Method and apparatus for wireless communication

Abstract

The invention discloses a method and an apparatus used for wireless communication. The method for the terminal is characterized by comprising the following steps: receiving a first signaling; receiving a first signal; wherein whether the first signal is associated to at least the former of both a reception quality of the first signal and an indication of the first signaling, the first ID identifying at least one ML model.

Description

Technical Field

[0001] The present application relates to a transmission method and device in a wireless communication system, and in particular to a scheme and device related to signal reception quality in a wireless communication system. Background Art

[0002] In NR R (release) 18, the research project of AI (Artificial Intelligence) / ML (Machine Learning) technology was established to explore its impact on system performance and system design. Compared with traditional processing methods, AI / ML has the characteristics of being based on training and requiring deployment. With the continuous enhancement of AI / ML technology, the application of AI / ML will be a potential important part of future wireless communication systems. Summary of the invention

[0003] For wireless communications, how to use the reception quality of signals to enhance AI / ML functions is a problem worth studying. In response to the above problems, the present application discloses a solution. In the absence of conflict, the embodiments and features in the first node of the present application can be applied to the second node, and vice versa. In the absence of conflict, the embodiments and features in the embodiments of the present application can be arbitrarily combined with each other.

[0004] As an example, the interpretation of the terms in the present application refers to the definitions of the TS38 series of specification protocols of 3GPP.

[0005] As an example, the interpretation of the terms in the present application refers to the definitions of the TS28 series of specification protocols of 3GPP.

[0006] The present application discloses a method in a first node used for wireless communication, characterized by comprising:

[0007] receiving a first signaling;

[0008] receiving a first signal;

[0009] Whether the first signal is associated with a first ID depends on at least the former of a reception quality of the first signal and an indication of the first signaling, and the first ID identifies at least one ML model.

[0010] As an embodiment, the first node is a terminal.

[0011] As an embodiment, the problem to be solved by the present application includes: how to determine the relationship between the signal and the ML model according to at least the former of the reception quality of the signal and the indication of the signaling.

[0012] As an embodiment, the above method is conducive to enhancing AI / ML functions according to the reception quality of the signal.

[0013] As an embodiment, the benefits of the above method include: being helpful in improving the performance of the communication system.

[0014] According to one aspect of the present application, the above method is characterized in that:

[0015] Whether the first signal is associated with the first ID depends on the indication of the first signaling and a relationship between the reception quality of the first signal and a first threshold.

[0016] As an embodiment, the problem to be solved by the present application includes: how to determine the relationship between the signal and the ML model according to the reception quality of the signal and the indication of the signaling.

[0017] According to one aspect of the present application, the above method is characterized in that:

[0018] When at least a portion of the first signal belongs to a target data set, the first signal is associated with the first ID; the first ID indicates the target data set.

[0019] As an embodiment, the benefits of the above method include: being conducive to optimizing the target data set, thereby improving the corresponding ML model.

[0020] According to one aspect of the present application, the above method is characterized in that:

[0021] When the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0022] As an embodiment, the benefits of the above method include: it is beneficial for the base station to control (at least partially) whether the first signal is associated with the first ID through the first signaling, thereby improving robustness.

[0023] As an embodiment, the above method is conducive to making full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference caused by the part of the first signal with low reception quality.

[0024] According to one aspect of the present application, the above method is characterized in that:

[0025] The first signaling schedules transmission of the first signal.

[0026] As an embodiment, the benefits of the above method include: being able to flexibly indicate whether the first signal is associated with the first ID.

[0027] As an embodiment, the benefits of the above method include: high indication flexibility.

[0028] According to one aspect of the present application, the above method is characterized in that it includes:

[0029] Sending a second signaling;

[0030] At least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal.

[0031] As an embodiment, the benefits of the above method include: being conducive to improving the performance monitoring of the ML model.

[0032] According to one aspect of the present application, the above method is characterized in that it includes:

[0033] receiving at least one reference signal;

[0034] At least one of the indication content of the second signaling and the triggering of the second signaling depends on the at least one reference signal.

[0035] As an embodiment, the benefits of the above method include: being conducive to improving reinforcement learning for ML models.

[0036] The present application discloses a method used in a second node of wireless communication, characterized by comprising:

[0037] Sending a first signaling;

[0038] sending a first signal;

[0039] Whether the first signal is associated with a first ID depends on at least the former of a reception quality of the first signal and an indication of the first signaling, and the first ID identifies at least one ML model.

[0040] As an embodiment, the second node is a base station.

[0041] According to one aspect of the present application, the above method is characterized in that:

[0042] Whether the first signal is associated with the first ID depends on the indication of the first signaling and a relationship between the reception quality of the first signal and a first threshold.

[0043] According to one aspect of the present application, the above method is characterized in that:

[0044] When at least a portion of the first signal belongs to a target data set, the first signal is associated with the first ID; the first ID indicates the target data set.

[0045] According to one aspect of the present application, the above method is characterized in that:

[0046] When the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0047] According to one aspect of the present application, the above method is characterized in that:

[0048] The first signaling schedules transmission of the first signal.

[0049] According to one aspect of the present application, the above method is characterized in that it includes:

[0050] receiving a second signaling;

[0051] At least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal.

[0052] As an embodiment, by receiving the second signaling or other feedback information, the second node can learn the reception quality of at least part of the first signal.

[0053] According to one aspect of the present application, the above method is characterized in that it includes:

[0054] sending at least one reference signal;

[0055] At least one of the indication content of the second signaling and the triggering of the second signaling depends on the at least one reference signal.

[0056] The present application discloses a first node used for wireless communication, characterized in that it includes:

[0057] A first receiver receives a first signaling;

[0058] The first receiver receives a first signal;

[0059] Whether the first signal is associated with a first ID depends on at least the former of a reception quality of the first signal and an indication of the first signaling, and the first ID identifies at least one ML model.

[0060] According to one aspect of the present application, the node is characterized in that:

[0061] Whether the first signal is associated with the first ID depends on the indication of the first signaling and the relationship between the reception quality of the first signal and a first threshold; the first threshold is predefined or configurable or reported by the terminal.

[0062] According to one aspect of the present application, the node is characterized in that:

[0063] When at least a portion of the first signal belongs to a target data set, the first signal is associated with the first ID; the first ID indicates the target data set.

[0064] According to one aspect of the present application, the node is characterized in that:

[0065] When the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0066] According to one aspect of the present application, the node is characterized in that:

[0067] The first signaling schedules transmission of the first signal.

[0068] According to one aspect of the present application, the node is characterized by comprising:

[0069] A first transmitter sends a second signaling;

[0070] At least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal.

[0071] According to one aspect of the present application, the node is characterized by comprising:

[0072] The first receiver receives at least one reference signal;

[0073] At least one of the indication content of the second signaling and the triggering of the second signaling depends on the at least one reference signal.

[0074] The present application discloses a second node used for wireless communication, characterized in that it includes:

[0075] A second transmitter sends a first signaling;

[0076] The second transmitter sends a first signal;

[0077] Whether the first signal is associated with a first ID depends on at least the former of a reception quality of the first signal and an indication of the first signaling, and the first ID identifies at least one ML model.

