Method and apparatus relating to information block reception in node for wireless communication

Abstract

Methods and apparatus related to information block reception in a node for wireless communication are disclosed. The method for the terminal is characterized by comprising the following steps: receiving a target information block; first indication information is sent, candidate values of the first indication information comprise a first candidate subset and a first candidate value, and the first candidate subset comprises a plurality of candidate values; wherein each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset also indicates information related to an ML model except that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

Description

Technical Field

[0001] This application relates to a transmission method and apparatus in a wireless communication system, particularly to a method and apparatus for transmitting wireless signals in a wireless communication system supporting a cellular network. Background Art

[0002] With the continuous progress and in-depth application of various technologies including AI (Artificial Intelligence) / ML (Machine Learning) technologies, the ability of the network side to process and utilize information will be increasingly enhanced. To make full use of the capabilities of the network side to optimize system scheduling, more effective information needs to be provided for the network side to make decisions. Summary of the Invention

[0003] How to enhance the reporting of feedback information of terminals applying ML technology is a problem worthy of research. In view of the above problem, this application discloses a solution. It should be noted that this application can be applied to a variety of wireless communication scenarios, such as 5G networks, 6G networks, the Internet of Things, etc., and achieve similar technical effects. In addition, adopting a unified solution in different scenarios (including but not limited to 5G networks, 6G networks, the Internet of Things) helps to reduce hardware complexity and cost. Without conflict, the embodiments and features in the first node of this application can be applied to the second node, and vice versa. Without conflict, the embodiments and features in the embodiments of this application can be combined with each other arbitrarily.

[0004] As an embodiment, the interpretation of the terms in this application refers to the definitions in the 3GPP specification protocol series TS38.

[0005] As an embodiment, the interpretation of the terms in this application refers to the definitions in the 3GPP specification protocol series TS28.

[0006] This application discloses a method in a first node for wireless communication, characterized by including:

[0007] Receiving a target information block;

[0008] Sending first indication information, the candidate values of the first indication information include a first candidate subset and a first candidate value, and the first candidate subset includes multiple candidate values;

[0009] Wherein, each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

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

[0011] As an embodiment, the problems to be solved by this application include: how to feedback information on whether the information block is correctly received and information related to the ML model.

[0012] As an embodiment, the problems to be solved by this application include: how to efficiently report more information to the base station for its decision-making.

[0013] As an embodiment, the above method can report additional information in the case where the target information block is not correctly received, so as to facilitate the subsequent retransmission of the target information block.

[0014] As an embodiment, the above method is beneficial to distinguish and indicate different states in which the target information block is not correctly received, and is beneficial for the network side to obtain more terminal reception information.

[0015] As an embodiment, AI / ML technology has great potential in improving communication performance; reporting information related to the ML model through the above method is beneficial to improving communication performance and efficiency.

[0016] As an embodiment, the advantages of the above method include: being beneficial to improving or enhancing the use effect of the ML model by reporting relevant information of the ML model.

[0017] As an embodiment, the advantage of the solution disclosed in this application is that the reception result of the target information block and the corresponding ML model-related information are jointly indicated, which is beneficial to saving indication overhead and has high utilization efficiency of the indication information.

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

[0019] Among the candidate values of the first indication information, only the first candidate value indicates that the target information block is correctly received.

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

[0021] The first candidate subset includes a second candidate value; the second candidate value indicates a first ID (identity), and the first ID identifies at least one ML model.

[0022] According to one aspect of this application, the above method is characterized in that

[0023] The first candidate subset includes a third candidate value, and the third candidate value indicates that the coding method corresponding to the first ML model is used for the retransmission of the target information block.

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

[0025] The first candidate subset includes a fourth candidate value, and the fourth candidate value indicates that the coding method corresponding to the traditional decoder is used for retransmission of the target information block.

[0026] According to one aspect of the present application, the above method is characterized in that

[0027] The first candidate subset includes a fifth candidate value, and the fifth candidate value indicates that the matching method corresponding to the first ML model is used for retransmission of the target information block.

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

[0029] The first candidate subset includes a sixth candidate value, and the sixth candidate value only indicates that the target information block is not correctly received.

[0030] According to one aspect of the present application, the above method is characterized in that

[0031] The target information block includes a transport block, and the first indication information includes a plurality of bits.

[0032] The present application discloses a method in a second node used for wireless communication, which is characterized by including:

[0033] Sending a target information block;

[0034] Receiving first indication information, candidate values of the first indication information include a first candidate subset and a first candidate value, and the first candidate subset includes a plurality of candidate values;

[0035] Wherein, each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0036] As an embodiment, the second node is a base station.

[0037] As an embodiment, the second node is a network-side device.

[0038] According to one aspect of the present application, the above method is characterized in that

[0039] Among the candidate values of the first indication information, only the first candidate value indicates that the target information block is correctly received.

[0040] According to one aspect of the present application, the above method is characterized in that

[0041] The first candidate subset includes a second candidate value; the second candidate value indicates a first ID, and the first ID identifies at least one ML model.

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

[0043] The first candidate subset includes a third candidate value, and the third candidate value indicates that the coding method corresponding to the first ML model is used for retransmission of the target information block.

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

[0045] The first candidate subset includes a fourth candidate value, and the fourth candidate value indicates that the coding method corresponding to the traditional decoder is used for retransmission of the target information block.

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

[0047] The first candidate subset includes a fifth candidate value, and the fifth candidate value indicates that the rapid matching method corresponding to the first ML model is used for retransmission of the target information block.

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

[0049] The first candidate subset includes a sixth candidate value, and the sixth candidate value only indicates that the target information block is not correctly received.

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

[0051] The target information block includes a transport block, and the first indication information includes a plurality of bits.

[0052] The present application discloses a first node used for wireless communication, characterized by including:

[0053] A first receiver for receiving a target information block;

[0054] A first transmitter for sending first indication information, the candidate values of the first indication information including a first candidate subset and a first candidate value, and the first candidate subset including a plurality of candidate values;

[0055] Wherein, each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0056] The present application discloses a second node used for wireless communication, which is characterized by including:

[0057] A second transmitter for sending a target information block;

[0058] A second receiver for receiving first indication information, where candidate values of the first indication information include a first candidate subset and a first candidate value, and the first candidate subset includes multiple candidate values;

[0059] Wherein, each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0060] As an embodiment, the present application has the following advantages:

[0061] · Enhances the indication that the information block is not correctly received;

[0062] · Facilitates improving the retransmission efficiency;

[0063] · Facilitates improving the ML model or enhancing the usage effect of the ML model;

[0064] · High utilization efficiency of the indication information;

[0065] · Provides more terminal reception information for the network side, which is beneficial to system optimization. Description of the Drawings

[0066] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objectives, and advantages of the present application will become more obvious:

[0067] Figure 1 Shows a processing flow chart of a first node according to an embodiment of the present application;

[0068] Figure 2 Shows a schematic diagram of a network architecture according to an embodiment of the present application;

[0069] Figure 3 Shows a schematic diagram of a radio protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0070] Figure 4 Shows a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application;

[0071] Figure 5 Shows a signal transmission flow chart according to an embodiment of the present application;

[0072] Figure 6 Shows an explanatory schematic diagram of candidate values of first indication information according to an embodiment of the present application;

[0073] Figure 7 Shows an explanatory schematic diagram of a first candidate subset according to an embodiment of the present application;

[0074] Figure 8 Shows an explanatory schematic diagram of a first candidate subset according to an embodiment of the present application;

[0075] Figure 9 Shows an explanatory schematic diagram of a first candidate subset according to an embodiment of the present application;

[0076] Figure 10 Shows an explanatory schematic diagram of a second candidate subset according to an embodiment of the present application;

[0077] Figure 11 Shows a schematic diagram of the deployment of RAN (Radio Access Network) domain AI / ML functions according to an embodiment of the present application;

[0078] Figure 12 Shows a schematic diagram of the deployment of UE's AI / ML functions according to an embodiment of the present application;

[0079] Figure 13 Shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application;

[0080] Figure 14 Shows a flowchart based on artificial intelligence or machine learning according to an embodiment of the present application;

[0081] Figure 15 Shows a structural block diagram of a processing device in a first node according to an embodiment of the present application;

[0082] Figure 16 Shows a structural block diagram of a processing device in a second node according to an embodiment of the present application. Detailed implementation manners

[0083] 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, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

[0084] Example 1

[0085] Embodiment 1 exemplifies a processing flowchart of a first node according to an embodiment of the present application, as shown in the accompanyingFigure 1 as shown

[0086] In Embodiment 1, the first node in the present application receives a target information block in step 101; and sends first indication information in step 102.

