A method and apparatus in a first node and a second node of a hybrid beamforming matrix

Abstract

The application discloses a kind of methods and devices in the first node and second node of mixed beamforming matrix. First node receives first listen PPDU;Send first channel information and second channel information;Wherein, the first channel information indicates C1 column vectors of first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix;The format of the first channel information is based on artificial intelligence, and the format of the second channel information is compressed beamforming matrix;The first beamforming matrix depends on measurement for the first listen PPDU;Any two column vectors in the C1 column vectors and the C2 column vectors correspond to different columns of the first beamforming matrix.This application is conducive to monitoring the performance of AI inference, while reducing air interface overhead.

Description

Technical Field

[0001] This application relates to methods and apparatus in wireless communication systems, and more particularly to methods and apparatus for AI (artificial intelligence) in wireless local area networks (WLANs). Background Technology

[0002] To adapt to the ever-emerging new business applications and narrow the performance gap with wired networks, each generation of the Wi-Fi (Wireless Fidelity) standard has been dedicated to improving wireless transmission rates. The existing 802.11 standard specifies various feedback methods for beamforming, such as CSI (Channel State Information) matrix feedback, uncompressed beamforming matrices, and compressed beamforming matrices. With the widespread application of AI (Artificial Intelligence) or ML (Machine Learning) technologies, the IEEE (Institute of Electrical and Electronics Engineers) 802.11 working group established an AI / ML research interest group to study the impact of related technologies on WLAN (Wireless Local Area Network), including application scenarios such as CSI feedback compression.

[0003] An AI model (sometimes called an ML model) is typically described by hyperparameters and model parameters. Hyperparameters are parameters that need to be pre-set during the training process of a machine learning model and cannot be automatically learned through conventional training processes. Examples include the AI ​​model structure, learning rate, and batch size. Model parameters, sometimes called parameter sets, are internal variables that the machine learning model automatically learns from data during training. AI models typically need to be trained and meet certain performance requirements before they can be used for inference. A portion of the training data can be used to test the performance of the AI ​​model. Generally speaking, the complexity and overhead of training are far greater than those of inference.

[0004] Typical model structures include classic models such as the Transformer structure, RNN (Recurrent Neural Network), and CNN (Conventional Neural Network), or hybrid models composed of multiple models. Summary of the Invention

[0005] For the application of AI / ML in WLAN or 802.11 series protocols, one problem that needs to be solved is how the STA (station) can feed back the AI-based channel matrix.

[0006] This application discloses a solution to the aforementioned problems. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined. It should be noted that although this application is initially intended for WLAN, it can also be used for wireless networks such as WMAN (Wireless Metropolitan Area Network), WRAN (Wireless Regional Area Network), and WSN (Wireless Specialty Network). Furthermore, adopting a unified solution for different scenarios (including but not limited to WLAN and WMAN scenarios) helps reduce hardware complexity and cost. Unless otherwise specified, the embodiments and features described in the first node device of this application can be applied to the second node device, and vice versa. Where necessary, the explanations of terms, nouns, functions, and variables in this application (unless otherwise specified) can be found in the definitions in the IEEE 802.11-2024 standard.

[0007] This application discloses a method used in the first node of a WLAN, characterized by comprising:

[0008] Receive the first listening PPDU (Physical Layer Protocol Data Unit).

[0009] Send the first channel information and the second channel information;

[0010] Wherein, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

[0011] One advantage of the above method is that it balances feedback accuracy and feedback performance through the feedback of mixed column vectors; furthermore, the above method provides the receiver with the ability to judge the accuracy of the first channel information, especially when the decoder used by the receiver is not completely reversible with the encoder of the first channel information.

[0012] Specifically, according to one aspect of this application, the above method is characterized in that the C1 column vectors corresponding to the C1 column of the first beamforming matrix depend on the carrier targeted by the first beamforming matrix.

[0013] The above method reduces the signaling overhead required to configure the spatial flow targeted by the C1 column vectors or the spatial flow targeted by the C2 column vectors.

[0014] Specifically, according to one aspect of this application, the above method is characterized by comprising:

[0015] Send the first MIMO (Multiple-Input Multiple-Output) control domain;

[0016] C1 and C2 depend on the indication of the first MIMO control domain.

[0017] The above method has good compatibility and provides the possibility for flexible configuration of C1 and C2.

[0018] Specifically, according to one aspect of this application, the above method is characterized in that the first MIMO control domain includes an identifier of a first AI encoder, and the generation of the first channel information depends on the inference of the first AI encoder.

[0019] The above method enables both communicating parties to have the same understanding of the reasoning characteristics of the first channel information, while avoiding excessive restrictions on the first AI encoder.

[0020] Specifically, according to one aspect of this application, the above method is characterized by comprising:

[0021] Receive the first MAC (Media Access Control) frame;

[0022] C1 and C2 depend on the indication of the first MAC frame.

[0023] The above method allows the recipient to flexibly instruct C1 and C2.

[0024] Specifically, according to one aspect of this application, the above method is characterized by comprising:

[0025] Receive the second MAC frame;

[0026] The indication of the second MAC frame depends on the correlation between at least one column vector among the C1 column vectors and at least one column vector among the C2 column vectors.

[0027] The above method helps the first node to know the accuracy of the AI-based channel information in a timely manner, and can switch the reporting mode or fine-tune the AI ​​model when necessary.

[0028] This application discloses a method used in a second node of a WLAN, characterized by comprising:

[0029] Send the first listening PPDU;

[0030] Receive first channel information and second channel information;

[0031] Wherein, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

[0032] Specifically, according to one aspect of this application, the above method is characterized in that the C1 column vectors corresponding to the C1 column of the first beamforming matrix depend on the carrier targeted by the first beamforming matrix.

[0033] Specifically, according to one aspect of this application, the above method is characterized by comprising:

[0034] Receive the first MIMO control domain;

[0035] C1 and C2 depend on the indication of the first MIMO control domain.

[0036] Specifically, according to one aspect of this application, the above method is characterized in that the first MIMO control domain includes an identifier of a first AI encoder, and the generation of the first channel information depends on the inference of the first AI encoder.

