Cooperative covert communication method, device and equipment

Abstract

The invention relates to the technical field of wireless communication, and discloses a cooperative covert communication method, device and equipment, and the method comprises the steps: predicting an energy detection threshold value and a false alarm probability threshold value of a potential monitoring node according to the first configuration information of the potential monitoring node; when it is detected that a short packet service arrives, modeling the monitoring behavior of the potential monitoring node based on an energy detection threshold, a false alarm probability threshold and a service effective time window corresponding to the short packet service, and constructing an assessment model of a covert communication success probability; based on the second configuration information of the short packet service, constructing an optimization model corresponding to the evaluation model of the covert communication success probability, and solving the optimization model to determine the optimal sending power and the optimal transmission time; and based on the optimal sending power and the optimal transmission time, sending the service data of the short packet service to each uplink base station through a preset unlicensed resource pool. Through joint optimization of the sending power and the transmission time, the success probability of covert communication is improved.

Description

Collaborative covert communication methods, devices and equipment Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a cooperative covert communication method, apparatus and device. Background Technology

[0002] In wireless communications for critical infrastructure, transmitting devices often need to send short but critical service data. However, existing covert communication methods, in order to combat third-party eavesdropping, typically rely on the strict assumption that the monitor possesses perfect information. They focus only on the risks within the transmission period, neglecting the effective time window constraints of the service information itself. This leads to a mismatch between the assessment of the success probability of covert communication and actual service requirements. Furthermore, the detection models used by existing covert communication methods operate in a batch processing manner, usually assuming that the monitor only makes a decision after obtaining all observation samples. This makes it difficult to characterize asynchronous eavesdropping behavior that involves long-term online observation and decision-making. Summary of the Invention

[0003] The main objective of this application is to provide a collaborative covert communication method, apparatus, and device, which aims to solve the technical problem that existing covert communication methods do not consider the effective time window constraints of the business information itself, resulting in a mismatch between the assessment of the success probability of covert communication and actual business needs.

[0004] To achieve the above objectives, this application proposes a cooperative covert communication method, the method comprising:

[0005] Based on the first configuration information of the potential monitoring nodes, the energy detection threshold and false alarm probability threshold of the potential monitoring nodes are predicted. The potential monitoring nodes are used to determine whether the transmitting terminal is conducting wireless communication. When a short packet service is detected, the monitoring behavior of the potential monitoring nodes is modeled based on the energy detection threshold, the false alarm probability threshold, and the effective time window of the service corresponding to the short packet service, and an evaluation model for the success probability of covert communication is constructed. Based on the second configuration information of the short packet service, an optimization model corresponding to the evaluation model for the success probability of covert communication is constructed, and the optimization model is solved to determine the optimal transmission power and the optimal transmission time. Based on the optimal transmission power and the optimal transmission time, the service data of the short packet service is sent to each uplink base station through a preset unlicensed resource pool.

[0006] Optionally, the step of predicting the energy detection threshold and false alarm probability threshold of the potential monitoring node based on the first configuration information of the potential monitoring node includes: constructing a received signal model of the potential monitoring node based on the first configuration information; converting the detection behavior corresponding to the received signal model into energy detection, establishing a behavior detection model of the potential monitoring node, wherein the behavior detection model is used to maximize the detection probability under the constraint that the false alarm probability of the potential monitoring node is less than or equal to a preset false alarm probability index; and predicting the energy detection threshold and false alarm probability threshold of the potential monitoring node based on the behavior detection model.

[0007] Optionally, the step of predicting the energy detection threshold and false alarm probability threshold of the potential monitoring node based on the behavior detection model includes: using the preset false alarm probability index as the false alarm probability threshold of the potential monitoring node; and calculating the energy detection threshold of the potential monitoring node based on the false alarm probability threshold and the behavior detection model.

[0008] Optionally, the effective time window of the service includes the transmission duration and the remaining validity period. The step of determining the success probability of covert communication based on the energy detection threshold, the false alarm probability threshold, and the effective time window of the short packet service includes: determining the detection probability of the potential monitoring node during the transmission duration according to the energy detection threshold; determining the false alarm probability of the potential monitoring node during the remaining validity period according to the false alarm probability threshold; and using the product of the detection probability and the false alarm probability as an evaluation model for the success probability of covert communication of the short packet service.

[0009] Optionally, the step of constructing an optimization model corresponding to the success probability of covert communication based on the second configuration information of the short packet service, and solving the optimization model to determine the optimal transmission power and optimal transmission time includes: determining a first constraint based on the effective time window of the service; determining the total received signal-to-noise ratio (SNR) of the received signals of each uplink base station after signal combining in the edge cloud according to the second configuration information, and determining the achievable rate of the short packet service under the target block error rate according to the total SNR; determining a second constraint according to the achievable rate; constructing an optimization model based on the first constraint and the second constraint, with maximizing the success probability of covert communication as the objective function; and solving the optimization model to determine the optimal transmission power and optimal transmission time.

[0010] Optionally, the step of solving the optimization model to determine the optimal transmission power and optimal transmission time includes: determining the minimum transmission duration that satisfies the second constraint under any transmission power; converting the objective function into a univariate optimization problem of the transmission power based on the minimum transmission duration; solving the univariate optimization problem to determine the relaxed transmission time when the probability of successful covert communication is maximized; rounding the relaxed transmission time down and up to obtain two integer transmission time candidate values; calculating the minimum transmission power candidate value corresponding to the integer transmission time candidate value under the conditions of satisfying the first constraint and the second constraint; determining the target covert communication success probability of the minimum transmission power candidate value, and taking the minimum transmission power candidate value and the integer transmission time candidate value corresponding to the larger target covert communication success probability as the optimal transmission power and optimal transmission time.

