Communication method and communication device

Abstract

The present application provides a communication method and a communication device. The method comprises: a first node determines first training data, the first training data comprising network experience information of a station (STA); and the first node sends the first training data to a second node, the first training data being used for model training of a first intelligent model, and the first intelligent model being used for reasoning a transmission strategy of a communication network where the first node is located. Model training of an intelligent model can be realized, so that the intelligent model is applied to a wireless network, and thus a transmission strategy for improving the experience of STAs can be reasoned, thereby improving the network communication performance.

Description

Communication method and communication device

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on December 21, 2023, with application number 202311791003.7 and application name “Communication Method and Communication Device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communications, and more particularly, to a communication method and a communication device. Background Art

[0003] Wireless communication is developing rapidly, and the fifth generation of mobile communication (5 th The 5G (5th generation) and sixth-generation wireless-fidelity (Wi-Fi) standards have already been commercialized, and the development and standardization of next-generation wireless technologies are in full swing. Wireless communications have become ubiquitous in all aspects of production and daily life, becoming an indispensable part. The rapid growth in the number of smart devices and the emergence of various new wireless applications (such as virtual reality, augmented reality, and holographic imaging) have made wireless networks unprecedentedly complex.

[0004] In the complex wireless networks of the future, if the current approach of determining communication transmission strategies for stations (STAs) based on channel conditions is still used, it may not match the network transmission environment and thus fail to guarantee STA transmission requirements. With the increasing complexity of wireless networks, the application of artificial intelligence (AI) technology to assist in wireless network management has become a hot topic of research. How to apply AI technology to wireless networks to improve communication performance has become a hot topic of research. Summary of the Invention

[0005] The embodiments of the present application provide a communication method and a communication device, which can implement the application of intelligent models to wireless networks and improve communication performance.

[0006] In a first aspect, a data transmission method is provided, which is described below by taking the first node executing the method as an example. The first node may be a communication device or a module (such as a chip or chip module) configured in (or used for) a communication device.

[0007] The method includes: a first node determining first training data, the first training data including network experience information of multiple stations (STAs). The first node sends the first training data to a second node, the first training data being used for model training of a first intelligent model, and the first intelligent model being used to infer a transmission strategy of a communication network in which the first node is located.

[0008] According to the above scheme, the first node can send the first training data containing the network experience information of the STA to the second node, so that through the transmission of the first training data in the network, the node that maintains the first intelligent model (called the intelligent node) can perform model training of the first intelligent model based on the first training data, realize the application of the first intelligent model to the wireless network, and be able to infer the transmission strategy that improves the network experience of the STA, thereby improving the network communication performance.

[0009] In conjunction with the first aspect, in certain implementations of the first aspect, the first training data further includes one or more of network state information corresponding to the network experience information, transmission strategy information corresponding to the network experience information, or collection time information. The collection time information indicates the time when the information in the first training data was collected.

[0010] According to the above solution, the training data used to train the first intelligent model may also include network status information corresponding to the STA's network experience information and the STA's transmission strategy information. This training data may also be determined by the first node and provided to the intelligent node via network transmission to implement model training of the first intelligent model. However, the present application is not limited to this, and the intelligent node may obtain this training data from other nodes other than the first node (such as a management node in the network) to implement model training of the first intelligent model.

[0011] In combination with the first aspect, in certain implementations of the first aspect, the first node is a first STA, and the second node is an intelligent node, wherein the first STA is an STA capable of accessing the intelligent node, and the intelligent node is a node that maintains the first intelligent model.

[0012] According to the above scheme, a STA with the ability to access an intelligent node can collect network experience information of multiple STAs and provide it to the intelligent node to enable the intelligent node to train the model of the first intelligent model, and then implement the application of the first intelligent model to the wireless network, and be able to infer the transmission strategy that improves the STA network experience, thereby improving the network communication performance.

[0013] In combination with the first aspect, in some implementations of the first aspect, the first node determines the first training data, including: the first STA sends a first request message to the access point AP, where the first request message is used to request the first training data; and the first STA receives the first training data from the AP.

[0014] According to the above solution, the first STA can obtain the network experience information of multiple STAs collected by the AP through the AP, and provide it to the intelligent node to enable the intelligent node to train the first intelligent model.

[0015] In combination with the first aspect, in certain implementations of the first aspect, the first node is an AP, and the second node is an intelligent node or a first STA, wherein the first STA is an STA with the ability to access an intelligent node, and the intelligent node is a node that maintains the first intelligent model.

[0016] According to the above scheme, the first node can be an AP, which provides the collected first training data to the intelligent node, or the AP provides the first training data to the first STA that has the ability to access the intelligent node, so that it can be transmitted to the intelligent node through the first STA to realize model training of the first intelligent model by the intelligent node.

[0017] In combination with the first aspect, in certain implementations of the first aspect, the first node determines the first training data, including: the AP receives a first request message from the first STA, where the first request message is used to request the first training data; the AP receives multiple first messages from multiple STAs, where each first message in the multiple first messages includes network experience information of each STA; the AP determines the first training data based on the multiple first messages.

[0018] According to the above scheme, the AP can collect network experience information of each STA from multiple STAs in response to the request of the first STA, and provide it to the first STA, so that the intelligent node can realize model training of the first intelligent model through the transmission of the first STA.

[0019] In one implementation, the first training data includes the first information of the multiple STAs, and the first information also includes at least one of the network status information corresponding to the network experience information of the STA, the transmission strategy information of the STA corresponding to the network experience information of the STA, or the acquisition time information.

[0020] In other words, the first information collected by the AP from multiple STAs also includes network status information and transmission strategy information corresponding to the network experience information. The first training data determined by the AP includes this information from the first information of the multiple STAs and is transmitted through the first STA, allowing the intelligent node to obtain all training data used to train the first intelligent model, thereby completing model training without having to obtain training data from other nodes.

[0021] In another implementation, the first training data is determined by the AP based on at least one of the network status information, the STA's transmission strategy information or the acquisition time information and the first information of the multiple STAs, wherein the acquisition time information is used to indicate the acquisition time of the information in the first training data.

[0022] That is to say, the first information obtained by the AP from multiple STAs includes the network experience information of the STAs, and the AP can have global observation capabilities to obtain the current network status information and the transmission strategy information of the current STA, so that the AP can determine the first training data including network status information, STA's transmission strategy information and STA's network experience information based on this information and the first information collected from the STA, and transmit it through the first STA, so that the intelligent node can obtain all the training data used to train the first intelligent model to complete the model training without having to obtain training data from other nodes.

[0023] In combination with the first aspect, in certain implementations of the first aspect, the first node determines the first training data, including: the AP receives a first request message from the first STA, where the first request message is used to request the first training data; the AP inputs the network status information and the transmission strategy information of multiple STAs into the second intelligent model to obtain the first training data output by the second intelligent model.

[0024] Exemplarily, the second intelligent model is a feedback model, the input of the feedback model is network status information and STA transmission strategy information, and the output of the feedback model includes STA network experience information.

[0025] According to the above solution, the AP can maintain an intelligent model for inferring STA network experience information. After receiving a request from the first STA, the AP can use the current network status information and the transmission strategy information of multiple STAs as input to the model to infer the network experience information of multiple STAs. This eliminates the need to collect network experience information from each STA after each request from the first STA. This can reduce signaling overhead and improve the efficiency of obtaining training data.

[0026] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the AP performs model training based on the network experience information of multiple STAs and the transmission strategy information and network status information corresponding to the network experience information to obtain a second intelligent model.

[0027] According to the above solution, the AP can obtain a second intelligent model capable of inferring the network experience information of STAs in the network through model training based on the historical network experience information of STAs, the historical transmission strategy information of STAs, and the corresponding network status information. However, the present application is not limited to this. The second intelligent model can also be configured on the AP after other network nodes complete model training.