[0087] In Embodiment 1, the candidate values of the first indication information include a first candidate subset and a first candidate value. The first candidate subset includes multiple candidate values; each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0088] As an embodiment, the target information block includes multiple bits.

[0089] As an embodiment, the target information block includes user data.

[0090] As an embodiment, the target information block includes at least one transport block.

[0091] As an embodiment, the target information block is a transport block.

[0092] As an embodiment, the target information block is transmitted after at least being channel-coded.

[0093] As an embodiment, the target information block is transmitted after at least being channel-coded, modulated, and resource-mapped.

[0094] As an embodiment, the target information block is transmitted after at least adding CRC (Cyclic Redundancy Check), channel-coding, rate matching, modulation, and resource mapping.

[0095] As an embodiment, the first indication information includes multiple bits.

[0096] As an embodiment, the first indication information includes RRC layer control information.

[0097] As an embodiment, the advantages of the above method include: high reliability of control information transmission.

[0098] As an embodiment, the first indication information includes physical layer control information.

[0099] As an embodiment, the advantages of the above method include: small control delay.

[0100] As an example, the first indication information includes UCI (Uplink Control Information).

[0101] As an example, the first indication information includes HARQ-ACK (Hybrid Automatic Repeat reQuest Acknowledgement) information.

[0102] As an example, the first indication information is transmitted after at least sequence generation and resource mapping.

[0103] As an example, the first indication information is transmitted after at least channel coding.

[0104] As an example, the first indication information is transmitted after at least channel coding, modulation, and resource mapping.

[0105] As an example, the first indication information is transmitted after at least CRC addition, channel coding, rate matching, modulation, and resource mapping.

[0106] As an example, the first indication information includes multiple bits, and the candidate values of the first indication information are the value range of the first indication information.

[0107] As an example, the first indication information includes 2 bits, the candidate values of the first indication information include 00, 01, 10, 11, and the value of the first indication information is one of 00, 01, 10, 11.

[0108] As a sub-example of the above example, the first candidate value is 11, and the first candidate subset includes 01, 10, 00.

[0109] As a sub-example of the above example, the first candidate subset includes two of 00, 01, 10, 11; the second candidate subset includes the other two of 00, 01, 10, 11, and each candidate value in the second candidate subset indicates that the target information block is correctly received, and the first candidate value is one of the second candidate subset.

[0110] As an example, the first indication information includes 3 bits, the candidate values of the first indication information include 000, 001, 010, 011, 100, 101, 110, 111, and the value of the first indication information is one of 000, 001, 010, 011, 100, 101, 110, 111.

[0111] As a sub - embodiment of the above - mentioned embodiment, the first candidate value is 111, and the first candidate subset includes 001, 010, 011, 100, 101, 110, 000.

[0112] As a sub - embodiment of the above - mentioned embodiment, the first candidate subset includes at least two of 000, 001, 010, 011, 100, 101, 110, 111; the second candidate subset includes the part of 000, 001, 010, 011, 100, 101, 110, 111 other than the first candidate subset. Each candidate value in the second candidate subset indicates that the target information block is correctly received, and the first candidate value is one of the second candidate subset.

[0113] As an embodiment, the candidate values of the first indication information are predefined.

[0114] As an embodiment, one of the candidate values of the first indication information is a decimal value.

[0115] As an embodiment, one of the candidate values of the first indication information can also be a predefined value other than a numerical value.

[0116] As an embodiment, the indication content of a candidate value of the first indication information is predefined.

[0117] As an embodiment, the indication content of a candidate value of the first indication information is configured.

[0118] As an embodiment, the first candidate value is one of the candidate values of the first indication information, and the first candidate subset is the part of the candidate values of the first indication information other than the first candidate value.

[0119] As an embodiment, the first candidate value is predefined.

[0120] As an embodiment, the value taken by the first indication information is one of the candidate values of the first indication information.

[0121] As an embodiment, one of the candidate values in the first candidate subset only indicates that the target information block is not correctly received.

[0122] As an embodiment, each candidate value in the first candidate subset indicates information other than that the target information block is not correctly received.

[0123] As an embodiment, at least one candidate value in the first candidate subset also indicates information other than that the target information block is not correctly received and depends on the ML model.

[0124] Example 2

[0125] Example 2 exemplifies a schematic diagram of a network architecture according to an embodiment of the present application, as shown in the appendix Figure 2 shown. The appendix Figure 2Describes the network architecture 200 of a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system. The 5G NR / LTE / LTE-A network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System) 200 or some other suitable term. The 5GS / EPS 200 includes at least one of UE (User Equipment) 201, RAN (Radio Access Network) 202, 5GC (5G Core Network) / EPC (Evolved Packet Core) 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet service 230. The 5GS / EPS may be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the 5GS / EPS provides packet-switched services. However, those skilled in the art will readily understand that the various concepts presented throughout this application can be extended to networks that provide circuit-switched services or other cellular networks. The RAN includes node 203 and other nodes 204. Node 203 provides user and control plane protocol termination towards UE 201. Node 203 may be connected to other nodes 204 via the Xn interface (e.g., backhaul) / X2 interface. Node 203 may also be referred to as a base station, base transceiver station, radio base station, radio transceiver, transceiver function, Basic Service Set (BSS), Extended Service Set (ESS), TRP (Transmitter Receiver Point), or some other suitable term. Node 203 provides an access point for UE 201 to the 5GC / EPC 210. Examples of UE 201 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, non-terrestrial base station communications, satellite mobile communications, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband Internet of Things devices, machine type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional devices.A person skilled in the art may also refer to the UE201 as a mobile station, subscriber station, mobile unit, subscriber unit, radio unit, remote unit, mobile device, radio device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable term. The node 203 is connected to the 5GC / EPC210 through the S1 / NG interface. The 5GC / EPC210 includes an MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMFs 214, an 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 processes the signaling between the UE201 and the 5GC / EPC210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through the S-GW / UPF 212, and the S-GW / UPF 212 itself is connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF 213 is connected to the Internet service 230. The Internet service 230 includes operator-corresponding Internet protocol services, which may specifically include the Internet, intranet, IMS (IP Multimedia Subsystem), and packet switching services.

[0126] It should be noted that the above-mentioned Embodiment 2 is merely a non-limiting implementation manner; the solution disclosed in this application is also applicable to other network architectures, such as the network architecture of the 6G system, etc.

[0127] As an embodiment, the UE201 corresponds to the first node in this application.

[0128] As an embodiment, the gNB203 corresponds to the second node in this application.

[0129] As an embodiment, the wireless link between the UE201 and the node 203 includes a cellular network link.

[0130] As an example, the gNB 203 is a macro cellular base station.

[0131] As an example, the gNB 203 is a micro cell base station.

[0132] As an example, the gNB 203 is a pico cell base station.