[0037] Specifically, according to one aspect of this application, the above method is characterized by comprising:

[0038] Send the first MAC frame;

[0039] C1 and C2 depend on the indication of the first MAC frame.

[0040] Specifically, according to one aspect of this application, the above method is characterized by comprising:

[0041] Send the second MAC frame;

[0042] The indication of the second MAC frame depends on the correlation between at least one column vector among the C1 column vectors and at least one column vector among the C2 column vectors.

[0043] This application discloses a first node used in a WLAN, characterized by comprising:

[0044] The first receiver receives the first listening PPDU;

[0045] The first transmitter transmits first channel information and second channel information;

[0046] Wherein, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

[0047] This application discloses a second node used in a WLAN, characterized by comprising:

[0048] The second transmitter sends the first listener PPDU;

[0049] The second receiver receives the first channel information and the second channel information;

[0050] Wherein, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix. Attached Figure Description

[0051] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0052] Figure 1 A communication flowchart of a first node according to an embodiment of this application is illustrated;

[0053] Figure 2 A schematic diagram of a network architecture according to an embodiment of this application is illustrated;

[0054] Figure 3 A schematic diagram of a wireless protocol stack according to an embodiment of this application is illustrated;

[0055] Figure 4 A schematic diagram of the hardware module of a communication device according to an embodiment of this application is illustrated;

[0056] Figure 5 A communication flowchart of a first node and a second node according to an embodiment of this application is illustrated;

[0057] Figure 6 A schematic diagram of a first AI encoder according to an embodiment of this application is illustrated;

[0058] Figure 7 A schematic diagram illustrating a reference encoder and a reference decoder according to one embodiment of this application is provided;

[0059] Figure 8 A structural block diagram of a processing apparatus in a first node according to an embodiment of this application is illustrated;

[0060] Figure 9 A structural block diagram of a processing apparatus in a second node according to an embodiment of this application is illustrated. Detailed Implementation

[0061] The technical solution of this application will be further described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0062] Example 1

[0063] Example 1 illustrates a signal processing flowchart in a first node according to an embodiment of this application, as shown in the attached diagram. Figure 1 As shown.

[0064] In step 101, the first node 100 receives the first listening PPDU; in step 102, it sends the first channel information and the second channel information.

[0065] Wherein, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

[0066] As an example, the first listening PPDU is an NDP (Null Data PPDU).

[0067] As an example, the first listening PPDU includes multiple LTFs (Long Training Fields).

[0068] As an example, the first channel information and the second channel information belong to the same MAC frame, such as an action frame.

[0069] As an example, the first node is a beamformer. The first node measures the first listening PPDU to obtain a channel matrix W, which is a complex matrix with P1 rows and P2 columns. Singular value decomposition is performed on the channel matrix W, i.e., W = UDV. H Wherein, the superscript H denotes the conjugate transpose, U is the left singular matrix, D is the singular value matrix, and the unitary matrix V is P2 row and P2 column;

[0070] Wherein, any one of the C1 column vectors and the C2 column vectors corresponds to a column vector of the unitary matrix V;

[0071] or,

[0072] The first beamforming matrix is ​​the unitary matrix V.

[0073] In the above embodiments, the specific channel estimation algorithm is determined by the supplier of the first node N1, such as Wiener filtering, FFT (Fast Fourier Transform) filtering, etc.

[0074] As a sub-implementation of the above embodiment, the second channel information is obtained by compressing the C2 column vectors using a given rotation; the first channel information is obtained by inference of the C1 column vectors by the first AI encoder.

[0075] As a sub-example of the above embodiment, the sum of C1 and C2 is P2.

[0076] As a sub-implementation of the above embodiment, C2 is 1, and the C2 column vectors correspond to the leftmost column vector of the first beamforming matrix; the C1 column vectors correspond one-to-one with the second column from the left to the (C1+1)th column from the left of the first beamforming matrix.

[0077] One advantage of the above embodiments is that they utilize traditional feedback methods to ensure the feedback accuracy of the spatial stream with the largest channel capacity (the leftmost column) while reducing the feedback overhead for other spatial streams.

[0078] As an example, the first channel information is obtained after inference by the first AI encoder, and the input of the first AI encoder for the first channel information depends on the measurement of the first listening PPDU.

[0079] Example 2

[0080] Example 2 illustrates a network architecture diagram according to an embodiment of this application, as shown in the attached diagram. Figure 2 As shown. Figure 2This section explains the architecture of an IEEE 802.11 local area network (LAN). A Basic Service Set (BSS) is a fundamental building block of an IEEE 802.11 LAN, consisting of a group of terminals that can communicate with each other. BSSs include Independent Basic Service Sets (IBSSs) and Basic Basic Service Sets. Terminals in an IBSS can communicate directly with each other, while terminals in a Basic Basic Service Set communicate with each other through a central terminal, also known as an access point. Basic Basic Service Sets can interconnect with other Basic Basic Service Sets via the access point through a Distribution System (DS). Multiple Basic Basic Service Sets interconnected via DS can form an Extended Basic Service Set (ESS) to extend the network coverage area. As shown in the figure, BSS231 is an IBSS, which includes terminals 201 and 202 that can communicate directly. As shown in the figure, BSS232 and BSS233 are basic service sets. Communication between terminals within a basic service set requires forwarding through an access point. For example, if terminal 203 in BSS232 needs to send data to terminal 204 in BSS232, it first needs to send the data to access point 211, and then access point 211 forwards the data to terminal 204; the reverse is also true. As shown in the figure, BSS232 and BSS233 together form ESS234. Access points 211 and 212, located within the same ESS234, use the same Service Set Identifier (SSID). BSS232 and BSS233 typically overlap. Terminals belonging to the same ESS234 can communicate with each other. As shown in the figure, terminal 203 located in BSS232 can communicate with terminal 205 or 206 located in BSS233. At this time, access point 211 and access point 212 have bridging functions respectively, and transmit data between different access points through the distribution system medium (DSM).

