A method for secure communication based on STAR-RIS assistance in a common sense integrated network

Abstract

The application discloses a kind of based on STAR-RIS auxiliary in the security communication method of integrated network, including the following steps: the channel characteristics between base station, active reconfigurable intelligent surface, user, target, multiple guards and fusion center are analyzed;In the case where the communication transmission power of guard is unknown base station, based on generalized likelihood ratio test and maximum likelihood estimation, the optimal detection threshold and minimum detection error probability are solved;Based on the independent detection result of each guard, the minimum security communication detection error probability of fusion center under hard decision fusion scheme and soft decision fusion scheme is solved;The transmission beamforming vector of base station, the active beamforming vector of active reconfigurable intelligent surface and the number of continuous transmission time slots are jointly optimized to maximize the total security transmission rate at the user, while satisfying the sensing performance and security performance constraints;The application balances the contradiction between sensing performance, security and security transmission efficiency.

Description

A secure communication method based on STAR-RIS assistance in a sensor-integrated network Technical Field

[0001] This invention relates to the field of secure communication technology, and specifically to a secure communication method based on STAR-RIS assistance in a sensor-integrated network. Background Technology

[0002] Reconfigurable smart surfaces (RIS) are a promising technology for improving wireless network performance. They dynamically adjust the wireless channel by changing the phase shift. However, most studies suggest that the transmitter and receiver must be located on the same side of the RIS, severely limiting its flexibility and effectiveness. Therefore, a reconfigurable smart surface capable of simultaneous reflection and transmission (STAR-RIS) has been proposed. Unlike traditional RIS, STAR-RIS can passively transmit and reflect incident signals to both sides simultaneously, increasing spatial freedom and achieving full-space coverage. However, signals reaching users through STAR-RIS experience double path loss; this "multiplicative fading" effect limits the performance improvement of STAR-RIS. To overcome this problem, the concept of active STAR-RIS has been proposed and studied. Active STAR-RIS integrates amplifiers in each component. These amplifiers can simultaneously adjust the phase and enhance the signal amplitude, effectively mitigating the "multiplicative fading" effect. By actively compensating for path loss, active STAR-RIS significantly improves signal reliability and increases the data transmission rate for secure communication.

[0003] In secure communication scenarios, the degree to which a watchdog knows the sender's power significantly impacts its detection performance. Existing research typically assumes that the watchdog knows the sender's accurate power. However, watchdogs often exist as external malicious nodes, making it difficult for them to obtain prior knowledge of the sender. Therefore, the original optimal detector likelihood ratio test (LRT) is no longer applicable to watchdogs. To address this issue, a joint estimation and detection framework based on the generalized likelihood ratio test (GLRT) has been proposed. Specifically, the watchdog can first use the estimate obtained from maximum likelihood estimation (MLE) to replace the unknown parameters, and then estimate the detection error probability through the generalized likelihood ratio test. This method has been proven to be optimal when the number of signal samples is small. However, the above method is for the case of a single watchdog and does not consider the case of multiple watchdogs. Summary of the Invention

[0004] Objective: This invention aims to provide a secure communication method based on STAR-RIS assisted in a sensor-integrated network, achieving low detection probability and high communication rate while meeting sensing performance requirements. The invention considers the scenario of multiple watchers with collusive relationships, where information sharing and collaborative decision-making with a fusion center can enhance the detection capability of security signals. This invention constructs a joint estimation and detection framework in non-ideal prior knowledge scenarios, where multiple collusive watchers first perform independent detection using generalized likelihood ratio tests and maximum likelihood estimation, and then collaborate with the fusion center for soft and hard decision fusion. This invention aims to overcome the shortcomings of traditional RIS systems, such as poor spatial flexibility and the inability to transmit when direct links between communicating parties are blocked, by integrating an active STAR-RIS capable of simultaneous reflection and transmission, while also addressing the impact of the "multiplicative fading" effect. The joint optimization framework for STAR-RIS's active beamforming and continuous time slot number aims to provide a reliable solution for achieving high-speed secure communication in millimeter-wave ISAC networks with collaborative detection by multiple collusive watchers and a fusion center.

[0005] Technical Solution: The present invention discloses a STAR-RIS-assisted secure communication method in an integrated sensing and communication network, applicable to millimeter-wave frequency bands. Direct links between the base station and users, targets, and monitors are blocked. Signal transmission and coverage are achieved through active reconfigurable smart surfaces. The method includes the following steps:

[0006] (1) Analyze the channel characteristics between the base station, active reconfigurable smart surface, user, target, multiple guards and fusion center, as well as the transmitted signal of the base station and the received signal of each node;

[0007] (2) When the communication transmission power of the base station is unknown to the guard, the optimal detection threshold and the minimum detection error probability are solved when each guard detects independently, based on the generalized likelihood ratio test and the maximum likelihood estimation.

[0008] (3) Based on the independent detection results of each guard, solve the minimum security communication detection error probability of the fusion center under the hard decision fusion scheme and the soft decision fusion scheme; analyze the security performance of the base station under different detection models; jointly optimize the transmit beamforming vector of the base station, the active beamforming vector of the active reconfigurable smart surface and the number of continuous transmission time slots to maximize the total secure transmission rate at the user and satisfy the perception performance and security performance constraints.

[0009] Furthermore, in step (1), the active reconfigurable smart surface includes multiple active units that can simultaneously adjust the phase and amplitude of reflection and transmission, for dynamically adjusting the wireless channels in the reflection space and transmission space to assist radar sensing and secure communication.

[0010] Furthermore, in step (2), the watcher estimates the communication transmission power based on the observation data of the current time slot or the observation data of the historical time slot using maximum likelihood estimation, and determines the optimal detection threshold and the minimum detection error probability based on the generalized likelihood ratio test.

[0011] Furthermore, when the watcher estimates and detects based on historical time-slot observation data, the expectation-maximization algorithm is used to iteratively solve for the maximum likelihood estimate of the communication transmission power.

[0012] Furthermore, in step (3), when the fusion center adopts the hard decision fusion scheme, it receives the binary decision results of each guardian and makes the final decision through the weighted sum rule; when the fusion center adopts the soft decision fusion scheme, it receives the energy measurement value of each guardian and performs a likelihood ratio test based on the total received signal of all guardians.

[0013] Furthermore, in step (3), the analysis of base station security performance is as follows: In the detection model based on historical observations, the security performance of the base station is evaluated by the expected detection error probability, wherein the expected detection error probability is calculated based on the probability distribution of the maximum likelihood estimate of the communication transmission power.