[0133] As an example, the gNB 203 is a femtocell.

[0134] As an example, the gNB 203 is a base station device that supports large delay differences.

[0135] As an example, the gNB 203 is an aerial platform device.

[0136] As an example, the gNB 203 is a satellite device.

[0137] Example 3

[0138] Embodiment 3 shows a schematic diagram of an embodiment of a radio protocol architecture for a user plane and a control plane according to the present application, as shown in the appendix Figure 3 as shown. Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3Show the radio protocol architecture of the control plane 300 for a first communication node device (UE, gNB or RSU (Road Side Unit), in-vehicle device or in-vehicle communication module in V2X (Vehicle to Everything)) and a second communication node device (gNB, UE or RSU in V2X, in-vehicle device or in-vehicle communication module), or between two UEs, using three layers: Layer 1 (L1), Layer 2 (L2) and Layer 3 (L3). L1 is the lowest layer and implements various PHY (Physical Layer) signal processing functions. L1 will be referred to as PHY301 in this document. Layer 2 (L2 layer) 305 is above PHY301 and is responsible for the link between the first communication node device and the second communication node device and between two UEs through PHY301. L2 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303 and a PDCP (Packet Data Convergence Protocol) sublayer 304, and these sublayers terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets, and provides handover support for the first communication node device between the second communication node devices. 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 disordered reception due to HARQ (Hybrid Automatic Repeat Qequest). The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell between the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in L3 in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layers using the RRC signaling between the second communication node device and the first communication node device.The radio protocol architecture of the user plane 350 includes Layer 1 (L1) and Layer 2 (L2). The radio protocol architecture for the first communication node device and the second communication node device in the user plane 350 is substantially the same as the corresponding layers and sub-layers in the control plane 300 for the physical layer 351, the PDCP sub-layer 354 in the L2 layer 355, the RLC sub-layer 353 in the L2 layer 355, and the MAC sub-layer 352 in the L2 layer 355. However, the PDCP sub-layer 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 an SDAP (Service Data Adaptation Protocol) sub-layer 356. The SDAP sub-layer 356 is responsible for the mapping between QoS (Quality of Service) flows and data radio bearers (DRBs, Data Radio Bearer) to support service diversity. Although not shown, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., an IP (Internet Protocol) layer) that terminates at the P-GW on the network side and an application layer that terminates at the other end of the connection (e.g., a remote UE, a server, etc.).

[0139] As an example, the Figure 3 radio protocol architecture in

[0140] As an example, the Figure 3 radio protocol architecture in

[0141] As an example, the target information block in this application is generated in the PHY351.

[0142] As an example, the target information block in this application is generated in the MAC sub-layer 352.

[0143] As an example, the target information block in this application is generated in the SDAP sub-layer 356.

[0144] As an example, the first indication information in this application is generated in the PHY301.

[0145] As an example, the first indication information in this application is generated in the MAC sub-layer 302.

[0146] Example 4

[0147] Example 4 shows a schematic diagram of the first communication device and the second communication device according to this application, as shown inFigure 4 as shown Figure 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.

[0148] The first communication device 410 includes a controller / processor 475, a memory 476, a receiving processor 470, a transmitting processor 416, a multi-antenna receiving processor 472, a multi-antenna transmitting processor 471, a transmitter / receiver 418, and an antenna 420.

[0149] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmitting processor 468, a receiving processor 456, a multi-antenna transmitting processor 457, a multi-antenna receiving processor 458, a transmitter / receiver 454, and an antenna 452.

[0150] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements the functionality of the L2 layer. In the transmission from the first communication device 410 to the second communication device 450, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation for the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for retransmission of lost packets and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). The transmit processor 416 implements encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and mapping of signal constellations 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 space precoding on the encoded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, to generate one or more spatial streams. The transmit processor 416 then maps each spatial stream to subcarriers, multiplexes with reference signals (e.g., pilots) in the time domain and / or frequency domain, and then uses the inverse fast Fourier transform (IFFT) to generate a physical channel carrying time-domain multi-carrier symbol streams. Subsequently, the multi-antenna transmit processor 471 performs transmit analog precoding / 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, and then provides them to different antennas 420.

[0151] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives signals through its respective antenna 452. Each receiver 454 recovers the information modulated onto the radio frequency carrier and converts the radio frequency stream into a baseband multi-carrier symbol stream and provides it to the receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 perform various signal processing functions of the L1 layer. The multi-antenna receive processor 458 performs receive analog precoding / beamforming operations on the baseband multi-carrier symbol stream from the receivers 454. The receive processor 456 uses the Fast Fourier Transform (FFT) to convert the baseband multi-carrier symbol stream after the receive analog precoding / beamforming operations from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receive processor 456, where the reference signal will be used for channel estimation, and the data signal recovers any spatial streams destined for the second communication device 450 after multi-antenna detection in the multi-antenna receive processor 458. The symbols on each spatial stream are demodulated and recovered in the receive processor 456, and soft decisions are generated. Subsequently, the receive processor 456 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 channel. Subsequently, the upper layer data and control signals are provided to the controller / processor 459. The controller / processor 459 performs the functions of the L2 layer. The controller / processor 459 may be associated with a memory 460 that stores program code and data. The memory 460 may be referred to as a computer-readable medium. In the transmission from the first communication device 410 to the second communication device 450, the controller / processor 459 provides demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover upper layer data packets from the core network. Subsequently, the upper layer data packets are provided to all protocol layers above the L2 layer. Various control signals may also be provided to the L3 for L3 processing.

[0152] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, the data source 467 is used to provide upper layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmit function at the first communication device 410 described in the transmission from the first communication device 410 to the second communication device 450, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocation, and implements the L2 layer functions for the user plane and the control plane. The controller / processor 459 is also responsible for retransmitting lost packets and signaling to the first communication device 410. The transmit processor 468 performs modulation mapping and channel coding processing. The multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing. Subsequently, the transmit processor 468 modulates the generated spatial streams into multi-carrier / single-carrier symbol streams, and after the analog precoding / beamforming operation in the multi-antenna transmit processor 457, provides them to different antennas 452 via the transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency symbol stream and then provides it to the antenna 452.

[0153] In the transmission from the second communication device 450 to the first communication device 410, the functions at the first communication device 410 are similar to the receive function at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives radio frequency signals through its corresponding antenna 420, converts the received radio frequency signals into baseband signals, and provides the baseband signals to the multi-antenna receive processor 472 and the receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 jointly implement the L1 layer functions. The controller / processor 475 implements the L2 layer functions. The controller / processor 475 may be associated with a memory 476 that stores program code and data. The memory 476 may be referred to as a computer-readable medium. In the transmission from the second communication device 450 to the first communication device 410, the controller / processor 475 provides demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover upper layer data packets from the UE 450. The upper layer data packets from the controller / processor 475 may be provided to the core network.

[0154] As an example, the first node in the present application includes the second communication device 450, and the second node in the present application includes the first communication device 410.

[0155] As a sub - embodiment of the above - mentioned embodiment, the first node is a user equipment, and the second node is a relay node.

[0156] As a sub - embodiment of the above - mentioned embodiment, the first node is a user equipment, and the second node is a base station device.

[0157] As a sub - embodiment of the above - mentioned embodiment, the first node is a relay node, and the second node is a base station device.

[0158] As an embodiment, the second communication device 450 includes: at least one processor and at least one memory, and the at least one memory includes computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor. The second communication device 450 is at least configured to: receive a target information block; send first indication information, and candidate values of the first indication information include a first candidate subset and a first candidate value, and the first candidate subset includes a plurality of candidate values; wherein each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0159] As a sub - embodiment of the above - mentioned embodiment, the second communication device 450 corresponds to the first node in this application.