[0028] In combination with the first aspect, in certain implementations of the first aspect, the second node is an intelligent node, which is a node that maintains the first intelligent model; the method also includes: the first node sends a second request message to the second node, and the second request message is used to request transmission policy information corresponding to the current network status information; the first node receives a response message from the second node, and the response message is used to indicate the first transmission policy information, and the first transmission policy information is the transmission policy information corresponding to the current network status information; the first node performs data transmission according to the first transmission policy information.

[0029] According to the above scheme, after the intelligent node completes the model training of the first intelligent model, the first node can request a transmission strategy from the intelligent node. The intelligent node can infer a transmission strategy that matches the current network status based on the first intelligent model, which can improve the STA network experience and achieve improved network communication performance.

[0030] In combination with the first aspect, in certain implementations of the first aspect, the first node sends a second request message to the second node, including: when the number of STAs whose network experience does not meet the requirements is greater than or equal to a quantity threshold, the first node sends the second request message to the second node.

[0031] According to the above scheme, when the number of STAs whose network experience does not meet the requirements reaches a number threshold, the first node requests a transmission strategy from the intelligent node, so that the intelligent node can infer a transmission strategy that matches the network environment based on the first intelligent model, thereby improving the network experience of the STA and thereby improving the network communication performance.

[0032] In combination with the first aspect, in certain implementations of the first aspect, the first node is a first STA, which is a STA capable of accessing an intelligent node; the first node sends a second request message to the second node, including: when the network experience of the first STA does not meet the requirements, the first STA sends a third request message to the AP, and the third request message is used to request the current network status information; the first STA receives the current network status information from the AP; the first STA sends the second request message to the AP, and the second request message includes the current network status information.

[0033] According to the above scheme, when the first STA's network experience does not meet the requirements, it can request a transmission strategy from the intelligent node to obtain a transmission strategy that matches the network environment and can improve the STA's network experience, which is inferred by the intelligent node based on the first intelligent model.

[0034] In combination with the first aspect, in certain implementations of the first aspect, the second request information includes the current network status information represented by natural language; and / or the response information includes the first transmission strategy information represented by natural language.

[0035] In combination with the first aspect, in certain implementations of the first aspect, the method also includes: the first node processes the current network transmission status based on a natural language representation method to obtain the current network transmission status information represented by a natural language; and / or, the first node parses the first transmission strategy information represented by a natural language in the response information based on a natural language representation method to obtain the first transmission strategy information.

[0036] According to the above solution, the first STA and the intelligent node convert interactive information through natural language representation, avoiding the need for predefined information sets (such as network status information sets and transmission policy information sets) in the protocol. This allows both parties to reach a consensus on the information set and select the appropriate information from the set for interaction. This reduces the storage overhead of both parties associated with storing predefined information sets, and the information types are not limited to the set, which improves scalability.

[0037] In combination with the first aspect, in some implementations of the first aspect, the first intelligent model is a large language model LLM, the input of the LLM is the first training data, and the output of the LLM is transmission strategy information.

[0038] According to the above solution, after LLM trains the basic model, it can be widely applied in various industries by simply introducing professional domain knowledge and fine-tuning some parameters. This can reduce the implementation complexity of AI technology in wireless networks.

[0039] In a second aspect, a communication method is provided. The method can be executed by a second node or a module (such as a chip or a chip module) configured at (or used for) the second node.

[0040] The method includes: a second node receives first training data from a first node, the first training data including network experience information of a STA; the second node performs model training on a first intelligent model based on the first training data to obtain the trained first intelligent model, and the first intelligent model is used to infer the transmission strategy in the communication network where the first node is located.

[0041] In conjunction with the second aspect, in certain implementations of the second aspect, the first training data further includes one or more of network status information corresponding to the network experience information, transmission strategy information corresponding to the network experience information, or collection time information. The collection time information indicates the time when the information in the first training data was collected.

[0042] In combination with the second aspect, in certain implementations of the second aspect, the method also includes: the second node receives a second request message from the first node, and the second request message is used to request transmission policy information corresponding to the current network status information; the second node sends a response message to the first node, and the response message is used to indicate the first transmission policy information, and the first transmission policy information is the transmission policy information corresponding to the current network status.

[0043] In combination with the second aspect, in certain implementations of the second aspect, the method further includes: the second node inputting current network status information into the first intelligent model to obtain the first transmission strategy information inferred by the first intelligent model.

[0044] In combination with the second aspect, in some implementations of the second aspect, the second request information includes the current network status information.

[0045] In combination with the second aspect, in certain implementations of the second aspect, the second request information includes the current network status information represented by natural language; and / or the response information includes the first transmission strategy information represented by natural language.

[0046] In combination with the second aspect, in some implementations of the second aspect, the first node is an access point AP or a station STA capable of accessing an intelligent node, and the second node is an intelligent node.

[0047] In combination with the second aspect, in some implementations of the second aspect, the first intelligent model is a large language model LLM, the input of the LLM is the first training data, and the output of the LLM is a transmission strategy.

[0048] In a third aspect, a communication device is provided. In one design, the device may include a module corresponding to each of the methods / operations / steps / actions described in the first aspect or any one of the embodiments of the first aspect. The module may be a hardware circuit, software, or a combination of hardware circuits and software. In one design, the communication device is applied to a first node, and the device includes: a processing unit for determining first training data, where the first training data includes network experience information of multiple stations (STAs); and a transceiver unit for sending the first training data to a second node, where the first training data is used for model training of a first intelligent model, where the first intelligent model is used to infer the transmission strategy of the communication network where the first node is located.

[0049] In conjunction with the third aspect, in certain implementations of the third aspect, the first training data further includes one or more of network status information corresponding to the network experience information, transmission strategy information corresponding to the network experience information, or collection time information. The collection time information indicates the time when the information in the first training data was collected.

[0050] In combination with the third aspect, in certain implementations of the third aspect, the first node is a first STA, and the second node is an intelligent node, wherein the first STA is an STA with the ability to access the intelligent node, and the intelligent node is a node that maintains the first intelligent model.

[0051] In combination with the third aspect, in some implementations of the third aspect, the transceiver unit is further used to send a first request message to an access point AP, where the first request message is used to request the first training data; the transceiver unit is further used to receive the first training data from the AP.

[0052] In combination with the third aspect, in certain implementations of the third aspect, the first node is an AP, and the second node is an intelligent node or a first STA, wherein the first STA is an STA with the ability to access an intelligent node, and the intelligent node is a node that maintains the first intelligent model.

[0053] In combination with the third aspect, in certain implementations of the third aspect, the transceiver unit is further used to receive a first request message from the first STA, where the first request message is used to request the first training data; the transceiver unit is further used to receive multiple first messages from multiple STAs, where each of the multiple first messages includes network experience information of each STA; the processing unit is specifically used to determine the first training data based on the multiple first messages.

[0054] In conjunction with the third aspect, in certain implementations of the third aspect, the first training data includes the first information of the multiple STAs, and the first information also includes at least one of network status information corresponding to the STA's network experience information, and transmission strategy information or acquisition time information of the STA corresponding to the STA's network experience information; or the first training data is determined by the AP based on the network status information, at least one of the STA's transmission strategy information or acquisition time information, and the first information of the multiple STAs. The acquisition time information is used to indicate the acquisition time of the information in the first training data.

[0055] In combination with the third aspect, in certain implementations of the third aspect, the transceiver unit is also used to receive a first request message from the first STA, and the first request message is used to request the first training data; the processing unit is specifically used to input the network status information and the transmission strategy information of multiple STAs into the second intelligent model to obtain the first training data output by the second intelligent model.