[0081] The BSS can identify user terminals via its SSID and other devices via its Basic Service Set Identifier (BSSID). The BSSID can be the MAC (Medium Access Control) address of access point 211. Access point 211 periodically broadcasts beacon frames including the BSSID, enabling any terminal within its wireless coverage area to associate with or re-associate with access point 211 to establish corresponding downlink 223 and uplink 224 (uplink and downlink can be collectively referred to as Wi-Fi links). The beacon may include: an identifier of the primary channel used by the corresponding access point 211 and a timing synchronization function for establishing or maintaining timing synchronization with access point 211.

[0082] A terminal is a single addressable instance of a MAC and a logical entity of a physical layer (PHY) interface for wireless medium (WM). In IEEE 802.11, a terminal is an addressable unit; in IBSS, a terminal can send beacon frames to announce the presence of a WLAN. In the Basic Services Set, to establish a Wi-Fi link with access point 211, terminal 203 is configured to perform passive or active scanning on frequency channels in one or more frequency bands (e.g., 2.4 GHz, 5 GHz, 6 GHz, or 60 GHz bands). To perform passive scanning, terminal 203 listens for beacons periodically sent by the corresponding access point 211. To perform active scanning, terminal 203 generates probe requests and sequentially transmits these probe requests on each channel to be scanned, and listens for probe responses from access point 211. Terminal 203 identifies or selects an access point 211 to associate with using scanning information obtained through passive or active scanning, and performs authentication and association operations to establish a communication link with the selected access point 211. Upon completion of the association operation, access point 211 assigns an association identifier (AID) to terminal 203, which is used by access point 211 to track terminal 203. Terminals include, but are not limited to, mobile phones, laptops, personal digital assistants (PDAs), media devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband IoT devices, machine-type communication devices, land vehicles, automobiles, in-vehicle equipment, in-vehicle communication units, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to the terminal as a non-AP (access point) workstation, a non-AP multi-link device (non-AP MLD), a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communication device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handheld device, a user agent, a mobile client, a client, or any other suitable term.

[0083] An access point is an entity that includes terminals and provides access to a distribution system (DS) for associated terminals via a wireless medium (WM). An access point includes both a terminal and a distribution system access function (DSAF). Access points may be referred to as AP multi-link devices (AP MLDs), base stations, base transceivers, radio base stations, radio transceivers, transceiver functions, Basic Service Sets (BSS), Extended Service Sets (ESS), Transmission Reception Points (TRPs), or other suitable terms. Access points can provide access to external networks to terminals in a WLAN via a corresponding WiFi link.

[0084] The Distribution System (DS) is the backbone network for data transmission between access points, often referred to as the backbone network, which is typically Ethernet. Access points use the Logical Link Control (LLC) layer as the upper layer, such as the IP (Internet Protocol) layer, to provide a unified interface. All user IP packets are transmitted through a gateway (GW), which provides IP address allocation and other functions for terminals within its corresponding network segment. The gateway is also called an IP router and connects to Internet Service 240. Internet Service 240 includes carrier-specific Internet Protocol services, specifically including the Internet, intranets, IMS (IP Multimedia Subsystem), and PS (Packet Switching) streaming services.

[0085] As an example, terminal 201 corresponds to the first node in this application, and terminal 202 corresponds to the second node in this application.

[0086] As one embodiment, the terminal 203 corresponds to the first node in this application, and the access point 211 corresponds to the second node in this application.

[0087] As an example, the terminal 201 supports Wi-Fi, such as the IEEE 802.11 protocol.

[0088] As an example, the terminal 201 is a DMG (directional multi-gigabit) terminal, a QoS (Quality of Service) terminal, a HT (High Throughput) terminal, an EHT (Extreme High Throughput) terminal, or an MLD (multi-link device) terminal, etc.

[0089] As an example, the terminal 203 is a DMG (directional multi-gigabit) terminal, a QoS (Quality of Service) terminal, a HT (High Throughput) terminal, an EHT (Extreme High Throughput) terminal, or an MLD (multi-link device) terminal, etc.

[0090] As an example, the access point 211 supports Wi-Fi or the IEEE 802.11 protocol.

[0091] As an example, the access point 211 is a base station, or a femtocell, or an access point (AP), or an MLD (multi-link device).

[0092] As an example, the wireless link from terminal 202 to terminal 201 is downlink 221, where terminal 201 is the source terminal and terminal 202 is the destination terminal, and downlink 221 is used to perform downlink transmission.

[0093] As an example, the wireless link from terminal 201 to terminal 202 is uplink 222, where terminal 201 is the source terminal and terminal 202 is the destination terminal, and uplink 222 is used to perform uplink transmission.

[0094] As an example, the wireless link from the access point 211 to the terminal 203 is a downlink 223, which is used to perform downlink transmission.

[0095] As an example, the wireless link from the terminal 203 to the access point 211 is an uplink 224, which is used to perform uplink transmission.

[0096] Example 3

[0097] Example 3 illustrates a schematic diagram of a wireless protocol stack according to an embodiment of this application, as shown in the attached diagram. Figure 3 As shown, IEEE 802.11 uses CSMA (carrier sense multiple access) / CA (collision avoidance) protocols to control access to the transmission medium. Figure 3 The wireless protocol architecture is illustrated using two layers: the data link layer and the physical layer. The physical layer, the lowest layer, implements various physical layer signal processing functions and will be referred to as PHY 301 in this paper. The data link layer, above the physical layer, is responsible for the link between terminals, or between a terminal and an access point. The data link layer includes MAC 302. MAC 302 works in conjunction with PHY 301 to complete data transmission and various management services.

[0098] MAC302 is responsible for encapsulating MSDU (MAC Service Data Unit) into MPDU (MAC Protocol Data Unit) frames, defining the frames, synchronizing frames, processing the destination and source MAC addresses, and handling frame errors. For data packets from higher layers or larger management packets, it performs packet fragmentation and defragmentation, and performs integrity protection and cryptographic encapsulation on data requiring protection. PHY301 is divided into two sub-layers: the Physical Layer Convergence Procedure (PLCP) sub-layer and the Physical Medium Dependent (PMD) sub-layer. The frames received from MAC302 have a PHY header added to the PLCP sublayer to generate a PPDU (PHY protocol data unit). Typically, the PPDU contains a pilot signal (preamble) and a PHY header to assist in the synchronization of received data, as well as demodulation of the MPDU. Then, the PMD is responsible for encoding and modulating the MPDU and transmitting it into the air using an antenna.