[0160] As an embodiment, the second communication device 450 includes: a memory storing a computer - readable instruction program, and the computer - readable instruction program generates actions when executed by at least one processor, and the actions include: receive a target information block; send first indication information, and candidate values of the first indication information include a first candidate subset and a first candidate value, and the first candidate subset includes a plurality of candidate values; wherein each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0161] As a sub - embodiment of the above - mentioned embodiment, the second communication device 450 corresponds to the first node in this application.

[0162] As an example, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 is at least configured to: send a target information block; receive first indication information, candidate values of the first indication information including a first candidate subset and a first candidate value, the first candidate subset including a plurality of candidate values; wherein, each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0163] As a sub - example of the above example, the first communication device 410 corresponds to the second node in this application.

[0164] As an example, the first communication device 410 includes: a memory storing a computer - readable instruction program, the computer - readable instruction program generating actions when executed by at least one processor, the actions including: sending a target information block; receiving first indication information, candidate values of the first indication information including a first candidate subset and a first candidate value, the first candidate subset including a plurality of candidate values; wherein, each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0165] As a sub - example of the above example, the first communication device 410 corresponds to the second node in this application.

[0166] As an example, the first node in this application includes the second communication device 450.

[0167] As an example, the second node in this application includes the first communication device 410.

[0168] As an example, at least one of {the antenna 452, the transmitter 454, the multi - antenna transmission processor 457, the transmission processor 468, the controller / processor 459, the memory 460, the data source 467} is used to send the first indication information in this application.

[0169] As an example, at least one of {the antenna 420, the receiver 418, the multi-antenna receiving processor 472, the receiving processor 470, the controller / processor 475, the memory 476} is used to receive the first indication information in the present application.

[0170] As an example, 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, the data source 467} is used to receive the target information block in the present application.

[0171] As an example, at least one of {the antenna 420, the transmitter 418, the multi-antenna transmitting processor 471, the transmitting processor 416, the controller / processor 475, the memory 476} is used to send the target information block in the present application.

[0172] Example 5

[0173] Example 5 exemplifies a signal transmission flowchart according to an embodiment of the present application, as shown in the appendix Figure 5 as shown. In the appendix Figure 5 the first node N1 and the second node N2 communicate through an air interface.

[0174] The first node N1 receives the target information block in step S511; and sends the first indication information in step S512.

[0175] The second node N2 sends the target information block in step S521; and receives the first indication information in step S522.

[0176] In Example 5, the candidate values of the first indication information include a first candidate subset and a first candidate value, the first candidate subset includes multiple candidate values; each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0177] As a sub-example of Example 5, among the candidate values of the first indication information, only the first candidate value indicates that the target information block is correctly received.

[0178] As a sub-example of Example 5, the first candidate subset includes a second candidate value; the second candidate value indicates a first ID, and the first ID identifies at least one ML model.

[0179] As a sub - embodiment of Embodiment 5, the first candidate subset includes a third candidate value, and the third candidate value indicates that the coding method corresponding to the first ML model is used for the re - transmission of the target information block.

[0180] As a sub - embodiment of Embodiment 5, the first candidate subset includes a fourth candidate value, and the fourth candidate value indicates that the coding method corresponding to the traditional decoder is used for the re - transmission of the target information block.

[0181] As a sub - embodiment of Embodiment 5, the first candidate subset includes a fifth candidate value, and the fifth candidate value indicates that the quick - matching method corresponding to the first ML model is used for the re - transmission of the target information block.

[0182] As a sub - embodiment of Embodiment 5, the first candidate subset includes a sixth candidate value, and the sixth candidate value only indicates that the target information block is not correctly received.

[0183] As a sub - embodiment of Embodiment 5, the target information block includes a transport block, and the first indication information includes multiple bits.

[0184] The various sub - embodiments of the above - mentioned Embodiment 5 can be combined with each other arbitrarily.

[0185] As an embodiment, the first node N1 is the first node in this application.

[0186] As an embodiment, the second node N2 is the second node in this application.

[0187] As an embodiment, the second node N2 and the first node N1 are a base station and a user equipment respectively.

[0188] As an embodiment, the second node N2 is the serving cell - maintaining base station of the first node N1.

[0189] As an embodiment, the air interface between the second node N2 and the first node N1 is the Uu interface.

[0190] As an embodiment, the air interface between the second node N2 and the first node N1 includes a cellular link.

[0191] As an embodiment, the air interface between the second node N2 and the first node N1 includes a wireless interface between a base station device and a user equipment.

[0192] As an embodiment, the air interface between the second node N2 and the first node N1 includes a wireless interface between a satellite device and a user equipment.

[0193] As an embodiment, the air interface between the second node N2 and the first node N1 includes a wireless interface between a relay device and a user equipment.

[0194] As an embodiment, in the above step S521, an initial transmission of the target information block is performed.

[0195] Example 6

[0196] Embodiment 6 exemplifies an explanatory schematic diagram of candidate values of first indication information according to an embodiment of the present application, as Figure 6 shown.

[0197] In Embodiment 6, the candidate values of the first indication information include a first candidate subset and a first candidate value; the first candidate value indicates that the target information block is correctly received; the first candidate subset includes K candidate values; each of the K candidate values indicates that the target information block is not correctly received; in addition to indicating that the target information block is not correctly received, at least K - 1 of the K candidate values also indicate other content, where K > 1.

[0198] As an embodiment, the vertical order between the rows in the Figure 6 table in the appendix does not represent a specific sorting of candidate values; no matter how the candidate values of the first indication information are sorted, it does not affect the effect of the solution disclosed in the present application.

[0199] As an embodiment, K is configurable.

[0200] As an embodiment, K is predefined.

[0201] As an embodiment, at least one candidate value in the first candidate subset also indicates other content other than that the target information block is not correctly received, and the other content is content within a predefined content range.

[0202] As an embodiment, at least one candidate value in the first candidate subset also indicates other content other than that the target information block is not correctly received, and the other content is content within a configured content range.

[0203] As an embodiment, at least one of the indication content #1,..., the indication content #K - 1, and the indication content #K depends on an ML model.

[0204] As an embodiment, the indication content #K exists.

[0205] As an embodiment, the indication content #K does not exist.

[0206] As an example, the indication content #1, ..., the indication content #K−1, and the indication content #K (if any) are different from each other.

[0207] As an example, the candidate values of the first indication information are all values with specific indication content.

[0208] As an example, when a value does not have specific indication content, this value does not belong to the candidate values of the first indication information.

[0209] As an example, the other information indicated by the candidate values in the first candidate subset (other than the incorrect reception of the target information block) is provided to the second node for its decision-making.

[0210] Example 7

[0211] Example 7 exemplifies an explanatory schematic diagram of a first candidate subset according to an embodiment of the present application, as Figure 7 shown.

[0212] In Example 7, the first candidate subset includes second candidate values; the second candidate values indicate a first ID, and the first ID identifies at least one ML model.

[0213] As an example, when the first indication information takes the value of the second candidate value: the receiving end of the first indication information can know, through the first ID indicated by the second candidate value, that the first node tends to use the ML model identified by the first ID to decode the target information block; based on the above-known content, the sending end of the target information block can optimize the retransmission of the target information block.

[0214] As an example, when the first indication information takes the value of the second candidate value: the receiving end of the first indication information can know, through the first ID indicated by the second candidate value, that the first node tends to use the speed matching method corresponding to the ML model identified by the first ID for retransmission; based on the above-known content, the sending end of the target information block can optimize the retransmission of the target information block.

[0215] As an example, an ML model is an AI model.

[0216] As an example, an ML model includes a mathematical algorithm that can be trained with data and human expert input as examples to replicate the decisions made by experts when providing the same information.

