Method and apparatus used for wireless communication

Abstract

Disclosed in the present application are a method and apparatus used for wireless communication. A method for a terminal, characterized by comprising: receiving first signaling; and 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 an indication of the first signaling, and the first ID identifies at least one ML model.

Description

Method and apparatus for wireless communication

[0001] This application claims priority to the Chinese patent application No. 202410725090.4, filed on June 5, 2024, with the State Intellectual Property Office, and entitled “Method and apparatus for wireless communication”, the whole content of which is incorporated herein by reference. TECHNICAL FIELD

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

[0003] In NR R(release)18, the research of AI(Artificial Intelligence) / ML(Machine Learning) technology is commissioned to explore its impact on system performance and system design. Compared with the traditional processing method, AI / ML has the characteristics of training and 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

[0004] For wireless communication, how to utilize the signal reception quality to enhance the AI / ML function is a problem worth studying. In view of the above problem, the present application discloses a solution. In the case of no conflict, the embodiments in the first node and the features in the embodiments of the present application can be applied to the second node, and vice versa. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other.

[0005] As an embodiment, the explanation of the terms in the present application is referred to the definition of the specification agreement TS38 series of 3GPP.

[0006] As an embodiment, the explanation of the terms in the present application is referred to the definition of the specification agreement TS28 series of 3GPP.

[0007] The present application discloses a method in a first node for wireless communication, characterized in that, comprising:

[0008] receiving a first signaling;

[0009] receiving a first signal;

[0010] Wherein, whether the first signal is associated to a first ID depends on at least the first of the reception quality of the first signal and the indication of the first signaling, the first ID identifying at least one ML model.

[0011] As one embodiment, the first node is a terminal.

[0012] As one embodiment, the problem to be solved by the present application includes how to determine the relationship between a signal and an ML model according to at least the reception quality of the signal and an indication of signaling.

[0013] As one embodiment, the above method is beneficial to implement the enhancement of AI / ML function according to the reception quality of the signal.

[0014] As one embodiment, the benefits of the above method include being beneficial to improve the performance of the communication system.

[0015] According to one aspect of the present application, the above method is characterized in that,

[0016] The first signal is associated to the first ID depending on the indication of the first signaling, and the relationship between the reception quality of the first signal and a first threshold.

[0017] As one embodiment, the problem to be solved by the present application includes how to determine the relationship between a signal and an ML model according to the reception quality of the signal and an indication of signaling.

[0018] According to one aspect of the present application, the above method is characterized in that,

[0019] When at least part of the first signal belongs to a target data set, the first signal is associated to the first ID; the first ID indicates the target data set.

[0020] As one embodiment, the benefits of the above method include being beneficial to optimize the target data set, thereby improving the corresponding ML model.

[0021] According to one aspect of the present application, the above method is characterized in that,

[0022] 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.

[0023] As one embodiment, the benefits of the above method include being beneficial to the base station to control (at least partially) whether the first signal is associated to the first ID through the first signaling, thereby improving robustness.

[0024] As an embodiment, the above method is advantageous in exploiting the part of the first signal with high reception quality to improve the ML model while avoiding potential interference from the part of the first signal with low reception quality.

[0025] According to an aspect of the present application, the above method is characterized in that,

[0026] The first signaling schedules transmission of the first signal.

[0027] As an embodiment, the above method is advantageous in flexibly indicating whether the first signal is associated to the first ID.

[0028] As an embodiment, the above method is advantageous in high indication flexibility.

[0029] According to an aspect of the present application, the above method is characterized in that, comprising:

[0030] transmitting second signaling;

[0031] wherein at least one of indication content of the second signaling and triggering of the second signaling depends on the first signal.

[0032] As an embodiment, the above method is advantageous in improving performance monitoring of the ML model.

[0033] According to an aspect of the present application, the above method is characterized in that, comprising:

[0034] receiving at least one reference signal;

[0035] 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.

[0036] As an embodiment, the above method is advantageous in improving reinforcement learning for the ML model.

[0037] The present application discloses a method in a second node used for wireless communication, characterized in that, comprising:

[0038] transmitting first signaling;

[0039] transmitting a first signal;

[0040] wherein whether the first signal is associated to a first ID depends on at least the former of reception quality of the first signal and indication of the first signaling, the first ID identifying at least one ML model.

[0041] As an embodiment, the second node is a base station.

[0042] According to an aspect of the present application, the above method is characterized in that,

[0043] whether the first signal is associated with the first ID depends on the indication of the first signaling, and a comparison between the reception quality of the first signal and a first threshold.

[0044] According to an aspect of the present application, the above method is characterized in that,

[0045] 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.

[0046] According to an aspect of the present application, the above method is characterized in that,

[0047] 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.

[0048] According to an aspect of the present application, the above method is characterized in that,

[0049] the first signaling schedules transmission of the first signal.

[0050] According to an aspect of the present application, the above method is characterized in that, comprising:

[0051] receiving second signaling;

[0052] wherein at least one of the indication content of the second signaling and the trigger of the second signaling depends on the first signal.

[0053] As an embodiment, by receiving the second signaling or other feedback information, the second node can know the reception quality of at least part of the first signal.

[0054] According to an aspect of the present application, the above method is characterized in that, comprising:

[0055] sending at least one reference signal;

[0056] wherein at least one of the indication content of the second signaling and the trigger of the second signaling depends on the at least one reference signal.

[0057] A first node for wireless communication is disclosed, which comprises:

[0058] a first receiver, configured to receive a first signaling;

[0059] a first receiver, configured to receive a first signal;

[0060] wherein whether the first signal is associated to a first ID depends on at least a first one of a reception quality of the first signal and an indication of the first signaling, the first ID identifying at least one ML model.

[0061] According to an aspect of the present application, the above node is characterized in that,

[0062] wherein whether the first signal is associated to the first ID depends on at least one of the indication of the first signaling and a comparison between the reception quality of the first signal and a first threshold; the first threshold is predefined or configurable or reported by the terminal.

[0063] According to an aspect of the present application, the above node is characterized in that,

[0064] when at least part of the first signal belongs to a target data set, the first signal is associated to the first ID; the first ID indicates the target data set.

[0065] According to an aspect of the present application, the above node is characterized in that,

[0066] when the first signaling does not indicate that the first signal does not belong to the target data set and a 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 a 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.

[0067] According to an aspect of the present application, the above node is characterized in that,

[0068] the first signaling schedules transmission of the first signal.

[0069] According to an aspect of the present application, the above node is characterized in that, comprising:

[0070] a first transmitter, configured to send a second signaling;

[0071] wherein at least one of an indication content of the second signaling and a trigger of the second signaling depends on the first signal.

[0072] According to an aspect of the present application, the node is characterized in that it comprises:

[0073] the first receiver, receiving at least one reference signal;

[0074] wherein at least one of the indication content of the second signaling and the trigger of the second signaling depends on the at least one reference signal.

[0075] The present application discloses a second node used for wireless communication, characterized in that it comprises:

[0076] the second transmitter, transmitting first signaling;

[0077] the second transmitter, transmitting a first signal;

[0078] wherein whether the first signal is associated to a first ID depends on at least a former one of a reception quality of the first signal and an indication of the first signaling, the first ID identifying at least one ML model.