[0078] As an embodiment, the present application has the following advantages:

[0079] It is beneficial to improve the performance of communication system;

[0080] It is beneficial to enhance AI / ML capabilities in wireless communications;

[0081] It is helpful to improve the ML model according to the reception quality of the signal;

[0082] High indication flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0084] Figure 1 A flow chart of determining a target reference signal according to an embodiment of the present application is shown;

[0085] Figure 2 A schematic diagram of a network architecture according to an embodiment of the present application is shown;

[0086] Figure 3 A schematic diagram showing an embodiment of a wireless protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0087] Figure 4 A schematic diagram showing a first communication device and a second communication device according to an embodiment of the present application is shown;

[0088] Figure 5 A flow chart showing transmission between a first node N1 and a second node N2 according to an embodiment of the present application is shown;

[0089] Figure 6 A schematic diagram illustrating that a first signal is associated with a first ID according to an embodiment of the present application;

[0090] Figure 7A schematic diagram illustrating that a first signal is associated with a first ID according to an embodiment of the present application;

[0091] Figure 8 A schematic diagram of RAN (Radio Access Network) domain (Domain) AI / ML function deployment according to an embodiment of the present application is shown;

[0092] Fig. 9 A schematic diagram showing the deployment of AI / ML functions of a UE according to an embodiment of the present application is shown;

[0093] Fig.10 A schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0094] Fig.11 A flowchart based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0095] Fig.12 A schematic diagram illustrating the relationship between the second signaling and the first signal according to an embodiment of the present application;

[0096] Fig.13 A schematic diagram illustrating the relationship between a second signaling, a first signal, and at least one reference signal according to an embodiment of the present application is shown;

[0097] Fig.14 A structural block diagram of a processing device used in a first node according to an embodiment of the present application is shown;

[0098] Fig.15 A structural block diagram of a processing device used in a second node according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0099] The technical solution of the present application will be further described in detail below in conjunction with the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be arbitrarily combined with each other without conflict. Based on considerations such as performance, flexibility, complexity, overhead, and compatibility, those skilled in the art are motivated to flexibly combine the embodiments in different drawings without conflict, including but not limited to the attached drawings. Figure 1 The embodiments and appendix Figure 5 -Attached Fig.12 The embodiments in Figure 5 The embodiments and appendix Figure 6 -Attached Fig.12 The embodiments in , etc.

[0100] Example 1

[0101] Embodiment 1 illustrates a flow chart of determining a target reference signal according to an embodiment of the present application, as shown in the attached figure. Figure 1 As shown in the attached Figure 1 In the first node 100 shown, each box represents a step.

[0102] In Embodiment 1, the first node 100 receives a first signaling in step 101 ; and receives a first signal in step 102 .

[0103] In embodiment 1, whether the first signal is associated with a first ID depends on at least the former of a reception quality of the first signal and an indication of the first signaling, and the first ID identifies at least one ML model.

[0104] As an embodiment, the first signaling is physical layer signaling.

[0105] As an embodiment, the first signaling is MAC layer signaling.

[0106] As an embodiment, the benefits of the above method include: a short delay in the effectiveness of the indication.

[0107] As an embodiment, the first signaling is higher layer signaling.

[0108] As an embodiment, the first signal is transmitted on a first physical layer channel.

[0109] As an embodiment, the first physical layer channel is a physical downlink channel.

[0110] As an embodiment, the first physical layer channel is a physical data channel.

[0111] As an embodiment, the first physical layer channel is a physical control channel.

[0112] As an embodiment, the first signal is used to transmit user data.

[0113] As an embodiment, the first signal is used to transmit at least one transmission block.

[0114] As an embodiment, the first signal is used to transmit control information.

[0115] As an embodiment, the first ID (identity) identifies at least one data set.

[0116] As an embodiment, the first ID is configured to the first node.

[0117] As an embodiment, the communicating parties reach a consensus on the content identified by the first ID.

[0118] As an embodiment, the number of ML models identified by the first ID is 1.

[0119] As an embodiment, the first ID identifies more than one ML model.

[0120] As an embodiment, a ML model is an AI model.

[0121] As an example, an ML model includes a mathematical algorithm that can be trained using data and human expert input as examples to replicate the decisions that an expert would make when provided with the same information.

[0122] As an embodiment, the first signal is associated with the first ID, including: the first signal is associated with a target data set; wherein the first ID indicates the target data set.

[0123] As an embodiment, the first signal is associated with the first ID, including: the target data set depends on the first signal; wherein the first ID indicates the target data set.

[0124] As an embodiment, whether the first signal is associated with the first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, including:

[0125] Whether the first signal is associated with the first ID depends on the reception quality of the first signal.

[0126] As an embodiment, whether the first signal is associated with the first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, including:

[0127] Whether the first signal is associated with the first ID depends on the reception quality of the first signal and the indication of the first signaling.

[0128] As an embodiment, whether the first signal is associated with the first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, including:

[0129] Whether the first signal is associated with the first ID depends on at least part of the reception quality of the first signal.

[0130] As an embodiment, whether the first signal is associated with the first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, including:

[0131] Whether the first signal is associated with the first ID depends on the reception quality of at least part of the first signal and the indication of the first signaling.

[0132] As an embodiment, the first signaling indicates whether the first signal is associated with the first ID.

[0133] As an embodiment, whether the first signal is associated with the first ID depends on the relationship between the reception quality of at least part of the first signal and a first threshold; the first threshold is predefined or configurable or reported by the terminal.

[0134] As an embodiment, whether the first signal is associated with the first ID depends on the indication of the first signaling, and the relationship between the reception quality of at least part of the first signal and a first threshold; the first threshold is predefined or configurable or reported by the terminal.

[0135] As an embodiment, the above method is conducive to improving the configuration flexibility of whether the first signal is associated with the first ID by using the indication of the first signaling.

[0136] As an embodiment, if the reception quality of at least part of the first signal is higher than a first threshold, the first signal is associated with the first ID; otherwise, the first signal is not associated with the first ID.

[0137] As an embodiment, if the first signaling does not indicate that the first signal is not associated with the first ID and the reception quality of at least part of the first signal is higher than a first threshold, the first signal is associated with the first ID; otherwise, the first signal is not associated with the first ID.

[0138] As an embodiment, if the first signaling indicates that the first signal is associated with the first ID and the reception quality of at least part of the first signal is higher than a first threshold, the first signal is associated with the first ID; otherwise, the first signal is not associated with the first ID.

[0139] As an embodiment, the first signal is a signal on a first physical layer channel and is transmitted on multiple REs (Resource Element); when the reception quality of a signal on at least one of the multiple REs on the first physical layer channel is higher than the first threshold, the reception quality of at least part of the first signal is higher than the first threshold.

[0140] As an embodiment, the reception quality of the first signal includes the reception power on each RE of at least one RE used to transmit the first signal.

[0141] As an embodiment, the reception quality of the first signal includes reception strength on each RE of at least one RE used to transmit the first signal.

[0142] As an embodiment, the first signal is a signal on a first physical layer channel and is transmitted on multiple REs; when the reception quality of the signal on each of the multiple REs on the first physical layer channel is not higher than the first threshold, the reception quality of any part of the first signal is not higher than the first threshold.

[0143] As an embodiment, if the reception quality of at least part of the first signal is not lower than a first threshold, the first signal is associated with the first ID; otherwise, the first signal is not associated with the first ID.

[0144] As an embodiment, if the first signaling does not indicate that the first signal is not associated with the first ID and the reception quality of at least part of the first signal is not lower than a first threshold, the first signal is associated with the first ID; otherwise, the first signal is not associated with the first ID.

[0145] As an embodiment, if the first signaling indicates that the first signal is associated with the first ID and the reception quality of at least part of the first signal is not lower than a first threshold, the first signal is associated with the first ID; otherwise, the first signal is not associated with the first ID.

[0146] As an embodiment, the first signal is a signal on a first physical layer channel and is transmitted on multiple REs; when the reception quality of a signal on at least one of the multiple REs on the first physical layer channel is not lower than the first threshold, the reception quality of at least part of the first signal is not lower than the first threshold.

[0147] As an embodiment, the first signal is a signal on a first physical layer channel and is transmitted on multiple REs; when the reception quality of the signal on each of the multiple REs on the first physical layer channel is lower than the first threshold, the reception quality of any part of the first signal is lower than the first threshold.

[0148] As an embodiment, the reception quality of a signal on an RE includes the reception power of the signal on the RE.

[0149] As an embodiment, the reception quality of a signal on an RE includes the reception strength of the signal on the RE.

[0150] As an embodiment, if the reception quality of at least part of the first signal is higher than a first threshold, the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0151] As an embodiment, if the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, then the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0152] As an embodiment, if the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, then the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0153] As an embodiment, when the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0154] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the first signal belongs to the target data set; when the first signaling does not indicate that the first signal belongs to the target data set or the reception quality of any part of the first signal is not higher than the first threshold, the first signal does not belong to the target data set.