[0217] As an example, multiple candidate values in the first candidate subset respectively indicate different IDs, and each ID in the different IDs identifies at least one ML model.

[0218] As an example, the first ID is any one of the different IDs.

[0219] Example 8

[0220] Example 8 exemplifies an illustrative schematic diagram of a first candidate subset according to an embodiment of the present application, as Figure 8 shown.

[0221] In Example 8, the first candidate subset includes a third candidate value, and the third candidate value indicates that the coding method corresponding to the first ML model is used for retransmission of the target information block.

[0222] As an example, the third candidate value indicating that the coding method corresponding to the first ML model is used for retransmission of the target information block includes: the third candidate value indicates that the coding method corresponding to the first ML model can be used for retransmission of the target information block.

[0223] As an example, the third candidate value indicating that the coding method corresponding to the first ML model is used for retransmission of the target information block includes: the third candidate value indicates that the coding method corresponding to the first ML model is preferentially used for retransmission of the target information block.

[0224] As an example, the third candidate value indicating that the coding method corresponding to the first ML model is used for retransmission of the target information block includes: the third candidate value indicates that the coding method corresponding to the first ML model can be preferentially used for retransmission of the target information block.

[0225] As an example, there are two candidate values in the first candidate subset: one of them indicates that one coding method corresponding to the first ML model can be used for retransmission of the target information block, and the other indicates that another coding method corresponding to the first ML model can be used for retransmission of the target information block.

[0226] As an example, the coding method corresponding to the first ML model is the coding method corresponding to the ID that identifies the first ML model.

[0227] As an example, the first ML model can be used for at least decoding.

[0228] As an example, the first ML model is an ML model determined to be applicable to decoding for at least one coding method; each coding method in the at least one coding method is the coding method corresponding to the first ML model.

[0229] As an example, generally speaking, for an information block encoded using the coding method corresponding to the first ML model, the receiving end can obtain better decoding performance by using the first ML model to perform decoding.

[0230] As an example, the coding method corresponding to the first ML model is reported by the first node.

[0231] As an example, the correspondence between the first ML model and the corresponding coding method is determined by configuration.

[0232] As an example, the first ML model and the corresponding coding method can be obtained through iterative training between the two communication parties.

[0233] As an example, the first candidate subset includes a fourth candidate value, and the fourth candidate value indicates that the coding method corresponding to the traditional decoder is used for retransmission of the target information block.

[0234] As an example, the fourth candidate value indicating that the coding method corresponding to the traditional decoder is used for retransmission of the target information block includes: the fourth candidate value indicates that the coding method corresponding to the traditional decoder can be used for retransmission of the target information block.

[0235] As an example, the fourth candidate value indicating that the coding method corresponding to the traditional decoder is used for retransmission of the target information block includes: the fourth candidate value indicates that the coding method corresponding to the traditional decoder is preferentially used for retransmission of the target information block.

[0236] As an example, the fourth candidate value indicating that the coding method corresponding to the traditional decoder is used for retransmission of the target information block includes: the fourth candidate value indicates that the coding method corresponding to the traditional decoder can be preferentially used for retransmission of the target information block.

[0237] As an example, the traditional decoder does not use an ML model to perform decoding.

[0238] As an example, the traditional decoder is applicable to decoding for one coding method, and the one coding method is the coding method corresponding to the traditional decoder.

[0239] As an example, the encoding in the present application includes channel encoding, and the decoding in the present application includes decoding corresponding to the channel encoding.

[0240] As an example, the encoding in the present application is channel encoding, and the decoding in the present application is channel decoding.

[0241] As an example, the encoding in the present application is source-channel joint encoding, and the decoding in the present application is decoding corresponding to the source-channel joint encoding.

[0242] As an example, the first ML model can be used for signal processing.

[0243] As an example, the first ML model is obtained through training.

[0244] As an example, a coding method is the coding method of LDPC codes.

[0245] As an example, a coding method is the coding method of Turbo codes.

[0246] As an example, a coding method is the coding method of Polar codes.

[0247] As an example, a coding method is the coding method of convolutional codes.

[0248] As an example, the coding method corresponding to the traditional decoder is one of the above coding methods.

[0249] As an example, a coding method corresponding to the first ML model is one of the above coding methods.

[0250] As an example, a coding method corresponding to the first ML model is a coding method in which information bits are input into a trained ML model at the sending end to output coded bits.

[0251] As an example, the retransmission of the target information block needs to be performed by the second node.

[0252] Example 9

[0253] Example 9 exemplifies an illustrative schematic diagram of a first candidate subset according to an embodiment of the present application, as Figure 9 shown.

[0254] In Example 9, the first candidate subset includes a fifth candidate value, and the fifth candidate value indicates that the speed matching method corresponding to the first ML model is used for the retransmission of the target information block.

[0255] As an example, the fifth candidate value indicating that the quick matching method corresponding to the first ML model is used for retransmission of the target information block includes: the fifth candidate value indicates that the quick matching method corresponding to the first ML model can be used for retransmission of the target information block.

[0256] As an example, the fifth candidate value indicating that the quick matching method corresponding to the first ML model is used for retransmission of the target information block includes: the fifth candidate value indicates that the quick matching method corresponding to the first ML model is preferentially used for retransmission of the target information block.

[0257] As an example, the fifth candidate value indicating that the quick matching method corresponding to the first ML model is used for retransmission of the target information block includes: the fifth candidate value indicates that the quick matching method corresponding to the first ML model can be preferentially used for retransmission of the target information block.

[0258] As an example, the quick matching method corresponding to the first ML model includes an RV (Redundancy Version) sequence.

[0259] As an example, the one RV sequence is composed of multiple elements.

[0260] As an example, in the one RV sequence, each element is an RV identified by an RV number.

[0261] As an example, in the one RV sequence, each element is an RV number.

[0262] As an example, an RV number is one of RV0, RV1, RV2, and RV3.

[0263] As an example, using the quick matching method corresponding to the first ML model for retransmission of the target information block includes: using the corresponding RV sequence in sequence (and in a sequence cycling manner) for subsequent retransmissions of the target information block.

[0264] As an example, the quick matching method corresponding to the first ML model is reported by the first node.

[0265] As an example, the correspondence between the first ML model and the corresponding quick matching method is determined by configuration.

[0266] As an example, the first ML model and the corresponding quick matching method can be obtained through iterative training between communication parties.

[0267] As an example, the first candidate subset includes a sixth candidate value, and the sixth candidate value only indicates that the target information block is not correctly received.

[0268] As an example, the sixth candidate value does not indicate information other than that the target information block is not correctly received.

[0269] As an example, when the first indication information takes the value of the sixth candidate value: the information obtained by the receiving end of the first indication information through the indication of the first indication information is only that the target information block is not correctly received.

[0270] As an example, the first candidate subset may include the second candidate value, the third candidate value, the fourth candidate value, the fifth candidate value, and the sixth candidate value, or may include only a part of the second candidate value, the third candidate value, the fourth candidate value, the fifth candidate value, and the sixth candidate value; without limitation, for example, the first candidate subset includes the second candidate value and the sixth candidate value, and does not include the third candidate value, the fourth candidate value, and the fifth candidate value; for another example, the first candidate subset includes the third candidate value and does not include the second candidate value, the fourth candidate value, the fifth candidate value, and the sixth candidate value.

[0271] Example 10

[0272] Example 10 exemplifies an illustrative schematic diagram of a second candidate subset according to an embodiment of the present application, as shown in the appendix Figure 10 as shown.