[0099] The MAC302 implements access control. For CSMA / CA-based access, it is managed by the distributed coordination function (DCF). If contention-free service is required, it can be managed by the point coordination function (PCF), which is built on top of the DCF. Alternatively, a hybrid coordination function (HCF) can be used, somewhere between DCF and PCF. In the DCF, carrier sensing determines the availability of the medium. Physical carrier sensing is provided by the PHY301, and virtual carrier sensing is provided by the network allocation vector (NAV). To ensure uninterrupted medium usage and data transmission, terminals can use RTS (request to send) / CTS (clear to send) switching. IEEE 802.11 frames typically include a duration field to reserve the medium usage time. Before attempting to transmit any data, the medium must be checked for idleness. If the network is busy, access must be delayed, and a backoff algorithm is used to avoid collisions. If the medium idle time is longer than DIFS (distributed interframe space), transmission can proceed immediately. In PCF, after the access point takes over the wireless medium, it polls the connected terminals for data to be transmitted according to the polling list. During contention-free periods, terminals must not transmit data unless the access point requests it with a polling frame. MAC302 can include error recovery functionality, meaning the sender is responsible for retransmitting each frame if an acknowledgment (ACK) is not received. MAC302 can prioritize latency-sensitive services. When there is only one transmission queue, latency-sensitive service data frames are placed at the front of the queue. When multiple transmission queues are used, one queue is dedicated to transmitting high-priority sensitive services. To achieve energy saving for terminals, they can periodically enter a sleep state. In the sleep state, the access point temporarily stores frames for each terminal in the sleep state. If there are temporary frames, the access point will inform the terminal in subsequent Beacon frames. Terminals woken up from power-saving mode can retrieve these temporary frames using PS (power save)-Poll frames. The MAC302's management functions also include channel management, connection management, quality of service, power control, security management, and time synchronization.Channel management includes channel scanning, channel measurement, and channel handover; connection management includes user authentication, association, reassociation, and disassociation, and point-to-point connections; quality of service includes QoS service scheduling and traffic management; power management includes transmit power management and adaptive transmit power control; security management includes key generation and distribution; and time synchronization includes higher-layer synchronization support. Although not illustrated, the terminal may also have several upper layers above the MAC302, including an LLC layer.

[0100] As an example, Appendix Figure 3 The wireless protocol architecture described herein is applicable to the first node in this application.

[0101] As an example, Appendix Figure 3 The wireless protocol architecture described herein is applicable to the second node in this application.

[0102] Example 4

[0103] Example 4 illustrates a hardware module schematic diagram of a communication device according to an embodiment of this application, as shown in the attached diagram. Figure 4 As shown. Figure 4 This is a block diagram of a first communication device 450 and a second communication device 410 communicating with each other in an access network.

[0104] The first 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.

[0105] The second communication device 410 includes a controller / processor 475, a memory 476, a data source 477, a receiver processor 470, a transmitter processor 416, a multi-antenna receiver processor 472, a multi-antenna transmitter processor 471, a transmitter / receiver 418, and an antenna 420.

[0106] In the transmission from the second communication device 410 to the first communication device 450, at the second communication device 410, upper-layer data packets from the Internet or from data source 477 are provided to the controller / processor 475. The Internet and data source 477 represent all protocol layers above the MAC layer. The controller / processor 475 implements the functionality of the MAC layer. In the transmission from the second communication device 410 to the first communication device 450, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, service-to-link mapping, and radio resource allocation to the first communication device 450. The controller / processor 475 is also responsible for retransmitting lost packets and signaling to the first communication device 450. The transmission processor 416 and the multi-antenna transmission processor 471 implement various signal processing functions for the PHY layer (i.e., the physical layer). Transmit processor 416 performs encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 410, and mapping of signal clusters based on various modulation schemes (e.g., Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), M-Phase Shift Keying (M-PSK), M-QAM). Multi-antenna transmit processor 471 performs digital spatial precoding and beamforming processing on the encoded and modulated symbols to generate one or more spatial streams. Transmit processor 416 then maps each spatial stream to subcarriers, multiplexes it with a reference signal (e.g., a pilot) in the time and / or frequency domains, and subsequently uses inverse fast Fourier transform (IFFT) to generate a physical channel carrying the time-domain multicarrier symbol stream. Multi-antenna transmit processor 471 then performs transmit analog precoding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multi-antenna transmitter processor 471 into an radio frequency stream, which is then provided to different antennas 420.

[0107] In the transmission from the second communication device 410 to the first communication device 450, at the first communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the radio frequency carrier and converts the radio frequency stream into a baseband multicarrier symbol stream, which is then provided to the receiver processor 456. The receiver processor 456 and the multi-antenna receiver processor 458 implement various signal processing functions of the PHY layer. The multi-antenna receiver processor 458 performs receive analog precoding / beamforming operations on the baseband multicarrier symbol stream from the receiver 454. The receiver processor 456 uses a Fast Fourier Transform (FFT) to convert the baseband multicarrier symbol stream after the receive analog precoding / beamforming operations from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiver processor 456, where the reference signal is used for channel estimation, and the data signal is recovered in the multi-antenna receiver processor 458 after multi-antenna detection to recover any spatial stream destined for or received by the first communication device 450. Symbols on each spatial stream are demodulated and recovered in the receive processor 456, generating soft decisions. The receive processor 456 then decodes and deinterleaves the soft decisions to recover the upper-layer data and control signals transmitted by the second communication device 410 over the physical channel. The upper-layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the MAC layer. The controller / processor 459 may be associated with a memory 460 storing program code and data. The memory 460 may be referred to as computer-readable media. During transmission from the second communication device 410 to the first communication device 450, the controller / processor 459 provides packet segmentation, decryption, header decompression, and control signal processing to recover the upper-layer data packets from the second communication device 410. The upper-layer data packets are then provided to all protocol layers above the MAC layer.