[0011] Furthermore, to achieve the above objectives, this application also proposes a cooperative covert communication device applied to a transmitting terminal, comprising: a threshold prediction module, used to predict the energy detection threshold and false alarm probability threshold of a potential monitoring node based on first configuration information of the potential monitoring node, wherein the potential monitoring node is used to determine whether the transmitting terminal is conducting wireless communication; a model building module, used to model the monitoring behavior of the potential monitoring node based on the energy detection threshold, the false alarm probability threshold, and the service effective time window corresponding to the short packet service when a short packet service is detected to arrive, and construct an evaluation model for the success probability of covert communication; a model solving module, used to construct an optimization model corresponding to the evaluation model for the success probability of covert communication based on second configuration information of the short packet service, and solve the optimization model to determine the optimal transmission power and the optimal transmission time; and a data transmission module, used to transmit the service data of the short packet service to each uplink base station through a preset unlicensed resource pool based on the optimal transmission power and the optimal transmission time.

[0012] In addition, to achieve the above objectives, this application also proposes a cooperative covert communication device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the cooperative covert communication method as described above.

[0013] This application does not rely on strict assumptions such as perfect synchronization of the eavesdropper or known transmission parameters. Instead, it predicts the detection parameters of potential monitoring nodes based on information obtained by the eavesdropper through wireless observation. Then, it combines the service validity time window unique to short packet services to evaluate the success probability of covert communication. The success determination of covert communication is expanded from focusing only on the detection situation during the transmission period to focusing on whether the eavesdropper makes a judgment on the existence of communication within the time range when the service information is still valid (i.e., the statistical test exceeds the threshold and determines that communication exists). This allows the performance evaluation of covert communication to cover the information transmission stage and the time stage after the information transmission is completed but still within the service validity period, thereby improving the success probability of covert communication. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 is a flowchart illustrating the first embodiment of the cooperative covert communication method of this application; Figure 2 is an architectural diagram illustrating the covert communication system of this application; Figure 3 is a flowchart illustrating the cooperative covert communication method of this application; Figure 4 is a flowchart illustrating the second embodiment of the cooperative covert communication method of this application; Figure 5 is a flowchart illustrating the signal detection of potential monitoring nodes of this application; Figure 6 is a flowchart illustrating the third embodiment of the cooperative covert communication method of this application; Figure 7 is a flowchart illustrating the fourth embodiment of the cooperative covert communication method of this application; Figure 8 is a schematic diagram illustrating the module structure of the cooperative covert communication device of this application; Figure 9 is a schematic diagram illustrating the device structure of the hardware operating environment involved in the cooperative covert communication method of this application.

[0017] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0019] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0020] Existing wireless communication systems typically employ a single-base station service model, with the choice of base station determined by downlink signal strength. Limited by channel fading and interference on a single link, terminal devices must maintain high transmission power to ensure reliable transmission of critical business data in complex industrial electromagnetic environments. This forced increase in transmission power for communication performance inevitably increases the signal's radiation range and energy characteristics, leading to a very high risk of detection. For third-party monitors with long-term passive eavesdropping capabilities, even if the business information is encrypted, they can still determine whether the terminal has transmitted data simply by detecting significant signal energy fluctuations in the air interface. To address the issue of third-party eavesdroppers potentially determining whether communication has occurred through wireless observation, the core objective of covert communication is to make it difficult for eavesdroppers to effectively distinguish between "communication present" and "communication absent" during information transmission, thereby concealing the existence of communication activities.

[0021] Since the effectiveness of covert communication directly depends on modeling the detection method and decision-making process of the eavesdropper, existing covert communication research usually starts with the analysis and design of the eavesdropper's detection model and constructs corresponding adversarial strategies based on the detection model that the eavesdropper may adopt. However, there are still shortcomings when facing short packet communication scenarios for critical infrastructure. For example, existing covert communication methods usually start from the sender's perspective, assuming that the eavesdropper can obtain sufficient information about the transmission process, simulating its detection behavior and designing adversarial strategies. This is often difficult to meet in actual systems, so although the relevant methods can give a lower bound on the covert performance, their guidance for the design of actual systems is limited. Covert communication models based on binary hypothesis testing usually assume that the eavesdropper completes sampling and makes a unified detection decision within a fixed observation window. It is difficult to reflect the actual situation such as the unknown start time of communication, asynchronous eavesdropping and sending, and real-time detection and real-time decision-making. Moreover, it only focuses on the detection of communication behavior by the eavesdropper during the transmission period, and its optimization variables are mostly transmission power and transmission duration. However, in critical infrastructure scenarios, the business information sent by terminal devices often has a clear effective time window. That is, after the time window expires, the business information no longer has any practical value to the legitimate recipient. Similarly, if the eavesdropper detects the communication behavior after the time window has ended, the detection result is no longer meaningful.

[0022] Therefore, this application provides a collaborative covert communication method. Without relying on a strict worst-case eavesdropping assumption, the terminal device makes reasonable inferences about the eavesdropper's behavior based on existing information, characterizes the eavesdropper's detection behavior when information is actually available, and introduces an effective time window for business information from the perspective of the business model. Based on this, the judgment conditions for successful covert communication are redefined. Under the condition of satisfying reliable transmission and delay constraints, combined with the receiving gain available to multiple receiving nodes on the system side, the probability of successful covert communication is improved by optimizing the transmission power and transmission time.

[0023] It should be noted that the executing entity in this embodiment can be a computing service device with data transmission, probability prediction, and program execution functions, such as a computer, or an electronic device capable of performing the above functions. The following description uses a sending terminal as an example to illustrate this embodiment and the subsequent embodiments.

