A method for secure downlink transmission of an unmanned aerial vehicle air-ground network based on RIS assistance

Abstract

This invention discloses a RIS-assisted downlink transmission method for UAV air-to-ground network security. The method includes: constructing a large-scale channel gain for the UAV-RIS link; implementing optimal phase configuration and beamforming for legitimate users based on CSI; calculating the received signals and instantaneous signal-to-noise ratio for legitimate users and effective eavesdroppers; introducing a channel statistical model to model the statistical characteristics of channel fading and node locations and derive the link distance PDF; deriving a closed-form expression for the Standard Operating Procedure (SOP) in scenarios with random eavesdropper locations and numbers; and performing precise quantitative analysis of system security performance based on the closed-form expression, dynamically adapting and updating the RIS phase configuration and receiver strategy. This invention effectively corrects the security assessment distortion problem of traditional methods, achieves accurate characterization of the security interruption probability in scenarios with uncertain eavesdropper locations and numbers, reduces the system security interruption probability, and improves transmission security and robustness. It is applicable to UAV air-to-ground secure communication in an integrated air-space-ground architecture.

Description

Technical Field

[0001] This invention relates to the fields of wireless communication and physical layer security technology, and in particular to a RIS-assisted downlink transmission method for UAV air-to-ground network security. Background Technology

[0002] As sixth-generation mobile communication networks evolve towards an integrated air-space-ground architecture, unmanned aerial vehicle (UAV)-assisted communication systems have become crucial solutions for emergency communications during natural disasters, temporary coverage of hotspot areas, and edge data acquisition due to their high mobility, flexible deployment capabilities, and high line-of-sight transmission probability. In traditional UAV downlink transmission, the UAV typically acts as an aerial base station, directly transmitting radio frequency signals to ground users. To overcome the impact of complex propagation environments on wireless links, reconfigurable intelligent surface (RIS) technology has emerged. RIS typically consists of numerous low-cost passive reflective elements, each of which can independently control the amplitude and phase of the incident signal. By intelligently adjusting the reflection coefficient of the RIS, the wireless propagation environment can be reconstructed, establishing a "virtual line-of-sight link," thereby enhancing signal coverage and improving link quality. However, given the broadcast characteristics and openness of wireless channels, downlinks are easily intercepted by unauthorized nodes. Traditional upper-layer encryption mechanisms rely on complex key management and high computational consumption, making them unsuitable for resource-constrained mobile terminals. Therefore, Physical Layer Security (PLS) technology has received widespread attention. Its core principle is to utilize the differences in physical characteristics (such as fading, noise, spatial location, etc.) between legitimate channels and eavesdropping channels, and to achieve secure information transmission at the physical layer through beamforming or artificial noise.

[0003] In existing RIS-assisted UAV relay downlink secure transmission, the key defects affecting system security performance are mainly as follows: many schemes focus on improving the receiving gain of legitimate links, ignoring the significant impact of the uncertainty and randomness of eavesdropper location on the system's security interruption probability (SOP). Furthermore, there is a lack of coordinated system optimization; existing research often employs step-by-step or decoupled optimization strategies. This local optimization approach severs the strong coupling between the parameters of each node, resulting in distorted calculated security interruption probabilities that fail to meet the requirements of high-level secure communication.

[0004] Therefore, it is necessary to propose a solution to improve one or more problems existing in the above-mentioned related technical solutions.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this disclosure is to provide a RIS-assisted method for secure downlink transmission of UAV air-to-ground networks, thereby overcoming, to at least some extent, one or more problems caused by the limitations and defects of related technologies.

[0007] A RIS-assisted downlink transmission method for UAV air-to-ground network security, according to an embodiment of this disclosure, includes the following steps: We perform link modeling from the UAV to the RIS to obtain a large-scale channel gain model for the UAV-RIS link; Based on the large-scale channel gain model and channel state information (CSI) of the UAV-RIS link, phase control and beamforming are performed on the RIS to obtain the optimal phase configuration and array gain of the RIS for legitimate users. Based on the large-scale channel gain model, optimal phase configuration, and array gain of the UAV-RIS link, the received signal models of legitimate users and eavesdroppers are constructed respectively, and the instantaneous signal-to-noise ratio of legitimate users and effective eavesdroppers is calculated. A channel statistical model is introduced to model the channel fading characteristics and the statistical characteristics of node locations of UAVs, legitimate users, and eavesdroppers, and the probability density function PDF of each link distance is derived. Based on the aforementioned channel statistical model, the probability density function PDF of each link distance, and the instantaneous signal-to-noise ratio of legitimate users and effective eavesdroppers, combined with the randomness of the number and location of eavesdroppers, a closed-form expression for the probability of system security interruption is derived under the scenario of UAV air-to-ground network transmission. Based on the closed-form expression of the system security interruption probability under the condition of random distribution of the number and location of eavesdroppers, and combined with the random characteristics of UAV location, the system dynamically adapts the RIS phase configuration and the legitimate user receiver strategy to achieve secure downlink transmission in UAV air-to-ground networks.

[0008] In an exemplary embodiment of this application, the step of performing link modeling from the UAV to the RIS to obtain a large-scale channel gain model of the UAV-RIS link includes: Using a drone as a signal source, confidential information is encoded to generate a transmission signal that meets the power normalization condition. The drone forwards the transmission signal to the RIS via an RF link. The drone-RIS link only considers large-scale path loss and has no small-scale fading. The large-scale channel gain of the drone-RIS link is modeled as a model related to the distance between the drone and the RIS and the path loss exponent. Furthermore, the RIS is a passive reflective element array and does not introduce additional noise.

[0009] In an exemplary embodiment of this application, the expression for the large-scale channel gain modeling of the UAV-RIS link is: in, Indicates the distance between the drone and the RIS. This is the path loss index.