[0079] As an embodiment, the present application has the following advantages:

[0080] It is beneficial to improve the performance of the communication system;

[0081] It is beneficial to enhance the AI / ML function in wireless communication;

[0082] It is beneficial to improve the ML model according to the reception quality of the signal;

[0083] The indication flexibility is high. BRIEF DESCRIPTION OF DRAWINGS

[0084] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments thereof, read in conjunction with the accompanying drawings:

[0085] Figure 1 shows a flowchart for determining a target reference signal according to an embodiment of the present application;

[0086] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of the present application;

[0087] Figure 3 shows a schematic diagram of an embodiment of a radio protocol architecture for the user and control planes according to an embodiment of the present application;

[0088] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application;

[0089] Figure 5 shows a flowchart of a transmission between a first node N1 and a second node N2 according to an embodiment of the present application;

[0090] FIG. 6 shows an illustrative diagram of a first signal being associated to a first ID according to an embodiment of the present application;

[0091] FIG. 7 shows an illustrative diagram of a first signal being associated to a first ID according to an embodiment of the present application;

[0092] FIG. 8 shows an illustrative diagram of RAN (Radio Access Network) domain AI / ML function deployment according to an embodiment of the present application;

[0093] FIG. 9 shows an illustrative diagram of UE AI / ML function deployment according to an embodiment of the present application;

[0094] FIG. 10 shows an illustrative diagram of an artificial intelligence or machine learning based processing system according to an embodiment of the present application;

[0095] FIG. 11 shows an illustrative diagram of an artificial intelligence or machine learning based flowchart according to an embodiment of the present application;

[0096] FIG. 12 shows an illustrative diagram of a relationship between a second signaling and a first signal according to an embodiment of the present application;

[0097] FIG. 13 shows an illustrative diagram of a relationship between a second signaling, a first signal and at least one reference signal according to an embodiment of the present application;

[0098] FIG. 14 shows a structural block diagram of a processing apparatus for use in a first node according to an embodiment of the present application;

[0099] FIG. 15 shows a structural block diagram of a processing apparatus for use in a second node according to an embodiment of the present application. DETAILED DESCRIPTION

[0100] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily without conflict. Based on performance, flexibility, complexity, overhead and compatibility, etc., the person skilled in the art has the motivation to combine the embodiments in different drawings flexibly without conflict, including but not limited to the embodiments in FIG. 1 and the embodiments in FIG. 5-FIG. 12, the embodiments in FIG. 5 and the embodiments in FIG. 6-FIG. 12, etc.

[0101] Embodiment 1

[0102] Embodiment 1 illustrates a flowchart of determining a target reference signal according to an embodiment of the present application, as shown in FIG. 1. In the first node 100 shown in FIG. 1, each block represents a step.

[0103] In Embodiment 1, the first node 100 receives the first signaling in step 101; receives the first signal in step 102.

[0104] In Embodiment 1, whether the first signal is associated to the first ID depends on at least the reception quality of the first signal and the indication of the first signaling, the first ID identifying at least one ML model.

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

[0106] As an embodiment, the first signaling is MAC layer signaling.

[0107] As an embodiment, the benefits of the above method include: the indication validity delay is small.

[0108] As an embodiment, the first signaling is higher layer signaling.

[0109] As an embodiment, the first signal is transmitted on a first physical layer channel.

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

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

[0112] As an embodiment, the first physical layer channel is a physical control channel.

[0113] As an embodiment, the first signal is used to transmit user data.

[0114] As an embodiment, the first signal is used to transmit at least one transport block.

[0115] As an embodiment, the first signal is used to transmit control information.

[0116] As an embodiment, the first ID (identity) identifies at least one data set.

[0117] As an embodiment, the first ID is configured to the first node.

[0118] As an embodiment, the communication parties reach a consensus on the content identified by the first ID.

[0119] As an embodiment, the number of ML models identified by the first ID is 1.

[0120] As one embodiment, the first ID identifies more than one ML model.

[0121] As one embodiment, one ML model is an AI model.

[0122] As one embodiment, one ML model comprises a mathematical algorithm that can be trained by data and human expert input as examples to replicate decisions made by experts in providing the same information.

[0123] As one embodiment, the first signal is associated to the first ID comprises: the first signal is associated to a target data set; wherein the first ID indicates the target data set.

[0124] As one embodiment, the first signal is associated to the first ID comprises: a target data set depends on the first signal; wherein the first ID indicates the target data set.

[0125] As one embodiment, whether the first signal is associated to the first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, comprising:

[0126] Whether the first signal is associated to the first ID depends on the reception quality of the first signal.

[0127] As one embodiment, whether the first signal is associated to the first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, comprising:

[0128] Whether the first signal is associated to the first ID depends on the reception quality of the first signal and the indication of the first signaling.

[0129] As one embodiment, whether the first signal is associated to the first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, comprising:

[0130] Whether the first signal is associated to the first ID depends on the reception quality of at least part of the first signal.

[0131] As one embodiment, whether the first signal is associated to the first ID depends on at least the former of the reception quality of the first signal and the indication of the first signaling, comprising:

[0132] Whether the first signal is associated to the first ID depends on the reception quality of at least part of the first signal and the indication of the first signaling.

[0133] As an embodiment, the first signaling indicates whether the first signal is associated to the first ID.

[0134] As an embodiment, whether the first signal is associated to the first ID depends on a comparison between a 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, whether the first signal is associated to the first ID depends on the indication of the first signaling, and a comparison between a 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.

[0136] As an embodiment, the above method is beneficial to improve the configuration flexibility of whether the first signal is associated to the first ID by the indication of the first signaling.

[0137] 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 to the first ID; otherwise, the first signal is not associated to the first ID.

[0138] As an embodiment, if the first signaling does not indicate that the first signal is not associated to 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 to the first ID; otherwise, the first signal is not associated to the first ID.

[0139] As an embodiment, if the first signaling indicates that the first signal is associated to 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 to the first ID; otherwise, the first signal is not associated to the first ID.

[0140] As an embodiment, the first signal is a signal on a first physical layer channel, and is transmitted on multiple REs (Resource Elements); the reception quality of at least part of the first signal is higher than the first threshold when the reception quality of the signal on at least one of the multiple REs on the first physical layer channel is higher than the first threshold.

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

[0142] As one embodiment, the reception quality of the first signal comprises a received strength of the signal on each of the at least one RE used to transmit the first signal.

[0143] As one embodiment, the first signal is a signal on a first physical layer channel and is transmitted on a plurality of REs; the reception quality of any portion of the first signal is not higher than the first threshold when the reception quality of the signal on each of the plurality of REs on the first physical layer channel is not higher than the first threshold.

[0144] As one embodiment, the first signal is associated to the first ID if at least a portion of the first signal has a reception quality not lower than a first threshold; otherwise, the first signal is not associated to the first ID.

[0145] As one embodiment, the first signal is associated to the first ID if the first signaling does not indicate that the first signal is not associated to the first ID and at least a portion of the first signal has a reception quality not lower than a first threshold; otherwise, the first signal is not associated to the first ID.

[0146] As one embodiment, the first signal is associated to the first ID if the first signaling indicates that the first signal is associated to the first ID and at least a portion of the first signal has a reception quality not lower than a first threshold; otherwise, the first signal is not associated to the first ID.

[0147] As one embodiment, the first signal is a signal on a first physical layer channel and is transmitted on a plurality of REs; at least a portion of the first signal has a reception quality not lower than the first threshold when the reception quality of the signal on at least one of the plurality of REs on the first physical layer channel is not lower than the first threshold.

[0148] As one embodiment, the first signal is a signal on a first physical layer channel and is transmitted on a plurality of REs; any portion of the first signal has a reception quality lower than the first threshold when the reception quality of the signal on each of the plurality of REs on the first physical layer channel is lower than the first threshold.

[0149] As one embodiment, the reception quality of the signal on one RE comprises a received power of the signal on this RE.

[0150] As one embodiment, the reception quality of the signal on one RE comprises a received strength of the signal on this RE.

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

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

[0153] As one embodiment, the first signal belongs to the target data set if a reception quality of at least part of the first signal is higher than a first threshold; otherwise, the first signal does not belong to the target data set.

[0154] As one embodiment, the first signal belongs to the target data set if a reception quality of at least part of the first signal is higher than a first threshold; otherwise, the first signal does not belong to the target data set.

[0155] As one embodiment, the first signal belongs to the target data set if a reception quality of at least part of the first signal is higher than a first threshold; otherwise, the first signal does not belong to the target data set.

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

[0157] As one embodiment, the first signal belongs to the target data set if a reception quality of at least part of the first signal is higher than a first threshold; otherwise, the first signal does not belong to the target data set.

