of the above method include: the second node is a device that provides wireless communication function services, can communicate with terminal devices, and is usually located on the network side.

[0056] As an example, the features of the above method include: the base station in this application includes a core network.

[0057] As an example, the features of the above method include: the base station in this application includes core network equipment.

[0058] As an example, the features of the above method include: the base station in this application includes an entity for deploying AI / ML models.

[0059] As an example, the features of the above method include: the base station in this application includes nodes for deploying AI / ML models.

[0060] As an example, the features of the above method include: the second node includes an OTT (Over-The-Top) Server.

[0061] As an example, the features of the above method include: the second node includes an eNB.

[0062] As an example, the features of the above method include: the second node supports AI / ML-based PA compensation.

[0063] As an example, the features of the above method include: the second node supports AI / ML-based PA nonlinear compensation.

[0064] As an example, the second node in this application includes OAM (Operation Administration and Maintenance).

[0065] As an example, the features of the above method include: the second node performs related receiver training using the K1 reference signals.

[0066] According to one aspect of this application, the above method is characterized in that the first signaling indicates K1 values, and the K1 power indications are respectively dependent on the K1 values.

[0067] According to one aspect of this application, the above method is characterized in that the power difference between any two adjacent transmission power values ​​among the K1 transmission power values ​​is the same.

[0068] According to one aspect of this application, the above method is characterized in that the transmission channel occupied by the inference dataset includes UL-SCH.

[0069] According to one aspect of this application, the above method is characterized by comprising:

[0070] Receive the first signal;

[0071] The K1 resource sets are associated with a first ID, and the first signal depends on the first ID.

[0072] As an example, the features of the above method include: the second node implements the inference of the first signal through the AI / ML model associated with the first ID.

[0073] According to one aspect of this application, the method is characterized in that the transmission power value of the first signal is equal to a first power value, the first power value depending on the first ID and the multi-antenna parameters of the first signal; the multi-antenna parameters include at least one of the number of layers associated with the first signal or the number of multiple users corresponding to the first signal.

[0074] According to one aspect of this application, the above method is characterized in that the minimum and maximum transmission power values ​​among the K1 transmission power values ​​are equal to a first power difference, and the first signaling indicates the first power difference.

[0075] This application discloses a first node used for uplink transmission in wireless communication, comprising:

[0076] A first receiver receives a first signaling instruction, which indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, and K1 being a positive integer greater than 1.

[0077] The first transmitter transmits K1 power indications and K1 reference signals with K1 transmission power values ​​in the K1 resource sets respectively;

[0078] The K1 transmission power values ​​are each dependent on the K1 power indicators; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

[0079] This application discloses a second node used for uplink transmission in wireless communication, comprising:

[0080] The second transmitter sends a first signaling instruction, which indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, and K1 being a positive integer greater than 1;

[0081] The second receiver receives K1 power indications and K1 reference signals from the K1 resource sets respectively;

[0082] The transmission power values ​​of the K1 reference signals are K1 transmission power values, and each of the K1 transmission power values ​​depends on the K1 power indicators; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

[0083] As an example, compared with conventional solutions, this application has the following advantages, but is not limited to:

[0084] The sender of the first signaling uses the K1 power indicators to reasonably configure the K1 transmission power values ​​of the K1 reference signals, thereby ensuring that the power corresponding to the K1 transmission power values ​​has a wide range of applicability and good flexibility and adaptability.

[0085] The sender of the first signaling uses the K1 power indicators to reasonably configure the power range of the training sequence used to train the receiver, thereby enabling the receiver to obtain a more general training sequence with a wider power variation range for training, so as to adapt to different PA operating points, and thus ensure the performance when the AI / ML model is used for PA compensation.

[0086] The K1 resource sets correspond to the same inference dataset, thereby ensuring that when the sender of the first signaling receives data in the inference dataset, the AI / ML model trained using the K1 reference signals sent in the K1 resource sets can be used for inference of the data in the inference dataset.

[0087] The above method enables the base station to configure the power range of the training sequence used for PA nonlinear compensation, ensuring that the input training sequence covers each different PA operating point, and that the base station receives the training sequence with the corresponding power value at the location of the corresponding resource set, thereby ensuring training performance and maximizing the gain brought by the introduction of PA nonlinear compensation in the AI / ML model. Attached Figure Description

[0088] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0089] Figure 1 illustrates a flowchart of the first node transmission according to an embodiment of this application;

[0090] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of this application;

[0091] Figure 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application;

[0092] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of this application;

[0093] Figure 5 shows a flowchart of transmission between a first node and a second node according to an embodiment of this application;

[0094] Figure 6 shows a flowchart of a first signal according to an embodiment of this application;

[0095] Figure 7 shows a schematic diagram of K1 resource sets according to an embodiment of this application;

[0096] Figure 8 shows a schematic diagram of a first ID according to an embodiment of this application;

[0097] Figure 9 shows a schematic diagram of K1 power indicators according to an embodiment of this application;

[0098] Figure 10 shows a schematic diagram of RAN domain AI / ML function deployment according to an embodiment of this application;

[0099] Figure 11 shows a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of this application;

[0100] Figure 12 shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application;

[0101] Figure 13 illustrates a schematic diagram of artificial intelligence or machine learning according to an embodiment of this application;

[0102] Figure 14 shows a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application;

[0103] Figure 15 shows a structural block diagram of a processing apparatus for a second node according to an embodiment of this application. Detailed Implementation

[0104] The technical solutions of this application will be further described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Considering performance, flexibility, complexity, overhead, and compatibility, those skilled in the art are motivated to flexibly combine the embodiments in different drawings without conflict, including but not limited to the embodiments in Figure 1 and the embodiments in Figures 5-15, the embodiments in Figure 5 and the embodiments in Figures 6-15, etc.

[0105] Example 1

[0106] Example 1 illustrates a flowchart of a first node transmission according to an embodiment of this application, as shown in Figure 1. In Figure 1, each block represents a step.

[0107] In step 101, the first node receives a first signaling, which indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, and K1 being a positive integer greater than 1; in step 102, it sends K1 power indications and sends K1 reference signals with K1 transmission power values ​​in the K1 resource sets respectively.

[0108] In Example 1, the K1 transmission power values ​​each depend on the K1 power indicators; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

[0109] As one example, the first node is a user equipment (UE).

[0110] As one example, the first node is a terminal.

[0111] As an example, at least one of the K1 power indicators and the K1 reference signals is transmitted simultaneously.

[0112] As an example, the earliest time-domain reference signal among the K1 power indicators and the K1 reference signals is transmitted simultaneously.

[0113] As an example, the K1 power indications are sent before the K1 reference signals.

[0114] As an example, the first signaling explicitly indicates the K1 resource sets.

[0115] As an example, the first signaling implicitly indicates the K1 resource sets.

[0116] As an example, the first signaling directly indicates the K1 resource sets.

[0117] As an example, the first signaling indirectly indicates the K1 resource sets.

[0118] As an example, the first signaling includes RRC (Radio Resource Control) signaling.