[0010] In an exemplary embodiment of this application, the step of performing phase control and beamforming on the RIS based on the large-scale channel gain model and channel state information (CSI) of the UAV-RIS link to obtain the optimal phase configuration and array gain of the RIS for legitimate users includes: Based on the Channel State Information (CSI), extract the phase of the channel fading coefficient between the nth reflection unit of the RIS and the legitimate user; Based on the phase of the channel fading coefficient, a RIS reflection coefficient matrix is ​​constructed, and the adjustable phase shift of the nth reflection unit is set to be opposite to the phase of the channel fading coefficient as the optimal phase configuration for legitimate users. Based on the large-scale channel gain model and optimal phase configuration of the UAV-RIS link, the signals reflected by N reflection units are coherently superimposed at the legitimate user, resulting in an array gain related to the number of reflection units N.

[0011] In an exemplary embodiment of this application, the steps of constructing received signal models for legitimate users and eavesdroppers based on the large-scale channel gain model, optimal phase configuration, and array gain of the UAV-RIS link, and calculating the instantaneous signal-to-noise ratio for legitimate users and effective eavesdroppers, include: Multiple receiving antennas are provided for legitimate users, and maximum ratio combining (MRC) technology is used as the receiving signal optimization processing strategy for legitimate users. Combining the large-scale channel gain model of the UAV-RIS link, the optimal phase configuration and array gain of the RIS for legitimate users, the equivalent small-scale fading coefficient of the Nakagami-m cascaded fading of the UAV-RIS-legitimate user link, the transmission distance between the legitimate user and the RIS and the corresponding path loss exponent are introduced. Combined with additive white Gaussian noise at the receiver, the legitimate user received signal is established. Based on the received signal from the legitimate user, and combined with signal processing using MRC technology, the instantaneous signal-to-noise ratio at the legitimate user's receiver is obtained; From all eavesdroppers in the airspace, randomly distributed eavesdroppers within the coverage area of ​​the RIS signal are identified and screened. The eavesdropper closest to the RIS among the effective eavesdroppers is set as the strongest eavesdropper. Based on the large-scale channel gain model of the UAV-RIS link, the channel power gain between the strongest eavesdropper and the RIS, their transmission distance and corresponding path loss exponent are combined, and the additive white Gaussian noise at the eavesdropper's receiver is combined to establish the strongest eavesdropper's received signal. The instantaneous signal-to-noise ratio of the receiver of the strongest eavesdropper is obtained based on the received signal of the strongest eavesdropper.

[0012] In an exemplary embodiment of this application, the expression for the instantaneous signal-to-noise ratio of the legitimate user receiver is: in, Let be the equivalent small-scale fading coefficient of the RIS to legitimate user link, which follows the... distributed, The distance from RIS to legitimate users, This is the path loss index. For array gain, This is additive white Gaussian noise at the receiver. For large-scale channel gain of the UAV-to-RIS link. The equivalent transmit power of the UAV-to-RIS link; The expression for the instantaneous signal-to-noise ratio of the receiver of the most powerful eavesdropper is: in, This represents the channel power gain of the link from RIS to the most powerful eavesdropper. The distance between the most powerful eavesdropper and RIS This is the path loss index. Additive white Gaussian noise at the location of the strongest eavesdropper. For large-scale channel gain of the UAV-to-RIS link. This represents the equivalent transmit power of the UAV-to-RIS link.

[0013] In an exemplary embodiment of this application, the step of introducing a channel statistical model to model the channel fading characteristics and the statistical characteristics of the node locations of drones, legitimate users, and eavesdroppers, and deriving the probability density function PDF for each link distance includes: The equivalent channel power gain of the legitimate link that has experienced Nakagami-m fading and has been processed by maximum ratio combining technology is modeled as a Gamma distribution, and the channel power gain of the RIS-strongest eavesdropper link that follows the Nakagami-m distribution is modeled as a Gamma distribution. The link distances between the drone and the RIS, the RIS and the legitimate user, and the RIS and the most powerful eavesdropper are all modeled as independent random variables. Based on a stochastic geometric framework, and combining the ground geometric distribution characteristics of the legitimate user and the three-dimensional spatial geometric distribution characteristics of the eavesdropper, the probability density function of each link distance is derived.

[0014] In an exemplary embodiment of this application, based on the channel statistical model, the probability density function PDF of each link distance, and the instantaneous signal-to-noise ratio of legitimate users and effective eavesdroppers, and considering the randomness of the number and location of eavesdroppers, a closed-form expression for the system security interruption probability is derived under different scenarios in the UAV air-to-ground network transmission scenario. The closed-form expression for the system security interruption probability is: in, The total security failure probability when there are K effective eavesdroppers. The probability of a normal link interruption when there is no eavesdropper. The base security breach probability is the presence of one valid eavesdropper. The total number of potential eavesdroppers, The number of effective eavesdroppers, Let K be the probability that all K potential eavesdroppers are effective eavesdroppers. The probability that a single potential eavesdropper becomes an effective eavesdropper; When the number of effective eavesdroppers At this time, there is no risk of eavesdropping on the UAV-RIS-legitimate user link. Establish a closed-form expression for the system security interruption probability SOP under this scenario. When the number of effective eavesdroppers In this case, the instantaneous security capacity of the system is defined as the non-negative value of the difference between the legitimate channel capacity and the eavesdropping channel capacity, and a closed expression for the security interruption probability SOP is established based on the instantaneous security capacity.

[0015] In one exemplary embodiment of this application, the number of effective eavesdroppers... At that time, the definition of the safety interruption probability SOP is: When the number of effective eavesdroppers At that time, the definition of the safety interruption probability SOP is: in, For the instantaneous signal-to-noise ratio of the legitimate user's receiver, The instantaneous signal-to-noise ratio at the receiver of the most powerful eavesdropper. To ensure a safe interruption threshold, The preset target safety rate threshold, It is a constant derived from the target safety rate threshold. This is a probability operator.