[0108] In the transmission from the first communication device 450 to the second communication device 410, at the first communication device 450, upper-layer data packets are provided to the controller / processor 459 using a data source 467. The data source 467 represents all protocol layers above the MAC layer. Similar to the transmission function at the second communication device 410 described in the transmission from the second communication device 410 to the first communication device 450, the controller / processor 459 performs header compression, encryption, packet segmentation and reordering, and mapping between services and links. The controller / processor 459 is also responsible for retransmitting lost packets and signaling to the second communication device 410. The transmit processor 468 performs modulation mapping and channel coding processing, and the multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding and beamforming processing. Subsequently, the transmit processor 468 modulates the resulting spatial stream into a multi-carrier symbol stream, which undergoes analog precoding / beamforming operations in the multi-antenna transmit processor 457 before being provided to different antennas 452 via the transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmitter processor 457 into a radio frequency symbol stream, and then provides it to the antenna 452.

[0109] In the transmission from the first communication device 450 to the second communication device 410, the function at the second communication device 410 is similar to the receiving function at the first communication device 450 described in the transmission from the second communication device 410 to the first communication device 450. Each receiver 418 receives radio frequency signals through its corresponding antenna 420, converts the received radio frequency signals into baseband signals, and provides the baseband signals to the multi-antenna receiving processor 472 and the receiving processor 470. The receiving processor 470 and the multi-antenna receiving processor 472 jointly implement the functions of the PHY layer. The controller / processor 475 implements the functions of the MAC layer. 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 computer-readable media. In the transmission from the first communication device 450 to the second communication device 410, the controller / processor 475 provides packet segmentation, decryption, header decompression, and control signal processing to recover the upper-layer data packets from the first communication device 450. Upper-layer packets from the controller / processor 475 can be provided to all protocol layers above the Internet or MAC layer.

[0110] As one embodiment, the first communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor, and the first communication device 450 at least: receives a first eavesdropping PPDU; transmits first channel information and second channel information; wherein the first channel information indicates C1 column vectors of a first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first eavesdropping PPDU; any two of the C1 column vectors and the C2 column vectors correspond to different columns of the first beamforming matrix.

[0111] As one embodiment, the first communication device 450 includes: a memory storing a computer-readable instruction program, which, when executed by at least one processor, produces actions including: receiving a first listening PPDU; transmitting first channel information and second channel information; wherein the first channel information indicates C1 column vectors of a first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

[0112] As one embodiment, the second 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 second communication device 410 at least: transmits a first eavesdropping PPDU; receives first channel information and second channel information; wherein the first channel information indicates C1 column vectors of a first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first eavesdropping PPDU; any two of the C1 column vectors and the C2 column vectors correspond to different columns of the first beamforming matrix.

[0113] As one embodiment, the second communication device 410 includes: a memory storing a computer-readable instruction program that, when executed by at least one processor, produces actions including: transmitting a first listening PPDU; receiving first channel information and second channel information; wherein the first channel information indicates C1 column vectors of a first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

[0114] As one embodiment, the first communication device 450 corresponds to the first node in this application.

[0115] As an example, the second communication device 410 corresponds to the second node in this application.

[0116] As one embodiment, the first communication device 450 is a terminal or a STA.

[0117] As one embodiment, the second communication device 410 is an MLD, a relay, or an access point.

[0118] As one embodiment, at least one of the antenna 452, the transmitter 454, the multi-antenna transmitter processor 457, the transmitter processor 468, or the controller / processor 459 is used to transmit the first channel information and the second channel information in this application.

[0119] As an example, at least one of the antenna 452, the receiver 454, the multi-antenna receiving processor 458, the receiving processor 456, or the controller / processor 459 is used to receive the first listening PPDU, or the first MAC frame, or the second MAC frame in this application.

[0120] As an example, at least one of the antenna 420, the receiver 418, the multi-antenna receiving processor 472, the receiving processor 470, or the controller / processor 475 is used to receive the first channel information and the second channel information in this application.

[0121] As an example, at least one of the antenna 420, the transmitter 418, the multi-antenna transmitter processor 471, the transmitter processor 416, or the controller / processor 475 is used to transmit the first listening PPDU, or the first MAC frame, or the second MAC frame in this application.

[0122] Example 5

[0123] Example 5 illustrates a communication flowchart of a first node and a second node according to an embodiment of this application, as shown in the attached diagram. Figure 5 As shown. (Attached) Figure 5 In the text, the step marked F1 is optional.

[0124] For the first node N1, in step S5101, the first listening PPDU is received; in step S5102, the first channel information and the second channel information are sent; and in step S5103, the second MAC frame is received.

[0125] For the second node N2, in step S5201, a first listening PPDU is sent; in step S5202, first channel information and second channel information are received; and in step S5203, a second MAC frame is sent.

[0126] In Example 5, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors among the C1 and C2 column vectors correspond to different columns of the first beamforming matrix; the indication of the second MAC frame depends on the correlation between at least one column vector among the C1 and at least one column vector among the C2.

[0127] Typically, the first node N1 is the beamformer and the second node N2 is the beamformer.

[0128] As an example, the first node N1 is a non-AP site, and the second node N2 is an AP.

[0129] As an example, the correlation is sometimes also referred to as similarity, including, for example, SGCS (Squared Generalized Cosine Similarity), GCS (Generalized Cosine Similarity), etc.

[0130] As an example, the second MAC frame indicates Where c1 = 1, 2, ..., C1; c2 = 1, 2, ..., C2; and These represent the c1th column vector in the C1 column vectors and the c2th column vector in the C2 column vectors, respectively, with the superscript H representing the conjugate transpose.

[0131] As an example, the second MAC frame indicates ,in, and These represent the c1th column vector in the C1 column vectors and the c2th column vector in the C2 column vectors, respectively, with the superscript H representing the conjugate transpose.

[0132] Typically, how the correlation indicated by the second MAC frame is utilized is either implemented or determined by the supplier of the first node N1; some non-limiting implementations are described below:

[0133] If the correlation indicated by the second MAC frame is significantly different from 0, the first node N1 determines that the accuracy of the first channel information does not meet the requirements and can re-adopt a non-AI approach, such as a compressed beamforming matrix, to send the C1 column vectors of the first beamforming matrix.