[0014] Furthermore, in step (3), the joint optimization is specifically as follows: the joint optimization problem is decomposed into a base station transmit beamforming optimization sub-problem, an active reconfigurable smart surface active beamforming optimization sub-problem, and a continuous time slot number optimization sub-problem; each sub-problem is solved iteratively through alternating optimization methods until convergence, and the optimized transmit beamforming vector, active beamforming vector, and continuous time slot number are obtained.

[0015] Furthermore, the joint optimization process satisfies the following constraints: the expected detection error probability of the fusion center is not lower than a preset security threshold; the perceived signal-to-interference-plus-noise ratio of the base station is not lower than a preset perception performance threshold; the transmit power of the base station and the reflection-transmission power of the active reconfigurable smart surface do not exceed their respective power budgets; and the amplitude of each unit of the active reconfigurable smart surface does not exceed its maximum amplitude limit.

[0016] Furthermore, the base station's transmitted signals include sensing signals when only sensing is performed, and a superposition of sensing and communication signals when secure communication is performed; the base station decides whether to send secure communication signals based on a preset prior probability in order to confuse the watchers and the fusion center.

[0017] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: This invention considers scenarios where multiple colluding watchers have unknown communication transmission power, and the joint fusion center uses soft-decision fusion and hard-decision fusion schemes for detection. It provides an estimation method for the detection error probability of the watchers and the fusion center in this scenario. It considers the use of finite block length transmission by the sender, making the studied network more efficient in some latency-sensitive applications. Furthermore, it considers an integrated system of active STAR-RIS and sensing, overcoming the shortcomings of traditional RIS which can only cover half-space and cause "multiplicative fading" effects, while also solving the problem of transmission failure when direct sensing and communication links are blocked. Finally, through a joint optimization strategy of the transmit beamforming vector, the active beamforming vector of STAR-RIS, and the number of consecutive time slots, it maximizes the total secure transmission rate while satisfying security and sensing constraints. It balances the contradiction between sensing performance, security performance, and secure transmission efficiency, providing a solution for achieving high-speed reliable communication in complex monitoring environments integrating sensing and communication. Attached Figure Description

[0018] Figure 1 is a modeling diagram of the STAR-RIS-enabled millimeter-wave ISAC network system of the present invention;

[0019] Figure 2 is a schematic diagram of the present invention;

[0020] Figure 3 shows the relationship between the mean variance of the estimated value and the number of time slots and the relationship between the average detection error probability and the number of time slots under different channel usage times of the present invention; Figure 3(a) shows the relationship between the mean variance of the estimated value and the number of time slots under different channel usage times; Figure 3(b) shows the relationship between the average detection error probability and the number of time slots under different channel usage times. Detailed Implementation

[0021] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0022] As shown in Figure 2, this embodiment of the invention provides a STAR-RIS-assisted secure communication method based on an integrated sensing and communication network in the millimeter-wave band. The direct links between the base station and the user, target, and monitor are blocked. Signal transmission and coverage are achieved through an active reconfigurable smart surface. The method includes the following steps:

[0023] This invention comprehensively considers the integration of sensing and communication, STAR-RIS empowerment, finite block length of millimeter waves, unknown communication transmission power of the monitoring party, joint estimation and detection by multiple colluding watchdogs and the fusion center, and the situation where the direct link between the two communicating parties is blocked. It maximizes the total secure transmission rate by jointly optimizing the transmit beamforming vector, the STAR-RIS active beamforming vector, and the number of consecutive time slots. Each watchdog node first independently performs maximum likelihood estimation (MLE) and generalized likelihood ratio test (GLRT) based on its own observation data, and then jointly with the fusion center, obtains the optimal detection error probability under soft decision fusion and hard decision fusion schemes.

[0024] 1. Channel characteristics and analysis of transmitted and received signals

[0025] 1.1 System Model Introduction

[0026] Figure 1 illustrates an integrated sensing and communication (ISAC) network assisted by an active reconfigurable smart surface (STAR-RIS) capable of simultaneous reflection and transmission. This network comprises a device equipped with… A transmitting base station with one antenna (arranged in a uniform linear array (ULA)), a receiving user with a single antenna, and a point target. A single-antenna watcher, a fusion center responsible for integrating watcher information and determining whether the base station is transmitting, and a system containing... STAR-RIS consists of a transmission-reflection element. For ease of implementation, all antennas of the base station are divided into... Root radar antenna and The root communication antenna, and satisfies The former is used for openly sensing potential targets, while the latter is used to transmit security signals to users in the event of joint detection by multiple colluding watchdogs and a fusion center. Assume the entire wireless space is divided into two half-spaces by STAR-RIS: a reflection space and a transmission space, where the sender and the sensed target are located in the former, and all other nodes are located in the latter. All nodes communicate in half-duplex mode with fully synchronized time slots. Assume the direct link between the base station and the user / target / watchdog is blocked. To address this issue, a [system / mechanism] is introduced... The active STAR-RIS consists of multiple reflective and transmissive elements arranged in a uniform planar array (UPA) to assist radar sensing and secure communication while ensuring a low probability of detection by watchers and fusion centers.

[0027] 1.2 Channel Gain

[0028] Assuming the base station's radar or communication antenna Communication link with STAR-RIS and the communication link between STAR-RIS and the user / caretaker. / Both adopt the Saleh-Valenzuela (SV) millimeter-wave channel model, the expression of which is as follows:

[0029]

[0030]

[0031] in / The number of resolvable subpaths of the link indicated by the subscript. / For large-scale channel fading, / Indicates the first Small-scale channel fading in striped paths. Furthermore, The first term related to the radar / communication antenna of the base station. The azimuth of the strip path; and These respectively represent the first or second information regarding base station radar or communication antennas. The azimuth and elevation angles of the strip path at STAR-RIS; and These represent the first and second links of the links indicated by the subscripts. The azimuth and elevation angles of the stripe path at STAR-RIS. Large-scale channel fading is represented as... .in This is the path loss when the reference distance is 1 meter. and These represent the distance and path loss index of the link indicated by the corresponding subscript, respectively.

[0032] Base station radar / communication antenna The array response vector of STAR-RIS is: ,

[0033] in and These represent the number of elements in the uniform planar array at STAR-RIS in the horizontal and vertical directions, respectively. .

[0034] The sensing link between STAR-RIS and the target Typically, this is a line-of-sight (LoS) link, which can be precisely determined using known distance and angular position information of the target of interest. Therefore, the perception link between STAR-RIS and the target can be represented as:

[0035]

[0036] in This indicates the large-scale channel fading between STAR-RIS and the target, i.e. . and These represent the distance and path loss index of the link indicated by the subscript, respectively. and These represent the azimuth and elevation angles of the link at STAR-RIS, respectively.

