Information security protection method and device

Abstract

The invention provides an information security protection method and device, and the method comprises the steps: a first node obtains first power control information based on an information security protection demand, the maximum transmission power of a second node, and channel information between the second node and the first node, the first power control information is power control information which is obtained through prediction and enables the signal-to-noise power ratio of a received signal of the first node to be maximum under the condition that the information security protection requirement is met, and the received signal comprises a signal from the second node. And the first node sends the first power control information to the second node, the first power control information is used for the second node to generate a first signal, the first node receives the first signal from the second node, and the first signal comprises data information and security protection information. The security protection information is used for protecting the information security of the data information. The communication security and reliability of wireless communication can be improved.

Description

Information security protection method and device Technical Field

[0001] The present application relates to the field of communications, and more specifically, to an information security protection method and device. Background Art

[0002] Wireless communication technology is now widely used in production and daily life, for example in vertical industries with extremely high reliability requirements, such as industrial control, telemedicine, and autonomous driving, as well as in massive machine-to-machine communication scenarios such as smart cities, smart homes, and environmental monitoring. Furthermore, wireless communication technology can be combined with artificial intelligence (AI) to implement AI tasks such as training and inference of AI models, meeting even more diverse needs.

[0003] With the widespread application of wireless communication technology, improving information security in wireless communications and reducing the risk of information leakage have become the focus of current research.

[0004] Summary of the Invention

[0005] The embodiments of the present application provide an information security protection method and device, which can improve the communication security and reliability of wireless communications.

[0006] In a first aspect, an information security protection method is provided, which can be executed by a communication device or a module (such as a chip, a chip system) configured in (or used for) a communication device.

[0007] The method includes: the first node obtains first power control information based on information security protection requirements, the maximum transmission power of the second node, and channel information between the second node and the first node, the first power control information is predicted to maximize the signal-to-noise power ratio of the received signal of the first node while meeting the information security protection requirements, and the received signal includes a signal from the second node; the first node sends the first power control information to the second node, and the first power control information is used for the second node to generate a first signal; the first node receives the first signal from the second node, wherein the first signal includes data information and security protection information, and the security protection information is used to protect the information security of the data information.

[0008] According to the above scheme, the security of data information transmission in the wireless channel can be improved, the probability of information leakage can be reduced, and the signal-to-noise ratio of the received signal at the receiving end can be maximized while meeting the security protection requirements of the data information, thereby improving the reliability of data information transmission.

[0009] In combination with the first aspect, in certain embodiments of the first aspect, the first power control information includes power control information corresponding to the data information and power control information corresponding to the security protection information.

[0010] According to the above scheme, the first power control information can include power control information for controlling the data information in the first signal and power control information for controlling the security protection information, so as to maximize the signal-to-noise ratio of the received signal at the receiving end while meeting the security protection requirements of the data information by performing power control on the data information and the security protection information respectively, thereby improving the reliability of data information transmission.

[0011] In combination with the first aspect, in certain embodiments of the first aspect, there are multiple second nodes, and the first node obtains the first power control information corresponding to the second node based on the information security budget, the maximum transmission power of the second node, and the channel information between the second node and the first node, including: the first node obtains the power control information corresponding to each second node based on the information security protection requirements, the maximum transmission power of each second node, and the channel information between the multiple second nodes and the first node; and the first node receives the first signal from the second node, including: the first node receives the superimposed signal of the first signals from the multiple second nodes.

[0012] According to the above solution, when a first node needs to receive a superposition of first signals from multiple second nodes, the first node obtains power control information corresponding to each second node based on information security requirements, the maximum transmission power of each second node, and channel information between the multiple second nodes and the first node. This ensures that the superposition of first signals from multiple second nodes received by the first node maximizes the signal-to-noise ratio of the received signal at the receiving end while meeting the data security requirements, thereby improving the reliability of data transmission.

[0013] In combination with the first aspect, in certain embodiments of the first aspect, the method also includes: the first node sends first information to the second node, the first information is used to request node feature information output by the kth layer of the first machine learning model, and the data information includes the node feature information output by the kth layer of the first machine learning model from the second node; and the method also includes: the first node determines the second node feature information corresponding to the kth layer of the first machine learning model based on the node feature information output by the kth layer of the first machine learning model.

[0014] According to the above scheme, when graph neural networks (GNNs) are applied to wireless communication networks, the node feature information obtained by each layer iteration needs to be transmitted in the wireless channel. The two adjacent communications have a high correlation, and the above information security protection method can be used to improve the security and reliability of information transmission.

[0015] In combination with the first aspect, in some embodiments of the first aspect, the first information includes the first power control information.

[0016] In combination with the first aspect, in certain embodiments of the first aspect, the method also includes: the first node inputs the first feature information and the second node feature information corresponding to the kth layer of the first machine learning model into the kth layer of the second machine learning model to obtain the aggregated node feature information output by the kth layer of the second machine learning model, wherein k is equal to 1, and the first feature information is the node feature information of the first node; or, k is an integer greater than 1, and the first feature information is the second node feature information corresponding to the k-1th layer of the first machine learning model.

[0017] According to the above scheme, the application of GNN in wireless communication networks can enable each communication node to obtain the characteristics and dependencies of adjacent nodes at different proximity depths in the environment where each node is located, thereby determining the state characteristics of the communication node itself, achieving higher quality communication, and realizing distributed control and scheduling of the network.

[0018] In combination with the first aspect, in certain embodiments of the first aspect, the method also includes: the first node inputs the second node feature information corresponding to the kth layer into the k+1th layer of the first machine learning model to obtain the node feature information output by the k+1th layer of the first machine learning model; the first node receives second power control information from the second node; and the first node sends a second signal to the second node, the second signal including the node feature information output by the k+1th layer of the first machine learning model and security protection information, and the second signal is generated based on the second power control information.

[0019] According to the above scheme, in addition to obtaining its own state characteristics by receiving feature information from each adjacent node, the first node also generates a second signal based on power control information containing the node feature information output by the k+1th layer of the first machine learning model of the first node, so that the adjacent nodes of the first node can also obtain their own state characteristics.

[0020] In a second aspect, an information security protection method is provided, which can be executed by a communication device or a module (such as a chip, a chip system) configured in (or used for) a communication device.

[0021] The method includes: a second node receives first power control information from a first node, and the first power control information is used by the second node to generate a first signal; the second node sends the first signal to the first node, and the first signal includes data information and security protection information, and the security protection signal is used to protect the information security of the data information.

[0022] In combination with the second aspect, in certain implementations of the second aspect, the first power control information includes power control information corresponding to the data information and power control information corresponding to the security protection information.

[0023] In combination with the second aspect, in certain implementations of the second aspect, the method also includes: the second node receives first information from the first node, the first information is used to request node feature information output by the kth layer of the first machine learning model, wherein the data information includes the node feature information output by the kth layer of the first machine learning model of the second node, and k is a positive integer.

[0024] In combination with the second aspect, in some implementations of the second aspect, the first information includes the first power control information.

[0025] In combination with the second aspect, in certain implementations of the second aspect, the method further includes: the second node inputs the second feature information into the kth layer of the first machine learning model to obtain the node feature information output by the kth layer of the first machine learning model, wherein k=1, and the second feature information is the node feature information of the second node; or, k is an integer greater than 1, and the second feature information is the first node feature information corresponding to the k-1th layer of the first machine learning model, and the first node feature information is determined based on the node feature information output by the kth layer of the first machine learning model from at least one of the first nodes.