[0158] As one 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, the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0159] As one 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, the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0160] As one 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 the first threshold, the first signal does not belong to the target data set.

[0161] As one 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.

[0162] As one 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 the first threshold, the first signal does not belong to the target data set.

[0163] As one embodiment, whether the first signal is associated to 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.

[0164] As one embodiment, whether the first signal is associated to 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.

[0165] As one embodiment, the above method facilitates improving configuration flexibility of whether the first signal is associated to the first ID with the indication of the first signaling.

[0166] As one embodiment, the reception quality of the first signal includes average received power on REs used to transmit the first signal.

[0167] As one embodiment, the reception quality of the first signal includes average received power on REs used to transmit the first signal.

[0168] As one 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.

[0169] As one 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, the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0170] As one 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, the first signal belongs to the target data set; otherwise, the first signal does not belong to the target data set.

[0171] As one 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.

[0172] As one 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 a first threshold, the first signal does not belong to the target data set.

[0173] As one embodiment, the first signal belongs to the target data set 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 does not belong 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 the first threshold.

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

[0175] As one embodiment, the first signal belongs to the target data set 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; otherwise, the first signal does not belong to the target data set.

[0176] As one embodiment, the first signal belongs to the target data set 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; otherwise, the first signal does not belong to the target data set.

[0177] As one embodiment, the first signal belongs to 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 the first signal is not lower than a first threshold; the first signal does not belong 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 the first threshold.

[0178] As one embodiment, the first signal belongs to the target data set 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 does not belong 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.

[0179] As one embodiment, the first signal belongs to the target data set 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 does not belong 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 the first threshold.

[0180] Embodiment 2

[0181] Example 2 illustrates a diagram of a network architecture according to one embodiment of the present application, as shown in FIG. 2.

[0182] FIG. 2 illustrates a network architecture 200. 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 in 3GPP future continued evolution; the network architecture 200 can be referred to as 5GS (5G System) / EPS (Evolved Packet System), or the network architecture 200 can be referred to as 6GS (6G System); the network architecture 200 includes a UE (User Equipment) 201, a RAN (Radio Access Network) 202, a core network 210, a HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and at least one of an Internet service 230. The network architecture 200 can be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As illustrated, the network architecture 200 provides packet-switched services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this application are amenable to use with networked systems including, but not limited to, other cellular systems that are deployed to operate in a packet-switched mode or other cellular systems that are deployed to operate in a circuit-switched mode. The RAN includes a node 203. The RAN can also include other nodes 204. The node 203 provides user and control plane protocol terminations toward the UE 201. The node 203 can be connected to the other nodes 204 via an Xn interface (e.g., backhaul) / X2 interface. The node 203 can 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 (Transmit Receive Point), or some other suitable terminology. The core network 210 is a 5GC (5G Core Network) / EPC (Evolved Packet Core), or the core network 210 is a 6GC; the node 203 provides an access point to the core network 210 for the UE 201.Examples of a UE 201 include a cellular phone, a smart phone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a non-tethered base station communication, a satellite mobile communication, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, a drone, a flying vehicle, a narrowband internet of things device, a machine type communication device, a land vehicle, a car, a wearable device, or any other similar functional device. Those skilled in the art will also The node 203 is connected by an S1 / NG interface to the core network 210. The core network 210 includes a MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF 214, a S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Date Network Gateway) / UPF 213. The MME / AMF / SMF 211 is a control node that handles signaling between the UE 201 and the core network 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocal) packets are transferred through the S-GW / UPF 212, which itself is connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation as well as other functions. The P-GW / UPF 213 is connected to the Internet services 230. The Internet services 230 include operator corresponding Internet protocol services, which can specifically include the Internet, an intranet, an IMS (IP Multimedia Subsystem), and a packet switching service.

[0183] As one embodiment, the first node comprises the UE 201.

[0184] As one embodiment, the second node comprises the node 203.

[0185] As one example, the wireless link between the UE 201 and the node 203 comprises a cellular network link.

[0186] Embodiment 3

[0187] Embodiment 3 illustrates a schematic diagram of an embodiment of a radio protocol architecture for the user and control planes according to one embodiment of the application, as shown in Figure 3.

[0188] Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3 showing three layers of the radio protocol architecture for 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: Layer 1, Layer 2, and Layer 3. Layer 1 (LI layer) is the lowest layer and implements various PHY (Physical layer) signal processing functions. The LI layer will be referred to as the PHY 301 herein. Layer 2 (L2 layer) 305 is above the PHY 301 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, a RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate the functions of 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 functions, such as ciphering of the data packets, and header compression. 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 the various radio resources (e.g., resource blocks) in one cell among the UEs. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in Layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and the use of RRC signaling between the second communication node device and the first communication node device for configuring the lower layers. The radio protocol architecture for the user plane 350 includes Layer 1 (LI layer) and Layer 2 (L2 layer), which are 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 for the first communication node device and the second communication node device, 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 the mapping between a QoS flow and a data radio bearer (DRB) to support the diversity of services. Although not illustrated, the first communication node device can have several upper layers above the L2 layer 355, including a network layer (e.g., IP layer) that terminates at a P-GW on the network side and an application layer that terminates at the other end of the connection (e.g., a remote UE, a server, etc.).

[0189] As one embodiment, the wireless protocol architecture in FIG. 3 is applicable to the first node.

[0190] As one embodiment, the wireless protocol architecture in FIG. 3 is applicable to the second node.

[0191] As one embodiment, the higher layer in this application refers to a layer above the physical layer.

[0192] As one embodiment, the first signaling is generated at the PHY 301.

[0193] As one embodiment, the first signaling is generated at the MAC sublayer 302.

[0194] As one embodiment, the first signaling is generated at the PHY 301 or the PHY 351.

[0195] As one embodiment, the at least one reference signal is generated at the PHY 301 or the PHY 351.

[0196] As one embodiment, the second signaling is generated at the RRC sublayer 306, the MAC sublayer 302 or the PHY 301.

[0197] Embodiment 4

[0198] Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to one embodiment of the present application, as shown in FIG. 4. FIG. 4 is a block diagram of a first communication device 410 and a second communication device 450 that communicate with each other in an access network.

[0199] 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 antennas 420.

[0200] 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 antennas 452.

[0201] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer packets from a core network are provided to the controller / processor 475. The controller / processor 475 implements functionality of the L2 layer. In the DL (DownLink), the controller / processor 475 provides header compression, ciphering, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocations for 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 LI layer (i.e., physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and 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 pre-coding on the coded and modulated symbols, including codebook-based and non-codebook-based pre-coding, and beamforming processing, generating one or more parallel streams. The transmit processor 416 then maps to each parallel stream to subcarriers, multiplexes the modulated symbols with reference signals (e.g., pilot) in time domain and / or frequency domain, and then performs an inverse fast Fourier transform (IFFT) to generate time domain OFDM streams. The multi-antenna transmit processor 471 then performs transmit analog pre-coding / beamforming operations on the time domain multi-carrier symbol streams. Each transmitter 418 converts the baseband multi-carrier symbol streams provided by the multi-antenna transmit processor 471 into radio frequency streams, which are then provided to different antennas 420.

[0202] In transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its respective antenna 452. Each receiver 454 recovers information modulated onto an RF carrier and converts the RF stream into a baseband, multicarrier symbol stream to be provided to a receive processor 456. The receive processor 456 and a multiple access receive processor 458 implement various signal processing functions of the Ll layer. The multiple access receive processor 458 performs receive analog precoding / beamforming operations on the baseband, multicarrier symbol stream from the receivers 454. The receive processor 456 converts the baseband, multicarrier symbol stream from the receive analog precoding / beamforming operations from the time domain to the frequency domain using a Fast Fourier Transform (FFT). In the frequency domain, the physical layer data signals and the reference signals are demultiplexed by the receive processor 456, with the reference signals to be used for channel estimation and the data signals to be recovered after multi-antenna detection in the multiple access receive processor 458 for any parallel streams destined to the second communication device 450. The symbols on each parallel stream are demodulated and recovered in the receive processor 456 and generate soft decisions. The receive processor 456 then decodes and de-interleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channels. The upper layer data and control signals are then provided to a controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 can be associated with a memory 460 that stores program codes and data. The memory 460 can be referred to as a computer-readable medium. In the DL, the controller / processor 459 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2 layer. Various control signals can also be provided to the L3 for L3 processing. The controller / processor 459 is also responsible for error detection using an acknowledgement (ACK) and / or negative acknowledgement (NACK) protocol to support HARQ operations.