[0273] In Example 10, the candidate values of the first indication information include a first candidate subset and a second candidate subset. Each candidate value in the second candidate subset indicates that the target information block is correctly received. The first candidate value is one of the second candidate subset, and at least one candidate value other than the first candidate value in the second candidate subset also indicates information related to the ML model other than that the target information block is correctly received.

[0274] As an example, the candidate values of the first indication information only include the first candidate subset and the second candidate subset.

[0275] As an example, the first candidate value only indicates that the target information block is correctly received.

[0276] As an example, one candidate value other than the first candidate value in the second candidate subset indicates the ID identifying the ML model.

[0277] As an example, the second candidate subset includes a seventh candidate value and an eighth candidate value; the seventh candidate value indicates that the target information block is correctly received when using an ML model to process the reception of the target information block; the eighth candidate value indicates that the target information block is not correctly received when using the ML model to process the reception of the target information block.

[0278] As an example, the second candidate subset includes a seventh candidate value and an eighth candidate value; the seventh candidate value indicates that the target information block can be correctly received when using an ML model to process the reception of the target information block; the eighth candidate value indicates that the target information block cannot be correctly received when using the ML model to process the reception of the target information block.

[0279] As an example, the value of the first indication information is the eighth candidate value, and the target information block is correctly received; when using the ML model to process the reception of the target information block, the target information block is not correctly received, and the correct reception of the target information block is based on the processing of an ML model other than the ML model or other types of processing modules.

[0280] As an example, the first node can attempt to process the reception of the target information block in different ways until the target information block is correctly received, or all methods cannot make the target information block be correctly received.

[0281] As an example, the indication content of a candidate value in the second candidate subset depends on the relationship between at least one ML model and the reception of the target information block.

[0282] As an example, a candidate value in the second candidate subset indicates that the target information block is correctly received when using an ML model to process the reception of the target information block.

[0283] As an example, a candidate value in the second candidate subset indicates that the target information block is correctly received when using an ML model to process the reception of the target information block; another candidate value in the second candidate subset indicates that the target information block is correctly received when using another ML model to process the reception of the target information block.

[0284] As an example, a candidate value in the second candidate subset indicates that the target information block can be correctly received when using an ML model to process the reception of the target information block.

[0285] As an example, one candidate value in the second candidate subset indicates that the target information block can be correctly received when using an ML model to process the reception of the target information block; another candidate value in the second candidate subset indicates that the target information block can be correctly received when using another ML model to process the reception of the target information block.

[0286] As an example, one candidate value in the second candidate subset indicates that the target information block can be correctly received when using each ML model among multiple ML models to process the reception of the target information block.

[0287] As an example, using an ML model to process the reception of the target information block includes: using this ML model to perform signal processing after receiving the signal carrying the target information block.

[0288] As an example, using an ML model to process the reception of the target information block includes: using this ML model to at least perform channel decoding for the target information block.

[0289] Example 11

[0290] Embodiment 11 exemplifies a schematic diagram of the deployment of RAN (Radio Access Network) domain AI / ML functions according to an embodiment of the present application, as shown in the appendix Figure 11 shown. The gNB in Embodiment 11 can be replaced with network devices such as an eNB, or a 6G base station, etc.

[0291] AI / ML-related functions include an ML training function (also referred to as AI training, or AI / ML training), an ML testing function, an ML inference function (also referred to as AI inference, or AI / ML inference), etc. The ML training function, the ML testing function, and the ML inference function can be deployed independently or co-located. The deployment of AI / ML-related functions can be achieved through software, such as the download and / or running of executable files; or can be achieved through a combination of software and hardware, such as accelerating a specific computing unit through hardware to improve the operation speed or save power consumption.

[0292] For the ML training function, it can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, the ML training function for MDA (Management Data Analytics) can be deployed in the MDAF (MDA function); the ML training for network data analysis can be deployed in the NWDAF (Network Data Analytics Function), that is, the ML training function is the MTLF (Model Training logical function).

[0293] For the ML inference function, it can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML inference function is the MDAF, or the ML inference function is the AnLF (Analytics logical function) located in the NWDAF.

[0294] Similarly, the ML testing function can also be deployed in a cross-domain management system or a domain-specific management system.

[0295] In Embodiment 11, the RAN domain ML training function 1402 is located in the RAN domain management function 1403; while 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,....

[0296] Appendix Figure 11 In, the management of the ML inference functions of multiple 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 dotted arrow in Appendix Figure 11 ).

[0297] 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.

[0298] It should be noted that Embodiment 11 is merely a non - restrictive implementation; optionally, the ML training function in the RAN domain may also be deployed at the base station; or optionally, some base stations deploy the ML inference function and the ML training function in the RAN domain, while some base stations only deploy the ML inference function.

[0299] As an embodiment, one gNB (or base station) in Embodiment 11 is the second node of the present application.

[0300] As an embodiment, the second node includes an Figure 11 AL / ML inference function among them, that is, 1404 or 1406.

[0301] Example 12

[0302] Embodiment 12 illustrates a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of the present application, as shown in the appendix Figure 12 shown. The RAN domain ML training function 1505 in the appendix Figure 12 is optional.

[0303] The UE function 1504 is deployed in the first node of the present application. The UE function 1504 includes an AI / ML inference function 1506; the AI / ML inference function 1506 uses an ML model (also called an AI model) for inference; an ML model usually needs to be trained before being used for AI / ML inference.

[0304] As an embodiment, the UE function 1504 includes a RAN domain ML training function 1505. The RAN domain ML training function 1505 runs training data through the ML model, obtains relevant losses, and adjusts the parameters of the ML model based on the calculated losses; the ML training includes at least one of ML initial training, ML re - training, and reinforcement learning.

[0305] The above - mentioned embodiments can reduce the complexity of the base station, or save the radio interface resources caused by reporting training data; however, the above - mentioned embodiments impose relatively high requirements on the processing capabilities of the UE side.

[0306] Optionally, the UE function 1504 further includes a CN domain ML training function ( Figure 12 not included in the appendix).

[0307] Optionally, the UE function 1504 further includes an AI / ML deployment function - Figure 12does not include a component for loading the ML model and data.

[0308] As an example, the first node indicates whether it supports the ML training function (RAN domain or CN domain) through capability reporting, and the capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0309] As an example, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0310] As an example, the first node loads the ML model.

[0311] Optionally, the UE function 1504 is an MnS (Management Service) Producer that provides data to the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for management or analysis (as shown by the double arrow 1507).

[0312] Optionally, the UE function 1504 is an MnS Consumer that loads data from the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for AI / ML-related management, such as management data requests, ML model activation, and / or ML training (as shown by the double arrow 1507).

[0313] As an example, the second signaling in this application includes the content obtained through the inference of the AI / ML inference function 1506.

[0314] As an example, the first node includes Figure 12 one AL / ML inference function 1506 among

[0315] As an example, the ML model is based on a Neural Network.

[0316] As an example, the ML model is based on a CNN (Conventional Neural Networks, Convolutional Neural Network).

[0317] As an example, the ML model is based on a Transformer architecture.

[0318] Example 13

[0319] Example 13 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 the appendix Figure 13 as follows. The appendix Figure 13 includes a first processor, a second processor, a third processor, and a fourth processor.

[0320] In Example 13, the first processor sends a first data set to the second processor and a second data set to the third processor; the second processor generates a target first type of parameter group according to the first data set, and the second processor sends the generated target first type of parameter group to the third processor; the third processor processes the second data set by using the target first type of parameter group to obtain a first type of output. Optionally, the third processor sends the first type of output to the fourth processor. In the appendix Figure 13 , the first type of feedback and the second type of feedback are optional; the second processor includes an ML training function; the third processor includes an ML inference function.

[0321] As an embodiment, the fourth processor includes an ML testing function.

[0322] As an embodiment, the fourth processor includes performance monitoring / evaluation of the ML model.