[0026] According to 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. The module may be a hardware circuit, software, or a combination of hardware circuit and software. In one design, the device includes: a processing unit for obtaining first power control information based on an information security protection requirement, a maximum transmission power of a second node, and channel information between the second node and the first node. The first power control information is power control information that is predicted to maximize the signal-to-noise power ratio of a received signal of the first node while satisfying the information security protection requirement, the received signal including a signal from the second node; a transceiver unit for transmitting the first power control information to the second node, the first power control information being used by the second node to generate a first signal; and the transceiver unit is further configured to receive the first signal from the second node, wherein the first signal includes data information and security protection information, the security protection information being used to protect the information security of the data information.

[0027] In combination with the third aspect, in certain implementations of the third aspect, the first power control information includes power control information corresponding to the data information and power control information corresponding to the security protection information.

[0028] In combination with the third aspect, in certain implementations of the third aspect, there are multiple second nodes, and the processing unit is specifically used to obtain the power control information corresponding to each second node based on the information security protection requirements, the maximum transmission power of each second node, and the channel information between the multiple second nodes and the first node; and the transceiver unit is specifically used to receive the superimposed signal of the first signal from the multiple second nodes.

[0029] In conjunction with the third aspect, in some implementations of the third aspect, the transceiver unit is further configured to send first information to the second node, where the first information is used to request node feature information output by the kth layer of the first machine learning model, and the data information includes the node feature information output by the kth layer of the first machine learning model from the second node;

[0030] The processing unit is also used to determine the second node feature information corresponding to the kth layer of the first machine learning model based on the node feature information output by the kth layer of the first machine learning model.

[0031] In combination with the third aspect, in certain implementations of the third aspect, the first information includes the first power control information.

[0032] In combination with the third aspect, in certain implementations of the third aspect, the processing unit is further used to input the first feature information and the second node feature information corresponding to the kth layer of the first machine learning model into the kth layer of the second machine learning model to obtain the aggregated node feature information output by the kth layer of the second machine learning model, wherein k is equal to 1, and the first feature information is the node feature information of the first node; or, k is an integer greater than 1, and the first feature information is the second node feature information corresponding to the k-1th layer of the first machine learning model.

[0033] In combination with the third aspect, in certain implementations of the third aspect, the processing unit is further used to input the second node feature information corresponding to the kth layer into the k+1th layer of the first machine learning model to obtain the node feature information output by the k+1th layer of the first machine learning model; the transceiver unit is further used to receive second power control information from the second node; and the transceiver unit is further used to send a second signal to the second node, the second signal including the node feature information and security protection information output by the k+1th layer of the first machine learning model, and the second signal is generated based on the second power control information.

[0034] 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. The module may be a hardware circuit, software, or a combination of hardware circuits and software. In one design, the device includes: a transceiver unit for receiving first power control information from a first node; a processing unit for generating a first signal based on the first power control information; the transceiver unit is further configured to send the first signal to the first node, the first signal including data information and security protection information, the security protection signal being configured to protect the information security of the data information.

[0035] In combination with the fourth aspect, in certain implementations of the fourth aspect, the first power control information includes power control information corresponding to the data information and power control information corresponding to the security protection information.

[0036] In combination with the fourth aspect, in certain implementations of the fourth aspect, the transceiver unit is also used to receive first information from the first node, and the first information is used to request node feature information output by the kth layer of the first machine learning model, wherein the data information includes the node feature information output by the kth layer of the first machine learning model of the second node, and k is a positive integer.

[0037] In combination with the fourth aspect, in certain implementations of the fourth aspect, the first information includes the first power control information.

[0038] In combination with the fourth aspect, in certain implementations of the fourth aspect, the processing unit is further used to input the second feature information into the kth layer of the first machine learning model to obtain the node feature information output by the kth layer of the first machine learning model, wherein k=1, and the second feature information is the node feature information of the second node; or, k is an integer greater than 1, and the second feature information is the first node feature information corresponding to the k-1th layer of the first machine learning model, and the first node feature information is determined based on the node feature information output by the kth layer of the first machine learning model from at least one of the first nodes.

[0039] In a fifth aspect, a communication device is provided, comprising a processor. The processor can implement the method of the first aspect or the second aspect and any possible implementation of the first aspect or 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 of the first aspect or the second aspect and any possible implementation of the first aspect or the second aspect. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface. In an 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.

[0040] In one implementation, the communication apparatus is a communication device. When the communication apparatus is a communication device, the communication interface may be a transceiver or an input / output interface.

[0041] In another implementation, the communication device is a chip or chip system configured in a communication device. When the communication device is a chip or chip system configured in a communication device, the communication interface may be an input / output interface.

[0042] Optionally, the transceiver may be a transceiver circuit. Optionally, the input / output interface may be an input / output circuit.

[0043] 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 of the first aspect or the second aspect, and any possible implementation of the first aspect or the second aspect.

[0044] 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.

[0045] 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 or second aspect and any possible implementation of the first aspect or second aspect.

[0046] In an eighth aspect, a computer-readable storage medium is provided, which stores a computer program (also referred to as code, or instructions) which, when run on a computer, enables the computer to execute the method in the above-mentioned first aspect or second aspect and any possible implementation of the first aspect or second aspect.

[0047] In a ninth aspect, a communication system is provided, comprising at least one first node and at least one second node as mentioned above.

[0048] It should be noted that the above-mentioned first node or second node can be a communication device, or a module (such as a chip, a chip system) configured in (or used for) a communication device. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] FIG1 is a schematic diagram of a communication system provided in an embodiment of the present application;

[0050] FIG2 is a schematic flowchart of graph neural network reasoning provided in an embodiment of the present application;

[0051] FIG3 is a schematic flow chart of an information security protection method provided in an embodiment of the present application;

[0052] FIG4 is another schematic flow chart of the information security protection method provided in an embodiment of the present application;

[0053] FIG5 is a schematic structural diagram of a communication device provided in an embodiment of the present application;

[0054] FIG6 is another schematic structural diagram of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The technical solution in this application will be described below with reference to the accompanying drawings.

[0056] 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.

[0057] The technical solutions of the embodiments of the present application can be applied to various communication systems, such as long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, fifth generation (5G) communication systems, wireless fidelity (WiFi) systems, and the communication methods provided in this application can also be applied to sixth generation (6G) communication systems and other communication systems evolved after 5G, future communication systems, or other communication systems. This application is not limited to this.

[0058] Figure 1 is a schematic diagram of the architecture of a communication system 1000 to which the present application can be applied. As shown in Figure 1 , the communication system includes a radio access network (RAN) 100. The radio access network 100 may include at least one access network device (such as 110a and 110b in Figure 1 ) and may also include at least one terminal (such as 120a-120j in Figure 1 ). The terminal and the access network device, as well as other terminals, may be connected to each other wirelessly. Figure 1 is merely a schematic diagram, and the communication system may also include other communication devices.

[0059] The communication nodes in the embodiments of the present application, such as the first node and / or the second node, can be access network devices, such as the access network device can be a base station, a node B, an evolved node B (eNodeB or eNB), a transmission reception point (TRP), a next generation node B (gNB) in a fifth generation (5G) mobile communication system, an access network device in an open radio access network (O-RAN or open RAN), a next generation base station in a sixth generation (6G) mobile communication system, a base station in a future mobile communication system, or an access node in a wireless fidelity (WiFi) system. The access network device can be a macro base station (such as 110a in Figure 1), a micro base station or an indoor station (such as 110b in Figure 1), a relay node or a donor node, etc. The specific technology and specific device form adopted by the access network device are not limited in this application.

[0060] In the embodiments of the present application, the communication nodes, such as the first node and / or the second node, may be terminal devices, which may also be referred to as terminals, user equipment (UE), mobile stations, mobile terminals, etc. Terminals can be widely used in various communication scenarios. These scenarios include, but are not limited to, at least one of the following: enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), massive machine-type communications (mMTC), device-to-device (D2D), vehicle-to-everything (V2X), machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearables, smart transportation, or smart city. A device may be a mobile phone, tablet computer, computer with wireless transceiver capabilities, wearable device, vehicle, drone, helicopter, airplane, ship, robot, robotic arm, or smart home device.