[0134] or,

[0135] The generation of the first channel information is based on the first AI encoder; if the absolute value of the difference between more than 10% of the correlations indicated by the most recently received T MAC frames for the first AI encoder and 0 exceeds a certain threshold, the first node N1 stops inference based on the first AI encoder; wherein, the second MAC frame is one of the T MAC frames, and T is a positive integer greater than 1.

[0136] As an example, the C1 column vectors corresponding to the C1 column of the first beamforming matrix depend on the carrier targeted by the first beamforming matrix.

[0137] As a sub-implementation of the above embodiment, if the first beamforming matrix targets a carrier with an even index, the C1 column vectors correspond to the leftmost C1 column of the first beamforming matrix, and the C2 column vectors correspond one-to-one with the C1+1th column from the left to the C2+C1th column from the left in the first beamforming matrix; if the first beamforming matrix targets a carrier with an odd index, the C1 column vectors correspond one-to-one with the C2+1th column from the left to the C2+C1th column from the left in the first beamforming matrix, and the C2 column vectors correspond to the leftmost C2 column of the first beamforming matrix.

[0138] The even and odd indices in the above sub-examples can be interchanged, which will not be elaborated further.

[0139] In the above sub-implementation, the receiver can obtain accurate channel information using a compression-based beamforming matrix. When the AI-based channel information is inaccurate, the receiver can use the correlation between carriers to obtain the column vectors of all C1+C2 columns on the left side of the first beamforming matrix through algorithms such as frequency domain difference.

[0140] In a first implementation, the first node N1 receives the first MAC frame in step S5101; the second node N2 sends the first MAC frame in step S5201.

[0141] C1 and C2 depend on the indication of the first MAC frame.

[0142] As an example, the first MAC frame is an NDP announcement frame that announces the transmission of the first listening PPDU.

[0143] As an example, the first MAC frame indicates the sum of C1 and C2, as well as C1.

[0144] In the above embodiment, the second node N2 uses the summation of C1 and C2 to indicate the number of spatial streams corresponding to the requested beamforming matrix.

[0145] As a sub-example of the above embodiment, the station information field (STA Infofield) in the first MAC frame indicates the sum of C1 and C2, as well as C1.

[0146] In a second implementation, the first node N1 sends a first MIMO control domain in step S5101; the second node N2 receives the first MIMO control domain in step S5201.

[0147] C1 and C2 depend on the indication of the first MIMO control domain.

[0148] As an example, in the first MIMO control domain, the first control information and the second control information belong to the same action frame, such as a beamforming feedback frame.

[0149] As an example, the first MIMO control domain indicates C1 and C2.

[0150] As an example, the first MIMO control domain indicates the identifier of the first AI encoder, and the generation of the first channel information depends on the inference of the first AI encoder.

[0151] As a sub-implementation of the above embodiment, the first channel information includes C1 sub-information, and the C1 sub-information corresponds one-to-one with the C1 column vectors of the first beamforming matrix; each of the C1 sub-information is obtained after the corresponding column vector has undergone one inference by the first AI encoder.

[0152] As an example, the identifier of the first AI encoder indicates a training dataset, and only the first node N1 of the first node N1 and the second node N2 obtains the first AI encoder.

[0153] The above embodiments prevent the second node N2 from knowing the specific parameters and structure of the first AI encoder, thus protecting product privacy. Furthermore, the second node N2 can train the first AI decoder using the training dataset indicated by the identifier of the first AI encoder. The first AI decoder is used to recover the beamforming vector based on the output of the first AI encoder.

[0154] As one embodiment, the identifier of the first AI encoder indicates a reference encoder or a reference decoder, and only the first node N1 of the first node N1 and the second node N2 obtains the first AI encoder.

[0155] The above embodiments also prevent the second node N2 from knowing the specific parameters and structure of the first AI encoder, thus protecting product privacy. Furthermore, the second node N2 can train (knowledge distillation) the first AI decoder through the reference encoder or the reference decoder. The first AI decoder is used to recover the beamforming vector based on the output of the first AI encoder.

[0156] As an example, the first MIMO control domain includes the sounding timestamp of the first listening PPDU.

[0157] Example 6

[0158] Example 6 illustrates a schematic diagram of a first AI encoder according to an embodiment of this application, as shown in the attached diagram. Figure 6 As shown. The first AI encoder is deployed on the first node.

[0159] In Example 6, the input to the first AI encoder includes at least one column vector, which, after inference by the first AI encoder, yields a first inference output. The first channel information includes the first inference output, or bits obtained by bit quantization of the first inference output. Typically, the first AI encoder is a trained AI model, which may employ structures such as transformers or CNNs.

[0160] As an example, the at least one column vector includes one of the C1 column vectors of the first beamforming matrix.

[0161] As a sub-implementation of the above embodiment, the first beamforming matrix is ​​for a first subcarrier, and the at least one column vector includes Q1 column vectors, each of which is for a Q1 subcarrier; the first subcarrier is one of the Q1 subcarriers.

[0162] Considering that the feedback of a typical WLAN beamforming matrix includes channel information for the beamforming matrix of each subcarrier (subcarrier group) in a plurality of subcarriers (subcarrier groups), the advantage of the above sub-implementation is that it allows the AI ​​encoder to obtain a higher compression ratio by taking advantage of the frequency domain correlation characteristics of the wireless channel, or to obtain better feedback accuracy at the same compression ratio.

[0163] As a sub-implementation of the above embodiment, the at least one column vector includes Q2 column vectors, each of which comes from Q2 reported beamforming matrices, and the first beamforming matrix is ​​the latest one among the Q2 reported beamforming matrices.

[0164] The advantage of the above sub-implementation is that it allows the AI ​​encoder to obtain a higher compression ratio by utilizing the temporal correlation characteristics of the wireless channel, or to obtain better feedback accuracy at the same compression ratio.

[0165] As an example, each column vector in the at least one column vector is a token.

[0166] As an example, the first AI encoder is the AI ​​model of the STA on the non-AP side.