[0037] Assume all nodes remain stationary, and all wireless links between nodes are quasi-static and statistically independent channels. Furthermore, the duration of each time slot is short enough that the channel fading coefficient decreases continuously. It remains constant within each time slot.

[0038] 1.3 Base Station Transmitted Signal Analysis

[0039] To detect targets, base stations continuously transmit sensing signals and receive echoes. Furthermore, to enhance transmission security, base stations transmit communication signals with a certain prior probability in each time slot. To confuse observers, base stations decide whether to transmit with equal probability.

[0040] In the In each time slot, if the base station transmits, it maps the message to a set of independent and identically distributed zero-mean complex Gaussian random symbol sequences. .in and They represent the first The sensing signals and communication signals transmitted by the base station in each channel transmission are independent of each other and uncorrelated. The number of times a channel is used for transmission within a time slot. Specifically, in the... In secondary channel transmission, the base station first generates a normalized sensing signal. and normalized communication signals Then, respectively, by sensing beamforming vectors and communication beamforming vector Encode the data, then amplify the power to obtain the final product. and Finally, the signals are merged and transmitted. Since the channel fading coefficient is constant across all time slots, the sensing beamforming vector and the communication beamforming vector are continuously... The number of time slots also remains constant. Correspondingly, under the two assumptions of a pure sensing scenario and a comprehensive sensing and communication scenario, the first time slot... The time-slot base station in the first time slot The transmitted signal used in secondary channel transmission is defined as follows:

[0041]

[0042] in Indicates the corresponding channel used for transmission. and . and These represent communication transmission power and sensing transmission power, respectively. Furthermore, The null hypothesis means that the base station only transmits sensing signals; As an alternative hypothesis, it is assumed that the base station transmits both sensing signals and communication signals.

[0043] In addition, sensing beamforming vector and communication beamforming vector and power budget and The following constraints must be met:

[0044]

[0045] in This represents the base station's total transmission power budget (composed of radar sensing power and communication transmission power).

[0046] 1.4 Analysis of Received Signals at Each Node

[0047] 1.4.1 Communication Model at the User End

[0048] When the base station is only performing radar sensing tasks, the signal received by the user is:

[0049]

[0050] When the base station simultaneously performs radar sensing tasks and transmits security signals to users, the signal received by the user is:

[0051]

[0052] in, Indicates the first In each time slot, the signal transmitted by the base station is transmitted through the active STAR-RIS and then reaches the user's communication link, where the dynamic noise generated by the STAR-RIS is present. Indicates the first Additive white Gaussian noise (AWGN) at the user location in each time slot. STAR-RIS and the noise at the user location are each a set of independent, identically distributed, zero-mean complex Gaussian random variables: and . Represents the transmission coefficients (TCs) at STAR-RIS, where and The first Each element is used to measure the correlated phase shift and amplitude during transmission.

[0053] It is important to note that when the number of channel transmissions is finite, the decoding error probability at the user end cannot be ignored. Accordingly, for a given decoding error probability... The secure transmission rate received by the user The lower bound can be approximated as:

[0054]

[0055] in, It is the inverse Q-function. It refers to the number of times the channel is used for transmission. This represents the signal-to-interference-plus-noise ratio at the user's location.

[0056] 1.4.2 Radar Sensing Model at Base Station

[0057] When a base station simultaneously performs radar sensing and security communication, the echo signal it receives from the target can be represented as:

[0058] (9)

[0059] in, Indicates the first The dynamic noise generated by the STAR-RIS in the link from the base station to the target after the signal is reflected by the active STAR-RIS in each time slot. Indicates the first The target reflected signal in each time slot is reflected back to the base station by the active STAR-RIS, generating dynamic noise in the STAR-RIS-based radar sensing echo link. Indicates the first The additive white Gaussian noise at the base station in each time slot. STAR-RIS and the noise at the base station are a set of independent and identically distributed zero-mean complex Gaussian random variables: , and . Represents the reflectance coefficient (RCs) at STAR-RIS, where ∈[0,2π) and The first Each element is used to determine the correlated phase shift and amplitude during reflection. .

[0060] definition Let the equivalent link from the communication / radar antenna to the target be represented, then the perceived signal-to-interference-plus-noise ratio at the base station can be expressed as:

[0061]

[0062] The perceived signal-to-interference-plus-noise ratio (SIR) of a base station within a time slot can be defined as follows: The minimum value is expressed as:

[0063]

[0064] 1.4.3 Signal Detection Model at the Guardian and Fusion Center

[0065] To determine whether a base station is communicating with a user, each monitor needs to distinguish between the following two assumptions:

[0066] (12)

[0067]

[0068] in, Indicates the first The base station transmits signals in each time slot, which are then transmitted through the active STAR-RIS signal to the next time slot. Dynamic noise generated by STAR-RIS in the link of the individual watchdog. Indicates the first In the first time slot Additive white Gaussian noise at each of the guard locations. Both the STAR-RIS and the noise at the guard locations are sequences of independent, identically distributed, zero-mean complex Gaussian random variables: and Record the first In the first time slot The received signal vector at each guard is: .

[0069] in [ The expression for ] is:

[0070]

[0071] Therefore, the first In each time slot, after all the watchers report their raw observation data, the total received signal at the fusion center can be expressed as: ,in This indicates that the data observed by the fusion center in each channel is the sum of the observation data transmitted by each watcher in the corresponding channel. Furthermore... .

[0072] 2. Optimal detectors for guards in different scenarios

[0073] In each time slot, each watcher receives a sequence of independent and identically distributed, zero-mean complex Gaussian random variables. Therefore, the symbols received by each watcher follow a Gaussian distribution. For the... A guard, in the assumption and The probability density functions are as follows:

[0074]

[0075]

[0076] in, .

[0077] Use respectively and This represents the assumptions of whether the base station has no communication transmission or has communication transmission. and These represent the binary decision made by the monitor regarding the presence of communication transmission at the base station. Monitor detection errors can be categorized into two types: false alarms and missed detections. Specifically, a false alarm occurs when there is no communication transmission at the base station (…). When the transmission was established, the guard mistakenly determined that a transmission had occurred. Missed detection refers to a situation where communication transmission occurs at the base station. When the custodian was established, the custodian mistakenly determined that no transmission had occurred. ).remember This represents the probability of a false alarm. This represents the probability of a missed detection. The detection error probability (DEP) of a guard is typically defined as... .in, and These are the prior probabilities of the base station remaining silent and transmitting communication, respectively. In secure communication literature, it is typically assumed that the caretaker uses a classic likelihood ratio test to minimize the error probability, and Therefore, when When the base station's transmission is secure, it can be considered that the base station's transmission is secure, which also means that the base station can... The probability of avoiding detection, among which For safety requirements, Small enough.

