Method and apparatus in node used for wireless communication and artificial intelligence

Abstract

Disclosed in the present application are a method and apparatus in a node used for wireless communication and artificial intelligence (AI). The method comprises: a node receiving first configuration information, wherein the first configuration information indicates a first RS resource group, and the first RS resource group comprises at least one uplink RS resource; and then sending first reporting information, measuring the at least one uplink RS resource, and calculating a first parameter, wherein the calculation of the first parameter depends on the measurement of the uplink RS resource and on the first reporting information, the first reporting information is obtained by means of inference, and the first configuration information is associated with the inference. In the present application, the performance of an AI / ML model related to first configuration information is monitored, so as to ensure the reliability of the AI / ML model, thereby improving the level of network intelligence.

Description

A method and apparatus in a node used for wireless communication and artificial intelligence

[0001] This application claims priority from the Chinese patent application No. 202411359446.3, filed on September 26, 2024, and entitled "A method and apparatus in a node used for wireless communication and artificial intelligence", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to a signal transmission method and apparatus in a wireless communication system, and in particular to a measurement method and apparatus. BACKGROUND

[0003] Using AI / ML (Artificial Intelligence / Machine Learning) technology to improve the performance of 5G networks is an important part of realizing the deep integration of 5G and AI / ML and building intelligent 5G-Advanced (5.5G) networks. The 3GPP (3rd Generation Partnership Project) standard organization has started to study the standardization of intelligent RAN (Radio Access Networks) since Rel-16 (Release-16), mainly focusing on intelligent use cases, data collection enhancement, potential impact on RAN nodes and interfaces, etc. In Rel-18, the establishment of AI / ML-based 5G air interface enhancement has officially started the international standardization work of the integration of 5G air interface and AI / ML, mainly focusing on the study of use cases, life cycle management (LCM), simulation verification, data collection, etc.

[0004] At present, the development of AI / ML has entered the stage of large models. Communication large models can realize autonomous networks and intelligent services, support network operation optimization, and improve network efficiency. The deep integration of communication and AI is an important direction for future communication evolution. AI will empower the development and upgrading of 5G, 5.5G, and 6G, bringing new management modes such as automatic management of frequency bands and traffic, real-time analysis of user data and network load, and prediction of network status. SUMMARY

[0005] The LCM of an AI / ML model is the management of an AI / ML model from creation to end, and the LCM of an AI / ML model in Rel-18 includes one or more of data collection, model training, model registration, model deployment, model configuration, model inference operation, model selection / switching / activation / deactivation / fallback operation, model monitoring, model update, and model transfer; wherein, in the AI / ML model monitoring, how to quickly identify the decline of the inference performance of the AI / ML model and the corresponding response mechanism after the decline of the inference performance of the AI / ML model are technical problems to be solved at present.

[0006] To solve the above problems, a solution is disclosed in the present application. It should be noted that, in the description of the above problems, the NR system is taken as an example, and the present application is also applicable to scenarios such as future 6G systems, and similar technical effects of the NR system can be achieved; further, although the original intention of the present application is for AI / ML scenarios, the present application can also be applied to other non-AI / ML scenarios; further, a unified design scheme for different scenarios (such as other non-AI / ML scenarios, including but not limited to Vehicle to Everything (V2X), capacity enhancement systems, near distance communication systems, NTN (Non Terrestrial Network), IoT (Internet of Things), URLLC (Ultra Reliable Low Latency Communication) network, etc.) can also help to reduce hardware complexity and cost. In the case of no conflict, the embodiments in any node of the present application and the features in the embodiments can be applied to any other node. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other.

[0007] In particular, the explanation of the terminology, nouns, functions, and variables in the present application (if not specifically stated) can refer to the definitions in TS38 series, TS37 series in the technical standards (TS) of 3GPP (the 3rd Generation Partnership Project). If necessary, refer to TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.300, TS38.304, TS38.305, TS38.321, TS38.331, TS37.355, TS38.423 in the 3GPP technical standards to assist in understanding the present application.

[0008] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification agreement TS38 series of 3GPP.

[0009] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification agreement TS37 series of 3GPP.

[0010] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification agreement Rel-17 version of 3GPP.

[0011] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification agreement Rel-18 version of 3GPP.

[0012] The present application discloses a method for a first node in wireless communication and artificial intelligence, comprising:

[0013] receiving first configuration information, the first configuration information indicating a first reference signal (RS) resource group, the first RS resource group comprising at least one uplink RS resource;

[0014] sending first reporting information; measuring the at least one uplink RS resource; and calculating a first parameter;

[0015] wherein the calculation of the first parameter depends on the measurement for the uplink RS resource and on the first reporting information; the first reporting information is obtained through inference, and the first configuration information is associated with the inference.

[0016] As an embodiment, the problem to be solved by the present application includes: how to improve the reliability of AI / ML model inference.

[0017] As an embodiment, the problem to be solved by the present application includes: how to reasonably utilize the distribution characteristics of the AI / ML model to monitor the performance of the AI / ML model with low complexity, and then improve the performance of the AI / ML model.

[0018] As an embodiment, the problem to be solved by the present application includes: how to quickly identify the decline of the inference performance of the AI / ML model in the AI / ML model monitoring.

[0019] As an embodiment, the characteristics of the above method include: the traditional terminal-side performance monitoring is usually based on the measurement in the downlink RS resource, and the first node in the present application realizes the performance monitoring of the AI / ML model through the measurement in the uplink RS resource and the corresponding calculation.

[0020] As an embodiment, the characteristics of the above method include: the first reporting information is generated by the first node according to the AI / ML model, and the measurement of the uplink RS resource is obtained from the network side for the reception in the uplink RS resource.

[0021] As an embodiment, the characteristics of the above method include: the present application assumes that the node for the AI / ML application of the first node is located outside the first node, for example, the manufacturer or operator of the first node specially sets an AI / ML node for the first node in the cloud or the core network, and the specially set AI / ML node can obtain the measurement result in the uplink RS resource from the base station or the network side, in combination with the first reporting information, to determine the performance of the AI / ML model.

[0022] As an embodiment, the characteristics of the above method include: the baseband processing part and the AI / ML part of the first node are separated, the AI / ML part is located in the cloud or the core network or the unified deployment device group, and then the implementation cost of the terminal-side AI / ML is reduced.

[0023] As an embodiment, the characteristics of the above method include: the first parameter is the KPI of the AI / ML model.

[0024] As an embodiment, the benefits of the above method include: the present application supports the deep integration of AI and communication, improves the adaptability and intelligent level of the communication system, and then improves the performance, efficiency and user experience of the communication system.

[0025] As an embodiment, the benefits of the above method include: it is helpful to identify the change of the inference performance of the AI / ML model in time.

[0026] As an embodiment, the benefits of the above method include: it improves the reliability and robustness of model inference.

[0027] As an embodiment, benefits of the above method include: improving quality of business decision.

[0028] As an embodiment, benefits of the above method include: improving flexibility and adaptability of the system.

[0029] According to an aspect of the present application, the above method is characterized in that the calculation of the first parameter is for performance monitoring of a first data set, and the inference associated with the first configuration information relies on the first data set.

[0030] As an embodiment, the above method is characterized in that the first data set corresponds to an AI / ML model.

[0031] As an embodiment, the above method is characterized in that the performance monitoring of the first data set is equivalent to performance monitoring of an AI / ML model.

[0032] According to an aspect of the present application, the above method is characterized in that the first RS resource group includes at least one downlink RS resource, and the calculation of the first parameter relies on measurement of the downlink RS resource.

[0033] As an embodiment, the above method is characterized in that both the downlink RS resource and the uplink RS resource are used in the calculation of the first parameter, and both the downlink RS resource and the uplink RS resource are used in performance evaluation of an AI / ML model, so as to improve accuracy of the AI / ML model and improve overall performance of the system.

[0034] According to an aspect of the present application, the above method is characterized in that the calculation of the first parameter is performed in a first entity and a second entity, the first entity performs part of the calculation of the first parameter associated with the downlink RS resource, the second entity performs part of the calculation of the first parameter associated with the uplink RS resource, the first entity and the second entity are non-co-located, and the first entity and the second entity rely on the terminal simultaneously.

[0035] As an embodiment, the above method is characterized in that the first entity corresponds to a baseband processing part of the first node, and the second entity corresponds to a part of an AI / ML application layer of the first node.

[0036] As an embodiment, the above method is characterized in that the first entity is located in the terminal, and the second entity is located outside the terminal.

[0037] As an embodiment, the above method is characterized in that the second entity is an entity for AI / ML built by a manufacturer or operator or equipment supplier of the first node in the cloud or network side for the first node.

[0038] According to an aspect of the present application, the method is characterized in that a first interface exists between the second entity and a serving node of the terminal, the first interface is non-wireless, and the serving node of the terminal sends the received wireless signal in the uplink RS resource to the second entity through the first interface.

[0039] As an embodiment, the method is characterized in that a non-wireless connection is established between the second entity and a base station or a core network, so that the channel-related information in the uplink RS resource from the base station or the core network can be obtained without air interface interaction, and the first parameter is more easily obtained to realize performance monitoring of the AI / ML model.

[0040] According to an aspect of the present application, the method is characterized in that the part in the first parameter calculation associated with the uplink RS resource includes predicting the first reporting information according to the received wireless signal in the uplink RS resource.

[0041] According to an aspect of the present application, the method is characterized in that the part in the first parameter calculation associated with the downlink RS resource includes predicting a downlink channel according to the received wireless signal in the downlink RS resource.

[0042] According to an aspect of the present application, the method is characterized in that the downlink RS resource and the uplink RS resource are associated.

[0043] According to an aspect of the present application, the method is characterized in that the first configuration information associated with the inference includes at least:

[0044] -the first configuration information is associated with an AI / ML model;

[0045] -the first configuration information is associated with an AI / ML model ID;

[0046] -the first configuration information is associated with a Functionality ID;

[0047] -the first configuration information is associated with a training data set;

[0048] -the first configuration information is associated with an inference data set;

[0049] -the first configuration information is associated with an Associated ID.