[0016] In an exemplary embodiment of this application, the steps of dynamically adapting the RIS phase configuration and legitimate user receiver strategy to achieve secure downlink transmission in the UAV air-to-ground network, based on the closed-form expression of the system security interruption probability under the condition of random distribution of the number and location of eavesdroppers, combined with the random characteristics of the UAV's location, include: Based on the closed-form expression of the system security interruption probability obtained under different random distributions of eavesdroppers, and with minimizing the system security interruption probability as the optimization objective, a dynamic adaptation model is established, which includes the phase configuration of each reflective unit of the reconfigurable smart surface and the maximum ratio merging weight coefficient of the legitimate user receiver. By combining channel statistical characteristics and link distance probability distribution, the optimization model is solved to obtain the optimal configuration parameters that minimize the probability of system security interruption. These parameters cover the optimal reflection phase of RIS and the optimal MRC merging weight of legitimate user terminals. Real-time monitoring of changes in the location of legitimate users, the spatial distribution of effective eavesdroppers, and the dynamic changes in channel statistical characteristics; based on the aforementioned SOP closed-form expression and random channel model, dynamically updating the above-mentioned optimal configuration parameters. Based on the updated optimal configuration parameters, the reflection phase of each reflection unit of the RIS is adjusted, and the maximum ratio combining strategy of the legitimate user receiver is configured to complete the secure downlink transmission of the UAV air-to-ground network.

[0017] This application proposes a RIS-assisted downlink transmission method for UAV air-to-ground network security. Firstly, it achieves accurate modeling of the UAV-RIS link, optimal RIS phase configuration and directional beamforming for legitimate users, and differentiates the received signals of legitimate users and effective eavesdroppers by combining real-time channel state information and calculating the instantaneous signal-to-noise ratio. Secondly, it introduces a channel statistical model to statistically model channel fading characteristics and node spatial distribution characteristics, deriving the probability density function of various critical link distances. Finally, it derives a closed-form expression for the probability of system security interruption under the condition of random distribution of eavesdropper numbers and locations, achieving accurate analysis and quantitative evaluation of the system's secure transmission performance. This scheme fully considers the impact of eavesdropper location uncertainty and spatial distribution randomness on system security performance, effectively correcting the problem of distorted security performance evaluation caused by neglecting the random characteristics of eavesdroppers in traditional secure transmission schemes. It provides accurate theoretical support and analytical basis for the secure transmission design of RIS-assisted UAV air-to-ground networks. On the other hand, based on the closed-form expression of the probability of security interruption under different random distributions of eavesdroppers, and with the optimization objective of minimizing the probability of system security interruption, a joint optimization model is constructed, which includes the phase configuration of each reflection unit of RIS and the maximum ratio combining weight coefficient of legitimate user receivers. Combining the dynamic changes of node location and channel statistical characteristics, the optimal transmission configuration parameters are updated in real time. This breaks the defect of the traditional local optimization strategy that isolates the strong coupling of key transmission parameters, and achieves the joint optimal matching of key transmission parameters. This significantly reduces the probability of system security interruption, effectively improves the security, stability and robustness of downlink secure transmission in UAV air-to-ground networks, and ensures the reliable and secure transmission of confidential information in complex air-to-ground channel environments. Attached Figure Description

[0018] 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. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0019] Figure 1 This diagram illustrates the steps of a RIS-assisted UAV air-to-ground network security downlink transmission method in an exemplary embodiment of this application. Figure 2 This diagram illustrates a RIS-assisted downlink transmission process for unmanned aerial vehicle (UAV) air-to-ground network security in an exemplary embodiment of this application. Figure 3 A model diagram of a RIS-assisted UAV air-to-ground network security downlink transmission method in an exemplary embodiment of this application is shown. Figure 4This diagram illustrates the impact of the total number of eavesdroppers M on the Standard Operating Procedure (SOP) in an exemplary embodiment of this application. Figure 5 This diagram illustrates the effect of the number of reflection units N on the SOP in an exemplary embodiment of this application. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0021] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0022] This example implementation first provides a RIS-assisted downlink transmission method for UAV air-to-ground network security. This method can be applied to a terminal device, such as a mobile terminal like a mobile phone, desktop computer, personal digital assistant, laptop, tablet, or smartwatch. (Reference) Figure 1 , Figure 2 , Figure 3 As shown, the method may include the following steps: Step S101: Perform link modeling from UAV to RIS to obtain a large-scale channel gain model for the UAV-RIS link; Step S102: Based on the large-scale channel gain model and channel state information (CSI) of the UAV-RIS link, perform phase control and beamforming on the RIS to obtain the optimal phase configuration and array gain of the RIS for legitimate users; Step S103: Based on the large-scale channel gain model, optimal phase configuration, and array gain of the UAV-RIS link, construct the received signal models for legitimate users and eavesdroppers respectively, and calculate the instantaneous signal-to-noise ratio for legitimate users and effective eavesdroppers; Step S104: Introduce a channel statistical model to model the channel fading characteristics and the statistical characteristics of the node locations of UAVs, legitimate users, and eavesdroppers, and derive the probability density function PDF for each link distance; Step S105: Based on the channel statistical model, the probability density function PDF of each link distance, and the instantaneous signal-to-noise ratio of legitimate users and effective eavesdroppers, and combined with the randomness of the number and location of eavesdroppers, the closed-form expression of the system security interruption probability is derived under the UAV air-to-ground network transmission scenario. Step S106: Based on the closed-form expression of the system security interruption probability under the case of random distribution of the number and location of eavesdroppers, and combined with the random characteristics of the UAV's location, dynamically adapt the RIS phase configuration and the legitimate user receiver strategy to achieve secure downlink transmission of the UAV air-to-ground network.