[0119] As an example, the first signaling includes one or more RRC IEs (Information Elements).

[0120] As one example, the first signaling includes one or more fields in an RRC IE.

[0121] As one example, the first signaling includes an SSB.

[0122] As an example, SSB in this application refers to Synchronization Signal Block.

[0123] As an example, the SSB mentioned in this application refers to: SS (Synchronization Signal) / PBCH block, the synchronization signal / physical broadcast channel block.

[0124] Typically, the PBCH (Physical Broadcast Channel), PSS (Primary Synchronization Signal), and SSS (Secondary Synchronization Signal) are received in consecutive symbols and form an SS / PBCH block.

[0125] As one embodiment, the first signaling includes one or more SIBs (System Information Blocks).

[0126] As one example, the first signaling includes one or more domains in RACH-ConfigCommon IE.

[0127] As one example, the first signaling includes one or more domains in RACH-ConfigCommonTwoStepRA IE.

[0128] As one example, the first signaling includes one or more domains in RACH-ConfigDedicated IE.

[0129] As one example, the first signaling includes one or more domains in RACH-ConfigGeneric IE.

[0130] As one example, the first signaling includes one or more domains in RACH-ConfigGenericTwoStepRA IE.

[0131] As one example, the first signaling includes one or more domains in RACH-ConfigTwoTA IE.

[0132] As an example, the name of the RRC signaling used to transmit the first signaling includes RACH.

[0133] As an example, the name of the RRC signaling used to transmit the first signaling includes Config.

[0134] As one example, the first signaling includes one or more domains in the CSI-ResourceConfig IE.

[0135] As one example, the first signaling includes one or more domains in CSI-ResourcePeriodicityAndOffset IE.

[0136] As one example, the first signaling includes one or more domains in the CSI-SSB-ResourceSet IE.

[0137] As one example, the first signaling includes one or more fields in the NZP-CSI-RS-Resource IE.

[0138] As one embodiment, the first signaling includes one or more fields in NZP-CSI-RS-ResourceSet IE.

[0139] As an example, the first signaling includes one or more fields in the PathlossReferenceRS IE.

[0140] As an example, the K1 resource sets are for inference.

[0141] As an example, the K1 resource sets are used for training.

[0142] As an example, the K1 resource sets are for prediction.

[0143] As an example, the meaning of the K1 resource sets corresponding to the same inference dataset includes: the K1 resource sets are used to receive data in the inference dataset.

[0144] As an example, the meaning of the K1 resource sets corresponding to the same inference dataset includes: the K1 resource sets are used for training the receiver that receives data in the inference dataset.

[0145] As an example, the meaning of the K1 resource sets corresponding to the same inference dataset is that the K1 resource sets are used for inference of the data in the inference dataset.

[0146] As an example, the meaning of the K1 resource sets corresponding to the same inference dataset includes: the K1 resource sets are used for prediction of data in the inference dataset.

[0147] As an example, the meaning of the K1 resource sets corresponding to the same inference dataset includes: the K1 resource sets are used to determine the PA of the receiver receiving the data in the inference dataset.

[0148] As an example, the same inference dataset is associated with the first ID.

[0149] As an example, the same inference dataset is associated with an associated ID.

[0150] As an example, the physical layer channels occupied by the data in the inference dataset include PUSCH (Physical Uplink Shared Channel).

[0151] As an example, the inference dataset is used for data transmission.

[0152] As an example, the inference dataset is not used for data collection.

[0153] As an example, the inference dataset is not used for training.

[0154] As an example, the inference dataset refers to a dataset that has undergone inference.

[0155] As an example, the inference dataset refers to a dataset that employs inference during reception.

[0156] As an example, the data in the inference dataset is PUSCH.

[0157] As an example, the data in the inference dataset is UL-SCH.

[0158] As an example, the K1 power indications are transmitted via UCI (Uplink Control Information).

[0159] As an example, the K1 power indications are transmitted via PUCCH (Physical Uplink Control Channel).

[0160] As an example, the K1 power indicators are transmitted via a physical layer channel.

[0161] As an example, the K1 power indications are transmitted via MAC (Medium Access Control) layer signaling.

[0162] As an example, the K1 power indications are transmitted via MAC CE (Control Elements).

[0163] As an example, the K1 power indicators respectively indicate the K1 transmission power values.

[0164] As an example, the K1 power indicators respectively indicate the K1 arrival power values ​​of the K1 reference signals on the second node side.

[0165] As an example, the K1 resource sets include at least one RS resource.

[0166] As an example, the K1 resource sets include at least one RS resource set.

[0167] As an example, the K1 resource sets are K1 RS resource sets.

[0168] As an example, the K1 resource sets are K1 PRACH (Physical Random Access Channel) resources.

[0169] As an example, the K1 resource sets are K1 PRACH resource sets.

[0170] As an example, the K1 resource sets are K1 RACH (Random Access Channel) opportunities.

[0171] As an example, each of the K1 reference signals includes an SRS (Sounding Reference Signal).

[0172] As an example, each of the K1 reference signals includes a DMRS (Demodulation Reference Signal).

[0173] As an example, each of the K1 reference signals includes a preamble sequence.

[0174] As an example, the K1 transmission power values ​​depending on the K1 power indicators include: the K1 transmission power values ​​are respectively equal to the K1 power values ​​indicated by the K1 power indicators.

[0175] As an example, the K1 transmission power values ​​depending on the K1 power indicators respectively include: the K1 power indicators respectively indicating the K1 transmission power values.

[0176] As an example, the K1 transmit power values ​​depending on the K1 power indications include: the K1 transmit power values ​​are equal to the sum of the K1 power values ​​indicated by the K1 power indications and the first path loss, where the first path loss is the path loss between the first node and the second node.

[0177] As an example, the power difference between at least two of the K1 transmission power values ​​is configurable.

[0178] As an example, the power difference between at least two of the K1 transmission power values ​​is predefined.

[0179] As a sub-implementation of the above two embodiments, the at least two transmission power values ​​are the largest and smallest transmission power values ​​among the K1 transmission power values.

[0180] As a sub-implementation of the above two embodiments, the at least two transmission power values ​​are the transmission power values ​​that are adjacent to each other among the K1 transmission power values.

[0181] As an example, the K1 transmission power values ​​are of equal step size, and the step size is configurable.

[0182] As an example, the K1 transmission power values ​​are of equal step size, and the step size is predefined.

[0183] As an example, the K1 transmission power values ​​are sorted in ascending order, and the power difference between any two adjacent transmission power values ​​in the K1 transmission power values ​​is the same.

[0184] As a sub-example of this embodiment, the power difference is configurable.

[0185] As a sub-example of this embodiment, the power difference is predefined.

[0186] As a sub-implementation of this embodiment, the first signaling indicates the power difference.

[0187] As an example, the unit of the K1 transmission power values ​​is dBm (millidecibels).

[0188] As an example, the unit of the power difference between any two of the K1 transmission power values ​​is dB (decibels).