[0203] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper layer packets to a controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmit function described at the first communication device 410 in the DL, the controller / processor 459 implements header compression, ciphering, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocations for the first communication device 410, implements L2 layer functionality for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. A transmit processor 468, in conjunction with a multi-antenna transmit processor 457, performs modulation mapping, channel coding processing, digital multi-antenna spatial processing, including codebook-based and non-codebook-based precoding, and beamforming processing, and then the transmit processor 468 generates parallel streams of symbols that are modulated onto different carriers, and the modulated symbol streams are then provided to different antennas 452 via transmitters 454 after analog precoding / beamforming at the multi-antenna transmit processor 457. Each transmitter 454 modulates a respective symbol stream, converts the modulated symbol stream from digital form to analog form, and transmits the analog signal via the corresponding antenna 452.

[0204] In the transmission from the second communication device 450 to the first communication device 410, the functionality at the first communication device 410 is similar to the functionality described in connection with the reception at the second communication device 450 in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives a signal from its respective antenna 420, converts the received signal to a baseband signal, and provides the baseband signal to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472, in conjunction with the controller / processor 475, implement the L1 layer functions. The controller / processor 475 implements L2 layer functionality. The controller / processor 475 can be associated with a memory 476 that stores program codes and data. The memory 476 can be referred to as a computer-readable medium. The controller / processor 475 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer packets from the second communication device 450. Upper layer packets from the controller / processor 475 can be provided to a core network. The controller / processor 475 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0205] As one embodiment, the second communication device 450 comprises: at least one processor and at least one memory including a computer program code; the at least one memory and the computer program code are configured to, with the at least one processor, cause the second communication device 450 to perform at least the following: receive first signaling; receive a first signal; wherein whether the first signal is associated to a first ID depends on at least a first one of a reception quality of the first signal and an indication of the first signaling, the first ID identifying at least one ML model.

[0206] As one embodiment, the second communication device 450 comprises: a memory storing a program of computer readable instructions to produce actions when executed by at least one processor, the actions comprising: receiving first signaling; receiving a first signal; wherein whether the first signal is associated to a first ID depends on at least a first one of a reception quality of the first signal and an indication of the first signaling, the first ID identifying at least one ML model.

[0207] As one embodiment, the first communication device 410 comprises: at least one processor and at least one memory including a computer program code; the at least one memory and the computer program code are configured to, with the at least one processor, cause the first communication device 410 to perform at least the following: transmit first signaling; transmit a first signal; wherein whether the first signal is associated to a first ID depends on at least a first one of a reception quality of the first signal and an indication of the first signaling, the first ID identifying at least one ML model.

[0208] As one embodiment, the first communication device 410 comprises: a memory storing a program of computer readable instructions to produce actions when executed by at least one processor, the actions comprising: transmitting first signaling; transmitting a first signal; wherein whether the first signal is associated to a first ID depends on at least a first one of a reception quality of the first signal and an indication of the first signaling, the first ID identifying at least one ML model.

[0209] As one embodiment, the first node in the present application comprises the second communication device 450.

[0210] As one embodiment, the second node in the present application comprises the first communication device 410.

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

[0212] As an embodiment, some or all of {the antennas 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460} are used to receive the first signaling; some or all of {the antennas 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475} are used to transmit the first signaling.

[0213] As an embodiment, some or all of {the antennas 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460} are used to receive the first signaling; some or all of {the antennas 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475} are used to transmit the first signaling.

[0214] As an embodiment, some or all of {the antennas 420, the receiver 418, the receive processor 470, the multi-antenna receive processor 472, the controller / processor 475, the memory 476} are used to receive the second signaling; some or all of {the antennas 452, the transmitter 454, the transmit processor 468, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, the data source 467} are used to transmit the second signaling.

[0215] Embodiment 5

[0216] Embodiment 5 illustrates a transmission flow chart between the first node N1 and the second node N2 according to an embodiment of the present application; as shown in FIG. 5. In FIG. 5, the second node N1 and the first node N2 are communication nodes for transmission over an air interface. In FIG. 5, the steps in the dashed box F1 and the steps in the dashed box F2 are optional. It should be noted that the sequence of the steps in FIG. 5 is only one specific implementation, and the sequence between the steps can be adjusted without conflict.

[0217] For the second node N2, at least one reference signal is transmitted in step S520; the first signaling is transmitted in step S521; the first signal is transmitted in step S522; the second signaling is received in step S523.

[0218] For the first node N1, at least one reference signal is received in step S510; the first signaling is received in step S511; the first signal is received in step S512; the second signaling is transmitted in step S513.

[0219] In Embodiment 5, the first signaling schedules transmission of the first signal; whether the first signal is associated to the first ID depends on at least the reception quality of the first signal and the indication of the first signaling; the first ID identifies at least one ML model.

[0220] As a sub-embodiment of Embodiment 5, whether the first signal is associated to the first ID depends on at least the indication of the first signaling and the comparison between the reception quality of the first signal and a first threshold; the first threshold is predefined or configurable or reported by the terminal.

[0221] As a sub-embodiment of Embodiment 5, when at least part of the first signal belongs to a target data set, the first signal is associated to 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.

[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.

[0223] 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.

[0224] Embodiment 5 and its respective sub-embodiments can be combined arbitrarily with each other.

[0225] 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.

[0226] As an embodiment, the air interface between the second node N1 and the first node N2 comprises a wireless interface between a base station device and a user equipment.

[0227] As an embodiment, the second node N2 and the first node N1 are a base station and a user equipment, respectively.

[0228] As an embodiment, the second node N2 and the first node N1 are both user equipments.

[0229] As an embodiment, the second node N2 is a serving cell maintaining base station of the first node N1.

[0230] As an embodiment, the second signaling comprises the first ID.

[0231] As an embodiment, a ML model is based on a neural network.

[0232] As an embodiment, a ML model is based on a CNN (Conventional Neural Networks).

[0233] As an embodiment, a ML model is based on a Transformer architecture.

[0234] As an embodiment, an output of a ML inference comprises channel information, e.g. a channel matrix, or a CSI.

[0235] As an embodiment, the steps in the dashed box F1 are present.

[0236] As an embodiment, the steps in the dashed box F1 are not present.

[0237] As an embodiment, the steps in the dashed box F2 are present.

[0238] As an embodiment, the steps in the dashed box F2 are not present.

[0239] Embodiment 6

[0240] Embodiment 6 illustrates a diagrammatic representation of a first signal being associated to a first ID according to an embodiment of the present application, as shown in FIG. 6.

[0241] In Embodiment 6, the first signal is associated to the first ID when at least part of the first signal belongs to a target data set; the first ID indicates the target data set.

[0242] As one embodiment, benefits of the above method include facilitating optimization of the target data set, thereby improving the corresponding ML model.

[0243] As one embodiment, the target data set is used for training of the at least one ML model.

[0244] As one embodiment, the target data set is used for reinforcement learning of the at least one ML model.

[0245] As one embodiment, the target data set is used for performance monitoring of the at least one ML model.

[0246] As one embodiment, the target data set is used for testing of the at least one ML model.

[0247] As one embodiment, the target data set is used as input for inference according to the at least one ML model.

[0248] As one embodiment, the first ID explicitly indicates the target data set.