[0155] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0156] As an embodiment, if the reception quality of at least part of the first signal is not lower than a first threshold, the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0157] As an embodiment, if the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is not lower than a first threshold, then the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0158] As an embodiment, if the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is not lower than a first threshold, then the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0159] As an embodiment, when the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is not lower than a first threshold, the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is lower than a first threshold, the first signal does not belong to the target data set.

[0160] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is not lower than a first threshold, the first signal belongs to the target data set; when the first signaling does not indicate that the first signal belongs to the target data set or the reception quality of any part of the first signal is lower than the first threshold, the first signal does not belong to the target data set.

[0161] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is not lower than a first threshold, the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is lower than a first threshold, the first signal does not belong to the target data set.

[0162] As an embodiment, whether the first signal is associated with the first ID depends on the relationship between the reception quality of the first signal and a first threshold; the first threshold is predefined or configurable or reported by the terminal.

[0163] As an embodiment, whether the first signal is associated with the first ID depends on the indication of the first signaling and the relationship between the reception quality of the first signal and a first threshold; the first threshold is predefined or configurable or reported by the terminal.

[0164] As an embodiment, the above method is conducive to improving the configuration flexibility of whether the first signal is associated with the first ID by using the indication of the first signaling.

[0165] As an embodiment, the reception quality of the first signal includes an average reception power on multiple REs used to transmit the first signal.

[0166] As an embodiment, the reception quality of the first signal includes an average reception strength on multiple REs used to transmit the first signal.

[0167] As an embodiment, if the reception quality of the first signal is higher than a first threshold, the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0168] As an embodiment, if the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of the first signal is higher than a first threshold, then the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0169] As an embodiment, if the first signaling indicates that the first signal belongs to the target data set and the reception quality of the first signal is higher than a first threshold, then the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0170] As an embodiment, when the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of the first signal is higher than a first threshold, the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0171] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of the first signal is higher than a first threshold, the first signal belongs to the target data set; when the first signaling does not indicate that the first signal belongs to the target data set or the reception quality of any part of the first signal is not higher than the first threshold, the first signal does not belong to the target data set.

[0172] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of the first signal is higher than a first threshold, the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0173] As an embodiment, if the reception quality of the first signal is not lower than a first threshold, the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0174] As an embodiment, if the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of the first signal is not lower than a first threshold, then the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0175] As an embodiment, if the first signaling indicates that the first signal belongs to the target data set and the reception quality of the first signal is not lower than a first threshold, then the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0176] As an embodiment, when the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of the first signal is not lower than a first threshold, the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is lower than a first threshold, the first signal does not belong to the target data set.

[0177] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of the first signal is not lower than a first threshold, the first signal belongs to the target data set; when the first signaling does not indicate that the first signal belongs to the target data set or the reception quality of any part of the first signal is lower than the first threshold, the first signal does not belong to the target data set.

[0178] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of the first signal is not lower than a first threshold, the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is lower than a first threshold, the first signal does not belong to the target data set.

[0179] Example 2

[0180] Embodiment 2 illustrates a schematic diagram of a network architecture according to an embodiment of the present application, as shown in the attached Figure 2 shown.

[0181] Attached Figure 2The network architecture 200 is described. The network architecture 200 is a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system, or the network architecture 200 is a 5G+ network architecture, or the network architecture 200 is a 6G network architecture, or the network architecture 200 is a network architecture adopted by 3GPP in the future evolution; the network architecture 200 may be called 5GS (5G System) / EPS (Evolved Packet System), or the network architecture 200 may be called 6GS (6G System); the network architecture 200 includes at least one of UE (User Equipment) 201, RAN (Radio Access Network) 202, core network 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220 and Internet service 230. The network architecture 200 may be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the network architecture 200 provides packet switching services, but it will be readily understood by those skilled in the art that the various concepts presented throughout this application can be extended to networks or other cellular networks that provide circuit switching services. The RAN includes a node 203. The RAN may also include other nodes 204. The node 203 provides user and control plane protocol terminations toward the UE 201. The node 203 may be connected to the other nodes 204 via an Xn interface (e.g., backhaul) / X2 interface. The node 203 may also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP (transmitting receiving node) or some other suitable terminology. The core network 210 is 5GC (5G Core Network) / EPC (Evolved Packet Core), or the core network 210 is 6GC; the node 203 provides an access point to the core network 210 for the UE 201.Examples of UE201 include cellular phones, smart phones, session initiation protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, non-terrestrial base station communications, satellite mobile communications, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband Internet of Things devices, machine type communication devices, land vehicles, cars, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to UE201 as a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communication device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable term. Node 203 is connected to the core network 210 via an S1 / NG interface. The core network 210 includes MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF214, S-GW (Service Gateway) / UPF (User Plane Function) 212 and P-GW (Packet Data Network Gateway) / UPF213. MME / AMF / SMF211 is a control node that handles signaling between UE201 and the core network 210. Generally, MME / AMF / SMF211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through S-GW / UPF212, and S-GW / UPF212 itself is connected to P-GW / UPF213. P-GW provides UE IP address allocation and other functions. P-GW / UPF213 is connected to Internet service 230. The Internet service 230 includes the operator's corresponding Internet protocol service, which may specifically include the Internet, intranet, IMS (IP Multimedia Subsystem) and packet switching services.

[0182] As an embodiment, the first node includes the UE201.

[0183] As an embodiment, the second node includes the node 203.

[0184] As an embodiment, the wireless link between the UE 201 and the node 203 includes a cellular network link.

[0185] Example 3

[0186] 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 the attached figure. Figure 3 shown.

[0187] Embodiment 3 shows a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to the present application, as shown in the attached Figure 3 shown. 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 3The radio protocol architecture of the control plane 300 between a first communication node device (UE, gNB or RSU in V2X) and a second communication node device (gNB, UE or RSU in V2X), or between two UEs is shown with three layers: Layer 1, Layer 2 and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (physical layer) signal processing functions. The L1 layer will be referred to as PHY301 in this article. Layer 2 (L2 layer) 305 is above PHY301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets, and provides support for inter-zone mobility of the first communication node device between the second communication node device. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell between the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layer 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 layer) and layer 2 (L2 layer). The radio protocol architecture used for the first communication node device and the second communication node device in the user plane 350 is substantially the same as the corresponding layers and sublayers in the control plane 300 for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 also includes a SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS flows and data radio bearers (DRBs) to support the diversity of services. Although not shown in the figure, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., an IP layer) terminated at the P-GW on the network side and an application layer terminated at the other end of the connection (e.g., a remote UE, a server, etc.).

[0188] As an example, Figure 3 The wireless protocol architecture is applicable to the first node.

[0189] As an example, Figure 3 The wireless protocol architecture is applicable to the second node.

[0190] As an embodiment, the higher layer in the present application refers to a layer above the physical layer.

[0191] As an embodiment, the first signaling is generated in the PHY301.

[0192] As an embodiment, the first signaling is generated in the MAC sublayer 302.

[0193] As an embodiment, the first signal is generated by the PHY301 or the PHY351.

[0194] As an embodiment, the at least one reference signal is generated by the PHY 301 or the PHY 351 .

[0195] As an embodiment, the second signaling is generated in the RRC sublayer 306, the MAC sublayer 302 or the PHY301.

[0196] Example 4

[0197] Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application, as shown in the attached figure. Figure 4 Attached Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0198] The first communication device 410 includes 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 .

[0199] The second communication device 450 includes 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.

[0200] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, the upper layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements the functionality of the L2 layer. In DL (DownLink), the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ 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 the L1 layer (i.e., the physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, as well as constellation mapping based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM). The multi-antenna transmit processor 471 performs digital spatial precoding on the coded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing to generate one or more Parallel streams. The transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols with a reference signal (e.g., pilot) in the time domain and / or frequency domain, and then uses an inverse fast Fourier transform (IFFT) to generate a physical channel carrying the time domain O stream. The multi-antenna transmit processor 471 then performs a transmit analog precoding / beamforming operation on the time domain multi-carrier symbol stream. Each transmitter 418 converts the baseband multi-carrier symbol stream provided by the multi-antenna transmit processor 471 into a radio frequency stream, and then provides it to different antennas 420.