[0189] As an example, the given transmission power value is the i-th transmission power value among the K1 transmission power values ​​sorted in ascending order, where i is a positive integer greater than 1 and less than K1. The given transmission power value is equal to the target power value plus the product of (i-1) and the target step size. The unit of the target power value is dBm, and the unit of the target step size is dB.

[0190] As a sub-example of this embodiment, the target power value is configurable or predefined.

[0191] As a sub-implementation of this embodiment, the target step size is configurable or predefined.

[0192] As a sub-implementation of this embodiment, the first signaling indicates the target step size.

[0193] As an example, the K1 resource sets are used for training the receiver of the second node in this application.

[0194] As an example, the K1 resource sets are associated with an Associated ID.

[0195] As an example, the K1 resource sets are associated with a Model ID.

[0196] As an example, the K1 resource sets are associated with a Functionality ID.

[0197] As an example, the K1 resource sets are associated with an AI / ML process.

[0198] As an example, the K1 reference signals sent from the K1 resource sets are used for data collection.

[0199] As an example, the K1 reference signals sent from the K1 resource sets are used for training.

[0200] Example 2

[0201] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in Figure 2.

[0202] Figure 2 illustrates network architecture 200. Network architecture 200 is the network architecture for LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), 5G systems, 5G-Advanced, and future 6G systems. The network architectures for LTE, LTE-A, 5G systems, 5G-Advanced, and future 6G systems are referred to as EPS (Evolved Packet System). The 5G NR or LTE network architecture may be referred to as 5GS (5G System) / EPS or some other suitable terminology; the 6G network architecture may be referred to as 6GS (6G System) / EPS or some other suitable terminology.

[0203] The network architecture 200 may include one or more UEs 201, a RAN (Radio Access Network) 202, a core network 210, an HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and an Internet service 230. The network architecture 200 may interconnect with other access networks, but these entities / interfaces are not shown for simplicity.

[0204] As shown in Figure 2, the network architecture 200 provides packet switching services; however, those skilled in the art will readily understand that the various concepts presented throughout this application can be extended to networks providing circuit-switched services or other cellular networks. The RAN 202 includes Node B 203 and other nodes 204. Node B 203 provides user and control plane protocol termination toward the UE 201. Node B 203 may be connected to other nodes 204 via an Xn interface (e.g., backhaul). Node B 203 may also be referred to as eNB (evolved Node B), gNB, base station, base transceiver station, wireless base station, wireless transceiver, transceiver function, Basic Service Set (BSS), Extended Service Set (ESS), TRP (Transmitter Receiver Point), or some other suitable term. Node B 203 provides UE 201 with an access point to the core network 210; the core network 210 is a 5GC (5G Core network) / EPC (Evolved Packet Core), or the core network 210 is a 6GC (6G Core network). Examples of the UE 201 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptops, personal digital assistants (PDAs), satellite radios, GPS devices, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband physical network devices, machine-type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to the UE 201 as a mobile station, subscriber station, mobile unit, subscriber unit, radio unit, remote unit, mobile device, radio device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, radio terminal, remote terminal, handheld device, user agent, mobile client, client, or any other suitable term. The Node B 203 is connected to the core network 210 via an S1 / NG interface.The core network 210 includes an MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MMEs / AMFs / SMFs 214, an S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Data Network Gateway) / UPF 213. The MME / AMF / SMF 211 is the control node that handles signaling between the UE 201 and the core network 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF 213 is connected to the Internet service 230. The Internet service 230 includes carrier-compliant Internet protocol services, specifically including the Internet, intranet, IMS (IP Multimedia Subsystem), and packet-switched streaming services.

[0205] As an example, the first node in this application includes the UE 201.

[0206] As an example, the second node in this application includes node B 203.

[0207] As an example, node B 203 is a macrocell base station.

[0208] As an example, node B 203 is a microcell base station.

[0209] As an example, node B 203 is a pico cell base station.

[0210] As an example, node B 203 is a femtocell.

[0211] As an example, node B 203 is a base station device that supports large latency differences.

[0212] As an example, node B 203 is a flight platform device.

[0213] As an example, node B 203 is a satellite device.

[0214] As one embodiment, the node B 203 is a test device (e.g., a transceiver device simulating part of the base station's functions, a signaling tester).

[0215] As an example, the UE 201 includes a mobile phone.

[0216] As an example, the UE 201 is a vehicle including a car.

[0217] As an example, the wireless link from the UE 201 to the node B 203 is an uplink, which is used to perform uplink transmissions.

[0218] As an example, the radio link from the node B 203 to the UE 201 is a downlink, which is used to perform downlink transmissions.

[0219] As an example, the wireless link between the node B 203 and the UE 201 includes a cellular link.

[0220] As an example, the node B 203 and the UE 201 are connected via the Uu air interface.

[0221] As an example, the node B 203 supports the deployment of network-side (NW-side) AI / ML models.

[0222] As an example, the UE 201 supports the deployment of UE-side AI / ML models.

[0223] As an example, the UE 201 supports a 5G system.

[0224] As an example, the node B 203 supports a 5G system.

[0225] As an example, the UE 201 supports at least a 6G system.

[0226] As an example, the node B 203 supports at least a 6G system.

[0227] As an example, the sender of the first signaling in this application includes the node B 203.

[0228] As an example, the recipient of the first signaling in this application includes the UE 201.

[0229] As an example, the sender of the K1 power indicators in this application includes the UE 201.

[0230] As an example, the receiver of the K1 power indications described in this application includes the node B 203.

[0231] As an example, the sender of the K1 reference signals in this application includes the UE 201.

[0232] As an example, the receiver of the K1 reference signals described in this application includes the node B 203.

[0233] As an example, the sender of the first signal in this application includes the UE 201.

[0234] As an example, the receiver of the first signal in this application includes the node B 203.

[0235] Example 3

[0236] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application, as shown in Figure 3.

[0237] Figure 3 is a schematic diagram illustrating an embodiment of the wireless protocol architecture for the user plane 350 and the control plane 300. Figure 3 shows the wireless protocol architecture for the control plane 300 between a first communication node device (UE or RSU in V2X, onboard equipment or onboard communication module) and a second node device (gNB, RSU in UE or V2X, onboard equipment or onboard communication module), or between two UEs, using three layers: Layer 1 (L1), Layer 2 (L2), and Layer 3 (L3). L1 is the lowest layer and implements various PHY (Physical layer) signal processing functions. L1 will be referred to herein as PHY 301. L2305 is above PHY 301 and is responsible for the link between the first node device and the second node device, or between two UEs, through PHY 301. L2305 includes a MAC sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. It also provides security through encrypted data packets and supports cross-cell mobility between the second communication node devices and the first communication node device. The RLC sublayer 303 provides upper-layer packet segmentation and reassembly, retransmission of lost packets, and packet reordering to compensate for out-of-order reception due to HARQ (Hybrid Automatic Repeat reQuest). The MAC sublayer 302 provides multiplexing between logical and transport channels. It is also responsible for allocating various radio resources (e.g., resource blocks) within a cell among the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. In control plane 300, the RRC sublayer 306 in L3 is responsible for acquiring radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the second and first communication node devices. The radio protocol architecture of user plane 350 includes layers 1 (L1) and 2 (L2). The radio protocol architecture for the first and second communication node devices in user plane 350 is largely the same as the corresponding layers and sublayers in control plane 300 for physical layer 351, PDCP sublayer 354 in L2355, RLC sublayer 353 in L2355, and MAC sublayer 352 in L2355. However, PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2355 in the user plane 350 also includes an SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS (Quality of Service) streams and Data Radio Bearer (DRB) to support service diversity. Although not illustrated, the first communication node device may have several upper layers above the L2355, including a network layer (e.g., IP (Internet Protocol) layer) terminating at the P-GW on the network side and an application layer terminating at the other end of the connection (e.g., a remote UE, server, etc.).