[0249] As one embodiment, the first ID implicitly indicates the target data set.

[0250] As one embodiment, the first signal is not associated to the first ID when the first signal does not belong to the target data set.

[0251] As one embodiment, at least part of the first signal belongs to the target data set if a reception quality of the at least part of the first signal is higher than the first threshold; otherwise, the first signal does not belong to the target data set.

[0252] As one embodiment, the above method facilitates taking advantage of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference from the part of the first signal with low reception quality.

[0253] As one embodiment, if the first signaling does not indicate that the first signal does not belong to the target dataset 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 dataset; otherwise, the first signal does not belong to the target dataset.

[0254] As one embodiment, if the first signaling indicates that the first signal belongs to the target dataset 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 dataset; otherwise, the first signal does not belong to the target dataset.

[0255] As one embodiment, when the first signaling does not indicate that the first signal does not belong to the target dataset 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 dataset; when the first signaling indicates that the first signal does not belong to the target dataset 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 dataset.

[0256] As one embodiment, when the first signaling indicates that the first signal belongs to the target dataset 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 dataset; when the first signaling does not indicate that the first signal belongs to the target dataset 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 dataset.

[0257] As one embodiment, when the first signaling indicates that the first signal belongs to the target dataset 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 dataset; when the first signaling indicates that the first signal does not belong to the target dataset 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 dataset.

[0258] As one embodiment, the above method has the benefit of facilitating the base station to control (at least partially) whether the first signal is associated to the first ID through the first signaling, thereby improving robustness.

[0259] As one embodiment, the above method facilitates to make full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference brought by the part of the first signal with low reception quality.

[0260] As an embodiment, when the reception quality of a portion of the first signal is not higher than the first threshold, the portion of the first signal does not belong to the target dataset.

[0261] As an embodiment, the first signal is a signal on a first physical layer channel and is transmitted on a plurality of REs; when the reception quality of the signal on at least one RE of the plurality of REs on the first physical layer channel is not higher than the first threshold, the signal on the at least one RE of the plurality of REs on the first physical layer channel does not belong to the target dataset.

[0262] As an embodiment, the signal on one RE of the plurality of REs is a portion of the first signal.

[0263] As an embodiment, if the reception quality of at least a portion of the first signal is not lower than the first threshold, the at least a portion of the first signal belongs to the target dataset; otherwise, the first signal does not belong to the target dataset.

[0264] As an embodiment, the above method is beneficial to make full use of the portion of the first signal with high reception quality to improve the ML model, while avoiding the potential interference brought by the portion of the first signal with low reception quality.

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

[0266] As an embodiment, if the first signaling indicates that the first signal belongs to the target dataset and the reception quality of at least a portion of the first signal is not lower than the first threshold, the at least a portion of the first signal belongs to the target dataset; otherwise, the first signal does not belong to the target dataset.

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

[0268] As an example, when the first signaling indicates that the first signal belongs to the target data set and a 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 a 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.

[0269] As an example, when the first signaling indicates that the first signal belongs to the target data set and a 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 a 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.

[0270] As an example, the above method has the benefit of facilitating the base station to control (at least partially) whether the first signal is associated to the first ID through the first signaling, thereby improving robustness.

[0271] As an example, the above method facilitates to make full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference brought by the part of the first signal with low reception quality.

[0272] As an example, when a 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.

[0273] As an example, the first signal is a signal on a first physical layer channel and is transmitted on multiple REs; when a 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 the at least one of the multiple REs on the first physical layer channel does not belong to the target data set.

[0274] As an example, the reception quality of a signal on an RE includes a received power of the signal on the RE.

[0275] As an example, the reception quality of a signal on an RE includes a received strength of the signal on the RE.

[0276] As an example, the first threshold is predefined.

[0277] As an example, the first threshold is a constant.

[0278] As one embodiment, the first threshold is configurable.

[0279] As one embodiment, the first threshold is reported by the first node.

[0280] As one embodiment, the unit of the first threshold is dBm.

[0281] Embodiment 7

[0282] Embodiment 7 illustrates a diagram showing that a first signal is associated to a first ID according to one embodiment of the present application, as shown in FIG. 7.

[0283] In embodiment 7, the first signal is associated to the first ID when information obtained from measurement on at least part of the first signal belongs to a target dataset; the first ID indicates the target dataset.

[0284] As one embodiment, the benefit of the above method includes facilitating optimization of the target dataset, thereby improving the corresponding ML model.

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

[0286] As one embodiment, the target dataset is used for reinforcement learning of the at least one ML model.

[0287] As one embodiment, the target dataset is used for performance monitoring of the at least one ML model.

[0288] As one embodiment, the target dataset is used for testing of the at least one ML model.

[0289] As one embodiment, the target dataset is used as input for inference according to the at least one ML model.

[0290] As one embodiment, the first ID explicitly indicates the target dataset.

[0291] As one embodiment, the first ID implicitly indicates the target dataset.

[0292] As one embodiment, the first signal is not associated to the first ID when the target dataset does not include information obtained from measurement on the first signal.

[0293] As one embodiment, if the reception quality of at least part of the first signal is higher than a first threshold, the information resulting from the measurement of the first signal belongs to the target dataset; otherwise, the target dataset does not include the information resulting from the measurement of the first signal.

[0294] As one embodiment, the above method is beneficial for taking advantage of the part of the first signal with high reception quality to improve the ML model while avoiding potential interference from the part of the first signal with low reception quality.

[0295] As one embodiment, if the first signaling does not indicate that the first signal is not associated to the first ID and the reception quality of at least part of the first signal is higher than a first threshold, the information resulting from the measurement of the first signal belongs to the target dataset; otherwise, the target dataset does not include the information resulting from the measurement of the first signal.

[0296] As one embodiment, if the first signaling indicates that the first signal is associated to the first ID and the reception quality of at least part of the first signal is higher than a first threshold, the information resulting from the measurement of the first signal belongs to the target dataset; otherwise, the target dataset does not include the information resulting from the measurement of the first signal.

[0297] As one embodiment, the above method is beneficial for taking advantage of the part of the first signal with high reception quality to improve the ML model while avoiding potential interference from the part of the first signal with low reception quality.

[0298] As one embodiment, the above method is beneficial for taking advantage of the part of the first signal with high reception quality to improve the ML model while avoiding potential interference from the part of the first signal with low reception quality.

[0299] As one embodiment, the reception quality of a signal on one RE includes the received power of the signal on this RE.

[0300] As one embodiment, the reception quality of a signal on one RE includes the received strength of the signal on this RE.

[0301] As an embodiment, the first signal is a signal on a first physical layer channel, and is transmitted on multiple REs; when the received quality of the signal on one RE on the first physical layer channel is higher than the first threshold, the first node can perform a hard decision determination of corresponding modulation symbol(s) for the signal on the one RE on the first physical layer channel, and perform channel estimation on the one RE according to the determined modulation symbol(s).

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

[0303] As an embodiment, when the first node performs measurement on the first signal, the obtained corresponding information includes a channel matrix.

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

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

[0306] As an embodiment, the information obtained according to the measurement on the at least part of the first signal includes a channel matrix on at least one RE.

[0307] As an embodiment, the information obtained according to the measurement on the at least part of the first signal includes a channel matrix on at least one RE on which the received quality of the carried signal is higher than the first threshold.

[0308] As an embodiment, the first node can also average the channel estimation results on the multiple REs on which the part of the first signal with the received quality higher than the first threshold is located.

[0309] As an embodiment, if the received quality of at least part of the first signal is not lower than the first threshold, the information obtained according to the measurement on the at least part of the first signal belongs to the target data set; otherwise, the target data set does not include the information obtained according to the measurement on the first signal.

[0310] As an embodiment, the above method is beneficial to making full use of the part of the first signal with high received quality to improve the ML model, while avoiding the potential interference brought by the part of the first signal with low received quality.