[0061] This application does not limit the specific technology and specific device form adopted by the communication node provided in the embodiment.

[0062] The following describes the relevant technologies and terms involved in the embodiments of this application.

[0063] 1. Graph Neural Network (GNN)

[0064] Traditional AI technology has superior performance in extracting features of Euclidean space data, but in actual application scenarios, many data do not belong to Euclidean space. Therefore, graph neural networks for processing graph data came into being. The graph data structure includes nodes and edges. GNN is a connection model that obtains the dependency relationship in the graph by passing information between nodes in the network. It can map high-dimensional graph data to a low-dimensional vector space. GNN updates the state of the node by reading the adjacent nodes at any depth of the node. If two nodes are reachable for communication, they can be called adjacent nodes. For example, node A can receive a signal from node B, and node B can receive the signal sent by node A. Then node A and node B are reachable for communication, and node A and node B are adjacent nodes. GNN still retains the hierarchical structure, and its operation of the kth layer can be specifically expressed as:

[0065]

[0066]

[0067] Wherein, k is an integer greater than or equal to 1, is the intermediate result of node v in the kth layer of GNN, e uv is the feature of the edge between node v and node u, N(v) is the set of identifiers of the adjacent nodes of node v, is the output result of node v in the kth layer of GNN. t (k) 、 It can be a function commonly used to implement a neural network such as a multi-layer perception (MLP), but the present application is not limited thereto. Wherein, k = 1, that is, when k-1 = 0, is the node feature x of node v v ,as well as, is the node feature x of node u u That is, in the first layer operation of GNN, is the node feature x of node u u , the intermediate result of the first layer It can be expressed as:

[0068]

[0069] as well as, is the node feature x of node v v , the output of the first layer It can be expressed as:

[0070]

[0071] When k is greater than 1, that is, in the second layer and subsequent layer operations of GNN, the intermediate results of the k-1 layers of each adjacent node are and the feature e of the edge between node u and node v uv As f t (k) Input, get the f corresponding to each adjacent node t (k) The sum of the outputs is the intermediate result of the kth layer of node v Then the intermediate results and the intermediate result obtained in the previous layer (i.e. k-1 layer) As Input, get The output result of the kth layer of the output node v Through multi-layer convolution operations, nodes can continuously aggregate information about adjacent nodes based on the topological structure and update their own status.

[0072] For example, as shown in Figure 2, after the graph data is input into GNN, GNN performs the first layer of graph convolution. For each node, the node features of each adjacent node u are and the feature e of the edge between node v and node u uv , where u∈N(v), input function f t (1) Get the intermediate result of the node v in the first layer of GNN Then the intermediate results and the node characteristics of the node v Input Function Get the output result of the node v in the first layer of GNN The first layer result is mapped to the second layer graph convolution through the activation function, and the second layer graph convolution of GNN is performed. In the second layer graph convolution, the intermediate result obtained based on the first layer of each adjacent node u is obtained. and the feature e of the edge between node v and node u uv , get the intermediate result of the second layer Based on the second-level intermediate results and the intermediate result of the first layer of node v Get the output result of the node v in the first layer of GNN After K iterations in this way, the inference is completed and the output result of the GNN is obtained. The adjacent node information aggregated according to the topological structure can be obtained to determine the status of each node. The output result includes the output vector of each node, and each output vector includes the output result of the Kth layer corresponding to a node. In the embodiment shown in Figure 4 of the present application, it is proposed to apply GNN to a wireless communication network. In order to improve the security and reliability of the transmission of each layer of the iteration result of the GNN in the wireless channel, it can be combined with the information security protection method provided by the present application. For details, please refer to the introduction of the embodiment shown in Figure 4 below.

[0073] 2. Local differential privacy (LDP) technology

[0074] LDP technology protects the information security of data by adding noise to the data. It can reduce the correlation of data. Even if an attacker obtains a piece of data, he cannot infer other data.

[0075] Assume that the information security budget is ε, M is a random function that takes a dataset as input, S is the set of outputs of the random function M, and any two adjacent datasets Q and Q′ are used as inputs of the random function M. The probabilities that the outputs belong to the set S are Pr(M(Q)∈S) and Pr(M(Q′)∈S), respectively. If the following inequality is satisfied:

[0076] Pr(M(Q)∈S)≤e ε Pr(M(Q′)∈S)+δ,

[0077] Then M satisfies the (ε, δ)-LDP. ε and δ are positive real numbers. The information security budget ε controls the similarity between the two probabilities. The smaller ε, the better the information security protection performance. Specifically, the lower the probability that an attacker can determine whether the input is Q or Q′ based on the output of the random function M. δ describes the probability of violating the aforementioned information security protection requirements. The smaller δ, the lower the probability of violating the information security protection requirements, and the better the information security protection performance. Adding Gaussian noise to data is an important method for implementing the (ε, δ)-LDP, and this method can also be called the Gaussian mechanism.

[0078] In order to improve information security in wireless communications and reduce the risk of information leakage, an embodiment of the present application proposes to achieve information security protection by adding Gaussian noise to data information at the transmitting end. However, during wireless communications, the channel has a channel fading effect on the transmission signal and there is noise in the channel. In order to minimize the problem that the signal cannot be correctly received by the receiving end due to the noise added to improve data information security, the present application further proposes that the power control information of the signal can be determined based on the information security requirements, the maximum transmission power of the transmitting end, and the channel information, so that the signal sent by the transmitting end can be accurately received by the receiving end while meeting the security protection requirements of the data information. This can improve the reliability of data information transmission.

[0079] FIG3 is a schematic flowchart of the information security protection method provided in an embodiment of the present application.

[0080] S301, the first node obtains first power control information based on the information security protection requirements, the maximum transmission power of the second node, and the channel information between the second node and the first node. The first power control information is the power control information predicted to maximize the signal to noise power ratio (SNR) of the received signal of the first node while meeting the information security protection requirements, and the received signal includes a signal from the second node.

[0081] The first node may obtain the maximum transmission power of the second node from the second node. Also, the first node may perform channel measurement to obtain channel information between the second node and the first node.

[0082] The information security protection requirement, also known as the information security budget, refers to the level of information security a communication node requires during information transmission. A first node can determine whether the level of information security of a received signal meets the information security protection requirement based on the information security threshold corresponding to the security protection requirement. For example, based on the information security threshold for meeting the information security protection requirement, the maximum transmission power of the second node, and the channel information, the first node can predict first power control information that maximizes the SNR of the first node's received signal while meeting the information security protection requirement.

[0083] The following describes how the first node predicts and obtains the first power control information.

[0084] In order to improve information security, the second node needs to treat the data information w sent to the first node as uv Overlay security protection information m uv , where u is the identifier of the second node, v is the identifier of the first node, and the security protection information m uv It may be Gaussian white noise, but this application does not limit this. The first node may determine a power control parameter based on the channel information, so that the transmission power of the to-be-transmitted signal (the signal including data information and security protection information) generated by the second node based on the power control parameter is within the maximum transmission power range of the second node, and the power control parameter can maximize the SNR of the received signal received by the first node after the signal is transmitted through the channel.

[0085] In the first embodiment, the received signal is a signal from the second node.

[0086] In one example, the signal can be pre-equalized at the second node (i.e., the signal transmitting end), which can also be called precoding, to equalize the interference of the channel on the signal. It can be expressed as:

[0087]

[0088] Among them, L is the upper limit of the norm of the data information That is, after the second node processes the data information and security protection information based on the power control parameter, the data information w in the generated signal uv The power coefficient is α uv is the power control parameter of data information, P u is the maximum transmission power of the second node, and the security protection information m uv The power coefficient is β uvTo protect the power control parameters of the information, the second node can use the equalization coefficient Pre-equalize the signal.