[0238] As an example, the wireless protocol architecture in Figure 3 is applicable to the first node in this application.

[0239] As an example, the wireless protocol architecture in Figure 3 is applicable to the second node in this application.

[0240] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0241] As an example, in this application, the first signaling is generated in MAC302 or MAC352.

[0242] As an example, in this application, the first signaling is generated in the PHY301 or PHY351.

[0243] As an example, in this application, the first signaling is generated in the RRC306.

[0244] As an example, the K1 power indicators described in this application are generated in the MAC302 or MAC352.

[0245] As an example, the K1 power indicators described in this application are generated in the PHY301 or PHY351.

[0246] As an example, the K1 reference signals described in this application are generated in the MAC302 or MAC352.

[0247] As an example, the K1 reference signals described in this application are generated in the PHY301 or PHY351.

[0248] As an example, in this application, the first signal is generated by MAC302 or MAC352.

[0249] As an example, in this application, the first signal is generated in the PHY301 or PHY351.

[0250] As an example, in this application, the first signal is generated in the RRC306.

[0251] Example 4

[0252] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of this application, as shown in Figure 4. Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0253] The first communication device 410 includes a controller / processor 475, a memory 476, a receiver processor 470, a transmitter processor 416, a multi-antenna receiver processor 472, a multi-antenna transmitter processor 471, a transmitter / receiver 418, and an antenna 420.

[0254] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmitting processor 468, a receiving processor 456, a multi-antenna transmitting processor 457, a multi-antenna receiving processor 458, a transmitter / receiver 454, and an antenna 452.

[0255] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper-layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements L2 functionality. In the DL, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operation, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for L1 (i.e., the physical layer). Transmit processor 416 performs encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and mapping of signal clusters based on various modulation schemes (e.g., Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), M-PSK, and M-Quadrature Amplitude Modulation (M-QAM)). Multi-antenna transmit processor 471 performs digital spatial precoding on the encoded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, generating one or more parallel streams. The transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols with a reference signal (e.g., a pilot) in the time and / or frequency domains, and then uses an inverse fast fourier transform (IFFT) to generate a physical channel carrying the time-domain multicarrier symbol stream. The multi-antenna transmit processor 471 then performs transmit analog precoding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multi-antenna transmit processor 471 into an RF stream, which is then provided to a different antenna 420.

[0256] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the radio frequency carrier and converts the radio frequency stream into a baseband multicarrier symbol stream, which is then provided to the receiver processor 456. The receiver processor 456 and the multi-antenna receiver processor 458 implement various L1 signal processing functions. The multi-antenna receiver processor 458 performs receive analog precoding / beamforming operations on the baseband multicarrier symbol stream from the receiver 454. The receiver processor 456 uses a Fast Fourier Transform (FFT) to convert the baseband multicarrier symbol stream after the receive analog precoding / beamforming operations from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiver processor 456, where the reference signal is used for channel estimation, and the data signal is recovered in the multi-antenna receiver processor 458 after multi-antenna detection to recover any parallel stream destined for the second communication device 450. Symbols on each parallel stream are demodulated and recovered in the receive processor 456, generating soft decisions. The receive processor 456 then decodes and deinterleaves the soft decisions to recover the upper-layer data and control signals transmitted by the first communication device 410 over the physical channel. The upper-layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements L2 functionality. The controller / processor 459 may be associated with a memory 460 storing program code and data. The memory 460 may be referred to as computer-readable media. In the DL, the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer packets from the core network. The upper-layer packets are then provided to all protocol layers above L2. Various control signals may also be provided to L3 for L3 processing. The controller / processor 459 is also responsible for error detection using Acknowledgement (ACK) and / or Negative Acknowledgement (NACK) protocols to support HARQ operation.

[0257] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper-layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above L2. Similar to the transmission functions at the first communication device 410 described in the DL, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communication device 410, implementing L2 functions for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. Transmit processor 468 performs modulation mapping and channel coding processing, while multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based and non-codebook-based precoding, and beamforming processing. Subsequently, transmit processor 468 modulates the generated parallel stream into a multi-carrier / single-carrier symbol stream. After analog precoding / beamforming operations in multi-antenna transmit processor 457, the stream is provided to different antennas 452 via transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by multi-antenna transmit processor 457 into a radio frequency symbol stream before providing it to antenna 452.

[0258] In the transmission from the second communication device 450 to the first communication device 410, the function at the first communication device 410 is similar to the receiving function at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives radio frequency signals through its corresponding antenna 420, converts the received radio frequency signals into baseband signals, and provides the baseband signals to the multi-antenna receiving processor 472 and the receiving processor 470. The receiving processor 470 and the multi-antenna receiving processor 472 jointly implement the L1 function. The controller / processor 475 implements the L2 function. The controller / processor 475 may be associated with a memory 476 storing program code and data. The memory 476 may be referred to as computer-readable media. The controller / processor 475 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer data packets from the second communication device 450. The upper-layer data packets from the controller / processor 475 may be provided to the core network. The controller / processor 475 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0259] As one embodiment, the second communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The second communication device 450 at least first receives a first signaling indicating K1 resource sets, the K1 resource sets corresponding to the same inference dataset, where K1 is a positive integer greater than 1; subsequently transmits K1 power indications, and transmits K1 reference signals in the K1 resource sets with K1 transmit power values ​​respectively; the K1 transmit power values ​​are respectively dependent on the K1 power indications; the power difference between at least two of the K1 transmit power values ​​is configurable or predefined.

[0260] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that generates actions when executed by at least one processor, the actions including: receiving a first signaling that indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, the K1 being a positive integer greater than 1; transmitting K1 power indications; and transmitting K1 reference signals in the K1 resource sets with K1 transmission power values ​​respectively.

[0261] As one embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 firstly transmits a first signaling instruction indicating K1 resource sets, the K1 resource sets corresponding to the same inference dataset, where K1 is a positive integer greater than 1; subsequently receives K1 power indications, and receives K1 reference signals in each of the K1 resource sets; the transmission power values ​​of the K1 reference signals are K1 transmission power values, each dependent on one of the K1 power indications; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

[0262] As one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program that generates actions when executed by at least one processor, the actions including: sending a first signaling indicating K1 resource sets, the K1 resource sets corresponding to the same inference dataset, the K1 being a positive integer greater than 1; subsequently receiving K1 power indications; and receiving K1 reference signals in each of the K1 resource sets.