[0050] According to an aspect of the present application, the method is characterized in that the first configuration information configures the first reporting information.

[0051] As an embodiment, ID in the present application refers to IDentify, to prove.

[0052] As an embodiment, ID in the present application refers to IDentification, identity proof.

[0053] As an embodiment, ID in the present application refers to IDentity, identity or identification.

[0054] As an embodiment, ID in the present application refers to IDentifier, identifier.

[0055] As an embodiment, ID in the present application refers to InDex, index.

[0056] According to an aspect of the present application, the above method is characterized in that the first node is a user equipment.

[0057] According to an aspect of the present application, the above method is characterized in that the first node is a terminal.

[0058] The present application discloses a method in a second node in wireless communication and artificial intelligence, comprising:

[0059] sending first configuration information, the first configuration information indicating a first RS resource group, the first RS resource group comprising at least one uplink RS resource;

[0060] receiving first reporting information;

[0061] Wherein, the receiver of the first configuration information comprises a terminal, the terminal measures the at least one uplink RS resource; and calculates a first parameter; the calculation of the first parameter depends on the measurement of the uplink RS resource and depends on the first reporting information; the first reporting information is obtained through reasoning, and the first configuration information is associated with the reasoning.

[0062] As an embodiment, the above method includes that the second node is a network device, and the network device includes at least one of a core network device and an access network device.

[0063] As an embodiment, the above method includes that the second node is a device providing wireless communication function service, which can communicate with a terminal device and is usually located at the network side.

[0064] As an embodiment, the above method includes that the second node is a base station.

[0065] As one embodiment, the method of the above includes that the second node is an eNB.

[0066] As one embodiment, the method of the above includes that the second node is a gNB.

[0067] As one embodiment, the method of the above includes that the second node includes a base station.

[0068] As one embodiment, the method of the above includes that the second node includes a core network.

[0069] As one embodiment, the method of the above includes that the second node includes a base station and a core network.

[0070] As one embodiment, the method of the above includes that the second node includes an entity for deploying an AI / ML model.

[0071] As one embodiment, the method of the above includes that the second node includes a node for deploying an AI / ML model.

[0072] As one embodiment, the method of the above includes that the base station in the application includes a core network.

[0073] As one embodiment, the method of the above includes that the base station in the application includes a core network device.

[0074] As one embodiment, the method of the above includes that the base station in the application includes an entity for deploying an AI / ML model.

[0075] As one embodiment, the method of the above includes that the base station in the application includes a node for deploying an AI / ML model.

[0076] According to one aspect of the application, the method of the above is characterized in that the calculation of the first parameter is for performance monitoring of a first data set, and the inference associated with the first configuration information depends on the first data set.

[0077] According to one aspect of the application, the method of the above is characterized in that the first RS resource group includes at least one downlink RS resource, and the calculation of the first parameter depends on the measurement of the downlink RS resource.

[0078] According to an aspect of the present application, the above method is characterized in that the calculation of the first parameter is performed in a first entity and a second entity, the first entity performs the part of the calculation of the first parameter associated with the downlink RS resource, the second entity performs the part of the calculation of the first parameter associated with the uplink RS resource, the first entity and the second entity are non-co-located, and the first entity and the second entity simultaneously depend on the terminal.

[0079] According to an aspect of the present application, the above method is characterized in that there is a first interface between the second entity and the base station, the first interface is non-wireless, and the base station sends the wireless signal received in the uplink RS resource to the second entity through the first interface.

[0080] According to an aspect of the present application, the above method is characterized in that the part of the calculation of the first parameter associated with the uplink RS resource comprises predicting the first report information according to the wireless signal received in the uplink RS resource.

[0081] According to an aspect of the present application, the above method is characterized in that the part of the calculation of the first parameter associated with the downlink RS resource comprises predicting a downlink channel according to the wireless signal received in the downlink RS resource.

[0082] According to an aspect of the present application, the above method is characterized in that the downlink RS resource and the uplink RS resource are associated.

[0083] According to an aspect of the present application, the above method is characterized in that the first configuration information associated with the inference means at least:

[0084] - the first configuration information is associated with an AI / ML model;

[0085] - the first configuration information is associated with an AI / ML model ID;

[0086] - the first configuration information is associated with a Functionality ID;

[0087] - the first configuration information is associated with a set of training data;

[0088] - the first configuration information is associated with a set of inference data;

[0089] - the first configuration information is associated with an Associated ID.

[0090] According to an aspect of the present application, the above method is characterized in that the first configuration information configures the first reporting information.

[0091] The present application discloses a device for a first node in wireless communication and artificial intelligence, comprising:

[0092] a first receiver, receiving first configuration information, the first configuration information indicating a first RS resource group, the first RS resource group comprising at least one uplink RS resource;

[0093] a first processor, sending first reporting information; measuring the at least one uplink RS resource; and calculating a first parameter;

[0094] wherein the calculation of the first parameter depends on the measurement for the uplink RS resource and on the first reporting information; the first reporting information is obtained through inference, and the first configuration information is associated with the inference.

[0095] The present application discloses a device for a second node in wireless communication and artificial intelligence, comprising:

[0096] a first transmitter, sending first configuration information, the first configuration information indicating a first RS resource group, the first RS resource group comprising at least one uplink RS resource;

[0097] a second processor, receiving first reporting information;

[0098] wherein the receiver of the first configuration information comprises a terminal, the terminal measuring the at least one uplink RS resource; and calculating a first parameter; the calculation of the first parameter depends on the measurement for the uplink RS resource and on the first reporting information; the first reporting information is obtained through inference, and the first configuration information is associated with the inference.

[0099] As an embodiment, compared with the conventional scheme, the present application has the following advantages, but is not limited to:

[0100] In the present application, AI and communication are deeply integrated, the adaptability and intelligence level of the communication system are improved, and the performance, efficiency and user experience of the communication system are improved;

[0101] From the perspective of the terminal side, the present application realizes the inclusion of both uplink RS resources and downlink RS resources into the performance monitoring of AI / ML models, so as to include more RS resources as much as possible to realize the performance monitoring and performance improvement of AI / ML models;

[0102] The AI / ML entity of the terminal is set outside the terminal, thereby reducing the implementation cost of the terminal, and also enjoying the performance gain of AI / ML. Attached Figure Description

[0103] 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:

[0104] Figure 1 illustrates a flowchart of the first node transmission according to an embodiment of this application;

[0105] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of this application;

[0106] 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;

[0107] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of this application;

[0108] Figure 5 illustrates a flowchart of the transmission between a first node and a second node according to an embodiment of this application;

[0109] Figure 6 illustrates a flowchart of a downlink RS transmission according to an embodiment of this application;

[0110] Figure 7 illustrates a flowchart of an uplink RS transmission according to an embodiment of this application;

[0111] Figure 8 shows a schematic diagram of a first entity and a second entity according to an embodiment of this application;

[0112] Figure 9 shows a schematic diagram of the calculation of the first parameter according to an embodiment of this application;

[0113] Figure 10 shows a schematic diagram of an AI / ML model according to an embodiment of this application;

[0114] Figure 11 shows a schematic diagram of RAN domain AI / ML function deployment according to an embodiment of this application;

[0115] Figure 12 shows a schematic diagram of the deployment of AI / ML functions of a UE according to an embodiment of this application;

[0116] Figure 13 shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application;

[0117] Figure 14 illustrates a schematic diagram of artificial intelligence or machine learning according to an embodiment of this application;

[0118] Figure 15 shows a structural block diagram of a processing apparatus for a first node according to an embodiment of this application;

[0119] Figure 16 illustrates a structural block diagram of a processing device in a second node according to an embodiment of the present application. DETAILED DESCRIPTION

[0120] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily without conflict. Based on performance, flexibility, complexity, overhead and compatibility, etc., the person skilled in the art has the motivation to combine the embodiments in different drawings flexibly without conflict, including but not limited to the embodiments in Figure 1 and the embodiments in Figures 5-16, the embodiments in Figure 5 and the embodiments in Figures 6-16, etc.

[0121] Embodiment 1

[0122] Embodiment 1 illustrates a flowchart of the first node transmission according to an embodiment of the present application, as shown in Figure 1. In Figure 1, each block represents a step. In particular, the order of the steps in the blocks does not represent a specific time sequence between the steps.

[0123] The first node receives first configuration information in step 101, the first configuration information indicating a first RS resource group, the first RS resource group including at least one uplink RS resource; sends first reporting information in step 102, measures the at least one uplink RS resource, and calculates a first parameter.

[0124] In Embodiment 1, the calculation of the first parameter depends on the measurement of the uplink RS resource and on the first reporting information; the first reporting information is obtained by inference, and the first configuration information is associated with the inference.

[0125] As an embodiment, the first configuration information is Prediction Configuration.

[0126] As an embodiment, the first configuration information is a set of Parameters.

[0127] As an embodiment, the first configuration information is all used for prediction.

[0128] As an embodiment, the first configuration information includes CSI-ReportConfig IE (Information Elements).

[0129] As an embodiment, the first configuration information includes CSI-ReportSubConfig IE.

[0130] As one embodiment, the name of the RRC signaling used to transmit the first configuration information comprises CSI (Channel State Information).

[0131] As one embodiment, the name of the RRC signaling used to transmit the first configuration information comprises CSI-RS (Channel State Information Reference Signal).

[0132] As one embodiment, the name of the RRC signaling used to transmit the first configuration information comprises Report.

[0133] As one embodiment, the name of the RRC signaling used to transmit the first configuration information comprises Config.

[0134] As one embodiment, the first configuration information comprises ServingCellConfig IE.

[0135] As one embodiment, the first configuration information comprises one or more fields in ServingCellConfig IE.

[0136] As one embodiment, the first configuration information comprises CSI-MeasConfig IE.

[0137] As one embodiment, the first configuration information comprises one or more fields in CSI-MeasConfig IE.