[0078] 2.1 The optimal detector LRT for the guard in the ideal prior knowledge scenario

[0079] A common assumption regarding the prior knowledge of the keeper is that the keeper has complete knowledge of the transmission or environmental information, such as noise variance, transmission power, and the codebook statistical model used by the base station. In this case, the keeper has prior knowledge of... and According to the Neyman-Pearson criterion, the watcher uses the least probable detection probability test (LRT) to obtain the minimum detection error probability. Therefore, the first... The likelihood ratio test for a single caretaker can be expressed as:

[0080]

[0081] Substituting equations (15) and (16) into equation (17) yields the first... The threshold for a single guard is: Accordingly, the watchman is in a single time slot. The test statistic is defined as follows: ,in Assume each watcher determines whether the base station is transmitting based on a threshold test using an energy detector: . exist Below is the scaling factor. / (2 A chi-squared distributed random variable, in Below is the scaling factor. / (2 A chi-squared distributed random variable. Therefore, exist and The likelihood function is:

[0082]

[0083]

[0084] Accordingly, the first A guard in a single time slot False alarm probability in and the probability of missed detection It can be represented as:

[0085]

[0086]

[0087] Then the first The first time slot The probability of detection error for each guard is:

[0088]

[0089] 2.2 GLRT, the optimal detector for the guardian in scenarios with non-ideal prior knowledge

[0090] It is worth noting that in most secure communication scenarios, the watchdog is an external malicious node, thus possessing very little prior knowledge of the transmission or environmental information. Consider a powerful watchdog: it knows nothing about the base station's communication transmission power, but all other environmental information can be obtained through various methods. In this case, the original likelihood ratio test (LRT) is no longer the optimal detector. However, this does not mean the watchdog is weak, as it can estimate the unknown communication transmission power through observation. In scenarios with unknown parameters, a common approach is to utilize the generalized likelihood ratio test (GLRT) and replace the unknown parameters with the maximum likelihood estimate by maximizing the likelihood function.

[0091] According to the Neyman-Pearson criterion, the generalized likelihood ratio test for the optimal detector of the watcher can be expressed as:

[0092]

[0093] in, They represent unknown parameters respectively. The estimated value, This represents the observed value. Therefore, even if the monitoring party does not know the communication transmission power, the corresponding optimal threshold and minimum detection error probability can still be calculated through the generalized likelihood ratio test.

[0094] 3. Analysis of the Detection Model for the Guard when the Transmission Power is Unknown

[0095] 3.1 Detection model of the guard based on current time slot observation

[0096] For the watcher, under normal circumstances, they can estimate the unknown communication transmission power based on the data observed in the current time slot. In scenarios with unknown communication transmission power, because... All of these are known, therefore, in the detection model based on the current time slot observations, the first... The generalized likelihood ratio test for a single guard can be written as:

[0097]

[0098] in, It is the first The communication transmission power obtained by the guard based on the current time slot observation The maximum likelihood estimate, obtained from equation (24), yields the optimal threshold for the guard: . Then by making Maximize the result.

[0099] right conduct Take the first derivative of , and simultaneously let The maximum likelihood estimate of communication transmission power can be derived. The expression is:

[0100]

[0101] Therefore, based on the current time slot observations, the first The minimum detection error probability of a single guard can be expressed as:

[0102]

[0103] 3.2 Detection Model of Guards Based on Historical Time Slot Observations

[0104] In practice, a watcher can not only construct a detector using observations from a single time slot, but also estimate unknown parameters using observations from historical time slots. Therefore, consider a more capable watcher that can flexibly utilize all historical observations for estimation and detection without prior knowledge. In scenarios with unknown communication transmission power, for the... The first guard, The generalized likelihood ratio test detector for each time slot can be expressed as:

[0105]

[0106] in It utilizes observations from all historical time slots. The obtained maximum likelihood estimate. In the detection of the... When the first time slot is defined, the first time slot is defined. A guard All historical observation vectors within each time slot are Because the monitor does not know whether the base station is transmitting communication in each time slot, a single time slot... observation vector The probability density function can be expressed as: and Assume the weighted sum of the following probability density functions:

[0107]

[0108] in, and The results are given by equations (15) and (16), respectively. Since historical observations consist of vectors from multiple time slots and the observation vectors from all time slots are independent and identically distributed, therefore... The probability density function can be expressed as the product of the probability density functions for each time slot:

[0109]

[0110] Perform on the likelihood function Take the first derivative of , and simultaneously let The maximum likelihood estimate of communication transmission power can be derived. The expression is:

[0111]

[0112]

[0113] in, Indicates the first The guard estimated that the base station was in the [number]th [location]. The probability of communication transmission in each time slot is expressed as:

[0114]

[0115] However, the The maximum likelihood estimate of a single guardian based on historical time-slot observations. It is a historical observation Sum of probabilities The function, and in equation (31) also with and Related. Therefore The closed-form expression cannot be directly derived. For equations with highly coupled variables, a feasible approach is to iteratively approximate the optimal solution using the Expectation-Maximization (EM) algorithm. The EM algorithm for solving unknown transmission power consists of two steps: first, calculating the expected value of the likelihood function using the estimated parameters from the previous iteration; second, updating the parameters to maximize the likelihood function. By iteratively alternating between these two steps, the EM algorithm can gradually approach the maximum likelihood estimate, eventually converging to a local optimum.

[0116] The EM algorithm can be used to obtain... The value of is then substituted into equation (27) to derive the first... The guard was in the first The optimal threshold for each time slot is:

[0117]

[0118] Therefore, based on historical time slot observations, in the first In the first time slot, for the first The minimum detection error probability of a single guard can be expressed as:

[0119]

[0120] 4. Analysis of the detection model for the fusion center

[0121] The optimal threshold and minimum detection error probability for each watcher operating independently based on observations from the current and historical time slots were discussed. Next, the security communication detection error probability of the fusion center using different decision-making schemes will be discussed in the presence of multiple colluding watchers, specifically divided into hard-decision fusion and soft-decision fusion schemes.