[0263] As an example, the first node in this application includes the second communication device 450.

[0264] As an example, the second node in this application includes the first communication device 410.

[0265] As an example, at least one of {the antenna 420, the transmitter 418, the transmission processor 416, the multi-antenna transmission processor 471, the controller / processor 475, and the memory 476} is used to transmit the first signaling; at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the first signaling.

[0266] As one embodiment, at least one of {the antenna 452, the transmitter 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to transmit K1 power indications and transmit K1 reference signals with K1 transmit power values ​​in the K1 resource sets respectively; at least one of {the antenna 420, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476} is used to receive K1 power indications and receive K1 reference signals in the K1 resource sets respectively.

[0267] As an example, at least one of {the antenna 452, the transmitter 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to transmit a first signal; at least one of {the antenna 420, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476} is used to receive the first signal.

[0268] Example 5

[0269] Example 5 illustrates a flowchart of a transmission between a first node and a second node according to an embodiment of this application, as shown in Figure 5. In Figure 5, the first node U1 and the second node N2 communicate via a wireless link.

[0270] For the first node U1, the first signaling is received in step S510; K1 power indications are sent in step S511; and K1 reference signals are sent in step S512.

[0271] For the second node N2, the first signaling is sent in step S520; K1 power indications are received in step S521; and K1 reference signals are received in step S513.

[0272] In Embodiment 5, the first signaling indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, and K1 being a positive integer greater than 1; the K1 reference signals are transmitted in the K1 resource sets with K1 transmit power values ​​respectively; the K1 transmit power values ​​depend on the K1 power indications respectively; the power difference between at least two of the K1 transmit power values ​​is configurable or predefined.

[0273] As an example, the first node U1 is the first node in this application.

[0274] As an example, the second node N2 is the second node in this application.

[0275] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between the base station equipment and the user equipment.

[0276] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between the relay node device and the user equipment.

[0277] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between user equipment and user equipment.

[0278] As one example, the second node N2 and the first node U1 communicate via the Uu interface.

[0279] As one example, the second node N2 is the maintenance base station of the serving cell of the first node U1.

[0280] Typically, the first signaling indicates K1 values, and the K1 power indications depend on the K1 values ​​respectively.

[0281] As an example, the K1 values ​​are K1 indices.

[0282] As an example, the K1 values ​​are each associated with one of the K1 resource sets.

[0283] As an example, the K1 values ​​are used to determine the K1 transmission power values.

[0284] As an example, the first transmission power value is the transmission power value that corresponds to the value j among the K1 transmission power values, where j is a positive integer greater than 1 and less than K1. The first transmission power value is equal to the candidate power value plus the product of (j-1) and the candidate step size. The unit of the candidate power value is dBm, and the unit of the candidate step size is dB.

[0285] As a sub-implementation of this embodiment, the candidate power value is configurable or predefined.

[0286] As a sub-implementation of this embodiment, the candidate step size is configurable or predefined.

[0287] As an example, the K1 values ​​are K1 power offset values, and the sum of the K1 power offset values ​​and the given power value corresponds to the expected K1 received power values ​​of the K1 reference channels at the receiving end.

[0288] As a sub-example of this embodiment, the K1 power indicators respectively indicate the K1 differences between the K1 transmit power values ​​and the K1 receive power values.

[0289] As a sub-example of this embodiment, the K1 power indicators respectively indicate the K1 differences between the K1 transmit power values ​​after removing path loss and the K1 receive power values.

[0290] Typically, the power difference between any two adjacent transmit power values ​​in the K1 transmit power values ​​is the same.

[0291] As an example, the K1 resource sets are orthogonal in the time domain.

[0292] As an example, the transmission of the K1 reference signals is time-division multiplexed.

[0293] As an example, the K1 reference signals are transmitted sequentially in the time domain.

[0294] As an example, the K1 reference signals are periodically transmitted in the time domain.

[0295] Typically, the transmission channels occupied by the inference dataset include UL-SCH.

[0296] As an example, the K1 reference signals are used for training the PA nonlinear compensation for the second node.

[0297] As an example, the K1 reference signals are used to train a receiver for receiving data from the inference dataset.

[0298] Typically, the minimum and maximum transmission power values ​​among the K1 transmission power values ​​are equal to a first power difference, and the first signaling indicates the first power difference.

[0299] As an example, the first signaling indicates K1.

[0300] As an example, the first signaling indicates the difference between the smallest transmit power value among the K1 transmit power values ​​and the path loss, K1, and the first power difference.

[0301] As an example, the first signaling indicates the difference between the largest transmit power value among the K1 transmit power values ​​and the path loss, K1, and the first power difference.

[0302] Example 6

[0303] Example 6 illustrates a flowchart of a first signal transmission according to an embodiment of this application, as shown in Figure 6. In Figure 6, the first node U3 and the second node N4 communicate via a wireless link.

[0304] For the first node U3, a first signal is sent in step S610.

[0305] For the second node N4, the first signal is received in step S620.

[0306] In Example 6, the K1 resource sets are associated with a first ID, and the first signal depends on the first ID.

[0307] As an example, the data carried by the first signal belongs to the inference dataset.

[0308] As an example, the first signal is associated with the inference dataset.

[0309] As an example, the first ID is a Model ID.

[0310] As an example, the first ID is a Functionality ID.

[0311] As an example, the first ID is an Associated ID.

[0312] As an example, the statement that the first signal depends on the first ID means that the reception of the first signal depends on the first ID.

[0313] As an example, the meaning of "the first signal depends on the first ID" includes: the reception of the first signal depends on the AI / ML model associated with the first ID.

[0314] As an example, the meaning of "the first signal depends on the first ID" includes: the reception of the first signal depends on the AI / ML process associated with the first ID.

[0315] As an example, the meaning of "the first signal depends on the first ID" includes: the reception of the first signal depends on the training process associated with the first ID.

[0316] As an example, the meaning of "the first signal depends on the first ID" includes: the reception of the first signal depends on the training results associated with the first ID.

[0317] As an example, the meaning of "the first signal depends on the first ID" includes: the reception of the first signal depends on the data collection associated with the first ID.

[0318] As an example, the meaning of "the first signal depends on the first ID" includes: the reception of the first signal depends on the receiver parameters associated with the first ID.

[0319] As an example, the meaning of "the first signal depends on the first ID" includes: the reception of the first signal depends on the model parameters associated with the first ID.

[0320] Typically, the transmit power value of the first signal is equal to a first power value, which depends on the first ID and the multi-antenna parameters of the first signal; the multi-antenna parameters include at least one of the number of layers associated with the first signal or the number of multiple users corresponding to the first signal.

[0321] As an example, the first ID is used to determine the upper limit of the first power value.

[0322] As an example, the first ID is used to determine the range of the first power value.

[0323] As an example, the first power value is linearly related to a first desired power value, and the first ID is used to determine the upper limit of the first desired power value.

[0324] As an example, the first power value is linearly related to the first desired power value, and the first ID is used to determine the range of the first desired power value.