[0023] This application proposes a RIS-assisted downlink transmission method for UAV air-to-ground network security. On one hand, it achieves accurate modeling of the UAV-RIS link, optimal RIS phase configuration and directional beamforming for legitimate users, and uses real-time channel state information to model differentiated received signals for legitimate users and effective eavesdroppers, calculating the instantaneous signal-to-noise ratio. Then, it introduces a channel statistical model to statistically model channel fading characteristics and node spatial distribution characteristics, deriving probability density functions for various critical link distances. Finally, it derives a closed-form expression for the probability of system security interruption under the condition of random distribution of eavesdropper numbers and locations, achieving accurate analysis and quantitative evaluation of system security transmission performance. This scheme fully considers the impact of eavesdropper location uncertainty and spatial distribution randomness on system security performance, effectively correcting the problem of distorted security performance evaluation caused by neglecting the random characteristics of eavesdroppers in traditional security transmission schemes. It provides accurate theoretical support and analytical basis for the design of RIS-assisted UAV air-to-ground network security transmission. On the other hand, based on the closed-form expression of the probability of security interruption under different random distributions of eavesdroppers, and with the optimization objective of minimizing the probability of system security interruption, a joint optimization model is constructed, which includes the phase configuration of each reflection unit of RIS and the maximum ratio combining weight coefficient of legitimate user receivers. Combining the dynamic changes of node location and channel statistical characteristics, the optimal transmission configuration parameters are updated in real time. This breaks the defect of the traditional local optimization strategy that isolates the strong coupling of key transmission parameters, and achieves the joint optimal matching of key transmission parameters. This significantly reduces the probability of system security interruption, effectively improves the security, stability and robustness of downlink secure transmission in UAV air-to-ground networks, and ensures the reliable and secure transmission of confidential information in complex air-to-ground channel environments.

[0024] Below, as Figures 1-5 As shown, a more detailed explanation will be given of a RIS-assisted downlink transmission method for UAV air-to-ground network security proposed in this example embodiment.

[0025] Step S101: Perform link modeling from UAV to RIS to obtain a large-scale channel gain model of the UAV-RIS link.

[0026] Specifically, the drone (R) acts as the signal source, first encoding the confidential information to be transmitted to generate a signal that meets the power normalization condition. Transmission signal Subsequently, the drone forwards the signal to RIS(A) via a short-range radio frequency link. This link only considers large-scale path loss and has no small-scale fading. The signal received by RIS is: The large-scale channel gain of the UAV-RIS link is modeled as a model related to the distance and path loss exponent between the UAV and the RIS. The large-scale channel gain model of the UAV-RIS link, i.e., the RA link, is as follows: in, This represents the distance between R and A. This represents the path loss exponent. It should be noted that the RIS, as a passive reflective element array, does not possess RF link or signal amplification capabilities, therefore it does not introduce additional noise; the noise in the system only exists at the subsequent active receiving nodes.

[0027] Step S102: Based on the large-scale channel gain model and channel state information (CSI) of the UAV-RIS link, perform phase control and beamforming on the RIS to obtain the optimal phase configuration and array gain of the RIS for legitimate users.

[0028] Specifically, based on Channel State Information (CSI), the phase of the channel fading coefficient between the nth reflection unit of the RIS and the legitimate user is extracted. The RIS is equipped with... The passive reflective unit, the first The reflection coefficient of a unit is defined as ,in Indicates the amplitude reflection coefficient. This indicates an adjustable phase shift.

[0029] Based on the phase of the channel fading coefficient, a RIS reflection coefficient matrix is ​​constructed. The adjustable phase shift of the nth reflection unit is set to be opposite to the phase of the aforementioned channel fading coefficient, serving as the optimal phase configuration for legitimate users. Specifically, to maximize the received signal quality at legitimate user (D), RIS employs passive beamforming technology for directional reflection of the incident signal. Specifically, assuming the system has perfect Channel State Information (CSI), RIS dynamically adjusts the phase shift of each unit to compensate for phase fluctuations in the cascaded channels. The optimal phase alignment strategy for legitimate users is designed as follows: in Indicates the first The phase of the channel fading coefficient between each reflection unit and the user.

[0030] Based on a large-scale channel gain model and optimal phase configuration of the UAV-RIS link, signals reflected by N reflection units are coherently superimposed at the legitimate user, resulting in an array gain related to the number of reflection units N. Specifically, under this phase configuration, signals reflected by N units achieve coherent superposition at the user, thereby obtaining the maximum array gain. Conversely, because the phase configuration of the RIS is specifically optimized for the user, the reflected signal cannot be coherently synthesized at the eavesdropper (E), so the eavesdropping link does not have the gain advantage of this array.

[0031] Step S103: Based on the large-scale channel gain model, optimal phase configuration, and array gain of the UAV-RIS link, construct the received signal models for legitimate users and eavesdroppers respectively, and calculate the instantaneous signal-to-noise ratio for legitimate users and effective eavesdroppers.

[0032] Specifically, multiple receiving antennas are provided for legitimate users, and maximum ratio combining (MRC) technology is used as an optimization processing strategy for the received signals at the legitimate user end.

[0033] Based on the signal transmission described above, the RIS reflects the incident signal to the receiver. For legitimate users, assuming they are equipped with... The antenna employs maximum ratio combining (MRC) technology to optimize reception performance. Combining the large-scale channel gain model of the UAV-RIS link, the optimal phase configuration and array gain of the RIS for legitimate users, the equivalent small-scale fading coefficient of the Nakagami-m cascaded fading of the UAV-RIS-legitimate user link, the transmission distance between the legitimate user and the RIS and the corresponding path loss exponent are introduced. Combined with additive white Gaussian noise at the receiver, the legitimate user received signal is established. The received signal at point D can then be expressed as: in, for The equivalent small-scale fading coefficient of the link follows distributed, for arrive distance, This is the path loss index. This is additive white Gaussian noise at the receiver.