[0167] Example 7

[0168] Example 7 illustrates a schematic diagram of a reference encoder and a reference decoder according to one embodiment of this application, as shown in the attached diagram. Figure 7 As shown. (Attached) Figure 7 In the table, parameters or functions identified by S800, S801, S802, ..., S808 belong to the reference encoder, and parameters or functions identified by S900, S901, S902, ..., S908 belong to the reference decoder.

[0169] The reference encoder and the reference decoder are inverse operations of each other, both employing a Transformer structure; the parameters and functions identified in block F800 represent a Transformer block, and the reference encoder typically includes multiple cascaded Transformer blocks. Figure 7 Only one example is shown; similarly, the parameters and functions identified in box F900 constitute a Transformer block, and the reference decoder typically comprises multiple cascaded Transformer blocks, as shown below. Figure 7 Only one example is shown in the image.

[0170] For the deployment of two AI models in a typical application scenario of beamforming matrix compression, the first AI encoder in Example 6 can adopt an auxiliary... Figure 7 The structure of the reference encoder described in the document, or to implement a similar function; the second node deploys attached... Figure 7 The reference decoder described above, or one that implements similar functionality, typically involves deploying a first AI encoder on the first node side and a first decoder on the second node side; the first AI encoder and the first decoder constitute a two-sided AI model. (See appendix.) Figure 7 The reference encoder and the reference decoder mentioned therein can be used to train or calibrate the first AI encoder and the first decoder. However, the first AI encoder and the first decoder used in actual inference do not need to completely reuse the structure / parameters / hyperparameters of the reference encoder and the reference decoder.

[0171] As an example, the first node trains the parameter set of the first AI encoder based on the parameter set of the reference decoder. The trained first AI encoder and the reference decoder are not necessarily completely inverse operations of each other, as long as the output of the inference data used for performance detection after passing through the first AI encoder and the reference decoder in sequence meets the error requirements of the inference data.

[0172] The KPIs (Key Performance Indicators) used to calculate errors may vary depending on the training algorithm or AI model structure. Common KPIs include the performance requirements of GCS (Generalized Cosine Similarity); SGCS (Squared Generalized Cosine Similarity); and NMSE (Normalized Mean Squared Error).

[0173] Since the reference decoder can be considered the inverse operation of the reference encoder, the specific implementation of the reference encoder is given below, while the reference decoder will not be described in detail.

[0174] The beamforming vector of the first measurement, such as at least one column vector as described in Embodiment 6, is input to the embedding layer S801; the feature dimension of each input is T, where T is the number of antenna ports in the first RS resource set; each input is a T-dimensional complex vector, which can be represented by two real vectors representing the real part and the imaginary part respectively; it is assumed below that each token is a T-dimensional real vector.

[0175] After the S801 embedding layer, the embedded representation of each token is a A dimensional real vector;

[0176] The dimension of the S802 location information is the same as the dimension of the embedded representation of the token;

[0177] The dimension of the Transformer block in F800 is as described The S803 normalization, S804 multi-head attention, S805 normalization, and S806 multi-head perception, which are included, adopt the classic Transformer structure.

[0178] The S807 linear layer, also known as the output linear layer, functions to output P1* tokens representing P1 tokens. Transform the matrix into For 3D vectors, typical transformation methods include linear operations using matrices; generally speaking, Less than That is, the S807 linear layer has completed the dimensionality reduction function;

[0179] The function of S808 vector quantization is to... A real vector of dimension is discretized into entries or codewords in a finite, learnable quantized codebook, i.e., appended... Figure 7 The first inference output in the process.

[0180] It should be noted that although the S808 is called Vector Quantization (VQ), it also supports SQ (Scalar Quantization).

[0181] As an example, the The real reference (SQ) quantization codebook of each dimension in the dimensional real vector is quantized into bits, where L is the The at least one codeword is L codewords, and the payload size of the first inference output is... 1 bit.

[0182] As an example, the A 3D real vector is divided into Each paragraph includes a real number; the Each segment in the segment is mapped to a codeword in the (VQ) quantization codebook according to criteria such as minimum SGSC or minimum NMSE; the mapped codeword corresponds to a... A dimensional vector, where each dimension corresponds to bits; that is, the output of S808 is bits; that is, the output of S808 is indivual dimensional vector, each of the The load size of the dimensional vector is 1 bit.

[0183] As a sub-implementation of the above embodiments, the at least one codeword is a codeword, and L is the... The load size of the first inference output is 1 bit.

[0184] As a sub-implementation of the above embodiments, the The value is 1, meaning that L is the... The load size of the first inference output is the same as the load size of the output of S808.

[0185] Provided that the inference performance meets the requirements, for example, if the error between the first recovered beamforming vector in S908 and the first measured beamforming vector in S800 meets the KPI requirements (e.g., does not exceed a specific threshold), the first AI encoder can adopt the same structure as the reference encoder or a different structure; this is determined by the supplier of the first node N1. The reference encoder and the reference decoder are used to train the first AI encoder and are not necessarily used for actual inference.

[0186] As an example, when C1 is greater than 1, the operation in Example 7 is performed once for each of the C1 column vectors.

[0187] Example 8

[0188] Example 8 illustrates a structural block diagram of a processing apparatus in a first node according to an embodiment of this application, as shown in the attached diagram. Figure 8 As shown. In the appendix Figure 8 In this context, the first node 1600 includes a first receiver 1601 and a first transmitter 1602; the first node 1600 is a terminal, sometimes also referred to as a station.

[0189] The first receiver 1601 receives the first listening PPDU;

[0190] The first transmitter 1602 transmits first channel information and second channel information;

[0191] Wherein, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

[0192] As an example, the C1 column vectors corresponding to the C1 column of the first beamforming matrix depend on the carrier targeted by the first beamforming matrix.

[0193] As one embodiment, the first transmitter 1602 transmits a first MIMO control domain;

[0194] C1 and C2 depend on the indication of the first MIMO control domain.

[0195] As an example, the first MIMO control domain includes an identifier of a first AI model, and the generation of the first channel information depends on the inference of the first AI model.