[0138] As one embodiment, the first configuration information comprises CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0139] As one embodiment, the first configuration information comprises one or more fields in CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0140] As one embodiment, the first configuration information comprises CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0141] As one embodiment, the first configuration information comprises one or more fields in CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0142] As one embodiment, the first configuration information includes a CSI-ReportSubConfigTriggerList IE.

[0143] As one embodiment, the first configuration information includes one or more fields in the CSI-ReportSubConfigTriggerList IE.

[0144] As one embodiment, the first configuration information includes a CSI-ReportConfig IE.

[0145] As one embodiment, the first configuration information includes one or more fields in the CSI-ReportConfig IE.

[0146] As one embodiment, the first configuration information includes a CSI-ReportSubConfig IE.

[0147] As one embodiment, the first configuration information includes one or more fields in the CSI-ReportSubConfig IE.

[0148] As one embodiment, the first configuration information includes one or more fields in the CSI-AperiodicTriggerStateList IE.

[0149] As one embodiment, the first configuration information includes one or more fields in the CSI-IM-Resource IE.

[0150] As one embodiment, the first configuration information includes one or more fields in the CSI-IM-ResourceSet IE.

[0151] As one embodiment, the first configuration information includes one or more fields in the CSI-ResourceConfig IE.

[0152] As one embodiment, the first configuration information includes one or more fields in the CSI-RS-ResourceConfigMobility IE.

[0153] As one embodiment, the first configuration information includes one or more fields in the NZP-CSI-RS-Resource IE.

[0154] As one embodiment, the first configuration information includes one or more fields in the NZP-CSI-RS-ResourceSet IE.

[0155] As an embodiment, the first RS resource group comprises one uplink RS resource.

[0156] As an embodiment, the first RS resource group comprises multiple uplink RS resources.

[0157] As an embodiment, the first RS resource group comprises one RS resource.

[0158] As an embodiment, the first RS resource group comprises multiple RS resources.

[0159] As an embodiment, the uplink RS resource comprises a SRS (Sounding Reference Signal) resource.

[0160] As an embodiment, the uplink RS resource corresponds to a SRI (Sounding Reference Signal Resource Indicator).

[0161] As an embodiment, the uplink RS resource corresponds to a SRS resource set.

[0162] As an embodiment, the first reporting information comprises uplink channel information.

[0163] As an embodiment, the first reporting information comprises a Precoding Matrix.

[0164] As an embodiment, the first reporting information comprises a Raw Channel Matrix.

[0165] As an embodiment, the first reporting information comprises an RSRP (Reference Signal Received Power).

[0166] As an embodiment, the first reporting information comprises an RSRQ (Reference Signal Received Quality).

[0167] As an embodiment, the first reporting information comprises an RSSI (Received Signal Strength Indicator).

[0168] As an embodiment, the first reporting information comprises an SNR (Signal To Noise Ratio).

[0169] As one embodiment, the measurement for the uplink RS resource comprises obtaining uplink channel information corresponding to the uplink RS resource.

[0170] As one embodiment, the measurement for the uplink RS resource comprises obtaining Precoding Matrix corresponding to the uplink RS resource.

[0171] As one embodiment, the measurement for the uplink RS resource comprises obtaining Raw Channel Matrix corresponding to the uplink RS resource.

[0172] As one embodiment, the measurement for the uplink RS resource comprises obtaining RSRP corresponding to the uplink RS resource.

[0173] As one embodiment, the measurement for the uplink RS resource comprises obtaining RSRQ corresponding to the uplink RS resource.

[0174] As one embodiment, the measurement for the uplink RS resource comprises obtaining RSSI corresponding to the uplink RS resource.

[0175] As one embodiment, the measurement for the uplink RS resource comprises obtaining SNR corresponding to the uplink RS resource.

[0176] As one embodiment, the measurement of the at least one uplink RS resource comprises generating uplink channel information from wireless signals received in the uplink RS resource.

[0177] As one embodiment, the measurement of the at least one uplink RS resource comprises estimating uplink channel information from wireless signals received in the uplink RS resource.

[0178] As one embodiment, the measurement of the at least one uplink RS resource comprises determining uplink channel information from wireless signals received in the uplink RS resource.

[0179] As one embodiment, the measurement of the at least one uplink RS resource is obtained according to a method other than prediction.

[0180] As one embodiment, the first parameter corresponds to a Performance Metric.

[0181] As one embodiment, the first node implements relevant execution of the settlement of the first parameter.

[0182] As an embodiment, the first node obtains the first parameter by comparing a result of the measurement of the first RS resource group indicated by the first configuration information and a prediction result obtained by the inference associated with the first configuration information.

[0183] As an embodiment, the first node obtains the first parameter by calculating a difference between a result of the measurement of the first RS resource group indicated by the first configuration information and a prediction result obtained by the inference associated with the first configuration information.

[0184] As an embodiment, the calculation of the first parameter relies on a difference between the result of the measurement of the uplink RS resource and the first reporting information.

[0185] As an embodiment, the first reporting information being obtained by inference means that the first reporting information is generated by an AI / ML method.

[0186] As an embodiment, the first reporting information being obtained by inference means that the first reporting information is generated by an AI / ML model.

[0187] As an embodiment, the first reporting information being obtained by inference means that the first reporting information is obtained by prediction.

[0188] As an embodiment, the first configuration information is used to configure an AI / ML model.

[0189] As an embodiment, the first configuration information is used to configure an inference data set, and the inference data set is used for the inference.

[0190] As an embodiment, the first configuration information is used to configure a training data set, and the training data set is used for the inference.

[0191] As one embodiment, the candidate of the first parameter includes one or more of GCS (Generalized Cosine Similarity), SGSC (Squared Generalized Cosine Similarity), NMSE (Normalized Mean Squared Error), truth ground CSI, equivalent MSE (equivalent Mean Squared Error), and numerical spectral efficiency gap.

[0192] As one embodiment, the candidate of the first parameter includes one or more of throughput, BLER (Block Error Rate), hypothetical BLER.

[0193] As one embodiment, the first parameter is NMSE.

[0194] As one embodiment, the first parameter is SGCS.

[0195] As one embodiment, the first parameter is truth ground CSI.

[0196] As one embodiment, the measurement includes measurement for at least one of RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), RSSI (Received Signal Strength Indicator), and SNR (Signal To Noise Ratio).

[0197] As one embodiment, the measurement includes measurement for RSRP.

[0198] As one embodiment, the measurement includes measurement for RSRQ.

[0199] As one embodiment, the measurement includes measurement for RSSI.

[0200] As one embodiment, the measurement includes measurement for SNR.

[0201] As one embodiment, the first node implements the correlation to perform the calculation of the first parameter.

[0202] As one embodiment, the measurement of the at least one uplink RS resource is implemented by the first node through an uplink RS in uplink RS resources received by a base station on a network side.

[0203] Embodiment 2

[0204] Embodiment 2 illustrates a schematic diagram of a network architecture according to one embodiment of the present application, as shown in FIG. 2.

[0205] FIG. 2 illustrates a network architecture 200. The network architecture 200 is a network architecture for LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), 5G systems, 5G-Advanced, and future 6G systems. The network architecture for LTE, LTE-A, 5G systems, 5G-Advanced, and future 6G systems is referred to as EPS (Evolved Packet System). The 5G NR or LTE network architecture can be referred to as 5GS (5G System) / EPS or some other suitable terminology; the 6G network architecture can be referred to as 6GS (6G System) / EPS or some other suitable terminology. The network architecture 200 can include one or more UEs 201, a RAN (Next Generation Radio Access Network) 202, a core network 210, a HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet services 230. The network architecture 200 can be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown in FIG. 2, the network architecture 200 provides packet-switched services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this application are amenable to use with networked systems providing circuit-switched services. The RAN 202 includes Node Bs 203 and other nodes 204. The Node Bs 203 provide user and control plane protocol terminations toward the UEs 201. The Node Bs 203 can be connected to the other nodes 204 via an Xn interface (e.g., backhaul). The Node Bs 203 can also be referred to as base stations, base transceiver stations, radio base stations, radio transceivers, transceiver functions, basic service sets (BSSs), extended service sets (ESSs), TRPs (Transmitter Receiver Points), or some other suitable terminology. The Node Bs 203 provide access points to the core network 210 for the UEs 201; the core network 210 is a 5GC (5G Core Network) / EPC (Evolved Packet Core), or alternatively, the core network 210 is a 6GC.Examples of a UE 201 include a cellular phone, a smart phone, a Session Initiation Protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, a drone, a flying vehicle, a narrowband physical web device, a machine type communication device, a land transport vehicle, a car, a wearable device, or any other similar functional device. Those skilled in the art will also The node 203 is connected by an SI / NG interface to the core network 210. The core network 210 includes a MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF 214, a 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 processes the signaling between the UE 201 and the 5G-CN / EPC 210. The MME / AMF / SMF 211 generally provides bearer and connection management. All user Internet Protocol (IP) packets are transferred through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation as well as other functions. The P-GW / UPF 213 is connected to the Internet services 230. The Internet services 230 include operator- correspondent Internet Protocol services, which can specifically include the Internet, intranet, IMS (IP Multimedia Subsystem), and packet-switched services.

[0206] As one embodiment, the first node described in this application includes the UE 201.

[0207] As one embodiment, the second node described in the present application comprises the node 203.

[0208] As one embodiment, the node 203 is a macro cell base station.

[0209] As one embodiment, the node 203 is a micro cell base station.

[0210] As one embodiment, the node 203 is a pico cell base station.

[0211] As one embodiment, the node 203 is a femto cell.

[0212] As one embodiment, the node 203 is a base station device supporting large latency difference.

[0213] As one embodiment, the node 203 is a flying platform device.

[0214] As one embodiment, the node 203 is a satellite device.

[0215] As one embodiment, the node 203 is a test device (e.g. a transceiver simulating part of the functions of a base station, a signaling tester).

[0216] As one embodiment, the UE 201 comprises a mobile phone.

[0217] As one embodiment, the UE 201 comprises a vehicle, including a car.

[0218] As one embodiment, the wireless link from the UE 201 to the node 203 is an uplink, which is used to perform uplink transmission.