[0034] Based on the received signal from the legitimate user, and combined with signal processing using MRC technology, the instantaneous signal-to-noise ratio at the legitimate user's receiver is obtained; The instantaneous signal-to-noise ratio (SNR) at point is: in, Let be the equivalent small-scale fading coefficient of the RIS to legitimate user link, which follows the... distributed, The distance from RIS to legitimate users, This is the path loss index. For array gain, This is additive white Gaussian noise at the receiver. For large-scale channel gain of the UAV-to-RIS link. The equivalent transmit power of the UAV-to-RIS link; From all eavesdroppers in the airspace, randomly distributed eavesdroppers within the RIS signal coverage area are identified and screened. The eavesdropper closest to the RIS among the effective eavesdroppers is designated as the strongest eavesdropper. Based on a large-scale channel gain model of the UAV-RIS link, combined with the channel power gain between the strongest eavesdropper and the RIS, their transmission distance, and the corresponding path loss exponent, and incorporating additive white Gaussian noise at the eavesdropper's receiver, the strongest eavesdropper's received signal is established. It is assumed that there are a total of M eavesdroppers in the airspace above the user, among which... K Only one randomly distributed effective eavesdropper... K Several eavesdroppers pose an eavesdropping threat, and the eavesdropper closest to the RIS is designated as the strongest eavesdropper (E). Since the RIS's beamforming strategy only performs phase alignment for the user, the eavesdropping link cannot obtain coherent array gain; therefore, the received signal-to-noise ratio at E is: in, This represents the channel power gain of the link from RIS to the most powerful eavesdropper. The distance between the most powerful eavesdropper and RIS This is the path loss index. Additive white Gaussian noise at the location of the strongest eavesdropper. For large-scale channel gain of the UAV-to-RIS link. This represents the equivalent transmit power of the UAV-to-RIS link.

[0035] Step S104: Introduce a channel statistical model to model the channel fading characteristics and the statistical characteristics of the node locations of UAVs, legitimate users, and eavesdroppers, and derive the probability density function PDF of each link distance.

[0036] Specifically, the equivalent channel power gain of a legitimate link that has undergone Nakagami-m fading and is processed by maximum ratio combining is modeled as a Gamma distribution, and the channel power gain of the RIS-strongest eavesdropper link that follows the Nakagami-m distribution is modeled as a Gamma distribution.

[0037] To facilitate subsequent security breach performance analysis, it is necessary to model the statistical characteristics of channel fading and node location. For legitimate links... Considering the channel experiences Nakagami- Fading and the receiver uses Antenna MRC combining technology, equivalent channel power gain It follows a Gamma distribution, that is... ,in Let be a scaling parameter related to the average channel power. Accordingly, for the eavesdropping link AE, it is assumed to follow a fading parameter of . of The distribution then determines its channel power gain. Also obeys the parameter as The Gamma distribution; for random variables that follow a Gamma distribution Its probability density function can be expressed as: in, for Values The probability density at time , For the rate parameter of the Gamma distribution, Let Gamma be the shape parameter of the distribution. for, For gamma function, These are sample values ​​of the channel gain. It is the channel gain variable of the link.

[0038] The link distances between UAVs and RIS, RIS and legitimate users, and RIS and the most powerful eavesdroppers are all modeled as independent random variables. Based on a stochastic geometric framework, and combining the ground geometric distribution characteristics of legitimate users and the three-dimensional spatial geometric distribution characteristics of eavesdroppers, the probability density function of each link distance is derived. To capture the inherent spatial randomness in satellite-air-ground networks, the link distance... and Modeled as independent random variables, their specific probability distribution depends on the geometric distribution characteristics of the user in the circular area on the ground and the eavesdropper in three-dimensional space. The relevant probability density function (PDF) is derived based on the random geometric framework.

[0039] Step S105: Based on the channel statistical model, the probability density function PDF of each link distance, and the instantaneous signal-to-noise ratio of legitimate users and effective eavesdroppers, and considering the randomness of the number and location of eavesdroppers, a closed-form expression for the system security interruption probability is derived under the UAV air-to-ground network transmission scenario, considering different scenarios. The closed-form expression for the system security interruption probability is: in, The total security failure probability when there are K effective eavesdroppers. The probability of a normal link interruption when there is no eavesdropper. The base security breach probability is the presence of one valid eavesdropper. The total number of potential eavesdroppers, The number of effective eavesdroppers, Let K be the probability that all K potential eavesdroppers are effective eavesdroppers. The probability that a single potential eavesdropper becomes an effective eavesdropper.

[0040] Specifically, when the number of effective eavesdroppers At this time, there is no risk of eavesdropping on the drone-RIS-legitimate user link. Establish a closed-form expression for the system security interruption probability SOP in this scenario.

[0041] when At this time, all M eavesdroppers fall outside the signal focusing area of ​​A. This scenario is considered to have no eavesdropping risk on the AD link, so the closed-form expression for the security interruption probability SOP is: Substituting the obtained parameters and formula, we get: The specific calculations show that: When the number of effective eavesdroppers In this case, the instantaneous security capacity of the system is defined as the non-negative value of the difference between the legitimate channel capacity and the eavesdropping channel capacity, and a closed expression for the security interruption probability SOP is established based on the instantaneous security capacity.

[0042] when At this time, the link is exposed to the risk of eavesdropping, and in this case, based on the aforementioned derived signal-to-noise ratio expression, the instantaneous security capacity of the AD downlink... Defined as the non-negative difference between the legal channel capacity and the eavesdropping channel capacity, i.e.: In an AD link, SOP (Security Operation) is when the confidentiality capacity falls below a certain threshold after successful D decoding. The probability of ( ), SOP is given by the following formula: Substituting the obtained parameters and formula, we get: The specific calculations show that: in, This represents the integral result of the legitimate link signal-to-noise ratio (CDF) and the spatial distribution of the eavesdropper (PDF) within the i-th interval. C t These are the binomial expansion coefficients, used to expand the binomial expansion. Decompose it into the sum of a finite number of exponential polynomials. Yes I i The function for performing series correction, where, Meanwhile, the PDFs of D and E are respectively Since there are M eavesdroppers in the system at this time, and K of them constitute effective eavesdropping, then at this time... Step S106: Based on the closed-form expression of the system security interruption probability under the case of random distribution of the number and location of eavesdroppers, and combined with the random characteristics of the UAV's location, dynamically adapt the RIS phase configuration and the legitimate user receiver strategy to achieve secure downlink transmission of the UAV air-to-ground network.