[0201] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the RF carrier and converts the RF stream into a baseband multi-carrier symbol stream and provides it to the receiving processor 456. The receiving processor 456 and the multi-antenna receiving processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receiving processor 458 performs a receiving analog precoding / beamforming operation on the baseband multi-carrier symbol stream from the receiver 454. The receiving processor 456 uses a fast Fourier transform (FFT) to convert the baseband multi-carrier symbol stream after the receiving analog precoding / beamforming operation from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiving processor 456, wherein the reference signal will be used for channel estimation, and the data signal is recovered after multi-antenna detection in the multi-antenna receiving processor 458 to any parallel stream destined for the second communication device 450. The symbols on each parallel stream are demodulated and recovered in the receiving processor 456, and soft decisions are generated. The receiving processor 456 then decodes and deinterleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channel. 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 layer. The controller / processor 459 may be associated with a memory 460 storing program codes and data. The memory 460 may be referred to as a computer-readable medium. In DL, the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover the upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2 layer. Various control signals may also be provided to L3 for L3 processing. The controller / processor 459 is also responsible for error detection using confirmation (ACK) and / or negative confirmation (NACK) protocols to support HARQ operations.

[0202] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmission function at the first communication device 410 described in DL, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communication device 410, and implements L2 layer functions for the user plane and the control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. The transmit processor 468 performs modulation mapping and channel coding processing, and the multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing. Then, the transmit processor 468 modulates the generated parallel stream into a multi-carrier / single-carrier symbol stream, which is then provided to different antennas 452 via the transmitter 454 after analog precoding / beamforming operations in the multi-antenna transmit processor 457. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency symbol stream, and then provides it to the antenna 452.

[0203] In the transmission from the second communication device 450 to the first communication device 410, the function at the first communication device 410 is similar to the reception function at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives a radio frequency signal through its corresponding antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to the multi-antenna reception processor 472 and the reception processor 470. The reception processor 470 and the multi-antenna reception processor 472 jointly implement the functions of the L1 layer. The controller / processor 475 implements the L2 layer functions. 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 transmission and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover the upper layer data packets from the second communication device 450. The upper layer data packets from the controller / processor 475 can be provided to the core network. The controller / processor 475 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0204] As an embodiment, the second communication device 450 includes: at least one processor and at least one memory, the at least one memory includes computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor. The second communication device 450 device at least: receives a first signal; receives a first signal; wherein whether the first signal is associated with a first ID depends on at least the former of the reception quality of the first signal and the indication of the first signal, and the first ID identifies at least one ML model.

[0205] As an embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program, wherein the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: receiving a first signal; receiving a first signal; wherein whether the first signal is associated with a first ID depends on at least the former of the reception quality of the first signal and the indication of the first signal, and the first ID identifies at least one ML model.

[0206] As an embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory includes computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor. The first communication device 410 device at least: sends a first signaling; sends a first signal; wherein whether the first signal is associated with a first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, and the first ID identifies at least one ML model.

[0207] As an embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program, wherein the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: sending a first signaling; sending a first signal; wherein whether the first signal is associated with a first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, and the first ID identifies at least one ML model.

[0208] As an embodiment, the first node in the present application includes the second communication device 450.

[0209] As an embodiment, the second node in the present application includes the first communication device 410.

[0210] As an embodiment, part or all of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, and the memory 460} are used to receive the first signaling; part or all of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, and the controller / processor 475} are used to send the first signaling.

[0211] As an embodiment, part or all of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, and the memory 460} are used to receive the first signal; part or all of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, and the controller / processor 475} are used to send the first signal.

[0212] As an embodiment, part or all of {the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, and the memory 460} are used to receive the at least one reference signal; part or all of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, and the controller / processor 475} are used to send the at least one reference signal.

[0213] As an embodiment, part or all of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, and the memory 476} are used to receive the second signaling; part or all of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467} are used to send the second signaling.

[0214] Example 5

[0215] Embodiment 5 illustrates a flow chart of transmission between a first node N1 and a second node N2 according to an embodiment of the present application; Figure 5 As shown in the attached Figure 5 In the embodiment, the second node N1 and the first node N2 are communication nodes transmitted via an air interface. Figure 5 In the embodiment, the steps in the dotted line box F1 and the steps in the dotted line box F2 are optional. Figure 5 The order of the steps in the embodiment is only a specific implementation method. The order of the steps can be adjusted without conflict.

[0216] For the second node N2, at least one reference signal is sent in step S520; a first signal is sent in step S521; a first signal is sent in step S522; and a second signal is received in step S523;

[0217] For the first node N1, at least one reference signal is received in step S510; a first signaling is received in step S511; a first signal is received in step S512; and a second signaling is sent in step S513.

[0218] In Embodiment 5, the first signaling schedules the transmission of the first signal; whether the first signal is associated with a first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, and the first ID identifies at least one ML model.

[0219] As a sub-embodiment of Example 5, whether the first signal is associated with the first ID depends on the indication of the first signaling, and the relationship between the reception quality of the first signal and a first threshold; the first threshold is predefined or configurable or reported by the terminal.

[0220] As a sub-embodiment of Example 5, when at least part of the first signal belongs to a target data set, the first signal is associated with the first ID; the first ID indicates the target data set; when the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0221] As a sub-embodiment of Embodiment 5, at least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal.

[0222] As a sub-embodiment of Embodiment 5, at least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal and the at least one reference signal.

[0223] The above-mentioned embodiment 5 and its various sub-embodiments can be combined with each other arbitrarily.

[0224] As an embodiment, the first node N1 is the first node in the present application, and the second node N2 is the second node in the present application.

[0225] As an embodiment, the air interface between the second node N1 and the first node N2 includes a wireless interface between a base station device and a user equipment.

[0226] As an embodiment, the second node N2 and the first node N1 are respectively a base station and a user equipment.

[0227] As an embodiment, the second node N2 and the first node N1 are both user equipment.

[0228] As an embodiment, the second node N2 is a base station maintaining a serving cell of the first node N1.

[0229] As an embodiment, the second signaling includes the first ID.

[0230] As an embodiment, an ML model is based on a neural network.

[0231] As an embodiment, an ML model is based on CNN (Conventional Neural Networks).

[0232] As an embodiment, an ML model is based on a Transformer architecture.

[0233] As an embodiment, an output of ML inference includes channel information, such as a channel matrix, or CSI.

[0234] As an embodiment, the steps in the dashed box F1 exist.

[0235] As an embodiment, the steps in the dashed box F1 do not exist.

[0236] As an embodiment, the steps in the dashed box F2 exist.

[0237] As an embodiment, the steps in the dashed box F2 do not exist.

[0238] Example 6

[0239] Embodiment 6 illustrates a schematic diagram of a first signal being associated with a first ID according to an embodiment of the present application, such as Figure 6 shown.

[0240] In Embodiment 6, when at least a portion of the first signal belongs to a target data set, the first signal is associated with the first ID; and the first ID indicates the target data set.

[0241] As an embodiment, the benefits of the above method include: being conducive to optimizing the target data set, thereby improving the corresponding ML model.

[0242] As an embodiment, the target dataset is used for training the at least one ML model.

[0243] As an embodiment, the target dataset is used for reinforcement learning of the at least one ML model.

[0244] As an embodiment, the target dataset is used for performance monitoring of the at least one ML model.

[0245] As an embodiment, the target dataset is used for testing the at least one ML model.

[0246] As an embodiment, the target dataset is used as input for reasoning according to the at least one ML model.

[0247] As an embodiment, the first ID explicitly indicates the target data set.

[0248] As an embodiment, the first ID implicitly indicates the target data set.

[0249] As an embodiment, when the first signal does not belong to the target data set, the first signal is not associated with the first ID.

[0250] As an embodiment, if the reception quality of at least part of the first signal is higher than the first threshold, then the at least part of the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0251] As an embodiment, the above method is conducive to making full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference caused by the part of the first signal with low reception quality.

