A secure communication method and system based on UAV-RIS

Abstract

The present invention proposes a secure communication method and system based on UAV‑RIS. By constructing a two-hop signal model, the ground base station is used to send information to the relay node through a direct link and an auxiliary link. At the same time, the target vehicle sends an interference signal to the relay node to reduce the risk of eavesdropping. In addition, the transmission power of the ground base station, the transmission power of the target vehicle, the amplification factor of the relay node and the flight trajectory of the UAV are used as parameters to construct an optimization problem with the goal of maximizing the security energy efficiency of the system. The firefly algorithm is used to obtain an initial flight trajectory data set that meets the conditions, and the DDPG algorithm is used to further optimize the flight trajectory of the UAV, thereby improving the security and energy efficiency of the communication system.

Description

Technical Field

[0001] The present invention relates to the field of secure communication technology, and in particular to a secure communication method and system based on UAV-RIS. Background Art

[0002] With the rapid advancement of Internet of Vehicles technology, the demand for communication between vehicles and base stations has increased dramatically. However, the inherent limitations of ground networks, such as obstacles and signal attenuation, often hinder the establishment of a stable direct communication link between vehicles and base stations. To overcome this problem, the industry introduced ground relay nodes as a bridge for signal forwarding to ensure smooth communication between vehicles and base stations. However, this solution also brings new security risks: while performing the duty of forwarding signals, relay nodes are at risk of stealing private information, posing a serious threat to communication security.

[0003] At the same time, the rapid development of unmanned aerial vehicle (UAV) technology has made its application in the field of Internet of Vehicles increasingly widespread. In particular, when the UAV is equipped with a reconfigurable intelligent surface (RIS), it can significantly enhance the transmission efficiency and quality of ground information by intelligently adjusting the wireless propagation environment. However, in the context of untrusted relay nodes, how to use the UAV-RIS-assisted Internet of Vehicles system to ensure the security of communication and improve communication efficiency has become a key issue that needs to be explored and solved. Summary of the invention

[0004] In order to solve the technical problem in the prior art that private information of a relay node is easily stolen when forwarding a signal, the present invention proposes a secure communication method and system based on UAV-RIS, which improves the security of communication.

[0005] The technical solution of the present invention is:

[0006] The secure communication method based on UAV-RIS comprises the following steps:

[0007] Step 1: Construct a two-hop signal model: The first hop in the two-hop signal model includes the ground base station sending the first information to the relay node via a direct link and a UAV reconfigurable intelligent surface auxiliary link, and the target vehicle sends an interference signal to the relay node; the second hop in the two-hop signal model includes the relay node forwarding the first information to the target vehicle, and the ground base station sending new information to the target vehicle;

[0008] Step 2: Taking the transmission power of the ground base station, the transmission power of the target vehicle, the amplification factor of the relay node, and the flight trajectory of the UAV as parameters, the first optimization problem is constructed with the goal of maximizing the system safety energy efficiency;

[0009] Step 3: Perform fractional programming on the first optimization problem to obtain a converted second optimization problem;

[0010] Step 4: Optimize and solve the second optimization problem to obtain the optimized relay node amplification factor, the transmission power of the ground base station and the transmission power of the target vehicle;

[0011] Step 5: Based on the optimized relay node amplification factor, the transmission power of the ground base station, and the transmission power of the target vehicle, the optimization algorithm is used to process the flight trajectory of the UAV for the first optimization problem, and a number of flight trajectories that meet the first condition are obtained as a data set; the first condition is a constraint condition on the flight trajectory of the UAV in the first optimization problem;

[0012] Step 6: Use the data set obtained in step 5 as the experience pool sample and use the optimization algorithm to optimize the target flight trajectory of the UAV.

[0013] A further preferred embodiment, in step 1, in the time slots, the signal received by the relay node in the first hop communication for:

[0014] s R n = P b H BR [n] x s1 n + P v G VR n x I n + n R n

[0015] in is the transmission power of the ground base station, is the transmission power of the target vehicle, n R [n] is the noise received by the relay node when receiving information, subject to n R [n] ∼ CN(0, s R 2 ) , for n R [n] The variance of the distribution, x s1 [n] The first information s1 sent by the ground base station, x I [n] A jamming signal sent to the target vehicle; H BR [n] represents the channel vector between the ground base station and the relay node; G VR [n] represents the channel vector from the target vehicle to the relay node;

[0016] In step 1, in time slots, the signal received by the target vehicle in the second hop communication s V [ n ] for:

[0017] s V [ n ]= ϖ G RV [ n ] s R [ n ]+ P b G BV [ n ] x s 2 [ n ]+ n V [ n ]

[0018] in is the amplification factor of the relay node, n V [n] is additive noise, subject to n V [n] ∼ CN(0, s V 2 ) , for n V [n] The variance of the distribution, x s2 [n] The new information s2 sent by the ground base station, G BV [ n ] is the channel vector from the ground base station to the UAV and then to the target vehicle.