[0325] As an example, the first power value is linearly related to the first offset value, and the multi-antenna parameters of the first signal are used to determine the first offset value.

[0326] As an example, the first power value is linearly related to the first offset value, and the multi-antenna parameters of the first signal are used to determine the upper limit of the first offset value.

[0327] As an example, the first power value is linearly related to the first offset value, and the multi-antenna parameters of the first signal are used to determine the range of the first offset value.

[0328] As one embodiment, the multi-antenna parameters include the number of layers associated with the first signal.

[0329] As a sub-implementation of this embodiment, the candidate number of layers associated with the first signal includes M1 layer values, each of which corresponds to M1 candidate offset values. The number of layers associated with the first signal is used to determine a first offset value from the M1 candidate offset values, and the first power value is linearly related to the first offset value.

[0330] As one embodiment, the multi-antenna parameters include the number of layers used by the first signal.

[0331] As a sub-implementation of this embodiment, the candidate number of layers used by the first signal includes M1 layer values, each of which corresponds to M1 candidate offset values. The number of layers associated with the first signal is used to determine a first offset value from the M1 candidate offset values, and the first power value is linearly related to the first offset value.

[0332] As one embodiment, the multi-antenna parameters include the number of multi-users corresponding to the first signal.

[0333] As a sub-implementation of this embodiment, the candidate number of multiple users corresponding to the first signal includes M2 multiple user values, each of which corresponds to M2 candidate offset values. The number of multiple users corresponding to the first signal is used to determine a first offset value from the M2 candidate offset values, and the first power value is linearly related to the first offset value.

[0334] As an example, step S610 is located after step S512 in Example 5.

[0335] As an example, step S620 is located after step S522 in Example 5.

[0336] Example 7

[0337] Example 7 illustrates a schematic diagram of K1 resource sets according to an embodiment of this application, as shown in Figure 7. In Figure 7, the K1 resource sets are orthogonal in the time domain, and the rectangles in the figure correspond to the resources occupied by each of the K1 resource sets in the time domain.

[0338] As an example, the reference signal transmitted in at least one of the K1 resource sets can be used for uplink channel measurement.

[0339] As an example, the reference signal transmitted from at least one of the K1 resource sets can be used for uplink demodulation that is not based on AI / ML.

[0340] As an example, each of the K1 resource sets includes time-domain resources.

[0341] As an example, each of the K1 resource sets includes frequency domain resources.

[0342] As an example, each of the K1 resource sets includes code domain resources.

[0343] As an example, each of the K1 resource sets includes spatial resources.

[0344] As an example, each of the K1 resource sets occupies a positive integer number of REs (Resource Elements).

[0345] Example 8

[0346] Example 8 illustrates a schematic diagram of the first ID according to this application, as shown in Figure 8. In Figure 8, the first ID is associated with the K1 resource sets, and the first signal carries the first ID.

[0347] As one embodiment, the first signal carrying the first ID includes: the scheduling signaling of the first signal indicating the first ID.

[0348] As one embodiment, the first signal carrying the first ID includes: the first signal being scrambled by the first ID.

[0349] As one embodiment, the first signal carrying the first ID includes: the UCI carried by the first signal indicating the first ID.

[0350] Example 9

[0351] Example 9 illustrates a schematic diagram of K1 power indicators according to this application, as shown in Figure 9. In Figure 9, the nonlinear distortion of the PA of the receiver of the second node is represented by the curve in the figure. The horizontal axis corresponds to the input power, and the vertical axis corresponds to the nonlinear distortion. The K1 power indicators in this application cover the operating region of the receiver of the second node, thereby enabling the training of a better parameter set under different operating regions of the receiver of the second node, and thus optimizing the performance of the AI / ML model for nonlinear compensation of the PA.

[0352] Example 10

[0353] Example 10 illustrates a schematic diagram of RAN domain AI / ML function deployment according to one embodiment of this application, as shown in Figure 10. In Figure 10, the gNB can be replaced with, for example, an eNB, or a network device such as a 6G base station.

[0354] In Example 10, the management of ML inference functions of multiple base stations is completed by the RAN domain management function 1002, that is, data interaction with the RAN domain MnS (Management Service) consumer / cross-domain management 1001 (as shown by the dashed arrow in Figure 10). The RAN domain ML training function 1003 is located in the RAN domain management function 1002; while the ML inference functions are located in the base stations, that is, the AI / ML inference function 1004 is located in gNB 1005, the AI / ML inference function 1006 is located in gNB 1007, and so on.

[0355] AI / ML related functions include ML training (also known as AI training or AI / ML training), ML testing, and ML inference (also known as AI inference or AI / ML inference), etc. ML training, ML testing, and ML inference functions can be deployed independently or co-located. Deployment of AI / ML related functions can be implemented through software, such as downloading and / or running executable files; or it can be implemented through a combination of software and hardware, such as accelerating specific computing units through hardware to improve computing speed or save power.

[0356] ML training functions can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, ML training functions for MDA (Management Data Analytics) can be deployed in MDAF (Management Data Analytics Function); ML training for network data analytics can be deployed in NWDAF (Network Data Analytics Function), meaning the ML training function is an MTLF (Model Training Logical Function).

[0357] The ML inference function can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML inference function is MDAF, or the ML inference function is AnLF (Analytics Logical Function) located in NWDAF.

[0358] Similarly, ML testing functionality can also be deployed in cross-domain management systems or domain-specific management systems.

[0359] Optionally, the management of ML inference function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1001.

[0360] It should be noted that Embodiment 10 is merely a non-limiting implementation method; optionally, the ML training function of the RAN domain may also be deployed in the base station; or optionally, some base stations may deploy both the ML inference function and the ML training function of the RAN domain, while some base stations may only deploy the ML inference function.

[0361] As an example, one of the gNBs (or base stations) in Example 10 is the second node of this application.

[0362] Example 11

[0363] Example 11 illustrates a schematic diagram of the AI / ML function deployment of a UE according to one embodiment of this application, as shown in Figure 11. In Figure 11, the RAN domain ML training function 1104 is optional.

[0364] UE function 1103 is deployed in the first node of this application, and the UE function 1103 includes AI / ML inference function 1105; the AI / ML inference function 1105 uses an ML model (also called an AI model) for inference; an ML model is typically trained before being used for AI / ML inference.

[0365] As an example, the UE function 1103 includes a RAN domain ML training function 1104, which runs training data through an ML model to obtain a relevant loss and adjusts the parameters of the ML model based on the calculated loss; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0366] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capabilities of the UE side.

[0367] Optionally, the UE function 1103 also includes a CN domain ML training function (not shown in Figure 11).

[0368] Optionally, the UE function 1103 also includes an AI / ML deployment function—not shown in Figure 11—for loading ML models and data.

[0369] As an example, the first node indicates whether it supports ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0370] As an example, the ML model and the associated metadata are loaded by the first node from a network device or a remote server.

[0371] Optionally, the UE function 1103 is an MnS producer that provides data to the CN domain MnF (Management Function) and / or the RAN domain MnF and / or the cross-domain management system 1101 for management or analysis (as shown by the double arrow 1102).