[0024] Based on this, the present application provides a collaborative covert communication method. Referring to FIG1, FIG1 is a flowchart of the first embodiment of the collaborative covert communication method of the present application.

[0025] In this embodiment, the cooperative covert communication method includes: step S10, predicting the energy detection threshold and false alarm probability threshold of the potential monitoring node according to the first configuration information of the potential monitoring node, wherein the potential monitoring node is used to determine whether the transmitting terminal is conducting wireless communication.

[0026] It should be noted that potential monitoring nodes are third-party devices or systems with passive eavesdropping capabilities existing within the communication environment. Their core function is to detect and determine whether communication has occurred by analyzing signal characteristics (such as energy) in the wireless channel, rather than participating in legitimate communication. The first configuration information is a set of basic parameters acquired by the transmitting terminal before initiating covert communication, used to infer the behavior of the monitoring node. Examples include potential monitoring node location estimation, noise power estimation, and false alarm probability constraints. The energy detection threshold is a threshold value set by the potential monitoring node in its detection algorithm, used to compare the energy of the received signal to determine whether "communication exists" or "communication does not exist." The false alarm probability threshold is a performance indicator upper limit preset by the potential monitoring node to control detection reliability, representing the maximum permissible probability that the potential monitoring node incorrectly determines that communication exists when communication has not actually occurred.

[0027] It should be understood that in this embodiment, the sending terminal does not explicitly obtain the real-time internal parameters of the potential monitoring node, but rather makes a forward-looking prediction of its key detection parameters based on a reasonable model of the behavior logic of the potential monitoring node and the available surrounding information.

[0028] In one example, referring to Figure 2, which is a schematic diagram of the architecture of the covert communication system of this application, the transmitting terminal is an IoT device with sudden service needs. It has the ability to perform unlicensed transmission based on a pre-configured communication resource pool, thereby avoiding the exposure of communication intent through control signaling interaction before transmission. The cooperative receiving node network corresponding to the transmitting terminal adopts a fully decoupled wireless access network architecture. The core feature of this architecture is the decoupling of uplink and downlink base stations and the separation of data plane and control plane. Under this architecture, uplink base stations can be deployed independently, which allows multiple uplink base stations (such as uplink base stations 1 to k) to be densely deployed around critical infrastructure to receive data from the transmitting terminal. The received data is then transmitted back to the edge cloud via the backhaul link for signal combining processing, such as using the MRC (Maximum Ratio Combining) algorithm, thereby improving the overall received signal-to-noise ratio without increasing the transmission power. At the same time, the resource pool information allocated to the terminal device is also synchronized by the edge cloud to the corresponding receiving base station via the fronthaul link, and the receiving base station will continuously receive data on the corresponding resource pool. Third-party monitoring nodes are located in unknown locations near critical infrastructure. They only have the ability to passively receive wireless signals and infer the communication behavior of terminal devices by observing the signal energy or statistical characteristics in the wireless channel.

[0029] In the aforementioned system, the sending terminal executes a short packet transmission task constrained by a valid service time window. Specifically, the sending terminal needs to transmit packets within the valid time window after the service information is generated, while ensuring reliable transmission requirements are met. Data transmission is completed within a certain window; if this window is exceeded, the business information will lose its practical significance to the receiver or monitor. A third-party potential monitoring node continuously observes the wireless channel online and calculates detection statistics in real time based on the acquired observation sample sequence. The potential monitoring node uses predetermined statistical detection rules (such as Shewhart sequential detection rules) to compare the real-time calculated statistics with a preset decision threshold. When the detection statistics exceed the decision threshold, the third-party monitoring node makes a "communication presence decision" (i.e., determines that communication has occurred). In this scenario, the goal of the transmitting terminal is to jointly optimize the transmission power while ensuring reliable transmission and not violating business time constraints. and transmission time To maximize the probability of successful covert communication throughout the entire effective business time window. .

[0030] It is understood that, in this embodiment, the first configuration information may include a preset upper limit for false alarm probability as specified in industry or safety monitoring standards. The transmitting terminal directly uses the preset upper limit value as the predicted false alarm probability threshold. Subsequently, based on the energy detection model, the transmitting terminal calculates the corresponding energy detection threshold through mathematical derivation based on the estimate of the noise power at the monitoring node and the false alarm probability threshold.

[0031] Optionally, the first configuration information can also be combined with historical interaction data or preset adversarial scenario templates. The sending terminal calls the corresponding parameter template (the template defines typical monitoring node capabilities and constraint assumptions) according to different security levels or scenario types, thereby directly mapping the predicted energy detection threshold and false alarm probability threshold.

[0032] Understandably, if certain key estimation information (such as the distance to the monitoring node) is unavailable, the transmitting terminal may adopt a conservative approach, such as assuming that the monitoring node is located at the nearest possible location, and thus calculate the most unfavorable (i.e. the most stringent) detection threshold prediction value for the transmitting terminal, so as to ensure that the concealment strategy designed in this worst case is still effective.

[0033] Step S20: When a short packet service is detected to arrive, the monitoring behavior of the potential monitoring node is modeled based on the energy detection threshold, the false alarm probability threshold, and the effective time window of the service corresponding to the short packet service, and an evaluation model for the success probability of covert communication is constructed.

[0034] It should be noted that short packet services are critical communication tasks with small data volumes but high requirements for transmission timeliness and reliability, such as status reporting or anomaly alarms in the Industrial Internet. The sending terminal needs to ensure reliable transmission within the effective time window after the service information is generated. Data transmission must be completed within the specified window. If this window is exceeded, the service information will lose its practical significance to the receiving end or the monitor. The probability of successful covert communication is the probability that no potential monitoring node makes a judgment on the existence of communication throughout the entire duration of the effective service time window. In this example, the determination time for successful covert communication includes the transmission duration (i.e., the transmission time). ) and remaining validity period .