[0056] In combination with the third aspect, in certain implementations of the third aspect, the processing unit is also used to perform model training based on the network experience information of multiple STAs and the transmission strategy information and network status information corresponding to the network experience information to obtain a second intelligent model.

[0057] In combination with the third aspect, in certain implementations of the third aspect, the second intelligent model is a feedback model, the input of the feedback model is network status information and STA's transmission strategy information, and the output of the feedback model includes STA's network experience information.

[0058] In combination with the third aspect, in certain implementations of the third aspect, the second node is an intelligent node, which is a node that maintains the first intelligent model; the transceiver unit is also used to send a second request message to the second node, and the second request message is used to request transmission policy information corresponding to the current network status information; the transceiver unit is also used to receive a response message from the second node, and the response message is used to indicate the first transmission policy information, and the first transmission policy information is the transmission policy information corresponding to the current network status information; the processing unit is also used to perform data transmission according to the first transmission policy information.

[0059] In combination with the third aspect, in certain implementations of the third aspect, the transceiver unit is specifically configured to send the second request information to the second node when the number of STAs whose network experience does not meet the requirements is greater than or equal to a quantity threshold.

[0060] In combination with the third aspect, in certain implementations of the third aspect, the first node is a first STA, which is a STA capable of accessing an intelligent node; the transceiver unit is specifically used to send a third request message to the AP when the network experience of the first STA does not meet the requirements, and the third request message is used to request the current network status information; the transceiver unit is also specifically used to receive the current network status information from the AP; the transceiver unit is also specifically used to send the second request message to the AP, and the second request message includes the current network status information.

[0061] In combination with the third aspect, in certain implementations of the third aspect, the second request information includes the current network status information represented by natural language; and / or the response information includes the first transmission strategy information represented by natural language.

[0062] In combination with the third aspect, in certain implementations of the third aspect, the processing unit is further used to process the current network transmission status based on a natural language representation method to obtain the current network transmission status information represented by natural language; and / or, the processing unit is further used to parse the first transmission strategy information represented by natural language in the response information based on a natural language representation method to obtain the first transmission strategy information.

[0063] In combination with the third aspect, in some implementations of the third aspect, the first intelligent model is a large language model LLM, the input of the LLM is the first training data, and the output of the LLM is transmission strategy information.

[0064] In a fourth aspect, a communication device is provided. In one design, the device may include a module corresponding to each of the methods / operations / steps / actions described in the second aspect or any one of the embodiments of the second aspect. The module may be a hardware circuit, software, or a combination of hardware circuits and software. In one design, the communication device is applied to a second node, and the device includes: a transceiver unit for receiving first training data from a first node, the first training data including network experience information of a STA; a processing unit for performing model training on a first intelligent model based on the first training data to obtain the trained first intelligent model, and the first intelligent model is used to infer a transmission strategy in the communication network where the first node is located.

[0065] In conjunction with the fourth aspect, in certain implementations of the fourth aspect, the first training data further includes one or more of network status information corresponding to the network experience information, transmission strategy information corresponding to the network experience information, or collection time information. The collection time information indicates the time when the information in the first training data was collected.

[0066] In combination with the fourth aspect, in certain implementations of the fourth aspect, the transceiver unit is also used to receive a second request message from the first node, and the second request message is used to request transmission policy information corresponding to the current network status information; the transceiver unit is also used to send a response message to the first node, and the response message is used to indicate the first transmission policy information, and the first transmission policy information is the transmission policy information corresponding to the current network status.

[0067] In combination with the fourth aspect, in certain implementations of the fourth aspect, the processing unit is further used to input current network status information into the first intelligent model to obtain the first transmission strategy information inferred by the first intelligent model.

[0068] In combination with the fourth aspect, in some implementations of the fourth aspect, the second request information includes the current network status information.

[0069] In combination with the fourth aspect, in certain implementations of the fourth aspect, the second request information includes the current network status information represented by natural language; and / or the response information includes the first transmission strategy information represented by natural language.

[0070] In combination with the fourth aspect, in certain implementations of the fourth aspect, the first node is an access point AP or a station STA capable of accessing an intelligent node, and the second node is an intelligent node.

[0071] In combination with the fourth aspect, in some implementations of the fourth aspect, the first intelligent model is a large language model LLM, the input of the LLM is the first training data, and the output of the LLM is a transmission strategy.

[0072] In a fifth aspect, a communication device is provided, comprising a processor. The processor can implement the method in any possible implementation of the first aspect to the second aspect and the first aspect to the second aspect. Optionally, the communication device further includes a memory, and the processor is coupled to the memory, and can be used to execute instructions in the memory to implement the method in any possible implementation of the first aspect to the second aspect and the first aspect to the second aspect. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface. In the embodiment of the present application, the communication interface can be a transceiver, a pin, a circuit, a bus, a module, or other types of communication interfaces, without limitation.

[0073] In one implementation, the communication apparatus is a communication device (such as a STA or an AP). When the communication apparatus is a communication device, the communication interface may be a transceiver or an input / output interface.

[0074] In another implementation, the communication device is a chip configured in a communication device. When the communication device is a chip configured in a communication device, the communication interface may be an input / output interface.

[0075] Optionally, the transceiver may be a transceiver circuit. Optionally, the input / output interface may be an input / output circuit.

[0076] In a sixth aspect, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive a signal through the input circuit and transmit a signal through the output circuit, so that the processor executes the method described in any possible implementation of the first and second aspects above.

[0077] In a specific implementation, the processor may be one or more chips, the input circuit may be an input pin, the output circuit may be an output pin, and the processing circuit may be a transistor, a gate circuit, a trigger, or various logic circuits. The input signal received by the input circuit may be, for example, but not limited to, received and input by a receiver, and the signal output by the output circuit may be, for example, but not limited to, output to and transmitted by a transmitter. The input circuit and the output circuit may be the same circuit, which functions as an input circuit and an output circuit at different times. The embodiments of the present application do not limit the specific implementation of the processor and various circuits.

[0078] In the seventh aspect, a computer program product is provided, which includes: a computer program (also referred to as code, or instructions), which, when executed, enables a computer to execute the method in the above-mentioned first aspect to the second aspect and any possible implementation of the first aspect to the second aspect.

[0079] In an eighth aspect, a computer-readable storage medium is provided, which stores a computer program (also referred to as code, or instructions). When the computer-readable storage medium is run on a computer, the computer executes the method in the above-mentioned first aspect to the second aspect and any possible implementation of the first aspect to the second aspect.

[0080] In a ninth aspect, a communication system is provided, comprising the aforementioned at least one first node and the aforementioned at least one second node. Optionally, the first node is the aforementioned first STA, the second node is the aforementioned AP, and the communication system further comprises the aforementioned at least one smart node. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] FIG1 is a schematic diagram of a wireless communication system applicable to an embodiment of the present application;

[0082] FIG2 is a schematic flow chart of a communication method provided in an embodiment of the present application;

[0083] FIG3 is another schematic flow chart of a communication method provided in an embodiment of the present application;

[0084] FIG4 is a schematic structural diagram of the communication device of the present application;

[0085] FIG5 is another schematic structural diagram of the communication device of the present application. DETAILED DESCRIPTION

[0086] The technical solution in this application will be described below with reference to the accompanying drawings.

[0087] In the embodiments of this application, " / " can indicate that the objects associated with each other are in an "or" relationship. For example, A / B can mean A or B. "And / or" can be used to describe the existence of three relationships between the associated objects. For example, "A and / or B" can mean: A exists alone, A and B exists simultaneously, and B exists alone. A and B can be singular or plural. To facilitate the description of the technical solutions of the embodiments of this application, the words "first" and "second" can be used to distinguish them in the embodiments of this application. The words "first" and "second" do not limit the quantity or order of execution, and the words "first" and "second" do not necessarily mean different. In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. The use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete way to facilitate understanding. In the embodiments of the present application, at least one (kind) can also be described as one (kind) or multiple (kinds), and multiple (kinds) can be two (kinds), three (kinds), four (kinds) or more (kinds), and this application does not limit it.