[0372] Optionally, the UE function 1103 is an MnS consumer that loads data from the CN domain MnF and / or RAN domain MnF and / or cross-domain management system 1101 for AI / ML-related management, such as managing data requests, ML model activation, and / or ML training (as shown by double arrow 1102).

[0373] As an example, the ML model is based on NN (Neural Networks).

[0374] As an example, the ML model is based on ANN (Artificial Neural Networks).

[0375] As an example, the ML model is based on CNN.

[0376] As an example, the ML model is based on the LLM (Large Language Model) architecture.

[0377] As an example, the ML model is based on the Transformer architecture.

[0378] As an example, the ML model is based on the GPT (Generative Pre-Trained) architecture.

[0379] As an example, the ML model is based on LSTM (Long Short-Term Memory network).

[0380] As an example, the ML model is based on MLP (MultiLayer Perceptron).

[0381] As an example, the ML model is based on GAN (Generative Adversarial Networks).

[0382] As an example, the ML model is based on a lightweight neural network.

[0383] As a sub-example of this embodiment, the lightweight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0384] Example 12

[0385] Example 12 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application, as shown in Figure 12. In Figure 12, the processing system based on artificial intelligence or machine learning includes a first processor, a second processor, a third processor, and a fourth processor.

[0386] In Example 12, the first processor sends a first dataset to the second processor and a second dataset to the third processor; the second processor generates a target first-class parameter set based on the first dataset, and sends the generated target first-class parameter set to the third processor; the third processor processes the second dataset using the target first-class parameter set to obtain a first-class output, optionally sending the first-class output to the fourth processor. In Figure 12, the first-class feedback and the second-class feedback are optional; the second processor includes ML training functionality; the third processor includes ML inference functionality.

[0387] As one embodiment, the fourth processor includes ML testing functionality.

[0388] As one embodiment, the fourth processor includes performance monitoring / evaluation of the ML model.

[0389] As one embodiment, the third processor sends a first type of feedback to the second processor; the first type of feedback is used to trigger the recalculation or update of the target first type of parameter set, that is, to trigger ML initial training or ML retraining.

[0390] As one embodiment, the fourth processor sends a second type of feedback to the first processor; the second type of feedback is used to generate the first dataset or the second dataset, or the second type of feedback is used to trigger the sending of the first dataset or the sending of the second dataset.

[0391] As one embodiment, the first processor generates the first dataset and the second dataset based on the measurement of the reference signal.

[0392] As one embodiment, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[0393] As one example, the third processor belongs to the first node.

[0394] As an example, the first dataset includes training data.

[0395] As one embodiment, the second processor is used to train an ML model, and the trained model is described by the target first class of parameter sets.

[0396] As an example, the second processor belongs to the first node; the above method avoids passing the first dataset to the second node.

[0397] As an example, the second processor belongs to the second node in this application; the above method supports joint training and optimizes system performance.

[0398] As an example, the second processor belongs to the core network; the above method supports network-wide joint training, further optimizing system performance.

[0399] As an example, the second dataset includes inference data.

[0400] As an example, the third processor constructs a model based on the target first type of parameter group, and then inputs the second dataset into the constructed model to obtain the first type of output.

[0401] As one embodiment, the output of the third processor includes the performance parameters described in this application.

[0402] As an example, the third processor generates a recovery dataset based on the first type of output, and the error between the recovery dataset and the second dataset is used to generate the first type of feedback.

[0403] As an example, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model fails to meet the requirements, the second processing opportunity will recalculate the target first type of parameter set.

[0404] As an example, when the error is too large or the update has not been performed for too long, the performance of the trained model is considered to be unsatisfactory.

[0405] As an example, the target first type of parameter group includes one or more of the following: convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, or parameters of the activation function.

[0406] As an example, the target first type of parameter group includes one or more of the following: convolution kernel size, number of convolution layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[0407] As an example, the target first type of parameter group corresponds to the first parameter set in this application.

[0408] As an example, the target first type of parameter group corresponds to the second parameter set in this application.

[0409] Example 13

[0410] Example 13 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application, as shown in Figure 13. In Figure 13, the first and second operations belong to a first stage, the third operation belongs to a second stage, the fourth operation belongs to a third stage, and the fifth operation belongs to a fourth stage; the arrowed lines indicate the sequence of the process.

[0411] As an example, the first operation includes AI / ML training, the second operation includes AI / ML testing, the third operation includes AI / ML emulation, the fourth operation includes AI / ML entity loading, and the fifth operation includes AI / ML inference.

[0412] As one embodiment, the first stage includes a training phase, the second stage includes an emulation phase, the third stage includes a deployment phase, and the fourth stage includes an inference phase.

[0413] As an example, the first stage includes AI / ML model training.

[0414] As an example, the first stage includes AI / ML model training and AI / ML testing.

[0415] As an example, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0416] As an example, the training of the AI / ML model depends on training data.

[0417] As an example, the AI / ML model training includes AI / ML entity validation.

[0418] As an example, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[0419] As an example, the AI / ML entity verification relies on verification data.

[0420] As an example, if the AI / ML entity verification results do not meet expectations, the AI / ML model will be retrained.

[0421] As an example, the AI / ML testing includes testing the validated AI / ML entities to estimate the performance of the trained AI / ML model.

[0422] As an example, if the AI / ML test results meet expectations, the AI / ML entity proceeds to the next stage; otherwise, the AI / ML model will be retrained.

[0423] As an example, the AI / ML test relies on test data.

[0424] As one embodiment, the second stage includes AI / ML simulation, which performs AI / ML entity reasoning in a simulation environment.

[0425] As an example, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using AI / ML entities.

[0426] As one embodiment, the second stage is optional.

[0427] As an example, the third stage includes AI / ML entity loading, which is to obtain trained AI / ML entities to obtain the desired AI / ML inference function.

[0428] As an example, the third stage is optional.

[0429] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0430] As an example, the fourth stage includes AI / ML inference.

[0431] Example 14

[0432] Example 14 illustrates a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application, as shown in Figure 14. In Figure 14, the processing apparatus 1400 in the first node includes a first receiver 1401 and a first transmitter 1402.

[0433] The first receiver 1401 receives a first signaling instruction, which indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, and K1 being a positive integer greater than 1.

[0434] The first transmitter 1402 transmits K1 power indications and transmits K1 reference signals with K1 transmission power values ​​in the K1 resource sets respectively;

[0435] In Example 14, the K1 transmit power values ​​each depend on the K1 power indications; the power difference between at least two of the K1 transmit power values ​​is configurable or predefined. As an example, the first transceiver 1401 transmits a first information block, which indicates that the first inference configuration is not applicable.

[0436] As an example, the first signaling indicates K1 values, and the K1 power indications are respectively dependent on the K1 values.

[0437] As an example, the power difference between any two adjacent transmission power values ​​in the K1 transmission power values ​​is the same.

[0438] As an example, the transmission channel occupied by the inference dataset includes UL-SCH.