[0035] Understandably, the success probability of covert communication can be determined by constructing a mathematical evaluation model based on probability multiplication. This evaluation model divides the effective time window of the service into a transmission duration and a remaining validity period. Within the transmission duration, at each time point, the success probability of covert communication is the probability of a monitoring node missing a detection in a single operation. Within the remaining validity period, at each time point, the success probability of covert communication is the probability of a monitoring node not triggering a false alarm in a single detection.

[0036] Step S30: Based on the second configuration information of the short packet service, construct an optimization model corresponding to the evaluation model of the success probability of covert communication, and solve the optimization model to determine the optimal transmission power and the optimal transmission time.

[0037] It should be noted that the second configuration information is a set of configuration parameters for sending resources and receiving network information. These parameters define the reliability and resource constraints of communication, such as the amount of data to be sent, the target block error rate, the number of available cooperative receiving nodes and their channel information (e.g., path loss), and the power adjustment range of the sending terminal. The optimal sending power and optimal transmission time are a set of parameters obtained by solving an optimization model. These parameters maximize the probability of successful covert communication within the entire effective service time window, while satisfying all service and system constraints.

[0038] Understandably, the optimization model is constrained by various constraints derived from the second configuration information, using transmission power and transmission duration as decision variables, with the objective function being to maximize the success probability of covert communication. The three conflicting design goals of covertness, reliability, and timeliness are unified into a solvable optimization framework through mathematical modeling. The transmitting terminal no longer adjusts power or time in isolation, but rather systematically searches for the optimal combination of transmission parameters to achieve stealth, under the hard constraints of how quickly and reliably data must be transmitted. This realizes a shift from single-point strategy adjustment to global resource collaborative optimization.

[0039] Step S40: Based on the optimal transmission power and the optimal transmission time, the service data of the short packet service is sent to each uplink base station through a preset unlicensed resource pool.

[0040] It should be noted that the pre-defined unlicensed resource pool refers to a set of specific time-frequency resource blocks that the network pre-allocates or configures for the sending terminal, which can be used directly without requesting through control signaling interaction before each communication. This pool is used to determine the time-frequency resources for communication transmission. Employing an unlicensed transmission mechanism avoids directly sending uplink control signaling such as scheduling requests, thereby eliminating potential exposure risks at the communication initiation stage.

[0041] Understandably, the transmitting terminal strictly uses optimized power and time parameters to send data on a pre-negotiated unlicensed resource pool, ensuring that the characteristics (energy and duration) of the transmitted signal conform to the design for optimal global concealment. Simultaneously, the unlicensed access mechanism avoids signaling interaction on the common control channel, making the entire communication process (from data arrival to completion of transmission) more abrupt and unpredictable to the monitoring node, thus further enhancing concealment from the access mechanism's perspective. Furthermore, the signal is received collaboratively by multiple uplink base stations, utilizing spatial diversity gain to ensure transmission reliability under low power and short duration conditions.

[0042] In one example, referring to Figure 3, which is a flowchart of the collaborative covert communication method of this application, the process is divided into two stages: system initialization and preparation, and online service transmission. First, in the first stage, configuration information related to the monitor and sending resources is acquired, specifically including three operations: acquiring sending resources and receiving network information, establishing parameter estimation for the monitor, and establishing a detection model for the monitor. After the service arrives, the process enters the second stage of online service transmission. First, service parameters are acquired. Based on the service parameters and the detection model, a covert communication risk assessment model is constructed. The sending parameters are jointly optimized using the covert communication risk assessment model to obtain the corresponding optimal sending power and optimal transmission time. Finally, link transmission is performed based on the strategy of optimal sending power and optimal transmission time.

[0043] In this embodiment, the detection parameters of potential monitoring nodes are predicted by existing configuration information, and then the probability of successful covert communication is evaluated by combining the service validity time window unique to short packet services. The success determination of covert communication is expanded from focusing only on the detection situation during the sending period to focusing on whether the eavesdropper makes a judgment on the existence of communication within the time range when the service information is still valid, thereby improving the success probability of covert communication.

[0044] Referring to Figure 4, which is a flowchart of the second embodiment of the collaborative covert communication method of this application, a second embodiment of the collaborative covert communication method of this application is proposed based on the first embodiment described above.

[0045] In the second embodiment, step S10 includes: step S101, constructing the received signal model of the potential monitoring node based on the first configuration information.

[0046] It should be noted that the received signal model is a mathematical model used to describe the signal received by a potential monitoring node at any observation time. It usually contains two mutually exclusive assumptions: the first is the null hypothesis (communication does not exist), which means that the potential monitoring node only receives environmental noise; the second is the alternative hypothesis (communication exists), which means that the signal received by the monitoring node contains the superposition of the transmitted signal from the transmitting terminal and noise.

[0047] Specifically, as an optional implementation, the transmitting terminal establishes an environmental perception model of potential monitoring nodes by estimating the parameters of the monitor, wherein location estimation... By using geographic information or security assessments, it is assumed that potential monitoring nodes are located at their most likely nearest deployment location. And calculate the large-scale channel gain at this location based on the path loss model; noise power estimation. Taking advantage of the strong correlation between environmental noise and temperature, and considering that the transmitting terminal and potential monitoring nodes are located in the same geographical area (with similar temperatures), the transmitting terminal uses idle periods to conduct long-term observations of local environmental noise, and uses the obtained local noise power estimate as the noise power estimate at the monitoring location (i.e., False alarm probability constraint It is the false alarm probability index followed by potential monitoring nodes. This is usually based on publicly available radio monitoring standards or security level presets.