[0311] As an embodiment, if the first signaling does not indicate that the first signal is not associated to 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 according to the measurement for the at least part of the first signal belongs to the target dataset; otherwise, the target dataset does not include the information obtained according to the measurement for the first signal.

[0312] As an embodiment, if the first signaling indicates that the first signal is associated to 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 according to the measurement for the at least part of the first signal belongs to the target dataset; otherwise, the target dataset does not include the information obtained according to the measurement for the first signal.

[0313] As an embodiment, the above method has the benefit of facilitating the base station to control (at least partially) whether the first signal is associated to the first ID through the first signaling, thereby improving robustness.

[0314] As an embodiment, the above method facilitates to make full use of the part of the first signal with high reception quality to improve the ML model, while avoiding potential interference brought by the part of the first signal with low reception quality.

[0315] As an embodiment, the reception quality of a signal on one RE includes the received power of the signal on this RE.

[0316] As an embodiment, the reception quality of a signal on one RE includes the received strength of the signal on this RE.

[0317] 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 one RE on the first physical layer channel is not lower than the first threshold, the first node can perform hard decision on the signal on the one RE on the first physical layer channel to determine corresponding modulation symbol(s) and perform channel estimation on the one RE according to the determined modulation symbol(s).

[0318] As an embodiment, the information obtained according to the measurement for the at least part of the first signal includes a channel matrix on at least one RE on which the reception quality of the carried signal is not lower than the first threshold.

[0319] As an embodiment, the first node can further average the channel estimation results on multiple REs on which the part of the first signal with reception quality not lower than the first threshold is located.

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

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

[0322] As an embodiment, the first threshold is configurable.

[0323] As an embodiment, the first threshold is reported by the first node.

[0324] As an embodiment, the unit of the first threshold is dBm.

[0325] Embodiment 8

[0326] Embodiment 8 illustrates a schematic diagram of RAN (Radio Access Network) domain AI / ML function deployment according to an embodiment of the present application; as shown in FIG. 8. The gNB in Embodiment 8 can be replaced by, for example, eNB, or 6G base station, and the like network device.

[0327] The AI / ML related functions include ML training function (also referred to as AI training, or AI / ML training), ML testing function, ML inference function (also referred to as AI inference, or AI / ML inference), and the like. The ML training function, the ML testing function, and the ML inference function can be deployed independently, or can be co-located. The deployment of the AI / ML related functions can be implemented through software, such as downloading and / or running of executable files; or can be implemented through software combined with hardware, such as acceleration of specific computing units through hardware to improve operation speed or save power consumption.

[0328] For the ML training function, it can be deployed in a cross-domain management system, or a domain-specific management system for managing a RAN domain or a CN (Core Network) domain. For example, the ML training function for MDA (Management Data Analytics) can be deployed in MDAF (MDA function); the ML training for network data analytics can be deployed in NWDAF (Network Data Analytics Function), i.e., the ML training function is MTLF (Model Training logical function).

[0329] For the ML inference function, it can also be deployed in the cross-domain management system or the domain-specific management system; for example, the ML inference function is MDAF, or the ML inference function is AnLF (Analytics logical function) located in the NWDAF.

[0330] Similarly, the ML test function can also be deployed in the cross-domain management system or the domain-specific management system.

[0331] In embodiment 8, the RAN domain ML training function 1402 is located in the RAN domain management function 1403; and the ML inference function is located in the base station, that is, the AI / ML inference function 1404 is located in the gNB 1405, the AI / ML inference function 1406 is located in the gNB 1407, and so on.

[0332] In FIG. 8, the management of the ML inference function of the plurality of base stations is completed by the RAN domain management function 1403, that is, data interaction is performed with the RAN domain MnS (Management Service) consumer / cross-domain management 1401 (as shown by the dashed arrow in FIG. 8).

[0333] Optionally, the management of the ML inference function can also be completed by the base station itself, that is, each base station can independently perform data interaction with the RAN domain MnS consumer / cross-domain management 1401.

[0334] It should be noted that embodiment 8 is only one non-limiting implementation; optionally, the ML training function of the RAN domain can also be deployed in the base station; or optionally, part of the base stations deploy the ML inference function and the ML training function of the RAN domain, and part of the base stations only deploy the ML inference function.

[0335] As an example, one gNB (or base station) in embodiment 8 is the second node of the present application.

[0336] As an example, the second node includes one AL / ML inference function in FIG. 8, that is, 1404 or 1406.

[0337] Embodiment 9

[0338] Embodiment 9 illustrates a schematic diagram of AI / ML function deployment of a UE according to one embodiment of the present application; as shown in FIG. 9. The RAN domain ML training function 1505 in FIG. 9 is optional.

[0339] The UE function 1504 is deployed in the first node of the present application, and includes an AI / ML inference function 1506; the AI / ML inference function 1506 uses an ML model (also referred to as an AI model) for inference; one ML model is usually trained before being used for AI / ML inference.

[0340] As one embodiment, the UE function 1504 includes a RAN-domain ML training function 1505, which runs training data through an ML model to derive a related loss, and adjusts parameters of the ML model based on the calculated loss; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0341] The above embodiment can reduce the complexity of the base station, or save the air interface resources caused by reporting training data; however, the above embodiment puts higher requirements on the processing capability of the UE side.

[0342] Optionally, the UE function 1504 further includes a CN-domain ML training function (not included in FIG. 9).

[0343] Optionally, the UE function 1504 further includes an AI / ML deployment function (not included in FIG. 9) for loading ML models and data.

[0344] As one embodiment, the first node indicates whether it supports ML training functions (RAN domain or CN domain) through capability reporting, and the capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0345] As one embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0346] As one embodiment, the first node loads the at least one ML model.

[0347] As one embodiment, the first node can load multiple ML models, the at least one ML model is 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 are identified by IDs other than the first ID.

[0348] Optionally, the UE function 1504 is a MnS producer providing data for management or analytics to the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 (as indicated by the double arrow 1507).

[0349] Optionally, the UE function 1504 is a MnS consumer loading 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 indicated by the double arrow 1507).

[0350] As an embodiment, the second signaling comprises content resulting from inference by the AI / ML inference function 1506.

[0351] As an embodiment, the first node comprises an AL / ML inference function 1506 in FIG. 9.

[0352] As an embodiment, the ML model is based on a neural network.

[0353] As an embodiment, the ML model is based on a CNN (Conventional Neural Networks).

[0354] As an embodiment, the ML model is based on a Transformer architecture.

[0355] Embodiment 10

[0356] Embodiment 10 illustrates a schematic diagram of an artificial intelligence or machine learning based processing system according to an embodiment of the present application; as shown in FIG. 10. FIG. 10 comprises a third processing machine, a fourth processing machine, a fifth processing machine, and a sixth processing machine.

[0357] In Embodiment 10, the third processor sends a first data set to the fourth processor, and sends a second data set to the fifth processor; the fourth processor generates a target first-type parameter group according to the first data set, and sends the generated target first-type parameter group to the fifth processor; the fifth processor processes the second data set using the target first-type parameter group to obtain a first-type output, and (optionally) sends the first-type output to the sixth processor. In FIG. 10, the first-type feedback and the second-type feedback are optional; the fourth processor comprises an ML training function; and the fifth processor comprises an ML inference function.

[0358] As an embodiment, the sixth processor comprises an ML testing function.

[0359] As an embodiment, the sixth processor comprises performance monitoring / evaluation of the ML model.

[0360] As an embodiment, the fifth processor sends a first-type feedback to the fourth processor, and the first-type feedback is used to trigger recalculation or update of the target first-type parameter group, i.e., trigger ML initial training or ML retraining.

[0361] As an embodiment, the sixth processor sends a second-type feedback to the third processor, and the second-type feedback is used to generate the first data set or the second data set, or the second-type feedback is used to trigger sending of the first data set or sending of the second data set.

[0362] As an embodiment, the third processor generates the first data set and the second data set according to measurement of a reference signal.

[0363] As an embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[0364] As an embodiment, the first-type output comprises the first channel information.