[0122] 4.1 Detection error probability of fusion center based on hard decision fusion

[0123] In the hard-combination decision-making approach, each watcher independently determines whether the base station is transmitting communication signals by measuring its own test statistic and sends its decision to the fusion center. The fusion center then merges the decisions of all watchers to ultimately determine whether the base station is transmitting communication signals. Indicates the first If a guard makes a decision that determines the base station is transmitting communication signals, then... ,otherwise The optimal fusion rule based on Bayesian hypothesis testing is as follows:

[0124]

[0125] in . This represents the weighted sum of the decisions made by each caretaker. It is important to note that, due to... , Meanwhile, the optimal weight that minimizes the detection error probability of the fusion center can be expressed as:

[0126]

[0127]

[0128] Accordingly, in existence In the case of one colluding caretaker, the detection error probability of the fusion center in the hard decision-making scheme is expressed as:

[0129]

[0130] Therefore, it can be deduced that, under a hard decision scheme based on current time-slot observations, the detection error probability of the fusion center is:

[0131]

[0132] Among them, when and They represent based on the current time slot. The first observation The false alarm probability and the missed detection probability of each guard are given by equation (26):

[0133] Similarly, under a hard decision-making scheme based on historical time slot observations, the first The detection error probability of the fusion center in each time slot can be expressed as:

[0134]

[0135] Among them, when . and These represent the observations of the first historical time slot. The guard was in the first The false alarm probability and the missed detection probability of each time slot are given by equation (33):

[0136] 4.2 Detection error probability of fusion center based on soft decision fusion

[0137] In this scheme, after each guardian makes its local decision, it reports its energy measurement value and optimal threshold to the fusion center, which then performs a likelihood ratio test.

[0138]

[0139] in, This represents the test statistic of the fusion center within a single time slot. The detection threshold at the fusion center is determined by the optimal threshold at each guard:

[0140]

[0141] in, The error value is obtained by the fusion center based on comprehensive factors such as transmission and environment. This makes the detection threshold of the fusion center slightly smaller than the total threshold, avoiding the randomness that would result from the fusion center directly comparing the test statistic with the total threshold (the sum of the thresholds at each guard).

[0142] In each time slot, the total number of symbols received by the fusion center still follows a Gaussian distribution. Therefore, for the fusion center, it assumes... and The probability density functions are as follows:

[0143]

[0144]

[0145] in, and They respectively represent the fusion center in the hypothesis and The average received power. Since the sensed signals and communication signals received by each guard are correlated with each other, and The expression can be derived as:

[0146] (44)

[0147] (45)

[0148] It can be deduced that, based on the current time slot Under the soft decision-making scheme of observation, the optimal threshold of the fusion center and minimum detection error probability for:

[0149]

[0150]

[0151] Similarly, under the soft decision-making scheme based on historical time slot observations, in the first... The optimal threshold for communication transmission power for the fusion center in each time slot and minimum detection error probability for:

[0152] (48)

[0153]

[0154] 5. Security Performance Analysis of Base Stations

[0155] In the current observation-based detection model, the parameters of the detection error probability are all known to the base station and can therefore be directly used to evaluate its security performance. However, in the historical observation-based detection model, the detection error probability is a function of the guard's historical observation vector and the maximum likelihood estimate of the communication transmission power, both of which are unknown to the base station. Therefore, the detection error probability of each guard based on historical observations is unknown and cannot be used to evaluate the base station's security performance. We will consider the worst-case scenario where each guard uses the optimal detector based on historical observations to further analyze its security performance from the base station's perspective.

[0156] 5.1 Expected Detection Error Probability of Base Station in Independent Guardian Scenario

[0157] When variables are unknown, a common approach is to use the expected detection error probability as an indicator of security performance. In the... In the first time slot, for the first Each guard, its probability of detection error yes and The function of . Therefore, from the perspective of the base station, its expected detection error probability can be derived as:

[0158]

[0159] in, It is the maximum likelihood estimate of the communication transmission power. The probability density function. Because It cannot be directly derived. Here, we obtain it through the asymptotic normality of maximum likelihood estimation. The probability distribution. Based on the asymptotic normality characteristic of maximum likelihood estimation. ,in, This represents Fisher information, used to measure information about unknown parameters. The expected amount of information. This represents the observed value.

[0160] When other regular expression conditions are met Follows a normal distribution Fisher information on communication transmission power It can be approximated as:

[0161] (51)

[0162] According to equations (32) and (33), in the first... In the first time slot, the base station targets the first The expected detection error probability of a single guard can be approximately expressed as:

[0163]

[0164] 5.2 Expected Detection Error Probability of Base Stations in Multi-Conspiracy Guardian Hard Decision Fusion Scenarios

[0165] According to equation (52), we can obtain the first... In the first time slot, the base station targets the first... The expected false alarm probability and expected false negative probability for each guard are as follows:

[0166]

[0167] Therefore, in scenarios involving multiple collusive caretakers and hard decision-making fusion, the focus is on the first... The detection performance of the fusion center under each time slot, and the expected detection error probability of the base station can be expressed as:

[0168] Accordingly, in hard decision fusion scenarios The expected detection error probability of a base station within a time slot can be defined as follows: The average value is expressed as:

[0169]

[0170] 5.3 Expected Detection Error Probability of Base Stations in Multi-Collateral Guardian Soft Decision Fusion Scenarios

[0171] Similar to the security performance analysis of base stations in independent guardian scenarios, the expected detection error probability of base stations in multi-collusive guardian soft decision fusion scenarios can be derived. However, it is important to note that the received signal at each time-slot fusion center comes from all guardians, including… There are several variables, and the threshold at the fusion center is not simply the sum of the thresholds of each watcher; errors caused by comprehensive factors such as transmission or environment also need to be considered. For ease of subsequent analysis, it is assumed that the comprehensive factors estimated by the base station are the same as those considered by the fusion center, i.e. .

[0172] Therefore, in a multi-conspirator soft decision fusion scenario, the expected detection error probability of the base station, considering the detection performance of the fusion center, can be expressed as:

[0173]

[0174] Accordingly, in soft decision fusion scenarios The expected detection error probability of a base station within a time slot can be defined as follows: The average value is expressed as:

[0175]

[0176] 6. Design and Optimization of Secure Transmission Strategies

[0177] Jointly optimize the transmit beamforming vector ( ), STAR-RIS active beamforming vector ( ) and number of consecutive time slots This approach maximizes the overall secure transmission rate at the user end while meeting the sensing performance requirements and security constraints of the base station. It is important to note that all monitors utilize optimal detectors for detection.