[0219] As one embodiment, the wireless link from the node 203 to the UE 201 is a downlink, which is used to perform downlink transmission.

[0220] As one embodiment, the wireless link between the node 203 and the UE 201 comprises a cellular network link.

[0221] As one embodiment, the node 203 and the UE 201 are connected through a Uu air interface.

[0222] As one embodiment, the sender of the first configuration information described in the present application comprises the node 203.

[0223] As an embodiment, the receiver of the first configuration information described in this application includes the UE 201.

[0224] As an embodiment, the sender of the first reporting information described in this application includes the UE 201.

[0225] As an embodiment, the receiver of the first reporting information described in this application includes the node 203.

[0226] As an embodiment, the node 203 supports the deployment of a network-side (NW-side) AI / ML model.

[0227] As an embodiment, the UE 201 supports the deployment of a UE-side AI / ML model.

[0228] As an embodiment, the UE 201 supports a 5G system.

[0229] As an embodiment, the node 203 supports a 5G system.

[0230] As an embodiment, the UE 201 at least supports a 6G system.

[0231] As an embodiment, the node 203 at least supports a 6G system.

[0232] Embodiment 3

[0233] Embodiment 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to an embodiment of the present application, as shown in FIG. 3.

[0234] Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3 shows the radio protocol architecture for the control plane 300 between a first communication node device (UE or RSU (Road Side Unit) in V2X (Vehicle to Everything), a vehicle mounted device or a vehicle mounted communication module) and a second node device (gNB, UE or RSU in V2X, a vehicle mounted device or a vehicle mounted 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 as the PHY 301 in this document. Layer 2 305 is above the PHY 301 and is responsible for the link between the first node device and the second node device, or between two UEs, through the PHY 301. Layer 2 305 includes a MAC (Medium Access Control) sublayer 302, a RLC (Radio Link Control) sublayer 303 and a PDCP (Packet Data Convergence Protocol) sublayer 304, which are terminated at the second node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security, by encrypting packets, and handover support for the first communication node device between second communication node devices. The RLC sublayer 303 provides segmentation and reassembly of upper layer packets, retransmission of lost packets, and reordering of packets 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. The MAC sublayer 302 is also responsible for allocating the various radio resources (e.g., resource blocks) in one cell among the UEs. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in Layer 3 in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the second communication node device and the first communication node device.The radio protocol architecture of the user plane 350 includes Layer 1 (L1) and Layer 2 (L2), which are substantially the same as the corresponding layers and sublayers in the control plane 300 for the first communication node device and the second communication node device, for the physical layer 351, the PDCP sublayer 354 in L2 355, the RLC sublayer 353 in L2 355, and the MAC sublayer 352 in L2 355, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead. Also included in L2 355 in the user plane 350 is the SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS (Quality of Service) flows and data radio bearers (DRBs) to support diverse traffic types. Although not illustrated, the first communication node device can have several upper layers above L2 355, including a network layer (e.g., IP (Internet Protocol) layer) that terminates at the P-GW on the network side and an application layer that terminates at the other end of the connection (e.g., a remote UE, a server, etc.).

[0235] As one embodiment, the radio protocol architecture in FIG. 3 is applicable to the first node in the present application.

[0236] As one embodiment, the radio protocol architecture in FIG. 3 is applicable to the second node in the present application.

[0237] As one embodiment, the first configuration information in the present application is generated at the RRC 306.

[0238] As one embodiment, the first configuration information in the present application is generated at the MAC 302 or the MAC 352.

[0239] As one embodiment, the first reporting information in the present application is generated at the PHY 301 or the PHY 351.

[0240] As one embodiment, the first reporting information in the present application is generated at the MAC 302 or the MAC 352.

[0241] As one embodiment, the first reporting information in the present application is generated at the RRC 306.

[0242] As one embodiment, the higher layer in the present application refers to a layer above the physical layer.

[0243] As one embodiment, the higher layer as described in the present application comprises a RRC layer.

[0244] As one embodiment, the higher layer signaling as described in the present application comprises a RRC IE.

[0245] As one embodiment, the higher layer signaling as described in the present application comprises a RRC message.

[0246] As one embodiment, the higher layer as described in the present application comprises a MAC layer.

[0247] As one embodiment, the higher layer signaling as described in the present application comprises a MAC CE.

[0248] Embodiment 4

[0249] Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to one embodiment of the present application, as shown in FIG. 4. FIG. 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.

[0250] The first communication device 410 comprises a controller / processor 475, a memory 476, a receive processor 470, a transmit processor 416, a multi-antenna receive processor 472, a multi-antenna transmit processor 471, a transmitter / receiver 418 and an antenna 420.

[0251] The second communication device 450 comprises a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, a multi-antenna transmit processor 457, a multi-antenna receive processor 458, a transmitter / receiver 454 and an antenna 452.

[0252] In transmissions from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements functionality of L2. In DL, the controller / processor 475 provides header compression, ciphering, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocations for second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, 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 Ll (i.e., physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450 and mapping onto signal constellations based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-ary phase shift keying (M-PSK), M-ary quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial pre-coding of the coded and modulated symbols, including codebook-based and non-codebook-based pre-coding and beamforming processing, to generate one or more parallel streams. The transmit processor 416 then maps to each of the parallel streams to subcarriers, multiplexes the modulated symbols in time domain and / or frequency domain with reference signals (e.g., pilot) and then performs an inverse fast Fourier transform (IFFT) to generate time domain multicarrier symbol streams. The multi-antenna transmit processor 471 then performs transmit analog pre-coding / beamforming operations on the time domain multicarrier symbol streams. Each transmitter 418 converts the baseband multicarrier symbol streams provided by the multi-antenna transmit processor 471 into radio frequency signals that are transmitted via the corresponding antennas 420.

[0253] In 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 respective antenna 452. Each receiver 454 recovers information modulated onto an RF carrier and converts the RF stream into a baseband, multicarrier symbol stream to receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 implement various signal processing functions of the LI. The multi-antenna receive processor 458 performs receive analog precoding / beamforming operation on the baseband, multicarrier symbol stream from the receivers 454. The receive processor 456 converts the baseband, multicarrier symbol stream from the receive analog precoding / beamforming operation from the time domain to the frequency domain using a Fast Fourier Transform (FFT). In the frequency domain, the physical layer data signals and the reference signals are demultiplexed by the receive processor 456, where the reference signals will be used for channel estimation, and the data signals are recovered after multi-antenna detection in the multi-antenna receive processor 458 for any parallel streams destined to the second communication device 450. The symbols on each parallel stream are demodulated and recovered in the receive processor 456 and generate soft decisions. The receive processor 456 then decodes and de-interleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channels. The upper layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2. The controller / processor 459 can be associated with a memory 460 that stores program codes and data. The memory 460 can be referred to as a computer-readable medium. In the DL, the controller / processor 459 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2. Various control signals can also be provided to the L3 for L3 processing. The controller / processor 459 is also responsible for error detection using an ACK and / or negative ACK (NACK) protocol to support HARQ operations.

[0254] 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 packets to a controller / processor 459. The data source 467 represents all protocol layers above L2. Similar to the transmit function described at the first communication device 410 in the DL, the controller / processor 459 implements header compression, ciphering, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocations for the first communication device 410, implements L2 layer functionality 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. A transmit processor 468, in conjunction with a multi-antenna transmit processor 457, performs modulation mapping, channel coding processing, digital multi-antenna spatial pre-coding including codebook-based and non-codebook-based precoding, and beamforming processing, and then the transmit processor 468 generates parallel streams of symbols that are modulated onto different carriers, and the modulated symbol streams are then provided to different antennas 452 via transmitters 454 after analog pre-coding / beamforming operations in the multi-antenna transmit processor 457. Each transmitter 454 converts a baseband symbol stream into a radio frequency signal that is transmitted via the corresponding antenna 452.

[0255] In the transmission from the second communication device 450 to the first communication device 410, the functionality at the first communication device 410 is similar to the functionality described in connection with the reception at the second communication device 450 in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives a radio frequency signal through its respective antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 together implement L1 functionality. A controller / processor 475 implements L2 functionality. The controller / processor 475 can be associated with a memory 476 that stores program codes and data. The memory 476 can be referred to as a computer-readable medium. The controller / processor 475 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer packets from the second communication device 450. Upper layer packets from the controller / processor 475 can be provided to a core network. The controller / processor 475 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0256] As one embodiment, the second communication device 450 comprises: at least one processor and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the performance of the actions including: receiving first configuration information, the first configuration information indicating a first RS resource set, the first RS resource set comprising at least one uplink RS resource; sending first reporting information; measuring the at least one uplink RS resource; and calculating a first parameter; the calculation of the first parameter relying on the measurement for the uplink RS resource and on the first reporting information; the first reporting information being inferable, the first configuration information being associated to the inference.

[0257] As one embodiment, the second communication device 450 comprises: a memory storing a computer readable program of instructions to, when executed by at least one processor, cause the performance of the actions including: receiving first configuration information, the first configuration information indicating a first RS resource set, the first RS resource set comprising at least one uplink RS resource; sending first reporting information; measuring the at least one uplink RS resource; and calculating a first parameter.

[0258] As one embodiment, the first communication device 410 comprises: at least one processor and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the performance of the actions including: sending first configuration information, the first configuration information indicating a first RS resource set, the first RS resource set comprising at least one uplink RS resource; receiving first reporting information; the recipient of the first configuration information comprising a terminal, the terminal measuring the at least one uplink RS resource; and calculating a first parameter; the calculation of the first parameter relying on the measurement for the uplink RS resource and on the first reporting information; the first reporting information being inferable, the first configuration information being associated to the inference.

[0259] As one embodiment, the first communication device 410 comprises: a memory storing a computer readable program of instructions to, when executed by at least one processor, cause the performance of the actions including: sending first configuration information, the first configuration information indicating a first RS resource set, the first RS resource set comprising at least one uplink RS resource; receiving first reporting information.

[0260] As one embodiment, the first node in the present application comprises the second communication device 450.