[0089] In another example, the signal can be equalized after being received by the first node (i.e., the signal receiving end). It can be expressed as:

[0090]

[0091] In this embodiment, illustratively, the reception of the signal from the second node by the first node can be represented as:

[0092]

[0093] Among them, h uv represents the weight coefficient of the channel between the first node and the second node, n uv represents the channel noise.

[0094] For the first node from R uv The unbiased estimate of w uv , and superimposed security protection information m uv The signal after the first node meets the information security protection requirements, then the power control parameter α uv and β uv The following constraints need to be met:

[0095]

[0096] Among them, ε v is the information security threshold corresponding to the information security protection requirement of the first node, and δ is the probability of not meeting the information security protection requirement. is the channel noise power.

[0097] To maximize the received signal R uv Taking the SNR as the target, the power control parameter α that meets the above constraints can be obtained uv Satisfy the following formula:

[0098]

[0099] in, is ε v A threshold value of the value range of is shown in formula (1). If but like Then α uv =1.

[0100] And, the power control parameter β that satisfies the above constraints can be obtained uv Satisfy the following formula:

[0101] β uv =1-α uv Formula (2)

[0102] Therefore, the first node can be based on the information security threshold ε v , the maximum transmission power P of the second node u and the channel information between the first node and the second node (such as the channel weight coefficient h between the first node and the second node obtained by the first node performing channel measurement). uv and / or channel noise power ), and obtain the power control parameter α uv and power control parameter β uv The first node may send first power control information to the second node, where the first power control information is used to indicate the power control parameter α uv and power control parameter β uv It can be understood that the “indication” in this application includes direct indication, indirect indication, explicit indication, and implicit indication.

[0103] In one example, the first power control information may indicate a power control parameter α uv and power control parameter β uv Alternatively, the first power control information may indicate one of the two power control parameters, and the second node may determine the power control parameter based on the power control parameter and the above α uv and β uv , and determine another power control parameter.

[0104] In another example, the first power control information may indicate α uv and β uv The second node can be calculated based on α uv and β uv The relationship between them determines the two power control parameters.

[0105] In another example, the first power control information indicates an identifier of a power control parameter, such as the first node determines an identifier corresponding to the power control parameter in a predefined power control parameter set based on the power control parameter, such as the predefined power control parameter set includes multiple power control parameters and an identifier of each power control parameter. The first node sends first power control information indicating an identifier corresponding to the power control parameter to the second node, and the second node determines a power control parameter corresponding to the identifier in the predefined power control parameter set based on the identifier of the power control parameter indicated by the first power control information. The first power control information may indicate α uv and β uv The second node uses the identifier corresponding to a power control parameter in the above α uv and β uv Alternatively, the first power control information may indicate identifiers of two power control parameters, and the second node determines the two power control parameters based on the identifiers.

[0106] In the second embodiment, there are multiple second nodes, and the received signal of the first node is a superposition of signals from the multiple second nodes. The first node obtains power control information corresponding to each second node based on an information security threshold, the maximum transmission power of each second node, and channel information between the multiple second nodes and the first node.

[0107] That is, multiple second nodes may send signals to the first node, and the signals of the multiple second nodes are superimposed in the channel, and the received signal of the first node is the superimposed signal of the signals from the multiple second nodes.

[0108] The plurality of second nodes may perform pre-equalization processing on the signal so that the first node can recover the data information based on the superimposed signal. The transmission signal of each second node may be represented as the pre-equalization processing For example, there are N second nodes, and the identifiers of the N second nodes are 1 to N, respectively, where N is an integer greater than 1. Then the signal sent by the second node with the identifier 1 (denoted as second node 1) can be expressed as:

[0109]

[0110] Where P1 is the maximum transmission power of the second node 1, w 1v Indicates the data information of the second node 1, m 1v represents the security protection information of the second node 1, α 1v represents the power control parameter of the data information of the second node 1, β uvis the power control parameter of the data information of the second node 1. The transmission signals of the second nodes with other identifiers can refer to the above-mentioned representation of the transmission signal of the second node 1, which will not be described here for brevity.

[0111] The multiple second nodes send their respective transmission signals to the first node, and the transmission signals of the multiple second nodes are superimposed in the channel, so that the reception signal received by the first node is R v , that is, the superposition signal of signals from multiple second nodes, can be expressed as follows:

[0112]

[0113] in, N(v) is a set of identifiers of the second nodes. For example, if there are N second nodes, the set of identifiers of the second nodes includes N identifiers corresponding to the N second nodes. For example, if the identifiers of the N second nodes are 1 to N, the received signal can be expressed as:

[0114]

[0115] Among them, h 1v is the channel information between the second node identified as 1 and the first node, h 2v is the channel information between the second node identified as 2 and the first node, h Nv is the channel information between the second node identified as N and the first node.

[0116] Then the received signal R v The SNR can be expressed as follows:

[0117]

[0118] For the first node from R v The unbiased estimate of w v , superimposed data information or the average value of the data information of multiple second nodes And R v If the information security protection requirements of the first node are met, then α uv and β uv The following constraints need to be met:

[0119]

[0120] in, Indicates that Defined as C v , or in other words, with C v express Contains C vC in the expression v Can be replaced by ε v The information security threshold corresponding to the information security protection requirement of the first node. is the channel noise power.

[0121] To maximize the received signal R uv Taking the SNR as the target, the power control parameter α that meets the above constraints can be obtained uv Satisfy the following formula:

[0122]

[0123] Where N is the number of second nodes in the set N(v), is ε v Another threshold value of the value range, It can be expressed as follows:

[0124]

[0125] And, the power control parameter β that satisfies the above constraints can be obtained uv Satisfy the following formula:

[0126]

[0127] in, and β′ uv It is obtained by using the pseudo-water injection method to solve the problem. The problem solved by the pseudo-water injection method can be expressed as:

[0128]

[0129] in, Indicates that Defined as In other words, express Include In the expression Can be replaced by

[0130] For the above problem to be solved, the first node can be solved by executing the steps of the pseudo-water injection method. The corresponding β uv The value of β′ uv :

[0131] 1. Initialization And the initialization set I = N(v)

[0132] 2. When D>0, calculate the water injection line N I Represents the number of elements in set I and determines the water injection strategy:

[0133] like Then set And update D=0, where Indicates that any u belongs to I;

[0134] Otherwise, set

[0135]

[0136] in, is the indicator function, which means that if A holds, then If A is not true, then In the above formula express:

[0137] when hour, when hour,

[0138] And, update

[0139]

[0140] and will satisfy The u is removed from I,

[0141] The first node can obtain the channel information h between the first node and each second node through channel measurement. uv For example, there are N second nodes, and the identifiers of the N second nodes are 1 to N respectively. The first node can measure the channel between itself and the second node with identifier u=1 to obtain the channel information h 1v , the first node can measure the channel information h between it and the second node with identifier u=2 2v , and measure the channel information between the first node and the second node. For example, the first node can measure the channel information h between the first node and the second node with identifier u=N. Nv It should be noted that the embodiment of the present application does not limit the order in which the first node measures the channel information between the first node and the plurality of second nodes.

[0142] The first node can be based on the information security threshold ε v The value range to which the value belongs, and the maximum transmission power P of each second node u , channel information between the first node and each second node (such as including channel weight coefficient h uv and channel noise power ), and obtain the power control parameter α corresponding to each second nodeuv and power control parameter β uv The first node sends power control information to each second node to inform each second node of its corresponding power control parameter.

[0143] S302: The first node sends first power control information to the second node, where the first power control information is used by the second node to generate a first signal.