[0252] As an embodiment, if the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, then the at least part of the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0253] As an embodiment, if the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, then the at least part of the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0254] As an embodiment, when the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0255] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling does not indicate that the first signal belongs to the target data set or the reception quality of any part of the first signal is not higher than the first threshold, the first signal does not belong to the target data set.

[0256] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0257] As an embodiment, the benefits of the above method include: it is beneficial for the base station to control (at least partially) whether the first signal is associated with the first ID through the first signaling, thereby improving robustness.

[0258] As an embodiment, the above method is conducive to making full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference caused by the part of the first signal with low reception quality.

[0259] As an embodiment, when the reception quality of a part of the first signal is not higher than the first threshold, the part of the first signal does not belong to the target data set.

[0260] As an embodiment, the first signal is a signal on a first physical layer channel and is transmitted on multiple REs; when the reception quality of a signal on at least one of the multiple REs on the first physical layer channel is not higher than the first threshold, the signal on at least one of the multiple REs on the first physical layer channel does not belong to the target data set.

[0261] As an embodiment, a signal on one RE among the multiple REs is a part of the first signal.

[0262] As an embodiment, if the reception quality of at least part of the first signal is not lower than the first threshold, then the at least part of the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0263] As an embodiment, the above method is conducive to making full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference caused by the part of the first signal with low reception quality.

[0264] As an embodiment, if the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is not lower than a first threshold, then the at least part of the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0265] As an embodiment, if the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is not lower than a first threshold, then the at least part of the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0266] As an embodiment, when the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is not lower than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is lower than a first threshold, the first signal does not belong to the target data set.

[0267] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is not lower than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling does not indicate that the first signal belongs to the target data set or the reception quality of any part of the first signal is lower than the first threshold, the first signal does not belong to the target data set.

[0268] As an embodiment, when the first signaling indicates that the first signal belongs to the target data set and the reception quality of at least part of the first signal is not lower than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is lower than a first threshold, the first signal does not belong to the target data set.

[0269] As an embodiment, the benefits of the above method include: it is beneficial for the base station to control (at least partially) whether the first signal is associated with the first ID through the first signaling, thereby improving robustness.

[0270] As an embodiment, the above method is conducive to making full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference caused by the part of the first signal with low reception quality.

[0271] As an embodiment, when the reception quality of a part of the first signal is lower than the first threshold, the part of the first signal does not belong to the target data set.

[0272] As an embodiment, the first signal is a signal on a first physical layer channel and is transmitted on multiple REs; when the reception quality of a signal on at least one of the multiple REs on the first physical layer channel is lower than the first threshold, the signal on at least one of the multiple REs on the first physical layer channel does not belong to the target data set.

[0273] As an embodiment, the reception quality of a signal on an RE includes the reception power of the signal on the RE.

[0274] As an embodiment, the reception quality of a signal on an RE includes the reception strength of the signal on the RE.

[0275] As an embodiment, the first threshold is predefined.

[0276] As an embodiment, the first threshold is a constant.

[0277] As an embodiment, the first threshold is configurable.

[0278] As an embodiment, the first threshold is reported by the first node.

[0279] As an embodiment, the unit of the first threshold is dBm.

[0280] Example 7

[0281] Embodiment 7 illustrates a schematic diagram of a first signal being associated with a first ID according to an embodiment of the present application, as shown in the attached figure. Figure 7 shown.

[0282] In Embodiment 7, when information obtained based on measurements of at least a portion of the first signal belongs to a target data set, the first signal is associated with the first ID; and the first ID indicates the target data set.

[0283] As an embodiment, the benefits of the above method include: being conducive to optimizing the target data set, thereby improving the corresponding ML model.

[0284] As an embodiment, the target dataset is used for training the at least one ML model.

[0285] As an embodiment, the target dataset is used for reinforcement learning of the at least one ML model.

[0286] As an embodiment, the target dataset is used for performance monitoring of the at least one ML model.

[0287] As an embodiment, the target dataset is used for testing the at least one ML model.

[0288] As an embodiment, the target dataset is used as input for reasoning according to the at least one ML model.

[0289] As an embodiment, the first ID explicitly indicates the target data set.

[0290] As an embodiment, the first ID implicitly indicates the target data set.

[0291] As an embodiment, when the target data set does not include information obtained according to measurements of the first signal, the first signal is not associated with the first ID.

[0292] As an embodiment, if the reception quality of at least part of the first signal is higher than a first threshold, the information obtained based on the measurement of at least part of the first signal belongs to the target data set; otherwise, the target data set does not include the information obtained based on the measurement of the first signal.

[0293] As an embodiment, the above method is conducive to making full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference caused by the part of the first signal with low reception quality.

[0294] As an embodiment, if the first signaling does not indicate that the first signal is not associated with the first ID and the reception quality of at least part of the first signal is higher than a first threshold, the information obtained based on the measurement of at least part of the first signal belongs to the target data set; otherwise, the target data set does not include the information obtained based on the measurement of the first signal.

[0295] As an embodiment, if the first signaling indicates that the first signal is associated with the first ID and the reception quality of at least part of the first signal is higher than a first threshold, the information obtained based on the measurement of at least part of the first signal belongs to the target data set; otherwise, the target data set does not include the information obtained based on the measurement of the first signal.

[0296] As an embodiment, the benefits of the above method include: it is beneficial for the base station to control (at least partially) whether the first signal is associated with the first ID through the first signaling, thereby improving robustness.

[0297] As an embodiment, the above method is conducive to making full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference caused by the part of the first signal with low reception quality.

[0298] As an embodiment, the reception quality of a signal on an RE includes the reception power of the signal on the RE.

[0299] As an embodiment, the reception quality of a signal on an RE includes the reception strength of the signal on the RE.

[0300] As an embodiment, the first signal is a signal on a first physical layer channel and is transmitted on multiple REs; when the reception quality of a signal on an RE on the first physical layer channel is higher than the first threshold, the first node can perform hard decision to determine a corresponding modulation symbol (symbol(s)) for the signal on the one RE on the first physical layer channel, and perform channel estimation on the one RE based on the determined modulation symbol.

[0301] As an embodiment, when the first node measures the first signal, the corresponding information obtained includes channel information.

[0302] As an embodiment, when the first node performs measurement on the first signal, the corresponding information obtained includes a channel matrix.

[0303] As an embodiment, the information obtained based on the measurement of at least part of the first signal includes channel information.

[0304] As an embodiment, the information obtained based on the measurement of at least part of the first signal includes a channel matrix.

[0305] As an embodiment, the information obtained according to the measurement of at least part of the first signal includes a channel matrix on at least one RE.

[0306] As an embodiment, the information obtained according to the measurement of at least part of the first signal includes: a channel matrix on at least one RE where the reception quality of the carried signal is higher than the first threshold.

[0307] As an embodiment, the first node may also average the channel estimation results on multiple REs where the portion of the first signal having reception quality higher than the first threshold is located.

[0308] As an embodiment, if the reception quality of at least part of the first signal is not lower than a first threshold, the information obtained based on the measurement of at least part of the first signal belongs to the target data set; otherwise, the target data set does not include the information obtained based on the measurement of the first signal.

[0309] As an embodiment, the above method is conducive to making full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference caused by the part of the first signal with low reception quality.

[0310] As an embodiment, if the first signaling does not indicate that the first signal is not associated with the first ID and the reception quality of at least part of the first signal is not lower than a first threshold, the information obtained based on the measurement of at least part of the first signal belongs to the target data set; otherwise, the target data set does not include the information obtained based on the measurement of the first signal.

[0311] As an embodiment, if the first signaling indicates that the first signal is associated with the first ID and the reception quality of at least part of the first signal is not lower than a first threshold, the information obtained based on the measurement of at least part of the first signal belongs to the target data set; otherwise, the target data set does not include the information obtained based on the measurement of the first signal.

[0312] As an embodiment, the benefits of the above method include: it is beneficial for the base station to control (at least partially) whether the first signal is associated with the first ID through the first signaling, thereby improving robustness.

[0313] As an embodiment, the above method is conducive to making full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference caused by the part of the first signal with low reception quality.