[0196] As one embodiment, the first receiver 1601 receives the first MAC frame;

[0197] C1 and C2 depend on the indication of the first MAC frame.

[0198] As one embodiment, the first receiver 1601 receives the second MAC frame;

[0199] The indication of the second MAC frame depends on the correlation between at least one column vector among the C1 column vectors and at least one column vector among the C2 column vectors.

[0200] As one embodiment, the first receiver 1601 includes the appendix to this application. Figure 4 The receiver 454 (including antenna 452), receiver processor 456, multi-antenna receiver processor 458, and controller / processor 459 are included.

[0201] As one embodiment, the first receiver 1601 includes the appendix to this application. Figure 4 The receiver 454 (including antenna 452), the receiver processor 456, the multi-antenna receiver processor 458, or the controller / processor 459 are at least one of them.

[0202] As one embodiment, the first transmitter 1602 includes the appendix to this application. Figure 4 The transmitter 454 (including antenna 452), the transmitter processor 468, the multi-antenna transmitter processor 457, and the controller / processor 459 are included.

[0203] As one embodiment, the first transmitter 1602 includes the appendix to this application. Figure 4 The receiver 454 (including antenna 452), the transmitter processor 468, the multi-antenna transmitter processor 457, or the controller / processor 459 are at least one of them.

[0204] Example 9

[0205] Example 9 illustrates a structural block diagram of a processing apparatus in a second node according to an embodiment of this application, as shown in the attached diagram. Figure 9 As shown. In the appendix Figure 9 In this context, the second node 1700 includes a second transmitter 1701 and a second receiver 1702; the second node 1700 is a station or an access point.

[0206] The second transmitter 1701 transmits the first listening PPDU;

[0207] The second receiver 1702 receives the first channel information and the second channel information;

[0208] In Example 9, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

[0209] As an example, the C1 column vectors corresponding to the C1 column of the first beamforming matrix depend on the carrier targeted by the first beamforming matrix.

[0210] As one embodiment, the second receiver 1702 receives the first MIMO control domain;

[0211] C1 and C2 depend on the indication of the first MIMO control domain.

[0212] As one embodiment, the first MIMO control domain includes an identifier of the first AI encoder, and the generation of the first channel information depends on the inference of the first AI encoder.

[0213] As one embodiment, the second transmitter 1701 transmits the first MAC frame;

[0214] C1 and C2 depend on the indication of the first MAC frame.

[0215] As one embodiment, the second transmitter 1701 transmits a second MAC frame;

[0216] The indication of the second MAC frame depends on the correlation between at least one column vector among the C1 column vectors and at least one column vector among the C2 column vectors.

[0217] As one embodiment, the second receiver 1702 includes the appendix to this application. Figure 4 The receiver 418 (including antenna 420), receiver processor 470, multi-antenna receiver processor 472, and controller / processor 475 are included.

[0218] As one embodiment, the second receiver 1702 includes the appendix to this application. Figure 4 The receiver 418 (including antenna 420), the receiver processor 470, the multi-antenna receiver processor 472, and the controller / processor 475 are at least one of them.

[0219] As one embodiment, the second transmitter 1701 includes the appendix to this application. Figure 4The transmitter 418 (including antenna 420), the transmitter processor 416, the multi-antenna transmitter processor 471, and the controller / processor 475 are included.

[0220] As one embodiment, the second transmitter 1701 includes the appendix to this application. Figure 4 The transmitter 418 (including antenna 420), the transmission processor 416, the multi-antenna transmission processor 471, or the controller / processor 475 are at least one of them.

[0221] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, hard disk, or optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiments can be implemented in hardware or in the form of software functional modules. This application is not limited to any specific combination of software and hardware. The first node in this application includes, but is not limited to, wireless communication devices such as mobile phones, tablets, laptops, network cards, low-power devices, vehicle communication devices, aircraft, airplanes, drones, and remote-controlled airplanes. The second node in this application includes, but is not limited to, wireless communication devices such as access points, wireless routers, sites or handheld terminals with access point functions, and transmission and reception points (TRPs). The above descriptions are merely preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A first node used in a WLAN, characterized in that, include: The first receiver receives the first listening PPDU; The first transmitter transmits first channel information and second channel information; Wherein, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

2. The first node according to claim 1, characterized in that, The C1 column vectors correspond to the C1 column of the first beamforming matrix, which depends on the carrier targeted by the first beamforming matrix.

3. The first node according to claim 1, characterized in that, include: The first transmitter sends the first MIMO control domain; C1 and C2 depend on the indication of the first MIMO control domain.

4. The first node according to claim 3, characterized in that, The first MIMO control domain includes the identifier of the first AI model, and the generation of the first channel information depends on the inference of the first AI model.

5. The first node according to claim 1, characterized in that, include: The first receiver receives the first MAC frame; C1 and C2 depend on the indication of the first MAC frame.

6. The first node according to any one of claims 1 to 5, characterized in that, include: The first receiver receives the second MAC frame; The indication of the second MAC frame depends on the correlation between at least one column vector among the C1 column vectors and at least one column vector among the C2 column vectors.

7. A second node used in a WLAN, characterized in that, include: The second transmitter sends the first listener PPDU; The second receiver receives the first channel information and the second channel information; Wherein, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

8. The second node according to claim 7, characterized in that, include: The second transmitter sends the second MAC frame; The indication of the second MAC frame depends on the correlation between at least one column vector among the C1 column vectors and at least one column vector among the C2 column vectors.

9. A method used in a first node of wireless communication, characterized in that, include: Receive the first listening PPDU; Send the first channel information and the second channel information; Wherein, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

10. A method used in a second node of wireless communication, characterized in that, include: Send the first listening PPDU; Receive first channel information and second channel information; Wherein, the first channel information indicates C1 column vectors of the first beamforming matrix, and the second channel information indicates C2 column vectors of the first beamforming matrix; the format of the first channel information is based on artificial intelligence, and the format of the second channel information is a compressed beamforming matrix; the first beamforming matrix depends on measurements for the first listening PPDU; any two column vectors of the C1 and C2 column vectors correspond to different columns of the first beamforming matrix.

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