[0048] In one example, referring to Figure 5, which is a flowchart of signal detection for a potential monitoring node in this application.

[0049] Throughout the entire time window, the monitor continuously samples and detects the channel (as illustrated on the left time axis); during each detection, it observes the energy statistics. With the preset optimal threshold The system compares the data; if the statistical value exceeds a threshold, it immediately determines that "communication exists"; otherwise, it continues to the next round of detection. When no communication exists, the signal received by the monitor is only environmental noise, and the information validity period of the service information sent by the terminal is limited. There is internal communication, and the signal received by the monitor is the transmitted signal plus noise.

[0050] Based on the calculated estimation information, the transmitting terminal locally simulates the Shewhart sequential detection mechanism for potential monitoring nodes. The Shewhart detection used by the potential monitoring nodes is a sequential change point detection method based on the Likelihood Ratio Test (LRT). As shown in Figure 5, the potential monitoring node at each time step... Calculate the statistical test value of the currently observed sample after receiving the sample signal. The received signal model of a potential monitoring node can be represented as:

[0051] in, For potential monitoring nodes at any time The observed samples, It is complex Gaussian white noise. In order to send a signal, For transmission power, This is the estimated distance between potential monitoring nodes and the transmitting terminal. This is the path loss index.

[0052] Step S102: Convert the detection behavior corresponding to the received signal model into energy detection, and establish a behavior detection model for the potential monitoring node. The behavior detection model is used to maximize the detection probability under the constraint that the false alarm probability of the potential monitoring node is less than or equal to a preset false alarm probability index.

[0053] It should be noted that, in a channel model considering only large-scale fading, the likelihood ratio test for an unknown signal is mathematically equivalent to energy detection. That is, the likelihood ratio statistic is determined. Is it greater than the likelihood ratio threshold? This is equivalent to determining the energy of the observed sample. Is it greater than the corresponding energy detection threshold? Therefore, in this embodiment, subsequent calculations can be performed based on the energy detection model.

[0054] Specifically, the behavioral objective of potential monitoring nodes is to detect the communication transmission of devices as quickly as possible, provided that the detectors are reliable and available. This is because the actual observational information that monitors can obtain is noise power. Therefore, in order to achieve the detection goal, monitors expect to meet the false alarm probability. Not exceeding the preset false alarm probability index upper limit Maximize the detection probability under the constraints. This behavior can be modeled as an optimization problem: the monitor's objective is to detect the device's communication transmission as quickly as possible, provided the detector is reliable and available. Since the actual observational information the monitor can obtain is noise power... Therefore, in order to achieve the detection goal, monitors expect to meet the false alarm probability. Not exceeding the preset upper limit Maximize the detection probability under the constraints. This behavior can be modeled as the following optimization problem:

[0055]

[0056] Among them, false alarm probability and detection probability The expressions are as follows:

[0057]

[0058] From the above formula, we can see that and All are about thresholds It is a monotonically decreasing function.

[0059] Step S103: Predict the energy detection threshold and false alarm probability threshold of the potential monitoring node based on the behavior detection model.

[0060] Furthermore, to ensure that the predicted monitoring behavior model is a binding code of conduct that the monitoring party must adhere to in practice, thereby making the countermeasures more targeted and effective, step S103 may include: using the preset false alarm probability index as the false alarm probability threshold of the potential monitoring node; and calculating the energy detection threshold of the potential monitoring node based on the false alarm probability threshold and the behavior detection model.

[0061] It should be noted that, due to and All are about thresholds It is a monotonically decreasing function. Therefore, in order to satisfy... Maximize under the premise The monitor should select the threshold that makes the false alarm probability constraint equal to the threshold as the optimal threshold, as follows:

[0062] Substituting the above formula into the behavior detection model, the sending terminal obtains the optimal energy detection threshold for the monitor. for:

[0063] This energy detection threshold The terminal to be sent will be used for covert communication risk assessment during the online phase.

[0064] In this embodiment, the core detection logic of the monitor (i.e., pursuing the maximum detection probability while controlling the false alarm rate) is reasonably inferred and simulated through a mathematical model, so that the sending terminal can predict the behavior of uncontrollable monitoring nodes based on available information (such as preset monitoring standards), thereby getting rid of unrealistic assumptions such as the monitor having perfect information or synchronization capabilities, and making the design of covert communication strategies closer to actual confrontation scenarios.

[0065] Referring to Figure 6, which is a flowchart of the third embodiment of the collaborative covert communication method of this application, a third embodiment of the collaborative covert communication method of this application is proposed based on the above embodiments.

[0066] In the second embodiment, the effective service time window includes the transmission duration and the remaining validity period. Step S20 includes: Step S201, determining the detection probability of the potential monitoring node during the transmission duration based on the energy detection threshold.

[0067] It should be noted that when a sudden short packet service arrives, the sending terminal enters the online processing flow, and the sending terminal determines the effective time window of this service information ( The unit is the number of channel uses, which refers to the maximum allowed time from information generation to expiration, and the amount of data to be sent. (in bits) and the target block error rate for reliable transmission .

[0068] It is understandable that the transmission parameters of the transmitting terminal and the detection capability of the monitoring node are linked through probabilistic statistical theory. Based on its own transmission power and the estimated channel conditions of the monitoring node, the transmitting terminal can calculate the signal-to-noise ratio (SNR) at the monitoring node's receiver. Based on this SNR and the monitoring node's fixed decision threshold, the probability of the monitoring node successfully detecting the signal can be derived using the performance analysis formula of the energy detector.