[0088] The technical solutions provided in the embodiments of the present application can be applied to various communication systems, for example, wireless local area network (WLAN) systems, such as wireless-fidelity (Wi-Fi), etc. The solutions provided in the embodiments of the present application can be applied to wireless local area network systems that support the Institute of Electrical and Electronics Engineers (IEEE) 802.11ax next-generation Wi-Fi protocol (such as 802.11bf, 802.11be, Wi-Fi 8, extremely high throughput (EHT), ultra high reliability (UHR), Wi-Fi AI, etc. 802.11 series protocols), and can also be applied to wireless personal area network systems based on ultra-wide band (UWB) (such as 802.15 series standards) and sensing systems (such as 802.11bf series standards). Among them, the 802.11ax standard can also be called the high-efficiency (HE) standard, and the 802.11be standard can also be called the extremely high throughput (EHT) standard. Among them, 802.11bf includes two major categories of standards: low-frequency (for example, sub7GHz) and high-frequency (for example, 60GHz). The implementation of sub7GHz mainly relies on standards such as 802.11ac, 802.11ax, 802.11be and next-generation standards, while the implementation of 60GHz mainly relies on standards such as 802.11ad, 802.11ay and next-generation standards. Among them, 802.11ad can also be called the directional multi-gigabit (DMG) standard, and 802.11ay can also be called the enhanced directional multi-gigabit (EDMG) standard.

[0089] The various aspects of the embodiments of the present application can also be applied to other networks that adopt various standards or protocols, such as high-performance wireless local area networks (HIPERLAN), wireless wide area networks (WWAN), wireless personal area networks (WPAN), or other networks now known or developed in the future.

[0090] The technical solutions of the embodiments of the present application can also be applied to various communication systems, such as: WLAN communication system, wireless fidelity (Wi-Fi) system, long term evolution (LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD), universal mobile telecommunication system (UMTS), world-wide interoperability for microwave access (WiMAX) communication system, fifth generation (5G) system or new radio (NR), sixth generation (6G) system, Internet of Things (IoT) network or vehicle to x (V2X), as well as new communication systems emerging in future communication development.

[0091] FIG1 is a schematic diagram of a communication system 100 provided in an embodiment of the present application. The communication system 100 includes at least one network device, which may be an access point (AP), as shown in FIG1 . Furthermore, the communication system 100 may also include at least one terminal, which may be a station (STA), as shown in FIG1 .

[0092] Optionally, the AP or STA provided herein may have certain intelligent model (or artificial intelligence (AI)) operation and maintenance capabilities, such as using the AI ​​model to reason about communication transmission decisions and also training the AI ​​model to achieve model optimization. For example, the AI ​​model may be a neural network model.

[0093] Optionally, the communication system 100 may further include an intelligent node, such as a server. The intelligent node may have the ability to operate and maintain an AI model and interact with the AP and / or STA. For example, the intelligent node may obtain model training data from the AP and / or STA to perform AI model training. The intelligent node may also respond to requests from the AP and / or STA, use the AI ​​model to infer the communication transmission policy of the communication network in which the AP and / or STA is located, and provide feedback to the AP and / or STA to improve system transmission performance.

[0094] For example, the AP may be understood as an access point entity, and the STA may be understood as a station entity. The AP and the STA may support a WLAN communication protocol, which may include an IEEE 802.11 series protocol.

[0095] The AP provided in the embodiment of the present application can be a device with wireless communication function, supports communication using the WLAN protocol, has the function of communicating with other devices in the WLAN network (such as STA or other AP), and of course, can also have the function of communicating with other devices. Alternatively, the AP is equivalent to a bridge connecting the wired network and the wireless network, and its main function is to connect various wireless network clients together and then connect the wireless network to the Ethernet. In the WLAN system, the access point can be called an access point station AP STA. The device with wireless communication function can be a complete device, or a chip or processing system installed in the complete device. The device installed with these chips or processing systems can implement the methods and functions of the embodiments of the present application under the control of the chip or processing system. The AP in the embodiment of the present application is a device that provides services for STA and can support the 802.11 series of protocols. For example, the access point can be an access point for a terminal device (such as a mobile phone) to enter a wired (or wireless) network. It can be deployed in a home, inside a building, inside a campus, or outdoors. For another example, an AP can be a communication entity such as a communication server, router, switch, or bridge; an AP can include various forms of macro base stations, micro base stations, and relay stations, or an AP can be a chip and processing system in these various forms of devices, thereby implementing the methods and functions of the embodiments of the present application. The access point in this application can be a high-efficiency (HE) AP or an extremely high-throughput EHT AP, or an access point applicable to future Wi-Fi protocols, etc.

[0096] The STA provided in the embodiments of the present application is a device with wireless communication capabilities, supports communication using the WLAN protocol, and has the ability to communicate with other stations or access points in the WLAN network. In a WLAN system, a STA can be referred to as a non-access point station (non-AP STA). For example, a STA can communicate with other devices in the WLAN by communicating with an AP. The device with wireless communication capabilities can be a complete device, or a chip or processing system installed in the complete device. The device installed with these chips or processing systems can implement the methods and functions of the embodiments of the present application under the control of the chip or processing system. For example, a station can be a wireless communication chip, a wireless sensor, or a wireless communication terminal, and can also be referred to as a user. For another example, a station can be a mobile phone that supports Wi-Fi communication capabilities, a tablet that supports Wi-Fi communication capabilities, a set-top box that supports Wi-Fi communication capabilities, a smart TV that supports Wi-Fi communication capabilities, a smart wearable device that supports Wi-Fi communication capabilities, a vehicle-mounted communication device that supports Wi-Fi communication capabilities, and a computer that supports Wi-Fi communication capabilities, etc.

[0097] The above-mentioned AP or STA may include a transmitter, a receiver, a memory, a processor, etc., wherein the transmitter and the receiver are used for sending and receiving packet structures respectively, the memory is used to store signaling information and store preset values ​​agreed in advance, etc., and the processor is used to parse signaling information, process related data, etc.

[0098] With the continuous evolution of WLAN application scenarios, WLAN systems will be applied to more scenarios or industries, such as the Internet of Things industry and the Internet of Vehicles industry. Devices that support WLAN communication (such as APs or STAs) can be sensor nodes in smart cities (such as smart water meters, smart electricity meters, and smart air detection nodes), smart devices in smart homes (such as smart cameras, projectors, displays, TVs, speakers, refrigerators, washing machines, etc.), IoT nodes and sensors in the Internet of Things, entertainment terminals (such as wearable devices such as AR and VR), smart devices in smart offices (such as printers, projectors, loudspeakers, speakers, etc.), infrastructure in daily life scenarios (such as vending machines, self-service navigation counters in supermarkets, self-service cash registers, self-service ordering machines, etc.), and equipment in large sports and music venues, etc. The specific forms of STAs and APs in the embodiments of this application are not limited and are only illustrative.

[0099] In order to support the development trend of high complexity of wireless networks, the application of artificial intelligence (AI) technology to assist in the management of wireless networks is considered. How to apply AI technology to wireless networks to improve communication performance has become a hot topic of current research. This application proposes that a first node can send first training data containing STA network experience information to a second node, so that through the transmission of the first training data in the network, the node maintaining the first intelligent model (referred to as an intelligent node) can perform model training of the first intelligent model based on the first training data, realize the application of the first intelligent model to the wireless network, and be able to infer a transmission strategy that improves the STA network experience, thereby improving network communication performance.

[0100] FIG2 is a schematic flow chart of a communication method 200 provided in an embodiment of the present application. The method 200 includes but is not limited to the following S201 and S202.