[0144] Correspondingly, the second node receives the first power control information from the first node. The second node determines a power control parameter for generating a first signal based on the first power control information. The first signal is a signal sent by the second node to the first node.

[0145] The following example provides the first power control information for indicating the power control parameter α: uv and power control parameter β uv It should be understood that the present application does not limit the indication method of the first power control information. It should be understood that the "indication" in the present application includes direct indication, indirect indication, explicit indication, and implicit indication.

[0146] In one example, the first power control information may indicate a power control parameter α uv and power control parameter β uv Alternatively, the first power control information may indicate one of the two power control parameters, and the second node may determine the power control parameter based on the power control parameter and the above α uv and β uv , and determine another power control parameter.

[0147] In another example, the first power control information may indicate α uv and β uv The second node can be calculated based on α uv and β uv The relationship between them determines the two power control parameters.

[0148] In another example, the first power control information indicates an identifier of a power control parameter. For example, the first node determines an identifier corresponding to the power control parameter in a predefined power control parameter set based on the power control parameter, and notifies the second node through the first power control information. The second node determines a power control parameter corresponding to the identifier in the predefined power control parameter set based on the identifier of the power control parameter indicated by the first power control information. The first power control information may indicate α uv and β uv The second node uses the identifier corresponding to a power control parameter in the above α uv and βuv Alternatively, the first power control information may indicate identifiers of two power control parameters, and the second node determines the two power control parameters based on the identifiers.

[0149] S303, the second node sends a first signal to the first node, where the first signal includes data information and first security protection information, and the first security protection information is used to protect the information security of the data information.

[0150] The second node determines a power control parameter α based on the first power control information uv and β uv After that, the first signal can be generated The first signal includes data information w uv and security protection informationm uv . First Signal It can be expressed as follows:

[0151]

[0152] Alternatively, the second node can perform pre-equalization on the signal, then the first signal It can be expressed as follows:

[0153]

[0154] Equalization coefficient It can be indicated by the first node to the second node. Or, the equalization coefficient The second node may determine the equalization coefficient after performing channel estimation. For example, if the channels in different transmission directions between the first node and the second node are mutually different, the second node may obtain the equalization coefficient after performing channel estimation. However, the present application is not limited thereto.

[0155] Accordingly, the first node receives the first signal from the second node.

[0156] In one embodiment, a first node receives a first signal from a second node, and the first node receives the first signal to obtain a received signal R uv , the first node can receive the signal R uv Decoded to get w uv .

[0157] In another embodiment, there are multiple second nodes, and the first node receives a superposition signal of the first signals from the multiple second nodes to obtain a received signal R v , and decode w from the received signal v , w v To overlay data information or w vis the mean value of the data information of multiple second nodes

[0158] According to the above scheme, the security of data information transmission in the wireless channel can be improved, the probability of information leakage can be reduced, and the signal-to-noise ratio of the received signal at the receiving end can be maximized while meeting the security protection requirements of the data information, thereby improving the reliability of data information transmission.

[0159] The previous article introduced the graph neural network GNN. This application proposes that GNN can be applied in wireless communication networks. Using GNN, each communication node can obtain the characteristics and dependencies of adjacent nodes at different proximity depths in the environment where its own node is located, thereby determining the state characteristics of the communication node itself, achieving higher quality communication, and realizing distributed control and scheduling of the network.

[0160] Each of the multiple communication nodes participating in GNN reasoning can be used as an adjacent node (denoted as node u) of other nodes (denoted as node v) to obtain the intermediate result of the previous layer (such as k-1 layer). and the feature e of the edge with node v uv , input the kth layer of the first machine learning model to obtain the node feature information output by the kth layer And send it to node v. And, each node also receives node feature information sent by each adjacent node of node v as node v. Get the intermediate result corresponding to the kth layer of the model, that is, the aggregated feature information of adjacent nodes Node v then takes the intermediate result obtained at layer k-1 and Input the kth layer of the second machine learning model to get the output of the kth layer It can be seen that the node feature information obtained by each layer iteration of node u needs to be transmitted in the wireless channel. The two adjacent communications have a high correlation. The information security protection method shown in Figure 3 can be used to improve the security and reliability of information transmission.

[0161] Figure 4 is a schematic flow chart of a communication method 400 provided in an embodiment of the present application. It should be noted that the parts in the embodiment shown in Figure 4 that are the same as those in the embodiment shown in Figure 3 can refer to the description in Figure 3, and for the sake of brevity, they will not be repeated here. In the embodiment shown in Figure 4, node u (i.e., an example of the second node) is an adjacent node of node v (i.e., an example of the first node). Node v may have one or more adjacent nodes, that is, there may be one or more nodes u. The communication method includes but is not limited to the following steps:

[0162] S401, node u broadcasts the maximum transmission power P u .

[0163] During the initialization process, each node broadcasts its maximum transmission power and receives the maximum transmission power broadcast by its neighboring nodes. Node v receives the maximum transmission power P of each node u. u , where u∈N(v), where N(v) is the set of identifiers of the node's neighboring nodes. After each node obtains the maximum transmission power of its neighboring nodes during initialization, it performs inference on each layer of the machine learning model. S402 to S409 below are the inference process for the kth layer.

[0164] S402: Node v obtains channel information from each node u to node v.

[0165] For the k-th layer reasoning, node v can obtain the channel information between node v and each node u through channel measurement. For example, there are N nodes u, whose identifiers are 1 to N respectively. Node v can measure the channel between itself and the node u with identifier u=1 to obtain the channel information corresponding to the k-th layer inference. Node v can measure the channel between itself and node u with identifier u=2, and obtain the channel information corresponding to the k-th layer inference. And measure the channel information between other nodes u. For example, node v can measure the channel between itself and node u with identifier u=N to obtain the channel information corresponding to the k-th layer inference. It should be noted that the embodiment of the present application does not limit the order of measuring the channel information between node v and multiple nodes u. The channel information corresponding to the k-th layer reasoning of node v and each node u can be expressed as in, Indicates channel information The amplitude of Indicates channel information phase.

[0166] S403 , node v obtains power control information corresponding to node u based on the information security threshold, the maximum transmission power of node u, and the channel information between node u and node v.

[0167] The power control information corresponding to the node u is used by the node u to generate the first signal.

[0168] In one embodiment, there are one or more nodes u, and node v receives the first signal from node u in S407. Then node v can be based on the information security threshold ε v , the maximum transmission power P of a node u u As well as the channel information between the node u and the node v, the power control information corresponding to the node u is obtained.

[0169] The power control information can be used to indicate the power control parameters and / or power control parameters in, is the power control parameter of the data information of node u, Is the power control parameter of the security protection information of node u. Node v obtains the power control parameter corresponding to node u and / or power control parameters The method can refer to the implementation method 1 in the embodiment shown in FIG3 , such as node v according to P u , information security threshold ε v and Determine the power control parameter based on the above formula (1) And determine the power control parameter based on the above formula (2) Thus, node v can determine the power control parameter corresponding to node u. and / or Node v can use this method to determine the power control parameters corresponding to each node u. and / or and corresponding power control information.

[0170] In another embodiment, there are multiple nodes u, and node v receives a superposition signal of first signals from multiple nodes u in S407. Then node v is based on the information security threshold ε v , the maximum transmission power of multiple nodes u and the channel information between multiple nodes u and node v, and obtain the power control information corresponding to each node u.

[0171] Node v obtains the power control parameter corresponding to node u and The method can refer to the second embodiment of the embodiment shown in FIG3 , such as node v according to ε v , P of a node u u and the channel information between the multiple nodes u and node v respectively Based on the above formula (3), the power control parameter corresponding to the node u is determined And determine the power control parameter based on the above formula (4) Thus, node v can determine the power control parameter and and corresponding power control information.