[0178] 6.1 Problem Modeling

[0179] Considering the additional constraints imposed by the active STAR-RIS and integrated sensing and communication systems, the joint optimization problem to maximize the total secure transmission rate can be formulated as follows:

[0180]

[0181] Constraints:

[0182]

[0183]

[0184] (58e)

[0185]

[0186]

[0187] And it is an integer.

[0188] Among them, equation (58b) represents the safety performance constraint. It is the minimum required value for the expected detection error probability. This represents the security level; Equation (58c) represents the perception performance constraint. (58d) is the minimum required signal-to-interference-plus-noise ratio (SINR) to ensure sensing performance; (58e) is the transmit beamforming constraint; and (58d) is the maximum reflection-to-transmission power constraint for active STAR-RIS. (58f) represents the power budget of the active STAR-RIS; (58f) represents the maximum amplitude constraint of each cell in the active STAR-RIS. Maximum reflection amplitude limit Maximum transmission amplitude limit; (58g) is the maximum transmit power constraint for the base station. It is the total transmission power budget of the base station (including radar sensing power and secure communication power).

[0189] However, due to the optimization variables in problem P1 and These problems are interconnected, making joint optimization difficult. To address this challenge, problem P1 is decomposed into three easily manageable subproblems: base station transmit beamforming optimization, STAR-RIS active beamforming optimization, and continuous time slot number optimization. These subproblems are then solved using an alternating optimization approach.

[0190] 6.2 Optimization of Base Station Transmit Beamforming

[0191] For a given feasible STAR-RIS active beamforming Total number of time slots Problem P1 can be simplified to optimizing only the base station's transmit beamforming vector. Given the STAR-RIS active beamforming vector and the total number of time slots, the sub-problem P2 of optimizing the base station's transmit beamforming vector can be formulated as follows:

[0192]

[0193] Constraints:

[0194] Using auxiliary variables The complex fractional form of problem P2 is decomposed into several independent inequality constraints, which can then be solved using the successive convex approximation (SCA) method. Therefore, problem P2 can be equivalently transformed into:

[0195]

[0196] The constraints are as follows:

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203] in, A convex approximation function representing the secure transmission rate. and These represent the equivalent channel gain vectors of the base station communication / radar antenna reaching the user via STAR-RIS transmission, respectively. It represents the user-side equivalent noise power, which includes the thermal noise of the user receiver and the total power of the active noise introduced by STAR-RIS at the user end. It represents the equivalent noise power at the base station, which includes the additive noise at the base station radar receiver and the total noise power introduced by STAR-RIS and reflected back to the base station. This represents the STAR-RIS power constraint constant term. The quadratic matrix representing the self-interference channel characteristics of the sensed signal reflected back to the base station via STAR-RIS. A quadratic matrix representing the interference channel characteristics of communication signals leaking to the base station after being reflected by STAR-RIS. and It is the equivalent channel matrix used to calculate the power consumption of the STAR-RIS transmission section. and It is the equivalent channel matrix used to calculate the power consumption of the STAR-RIS reflection section.

[0204] Since the objective function (60a) and constraints (60b), (60c), and (60e) are all non-convex, direct solutions remain difficult. Therefore, a successive convex approximation method is used to construct their convex surrogate functions.

[0205] In the objective function (60a) It's about auxiliary variables. Since the function is a joint convex function, a lower bound for (60a) can be obtained using a first-order Taylor expansion:

[0206]

[0207] in Indicates the lower bound of the communication rate gain term; This represents the upper bound of the decoding error loss term. It is the first A feasible solution is given in the next iteration. At this point, the objective function (60a) is approximately convex, but problem P2.1 still has non-convex constraints (60b), (60c), and (60e). Similarly, in the iterative feasible solution... We expand these functions using a first-order Taylor expansion to find the linear lower bound of the convex functions. Accordingly, (60b), (60c), and (60e) can be approximated as:

[0208]

[0209]

[0210]

[0211] in, It is the first The feasible solution of the transmit beamforming vector in the next iteration represents the value obtained in the previous iteration, which is treated as a constant in the current step. At this time, (60b), (60c), and (60e) are all approximately convex constraints. Therefore, problem P2 is ultimately transformed into a convex problem:

[0212]

[0213] The constraints are as follows:

[0214]

[0215] This problem can be solved directly using convex optimization tools such as CVX to obtain the optimal solution. .

[0216] 6.3 STAR-RIS Active Beamforming Optimization

[0217] Keeping the transmit beamforming vector in each time slot and the total number of time slots fixed, the transmit beamforming vector is obtained from problem P2. Given the base station transmit beamforming vector and the total number of time slots, the STAR-RIS active beamforming vector optimization subproblem P3, aimed at maximizing the total secure transmission rate, can be expressed as:

[0218]

[0219] The constraints are as follows:

[0220]

[0221] Referring to the approach used for problem P2, we first introduce auxiliary variables to decompose the complex fractional form into several independent inequality constraints, thus transferring the complexity to the constraints. However, it can be observed that the optimization variables at this point... Sandwiched in the middle of the channel matrix, it is difficult to directly extract it for differentiation or to make a convex approximation. Utilizing... , Unify all constraints into a set of conditions. For quadratic form problems, matrix optimization is transformed into vector optimization, allowing for subsequent Taylor expansion and iterative solutions using successive convex approximation methods. Problem P3 can be further transformed into:

[0222]

[0223] The constraints are as follows:

[0224]

[0225]

[0226]

[0227]

[0228]

[0229]

[0230]

[0231] in, , Because the signal in the radar sensing link undergoes two-way reflection, the corresponding power expression will appear. The fourth term. The fourth term is extremely difficult to solve, so it is written as a quartic term. The quadratic form of the constraint transforms the quadratic constraint into a linear constraint for subsequent processing. express The Column. And , , , , , , , , , , , , The expression for is given by equation (68).

[0232]

[0233]

[0234]

[0235]

[0236]

[0237]

[0238]

[0239]

[0240]

[0241]

[0242]

[0243]

[0244] This represents the receiver rate constraint coefficient matrix, used to reconstruct the signal-to-interference-plus-noise ratio (SIR) constraint at the user end. , , , This represents the sensing performance constraint coefficient matrix, used to reconstruct the signal-to-interference-plus-noise ratio (SIR) constraint of the base station radar echo. , This represents the power budget constraint coefficient matrix, used to reconstruct the total power consumption constraint of STAR-RIS. , This represents the active noise power constant matrix, which is related to the active characteristics of STAR-RIS. , , This represents the intermediate variable matrix of the sensing link, used for the mathematical derivation of two-way reflection paths. Specifically, It includes communication beamforming vectors and user channels. Includes radar beam and user channel, representing the interference power of radar signal to communication users. Includes noise variance, representing the noise power term at the user end, and representing the received useful signal power term. / These correspond to the energy terms of the sensing and communication signals after reflection and echo, respectively. This represents the two-way noise term unique to radar sensing. This corresponds to the self-interference noise term at the radar receiver. Describe the signal power (including reflection and transmission) after STAR-RIS amplification. Describe the additional noise power introduced by STAR-RIS active devices. This corresponds to the dynamic noise constant term introduced during the reflection / transmission process. The corresponding noise amplification term includes the number of guards. , , This is for simplification The more fundamental intermediate term defined by the expression includes the STAR-RIS to target channel and its conjugate transpose.