[0043] Specifically, based on the closed-form expression of the system security interruption probability obtained under different random distributions of eavesdroppers, and with minimizing the system security interruption probability as the optimization objective, a dynamic adaptation model is established, which includes the phase configuration of each reflective unit of the reconfigurable smart surface and the maximum ratio merging weight coefficient of the legitimate user receiver. By combining channel statistical characteristics and link distance probability distribution, the optimization model is solved to obtain the optimal configuration parameters that minimize the probability of system security interruption. These parameters cover the optimal reflection phase of RIS and the optimal MRC merging weight of legitimate user terminals. Real-time monitoring of changes in the location of legitimate users, the spatial distribution of effective eavesdroppers, and the dynamic changes in channel statistical characteristics; based on the aforementioned SOP closed-form expression and random channel model, dynamically updating the above-mentioned optimal configuration parameters. Based on the updated optimal configuration parameters, the reflection phase of each reflection unit of the RIS is adjusted, and the maximum ratio combining strategy of the legitimate user receiver is configured to complete the secure downlink transmission of the UAV air-to-ground network.

[0044] To address the problem that existing solutions often assume fixed or known locations of eavesdroppers, leading to calculated Standard Operating Procedures (SOPs) that deviate from actual risks, this application incorporates the uncertainty of eavesdropper locations and the randomness of their distribution into the evaluation system through precise modeling based on stochastic geometry. This corrects the overly optimistic bias of previous models when facing randomly moving eavesdroppers, enabling a realistic analysis of the system's security capacity and providing an accurate theoretical basis for high-level secure communication systems that aligns with real-world scenarios.

[0045] To address the shortcomings of existing technologies that often employ local optimization strategies, resulting in the artificial separation of parameters between the UAV, RIS, and receiver, and failing to handle the strong coupling between parameters, this application constructs a global collaborative optimization mechanism for UAV position, RIS phase, and receiver strategy. This mechanism fully utilizes the strong coupling relationship between the parameters of each node, achieving complementary advantages through joint adjustment. Under the same channel conditions, it more effectively widens the capacity difference between the legitimate channel and the eavesdropping channel than traditional decoupling schemes, thereby achieving a significant reduction in SOP.

[0046] This application not only solves the problem of large deviations in SOP calculation, but also provides a secure transmission scheme that can operate stably under strongly coupled parameters. Through precise channel modeling and collaborative optimization, the system can maintain a stable low interruption probability under complex electromagnetic environments and uncertain eavesdropping threats, effectively meeting the security requirements of scenarios such as emergency communication and military communication.

[0047] To verify the effectiveness of the technical solution of this invention, this embodiment constructs a typical urban canyon emergency communication scenario, in which the drone cannot directly establish a reliable connection with the ground user and needs to use RIS (Radio Router Array) on the building surface for auxiliary transmission. The main parameters in this embodiment are shown in Table 1 below: Table 1: Main Parameters The specific implementation steps of this embodiment are as follows: Initial configuration: For equipment deployment preparation, in the urban canyon scenario, RIS devices are installed on the exterior facades of high-rise buildings. This ensures the RIS coverage area covers the 3D hollow frustum area where target ground users are distributed, while also ensuring unobstructed access between the UAV's flight airspace and the RIS, meeting line-of-sight transmission requirements. Based on emergency communication mission needs, the UAV is initially deployed in a pre-defined airspace within the RIS communication coverage area, with its flight altitude balancing signal transmission distance and ground obstacle avoidance.

[0048] Parameter initialization settings: Configure core parameters according to the above system parameter table, set the UAV transmit power to 30dBm and adjust the carrier frequency to 2.4GHz; flexibly select the number of RIS reflector units and user antennas according to communication requirements; configure the path loss index according to the link type, and give the Nakagami fading parameters for the legitimate channel and the eavesdropping channel; set the target security rate threshold as the core indicator for subsequent security interruption probability assessment.

[0049] Channel state information acquisition involves obtaining complete channel state information through channel sounding technology, including link channel information from the UAV to the RIS, and channel fading coefficients from each RIS reflection unit to legitimate users and potential eavesdroppers, providing data support for subsequent phase optimization and beamforming.

[0050] RIS phase optimization and beamforming implementation: like Figure 4 , Figure 5 As shown, RIS is equipped with One passive reflective unit. The reflection coefficient of a unit is defined as ,in Indicates the amplitude reflection coefficient. This indicates an adjustable phase shift.

[0051] Assuming the system has perfect Channel State Information (CSI), in order to maximize the received signal quality at legitimate users, the RIS will dynamically adjust the phase shift of each unit to compensate for phase fluctuations in the cascaded channel. For legitimate users... The optimal phase alignment strategy is designed as follows: in, Indicates the first The phase of the channel fading coefficient between each reflection unit and the user.

[0052] Under this phase configuration, via The signals reflected by each unit are coherently superimposed at the user end to obtain the maximum array gain, the gain intensity of which is proportional to the signal strength. .

[0053] The maximum ratio of user-end merged reception, and the configuration of legitimate users. Root antenna ( User receiver to The signals of each branch are weighted, and the weighting coefficients are set as the conjugate of the channel coefficients of each branch.

[0054] The instantaneous signal-to-noise ratio (SNR) after MRC processing is expressed as: Its expression is: in: For the drone's transmission power, The beamforming gain brought to RIS This represents the equivalent channel power gain after MRC combining. Let be the power spectral density of additive white Gaussian noise. Meanwhile, the instantaneous signal-to-noise ratio at the eavesdropper's location is... Because the RIS phase is not aligned with the eavesdropping channel, the received signals cannot be coherently superimposed.

[0055] Channel statistical modeling and SOP calculation: Channel statistical characteristic modeling: For legitimate links, considering Nakagami fading and MRC combining techniques, the equivalent channel power gain is modeled as a Gamma distribution; the channel power gain of the eavesdropping link AE is also modeled as a Gamma distribution. Based on a stochastic geometric framework, the link distance is derived. and The PDF shows that the user distance follows a geometric distribution within a hollow frustum region, while the eavesdropper distance follows a random distribution in three-dimensional space.

[0056] Calculation of the probability of security interruption: when When there is no effective eavesdropper, it is determined that there is no risk of eavesdropping on the AD link, and SOP=OP. when When there is an effective eavesdropper, based on the definition of instantaneous security capacity, combined with the aforementioned signal-to-noise ratio and channel statistical model, the relevant parameters and formulas are substituted into the SOP calculation formula, and the accurate SOP closed-form result is obtained through numerical calculation.