[0261] As an embodiment, the second node described in the present application comprises the first communication device 410.

[0262] As an embodiment, at least one of {the antenna 420, the transmitter 418, the transmission processor 416, the multi-antenna transmission processor 471, the controller / processor 475, the memory 476} is used to send the first configuration information described in the present application; at least one of {the antenna 452, the receiver 454, the reception processor 456, the multi-antenna reception processor 458, the controller / processor 459, the memory 460, the data source 467} is used to receive the first configuration information described in the present application.

[0263] As an embodiment, at least one of {the antenna 452, the transmitter / receiver 454, the transmission processor 468, the reception processor 456, the multi-antenna transmission processor 457, the multi-antenna reception processor 458, the controller / processor 459, the memory 460, the data source 467} is used to send the first reporting information; at least one of {the antenna 420, the receiver 418, the reception processor 470, the multi-antenna reception processor 472, the controller / processor 475, the memory 476} is used to receive the first reporting information.

[0264] Embodiment 5

[0265] Embodiment 5 illustrates a flowchart of the transmission between the first node and the second node according to an embodiment of the present application, as shown in FIG. 5. In FIG. 5, the first node U1 communicates with the second node N2 through a wireless link. It is particularly pointed out that the sequence in the present embodiment does not limit the sequence of signal transmission and the sequence of implementation in the present application.

[0266] For the first node U1, the first configuration information is received in step S510; the first reporting information is sent in step S511; at least one uplink RS resource is measured in step S512; and the first parameter is calculated in step S513.

[0267] For the second node N2, the first configuration information is sent in step S520; and the first reporting information is received in step S521.

[0268] In embodiment 5, the first configuration information indicates a first RS resource group, the first RS resource group comprising the at least one uplink RS resource; the calculation of the first parameter depends on the measurement for the uplink RS resource and on the first reporting information; the first reporting information is obtained through reasoning, and the first configuration information is associated with the reasoning.

[0269] As one embodiment, the first node U1 is the first node as described in the present application.

[0270] As one embodiment, the second node N2 is the second node as described in the present application.

[0271] As one embodiment, the air interface between the second node N2 and the first node U1 comprises a wireless interface between a base station device and a user equipment device.

[0272] As one embodiment, the air interface between the second node N2 and the first node U1 comprises a wireless interface between a relay node device and a user equipment device.

[0273] As one embodiment, the air interface between the second node N2 and the first node U1 comprises a wireless interface between a user equipment device and a user equipment device.

[0274] As one embodiment, the second node N2 and the first node U1 communicate over a Uu interface.

[0275] As one embodiment, the second node N2 is a serving cell maintaining base station of the first node U1.

[0276] Typically, the calculation of the first parameter is for performance monitoring of a first data set, and the inference associated with the first configuration information relies on the first data set.

[0277] As one embodiment, the first data set comprises an inference data set.

[0278] As one embodiment, the first data set comprises a training data set.

[0279] As one embodiment, the calculation of the first parameter is used for the performance monitoring of the first data set.

[0280] As one embodiment, the calculation of the first parameter is used for evaluating performance of the first data set.

[0281] As one embodiment, the calculation of the first parameter is used for performance maintenance of the first data set.

[0282] As one embodiment, the calculation of the first parameter is used for updating of the first data set.

[0283] As one embodiment, the first data set is used for inference of the AI / ML model associated with the first configuration information.

[0284] Typically, the measuring the at least one uplink RS resource comprises receiving a measurement result of the at least one uplink RS resource from the second node or the network side.

[0285] Typically, the first configuration information configures the first reporting information.

[0286] As an embodiment, the first configuration information indicates the first reporting information.

[0287] As an embodiment, the first configuration information configures at least one of a time domain resource or a frequency domain resource occupied by the first reporting information.

[0288] As an embodiment, the first configuration information configures a trigger condition of the first reporting information.

[0289] As an embodiment, the step S512 is before the step S511.

[0290] Embodiment 6

[0291] Embodiment 6 illustrates a flowchart of downlink RS transmission according to an embodiment of the present application, as shown in FIG. 6. In FIG. 6, each block represents a step.

[0292] For the first node U3, in step S630, receiving a downlink RS in a downlink RS resource.

[0293] For the second node N4, in step S640, transmitting a downlink RS in a downlink RS resource.

[0294] In embodiment 6, the first RS resource group in the present application comprises at least one downlink RS resource, and the calculation of the first parameter depends on the measurement of the downlink RS resource.

[0295] As an embodiment, the first RS resource group comprises one downlink RS resource.

[0296] As an embodiment, the first RS resource group comprises multiple downlink RS resources.

[0297] As an embodiment, the downlink RS resource in the present application comprises an SSB.

[0298] As an embodiment, the SSB in the present application refers to a Synchronization Signal Block.

[0299] As an embodiment, the SSB in the present application refers to a SS (Synchronization Signal) / PBCH (Physical Broadcast Channel) block.

[0300] Typically, the receiving occasions of the PBCH, PSS (Primary Synchronization Signal) and SSS (Secondary Synchronization Signal) are in consecutive symbols, and form a SS / PBCH block.

[0301] As an embodiment, the downlink RS resource in the present application includes a CSI-RS resource.

[0302] As an embodiment, the downlink RS resource in the present application includes a NZP CSI-RS resource.

[0303] As an embodiment, the downlink RS resource in the present application includes a TRS (Tracking Reference Signal) resource.

[0304] As an embodiment, the downlink RS resource in the present application includes a PRS (Positioning Reference Signal) resource.

[0305] As an embodiment, the measurement on the downlink RS resource includes obtaining downlink channel information corresponding to the downlink RS resource.

[0306] As an embodiment, the measurement on the downlink RS resource includes obtaining a Precoding Matrix corresponding to the downlink RS resource.

[0307] As an embodiment, the measurement on the downlink RS resource includes obtaining a Raw Channel Matrix corresponding to the downlink RS resource.

[0308] As an embodiment, the measurement on the downlink RS resource includes obtaining an RSRP corresponding to the downlink RS resource.

[0309] As an embodiment, the measurement on the downlink RS resource includes obtaining an RSRQ corresponding to the downlink RS resource.

[0310] As an embodiment, the measurement on the downlink RS resource includes obtaining an RSSI corresponding to the downlink RS resource.

[0311] As one embodiment, the measurement of the downlink RS resource comprises obtaining SNR corresponding to the downlink RS resource.

[0312] As one embodiment, the measurement of the at least one downlink RS resource comprises generating downlink channel information according to the wireless signal received in the downlink RS resource.

[0313] As one embodiment, the measurement of the at least one downlink RS resource comprises estimating downlink channel information according to the wireless signal received in the downlink RS resource.

[0314] As one embodiment, the measurement of the at least one downlink RS resource comprises determining downlink channel information according to the wireless signal received in the downlink RS resource.

[0315] As one embodiment, the measurement of the at least one downlink RS resource is obtained according to a method other than prediction.

[0316] As one embodiment, the first parameter corresponds to a performance metric.

[0317] Typically, the association to the part of the uplink RS resource in the calculation of the first parameter comprises predicting the first reporting information according to the wireless signal received in the uplink RS resource.

[0318] As one embodiment, the step 630 is before the step S513 in FIG. 5.

[0319] As one embodiment, the step 640 is before the step S521 in FIG. 5.

[0320] Embodiment 7

[0321] Embodiment 7 illustrates a flowchart of uplink RS transmission according to one embodiment of the present application, as shown in FIG. 7. In FIG. 7, each block represents a step.

[0322] For the first node U5, in step S750, transmitting uplink RS in uplink RS resource.

[0323] For the second node N6, in step S760, receiving uplink RS in downlink RS resource.

[0324] In embodiment 7, the association to the part of the uplink RS resource in the calculation of the first parameter comprises predicting the first reporting information according to the wireless signal received in the uplink RS resource.

[0325] As an embodiment, the first reporting information comprises uplink channel information.

[0326] As an embodiment, the step S760 comprises measuring at least one uplink RS resource.

[0327] As an embodiment, the downlink RS resource in the present application and the uplink RS resource in the present application are associated.

[0328] As an embodiment, the downlink RS resource and the uplink RS resource are QCL (Quasi Co-Location).

[0329] As an embodiment, the downlink RS resource and the uplink RS resource are associated to an AL / ML model.

[0330] As an embodiment, the downlink RS resource and the uplink RS resource are associated to an Associated ID.

[0331] As an embodiment, the downlink RS resource and the uplink RS resource are associated to a training data set.

[0332] As an embodiment, the downlink RS resource and the uplink RS resource are associated to an inference data set.

[0333] As an embodiment, the downlink RS resource and the uplink RS resource are associated to a data set.

[0334] As an embodiment, the QCL in the present application refers to Quasi Co-Location.

[0335] As an embodiment, the QCL in the present application refers to Quasi Co-Located.

[0336] As an embodiment, the QCL in the present application comprises QCL parameters.

[0337] As an embodiment, the QCL in the present application comprises QCL assumption.

[0338] As an embodiment, the QCL type in the present application comprises TypeA, TypeB, TypeC and TypeD.

[0339] As an embodiment, the QCL parameters of Type A in the present application include Doppler shift, Doppler spread, average delay and delay spread; the QCL parameters of Type B in the present application include Doppler shift and Doppler spread; the QCL parameters of Type C in the present application include Doppler shift and average delay; the QCL parameters of Type D in the present application include spatial Rx parameter.

[0340] As an embodiment, the QCL in the present application includes at least one of Doppler shift, Doppler spread, average delay, delay spread, spatial Tx parameter or spatial Rx parameter.

[0341] As an embodiment, the specific definitions of Type A, Type B, Type C and Type D in the present application refer to clause 5.1.5 of 3GPP TS 38.214.

[0342] As an embodiment, the step 750 is before the step S513 in FIG. 5.

[0343] As an embodiment, the step 760 is before the step S521 in FIG. 5.

[0344] Embodiment 8

[0345] Embodiment 8 illustrates a schematic diagram of a first entity and a second entity according to an embodiment of the present application, as shown in FIG. 8. In FIG. 8, the first node in the present application is associated to a first entity 801 and a second entity 802.