[0245] For constraints (67b) and (67c), in the iterative feasible solution The function is expanded using a first-order Taylor series to find a linear lower bound for the convex function. Therefore, it can be approximately transformed into:

[0246]

[0247]

[0248] in, It is the first The given feasible solution in the next iteration represents the known solution calculated in the previous iteration, used to calculate the gradient point of the Taylor expansion in the current iteration. At this point, constraints (67b) and (67c) are approximately convex constraints. The core difficulty with constraint (67e) lies in its relation to... The quartic function is commonly approximated as a convex constraint using a second-order Taylor expansion and eigenvalue boundary treatment. Therefore, constraint (67e) can be rewritten as:

[0249]

[0250] in, The constructed sensing constraint coefficient matrix is ​​used to transform the complex signal-to-interference-plus-noise ratio inequality constraint into a standard one. Form. For quartic terms Its upper bound can be obtained through second-order Taylor expansion and the largest eigenvalue of the matrix: .in It is a matrix The largest eigenvalue is used to construct a sufficiently large positive definite matrix to ensure the strict convexity of the convex approximation function. It is the first A given feasible solution in the next iteration. And because... You can get Further transform the fourth term into a term about Secondary constraints:

[0251]

[0252] in It contains the gradient information of the function at the current point and is used to construct the linear part of the convex surrogate function. It is the gradient vector obtained by expanding the first-order Taylor series. After inverse vectorization matrix.

[0253] First, transform the inner product of the two vectors into the trace of their corresponding matrices, i.e. .because It is a Hermitian matrix whose conjugate transpose is equal to itself, therefore we can obtain Based on the cyclic property of the matrix trace, we have: .because It is a scalar, and the trace of a scalar is equal to itself. Ultimately, we can obtain: .

[0254] because It is still nonconvex. Further transformation into real-valued form to avoid the nonconvexity of the complex field yields:

[0255]

[0256] in, , complex vector The new vector formed by stacking the real and imaginary parts of the vector has twice the length of the original vector. This ensures that mathematical equivalence remains unchanged after transforming complex number problems into real number problems. Its upper bound can be obtained by second-order Taylor expansion: .

[0257] in It is a matrix The maximum eigenvalue ensures that the constructed convex surrogate function always serves as the upper bound of the original non-convex function, thus guaranteeing the convergence of the successive convex approximation algorithm. In convex approximation constraints, it serves as a coefficient of the first-order term, guiding the optimization variables to move in the correct direction.

[0258] Therefore, constraint (70e) can ultimately be transformed into:

[0259]

[0260] Similarly, constraint (67f) can be approximated as:

[0261]

[0262] in The total power consumption coefficient matrix of STAR-RIS is represented by a matrix that transforms the complex power constraints into a standard quadratic form for convex approximation. Used to simulate complex number multiplication in the real number field. It is a matrix The maximum eigenvalue ensures that the constructed convex surrogate function always serves as the upper bound of the original non-convex function, thus guaranteeing the convergence of the successive convex approximation algorithm. In convex approximation constraints, it serves as a coefficient of the first-order term, guiding the optimization variables to move in the correct direction. It contains the gradient information of the function at the current point, which is used to construct the linear part of the convex surrogate function. It is the gradient vector obtained by expanding the first-order Taylor series. After inverse vectorization matrix. It is a matrix The largest eigenvalue is used to construct a sufficiently large positive definite matrix to ensure the strict convexity of the convex approximation function.

[0263] Therefore, problem P3 is ultimately transformed into a convex problem:

[0264]

[0265] The constraints are as follows:

[0266]

[0267] This problem can be solved directly using convex optimization tools such as CVX to obtain the optimal solution. .

[0268] 6.4 Optimization of the Number of Continuous Time Slots

[0269] Keeping the transmit beamforming vector and the STAR-RIS active beamforming vector fixed in each time slot, i.e., obtaining the beamforming vector from problems P2 and P3. After determining all beamforming vectors, the subproblem P4, which optimizes the number of consecutive time slots, can be formulated as follows:

[0270]

[0271] The constraints are as follows:

[0272]

[0273] For the objective function (77a), it is obvious It is about A monotonically increasing function, in addition It is about The function is a monotonically decreasing function. Therefore, this optimization problem can be transformed into an integer nonlinear programming problem, using... To obtain the maximum number of time slots This maximizes the overall secure transmission rate. Solve using the binary search method and take the integer part.

[0274] To verify the feasibility and performance optimization of this invention, Figure 3 shows the MATLAB simulation results of the estimation and detection performance of the independent watcher based on historical time-slot observations. In Figure 3(a), the watcher's response to the estimated value is shown. The average variance varies with the number of observation slots T and the number of channel uses L. Figure 3(b) shows the variation of the average variance with different number of channel uses L. The average detection error probability of a guard The variation with the number of observation time slots T. Furthermore, each data point was obtained through 10,000 Monte Carlo simulations. As can be observed from the graph, The variance decreases with increasing observation slots and channel usage frequency. This demonstrates that a larger sample size improves the estimation accuracy of unknown parameters, thus proving the effectiveness of the model based on historical observations. It also shows that when the channel usage frequency L is large, fewer observation slots are needed to achieve the same estimation accuracy. This is because increasing the channel usage frequency leads to more observations, thereby improving the estimation effect. As the number of observations approaches infinity, the estimated value approaches the true value. Furthermore, it can be observed that as the estimation becomes more accurate, the corresponding average detection error probability decreases. This also verifies that estimation errors lead to uncertainty for the watcher regarding whether communication transmission has occurred, and that the detection model based on historical slot observations does indeed aid in detection.