[0069] Specifically, as an optional implementation, the estimated received signal-to-noise ratio from the transmitting terminal to the monitoring node is... and energy detection threshold Substituting into the detection probability formula for the energy detector under Rayleigh fading or Gaussian channels, the detection probability can be calculated under a typical additive white Gaussian noise channel model as follows:

[0070] Estimate the received signal-to-noise ratio The calculation formula is as follows:

[0071] in, For transmission power, For the estimated distance between monitoring nodes, This is the path loss index. This represents the estimated noise power of the monitoring node.

[0072] Therefore, based on the energy detection threshold, the detection probability of the potential monitoring node during the transmission duration is determined as follows:

[0073] in, For the duration of transmission, successful covert communication indicates that the monitor has not detected the signal.

[0074] Step S202: Determine the false alarm probability of the potential monitoring node in the remaining validity period based on the false alarm probability threshold.

[0075] It should be noted that the risk assessment of covert communication must cover the entire lifecycle of the communication activity. During the transmission period, the risk mainly stems from the capture of signal energy (missed detection); while in the remaining validity period after transmission ends, the risk transforms into false alarms caused by pure background noise fluctuations exceeding the decision threshold of the monitoring node. By incorporating the risk of false alarms in this stage into the overall assessment, the criteria for determining the success or failure of covert communication become more stringent and comprehensive.

[0076] Specifically, as an optional implementation, in order to meet its preset performance indicators, the false alarm probability of a potential monitoring node is constrained to not exceed a fixed value, namely the false alarm probability threshold. During the remaining validity period, since the transmitting terminal has stopped transmitting, the input to the monitoring node's receiver is only noise. Therefore, the probability of a false alarm occurring in any single observation within the remaining validity period is equal to the upper limit set by its design. The sending terminal uses this value as the basis for calculating the probability of remaining validity period risk.

[0077] Therefore, based on the false alarm probability threshold, the false alarm probability of the potential monitoring node in the remaining validity period is determined as follows:

[0078] in, For the remaining validity period, successful covert communication indicates that the monitor has not made a misjudgment due to noise fluctuations.

[0079] Step S203: The product of the detection probability and the false alarm probability is used as the evaluation model for the success probability of covert communication of the short packet service.

[0080] It should be noted that the monitoring node makes independent decisions at each observation point within the entire effective service time window. Therefore, the probability of remaining concealed (i.e., not triggering the "communication exists" decision at any point) throughout the entire window is equal to the product of the probabilities of not triggering the decision at any of the individual points. During the transmission duration, what needs to be avoided is correct detection; during the remaining validity period, what needs to be avoided is false alarms, as follows:

[0081] This refers to the probability of successfully achieving covert communication within the entire effective business time window.

[0082] In this embodiment, the effective time window of the service is clearly divided into the transmission duration and the remaining validity period. Based on this, the probability of successful covert communication is precisely defined as the product of the probabilities that the monitoring node does not make a misjudgment in either of these two periods. This not only considers the probability of being missed during transmission but also takes into account the risk of false alarms caused by environmental noise after transmission is completed and before the information expires, thereby improving the probability of successful covert communication.

[0083] Referring to Figure 7, which is a flowchart of the fourth embodiment of the collaborative covert communication method of this application, a fourth embodiment of the collaborative covert communication method of this application is proposed based on the above embodiments.

[0084] In the fourth embodiment, step S30 includes: step S301, determining a first constraint based on the effective time window of the service.

[0085] It should be noted that the first constraint is the effective time constraint of the business, that is... In critical infrastructure scenarios, the validity of status information, control commands, or alarms has a strict time limit; beyond this limit, successful information transmission becomes meaningless. Therefore, any transmission strategy must first ensure completion within this rigid time window.

[0086] Step S302: Determine the total received signal-to-noise ratio (SNR) of the received signals of each uplink base station after signal combining in the edge cloud according to the second configuration information, and determine the achievable rate of the short packet service under the target block error rate according to the total received SNR.

[0087] Step S303: Determine the second constraint based on the achievable rate.

[0088] It should be noted that the total received signal-to-noise ratio (SNR) is the equivalent SNR obtained after signal combining (such as maximum ratio combining) of the received signals from all cooperating uplink base stations at the edge cloud, reflecting the overall signal quality at the receiver. The achievable rate refers to the maximum reliable data transmission rate that the physical layer transmission can achieve under given conditions of finite code length (corresponding to short packets), target block error rate, and current total received SNR.

[0089] Understandably, unlike traditional infinite code length analysis based on Shannon capacity, short packet communication must consider the performance loss caused by finite code length. By introducing the achievable rate formula under finite code length theory, it is possible to more accurately describe the performance loss in finite transmission time. Within, with a certain error probability The minimum rate required for reliable transmission of L bits of data.

[0090] Specifically, as an optional implementation, the transmission power is determined according to the second configuration information. Sending duration and target block error rate The received signal-to-noise ratio (SNR) of each link to the base station is calculated as follows:

[0091] in, for The total received signal-to-noise ratio of each uplink base station communication link It is the inverse function of the complementary cumulative distribution function of the standard Gaussian distribution.

[0092] The second constraint determined by the above formula is:

[0093] in, For reliable transmission constraints, this is used to ensure that the short packet service data can be successfully received by the legitimate receiving end with a level of reliability no less than the target reliability.

[0094] Step S304: Based on the first constraint and the second constraint, construct an optimization model with the objective function of maximizing the success probability of the covert communication.

[0095] Specifically, as an optional implementation, the transmitting terminal uses a transmission power... and transmission duration To optimize the variables, we establish the following optimization problem P1:

[0096]

[0097]

[0098] in, and These are the minimum and maximum values ​​of the set transmission power, respectively. This indicates that the transmission duration is a positive integer. By combining the constraints of finite code length reliable transmission (supporting single / multiple receiving nodes) with the constraints of the service's effective time window, the transmission power and transmission duration are jointly optimized to maximize the probability of successful covert communication within the validity period of the service data.