[0172] S404, node v sends power control information to each node u.

[0173] Node v notifies each node u of its corresponding power control parameters through power control information. The specific implementation method of the power control information indicating the power control parameters can be referred to the above description, which will not be repeated here for the sake of brevity.

[0174] Optionally, the node v may send first information to each node u, where the first information is used to request node feature information output by the kth layer of the first machine learning model. After receiving the first information, each node u sends the node feature information output by the kth layer of the first machine learning model obtained by each node u to node v.

[0175] The node v may send power control information and first information to each node u respectively. Alternatively, the first information may include the power control information. Alternatively, the power control information may be used as the first information to request node feature information output by the kth layer of the first machine learning model. After receiving the power control information, node u sends the corresponding node feature information to node v.

[0176] S405, node u inputs the second feature information into the kth layer of the first machine learning model to obtain the node feature information output by the kth layer of the first machine learning model.

[0177] The second characteristic information is recorded as If k=1, the second characteristic information is the node feature information x of node u u ,Right now The node feature information output by the kth layer of the first machine learning model is f t (1) ({x u ,e uv}).

[0178] If k is an integer greater than 1, the second feature information The adjacent node feature information corresponding to the k-1th layer of the first machine learning model obtained for node u. The adjacent node feature information corresponding to the k-1th layer of the first machine learning model Please refer to the node v determined in S408 below. implementation method.

[0179] For example, the kth layer of the first machine learning model can be represented as a function f t (k) , the second feature information Input the kth layer of the first machine learning model and obtain the node feature information output by the kth layer of the first machine learning model Among them, e uv is the feature information of the edge between node u and node v.

[0180] S406, node u generates a first signal based on the power control information, where the first signal includes node feature information and security protection information output by the kth layer of the first machine learning model.

[0181] Node u may need feature information about the node Overlay security protection information m uv , the security protection information m uv It can be Gaussian white noise, but this is not limited in the application. Node u can determine the power control parameter based on the power control information and Based on the power control parameter and Node feature information and security protection informationm uv , generating a first signal.

[0182] In one example, the first signal can be expressed as:

[0183]

[0184] Among them, L is the data information Norm of The upper limit of The equalization coefficient may be sent by node v to node u. For example, the first information may include the equalization coefficient, or when the channels are mutually different, node u may obtain the equalization coefficient after performing channel estimation. This application does not limit this.

[0185] In another example, if the node v receives the first signal from each node u in S407, the node u may not perform pre-equalization processing. The first signal may be expressed as:

[0186]

[0187] According to the above scheme, node u performs power control on the node characteristic information and security protection information to be sent based on the power control information, which can improve the security of data information transmission in the wireless channel, reduce the probability of information leakage, and maximize the signal-to-noise ratio of the received signal at the receiving end while meeting the security protection requirements of the data information, thereby improving the reliability of data information transmission.

[0188] S407: Each node u sends a first signal to node v.

[0189] Node v receives the first signal from each node u.

[0190] In one embodiment, there are multiple nodes u, and node v receives a superposition signal of first signals from multiple nodes u to obtain a received signal

[0191] If the multiple nodes u send their respective first signals simultaneously, the first signals of the multiple nodes u are superimposed in the channel, and the node v receives and obtains the received signal For example, the received signal It can be expressed as:

[0192]

[0193] in, Including the channel noise between multiple nodes u and v, is the channel noise of the channel between node u and node v.

[0194] For example, if there are N second nodes, the set of second node identifiers includes N identifiers corresponding to the N second nodes. For example, if the identifiers of the N second nodes are 1 to N respectively, the first node receives the superposition signal of the first signals of the N second nodes, and the received signal obtained is It can be expressed as:

[0195]

[0196] in, is the channel information corresponding to the k-th layer inference between the second node identified as 1 and the first node, is the channel information corresponding to the k-th layer inference between the second node identified as 2 and the first node, Channel information corresponding to the k-th layer inference between the second node identified as N and the first node.

[0197] In another embodiment, the node v receives the first signal of each node u respectively, and obtains the received signal R corresponding to each node u. uv For example, the received signal R uv It can be expressed as:

[0198]

[0199] in, is the channel noise of the channel between node u and node v.

[0200] S408, node v estimates the adjacent node feature information corresponding to the kth layer of the first machine learning model based on the received signal obtained by receiving the first signal.

[0201] For example, the received signal is the superimposed signal R received by the first node after the first signals of the above-mentioned multiple nodes u are superimposed in the channel. v Or, if there are multiple nodes u, node v receives the first information of each node u respectively, and obtains the receiving signal R corresponding to each node u uv Then, the node v receives the signal R corresponding to the multiple nodes u. uv Perform superposition processing to obtain the superposition signal R v ,Right now

[0202]

[0203] Node v is based on the above superposition signal R v Estimate the adjacent node feature information corresponding to the kth layer of the first machine learning model Should The node feature information output from the kth layer of the first machine learning model for each node u The mean of the superimposed signal is:

[0204]

[0205] Where E[x] represents the mean of x.

[0206] For another example, node v receives the first signal of each node u respectively, and obtains the received signal R corresponding to each node u uv Node v can receive signals R uv , estimate the node feature information of the k-th layer output of the first machine learning model of the corresponding node u Node v gets each node feature information The mean of the superimposed signal Please refer to the previous expression.

[0207] S409, node v inputs the first feature information and the adjacent node feature information corresponding to the kth layer of the first machine learning model into the kth layer of the second machine learning model to obtain the aggregated node feature information output by the kth layer of the second machine learning model.

[0208] For example, the kth layer of the second machine learning model can be represented as a function Node v takes the first feature information and the adjacent node feature information corresponding to the kth layer of the first machine learning model Input to the kth layer of the second machine learning model to obtain the aggregate node feature information of the kth layer output of the second machine learning model Right now:

[0209]

[0210] If k = 1, is the node feature information x of node v v ,Right now The characteristic information of the aggregation node is

[0211] If k is an integer greater than 1, Aggregate node feature information output by the k-1th layer of the second machine learning model obtained for node v.

[0212] Each of the multiple communication nodes participating in GNN reasoning is reasoned as node u in the k-th layer reasoning And based on the power control information from each adjacent node (i.e., node v), a first signal is generated and sent to the corresponding adjacent nodes respectively. In addition, each communication node also predicts the power control information of each adjacent node (i.e., node u) as node v and notifies each adjacent node, and then receives the first signal from each adjacent node that has been processed with the power control information and contains security protection information, and obtains the adjacent node feature information corresponding to the kth layer of the first machine learning model. Thus, the k-th layer reasoning of the second machine learning model is input to obtain the k-th layer output result of the GNN. Each node then performs the k+1-th layer reasoning of the GNN. You can refer to the process from S402 to S409. For the sake of brevity, it will not be repeated here. Each communication node can complete the reasoning process through the K-layer reasoning of the GNN, and each communication node obtains the GNN reasoning result containing the output results of each layer in the K layers. Wherein, K is a positive integer, which can be determined according to the specific implementation, and this application does not limit this.

[0213] It should be noted that, based on information security protection requirements, each node can exchange power control information at the GNN inference layer where information security protection is required, and generate a signal containing security protection information based on the power control information. At the GNN inference layer where information security protection is not required, there is no need to exchange power control information, and the signal containing node feature information sent may not contain a signal containing security protection information. This can be implemented according to specific implementation requirements and is not limited in this application.

[0214] GNNs are used in wireless communication networks, enabling each communication node to obtain the characteristics and dependencies of neighboring nodes at different depths within its environment. This allows the node's own state to be determined, enabling higher-quality communication and distributed network control and scheduling. During GNN inference, information security is protected by adding noise to the transmitter and controlling transmit power, improving both information security and transmission reliability.