[0101] S201: A first node determines first training data, where the first training data includes network experience information of a plurality of STAs.

[0102] Among them, S201 is an optional step.

[0103] For example, the first node may collect network experience information of multiple STAs, and determine the first training data according to the network experience information of the multiple STAs.

[0104] The network experience information of the STA may indicate the STA's experience evaluation of the communication network, such as reflecting the STA's satisfaction with the service quality (and / or network quality). For example, the network experience information may indicate one of the multiple candidate evaluation levels of the STA for the service quality (and / or network quality). Exemplarily, the multiple candidate evaluation levels may include high, medium, and low. Or the multiple candidate evaluation levels may include excellent, good, average, and poor, which is not limited in this application. For another example, the network experience information may indicate the gap between the STA's service quality (and / or network quality) and the expected quality, or the STA's satisfaction with the service quality (and / or network quality) and the expected quality. The network experience information of the STA may also indicate the transmission delay, bit error rate, packet loss rate, throughput, etc. of the current service data. It should be understood that the specific name of the network experience information is not limited in this application. The network experience information may also be referred to as network evaluation information, quality of experience (QoE) information, or quality of service (QoS) information.

[0105] S202, the first node sends first training data to the second node, the first training data is used for model training of a first intelligent model, and the first intelligent model is used to infer the transmission strategy of the communication network where the first node is located.

[0106] After the first node determines the first training data, it can send the first training data to the second node, so that the first training data can be transmitted to the intelligent node. The intelligent node is a node that maintains the first intelligent model. For example, the intelligent node can be a server, or it can be a node in the network with AI capabilities (such as a model training module and a model inference module). The intelligent node can train the first intelligent model based on the first training data. The trained first intelligent model can infer the transmission strategy of the communication network where the first node is located.

[0107] In one embodiment, the first node may be a first STA, and the second node may be an intelligent node. The first STA has the ability to access the intelligent node. The first STA may collect first training data containing network experience information of multiple STAs from an AP to which the first STA is connected, and then send the first training data to the intelligent node, so that the intelligent node can perform model training of the first intelligent model based on the first training data.

[0108] In another embodiment, the first node may be an AP, and the second node may be a first STA capable of accessing an intelligent node. The AP may collect network experience information from multiple STAs with which it has established communication connections, determine first training data, and send the first training data to the first STA, so that the STA can send the first training data to the intelligent node, enabling the intelligent node to perform model training of the first intelligent model based on the first training data.

[0109] In another embodiment, the first node may be an AP, and the second node may be an intelligent node. The AP has the ability to access the intelligent node. The AP may collect network experience information from multiple STAs that have established communication connections with it, determine first training data, and send the first training data to the intelligent node, so that the intelligent node can perform model training of the first intelligent model based on the first training data.

[0110] Specifically, the intelligent node performs model training on the first intelligent model, and may use the network experience information and network status information of the multiple STAs, as well as the transmission strategy information of each of the multiple STAs, as training data. Through model training, a trained first intelligent model is obtained. That is, the intelligent node updates the model parameters of the first intelligent model through model training based on the training data. The obtained first intelligent model with updated model parameters is the trained first intelligent model, enabling the first intelligent model to infer a transmission strategy that conforms to the network status based on the network status information.

[0111] The network status information indicates the communication network status corresponding to the STA's network experience information. The STA's network experience information specifically indicates the STA's evaluation of the communication network experience under the communication network status. This network status information may include, but is not limited to, one or more of the following: the number of active devices in the communication network where the first node resides, communication throughput, transmission efficiency, bit error rate, packet loss rate, bandwidth, latency, number of sent / received messages, and received energy. The network status may be one or more of the measurement quantities defined in the IEEE 802.11k standard.

[0112] The STA's transmission strategy information can be used to indicate the transmission strategy corresponding to the STA's network experience information. The STA's network experience information specifically indicates the network experience evaluation of the STA using the transmission strategy for communication in the communication network. The transmission strategy information can indicate the number of data streams (or data layers) used by the STA during communication, the channel coding method used, the channel coding code rate, the modulation order, the bandwidth, and one or more of channel aggregation / bonding.

[0113] Optionally, the first training data may further include the network status information and / or the transmission strategy information of the STA. Alternatively, the intelligent node may obtain the network status information and / or the transmission strategy information of the STA from other nodes.

[0114] The first training data also includes acquisition time information, which is used to indicate the acquisition time of other information in the first training data (i.e., information other than the acquisition time information). The intelligent node can determine the acquisition time of other information in the first training data based on the acquisition time information. For example, if the first training data does not include network status information and / or STA's transmission strategy information, the first training data includes information for indicating the acquisition time of STA's network experience information. The intelligent node can determine the network status information and STA's transmission strategy information of the corresponding time based on the acquisition time, obtain the network status information corresponding to the STA's network experience information and the STA's transmission strategy information, and thus perform model training of the first intelligent model.

[0115] In one implementation, the first intelligent model may be a large language model (LLM).

[0116] Unlike other AI technologies, LLM exhibits an emergent phenomenon. After training the basic model, it can be widely applied across various industries simply by incorporating specialized domain knowledge and fine-tuning certain parameters. LLM model training involves three stages: pre-training. Next, fine-tuning based on domain knowledge to support downstream tasks. Finally, feedback-based refinement and fine-tuning enable further alignment with downstream tasks during use. The pre-training stage does not involve domain knowledge; it trains the basic model based on big data and context. The second training stage, though fine-tuning, also falls under the category of pre-training; it utilizes domain knowledge to make the basic model more suitable for downstream tasks. In current wireless systems, numerous rule-based solutions exist. These solutions or rules are derived through extensive simulations and expert experience. Therefore, these rules can serve as data for the second training stage. Alternatively, communication network simulation can be used to perform the second training stage.

[0117] After the second training process, LLM already has basic wireless domain knowledge and can infer transmission strategies based on the communication network status. However, the transmission strategy inferred at this time may not be aligned with the STA's preferences / experiences. Therefore, this application proposes that for the first intelligent model obtained after the second training process, the intelligent node can obtain the first training data including the network experience information of multiple STAs from the first node, thereby fine-tuning the model parameters of the first intelligent model through model training, so that the first intelligent model can output a transmission strategy that conforms to the network status and the STA's preferences / experiences. The first intelligent model obtained after the second training process can be pre-configured in the intelligent node, or obtained by the intelligent node based on simulation execution model training. This application does not limit this.

[0118] Exemplarily, the intelligent node performs model training on the first intelligent model based on the first training data using a reinforcement learning from human feedback (RLHF) algorithm or a direct preference optimization (DPO) algorithm. The intelligent node may also use other algorithms, and this application does not limit the algorithm used by the intelligent node to perform model training on the first intelligent model.

[0119] The first intelligent model may also be other AI models, such as other neural network models, which is not limited in this application.

[0120] According to the above scheme, the intelligent node can obtain the first training data from the first node, so that the intelligent node can perform model training on the first intelligent model based on the obtained network experience information of multiple STAs, and train the first intelligent model by referring to the network experience information of the STA, so that the first intelligent model can be used for inference of the transmission strategy to obtain a transmission strategy with a high degree of match with the network status, thereby improving the communication performance of the network and meeting the transmission needs of the STA.

[0121] FIG3 is a schematic flow chart of a communication method 300 provided in an embodiment of the present application. In the method 300, the first STA is a STA capable of accessing a smart node. The method 300 includes but is not limited to the following steps S301 to S305:

[0122] S301: A first STA sends first request information to an AP, where the first request information is used to request first training data.

[0123] Correspondingly, the AP receives the first request information from the first STA and determines that the first STA requests the first training data.