[0099] Step S305: Solve the optimization model to determine the optimal transmission power and optimal transmission time.

[0100] Furthermore, to enable the complex joint optimization model to be solved quickly on a computationally limited transmitting terminal, step S305 may include: determining the minimum transmission duration that satisfies the second constraint under any transmission power; converting the objective function into a univariate optimization problem of the transmission power based on the minimum transmission duration; solving the univariate optimization problem to determine the relaxed transmission time when the probability of successful covert communication is maximized; rounding the relaxed transmission time down and up to obtain two integer transmission time candidate values; calculating the minimum transmission power candidate value corresponding to the integer transmission time candidate value under the first and second constraints; determining the target covert communication success probability of the minimum transmission power candidate value, and taking the minimum transmission power candidate value and the integer transmission time candidate value corresponding to the larger target covert communication success probability as the optimal transmission power and optimal transmission time.

[0101] It should be noted that the minimum transmission duration refers to a given transmission power. such that the second constraint holds and The minimum positive integer value represents the transmission power. Under these conditions, the minimum time required to reliably complete the transmission is considered. The relaxed transmission time refers to the optimal solution obtained when the constraint that the transmission duration must be an integer is temporarily ignored during the solution process, and the transmission duration is treated as a continuous variable. The integer transmission time candidate value is the physically executable time length obtained by rounding down the relaxed solution. The minimum transmission power candidate value refers to the minimum transmission power required to just satisfy the reliable transmission constraint for a given integer transmission time.

[0102] Specifically, as an optional implementation, to address the solution complexity of the original bivariate mixed integer programming problem, an efficient dimensionality reduction and near-optimal solution strategy can be adopted. This involves adjusting the constraints... Rewrite it in the following form:

[0103] The sending terminal is based on Follow and Monotonically decreasing, while Follow and Given the monotonically increasing property, choose the minimum. Make ,Right now Should be constrained On the boundary of the restricted area. Therefore, for any transmission power The optimal sending duration should satisfy the constraints. and Minimum transmission duration :

[0104] By Substituting the objective function, the bivariate optimization problem can be transformed into a problem concerning transmission power. Single variable optimization problem ,as follows:

[0105] Its feasible region is limited by physical power. With time constraints The intersection of the limited power ranges.

[0106] Next, regarding The discrete integer properties lead to optimization problems For the non-differentiable problem, the transmitting terminal uses a continuous relaxation strategy to solve it: 1. Relaxation calculation: Introduce continuous variables Calculate to satisfy the equation real solutions (This can be solved using numerical methods or analytical formulas), construct the relaxed objective function. .

[0107] 2. Single-peak search: utilizing a relaxed objective function Given the existence of maxima within the feasible region, numerical search algorithms (such as the bisection method or the golden section method) can be used to quickly locate the optimal power point that maximizes the concealment probability. and the corresponding relaxation transmission duration .

[0108] Furthermore, the relaxed solution is mapped back to a physically feasible integer solution. The transmitting terminal calculates the relaxed transmission duration for each solution. floor value and round up ,use The equation calculates the minimum transmission power for the corresponding transmission duration. By comparing the success probabilities of covert communication in the two cases, the power and duration corresponding to the higher probability are selected as the final optimal transmission strategy. .

[0109] Finally, the transmitting terminal sets the transmit power to and during the duration Internally, it uses an unauthorized resource pool to send business data to the receiving node.

[0110] In this embodiment, a model is constructed with the goal of maximizing the success probability of covert communication by integrating the first constraint of the effective service time window and the second constraint of reliable transmission based on finite code length theory and considering the cooperative reception gain of multiple nodes. By solving this model, the optimal combination of transmission power and time can be automatically found to achieve the best covertness throughout the entire service validity period, while ensuring timely and reliable information delivery. This achieves optimal system performance under multiple stringent indicators.

[0111] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the collaborative covert communication method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0112] This application also provides a cooperative covert communication device. Referring to Figure 8, the cooperative covert communication device is applied to a transmitting terminal and includes: a threshold prediction module 10, used to predict the energy detection threshold and false alarm probability threshold of a potential monitoring node based on the first configuration information of the potential monitoring node, wherein the potential monitoring node is used to determine whether the transmitting terminal is conducting wireless communication; a model building module 20, used to model the monitoring behavior of the potential monitoring node based on the energy detection threshold, the false alarm probability threshold, and the service effective time window corresponding to the short packet service when the arrival of the short packet service is detected, and to build an evaluation model for the success probability of covert communication; a model solving module 30, used to build an optimization model corresponding to the evaluation model for the success probability of covert communication based on the second configuration information of the short packet service, and to solve the optimization model to determine the optimal transmission power and the optimal transmission time; and a data transmission module 40, used to send the service data of the short packet service to each uplink base station through a preset unlicensed resource pool based on the optimal transmission power and the optimal transmission time.

[0113] The collaborative covert communication device provided in this application, employing the collaborative covert communication method in the above embodiments, can solve the technical problem that existing covert communication methods do not consider the effective time window constraint of the business information itself, resulting in a low probability of successful covert communication. Compared with the prior art, the beneficial effects of the collaborative covert communication device provided in this application are the same as those of the collaborative covert communication method provided in the above embodiments, and other technical features in the collaborative covert communication device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0114] This application provides a cooperative covert communication device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the cooperative covert communication method in the first embodiment described above.

[0115] Referring now to Figure 9, a schematic diagram of a cooperative covert communication device suitable for implementing embodiments of this application is shown. The cooperative covert communication device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The cooperative covert communication device shown in Figure 9 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of this application.