[0314] As an embodiment, the reception quality of a signal on an RE includes the reception power of the signal on the RE.

[0315] As an embodiment, the reception quality of a signal on an RE includes the reception strength of the signal on the RE.

[0316] As an embodiment, the first signal is a signal on a first physical layer channel and is transmitted on multiple REs; when the reception quality of a signal on an RE on the first physical layer channel is not lower than the first threshold, the first node can perform hard decision to determine a corresponding modulation symbol (symbol(s)) for the signal on the one RE on the first physical layer channel, and perform channel estimation on the one RE based on the determined modulation symbol.

[0317] As an embodiment, the information obtained according to the measurement of at least part of the first signal includes: a channel matrix on at least one RE where the reception quality of the carried signal is not lower than the first threshold.

[0318] As an embodiment, the first node may also average the channel estimation results on multiple REs where the portion of the first signal whose reception quality is not lower than the first threshold is located.

[0319] As an embodiment, the first threshold is predefined.

[0320] As an embodiment, the first threshold is a constant.

[0321] As an embodiment, the first threshold is configurable.

[0322] As an embodiment, the first threshold is reported by the first node.

[0323] As an embodiment, the unit of the first threshold is dBm.

[0324] Example 8

[0325] Embodiment 8 illustrates a schematic diagram of RAN (Radio Access Network) domain (Domain) AI / ML function deployment according to an embodiment of the present application; as shown in the attached Figure 8 The gNB in ​​Example 8 may be replaced by a network device such as an eNB or a 6G base station.

[0326] AI / ML related functions include ML training function (also called AI training, or AI / ML training), ML testing function, ML reasoning (also called AI reasoning, or AI / ML reasoning) function, etc. ML training function, ML testing function, and ML reasoning function can be deployed independently or co-located. The deployment of AI / ML related functions can be achieved through software, such as downloading and / or running executable files; it can also be achieved through a combination of software and hardware, such as accelerating specific computing units through hardware to increase computing speed or save power consumption.

[0327] For ML training functions, they can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, the ML training function for MDA (Management Data Analytics) can be deployed in MDAF (MDA function); ML training for network data analysis can be deployed in NWDAF (Network Data Analytics Function), that is, the ML training function is MTLF (Model Training logical function).

[0328] The ML reasoning function can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML reasoning function is MDAF, or the ML reasoning function is AnLF (Analytics logical function) located in NWDAF.

[0329] Similarly, ML testing capabilities can also be deployed in cross-domain management systems, or domain-specific management systems.

[0330] In Example 8, the RAN domain ML training function 1402 is located in the RAN domain management function 1403; and the ML reasoning function is located in the base station, that is, the AI / ML reasoning function 1404 is located in the gNB1405, the AI / ML reasoning function 1406 is located in the gNB1407, and so on.

[0331] Attached Figure 8 In the example, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 1403, that is, data interaction with the RAN domain MnS (Management Service) consumer / cross-domain management 1401 (see attached Figure 8 ).

[0332] Optionally, the management of the ML reasoning function may also be performed by the base station itself, that is, each base station may independently interact with the RAN domain MnS consumer / cross-domain management 1401 for data.

[0333] It should be noted that Example 8 is merely a non-restrictive implementation method; optionally, the ML training function of the RAN domain may also be deployed in the base station; or optionally, some base stations deploy the ML reasoning function and the ML training function of the RAN domain, while some base stations only deploy the ML reasoning function.

[0334] As an embodiment, a gNB (or base station) in Embodiment 8 is the second node of the present application.

[0335] As an embodiment, the second node includes an attached Figure 8 An AL / ML reasoning function, namely 1404 or 1406.

[0336] Example 9

[0337] Embodiment 9 illustrates a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of the present application; Fig. 9 Attached Fig. 9 The RAN domain ML training functionality 1505 is optional.

[0338] The UE function 1504 is deployed in the first node of the present application, and the UE function 1504 includes an AI / ML reasoning function 1506; the AI / ML reasoning function 1506 uses an ML model (also called an AI model) for reasoning; an ML model is usually trained before being used for AI / ML reasoning.

[0339] As an embodiment, the UE function 1504 includes a RAN domain ML training function 1505, and the RAN domain ML training function 1505 runs the training data through the ML model to derive relevant losses, and adjusts the parameters of the ML model based on the calculated losses; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0340] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capability of the UE side.

[0341] Optionally, the UE function 1504 also includes a CN domain ML training function ( Fig. 9 not included).

[0342] Optionally, the UE function 1504 also includes an AI / ML deployment function: Fig. 9Not included, used to load ML models and data.

[0343] As an embodiment, the first node indicates whether the ML training function is supported (RAN domain or CN domain) through capability reporting, and the capability reporting is RRC signaling, or NAS (Non-Access Stratum) signaling.

[0344] As an embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0345] As an embodiment, the first node loads and obtains the at least one ML model.

[0346] As an embodiment, the first node may load the obtained multiple ML models, the at least one ML model being a proper subset of the multiple ML models, and the ML models other than the at least one ML model in the multiple ML models being identified by an ID other than the first ID.

[0347] Optionally, the UE function 1504 is a MnS (Management Service) producer, providing data to the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for management or analysis (as shown by the double arrow 1507).

[0348] Optionally, the UE function 1504 is an MnS consumer, which loads data from the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for AI / ML related management, such as management data requests, ML model activation, and / or ML training, etc. (as shown by the double arrow 1507).

[0349] As an embodiment, the second signaling in the present application includes content obtained through reasoning by the AI / ML reasoning function 1506.

[0350] As an embodiment, the first node includes an attached Fig. 9 An AL / ML reasoning function 1506 in.

[0351] As an embodiment, the ML model is based on a neural network.

[0352] As an embodiment, the ML model is based on CNN (Conventional Neural Networks).

[0353] As an embodiment, the ML model is based on the Transformer architecture.

[0354] Example 10

[0355] Embodiment 10 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application; Fig.10 Attached Fig.10 Includes a third processor, a fourth processor, a fifth processor and a sixth processor.

[0356] In Example 10, the third processor sends a first data set to the fourth processor and a second data set to the fifth processor; the fourth processor generates a target first-category parameter group based on the first data set, and the fourth processor sends the generated target first-category parameter group to the fifth processor; the fifth processor processes the second data set using the target first-category parameter group to obtain a first-category output, and (optionally) the fifth processor sends the first-category output to the sixth processor. Fig.10 In the embodiment, the first type of feedback and the second type of feedback are optional; the fourth processor includes ML training function; and the fifth processor includes ML reasoning function.

[0357] As an embodiment, the sixth processor includes ML testing functionality.

[0358] As an embodiment, the sixth processor includes performance monitoring / evaluation of the ML model.

[0359] As an embodiment, the fifth processor sends the first type of feedback to the fourth processor, and the first type of feedback is used to trigger recalculation or updating of the target first type parameter group, that is, triggering ML initial training or ML retraining.

[0360] As an embodiment, the sixth processor sends a second type of feedback to the third processor, and the second type of feedback is used to generate the first data set or the second data set, or the second type of feedback is used to trigger the sending of the first data set or the sending of the second data set.

[0361] As an embodiment, the third processor generates the first data set and the second data set based on measurement of a reference signal.

[0362] As an embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[0363] As an embodiment, the first type of output includes the first channel information.

[0364] As an embodiment, the first type of output includes an index of the target reference signal.

[0365] As an embodiment, the second data set includes measurements for the first reference signal, or includes measurements for the second reference signal.

[0366] As an embodiment, the first data set includes training data (Training Data).

[0367] As an embodiment, the fourth processor is used to train the ML model, and the trained model is described by the target first-category parameter group.

[0368] As an embodiment, the fourth processor belongs to the first node.

[0369] The above embodiment avoids transferring the first data set to the second node.

[0370] As an embodiment, the fourth processor belongs to the second node.

[0371] The above embodiments support joint training and optimize system performance.

[0372] As an embodiment, the fourth processor belongs to the core network.

[0373] The above embodiments support joint training of the entire network, further optimizing system performance.

[0374] As an embodiment, the second data set includes inference data.