[0346] Typically, the computation of the first parameter is performed in a first entity and a second entity, the first entity performs the part of the computation of the first parameter associated to the downlink RS resources, the second entity performs the part of the computation of the first parameter associated to the uplink RS resources, the first entity and the second entity are non co-located, the first entity and the second entity are simultaneously dependent on the first node.

[0347] As one embodiment, the first entity comprises a RAN (Access Network) part of the first node.

[0348] As one embodiment, the first entity comprises a CN (Core Network) part of the first node.

[0349] As one embodiment, the second entity comprises an AI / ML part of the first node.

[0350] As one embodiment, the second entity performs measurements of at least one uplink RS resource.

[0351] As one embodiment, the second entity comprises an Application Layer of the first node.

[0352] As one embodiment, the second entity comprises a CN part of the first node for AI / ML.

[0353] As one embodiment, the first entity and the second entity being non co-located means that there is no wired connection between the first entity and the second entity.

[0354] As one embodiment, the first entity and the second entity being non co-located means that there is no interface between the first entity and the second entity.

[0355] As one embodiment, the first entity and the second entity being non co-located means that the first entity and the second entity are transparent to 3GPP.

[0356] As one embodiment, the first entity and the second entity being non co-located means that the first entity and the second entity are respectively a local entity of the first node and a network side entity of the first node.

[0357] As one embodiment, the first entity and the second entity are both entities for the first node.

[0358] As one embodiment, the first entity and the second entity are both associated to an ID of the first node.

[0359] As an embodiment, the ID of the first node in the present application comprises a TMSI (Temporary Mobile Subscriber Identity) of the first node.

[0360] As an embodiment, the ID of the first node in the present application comprises a UE ID of the first node.

[0361] As an embodiment, the ID of the first node in the present application comprises an AI / ML model ID of the first node.

[0362] As an embodiment, the first entity and the second entity both belong to the first node.

[0363] As an embodiment, the first entity and the second entity interact through a wireless interface.

[0364] As an embodiment, the interaction between the first entity and the second entity is transparent on the network side.

[0365] Embodiment 9

[0366] Embodiment 9 illustrates a schematic diagram of the calculation of the first parameter according to an embodiment of the present application, as shown in FIG. 9. In FIG. 9, the calculation of the first parameter involves a UE RAN and CN entity, a UE AI entity, a network RAN entity, and a network AI entity.

[0367] As an embodiment, the UE RAN and CN entity corresponds to the processing of the RAN side of the first node in the present application.

[0368] As an embodiment, the UE RAN and CN entity corresponds to the processing of the core network of the first node in the present application.

[0369] As an embodiment, the UE RAN and CN entity comprises the first entity in the present application.

[0370] As an embodiment, the UE RAN and CN entity measures the at least one downlink RS resource.

[0371] As an embodiment, the UE RAN and CN entity predicts a downlink channel according to the wireless signal received in the downlink RS resource.

[0372] As an embodiment, the UE RAN and CN entity performs the part of the calculation of the first parameter associated with the downlink RS resource.

[0373] As an embodiment, the UE AI entity corresponds to an AI / ML model of the first node in the application.

[0374] As an embodiment, the UE AI entity comprises the second entity in the application.

[0375] As an embodiment, the UE AI entity measures the at least one uplink RS resource.

[0376] As an embodiment, the UE AI entity predicts an uplink channel according to the wireless signal received in the uplink RS resource.

[0377] As an embodiment, the UE AI entity performs the part in the calculation of the first parameter that is associated with the uplink RS resource.

[0378] As an embodiment, the network RAN entity corresponds to a second node in the application.

[0379] As an embodiment, the network RAN entity corresponds to a serving cell of the first node in the application.

[0380] As an embodiment, the network RAN entity corresponds to a base station corresponding to the serving cell of the first node in the application.

[0381] As an embodiment, the network RAN entity receives the first reporting information and forwards the first reporting information to the network AI entity.

[0382] As an embodiment, the network AI entity forwards the first reporting information to the UE AI entity.

[0383] As an embodiment, the network RAN entity receives the RS signal in the uplink RS resource and forwards the uplink channel information generated according to the RS signal in the uplink RS resource to the network AI entity.

[0384] As an embodiment, the network AI entity forwards the uplink channel information to the UE AI entity.

[0385] As an embodiment, the network RAN entity receives the RS signal in the uplink RS resource and forwards the received RS signal in the uplink RS resource to the network AI entity.

[0386] As an embodiment, the network AI entity forwards the received RS signal in the uplink RS resource to the UE AI entity.

[0387] As one embodiment, the interface between the UE RAN and CN entities and the UE AI entity is transparent to 3GPP.

[0388] As one embodiment, the UE RAN and CN entities and the network RAN entity interact over the Uu interface.

[0389] As one embodiment, the network RAN entity and the network AI entity interact over the eXn interface.

[0390] As one sub-embodiment of this embodiment, the eXn interface is non-wireless.

[0391] As one embodiment, the UE AI entity and the network AI entity interact over the Xyz interface.

[0392] As one sub-embodiment of this embodiment, the Xyz interface is non-wireless.

[0393] Embodiment 10

[0394] Embodiment 10 illustrates a diagram of an AI / ML model according to one embodiment of the present application, as shown in FIG. 10.

[0395] In Embodiment 10, the first configuration information is associated to the first data set, which is associated to an AI / ML model.

[0396] As one embodiment, the first configuration information is associated to an AI / ML model.

[0397] As one embodiment, the first configuration information is associated to an AI / ML model ID.

[0398] As one embodiment, the first configuration information is associated to a Functionality ID.

[0399] As one embodiment, the first configuration information is associated to a training data set.

[0400] As one embodiment, the first configuration information is associated to an inference data set.

[0401] As one embodiment, the first configuration information is associated to an Associated ID.

[0402] As one embodiment, the first data set comprises a training data set.

[0403] As one embodiment, the first data set comprises an inference data set.

[0404] Embodiment 11

[0405] Embodiment 11 illustrates a schematic diagram of RAN-domain AI / ML function deployment according to an embodiment of the present application, as shown in FIG. 11. In FIG. 11, gNB can be replaced by eNB, or 6G base station, or other network equipment.

[0406] In Embodiment 11, the management of ML inference functions of multiple base stations is done by RAN-domain management function 1102, i.e., data interaction with RAN-domain MnS (Management Service) consumer / cross-domain management 1101 (as shown by the dashed arrow in FIG. 11). RAN-domain ML training function 1103 is located in RAN-domain management function 1102; while ML inference functions are located in base stations, i.e., AI / ML inference function 1104 is located in gNB 1105, AI / ML inference function 1106 is located in gNB 1107, and so on.

[0407] AI / ML related functions include ML training function (also referred to as AI training, or AI / ML training), ML testing function, ML inference function (also referred to as AI inference, or AI / ML inference), and so on. ML training function, ML testing function, and ML inference function can be deployed independently, or co-located. The deployment of AI / ML related functions can be implemented by software, such as the download and / or running of executable files; or implemented by software combined with hardware, such as specific computing units accelerated by hardware to improve operation speed or save power consumption.

[0408] For ML training function, it can be deployed in cross-domain management system, or domain-specific management system for managing RAN domain or CN (Core Network) domain. For example, ML training function for MDA (Management Data Analytics) can be deployed in MDAF (Management Data Analytic Function); ML training for network data analytics can be deployed in NWDAF (NetWork Data Analytics Function), i.e., ML training function is MTLF (Model Training Logical Function).

[0409] For the ML inference function, it can also be deployed in the cross-domain management system or the domain-specific management system; for example, the ML inference function is the MDAF, or the ML inference function is the AnLF (Analytics Logical Function) located in the NWDAF.

[0410] Similarly, the ML test function can also be deployed in the cross-domain management system or the domain-specific management system.

[0411] Optionally, the management of the 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 1101 for data.

[0412] It should be noted that embodiment 11 is only one non-limiting implementation; optionally, the ML training function of the RAN domain can also be deployed in the base station; or optionally, part of the base stations deploy the ML inference function and the ML training function of the RAN domain, and part of the base stations only deploy the ML inference function.

[0413] As an embodiment, one gNB (or base station) in embodiment 11 is the second node of the application.

[0414] Embodiment 12

[0415] Embodiment 12 illustrates a schematic diagram of AI / ML function deployment of a UE according to an embodiment of the application, as shown in FIG. 12. In FIG. 12, the RAN domain ML training function 1204 is optional.

[0416] The UE function 1203 is deployed in the first node of the application, and the UE function 1203 includes an AI / ML inference function 1205; the AI / ML inference function 1205 uses a ML model (also referred to as an AI model) for inference; a ML model usually needs to be trained before being used for AI / ML inference.

[0417] As an embodiment, the UE function 1203 includes the RAN domain ML training function 1204, which runs training data through a ML model to derive a related loss, and adjusts 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.

[0418] 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 have higher requirements for the processing capability of the UE side.

[0419] Optionally, the UE function 1203 further includes a CN domain ML training function (not included in FIG. 12).

[0420] Optionally, the UE function 1203 further includes an AI / ML deployment function (not included in FIG. 12) for loading ML models and data.

[0421] As an embodiment, the first node indicates whether the ML training function (RAN domain or CN domain) is supported through capability reporting, and the capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0422] As an embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0423] Optionally, the UE function 1203 is an MnS producer that provides data to the CN domain MnF and / or the RAN domain MnF and / or the cross-domain management system 1201 for management or analysis (as indicated by the double-headed arrow 1202).

[0424] Optionally, the UE function 1203 is an MnS consumer that loads data from the CN domain MnF and / or the RAN domain MnF and / or the cross-domain management system 1201 for AI / ML-related management, such as management data requests, ML model activation, and / or ML training, etc. (as indicated by the double-headed arrow 1202).

[0425] As an embodiment, the ML model is based on a NN (Neural Networks).

[0426] As an embodiment, the ML model is based on an ANN (Artificial Neural Networks).