[0124] The first request information may be carried in a radio frame sent by the first STA to the AP. The radio frame may be a management frame. The management frame may be a management frame in a currently defined frame format, such as one of the indication fields or the reserved field multiplexed with the first request information. Alternatively, the management frame may be a management frame in a frame format designed specifically for the first request information. This application is not limited to this.

[0125] S302: The AP determines first training data, where the first training data includes network experience information of multiple STAs.

[0126] Among them, S302 is an optional step.

[0127] The manner in which the AP determines the first training data may include but is not limited to the following two implementations, which are respectively introduced below.

[0128] In the first implementation mode, an AP receives first information from a plurality of STAs, where each piece of first information includes network experience information of a corresponding STA.

[0129] To determine the first training data, the AP may send second information to multiple STAs that have established communication connections with the AP, where the second information is used to request the first information. The multiple STAs, in response to the AP's request, send the first information to the AP. Each piece of first information includes network experience information of the STA that sent the first information.

[0130] The second information may be carried in a radio frame used for a status request, and the first information may be carried in a radio frame used for reporting a measurement report. The radio frame carrying the first information and / or the second information may be a radio frame that reuses a currently defined frame format, or a radio frame in a newly defined frame format. This application does not impose any restrictions on this.

[0131] The AP may determine first training data based on first information of multiple STAs, where the first training data includes network experience information of the multiple STAs, and further includes transmission strategy information of the STAs corresponding to the network experience information and network status information corresponding to the network experience information.

[0132] It can be considered that the AP requests the STA's real-time network experience information, and the network experience information can be called the STA's current network experience information, the STA's transmission strategy information corresponding to the network experience information can be called the STA's current transmission strategy, and the network status information can be called the current network status information.

[0133] In one example, the AP may have global network observation capabilities, enabling it to observe and obtain the current transmission strategy information and network status information of the STA. Upon receiving the first message, the AP determines the first training data based on the STA's network experience information in the first message and the STA's transmission strategy information and network status information observed and obtained by the AP.

[0134] In another example, the first information sent by the STA may also include the STA's current transmission policy information and / or current network status information. The second information sent by the AP and the multiple STAs is not only used to request the STA's network experience information, but also used to request the transmission policy information and / or corresponding network status information corresponding to the STA's network experience information. Then the first information sent by the multiple STAs to the AP includes the corresponding information requested by the second information, that is, the first information includes the STA's network experience information, and the first information also includes the STA's transmission policy information and / or corresponding network experience information corresponding to the network experience information. The first training data includes the first information of the multiple STAs. If the first information only includes the STA's transmission policy information (or current network status information), the AP determines the first training data based on the observed STA's transmission policy information (or network status information) and the first information.

[0135] Optionally, the first training data further includes acquisition time information, where the acquisition time information is used to indicate the acquisition time of other information in the first training data (ie, other information other than the acquisition time information).

[0136] For example, the first information may include information for indicating the collection time of the STA's network experience information. The AP may determine the network status information and / or the STA's transmission strategy information corresponding to the collection time based on the collection time, and obtain the network status information and / or the STA's transmission strategy information corresponding to the STA's network experience information. The first training data may also include the collection time information, so that the intelligent node can determine the time corresponding to the information in the first training data after obtaining the first training data.

[0137] It should be understood that the first training data sent by the AP to the first STA does not carry information of each STA, and privacy can be protected.

[0138] In the second implementation mode, the AP may maintain a second intelligent model, and the AP may use the second intelligent model to infer the first training data. The input of the second intelligent model is the network status information and the transmission strategy information of the STA, and the output is the first training data.

[0139] Exemplarily, the second intelligent model may be a reward model (RM).

[0140] After receiving the first request information from the first STA, the AP may input the current network state information and the current transmission strategy information of the multiple STAs into the second intelligent model, and infer the network experience information of the multiple STAs using the second intelligent model. The network experience information of the multiple STAs is specifically the network experience information of the multiple STAs corresponding to the current network state information and the current transmission strategy information of the multiple STAs, as inferred by the second intelligent model.

[0141] The AP can determine the first training data based on the output of the second intelligent model. In one implementation, the output of the second intelligent model is the network experience information of the multiple STAs inferred by the second intelligent model. The AP determines the first training data based on the output of the second intelligent model and the current network status information and the current transmission strategy information of the multiple STAs. The first training data includes the network experience information of the multiple STAs, the current network status information and the current transmission strategy information of the multiple STAs. In another implementation, the second intelligent model outputs the first training data, that is, the second intelligent model not only outputs the network experience information of the multiple STAs inferred by the second intelligent model, but also outputs the input information of the second intelligent model, that is, the current network status information and the transmission strategy information of the multiple STAs. The output of the second intelligent model obtained by the AP is the first training data.

[0142] In the second embodiment, the AP can obtain the network experience information of the STA based on the second intelligent model inference, without having to collect network experience information from multiple STAs that have established connections each time the first STA requests the first training data. This can reduce the overhead of wireless resources and improve the efficiency of obtaining the first training data.

[0143] The second intelligent model may be pre-configured in the AP, for example, after a node in the network (such as a server, etc.) performs model training to obtain the second intelligent model, the second intelligent model is configured for the AP. Alternatively, the second intelligent model may be obtained by the AP performing model training. The AP may perform model training, and the AP may first collect training data for model training of the second intelligent model, such as the AP may obtain network status information and STA's transmission policy information based on network observation or from the STA, and the AP may collect the STA's network experience information corresponding to the network status information and transmission policy information from the STA with which the AP establishes a communication connection. The AP may use the network status information and the STA's transmission policy information as training data, and the STA's network experience information as verification information for model training, perform model training, and obtain the trained second intelligent model, so that the second intelligent model can infer the corresponding network experience information based on the input network status information and transmission policy information. After the AP obtains the second intelligent model through model training, it can use the network status information and the STA's transmission strategy information as the input of the second intelligent model to obtain the network experience information of the STA inferred by the second intelligent model, without having to collect network experience information from multiple STAs that have established connections every time the first STA requests the first training data. This can reduce the overhead of wireless resources and improve the efficiency of obtaining the first training data.

[0144] S303: The AP sends first training data to the first STA.

[0145] Accordingly, the first STA receives the first training data from the AP.

[0146] S304: The first STA sends first training data to the smart node.

[0147] After the first STA obtains the first training data from the AP, it may send the first training data to the intelligent node so that the intelligent node may perform model training of the first intelligent model based on the first training data.

[0148] S305, the intelligent node performs model training on the first intelligent model according to the first training data to obtain a trained first intelligent model, and the first intelligent model is used to infer the transmission strategy of the communication network where the first node is located.

[0149] After obtaining the first training data from the first STA, the intelligent node performs model training, such as using the RLHF algorithm or the DPO algorithm. The obtained first intelligent model with updated model parameters is the trained first intelligent model. This first intelligent model can be used to infer the transmission strategy of the communication network where the first node is located.

[0150] The first STA can request transmission policy information from the intelligent node. The intelligent node can use the transmission policy information inferred by the first intelligent model and feed it back to the first STA. Specifically, the first STA can send a second request message to the intelligent node, requesting the transmission policy information corresponding to the current network status information. After receiving the second request message, the intelligent node can input the current network status information into the first intelligent model to obtain the first transmission policy information output by the first intelligent model. The intelligent node then sends a response message to the first STA, indicating the first transmission policy information.

[0151] The second request information sent by the first STA to the smart node may include the current network status information, or the smart node may obtain the current network status information in other ways, which is not limited in this application.