[0323] As an embodiment, the third processor sends a first type of feedback to the second processor, and the first type of feedback is used to trigger recalculation or update of the target first type of parameter group, that is, to trigger ML initial training or ML retraining.

[0324] As an embodiment, the fourth processor sends a second type of feedback to the first processor, and the second type of feedback is used to generate the first data set or the second data set, or the second type of feedback is used to trigger the sending of the first data set or the sending of the second data set.

[0325] As an embodiment, the first processor generates the first data set and the second data set according to the measurement of a reference signal.

[0326] As an embodiment, the first type of output includes the first channel information.

[0327] As an embodiment, the first type of output includes the index of the target reference signal.

[0328] As an example, the second data set includes measurements for the first reference signal or includes measurements for the second reference signal.

[0329] As an example, the first data set includes training data.

[0330] As an example, the second processor is used to train an ML model, and the trained model is described by the target first type of parameter group.

[0331] As an example, the third processor constructs a model according to the target first type of parameter group, and then inputs the second data set into the constructed model to obtain the first type of output.

[0332] As an example, the third processor generates a recovery data set according to the first type of output, and the error between the recovery data set and the second data set is used to generate the first type of feedback.

[0333] As an example, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model does not meet the requirements, the second processor will recalculate the target first type of parameter group.

[0334] As an example, when the error is too large or there is no update for too long, the performance of the trained model is considered not to meet the requirements.

[0335] As an example, the target first type of parameter group includes one or more of: convolution kernel size, number of convolution layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[0336] As an example, the target first type of parameter group includes one or more of: convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, or parameters of the activation function.

[0337] Example 14

[0338] Example 14 exemplifies a flowchart based on artificial intelligence or machine learning according to an embodiment of the present application, as shown in the appendix Figure 14 shown. The appendix Figure 14 includes a first operation, a second operation, a third operation, a fourth operation, and a fifth operation. In Example 14, the first operation and the second operation belong to the first stage, the third operation belongs to the second stage, the fourth operation belongs to the third stage, and the fifth operation belongs to the fourth stage. In the appendix Figure 14 The lines with arrows indicate the order of the process.

[0339] As an example, the first operation includes AI / ML training, the second operation includes AI / ML testing, the third operation includes AI / ML emulation, the fourth operation includes AI / ML entity loading, and the fifth operation includes AI / ML inference.

[0340] As an example, 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.

[0341] As an example, the first phase includes AI / ML model training.

[0342] As an example, the first phase includes AI / ML model training and AI / ML testing.

[0343] As an example, the AI / ML model training includes the initial training and re-training of one or a group of AI / ML entities.

[0344] As an example, the AI / ML model training depends on training data.

[0345] As an example, the AI / ML model training includes AI / ML entity validation.

[0346] As an example, the AI / ML entity validation is used to evaluate the performance of the AI / ML entity.

[0347] As an example, the AI / ML entity validation depends on validation data.

[0348] As an example, if the result of the AI / ML entity validation does not meet the expectation, the AI / ML model will be re-trained.

[0349] As an example, the AI / ML testing includes testing the validated AI / ML entity to evaluate the performance of the trained AI / ML model.

[0350] As an example, if the result of the AI / ML test meets the expectation, the AI / ML entity proceeds to the next stage; otherwise, the AI / ML model will be retrained.

[0351] As an example, the AI / ML test relies on test data.

[0352] As an example, the second stage includes AI / ML simulation, and the AI / ML simulation performs inference of the AI / ML entity in a simulation environment.

[0353] As an example, the AI / ML simulation estimates the performance of the AI / ML entity inference in a simulation environment before using the AI / ML entity.

[0354] As an example, the second stage is optional.

[0355] As an example, the third stage includes AI / ML entity loading, and the AI / ML entity loading is to obtain the trained AI / ML entity to obtain the desired AI / ML inference function.

[0356] As an example, the third stage is optional.

[0357] As an example, when the training function and the inference function are co-located, the third stage is no longer required.

[0358] As an example, the fourth stage includes AI / ML inference.

[0359] Example 15

[0360] Example 15 illustrates a structural block diagram of a processing device in a first node according to an embodiment of the present application, as shown in the appendix Figure 15 shown. In the appendix Figure 15 In it, the processing device A00 in the first node includes a first receiver A01 and a first transmitter A02.

[0361] As an example, the first node is a user equipment.

[0362] As an example, the first node is a relay node.

[0363] As an example, the first node is a vehicle-mounted communication device.

[0364] As an example, the first receiver A01 includes the appendix of the present application Figure 4at least one of antenna 452, receiver 454, multi-antenna receiving processor 458, receiving processor 456, controller / processor 459, memory 460, and data source 467 in

[0365] As an example, the first receiver A01 includes the attachment of this application Figure 4 at least the first five of antenna 452, receiver 454, multi-antenna receiving processor 458, receiving processor 456, controller / processor 459, memory 460, and data source 467 in

[0366] As an example, the first receiver A01 includes the attachment of this application Figure 4 at least the first four of antenna 452, receiver 454, multi-antenna receiving processor 458, receiving processor 456, controller / processor 459, memory 460, and data source 467 in

[0367] As an example, the first receiver A01 includes the attachment of this application Figure 4 at least the first three of antenna 452, receiver 454, multi-antenna receiving processor 458, receiving processor 456, controller / processor 459, memory 460, and data source 467 in

[0368] As an example, the first receiver A01 includes the attachment of this application Figure 4 at least the first two of antenna 452, receiver 454, multi-antenna receiving processor 458, receiving processor 456, controller / processor 459, memory 460, and data source 467 in

[0369] As an example, the first transmitter A02 includes the attachment of this application Figure 4 at least one of antenna 452, transmitter 454, multi-antenna transmitting processor 457, transmitting processor 468, controller / processor 459, memory 460, and data source 467 in

[0370] As an example, the first transmitter A02 includes the attachment of this application Figure 4 at least the first five of antenna 452, transmitter 454, multi-antenna transmitting processor 457, transmitting processor 468, controller / processor 459, memory 460, and data source 467 in

[0371] As an example, the first transmitter A02 includes the attachment of this application Figure 4 at least the first four of antenna 452, transmitter 454, multi-antenna transmitting processor 457, transmitting processor 468, controller / processor 459, memory 460, and data source 467 in

[0372] As an example, the first transmitter A02 includes at least the first three of the antenna 452, transmitter 454, multi-antenna transmission processor 457, transmission processor 468, controller / processor 459, memory 460, and data source 467 attached in this application. Figure 4

[0373] As an example, the first transmitter A02 includes at least the first two of the antenna 452, transmitter 454, multi-antenna transmission processor 457, transmission processor 468, controller / processor 459, memory 460, and data source 467 attached in this application. Figure 4

[0374] As an example, the first receiver A01 receives a target information block;

[0375] The first transmitter A02 sends first indication information, and the candidate values of the first indication information include a first candidate subset and a first candidate value, and the first candidate subset includes multiple candidate values;

[0376] Wherein, each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0377] As an example, among the candidate values of the first indication information, only the first candidate value indicates that the target information block is correctly received.

[0378] As an example, the first candidate subset includes a second candidate value; the second candidate value indicates a first ID (identity), and the first ID identifies at least one ML model.

[0379] As an example, the first candidate subset includes a third candidate value, and the third candidate value indicates that the coding method corresponding to the first ML model is used for retransmission of the target information block.

[0380] As an example, the first candidate subset includes a fourth candidate value, and the fourth candidate value indicates that the coding method corresponding to the traditional decoder is used for retransmission of the target information block.

[0381] As an example, the first candidate subset includes a fifth candidate value, and the fifth candidate value indicates that the matching method corresponding to the first ML model is used for retransmission of the target information block.