[0427] As an embodiment, the ML model is based on a CNN (Convolutional Neural Networks).

[0428] As one embodiment, the ML model is based on a LLM (Large Language Model) architecture.

[0429] As one embodiment, the ML model is based on a Transformer architecture.

[0430] As one embodiment, the ML model is based on a GPT (Generative Pre-Trained) architecture.

[0431] As one embodiment, the ML model is based on an LSTM (Long Short-Term Memory) architecture.

[0432] As one embodiment, the ML model is based on an MLP (MultiLayer Perceptron) architecture.

[0433] As one embodiment, the ML model is based on a GAN (Generative Adversarial Nets) architecture.

[0434] As one embodiment, the ML model is based on a light-weight neural network.

[0435] As one sub-embodiment of this embodiment, the light-weight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0436] Embodiment 13

[0437] Embodiment 13 illustrates a schematic diagram of an artificial intelligence or machine learning based processing system according to one embodiment of the present application, as shown in FIG. 13. In FIG. 13, the artificial intelligence or machine learning based processing system includes a first processing machine, a second processing machine, a third processing machine, and a fourth processing machine.

[0438] In embodiment 13, the first processing machine sends a first data set to the second processing machine, and sends a second data set to the third processing machine; the second processing machine generates a target first-class parameter group according to the first data set, and sends the generated target first-class parameter group to the third processing machine; the third processing machine processes the second data set using the target first-class parameter group to obtain a first-class output, and optionally, the third processing machine sends the first-class output to the fourth processing machine. In FIG. 13, a first-class feedback and a second-class feedback are optional; the second processing machine includes an ML training function; and the third processing machine includes an ML inference function.

[0439] As an embodiment, the fourth processor comprises an ML test function.

[0440] As an embodiment, the fourth processor comprises performance monitoring / evaluation of the ML model.

[0441] As an embodiment, the third processor sends first type feedback to the second processor; the first type feedback is used to trigger recalculation or update of the target first type parameter group, i.e. trigger ML initial training or ML retraining.

[0442] As an embodiment, the fourth processor sends second type feedback to the first processor; the second type feedback is used to generate the first data set or the second data set, or the second type feedback is used to trigger sending of the first data set or sending of the second data set.

[0443] As an embodiment, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[0444] As an embodiment, the third processor belongs to the first node.

[0445] As an embodiment, the first data set comprises training data.

[0446] As an embodiment, the second processor is used to train an ML model, and the trained model is described by the target first type parameter group.

[0447] As an embodiment, the second processor belongs to the first node; the above method avoids passing the first data set to the second node.

[0448] As an embodiment, the second processor belongs to the second node in the present application; the above method supports joint training and optimizes system performance.

[0449] As an embodiment, the second processor belongs to a core network; the above method supports network-wide joint training and further optimizes system performance.

[0450] As an embodiment, the second data set comprises inference data.

[0451] As an embodiment, the third processor constructs a model according to the target first type parameter group, and then inputs the second data set into the constructed model to obtain the first type output.

[0452] As one embodiment, the output of the third processor includes the first parameter.

[0453] As one embodiment, the second data set includes the M configuration messages.

[0454] As one embodiment, the second data set includes the M RS resource groups.

[0455] As one embodiment, at least one of the first processor, the second processor, the third processor and the fourth processor is associated to the first entity in the present application.

[0456] As one embodiment, the first type of feedback includes the first parameter.

[0457] As one embodiment, the second processor is associated to the first entity.

[0458] As one embodiment, the third processor generates a recovery data set according to the first type of output, and an error of the recovery data set and the second data set is used to generate the first type of feedback.

[0459] As one embodiment, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model cannot meet the requirement, the second processor will recalculate the target first type of parameter group.

[0460] As one embodiment, when the error is too large or the update is not performed for too long a time, the performance of the trained model is considered to be unable to meet the requirement.

[0461] As one embodiment, the target first type of parameter group includes one or more of a convolution kernel, a pool core, a pooling function, an activation function, a parameter of the pooling function, or a parameter of the activation function.

[0462] As one embodiment, the target first type of parameter group includes one or more of a convolution kernel size, a convolution layer number, a convolution step, a pool core size, a pool core step, a pooling function, an activation function, or a feature map number.

[0463] Embodiment 14

[0464] Embodiment 14 illustrates a schematic diagram based on artificial intelligence or machine learning according to one embodiment of the present application, as shown in FIG. 14. In FIG. 14, the first operation and the second operation belong to the first stage, the third operation belongs to the second stage, the fourth operation belongs to the third stage, and the fifth operation belongs to the fourth stage; the line with an arrow represents the order of the flow.

[0465] As one embodiment, the first operation comprises AI / ML training, the second operation comprises AI / ML testing, the third operation comprises AI / ML emulation, the fourth operation comprises AI / ML entity loading, and the fifth operation comprises AI / ML inference.

[0466] As one embodiment, the first phase comprises a training phase, the second phase comprises an emulation phase, the third phase comprises a deployment phase, and the fourth phase comprises an inference phase.

[0467] As one embodiment, the first phase comprises AI / ML model training.

[0468] As one embodiment, the first phase comprises AI / ML model training and AI / ML testing.

[0469] As one embodiment, the AI / ML model training comprises initial training and re-training of one or a set of AI / ML entities.

[0470] As one embodiment, the AI / ML model training relies on training data.

[0471] As one embodiment, the AI / ML model training comprises AI / ML entity validation.

[0472] As one embodiment, the AI / ML entity validation is used to evaluate the performance of the AI / ML entity.

[0473] As one embodiment, the AI / ML entity validation relies on validation data.

[0474] As one embodiment, if the result of AI / ML entity validation does not meet expectations, the AI / ML model will be re-trained.

[0475] As one embodiment, the AI / ML testing comprises testing the validated AI / ML entity to evaluate the performance of the trained AI / ML model.

[0476] As one embodiment, if the result of AI / ML testing meets expectations, the AI / ML entity proceeds to the next phase; otherwise, the AI / ML model will be re-trained.

[0477] As one embodiment, the AI / ML testing relies on test data.

[0478] As one embodiment, the second stage includes AI / ML simulation, which simulates the inference of the AI / ML entity in a simulation environment.

[0479] As one embodiment, the AI / ML simulation estimates the performance of the inference of the AI / ML entity in a simulation environment before the AI / ML entity is used.

[0480] As one embodiment, the second stage is optional.

[0481] As one embodiment, the third stage includes AI / ML entity loading, which is to obtain the trained AI / ML entity to obtain the desired AI / ML inference function.

[0482] As one embodiment, the third stage is optional.

[0483] As one embodiment, the third stage is no longer needed when the training function and the inference function are co-located.

[0484] As one embodiment, the fourth stage includes AI / ML inference.

[0485] Embodiment 15

[0486] Embodiment 15 illustrates a structural block diagram of a processing apparatus in a first node according to one embodiment of the present application, as shown in FIG. 15. In FIG. 15, the processing apparatus 1500 in the first node includes a first receiver 1501 and a first processor 1502.

[0487] In embodiment 15, the first receiver 1501 receives first configuration information, the first configuration information indicating a first RS resource group, the first RS resource group including at least one uplink RS resource;

[0488] The first processor 1502 transmits first reporting information; measures the at least one uplink RS resource; and calculates a first parameter;

[0489] In embodiment 15, the calculation of the first parameter relies on the measurement of the uplink RS resource and relies on the first reporting information; the first reporting information is obtained through inference, and the first configuration information is associated with the inference. As one embodiment, the calculation time refers to the CPU occupation time.

[0490] As an embodiment, the first parameter is associated with a performance monitoring of a first data set, and the inference associated with the first configuration information depends on the first data set.

[0491] As an embodiment, the first RS resource group comprises at least one downlink RS resource, and the calculation of the first parameter depends on a measurement on the downlink RS resource.

[0492] As an embodiment, the calculation of the first parameter is performed in a first entity and a second entity, the first entity performs a part of the calculation of the first parameter associated with the downlink RS resource, the second entity performs a part of the calculation of the first parameter associated with the uplink RS resource, the first entity and the second entity are non-co-located, and the first entity and the second entity simultaneously depend on the terminal.

[0493] As an embodiment, there is a first interface between the second entity and a serving node of the terminal, the first interface is non-wireless, and the serving node of the terminal sends a wireless signal received in the uplink RS resource to the second entity through the first interface.

[0494] As an embodiment, the part of the calculation of the first parameter associated with the uplink RS resource comprises predicting the first reported information from the wireless signal received in the uplink RS resource.

[0495] As an embodiment, the part of the calculation of the first parameter associated with the downlink RS resource comprises predicting a downlink channel from the wireless signal received in the downlink RS resource.

[0496] As an embodiment, the downlink RS resource and the uplink RS resource are associated.

[0497] As an embodiment, the meaning of the first configuration information associated with the inference comprises at least:

[0498] - the first configuration information is associated with an AI / ML model;

[0499] - the first configuration information is associated with an AI / ML model ID;

[0500] - the first configuration information is associated with a Functionality ID;

[0501] - the first configuration information is associated with a training data set;

[0502] - the first configuration information is associated with an inference data set;

[0503] - the first configuration information is associated to an Associated ID.

[0504] As one embodiment, the first configuration information configures the first reporting information.

[0505] As one embodiment, the first receiver 1501 receives a downlink RS in the downlink RS resource.

[0506] As one embodiment, the first processor 1502 performs the calculation of the first parameter associated to part of the downlink RS resource.

[0507] As one embodiment, the first processor 1502 predicts a downlink channel according to the received wireless signal in the downlink RS resource.

[0508] As one embodiment, the first node 1500 is a user equipment.

[0509] As one embodiment, the first node 1500 is a terminal.

[0510] As one embodiment, the first node 1500 is a relay node equipment.

[0511] As one embodiment, the first receiver 1501 comprises 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, the data source 467} in embodiment 4.

[0512] As one embodiment, the first processor 1502 comprises at least one of {the antenna 452, the transmitter / receiver 454, the transmitting processor 468, the receiving processor 456, the multi-antenna transmitting processor 457, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} in embodiment 4.