[0365] As an embodiment, the first-type output comprises an index of the target reference signal.

[0366] As an embodiment, the second data set comprises measurement of the first reference signal, or comprises measurement of the second reference signal.

[0367] As an embodiment, the first data set comprises training data.

[0368] As an embodiment, the fourth processor is configured to train the ML model, and the trained model is described by the target first-type parameter set.

[0369] As an embodiment, the fourth processor belongs to the first node.

[0370] The above embodiment avoids transmitting the first data set to the second node.

[0371] As an embodiment, the fourth processor belongs to the second node.

[0372] The above embodiment supports joint training and optimizes system performance.

[0373] As an embodiment, the fourth processor belongs to the core network.

[0374] The above embodiment supports joint training across the network and further optimizes system performance.

[0375] As an embodiment, the second data set includes inference data.

[0376] As an embodiment, the fifth processor belongs to the first node.

[0377] As an embodiment, the fifth processor constructs a model according to the target first-type parameter set, and then inputs the second data set into the constructed model to obtain the first-type output.

[0378] As an embodiment, the fifth processor generates a recovery data set according to the first-type output, and the error of the recovery data set and the second data set is used to generate the first-type feedback.

[0379] As an embodiment, the first-type 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 set.

[0380] As an embodiment, when the error is too large or the model has not been updated for too long a time, the performance of the trained model is considered to not meet the requirements.

[0381] As an embodiment, the target first-type parameter set includes one or more of the following: convolution kernel size, convolution layer number, convolution step size, pooling kernel size, pooling kernel step size, pooling function, activation function, or feature map number.

[0382] As an embodiment, the target first-type parameter set includes one or more of the following: convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, or parameters of the activation function.

[0383] Embodiment 11

[0384] Embodiment 11 illustrates an AI or ML based flowchart according to an embodiment of the present application; as shown in FIG. 11. FIG. 11 includes a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation. In Embodiment 11, the third and fourth operations belong to a first phase, the fifth operation belongs to a second phase, the sixth operation belongs to a third phase, and the seventh operation belongs to a fourth phase. In FIG. 11, the lines with arrows represent the order of the flow.

[0385] 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.

[0386] 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 inference phase.

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

[0388] As an embodiment, the first phase includes AI / ML model training and AI / ML testing.

[0389] As an embodiment, the AI / ML model training includes initial training and re-training of one or a set of AI / ML entities.

[0390] As an embodiment, the AI / ML model training relies on training data.

[0391] As an embodiment, the AI / ML model training includes AI / ML entity validation.

[0392] As an embodiment, the AI / ML entity validation is used to evaluate the performance of the AI / ML entity.

[0393] As one embodiment, the AI / ML entity validation relies on validation data.

[0394] As one embodiment, if the result of the AI / ML entity validation does not meet the expectation, the AI / ML model will be retrained.

[0395] As one embodiment, the AI / ML testing includes testing the validated AI / ML entity to estimate the performance of the trained AI / ML model.

[0396] As one embodiment, if the result of the AI / ML testing meets the expectation, the AI / ML entity proceeds to the next stage; otherwise, the AI / ML model will be retrained.

[0397] As one embodiment, the AI / ML testing relies on testing data.

[0398] As one embodiment, the second stage includes AI / ML simulation, which simulates the inference of the AI / ML entity in a simulation environment.

[0399] As one embodiment, the AI / ML simulation estimates the performance of the inference of the AI / ML entity in a simulation environment before the AI / ML entity is used.

[0400] As one embodiment, the second stage is optional.

[0401] As one embodiment, the third stage includes AI / ML entity loading, which is to obtain the trained AI / ML entity to obtain the desired AI / ML inference function.

[0402] As one embodiment, the third stage is optional.

[0403] As one embodiment, the third stage is not needed when the training function and the inference function are co-located.

[0404] As one embodiment, the fourth stage includes AI / ML inference.

[0405] Embodiment 12

[0406] Embodiment 12 illustrates an explanatory diagram of the relationship between the second signaling and the first signal according to one embodiment of the present application; as shown in FIG. 12.

[0407] In embodiment 12, the first signal is associated to the first ID, and at least one of the indication content of the second signaling and the trigger of the second signaling relies on the first signal.

[0408] As one embodiment, the second signaling is physical layer signaling.

[0409] As one embodiment, the second signaling is MAC layer signaling.

[0410] As one embodiment, the benefit of the above method includes: indicating a small validation delay.

[0411] As one embodiment, the second signaling is higher layer signaling.

[0412] As one embodiment, the second signaling includes information for performance monitoring / evaluation of the ML model.

[0413] As one embodiment, the indication content of the second signaling depends on the first signal.

[0414] As one embodiment, the indication content of the second signaling depends on the ML model.

[0415] As one embodiment, the indication content of the second signaling depends on one or more ML models of the at least one ML model.

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

[0417] As one embodiment, the indication content of the second signaling includes an output inferred according to one ML model of the at least one ML model, and an input of the inference depends on the target data set.

[0418] As one embodiment, the input of the inference includes the target data set.

[0419] As one embodiment, the indication content of the second signaling includes an output inferred according to one ML model of the at least one ML model, and an input of the inference includes the target data set.

[0420] As one embodiment, the output inferred according to one ML model of the at least one ML model includes channel information, such as a channel matrix or CSI (Channel State Information).

[0421] As one embodiment, the trigger of the second signaling depends on the first signal.

[0422] As one embodiment, the trigger of the second signaling depends on the ML model.

[0423] As an embodiment, the triggering of the second signaling depends on one or more ML models in the at least one ML model.

[0424] As an embodiment, the triggering of the second signaling depends on an output inferred from one ML model in the at least one ML model, an input of the inference depending on the target dataset.

[0425] As an embodiment, the output inferred from one ML model in the at least one ML model comprises a SINR (Signal-to-Interference-and-Noise Ratio), the second signaling being triggered when the SINR is less than a predefined or configured threshold.

[0426] As an embodiment, the output inferred from one ML model in the at least one ML model comprises a SINR, the second signaling being triggered when the SINR is not less than a predefined or configured threshold.

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

[0428] As an embodiment, the second signaling is triggered when a proportion of the first signal in which the reception quality is higher than the first threshold is lower than a second threshold; the second threshold is greater than 0 and less than 1.

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

[0430] As an embodiment, the second threshold is configurable.

[0431] As an embodiment, the second threshold is equal to 0.8.

[0432] Embodiment 13

[0433] Embodiment 13 illustrates an explanatory diagram of the relationship between the second signaling, the first signal and the at least one reference signal according to an embodiment of the present application; as shown in FIG. 13.

[0434] In embodiment 13, the first signal is associated with the first ID, at least one of the indication content of the second signaling and the triggering of the second signaling depending on the first signal and the at least one reference signal.

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

[0436] As an embodiment, the trigger of the second signaling depends on the first signal and the at least one reference signal.

[0437] As an embodiment, the target channel quality is the best channel quality in a first channel quality group; the output inferred according to one of the at least one ML model comprises a first channel quality, and the corresponding input comprises the target dataset; the first channel quality group comprises the first channel quality.

[0438] As an embodiment, each of the at least one reference signal is taken as input, and a ML model is used to infer one channel quality in the first channel quality group as output.

[0439] As an embodiment, one of the first channel quality group is an estimated average received power under a given precoding.

[0440] As an embodiment, one of the first channel quality group is a SINR.

[0441] As an embodiment, one of the first channel quality group is calculated according to a measurement on each of the at least one reference signal.

[0442] As an embodiment, for the target dataset, the corresponding channel quality is an estimated average received power under a given precoding; and for each of the at least one reference signal, the corresponding channel quality is a RSRP (Reference Signal Received Power).

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

[0444] As an embodiment, the second signaling is MAC layer signaling.

[0445] As an embodiment, the above method has the advantage of small indication effective delay.

[0446] As an embodiment, the second signaling is higher layer signaling.