[0215] In order to achieve the above-mentioned use of GNN reasoning in a wireless network, the expected reasoning performance can be obtained. The above-mentioned first machine learning model and the second machine learning model need to match the wireless network environment during the training process. The data processing nodes in the network can use training data to perform model training. The data processing nodes can be core network nodes, servers or OAM, etc., but the present application is not limited to this. The training data can be obtained by collecting data from communication nodes in a wireless network. The training data includes node feature information (i.e., node data), association data between adjacent nodes (i.e., edge data, for example, channel information between adjacent nodes, etc.), and channel information for security protection, node maximum transmission power, and security requirement data. Communication nodes may include but are not limited to terminals and / or wireless access network nodes, etc.

[0216] After collecting the training data, the data processing node can perform data preprocessing and execute the training process of the GNN model (including the first machine learning model and the second machine learning model) based on the preprocessed training data. Each model training in this process includes iterative forward propagation and reverse gradient update. During the forward propagation process, it is necessary to simulate the wireless channel environment as well as information security protection and power control. Specifically, the forward propagation process can refer to the reasoning process of the embodiment shown in Figure 4. After each forward propagation, the gradient is updated and the model parameters are adjusted. The next model training is performed until the GNN model parameters converge to less than the preset threshold, and the trained GNN model, i.e., the first machine learning model and the second machine learning model, is obtained. This allows the GNN model to be applied to wireless networks and obtains expected reasoning performance.

[0217] The method provided by the present application is described in detail above in conjunction with the accompanying drawings. The following drawings illustrate the communication apparatus and communication equipment provided by the present application. In order to implement the various functions in the method provided by the present application, each network element may include a hardware structure and / or a software module, and implement the above functions in the form of a hardware structure, a software module, or a hardware structure plus a software module. Whether one of the above functions is executed in the form of a hardware structure, a software module, or a hardware structure plus a software module depends on the specific application and design constraints of the technical solution.

[0218] FIG5 is a schematic block diagram of a communication device provided in the present application. As shown in FIG5 , the communication device 500 may include a transceiver unit 520 .

[0219] In one possible design, the communication device 500 may correspond to the first node in the above method. When the communication device 500 corresponds to the first node, the communication device 500 may be a communication device, or the communication device 500 may be configured in (or used for) a chip in a communication device, or other devices, modules, circuits or units that can implement the method of the first node.

[0220] It should be understood that the communication device 500 may include a unit for executing the method performed by the first node in the above method embodiment. In addition, each unit in the communication device 500 and the above other operations and / or functions are respectively for implementing the corresponding processes of the above method embodiment.

[0221] Optionally, the communication device 500 may further include a processing unit 510, which may be configured to process instructions or data to implement corresponding operations.

[0222] It should also be understood that when the communication device 500 is a chip configured in (or used for) the first node, the transceiver unit 520 in the communication device 500 can be the input / output interface or circuit of the chip, and the processing unit 510 in the communication device 500 can be the processor in the chip.

[0223] Optionally, the communication device 500 may further include a storage unit 530, which may be used to store instructions or data. The processing unit 510 may execute the instructions or data stored in the storage unit to enable the communication device to perform corresponding operations.

[0224] It should also be understood that the specific process of each unit executing the above corresponding steps has been described in detail in the above method, and for the sake of brevity, it will not be repeated here.

[0225] In another possible design, the communication device 500 may correspond to the second node in the above method. When the communication device 500 corresponds to the second node, the communication device 500 may be a communication device, or the communication device 500 may be configured in (or used for) a chip in a communication device, or other devices, modules, circuits or units that can implement the method of the second node.

[0226] It should be understood that the communication device 500 may include a unit for executing the method performed by the second node in the above method embodiment. In addition, each unit in the communication device 500 and the above other operations and / or functions are respectively for implementing the corresponding processes in the above method embodiment.

[0227] Optionally, the communication device 500 may further include a processing unit 510, which may be configured to process instructions or data to implement corresponding operations.

[0228] It should also be understood that when the communication device 500 is a chip configured in (or used for) the second node, the transceiver unit 520 in the communication device 500 can be the input / output interface or circuit of the chip, and the processing unit 510 in the communication device 500 can be the processor in the chip.

[0229] Optionally, the communication device 500 may further include a storage unit 530, which may be used to store instructions or data. The processing unit 510 may execute the instructions or data stored in the storage unit to enable the communication device to perform corresponding operations.

[0230] It should be understood that the transceiver unit 520 in the communication device 500 can be implemented through a communication interface (such as a transceiver, a transceiver circuit, an input / output interface, or a pin), for example, it can correspond to the transceiver 620 in the communication device 600 shown in Figure 6. The processing unit 510 in the communication device 500 can be implemented through at least one processor, for example, it can correspond to the processor 610 in the communication device 600 shown in Figure 6. The processing unit 510 in the communication device 500 can also be implemented through at least one logic circuit. The storage unit 530 in the communication device 500 can correspond to the memory 630 in the communication device 600 shown in Figure 6.

[0231] Figure 6 is a schematic diagram of the structure of a communication device 600 provided in an embodiment of the present application. As shown in Figure 6, the communication device 600 includes one or more processors 610. Processor 610 can be used for internal processing of the device to implement certain control processing functions. Optionally, processor 610 includes instructions 611. Optionally, processor 610 can store data.

[0232] Optionally, the communication device 600 includes one or more memories 630 for storing instructions 631. Optionally, data may also be stored in the memories 630. The processor and memory may be provided separately or integrated together.

[0233] Optionally, the communication device 600 may further include a transceiver 620 and / or an antenna 640. The transceiver 620 may be used to send information to or receive information from other devices. The transceiver 620 may be referred to as a transceiver, a transceiver circuit, an input / output interface, etc., and is used to implement the transceiver functions of the communication device 600 via the antenna 640. Optionally, the transceiver 620 includes a transmitter and a receiver.

[0234] The communication device 600 can be applied to the communication device in the system shown in Figure 1. The communication device 600 can correspond to the first node or the second node, and the communication device 600 can be the communication device itself. Alternatively, the communication device 600 is configured in the communication device, such as a chip or module configured in the communication device. The communication device 600 can perform the operations of the first node or the second node in the above-mentioned method embodiment.

[0235] In this application, the processor can be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and can implement or execute the various methods, steps, and logic block diagrams of this application. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method in conjunction with this application can be directly implemented as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor.

[0236] In this application, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or a volatile memory, such as a random-access memory (RAM). The memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in this application may also be a circuit or any other device that can implement a storage function, for storing program instructions and / or data.

[0237] The present application also provides a processing device, including a processor and a (communication) interface; the processor is used to execute the method provided by the above method embodiment.

[0238] It should be understood that the processing device may be one or more chips. For example, the processing device may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0239] The present application also provides a computer-readable storage medium, which stores a computer program or instruction. When the computer program or instruction is executed, the method performed by the second node or the first node device in the aforementioned method embodiment is implemented. In this way, the functions described in the above embodiments can be implemented in the form of software functional units and sold or used as independent products. Based on this understanding, the technical solution of the present application can be essentially or in other words, the part that contributes or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a second node, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory RAM, a magnetic disk or an optical disk.

[0240] According to the method provided in the present application, the present application also provides a computer program product, which includes: computer program code, which, when executed by one or more processors, enables the device including the processor to execute the method shown in Figures 3 and 4.

[0241] The technical solution provided in this application can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in this application is generated in whole or in part. The above-mentioned computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. 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 includes one or more available media integrated. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a digital video disc (DVD)), or a semiconductor medium, etc.

[0242] According to the method provided in the present application, the present application also provides a system, which includes the aforementioned one or more first node devices. The system may further include the aforementioned multiple second nodes.