[0513] Embodiment 16

[0514] Embodiment 16 illustrates a structural block diagram of a processing apparatus in a second node according to an embodiment of the application, as shown in FIG. 16. In FIG. 16, the processing apparatus 1600 in the second node comprises a first transmitter 1601 and a second processor 1602.

[0515] In embodiment 16, the first transmitter 1601 transmits first configuration information, the first configuration information indicating a first RS resource group, the first RS resource group comprising at least one uplink RS resource;

[0516] The second processor 1602 receives the first reporting information;

[0517] In embodiment 16, the receiver of the first configuration information comprises a terminal, the terminal measures the at least one uplink RS resource; and calculates a first parameter; the calculation of the first parameter depends on the measurement for the uplink RS resource and depends on the first reporting information; the first reporting information is obtained through inference, and the first configuration information is associated with the inference.

[0518] As an embodiment, the calculation of the first parameter is performance monitoring for a first data set, and the inference associated with the first configuration information depends on the first data set.

[0519] As an embodiment, the first RS resource group comprises at least one downlink RS resource, and the calculation of the first parameter depends on the measurement for the downlink RS resource.

[0520] As an embodiment, the calculation of the first parameter is performed in a first entity and a second entity, the first entity performs a part of the calculation of the first parameter associated with the downlink RS resource, the second entity performs a part of the calculation of the first parameter associated with the uplink RS resource, the first entity and the second entity are non-co-located, and the first entity and the second entity simultaneously depend on the terminal.

[0521] As an embodiment, there is a first interface between the second entity and the base station, the first interface is non-wireless, and the base station sends the wireless signal received in the uplink RS resource to the second entity through the first interface.

[0522] As an embodiment, the part of the calculation of the first parameter associated with the uplink RS resource comprises predicting the first reporting information according to the wireless signal received in the uplink RS resource.

[0523] As an embodiment, the part of the calculation of the first parameter associated with the downlink RS resource comprises predicting a downlink channel according to the wireless signal received in the downlink RS resource.

[0524] As an embodiment, the downlink RS resource and the uplink RS resource are associated.

[0525] As an embodiment, the meaning that the first configuration information is associated with the inference comprises at least:

[0526] -the first configuration information is associated with an AI / ML model;

[0527] - the first configuration information is associated to an AI / ML model ID;

[0528] - the first configuration information is associated to a Functionality ID;

[0529] - the first configuration information is associated to a training data set;

[0530] - the first configuration information is associated to an inference data set;

[0531] - the first configuration information is associated to an Associated ID.

[0532] As an embodiment, the first configuration information configures the first reporting information.

[0533] As an embodiment, the second processor 1602 performs the calculation of the first parameter associated to part of the uplink RS resource.

[0534] As an embodiment, the second processor 1602 predicts an uplink channel according to the wireless signal received in the uplink RS resource.

[0535] As an embodiment, the first transmitter 1601 forwards the predicted uplink channel to an AI node in a core network.

[0536] As an embodiment, the first transmitter 1601 forwards the first reporting information to an AI node in a core network.

[0537] As an embodiment, the second node 1600 is a base station device.

[0538] As an embodiment, the second node 1600 is a user equipment.

[0539] As an embodiment, the second node 1600 is a TRP.

[0540] As an embodiment, the first transmitter 1601 comprises at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} in embodiment 4.

[0541] As an embodiment, the second processing machine 1602 includes at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the receiver 418, the receive processor 470, the multi-antenna transmit processor 471, the multi-antenna receive processor 472, the controller / processor 475, the memory 476} in embodiment 4.

[0542] Those skilled in the art can understand that all or part of the steps in the above method can be instructed by a program to complete the relevant hardware, and the program can be stored in a computer readable storage medium, such as a read-only memory, a hard disk, or an optical disk. Alternatively, all or part of the steps of the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiments can be implemented in the form of hardware or in the form of a software function module, and the present application is not limited to any specific form of combination of software and hardware. The user equipment, terminal and UE in the present application include but are not limited to unmanned aerial vehicles, communication modules on unmanned aerial vehicles, remote control aircraft, aircraft, small aircraft, mobile phones, tablets, notebooks, vehicle-mounted communication devices, vehicles, vehicles, RSUs, wireless sensors, network cards, Internet of Things 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, network cards, vehicle-mounted communication devices, low-cost mobile phones, low-cost tablets, and other wireless communication devices. The base station or system device in the present application includes but is not limited to macro cellular base stations, micro cellular base stations, small cellular base stations, home base stations, relay base stations, eNB (evolved Node B), gNB, TRP, GNSS (Global Navigation Satellite System), relay satellites, satellite base stations, aerial base stations, RSUs, unmanned aerial vehicles, test equipment such as wireless communication devices that simulate part of the functions of base stations or signaling testers, and the like.

[0543] Those skilled in the art will understand that the present application can be implemented by other specified forms without departing from the core or essential characteristics thereof. Therefore, the presently disclosed embodiments should in no way be considered as descriptive rather than limiting. The scope of the application is determined by the appended claims rather than the preceding description, and all modifications within the equivalent meaning and range of the claims are considered to be included therein.

Claims

1. A method in a terminal used for wireless communication and artificial intelligence, characterized by, comprises: receiving first configuration information, the first configuration information indicating a first RS resource group, the first RS resource group comprising at least one uplink RS resource; transmitting first reporting information; measuring the at least one uplink RS resource; and computing a first parameter; wherein the computing of the first parameter relies on the measuring of the uplink RS resource and on the first reporting information; the first reporting information being derived from an inference, the first configuration information being associated to the inference.

2. A method in a terminal according to claim 1, characterized by, the computing of the first parameter is for performance monitoring of a first data set, the inference associated to the first configuration information relying on the first data set.

3. A method in a terminal according to claim 1 or 2, characterized by, the first RS resource group comprises at least one downlink RS resource, the computing of the first parameter relying on a measurement of the downlink RS resource.

4. A method in a terminal according to claim 3, characterized by, the computing of the first parameter is performed in a first entity and a second entity, the first entity performing a part of the computing of the first parameter associated to the downlink RS resource, the second entity performing a part of the computing of the first parameter associated to the uplink RS resource, the first entity and the second entity being non-co-located, the first entity and the second entity simultaneously relying on the terminal.

5. A method in a terminal according to claim 4, characterized by, a first interface exists between the second entity and a serving node of the terminal, the first interface being non-wireless, the serving node of the terminal transmitting a wireless signal received in the uplink RS resource to the second entity through the first interface.

6. A method in a terminal according to claim 4 or 5, characterized by, the part of the computing of the first parameter associated to the uplink RS resource comprises predicting the first reporting information from the wireless signal received in the uplink RS resource.

7. A method in a terminal according to any of claims 4 to 6, characterized by, the part of the computing of the first parameter associated to the downlink RS resource comprises predicting a downlink channel from the wireless signal received in the downlink RS resource.

8. A method in a terminal according to any of claims 1 to 7, characterized by, the downlink RS resource and the uplink RS resource are associated.

9. A method in a terminal according to any of claims 1 to 8, characterized by, the meaning of the first configuration information being associated to the inference comprises at least: the first configuration information being associated to an AI / ML model; the first configuration information being associated to an AI / ML model ID; the first configuration information being associated to a Functionality ID; the first configuration information being associated to a training data set; the first configuration information being associated to an inference data set; the first configuration information being associated to an Associated ID.

10. A terminal, comprising: one or more processors and a memory; the memory being coupled to the one or more processors, the memory being configured to store computer program codes, the computer program codes comprising computer instructions, the one or more processors invoking the computer instructions to cause the terminal to perform the method of any one of claims 1-9.

11. A method in a base station for wireless communication with artificial intelligence, characterized by, comprises: transmitting first configuration information, the first configuration information indicating a first RS resource group, the first RS resource group comprising at least one uplink RS resource; receiving first reporting information; The first configuration information is received by a terminal, the terminal measures the at least one uplink RS resource, and calculates a first parameter; the calculation of the first parameter depends on the measurement of the uplink RS resource and on the first reporting information; the first reporting information is obtained through inference, and the first configuration information is associated with the inference.

12. A method in a base station according to claim 11, characterized by The calculation of the first parameter is for performance monitoring of a first data set, and the inference associated with the first configuration information depends on the first data set.

13. A method in a base station according to claim 11 or 12, characterized by The first RS resource group includes at least one downlink RS resource, and the calculation of the first parameter depends on measurement of the downlink RS resource.

14. A method in a base station according to claim 13, characterized by The calculation of the first parameter is performed in a first entity and a second entity, the first entity performs part of the calculation of the first parameter associated with the downlink RS resource, the second entity performs part of the calculation of the first parameter associated with the uplink RS resource, the first entity and the second entity are non-co-located, and the first entity and the second entity simultaneously depend on the terminal.

15. A method in a base station according to claim 14, characterized by There is a first interface between the second entity and the base station, the first interface is non-wireless, and the base station sends wireless signals received in the uplink RS resource to the second entity through the first interface.

16. A method in a base station according to claim 14 or 15, characterized by The part of the calculation of the first parameter associated with the uplink RS resource includes predicting the first reporting information from the wireless signals received in the uplink RS resource.

17. A method in a base station according to any of claims 14 to 16, characterized by The part of the calculation of the first parameter associated with the downlink RS resource includes predicting a downlink channel from the wireless signals received in the downlink RS resource.

18. A method in a base station according to any of claims 11 to 17, characterized by The downlink RS resource and the uplink RS resource are associated.

19. A method in a base station according to any of claims 11 to 18, characterized by The meaning that the first configuration information is associated with the inference includes at least: The first configuration information is associated with an AI / ML model; The first configuration information is associated with an AI / ML model ID; The first configuration information is associated with a Functionality ID; The first configuration information is associated with a training data set; The first configuration information is associated with an inference data set; The first configuration information is associated with an Associated ID.

20. A base station, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is configured to store computer program code, the computer program code includes computer instructions, and the one or more processors invoke the computer instructions to enable the base station to perform the method according to any one of claims 11-19.

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