[0439] As an example, the first transmitter 1402 sends a first signal; the K1 resource sets are associated with a first ID, and the first signal depends on the first ID.

[0440] As an example, the transmission power value of the first signal is equal to the first power value, which depends on the first ID and the multi-antenna parameters of the first signal; the multi-antenna parameters include at least one of the number of layers associated with the first signal or the number of multiple users corresponding to the first signal.

[0441] As an example, the minimum and maximum transmission power values ​​among the K1 transmission power values ​​are equal to a first power difference, and the first signaling indicates the first power difference.

[0442] As an example, the first node 1400 is a user equipment.

[0443] As an example, the first node 1400 is a Handset.

[0444] As an example, the first node 1400 is a terminal.

[0445] As an example, the first receiver 1401 includes at least one of the following in embodiment 4: the antenna 452, the receiver 454, the receiver processor 456, the multi-antenna receiver processor 458, the controller / processor 459, the memory 460, and the data source 467.

[0446] As an example, the first transmitter 1402 includes at least one of the following in embodiment 4: the antenna 452, the transmitter 454, the transmission processor 468, the multi-antenna transmission processor 457, the controller / processor 459, the memory 460, and the data source 467.

[0447] Example 15

[0448] Example 15 illustrates a structural block diagram of a processing apparatus for a second node according to an embodiment of the present application, as shown in Figure 15. In Figure 15, the processing apparatus 1500 in the second node includes a second transmitter 1501 and a second receiver 1502.

[0449] The second transmitter 1501 sends a first signaling instruction, which indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, and K1 being a positive integer greater than 1;

[0450] The second receiver 1502 receives K1 power indications and K1 reference signals in the K1 resource sets respectively;

[0451] In Example 15, the transmission power values ​​of the K1 reference signals are K1 transmission power values, and the K1 transmission power values ​​depend on the K1 power indicators respectively; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

[0452] As an example, the first signaling indicates K1 values, and the K1 power indications are respectively dependent on the K1 values.

[0453] As an example, the power difference between any two adjacent transmission power values ​​in the K1 transmission power values ​​is the same.

[0454] As an example, the transmission channel occupied by the inference dataset includes UL-SCH.

[0455] As one embodiment, the second receiver 1502 receives a first signal; the K1 resource sets are associated with a first ID, and the first signal depends on the first ID.

[0456] As an example, the transmission power value of the first signal is equal to the first power value, which depends on the first ID and the multi-antenna parameters of the first signal; the multi-antenna parameters include at least one of the number of layers associated with the first signal or the number of multiple users corresponding to the first signal.

[0457] As an example, the minimum and maximum transmission power values ​​among the K1 transmission power values ​​are equal to a first power difference, and the first signaling indicates the first power difference.

[0458] As an example, the second node 1500 is a base station device.

[0459] As one embodiment, the second node 1500 is a user equipment.

[0460] As an example, the second node 1500 is a TRP.

[0461] As an example, the second transmitter 1501 includes at least one of the following in embodiment 4: the antenna 420, the transmitter 418, the transmission processor 416, the multi-antenna transmission processor 471, the controller / processor 475, and the memory 476.

[0462] As one embodiment, the second receiver 1502 includes at least one of the following in embodiment 4: the antenna 420, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476.

[0463] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, hard disk, or optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiments can be implemented in hardware or in the form of software functional modules. This application is not limited to any specific combination of software and hardware. The user equipment, terminal, and UE in this application include, but are not limited to, drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablets, laptops, vehicle-mounted communication equipment, vehicles, RSUs, wireless sensors, internet cards, IoT terminals, RFID (Radio Frequency Identification) terminals, NB-IoT (Narrow Band Internet of Things) terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablets, and other wireless communication devices. The base station or system equipment in this application includes, but is not limited to, macrocell base stations, microcell base stations, small cell base stations, home base stations, relay base stations, eNB (evolved Node B), gNB, TRP, GNSS (Global Navigation Satellite System), relay satellites, satellite base stations, airborne base stations, RSUs, unmanned aerial vehicles, and test equipment, such as transceivers or signaling testers that simulate some functions of a base station, and other wireless communication equipment.

[0464] Those skilled in the art will understand that the present invention can be practiced in other specified forms without departing from its core or essential characteristics. Therefore, the embodiments disclosed herein should in any way be considered descriptive rather than restrictive. The scope of the invention is defined by the appended claims rather than the foregoing description, and all modifications within their equivalent meaning and scope are considered to be included therein.

Claims

1. A first node used for uplink transmission in wireless communication, characterized in that... include: A first receiver receives a first signaling instruction, which indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, and K1 being a positive integer greater than 1. The first transmitter transmits K1 power indications and K1 reference signals with K1 transmission power values ​​in the K1 resource sets respectively; The K1 transmission power values ​​are each dependent on the K1 power indicators; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

2. The first node according to claim 1, characterized in that, The first signaling indicates K1 values, and the K1 power indications are respectively dependent on the K1 values.

3. The first node according to claim 1 or 2, characterized in that, The power difference between any two adjacent transmission power values ​​in the K1 transmission power values ​​is the same.

4. The first node according to any one of claims 1 to 3, characterized in that, The transmission channels occupied by the inference dataset include UL-SCH.

5. The first node according to any one of claims 1 to 4, characterized in that, Its features include: The first transmitter sends a first signal; The K1 resource sets are associated with a first ID, and the first signal depends on the first ID.

6. The first node according to claim 5, characterized in that, The transmission power value of the first signal is equal to the first power value, which depends on the first ID and the multi-antenna parameters of the first signal; the multi-antenna parameters include at least one of the number of layers associated with the first signal or the number of multiple users corresponding to the first signal.

7. The first node according to any one of claims 1 to 6, characterized in that, The minimum and maximum transmission power values ​​among the K1 transmission power values ​​are equal to the first power difference, and the first signaling indicates the first power difference.

8. A second node for uplink transmission in wireless communication, characterized in that, include: The second transmitter sends a first signaling instruction, which indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, and K1 being a positive integer greater than 1; The second receiver receives K1 power indications and K1 reference signals from the K1 resource sets respectively; The transmission power values ​​of the K1 reference signals are K1 transmission power values, and each of the K1 transmission power values ​​depends on the K1 power indicators; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

9. A method for power control of a first node used in wireless communication, characterized in that... include: Receive a first signaling, which indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, where K1 is a positive integer greater than 1; Send K1 power indications, and send K1 reference signals with K1 transmission power values ​​in the K1 resource sets respectively; The K1 transmission power values ​​are each dependent on the K1 power indicators; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

10. A method for power control of a second node in wireless communication, characterized in that, include: Send a first signaling instruction, which indicates K1 resource sets, the K1 resource sets corresponding to the same inference dataset, where K1 is a positive integer greater than 1; Receive K1 power indications and K1 reference signals in the K1 resource sets respectively; The transmission power values ​​of the K1 reference signals are K1 transmission power values, and each of the K1 transmission power values ​​depends on the K1 power indicators; the power difference between at least two of the K1 transmission power values ​​is configurable or predefined.

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