[0243] 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.

[0244] 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. An information security protection method, characterized in that: include: The first node obtains first power control information based on an information security protection requirement, a maximum transmission power of a second node, and channel information between the second node and the first node, where the first power control information is power control information that is predicted to maximize a signal-to-noise power ratio of a received signal of the first node while satisfying the information security protection requirement, where the received signal includes a signal from the second node. The first node sends the first power control information to the second node, where the first power control information is used by the second node to generate a first signal; The first node receives the first signal from the second node, wherein the first signal includes data information and security protection information, and the security protection information is used to protect information security of the data information.

2. The method according to claim 1, characterized in that The first power control information includes power control information corresponding to the data information and power control information corresponding to the security protection information.

3. The method according to claim 1 or 2, characterized in that There are multiple second nodes, The first node obtains first power control information corresponding to the second node based on the information security budget, the maximum transmission power of the second node, and channel information between the second node and the first node, including: The first node obtains power control information corresponding to each of the second nodes based on the information security protection requirement, the maximum transmission power of each of the second nodes, and channel information between the multiple second nodes and the first node; as well as, The first node receives the first signal from the second node, comprising: The first node receives a superimposed signal of the first signals from a plurality of the second nodes.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: The first node sends first information to the second node, where the first information is used to request node feature information output by the kth layer of the first machine learning model, and the data information includes the node feature information output by the kth layer of the first machine learning model from the second node; And, the method further comprises: The first node determines the second node feature information corresponding to the kth layer of the first machine learning model based on the node feature information output by the kth layer of the first machine learning model.

5. The method according to any one of claims 4, characterized in that The first information includes the first power control information.

6. The method according to claim 4 or 5, characterized in that The method further comprises: The first node inputs the first feature information and the second node feature information corresponding to the kth layer of the first machine learning model into the kth layer of the second machine learning model to obtain the aggregated node feature information output by the kth layer of the second machine learning model. Wherein, k is equal to 1, and the first feature information is the node feature information of the first node; or, k is an integer greater than 1, and the first feature information is the second node feature information corresponding to the k-1th layer of the first machine learning model.

7. The method according to any one of claims 4 to 6, characterized in that The method further comprises: The first node inputs the second node feature information corresponding to the k-th layer into the k+1-th layer of the first machine learning model to obtain the node feature information output by the k+1-th layer of the first machine learning model; The first node receives second power control information from the second node; The first node sends a second signal to the second node, where the second signal includes node feature information and security protection information output by the k+1th layer of the first machine learning model, and the second signal is generated based on the second power control information.

8. An information security protection method, characterized in that: include: The second node receives first power control information from the first node, where the first power control information is used by the second node to generate a first signal; The second node sends the first signal to the first node, where the first signal includes data information and security protection information, and the security protection signal is used to protect information security of the data information.

9. The method according to claim 8, characterized in that The first power control information includes power control information corresponding to the data information and power control information corresponding to the security protection information.

10. The method according to claim 8 or 9, characterized in that The method further comprises: The second node receives first information from the first node, where the first information is used to request node feature information output by the kth layer of the first machine learning model, wherein the data information includes the node feature information output by the kth layer of the first machine learning model of the second node, and k is a positive integer.

11. The method according to claim 10, characterized in that The first information includes the first power control information.

12. The method according to claim 10 or 11, characterized in that The method further comprises: The second node inputs the second feature information into the kth layer of the first machine learning model to obtain node feature information output by the kth layer of the first machine learning model. Wherein, k=1, the second feature information is the node feature information of the second node; or, k is an integer greater than 1, the second feature information is the first node feature information corresponding to the k-1th layer of the first machine learning model, and the first node feature information is determined based on the node feature information output from the kth layer of the first machine learning model from at least one of the first nodes.

13. An information security protection device, characterized in that: include: a processing unit, configured to obtain first power control information based on an information security protection requirement, a maximum transmission power of the second node, and channel information between the second node and the first node, the first power control information being power control information predicted to maximize a signal-to-noise power ratio of a received signal of the first node while satisfying the information security protection requirement, the received signal including a signal from the second node; a transceiver unit, configured to send the first power control information to the second node, where the first power control information is used by the second node to generate a first signal; The transceiver unit is further configured to receive the first signal from the second node, wherein the first signal includes data information and security protection information, and the security protection information is configured to protect information security of the data information.

14. The device according to claim 13, characterized in that The first power control information includes power control information corresponding to the data information and power control information corresponding to the security protection information.

15. The device according to claim 13 or 14, characterized in that There are multiple second nodes, The processing unit is specifically configured to obtain power control information corresponding to each second node based on the information security protection requirement, the maximum transmission power of each second node, and channel information between the plurality of second nodes and the first node; as well as, The transceiver unit is specifically configured to receive a superimposed signal of the first signals from a plurality of second nodes.

16. The device according to any one of claims 13 to 15, characterized in that The transceiver unit is further configured to send first information to the second node, where the first information is used to request node feature information output by the kth layer of the first machine learning model, and the data information includes the node feature information output by the kth layer of the first machine learning model from the second node; The processing unit is also used to determine the second node feature information corresponding to the kth layer of the first machine learning model based on the node feature information output by the kth layer of the first machine learning model.

17. The device according to any one of claims 16, characterized in that The first information includes the first power control information.

18. The device according to claim 16 or 17, characterized in that The processing unit is further configured to input the first feature information and the second node feature information corresponding to the kth layer of the first machine learning model into the kth layer of the second machine learning model to obtain the aggregated node feature information output by the kth layer of the second machine learning model. Wherein, k is equal to 1, and the first feature information is the node feature information of the first node; or, k is an integer greater than 1, and the first feature information is the second node feature information corresponding to the k-1th layer of the first machine learning model.

19. The device according to any one of claims 16 to 18, characterized in that The processing unit is further configured to input the second node feature information corresponding to the kth layer into the k+1th layer of the first machine learning model to obtain the node feature information output by the k+1th layer of the first machine learning model; The transceiver unit is further configured to receive second power control information from the second node; The transceiver unit is also used to send a second signal to the second node, where the second signal includes node feature information and security protection information output by the k+1th layer of the first machine learning model, and the second signal is generated based on the second power control information.

20. An information security protection device, characterized in that: include: a transceiver unit, configured to receive first power control information from a first node; a processing unit, configured to generate a first signal according to the first power control information; The transceiver unit is further configured to send the first signal to the first node, where the first signal includes data information and security protection information, and the security protection signal is configured to protect information security of the data information.

21. The device according to claim 20, characterized in that The first power control information includes power control information corresponding to the data information and power control information corresponding to the security protection information.

22. The device according to claim 20 or 21, characterized in that The transceiver unit is also used to receive first information from the first node, and the first information is used to request node feature information output by the kth layer of the first machine learning model, wherein the data information includes the node feature information output by the kth layer of the first machine learning model of the second node, and k is a positive integer.

23. The device according to claim 22, characterized in that The first information includes the first power control information.

24. The device according to claim 22 or 23, characterized in that The processing unit is further configured to input the second feature information into the kth layer of the first machine learning model to obtain node feature information output by the kth layer of the first machine learning model. Wherein, k=1, the second feature information is the node feature information of the second node; or, k is an integer greater than 1, the second feature information is the first node feature information corresponding to the k-1th layer of the first machine learning model, and the first node feature information is determined based on the node feature information output from the kth layer of the first machine learning model from at least one of the first nodes.

25. A communication device, characterized in that: The communication device comprises a processor connected to a memory, the memory being used to store a computer program, and 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 7, or performs the method as claimed in any one of claims 8 to 12.

26. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program. When the computer program is executed, the method according to any one of claims 1 to 7 is executed, or the method according to any one of claims 8 to 12 is executed.

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