[0375] As an embodiment, the fifth processor belongs to the first node.

[0376] As an embodiment, the fifth processing machine constructs a model according to the target first-category parameter group, and then inputs the second data set into the constructed model to obtain the first-category output.

[0377] As an embodiment, the fifth processing machine generates a restored data set according to the first type of output, and an error between the restored data set and the second data set is used to generate the first type of feedback.

[0378] As an 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 requirements, the fourth processor will recalculate the target first type parameter group.

[0379] As an embodiment, when the error is too large or has not been updated for too long, the performance of the trained model is considered to be unable to meet the requirements.

[0380] As an embodiment, the target first category parameter group includes: one or more of: convolution kernel size, number of convolution layers, convolution step size, pooling kernel size, pooling kernel step size, pooling function, activation function, or number of feature maps.

[0381] As an embodiment, the target first-category parameter group includes: one or more of a convolution kernel, a pooling kernel, a pooling function, an activation function, parameters of a pooling function, or parameters of an activation function.

[0382] Embodiment 11

[0383] Embodiment 11 illustrates a flowchart based on artificial intelligence or machine learning according to an embodiment of the present application; Fig.11 Attached Fig.11 Including a third operation, a fourth operation, a fifth operation, a sixth operation and a seventh operation. In Example 11, the third operation and the fourth operation belong to the first stage, the fifth operation belongs to the second stage, the sixth operation belongs to the third stage, and the seventh operation belongs to the fourth stage. Fig.11 In the diagram, the lines with arrows indicate the sequence of the processes.

[0384] As an embodiment, the third operation includes AI / ML training, the fourth operation includes AI / ML testing, the fifth operation includes AI / ML emulation, the sixth operation includes AI / ML entity loading, and the seventh operation includes AI / ML inference.

[0385] As an embodiment, the first phase includes a training phase, the second phase includes an emulation phase, the third phase includes a deployment phase, and the fourth phase includes an emulation phase.

[0386] As an embodiment, the first stage includes AI / ML model training.

[0387] As an embodiment, the first stage includes AI / ML model training and AI / ML testing.

[0388] As an embodiment, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0389] As an embodiment, the AI / ML model training relies on training data.

[0390] As an embodiment, the AI / ML model training includes AI / ML entity validation.

[0391] As an embodiment, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[0392] As an embodiment, the AI / ML entity verification relies on verification data.

[0393] As an example, if the result of AI / ML entity verification does not meet expectations, the AI / ML model will be retrained.

[0394] As an embodiment, the AI / ML testing includes testing the verified AI / ML entity to estimate the performance of the trained AI / ML model.

[0395] As an example, if the result of the AI / ML test meets expectations, the AI / ML entity proceeds to the next stage; otherwise the AI / ML model will be retrained.

[0396] As an embodiment, the AI / ML testing relies on test data.

[0397] As an embodiment, the second stage includes AI / ML simulation, which performs inference of AI / ML entities in a simulation environment.

[0398] As an embodiment, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using the AI / ML entity.

[0399] As an embodiment, the second stage is optional.

[0400] As an embodiment, the third stage includes AI / ML entity loading, and the AI / ML entity loading is to obtain the trained AI / ML entity to obtain the desired AI / ML reasoning function.

[0401] As an embodiment, the third stage is optional.

[0402] As an example, when the training function and the inference function are co-located, the third stage is no longer required.

[0403] As an embodiment, the fourth stage includes AI / ML reasoning.

[0404] Example 12

[0405] Embodiment 12 illustrates a schematic diagram illustrating the relationship between the second signaling and the first signal according to an embodiment of the present application; Fig.12 shown.

[0406] In Embodiment 12, the first signal is associated with the first ID, and at least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal.

[0407] As an embodiment, the second signaling is physical layer signaling.

[0408] As an embodiment, the second signaling is MAC layer signaling.

[0409] As an embodiment, the benefits of the above method include: a short delay in the effectiveness of the indication.

[0410] As an embodiment, the second signaling is higher layer signaling.

[0411] As an embodiment, the second signaling includes information for performance monitoring / evaluation of the ML model.

[0412] As an embodiment, the indication content of the second signaling depends on the first signal.

[0413] As an embodiment, the indication content of the second signaling depends on the ML model.

[0414] As an embodiment, the indication content of the second signaling depends on one or more ML models of the at least one ML model.

[0415] As an embodiment, the indication content of the second signaling depends on the output obtained by reasoning according to one ML model among the at least one ML model, and the input of the reasoning depends on the target data set.

[0416] As an embodiment, the indication content of the second signaling includes an output obtained by reasoning according to one ML model among the at least one ML model, and the input of the reasoning depends on the target data set.

[0417] As an embodiment, the input of the reasoning includes the target data set.

[0418] As an embodiment, the indication content of the second signaling includes an output obtained by reasoning according to one ML model among the at least one ML model, and the input of the reasoning includes the target data set.

[0419] As an embodiment, the output obtained by inference according to one ML model among the at least one ML model includes channel information, such as a channel matrix, or CSI (Channel State Information).

[0420] As an embodiment, the triggering of the second signaling depends on the first signal.

[0421] As an embodiment, the triggering of the second signaling depends on an ML model.

[0422] As an embodiment, the triggering of the second signaling depends on one or more ML models of the at least one ML model.

[0423] As an embodiment, the triggering of the second signaling depends on the output obtained by inference according to one ML model among the at least one ML model, and the input of the inference depends on the target data set.

[0424] As an embodiment, the output obtained by reasoning according to one ML model among the at least one ML model includes SINR (Signal-to-Interference-and-Noise Ratio), and when the SINR is less than a predefined or configured threshold, the second signaling is triggered.

[0425] As an embodiment, the output obtained by reasoning according to one ML model among the at least one ML model includes SINR, and when the SINR is not less than a predefined or configured threshold, the second signaling is triggered.

[0426] As an embodiment, when the proportion of the portion of the first signal whose reception quality is higher than the first threshold in the first signal exceeds a second threshold, the second signaling is triggered; the second threshold is greater than 0 and less than 1.

[0427] As an embodiment, when the proportion of the portion of the first signal whose reception quality is higher than the first threshold in the first signal is lower than a second threshold, the second signaling is triggered; and the second threshold is greater than 0 and less than 1.

[0428] As an embodiment, the second threshold is predefined.

[0429] As an embodiment, the second threshold is configurable.

[0430] As an embodiment, the second threshold is equal to 0.8.

[0431] Example 13

[0432] Embodiment 13 illustrates a schematic diagram illustrating the relationship between the second signaling, the first signal, and at least one reference signal according to an embodiment of the present application; Fig.13 shown.

[0433] In Embodiment 13, the first signal is associated with the first ID, and at least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal and at least one reference signal.

[0434] As an embodiment, the indication content of the second signaling depends on the first signal and the at least one reference signal.

[0435] As an embodiment, the triggering of the second signaling depends on the first signal and the at least one reference signal.

[0436] As an embodiment, the target channel quality is the best channel quality in the first channel quality group; the output inferred from one ML model in the at least one ML model includes the first channel quality, and the corresponding input includes the target data set; the first channel quality group includes the first channel quality.

[0437] As an embodiment, each reference signal of the at least one reference signal is taken as input, and reasoning is performed according to the ML model to output a channel quality in the first channel quality group.

[0438] As an embodiment, one channel quality in the first channel quality group is an estimated average received power under a given precoding.

[0439] As an embodiment, one of the channel qualities in the first channel quality group is SINR.

[0440] As an embodiment, a channel quality in the first channel quality group is obtained by calculating based on the measurement of each reference signal in the at least one reference signal.

[0441] As an embodiment, for the target data set, the corresponding channel quality is the estimated average received power under a given precoding; for each reference signal of the at least one reference signal, the corresponding channel quality is RSRP (Reference Signal Received Power).

[0442] As an embodiment, the second signaling is physical layer signaling.

[0443] As an embodiment, the second signaling is MAC layer signaling.

[0444] As an embodiment, the benefits of the above method include: a short delay in the effectiveness of the indication.

[0445] As an embodiment, the second signaling is higher layer signaling.