[0152] In one embodiment, a STA connected to an AP can report to the AP that its current network experience does not meet its requirements. If the number of STAs experiencing unsatisfactory network experiences is greater than or equal to a threshold, the AP notifies a first STA. The first STA then sends a second request to an intelligent node, requesting transmission policy information appropriate for the current network state information. The intelligent node inputs the current network state information into a first intelligent model. The first intelligent model then infers transmission policy information corresponding to the current network state information and feeds it back to the first STA. The first STA can send this transmission policy information to the AP, which determines the transmission policy for the AP and / or each STA based on the transmission policy information. Specifically, the AP can directly adopt the transmission policy indicated by the transmission policy information fed back by the intelligent node, or use the transmission policy information fed back by the intelligent node as a reference for the AP to make overall adjustments and determine the transmission policy for the AP and / or each STA. This allows the AP and / or STA to adopt a transmission policy that matches the current network state to meet the STA's transmission requirements, improve the STA's network experience, and thereby enhance network communication performance.

[0153] In another embodiment, if a first STA's network experience does not meet its requirements, the first STA may send a third request to the AP, requesting current network status information. In response to the first STA's request, the AP sends the current network status information to the first STA. After obtaining the current network status information from the AP, the first STA sends a second request to the intelligent node, requesting the first STA's transmission policy information corresponding to the current network status information. The second request includes the current network status information obtained by the first STA from the AP. After receiving the second request, the intelligent node uses the current network status information in the second request as input to a first intelligent model and uses the first intelligent model to infer first transmission policy information. The first transmission policy is the transmission policy information corresponding to the current network status of the first STA. The intelligent node indicates the first transmission policy information in a response message, allowing the first STA to obtain the first transmission policy information through the response message and perform data transmission based on the first transmission policy information. This allows the first STA to use a transmission policy that matches the current network status for data transmission, thereby meeting the first STA's transmission requirements, improving the first STA's network experience, and thereby improving network communication performance.

[0154] Optionally, the second request information sent by the first STA specifically includes current network status information represented by natural language, and / or the response information sent by the intelligent node specifically includes first transmission strategy information represented by natural language.

[0155] Specifically, the first STA can process the current network status information in a natural language representation manner, obtain the current network status information represented by the natural language, and send it to the intelligent node through the second request information. After the intelligent node receives the second request information, it can parse the current network status information represented by the natural language based on the natural language representation manner to obtain the current network status information. Similarly, the intelligent node can process the first transmission strategy information in a natural language representation manner, obtain the first transmission strategy information represented by the natural language, and send it to the first STA through the response information. After the first STA receives the response information, it can parse the first transmission strategy information represented by the natural language based on the natural language representation manner to obtain the first transmission strategy information. Optionally, the first STA / intelligent node can convert between information and information represented by the natural language based on a model with natural language representation capabilities.

[0156] The first STA and the intelligent node convert interactive information through natural language representation, avoiding the need for predefined information sets (such as network status information sets and transmission policy information sets) in the protocol. This allows both parties to reach a consensus on the information set and select the appropriate information from the set for interaction. This reduces the storage overhead of both parties associated with storing predefined information sets, and information types are not restricted to the set, which improves scalability.

[0157] In another implementation, the AP may be capable of accessing an intelligent node. The AP may then provide first training data to the intelligent node. For example, the AP may determine the first training data and send the first training data to the intelligent node. The intelligent node may then perform model training on the first intelligent model based on the first training data from the AP, thereby obtaining a trained first intelligent model. The manner in which the AP determines the first training data can be found in the description of S302 above, and the manner in which the intelligent node performs model training can be found in the description of S305 above, and will not be further elaborated here.

[0158] According to the above scheme, the intelligent node can obtain the network experience information of the STA as the training data of the first intelligent model from the first node, so that the intelligent node can complete the model training of the first intelligent model based on the network experience information of the STA, so that the first intelligent model can infer the transmission strategy information that matches the current network status based on the current network status information, so that the STA can apply the transmission strategy information to match the current network status, thereby meeting the STA transmission requirements, improving the STA's network experience, and thereby improving the network communication performance.

[0159] It is understood that in order to implement the functions in the above embodiments, the first node, the second node, the STA, the AP, and the intelligent node include hardware structures and / or software modules corresponding to the execution of each function. It should be readily apparent to those skilled in the art that, in combination with the units and method steps of the various examples described in the embodiments disclosed in this application, this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in hardware or in a manner driven by computer software depends on the specific application scenario and design constraints of the technical solution.

[0160] Figures 4 and 5 are schematic diagrams of the structures of possible communication devices provided in the embodiments of the present application. These communication devices can be used to implement the functions of the first node or the second node in the above method embodiment, and thus can also achieve the beneficial effects possessed by the above method embodiment. In the embodiment of the present application, the communication device can be a STA as shown in Figure 1, or an AP as shown in Figure 1, or an intelligent node for maintaining the first intelligent model, or a module (such as a chip or chip system) applied to each node.

[0161] The communication device 400 includes a transceiver unit 420, which can be used to receive or send information. The communication device 400 can also include a processing unit 410, which can be used to process instructions or data to implement corresponding operations.

[0162] It should be understood that when the communication device 400 is a chip configured in (or used in) a communication device, the transceiver unit 420 in the communication device 400 can be the input / output interface or circuit of the chip, and the processing unit 410 in the communication device 400 can be the processor in the chip.

[0163] Optionally, the communication device 400 may further include a storage unit 430, which may be used to store instructions or data. The processing unit 410 may execute the instructions or data stored in the storage unit to enable the communication device to perform corresponding operations.

[0164] The communication device 400 may be used to implement the functions of the first node or the second node in the method embodiment shown in FIG. 2 .

[0165] When communication device 400 is used to implement the functions of a terminal in the method embodiment shown in FIG2 : processing unit 410 is configured to determine first training data, where the first training data includes network experience information of multiple STAs. Transceiver unit 420 is configured to send the first training data to a second node. The first training data is used to train a first intelligent model, which is used to infer the transmission strategy of the communication network in which the communication device is located.

[0166] When communication device 400 is used to implement the functions of the network device in the method embodiment shown in Figure 2 , transceiver unit 420 is configured to receive first training data from a first node, where the first training data includes network experience information of a STA. Processing unit 410 is configured to perform model training on a first intelligent model based on the first training data to obtain a trained first intelligent model, which is used to infer a transmission policy in the communication network where the first node resides.

[0167] For a more detailed description of the processing unit 410 and the transceiver unit 420 , reference may be made to the relevant description in the method embodiment shown in FIG. 2 .

[0168] It should be understood that the transceiver unit 420 in the communication device 400 can be implemented through a communication interface (such as a transceiver, a transceiver circuit, an input / output interface, or a pin, etc.). When the communication interface is a transceiver, the transceiver can be composed of a receiver and / or a transmitter. The processing unit 410 in the communication device 400 can be implemented by at least one processor. The processing unit 410 in the communication device 400 can also be implemented by at least one logic circuit. Optionally, the communication device 400 also includes a storage unit, which can be implemented by a memory.

[0169] As shown in Figure 5, communication device 500 includes a processor 510 and an interface circuit 520. Processor 510 and interface circuit 520 are coupled to each other. It is understood that interface circuit 520 can be a transceiver or an input / output interface. Optionally, communication device 500 may also include a memory 530 for storing instructions executed by processor 510, input data required by processor 510 to execute instructions, or data generated after processor 510 executes instructions.

[0170] In one implementation, the memory 530 may also be integrated into the processor 510 or independent of the processor 510 .

[0171] When the communication device 500 is used to implement the method shown in FIG. 2 , the processor 510 is used to implement the functions of the processing unit 410 , and the interface circuit 520 is used to implement the functions of the transceiver unit 420 .

[0172] When the aforementioned communication device is a chip used in a STA, the STA chip can implement the STA functions described in the aforementioned method embodiments. The STA chip receives information from other modules in the STA (e.g., a radio frequency module or antenna), which is information sent by the AP to the STA; or the STA chip sends information to other modules in the STA (e.g., a radio frequency module or antenna), which is information sent by the STA to the AP.