[0057] Global parameter collaborative optimization and dynamic adjustment: The optimization objective is to minimize the system SOP and maximize the security capacity. Based on the above SOP calculation results, a collaborative optimization model for RIS phase configuration and receiver strategy is constructed.

[0058] A dynamic adjustment mechanism monitors in real time the distribution changes of legitimate users and eavesdroppers, as well as fluctuations in channel fading characteristics. It dynamically adjusts the UAV's flight position (optimizing the RA link distance), the phase shift parameters of each reflector in the RIS, and the merging strategy of the user's receiving antennas to ensure that the globally optimal configuration is maintained under conditions of strong parameter coupling. For example, when the position of the strongest eavesdropper changes, the optimal phase of the RIS is recalculated and the UAV's position is adjusted to further widen the capacity difference between the legitimate link and the eavesdropping link.

[0059] System performance verification and evaluation: Performance monitoring involves real-time collection of key indicators such as the system's SOP, the signal-to-noise ratio received by legitimate users, and the signal-to-noise ratio received by eavesdroppers through the security performance evaluation module. This allows for comparison of performance differences under different numbers of RIS reflector units and user antennas.

[0060] The results analysis verifies the effectiveness of the proposed method in complex urban canyon scenarios. The analysis focuses on the improvement effect of stochastic geometric modeling on the accuracy of SOP evaluation and the reduction of SOP by global collaborative optimization compared with traditional step-by-step optimization, ensuring that the system meets the high-level security communication requirements of scenarios such as emergency communication.

[0061] This embodiment realizes secure downlink transmission of UAV air-to-ground network with RIS assistance, effectively solves the problem of security assessment distortion caused by the randomness of eavesdropper distribution, and significantly reduces the probability of system security interruption through global collaborative optimization, providing a reliable technology for secure communication in complex scenarios. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0062] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0063] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

Claims

1. A RIS-assisted downlink transmission method for UAV air-to-ground network security, characterized in that, Includes the following steps: We perform link modeling from the UAV to the RIS to obtain a large-scale channel gain model for the UAV-RIS link; Based on the large-scale channel gain model and channel state information (CSI) of the UAV-RIS link, phase control and beamforming are performed on the RIS to obtain the optimal phase configuration and array gain of the RIS for legitimate users. Based on the large-scale channel gain model, optimal phase configuration, and array gain of the UAV-RIS link, the received signal models of legitimate users and eavesdroppers are constructed respectively, and the instantaneous signal-to-noise ratio of legitimate users and effective eavesdroppers is calculated. A channel statistical model is introduced to model the channel fading characteristics and the statistical characteristics of node locations of UAVs, legitimate users, and eavesdroppers, and the probability density function PDF of each link distance is derived. Based on the aforementioned channel statistical model, the probability density function PDF of each link distance, and the instantaneous signal-to-noise ratio of legitimate users and effective eavesdroppers, combined with the randomness of the number and location of eavesdroppers, a closed-form expression for the probability of system security interruption is derived under the scenario of UAV air-to-ground network transmission. Based on the closed-form expression of the system security interruption probability under the condition of random distribution of the number and location of eavesdroppers, and combined with the random characteristics of UAV location, the system dynamically adapts the RIS phase configuration and the legitimate user receiver strategy to achieve secure downlink transmission in UAV air-to-ground networks.

2. The RIS-assisted UAV air-to-ground network security downlink transmission method according to claim 1, characterized in that, The steps for performing link modeling from the UAV to the RIS to obtain a large-scale channel gain model of the UAV-RIS link include: Using a drone as a signal source, confidential information is encoded to generate a transmission signal that meets the power normalization condition. The drone forwards the transmission signal to the RIS via an RF link. The drone-RIS link only considers large-scale path loss and has no small-scale fading. The large-scale channel gain of the drone-RIS link is modeled as a model related to the distance between the drone and the RIS and the path loss exponent. Furthermore, the RIS is a passive reflective element array and does not introduce additional noise.

3. The RIS-assisted UAV air-to-ground network security downlink transmission method according to claim 2, characterized in that, The expression for the large-scale channel gain modeling of the UAV-RIS link is: in, Indicates the distance between the drone and the RIS. This is the path loss index.

4. The RIS-assisted UAV air-to-ground network security downlink transmission method according to claim 1, characterized in that, Based on the large-scale channel gain model and channel state information (CSI) of the UAV-RIS link, the steps for performing phase control and beamforming on the RIS to obtain the optimal phase configuration and array gain of the RIS for legitimate users include: Based on the Channel State Information (CSI), extract the phase of the channel fading coefficient between the nth reflection unit of the RIS and the legitimate user; Based on the phase of the channel fading coefficient, a RIS reflection coefficient matrix is ​​constructed, and the adjustable phase shift of the nth reflection unit is set to be opposite to the phase of the channel fading coefficient as the optimal phase configuration for legitimate users. Based on the large-scale channel gain model and optimal phase configuration of the UAV-RIS link, the signals reflected by N reflection units are coherently superimposed at the legitimate user, resulting in an array gain related to the number of reflection units N.