[0275] This invention aims to address the challenge of achieving secure communication with finite block lengths while meeting the constraints of perception and security performance in scenarios where the direct link between communicating parties is blocked in an ISAC network. The invention constructs a joint estimation and detection framework where multiple colluding watchmen first perform independent detection using generalized likelihood ratio testing and maximum likelihood estimation in a non-ideal prior knowledge scenario, and then collaborate with the fusion center to perform soft and hard decision fusion. This framework yields an estimate of the unknown communication transmission power and the corresponding detection error probability in this scenario. Furthermore, by introducing active STAR-RIS, the invention overcomes the limitations of traditional RIS, which only provides half-space coverage and suffers from multiplicative fading. Finally, the invention designs a joint optimization strategy for transmit beamforming, STAR-RIS active beamforming, and the number of consecutive time slots, providing a reliable solution for achieving low detection probability and high-speed secure communication in millimeter-wave ISAC networks with collaborative detection by multiple colluding watchmen and the fusion center.

[0276] The technical meanings of all parameters are shown in Tables 1-10.

[0277] Table 1. Technical Meaning of Parameters

[0278] ;

[0279] Table 2. Technical Meaning of Parameters

[0280] ;

[0281] Table 3. Technical Meaning of Parameters

[0282] ;

[0283] Table 4. Technical Meaning of Parameters

[0284] ;

[0285] Table 5. Technical Meaning of Parameters

[0286] ;

[0287] Table 6. Technical Meaning of Parameters

[0288] ;

[0289] Table 7. Technical Meaning of Parameters

[0290] ;

[0291] Table 8. Technical Meaning of Parameters

[0292] ;

[0293] Table 9. Technical Meaning of Parameters

[0294] ;

[0295] Table 10 Parameter Technical Meaning 10

[0296] .

Claims

1. A secure communication method based on STAR-RIS assistance in a sensor-integrated network, characterized in that, Includes the following steps: (1) Analyze the channel characteristics between the base station, active reconfigurable smart surface, user, target, multiple guards and fusion center, as well as the transmitted signal of the base station and the received signal of each node; (2) When the communication transmission power of the base station is unknown to the guard, the optimal detection threshold and the minimum detection error probability are solved when each guard detects independently, based on the generalized likelihood ratio test and the maximum likelihood estimation. (3) Based on the independent detection results of each guard, solve for the minimum security communication detection error probability of the fusion center under the hard decision fusion scheme and the soft decision fusion scheme; The security performance of base stations under different detection models is analyzed; the transmit beamforming vector of the base station, the active beamforming vector of the active reconfigurable smart surface, and the number of continuous transmission time slots are jointly optimized to maximize the total secure transmission rate at the user while satisfying both sensing and security performance constraints; an integrated sensing and communication (ISAC) network assisted by the STAR-RIS active reconfigurable smart surface capable of simultaneous reflection and transmission is constructed, and the network includes... A transmitting base station with one antenna arranged in a uniform linear array ULA, a receiving user with one single antenna, and a point target. A single-antenna watcher, a fusion center that integrates watcher information and determines whether the base station is transmitting, and Active STAR-RIS is composed of multiple reflective and transmissive units; All antennas of the base station are divided into Root radar antenna and The root communication antenna, and satisfies ； Radar antennas are used to detect potential targets in the open. The communication antenna is used to transmit security signals to users in the event of joint detection by multiple colluding watchdogs and fusion centers; the entire wireless space is divided into two half-spaces by STAR-RIS: a reflection space and a transmission space, where the transmitter and the sensing target are located in... The root radar antenna, and all other nodes are located at The communication antenna is used; all nodes communicate in half-duplex mode and their time slots are fully synchronized; the direct link between the base station and the user / target / guardian is blocked. An active STAR-RIS, composed of units capable of both simultaneous reflection and transmission, is arranged in a uniform planar array UPA to assist radar sensing and secure communication. The base station's transmitted signal includes sensing signals when sensing is performed only, and a superposition of sensing and communication signals when secure communication is performed. The base station determines whether to send secure communication signals based on a preset prior probability to confuse the watcher and the fusion center.

2. The secure communication method based on STAR-RIS assistance in a sensor-integrated network according to claim 1, characterized in that, In step (1), the active reconfigurable smart surface includes multiple active units that can simultaneously adjust the phase and amplitude of reflection and transmission, for dynamically adjusting the wireless channels in the reflection space and transmission space to assist radar sensing and secure communication.

3. The secure communication method based on STAR-RIS assistance in a sensor-integrated network according to claim 1, characterized in that, In step (2), the watcher estimates the communication transmission power based on the observation data of the current time slot or the observation data of the historical time slot using the maximum likelihood estimation, and determines the optimal detection threshold and the minimum detection error probability based on the generalized likelihood ratio test.

4. A secure communication method based on STAR-RIS assistance in a sensor-integrated network according to claim 3, characterized in that, When the watcher estimates and detects based on historical time-slot observation data, the expectation-maximization algorithm is used to iteratively solve for the maximum likelihood estimate of the communication transmission power.

5. A secure communication method based on STAR-RIS assistance in a sensor-integrated network according to claim 1, characterized in that, In step (3), when the fusion center adopts the hard decision fusion scheme, it receives the binary decision results of each guardian and makes the final decision through the weighted sum rule; When the fusion center adopts a soft decision fusion scheme, it receives the energy measurement values ​​of each custodian and performs a likelihood ratio test based on the total received signal of all custodians.

6. A secure communication method based on STAR-RIS assistance in a sensor-integrated network according to claim 1, characterized in that, In step (3), the security performance of the base station is analyzed as follows: In the detection model based on historical observations, the security performance of the base station is evaluated by the expected detection error probability, wherein the expected detection error probability is calculated based on the probability distribution of the maximum likelihood estimate of the communication transmission power.

7. A secure communication method based on STAR-RIS assistance in a sensor-integrated network according to claim 1, characterized in that, In step (3), the joint optimization is specifically as follows: the joint optimization problem is decomposed into a base station transmit beamforming optimization sub-problem, an active reconfigurable smart surface active beamforming optimization sub-problem, and a continuous time slot number optimization sub-problem; each sub-problem is solved iteratively through alternating optimization methods until convergence, and the optimized transmit beamforming vector, active beamforming vector, and continuous time slot number are obtained.

8. A secure communication method based on STAR-RIS assistance in a sensor-integrated network according to claim 7, characterized in that, The joint optimization process satisfies the following constraints: the expected detection error probability of the fusion center is not lower than a preset safety threshold; the perceived signal-to-interference-plus-noise ratio of the base station is not lower than a preset perception performance threshold; the transmit power of the base station and the reflection-transmission power of the active reconfigurable smart surface do not exceed their respective power budgets; and the amplitude of each unit of the active reconfigurable smart surface does not exceed its maximum amplitude limit.

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