[0382] As an example, the first candidate subset includes a sixth candidate value, and the sixth candidate value only indicates that the target information block is not correctly received.

[0383] As an example, the target information block includes a transport block, and the first indication information includes a plurality of bits.

[0384] Example 16

[0385] Example 16 illustrates a structural block diagram of a processing device in a second node according to an embodiment of the present application, as shown in the appendix. Figure 16 In the appendix, the processing device B00 in the second node includes a second transmitter B01 and a second receiver B02. Figure 16

[0386] As an example, the second node is a base station.

[0387] As an example, the second node is a satellite device.

[0388] As an example, the second node is a relay node.

[0389] As an example, the second node is one of a test device, a test equipment, and a test instrument.

[0390] As an example, the second transmitter B01 includes 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 appendix of the present application. Figure 4

[0391] As an example, the second transmitter B01 includes at least the first five 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 appendix of the present application. Figure 4

[0392] As an example, the second transmitter B01 includes at least the first four 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 appendix of the present application. Figure 4

[0393] As an example, the second transmitter B01 includes at least the first three 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 appendix of the present application. Figure 4

[0394] ​​​​​As an example, the second transmitter B01 includes at least the first two 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 attached to this application. Figure 4

[0395] As an example, the second receiver B02 includes 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 attached to this application. Figure 4

[0396] As an example, the second receiver B02 includes at least the first five 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 attached to this application. Figure 4

[0397] As an example, the second receiver B02 includes at least the first four 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 attached to this application. Figure 4

[0398] As an example, the second receiver B02 includes at least the first three 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 attached to this application. Figure 4

[0399] As an example, the second receiver B02 includes at least the first two 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 attached to this application. Figure 4

[0400] As an example, the second transmitter B01 transmits a target information block;

[0401] The second receiver B02 receives first indication information, and candidate values of the first indication information include a first candidate subset and a first candidate value, and the first candidate subset includes multiple candidate values;

[0402] Wherein, each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model other than that the target information block is not correctly received; the first candidate value indicates that the target information block is correctly received.

[0403] As an example, among the candidate values of the first indication information, only the first candidate value indicates that the target information block is correctly received.

[0404] As an example, the first candidate subset includes a second candidate value; the second candidate value indicates a first ID, and the first ID identifies at least one ML model.

[0405] As an example, the first candidate subset includes a third candidate value, and the third candidate value indicates that the coding method corresponding to the first ML model is used for retransmission of the target information block.

[0406] As an example, the first candidate subset includes a fourth candidate value, and the fourth candidate value indicates that the coding method corresponding to the traditional decoder is used for retransmission of the target information block.

[0407] As an example, the first candidate subset includes a fifth candidate value, and the fifth candidate value indicates that the quick matching method corresponding to the first ML model is used for retransmission of the target information block.

[0408] As an example, the first candidate subset includes a sixth candidate value, and the sixth candidate value only indicates that the target information block is not correctly received.

[0409] As an example, the target information block includes a transport block, and the first indication information includes multiple bits.

[0410] Those of ordinary skill in the art can understand that all or part of the steps in the above methods can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a hard disk, or an optical disc, etc. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiments can be implemented in a hardware form or in the form of a software functional module. This application is not limited to any specific form of the combination of software and hardware. The user equipment, terminal, and UE in this application include, but are not limited to, drones, communication modules on drones, remote control aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication devices, transportation means, vehicles, RSU, wireless sensors, network cards, Internet of Things terminals, RFID (Radio Frequency Identification) terminals, NB-IoT (Narrow Band Internet of Things) terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, network cards, vehicle-mounted communication devices, low-cost mobile phones, low-cost tablet computers, and other wireless communication devices. The base station or system equipment in this application includes, but is not limited to, macrocell base stations, microcell base stations, small cell base stations, home base stations, relay base stations, eNB (evolved Node B), gNB, TRP, GNSS (Global Navigation Satellite System), relay satellites, satellite base stations, aerial base stations, RSU, drones, test equipment, such as transceiver devices or signaling testers that simulate some functions of base stations, and other wireless communication devices.

[0411] Those skilled in the art should understand that the present invention can be implemented in other specified forms without departing from its core or basic characteristics. Therefore, the currently disclosed embodiments should be considered as illustrative rather than restrictive in any case. The scope of the invention is determined by the appended claims rather than the preceding description, and all modifications within the equivalent meaning and scope thereof are considered to be included therein.

Claims

1. A method for a terminal, characterized in that: include: receiving a target information block; Sending first indication information, wherein the candidate values ​​of the first indication information include a first candidate subset and a first candidate value, and the first candidate subset includes multiple candidate values; Each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model in addition to that the target information block is not correctly received; and the first candidate value indicates that the target information block is correctly received.

2. The method according to claim 1, characterized in that Among the candidate values ​​of the first indication information, only the first candidate value indicates that the target information block is correctly received.

3. The method according to claim 1 or 2, characterized in that: The first candidate subset includes a second candidate value; the second candidate value indicates a first ID, and the first ID identifies at least one ML model.

4. The method according to any one of claims 1 to 3, characterized in that The first candidate subset includes a third candidate value, and the third candidate value indicates that the encoding method corresponding to the first ML model is used for retransmission of the target information block.

5. The method according to any one of claims 1 to 4, characterized in that The first candidate subset includes a fourth candidate value, and the fourth candidate value indicates that a coding method corresponding to a traditional decoder is used for retransmission of the target information block.

6. The method according to any one of claims 1 to 5, characterized in that The first candidate subset includes a fifth candidate value, and the fifth candidate value indicates that a quick matching method corresponding to the first ML model is used for retransmission of the target information block.

7. The method according to any one of claims 1 to 6, characterized in that The first candidate subset includes a sixth candidate value, and the sixth candidate value only indicates that the target information block is not correctly received.

8. The method according to any one of claims 1 to 7, characterized in that The target information block includes a transmission block, and the first indication information includes a plurality of bits.

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

10. A method for a base station, characterized in that: include: Send target information block; receiving first indication information, wherein the candidate values ​​of the first indication information include a first candidate subset and a first candidate value, and the first candidate subset includes a plurality of candidate values; Each candidate value in the first candidate subset indicates that the target information block is not correctly received, and at least one candidate value in the first candidate subset further indicates information related to the ML model in addition to that the target information block is not correctly received; and the first candidate value indicates that the target information block is correctly received.

11. The method according to claim 10, characterized in that Among the candidate values ​​of the first indication information, only the first candidate value indicates that the target information block is correctly received.

12. The method according to claim 10 or 11, characterized in that: The first candidate subset includes a second candidate value; the second candidate value indicates a first ID, and the first ID identifies at least one ML model.

13. The method according to any one of claims 10 to 12, characterized in that The first candidate subset includes a third candidate value, and the third candidate value indicates that the encoding method corresponding to the first ML model is used for retransmission of the target information block.

14. The method according to any one of claims 10 to 13, characterized in that The first candidate subset includes a fourth candidate value, and the fourth candidate value indicates that a coding method corresponding to a traditional decoder is used for retransmission of the target information block.

15. The method according to any one of claims 10 to 14, characterized in that The first candidate subset includes a fifth candidate value, and the fifth candidate value indicates that a quick matching method corresponding to the first ML model is used for retransmission of the target information block.

16. The method according to any one of claims 10 to 15, characterized in that The first candidate subset includes a sixth candidate value, and the sixth candidate value only indicates that the target information block is not correctly received.

17. The method according to any one of claims 10 to 16, characterized in that The target information block includes a transmission block, and the first indication information includes a plurality of bits.

18. A base station, characterized in that: The base station includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the base station to perform the method according to any one of claims 10 to 17.

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