5. The RIS-assisted downlink transmission method for UAV air-to-ground network security according to claim 1, characterized in that, Based on the large-scale channel gain model, optimal phase configuration, and array gain of the UAV-RIS link, the steps for constructing the received signal models for legitimate users and eavesdroppers, and calculating the instantaneous signal-to-noise ratio for legitimate users and effective eavesdroppers include: Multiple receiving antennas are provided for legitimate users, and maximum ratio combining (MRC) technology is used as the receiving signal optimization processing strategy for legitimate users. Combining the large-scale channel gain model of the UAV-RIS link, the optimal phase configuration and array gain of the RIS for legitimate users, the equivalent small-scale fading coefficient of the Nakagami-m cascaded fading of the UAV-RIS-legitimate user link, the transmission distance between the legitimate user and the RIS and the corresponding path loss exponent are introduced. Combined with additive white Gaussian noise at the receiver, the legitimate user received signal is established. Based on the received signal from the legitimate user, and combined with signal processing using MRC technology, the instantaneous signal-to-noise ratio at the legitimate user's receiver is obtained; From all eavesdroppers in the airspace, randomly distributed eavesdroppers within the coverage area of ​​the RIS signal are identified and screened. The eavesdropper closest to the RIS among the effective eavesdroppers is set as the strongest eavesdropper. Based on the large-scale channel gain model of the UAV-RIS link, the channel power gain between the strongest eavesdropper and the RIS, their transmission distance and corresponding path loss exponent are combined, and the additive white Gaussian noise at the eavesdropper's receiver is combined to establish the strongest eavesdropper's received signal. The instantaneous signal-to-noise ratio of the receiver of the strongest eavesdropper is obtained based on the received signal of the strongest eavesdropper.

6. The RIS-assisted downlink transmission method for UAV air-to-ground network security according to claim 5, characterized in that, The expression for the instantaneous signal-to-noise ratio of the legitimate user receiver is: in, Let be the equivalent small-scale fading coefficient of the RIS to legitimate user link, which follows the... distributed, The distance from RIS to legitimate users, This is the path loss index. For array gain, This is additive white Gaussian noise at the receiver. For large-scale channel gain of the UAV-to-RIS link. The equivalent transmit power of the UAV-to-RIS link; The expression for the instantaneous signal-to-noise ratio of the receiver of the most powerful eavesdropper is: in, This represents the channel power gain of the link from RIS to the most powerful eavesdropper. The distance between the most powerful eavesdropper and RIS This is the path loss index. Additive white Gaussian noise at the location of the strongest eavesdropper. For large-scale channel gain of the UAV-to-RIS link. This represents the equivalent transmit power of the UAV-to-RIS link.

7. The RIS-assisted UAV air-to-ground network security downlink transmission method according to claim 6, characterized in that, The step of introducing a channel statistical model to model the channel fading characteristics and the statistical characteristics of the node locations of UAVs, legitimate users, and eavesdroppers, and deriving the probability density function PDF for each link distance includes: The equivalent channel power gain of the legitimate link that has experienced Nakagami-m fading and has been processed by maximum ratio combining technology is modeled as a Gamma distribution, and the channel power gain of the RIS-strongest eavesdropper link that follows the Nakagami-m distribution is modeled as a Gamma distribution. The link distances between the drone and the RIS, the RIS and the legitimate user, and the RIS and the most powerful eavesdropper are all modeled as independent random variables. Based on a stochastic geometric framework, and combining the ground geometric distribution characteristics of the legitimate user and the three-dimensional spatial geometric distribution characteristics of the eavesdropper, the probability density function of each link distance is derived.

8. The RIS-assisted UAV air-to-ground network security downlink transmission method according to claim 1, characterized in that, Based on the aforementioned channel statistical model, the probability density function (PDF) of each link distance, and the instantaneous signal-to-noise ratio of legitimate users and effective eavesdroppers, and considering the randomness of the number and location of eavesdroppers, a closed-form expression for the system security interruption probability is derived under different scenarios in the UAV air-to-ground network transmission scenario. The closed-form expression for the system security interruption probability is: in, The total security failure probability when there are K effective eavesdroppers. The probability of a normal link interruption when there is no eavesdropper. The base security breach probability is the presence of one valid eavesdropper. The total number of potential eavesdroppers, The number of effective eavesdroppers, Let K be the probability that all K potential eavesdroppers are effective eavesdroppers. The probability that a single potential eavesdropper becomes an effective eavesdropper; When the number of effective eavesdroppers At this time, there is no risk of eavesdropping on the UAV-RIS-legitimate user link. Establish a closed-form expression for the system security interruption probability SOP under this scenario. When the number of effective eavesdroppers In this case, the instantaneous security capacity of the system is defined as the non-negative value of the difference between the legitimate channel capacity and the eavesdropping channel capacity, and a closed expression for the security interruption probability SOP is established based on the instantaneous security capacity.

9. The RIS-assisted downlink transmission method for UAV air-to-ground network security according to claim 8, characterized in that, The number of effective eavesdroppers At that time, the definition of the safety interruption probability SOP is: When the number of effective eavesdroppers At that time, the definition of the safety interruption probability SOP is: in, For the instantaneous signal-to-noise ratio of the legitimate user's receiver, The instantaneous signal-to-noise ratio at the receiver of the most powerful eavesdropper. To ensure a safe interruption threshold, The preset target safety rate threshold, It is a constant derived from the target safety rate threshold. This is a probability operator.

10. The RIS-assisted UAV air-to-ground network security downlink transmission method according to claim 1, characterized in that, The steps for achieving secure downlink transmission in the UAV air-to-ground network, based on the closed-form expression of the system security interruption probability under the condition of random distribution of the number and location of eavesdroppers, combined with the random characteristics of UAV location, dynamically adapting RIS phase configuration and legitimate user receiver strategies, include: Based on the closed-form expression of the system security interruption probability obtained under different random distributions of eavesdroppers, and with minimizing the system security interruption probability as the optimization objective, a dynamic adaptation model is established, which includes the phase configuration of each reflective unit of the reconfigurable smart surface and the maximum ratio merging weight coefficient of the legitimate user receiver. By combining channel statistical characteristics and link distance probability distribution, the optimization model is solved to obtain the optimal configuration parameters that minimize the probability of system security interruption. These parameters cover the optimal reflection phase of RIS and the optimal MRC merging weight of legitimate user terminals. Real-time monitoring of changes in the location of legitimate users, the spatial distribution of effective eavesdroppers, and the dynamic changes in channel statistical characteristics; based on the aforementioned SOP closed-form expression and random channel model, dynamically updating the above-mentioned optimal configuration parameters. Based on the updated optimal configuration parameters, the reflection phase of each reflection unit of the RIS is adjusted, and the maximum ratio combining strategy of the legitimate user receiver is configured to complete the secure downlink transmission of the UAV air-to-ground network.

Images