[0173] When the communication device is a module applied to an AP, the AP module can implement the functions of the AP in the above-mentioned method embodiment. The AP module receives information from other modules in the AP (such as a radio module or antenna), and the information is sent by the STA to the AP; or the AP module sends information to other modules in the AP (such as a radio module or antenna), and the information is sent by the AP to the STA. The AP module here can be the baseband chip of the AP, or it can be a DU or other module. The DU here can be a DU in the open radio access network (O-RAN) architecture.

[0174] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0175] The method steps in the embodiments of the present application can be implemented in hardware or in software instructions that can be executed by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disk, mobile hard disk, CD-ROM or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. The storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. In addition, the ASIC can be located in an access network device or a terminal device. The processor and storage medium can also exist in the access network device or the terminal device as discrete components.

[0176] According to the method provided in the embodiment of the application, the embodiment of the present application also provides a computer program product, which includes: computer program code, when the computer program code is executed by one or more processors, it enables the device including the processor to execute the method of the embodiment shown in Figure 2.

[0177] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are performed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, or other programmable device.

[0178] According to the method provided in an embodiment of the present application, an embodiment of the present application also provides a computer-readable storage medium, which stores the above-mentioned computer program or instructions. When the computer program or instructions are executed by one or more processors, the device including the processor executes the method of the embodiment shown in Figure 2.

[0179] As described above, the computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video disk; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or can include both volatile and non-volatile types of storage media.

[0180] According to the method provided in the embodiment of the present application, the embodiment of the present application also provides a communication system, including the one or more terminals mentioned above. The system may further include the one or more network devices mentioned above.

[0181] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the devices described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0182] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this solution based on actual needs.

[0183] In the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0184] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A communication method, characterized in that: include: The first node determines first training data, where the first training data includes network experience information of multiple site STAs; The first node sends the first training data to the second node, the first training data is used for model training of a first intelligent model, and the first intelligent model is used to infer a transmission strategy of a communication network where the first node is located.

2. The method according to claim 1, characterized in that: The first training data also includes one or more of the following: The network state information corresponding to the network experience information, the transmission strategy information or the collection time information corresponding to the network experience information, wherein the collection time information is used to indicate the collection time of the information in the first training data.

3. The method according to claim 1 or 2, characterized in that: The first node is a first STA, and the second node is an intelligent node, wherein the first STA is an STA capable of accessing the intelligent node, and the intelligent node is a node that maintains the first intelligent model.

4. The method according to claim 3, characterized in that: The first node determines first training data, including: The first STA sends first request information to an access point AP, where the first request information is used to request the first training data; The first STA receives the first training data from the AP.

5. The method according to claim 1 or 2, characterized in that: The first node is an AP, and the second node is an intelligent node or a first STA, wherein the first STA is a STA capable of accessing an intelligent node, and the intelligent node is a node that maintains the first intelligent model.

6. The method according to claim 5, characterized in that The first node determines first training data, including: The AP receives first request information from the first STA, where the first request information is used to request the first training data; The AP receives a plurality of first information from a plurality of STAs, each of the plurality of first information comprising network experience information of each STA; The AP determines the first training data according to the multiple pieces of first information.

7. The method according to claim 6, characterized in that The first training data includes the first information of the multiple STAs, and the first information also includes at least one of network state information corresponding to the network experience information of the STA, transmission strategy information or collection time information of the STA corresponding to the network experience information of the STA; or, The first training data is determined by the AP according to at least one of network status information, transmission strategy information or acquisition time information of the STA and the first information of the multiple STAs, The collection time information is used to indicate the collection time of the information in the first training data.

8. The method according to claim 5, characterized in that The first node determines first training data, including: The AP receives first request information from the first STA, where the first request information is used to request the first training data; The AP inputs network status information and transmission strategy information of multiple STAs into a second intelligent model to obtain the first training data output by the second intelligent model.

9. The method according to claim 8, characterized in that The method further comprises: The AP performs model training according to the network experience information of multiple STAs and the transmission strategy information and network status information corresponding to the network experience information to obtain a second intelligent model.

10. The method according to claim 8 or 9, characterized in that: The second intelligent model is a feedback model, the input of the feedback model is network status information and STA transmission strategy information, and the output of the feedback model includes STA's network experience information.

11. The method according to any one of claims 1 to 10, characterized in that The second node is an intelligent node, and the intelligent node is a node that maintains the first intelligent model; the method further includes: The first node sends second request information to the second node, where the second request information is used to request transmission strategy information corresponding to current network status information; The first node receives response information from the second node, where the response information is used to indicate first transmission strategy information, where the first transmission strategy information is the transmission strategy information corresponding to the current network status information; The first node performs data transmission according to the first transmission strategy information.

12. The method according to claim 11, characterized in that The first node sending second request information to the second node includes: When the number of STAs whose network experience does not meet the requirements is greater than or equal to the number threshold, the first node sends the second request information to the second node.

13. The method according to claim 11 or 12, characterized in that: The first node is a first STA, and the first STA is a STA capable of accessing a smart node; The first node sending second request information to the second node includes: When the network experience of the first STA does not meet the requirement, the first STA sends third request information to the AP, where the third request information is used to request the current network status information; The first STA receives the current network status information from the AP; The first STA sends the second request information to the AP, where the second request information includes the current network status information.

14. The method according to any one of claims 11 to 13, characterized in that The second request information includes the current network status information represented by natural language; and / or, The response information includes the first transmission strategy information represented by natural language.

15. The method according to claim 14, characterized in that The method further comprises: The first node processes the current network transmission state based on a natural language representation method to obtain the current network transmission state information represented by the natural language; and / or, The first node parses the first transmission strategy information represented by natural language in the response information based on a natural language representation method to obtain the first transmission strategy information.

16. The method according to any one of claims 1 to 15, characterized in that The first intelligent model is a large language model LLM, the input of the LLM is the first training data, and the output of the LLM is transmission strategy information.

17. A data transmission method, characterized in that: include: The second node receives first training data from the first node, where the first training data includes network experience information of the STA; The second node performs model training on the first intelligent model according to the first training data to obtain the trained first intelligent model, and the first intelligent model is used to infer the transmission strategy in the communication network where the first node is located.

18. The method according to claim 17, characterized in that The first training data also includes one or more of the following: The network state information corresponding to the network experience information, the transmission strategy information or the collection time information corresponding to the network experience information, wherein the collection time information is used to indicate the collection time of the information in the first training data.

19. The method according to claim 17 or 18, characterized in that The method further comprises: The second node receives second request information from the first node, where the second request information is used to request transmission strategy information corresponding to current network status information; The second node sends response information to the first node, where the response information is used to indicate first transmission strategy information, and the first transmission strategy information is the transmission strategy information corresponding to the current network status.

20. The method according to claim 19, characterized in that The method further comprises: The second node inputs the current network status information into the first intelligent model to obtain the first transmission strategy information inferred by the first intelligent model.

21. The method according to claim 19 or 20, characterized in that The second request information includes the current network status information.

22. The method according to claim 21, characterized in that The second request information includes the current network status information represented by natural language; and / or, The response information includes the first transmission strategy information represented by natural language.

23. The method according to any one of claims 17 to 22, characterized in that The first node is an access point AP or a station STA capable of accessing an intelligent node, and the second node is an intelligent node.

24. The method according to any one of claims 17 to 23, characterized in that The first intelligent model is a large language model LLM, the input of the LLM is the first training data, and the output of the LLM is a transmission strategy.

25. A communication device, characterized in that: The device comprises a processor coupled to a memory, the memory being used to store a computer program, the processor being used to execute the computer program stored in the memory so that the communication device performs the method as claimed in any one of claims 1 to 16; or so that the communication device performs the method as claimed in any one of claims 17 to 24.

26. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 24.

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