[0446] As an embodiment, the second signaling indicates the input of the ML model corresponding to the target channel quality.

[0447] As an embodiment, when the input of the ML model corresponding to the target channel quality is the target data set, the second signaling is triggered.

[0448] As an embodiment, the at least one reference signal is a downlink reference signal.

[0449] As an embodiment, the at least one reference signal is used for channel detection.

[0450] As an embodiment, the at least one reference signal is a CSI-RS (Channel State Information Reference Signal).

[0451] Embodiment 14

[0452] Embodiment 14 illustrates a structural block diagram of a processing device used in a first node according to an embodiment of the present application; Fig.14 As shown in the attached Fig.14 In the embodiment, the processing device A00 in the first node includes a first receiver A01 and a first transmitter B02.

[0453] As an embodiment, the first node is user equipment.

[0454] As an embodiment, the first node is a relay node.

[0455] As an embodiment, the first node is a vehicle-mounted communication device.

[0456] As an embodiment, the first receiver A01 includes the attached Figure 4 At least one of the antenna 452, receiver 454, multi-antenna reception processor 458, reception processor 456, controller / processor 459, memory 460 and data source 467.

[0457] As an embodiment, the first transmitter A02 includes the attached Figure 4 At least one of the antenna 452, transmitter 454, multi-antenna transmit processor 457, transmit processor 468, controller / processor 459, memory 460 and data source 467.

[0458] As an embodiment, the first receiver A01 receives a first signal; the first receiver A01 receives a first signal; wherein, whether the first signal is associated with a first ID depends on at least the former of the reception quality of the first signal and the indication of the first signal, and the first ID identifies at least one ML model.

[0459] As an embodiment, whether the first signal is associated with the first ID depends on the indication of the first signaling and the relationship between the reception quality of the first signal and a first threshold.

[0460] As an embodiment, when at least a portion of the first signal belongs to a target data set, the first signal is associated with the first ID; the first ID indicates the target data set.

[0461] As an embodiment, when the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0462] As an embodiment, the first signaling schedules the transmission of the first signal.

[0463] As an embodiment, the first transmitter A02 sends a second signaling, wherein at least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal.

[0464] As an embodiment, the first receiver A01 receives at least one reference signal; wherein at least one of the indication content of the second signaling and the triggering of the second signaling depends on the at least one reference signal.

[0465] Embodiment 15

[0466] Embodiment 15 illustrates a structural block diagram of a processing device used in a second node according to an embodiment of the present application; Fig.15 As shown in the attached Fig.15 In the embodiment, the processing device B00 in the second node includes a first transmitter B01 and a second receiver B02.

[0467] As an embodiment, the second node is a base station.

[0468] As an embodiment, the second node is a satellite device.

[0469] As an embodiment, the second node is a relay node.

[0470] As an embodiment, the second node is one of a test device, a test equipment, and a test instrument.

[0471] As an embodiment, the second transmitter B01 includes the attached Figure 4 At least one of the antenna 420, transmitter 418, multi-antenna transmit processor 471, transmit processor 416, controller / processor 475 and memory 476.

[0472] As an embodiment, the second receiver B02 includes the attached Figure 4 At least one of the antenna 420, the receiver 418, the multi-antenna reception processor 472, the reception processor 470, the controller / processor 475 and the memory 476.

[0473] As an embodiment, the second transmitter B01 sends a first signal; the second transmitter B01 sends a first signal; wherein, whether the first signal is associated with a first ID depends on at least the former of the reception quality of the first signal and the indication of the first signal, and the first ID identifies at least one ML model.

[0474] As an embodiment, whether the first signal is associated with the first ID depends on the indication of the first signaling and the relationship between the reception quality of the first signal and a first threshold.

[0475] As an embodiment, when at least a portion of the first signal belongs to a target data set, the first signal is associated with the first ID; the first ID indicates the target data set.

[0476] As an embodiment, when the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

[0477] As an embodiment, the first signaling schedules the transmission of the first signal.

[0478] As an embodiment, the second receiver B02 receives a second signaling, wherein at least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal.

[0479] As an embodiment, the second transmitter B01 sends at least one reference signal; wherein at least one of the indication content of the second signaling and the triggering of the second signaling depends on the at least one reference signal.

[0480] A person of ordinary skill in the art can understand that all or part of the steps in the above method can be completed by instructing the relevant hardware through a program, 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. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiment 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 software and hardware combination. The user equipment, terminal and UE in the present application include but are not limited to drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication equipment, transportation tools, vehicles, RSUs, wireless sensors, Internet cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication, Machine Type Communication) terminals, eMTC (enhanced MTC, enhanced MTC) terminals, data cards, Internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablet computers and other wireless communication devices. The base stations or system equipment in this application include but are not limited to macrocell base stations, microcell base stations, small cell base stations, home base stations, relay base stations, eNB, gNB, TRP (Transmitter Receiver Point), GNSS, relay satellites, satellite base stations, aerial base stations, RSU (Road Side Unit), drones, test equipment, such as wireless communication equipment such as transceivers or signaling testers that simulate some functions of base stations.

[0481] It should be understood by those skilled in the art that the present invention may be implemented in other specified forms without departing from its core or essential features. Therefore, the embodiments disclosed herein should be considered illustrative rather than restrictive in any way. The scope of the invention is determined by the appended claims rather than the preceding description, and all modifications within their equivalent meanings and regions are considered to be included therein.

Claims

1. A method for a terminal, characterized in that: include: receiving a first signaling; receiving a first signal; Whether the first signal is associated with a first ID depends on at least the former of a reception quality of the first signal and an indication of the first signaling, and the first ID identifies at least one ML model.

2. The method according to claim 1, characterized in that: Whether the first signal is associated with the first ID depends on the indication of the first signaling and the relationship between the reception quality of the first signal and a first threshold; the first threshold is predefined or configurable or reported by the terminal.

3. The method according to claim 1 or 2, characterized in that: When at least a portion of the first signal belongs to a target data set, the first signal is associated with the first ID; the first ID indicates the target data set.

4. The method according to claim 3, characterized in that When the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

5. The method according to any one of claims 1 to 4, characterized in that The first signaling schedules transmission of the first signal.

6. The method according to any one of claims 1 to 5, characterized in that The method comprises: sending a second signaling; At least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal.

7. The method according to claim 6, characterized in that The method comprises: receiving at least one reference signal; At least one of the indication content of the second signaling and the triggering of the second signaling depends on the at least one reference signal.

8. A terminal, characterized in that: The terminal includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the terminal to execute the method according to any one of claims 1 to 7.

9. A method for a base station, characterized in that: include: Sending a first signaling; Sending a first signal; Whether the first signal is associated with a first ID depends on at least the former of a reception quality of the first signal and an indication of the first signaling, and the first ID identifies at least one ML model.

10. The method according to claim 9, characterized in that Whether the first signal is associated with the first ID depends on the indication of the first signaling and the relationship between the reception quality of the first signal and a first threshold; the first threshold is predefined or configurable or reported by the terminal.

11. The method according to claim 9 or 10, characterized in that: When at least a portion of the first signal belongs to a target data set, the first signal is associated with the first ID; the first ID indicates the target data set.

12. The method according to claim 11, characterized in that When the first signaling does not indicate that the first signal does not belong to the target data set and the reception quality of at least part of the first signal is higher than a first threshold, the at least part of the first signal belongs to the target data set; when the first signaling indicates that the first signal does not belong to the target data set or the reception quality of any part of the first signal is not higher than a first threshold, the first signal does not belong to the target data set.

13. The method according to any one of claims 9 to 12, characterized in that The first signaling schedules transmission of the first signal.

14. The method according to any one of claims 9 to 13, characterized in that The method comprises: receiving a second signaling; At least one of the indication content of the second signaling and the triggering of the second signaling depends on the first signal.

15. The method according to claim 14, characterized in that The method comprises: sending at least one reference signal; At least one of the indication content of the second signaling and the triggering of the second signaling depends on the at least one reference signal.

16. A base station, characterized in that: The base station includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the base station to perform the method according to any one of claims 9 to 15.

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