[0447] As an embodiment, the second signaling indicates the input of the ML model corresponding to the target channel quality.

[0448] As an embodiment, the second signaling is triggered when an input of the ML model corresponding to the target channel quality is the target dataset.

[0449] As an embodiment, the at least one reference signal is a downlink reference signal.

[0450] As an embodiment, the at least one reference signal is used for channel sounding.

[0451] As an embodiment, the at least one reference signal is a CSI-RS (Channel State Information Reference Signal).

[0452] Embodiment 14

[0453] Embodiment 14 illustrates a structure block diagram of a processing apparatus in a first node according to an embodiment of the present application; as shown in FIG. 14. In FIG. 14, the processing apparatus A00 in the first node includes a first receiver A01 and a first transmitter B02.

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

[0455] As an embodiment, the first node is a relay node.

[0456] As an embodiment, the first node is a vehicle-mounted communication device.

[0457] As an embodiment, the first receiver A01 includes at least one of the antenna 452, the receiver 454, the multi-antenna receiving processor 458, the receiving processor 456, the controller / processor 459, the memory 460 and the data source 467 in FIG. 4 of the present application.

[0458] As an embodiment, the first transmitter A02 includes at least one of the antenna 452, the transmitter 454, the multi-antenna transmitting processor 457, the transmitting processor 468, the controller / processor 459, the memory 460 and the data source 467 in FIG. 4 of the present application.

[0459] As an embodiment, the first receiver A01 receives the first signaling; the first receiver A01 receives the first signal; wherein whether the first signal is associated to the first ID depends on at least the first of the receiving quality of the first signal and the indication of the first signaling, the first ID identifying at least one ML model.

[0460] As one embodiment, whether the first signal is associated to 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.

[0461] As one embodiment, the first signal is associated to the first ID when at least part of the first signal belongs to a target data set; the first ID indicates the target data set.

[0462] As one embodiment, 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 does not belong to the target data set and the reception quality of the at least part of the first signal is higher than a first threshold; the first signal does not belong 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.

[0463] As one embodiment, the first signaling schedules transmission of the first signal.

[0464] As one embodiment, the first transmitter A02, transmits second signaling; wherein at least one of an indication content of the second signaling and a trigger of the second signaling depends on the first signal.

[0465] As one 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 trigger of the second signaling depends on the at least one reference signal.

[0466] Embodiment 15

[0467] Embodiment 15 illustrates a structural block diagram of a processing apparatus in a second node according to one embodiment of the present application; as shown in FIG. 15. In FIG. 15, the processing apparatus B00 in the second node includes a first transmitter B01 and a second receiver B02.

[0468] As one embodiment, the second node is a base station.

[0469] As one embodiment, the second node is a satellite device.

[0470] As one embodiment, the second node is a relay node.

[0471] As one embodiment, the second node is one of a test apparatus, a test device, a test meter.

[0472] As an embodiment, the second transmitter B01 comprises at least one of the antenna 420, the transmitter 418, the multi-antenna transmission processor 471, the transmission processor 416, the controller / processor 475 and the memory 476 in the application FIG. 4.

[0473] As an embodiment, the second receiver B02 comprises 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 in the application FIG. 4.

[0474] As an embodiment, the second transmitter B01 transmits the first signaling; the second transmitter B01 transmits the first signal; wherein whether the first signal is associated to the first ID depends on at least the first one of the reception quality of the first signal and the indication of the first signaling, the first ID identifying at least one ML model.

[0475] As an embodiment, whether the first signal is associated to the first ID depends on the indication of the first signaling and a comparison between the reception quality of the first signal and a first threshold.

[0476] As an embodiment, the first signal is associated to the first ID when at least part of the first signal belongs to a target data set; the first ID indicates the target data set.

[0477] As an embodiment, at least part of the first signal belongs to a 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 the at least part of the first signal is higher than a first threshold; the first signal does not belong 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.

[0478] As an embodiment, the first signaling schedules the transmission of the first signal.

[0479] As an embodiment, the second receiver B02 receives the second signaling; wherein at least one of the indication content of the second signaling and the trigger of the second signaling depends on the first signal.

[0480] As an embodiment, the second transmitter B01 transmits at least one reference signal; wherein at least one of the indication content of the second signaling and the trigger of the second signaling depends on the at least one reference signal.

[0481] Those skilled in the art can understand that all or part of the steps in the foregoing method can be instructed by programs to the relevant hardware, and the programs can be stored in a computer readable storage medium, such as a read-only memory, a hard disk, an optical disk or the like. Alternatively, all or part of the steps of the foregoing embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the foregoing embodiments can be implemented in the form of hardware or in the form of a software function module, and the present application is not limited to any specific form of combination of software and hardware. The user equipment, terminal and UE in the present application include but are not limited to unmanned aerial vehicles, communication modules on unmanned aerial vehicles, remote control aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication devices, vehicles, vehicles, RSUs, wireless sensors, network cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, network cards, vehicle-mounted communication devices, low-cost mobile phones, low-cost tablet computers and other wireless communication devices. The base station or system device in the present application includes but is not limited to macro cellular base stations, micro cellular base stations, small cellular base stations, home base stations, relay base stations, eNBs, gNBs, TRPs (Transmitter Receiver Points), GNSSs, relay satellites, satellite base stations, air base stations, RSUs (Road Side Units), unmanned aerial vehicles, test equipment such as wireless communication devices that simulate part of the functions of base stations or signaling testers, and the like.

[0482] Those skilled in the art will understand that the application can be implemented by other specified forms without departing from the core or essential characteristics thereof. Therefore, the presently disclosed embodiments should in no way be considered as descriptive rather than limiting. The scope of the application is determined by the appended claims rather than the preceding description, and all modifications within the equivalent meaning and range of the claims are considered to be included therein.

Claims

1. A method for a terminal, characterized by, comprising: receiving first signaling; receiving a first signal; wherein whether the first signal is associated to a first ID depends on at least a first one of a reception quality of the first signal and an indication of the first signaling, the first ID identifying at least one ML model.

2. The method of claim 1, wherein, whether the first signal is associated to the first ID depends on the indication of the first signaling, and a comparison 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 part of the first signal belongs to a target data set, the first signal is associated to the first ID; the first ID indicates the target data set.

4. The method of claim 3, wherein, when the first signaling does not indicate that the first signal does not belong to the target data set and a 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 a 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 of any of claims 1-5, wherein: the method comprises sending second signaling; wherein at least one of an indication content of the second signaling and a triggering of the second signaling depends on the first signal.

7. The method of claim 6, wherein: the method comprises receiving 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.

8. A terminal, comprising: one or more processors and a memory; the memory coupled to the one or more processors, the memory configured to store computer program code comprising computer instructions, the one or more processors configured to invoke the computer instructions to cause the terminal to perform the method of any of claims 1-7.

9. A method for a base station, characterized by, comprising: sending first signaling; sending a first signal; wherein whether the first signal is associated to a first ID depends on at least a first one of a reception quality of the first signal and an indication of the first signaling, the first ID identifying at least one ML model.

10. The method of claim 9, wherein, whether the first signal is associated to the first ID depends on the indication of the first signaling, and a comparison 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 part of the first signal belongs to a target data set, the first signal is associated to the first ID; the first ID indicates the target data set.

12. The method of claim 11, wherein, when the first signaling does not indicate that the first signal does not belong to the target data set and a 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 a 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 of any one of claims 9-13, characterized in that, The method comprises receiving second signaling. At least one of an indication content of the second signaling and a trigger of the second signaling depends on the first signal.

15. The method of claim 14, characterized in that, The method comprises transmitting at least one reference signal. At least one of the indication content of the second signaling and the trigger of the second signaling depends on the at least one reference signal.

16. A base station, characterized in that, The base station comprises one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is configured to store computer program codes including computer instructions, and the one or more processors are configured to invoke the computer instructions to cause the base station to perform the method of any one of claims 9-15.

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