[0116] As shown in Figure 9, the cooperative covert communication device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the cooperative covert communication device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows cooperative covert communication devices to exchange data wirelessly or wiredly with other devices. While cooperative covert communication devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0117] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0118] The collaborative covert communication device provided in this application, employing the collaborative covert communication method in the above embodiments, can solve the technical problem that existing covert communication methods do not consider the effective time window constraint of the business information itself, resulting in a low probability of successful covert communication. Compared with the prior art, the beneficial effects of the collaborative covert communication device provided in this application are the same as those of the collaborative covert communication method provided in the above embodiments, and other technical features in this collaborative covert communication device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0119] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0120] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0121] The above description is only a part of the embodiments of this application and does not limit the scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of this application.

Claims

1. A cooperative covert communication method, characterized in that, The method, applied to a transmitting terminal, includes: predicting an energy detection threshold and a false alarm probability threshold for a potential monitoring node based on first configuration information of the potential monitoring node, wherein the potential monitoring node is used to determine whether the transmitting terminal is conducting wireless communication; when a short packet service is detected, modeling the monitoring behavior of the potential monitoring node based on the energy detection threshold, the false alarm probability threshold, and the effective time window of the service corresponding to the short packet service, and constructing an evaluation model for the success probability of covert communication; constructing an optimization model corresponding to the evaluation model for the success probability of covert communication based on second configuration information of the short packet service, and solving the optimization model to determine the optimal transmission power and the optimal transmission time; and transmitting the service data of the short packet service to each uplink base station through a preset unlicensed resource pool based on the optimal transmission power and the optimal transmission time.

2. The cooperative covert communication method as described in claim 1, characterized in that, The step of predicting the energy detection threshold and false alarm probability threshold of the potential monitoring node based on the first configuration information of the potential monitoring node includes: constructing a received signal model of the potential monitoring node based on the first configuration information; converting the detection behavior corresponding to the received signal model into energy detection, establishing a behavior detection model of the potential monitoring node, wherein the behavior detection model is used to maximize the detection probability under the constraint that the false alarm probability of the potential monitoring node is less than or equal to a preset false alarm probability index; and predicting the energy detection threshold and false alarm probability threshold of the potential monitoring node based on the behavior detection model.

3. The cooperative covert communication method as described in claim 2, characterized in that, The step of predicting the energy detection threshold and false alarm probability threshold of the potential monitoring node based on the behavior detection model includes: using the preset false alarm probability index as the false alarm probability threshold of the potential monitoring node; and calculating the energy detection threshold of the potential monitoring node based on the false alarm probability threshold and the behavior detection model.

4. The cooperative covert communication method as described in claim 1, characterized in that, The effective time window for the service includes the transmission duration and the remaining validity period. The step of modeling the monitoring behavior of the potential monitoring node and constructing an evaluation model for the success probability of covert communication based on the energy detection threshold, the false alarm probability threshold, and the effective time window for the short packet service includes: determining the detection probability of the potential monitoring node during the transmission duration according to the energy detection threshold; determining the false alarm probability of the potential monitoring node during the remaining validity period according to the false alarm probability threshold; and using the product of the detection probability and the false alarm probability as the evaluation model for the success probability of covert communication of the short packet service.

5. The cooperative covert communication method as described in any one of claims 1 to 4, characterized in that, The steps of constructing an optimization model corresponding to the evaluation model of the success probability of covert communication based on the second configuration information of the short packet service, and solving the optimization model to determine the optimal transmission power and optimal transmission time include: determining a first constraint based on the effective time window of the service; determining the total received signal-to-noise ratio (SNR) of the received signals of each uplink base station after signal combining in the edge cloud according to the second configuration information, and determining the achievable rate of the short packet service under the target block error rate according to the total SNR; determining a second constraint according to the achievable rate; constructing an optimization model based on the first constraint and the second constraint, with maximizing the success probability of covert communication as the objective function; and solving the optimization model to determine the optimal transmission power and optimal transmission time.

6. The cooperative covert communication method as described in claim 5, characterized in that, The steps of solving the optimization model to determine the optimal transmission power and optimal transmission time include: determining the minimum transmission duration that satisfies the second constraint under any transmission power; converting the objective function into a univariate optimization problem of the transmission power based on the minimum transmission duration; solving the univariate optimization problem to determine the relaxed transmission time when the probability of successful covert communication is maximized; rounding the relaxed transmission time down and up to obtain two integer transmission time candidate values; calculating the minimum transmission power candidate value corresponding to the integer transmission time candidate value under the conditions of satisfying the first constraint and the second constraint; determining the target covert communication success probability of the minimum transmission power candidate value, and taking the minimum transmission power candidate value and the integer transmission time candidate value corresponding to the larger target covert communication success probability as the optimal transmission power and optimal transmission time.

7. A cooperative covert communication device, characterized in that, The device, applied to a transmitting terminal, includes: a threshold prediction module, used to predict the energy detection threshold and false alarm probability threshold of a potential monitoring node based on first configuration information of the potential monitoring node, wherein the potential monitoring node is used to determine whether the transmitting terminal is conducting wireless communication; a model building module, used to model the monitoring behavior of the potential monitoring node based on the energy detection threshold, the false alarm probability threshold, and the service effective time window corresponding to the short packet service when the arrival of the short packet service is detected, and to build an evaluation model for the success probability of covert communication; a model solving module, used to build an optimization model corresponding to the evaluation model for the success probability of covert communication based on second configuration information of the short packet service, and to solve the optimization model to determine the optimal transmission power and the optimal transmission time; and a data transmission module, used to transmit the service data of the short packet service to each uplink base station through a preset unlicensed resource pool based on the optimal transmission power and the optimal transmission time.

8. A cooperative covert communication device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the cooperative covert communication method as described in any one of claims 1 to 6.

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