[0019] In a further preferred embodiment, the channel vector between the ground base station and the relay node is H BR [ n ] for:

[0020] H BR [ n ]= h BR [ n ]+ h UR H [ n ] I 1 [ n ] h BU [ n ]

[0021] in represents the direct link between the ground base station and the relay node, h BR n = x 0 d BR [n] d 0 - α 1 h ̃ BR [n] , is the reference distance set The path loss at is the communication attenuation coefficient, d BR [n] For the The distance between the time slot ground base station and the relay node; h ̃ BR [n] is a complex number that follows an independent and identically distributed cyclic complex Gaussian distribution;

[0022] h UR [n] represents the link from the drone to the relay node, h UR H [n] express h UR [n] The conjugate transpose of h BU [n] Represents the link from the ground base station to the drone:

[0023]

[0024] h UR [ n ]= x 0 d UR [ n ] - α 2 K Rc 1+ K Rc h ̄ UR L [ n ]+ 1 1+ K Rc h ̄ UR N [ n ]

[0025] in Indicates the distance from the ground base station to the drone. Indicates the distance from the drone to the relay node, is the open space attenuation coefficient, h ̄ BU N [ n ] represents the non-line-of-sight component between the ground base station and the UAV, h ̄ UR N [ n ] represents the non-line-of-sight component between the UAV and the relay node, h ̄ BU L [ n ] represents the line-of-sight component between the ground base station and the UAV, h ̄ UR L [ n ] represents the line-of-sight component between the UAV and the relay node; is the Rice factor;

[0026] I 1 [ n ] The first-hop phase shift matrix for the reconfigurable smart surface of the UAV:

[0027] I 1 [n]=diag( e j ϑ 1 1 [n] , e j ϑ 2 1 [n] ,..., e j ϑ M 1 [n] )∈ C M×M

[0028] ϑ i 1 [n]∈[0,2π),∀i∈M Represents the first in the reconfigurable smart surface of drones The first jump phase shift of a passive reflective element; the reconfigurable smart surface of the UAV is composed of passive reflective elements, is the number of rows of the array composed of passive reflective elements, is the number of columns of the array composed of passive reflective elements; is the imaginary number symbol;

[0029] A further preferred solution is that the channel vector from the target vehicle to the relay node G VR [n] for:

[0030] G VR [n]= h RV [n]+ h UR H [n] I 1 [n] h VU [n]⋅ e j 2 πn f RUV

[0031] in h RV [ n ] is the direct link between the relay node and the target vehicle, h RV [ n ]= x 0 d RV [ n ] - α 1 e j 2 πn f RV , d RV [ n ] is the distance between the relay node and the target vehicle, is the Doppler frequency shift between the relay node and the target vehicle;

[0032] h VU [n] For a direct link between the target vehicle and the drone:

[0033] h VU [ n ]= e 3 ⋅ K Rc 1+ K Rc h ̄ VU L [ n ]+ 1 1+ K Rc h ̄ VU N [ n ]

[0034] e 3 = x 0 d VU [ n ] - α 2

[0035] in d VU [ n ] is the distance from the target vehicle to the UAV, h ̄ VU N [n] is the non-line-of-sight component between the target vehicle and the UAV, h ̄ VU L [ n ] is the line-of-sight component between the target vehicle and the UAV.

[0036] A further preferred solution is to calculate the channel vector from the ground base station to the drone and then to the target vehicle. G BV [ n ] for:

[0037] G BV [ n ]= h VU H [ n ] I 2 [ n ] h BU [ n ] ⋅ e j 2 πn f BUV

[0038] in h VU H [ n ] for h VU [n] The conjugate transpose of is the Doppler frequency shift between the ground base station-UAV-target vehicle; I 2 [ n ] The second hop phase shift matrix for the reconfigurable smart surface of the UAV:

[0039] I 2 [n]=diag( e j ϑ 1 2 [n] , e j ϑ 2 2 [n] ,..., e j ϑ M 2 [n] )∈ C M×M

[0040] ϑ i 2 [n]∈[0,2π),∀i∈M Represents the first in the reconfigurable smart surface of drones The second hop phase shift of a passive reflective element.

[0041] A further preferred solution is the line-of-sight component between the ground base station and the drone. h ̄ BU L [ n ] for:

[0042] h ̄ BU L n = 1, e j 2 π d x l cos i BU n cos f BU n ,..., e j 2 π M x - 1 d x l cos i BU n cos f BU n T ⊗ 1, e j 2 π d y l cos i BU [ n ] sin f BU [ n ] ,..., e j 2 π M y - 1 d y l cos i BU [ n ] sin f BU [ n ] T ,

[0043] cos i BU [ n ] cos f BU [ n ]= x u [ n ] - x B d BU [ n ] , cos i BU [ n ] sin f BU [ n ]= y u [ n ] - y B d BU [ n ]

[0044] Line-of-sight component between the drone and the relay node h ̄ UR L [ n ] for:

[0045] ,

[0046] cos i UR [ n ] cos f UR [ n ]= x u [ n ] - x R d UR [ n ] , cos i UR [ n ] sin f UR [ n ]= y u [ n ] - y R d UR [ n ]

[0047] The line-of-sight component between the target vehicle and the UAV h ̄ VU L [ n ] for:

[0048] h ̄ VU L [ n ]= 1, e j 2 π d x l cos i VU n cos f VU n ,..., e j 2 π M x - 1 d x l cos i VU n cos f VU n T ⊗ 1, e j 2 π d y l cos i VU [ n ] sin f VU [ n ] ,..., e j 2 π M y - 1 d y l cos i VU [ n ] sin f VU [ n ] T ,

[0049] cos i VU n cos f VU n = x u n - x v n d VU n , cos i VU [ n ] sin f VU [ n ]= y u [ n ]- y v [ n ] d VU [ n ]

[0050] in is the spacing between adjacent passive reflective elements in the same row on the reconfigurable smart surface, is the spacing between adjacent passive reflective elements in the same column on the reconfigurable smart surface, represents the wavelength, d BU [ n ] is the distance from the ground base station to the drone, d UR [ n ] is the distance from the drone to the relay node, is the distance from the target vehicle to the UAV; is the spatial three-dimensional coordinate of the ground base station, is the spatial three-dimensional coordinate of the relay node, x u [ n ], y u [ n ], z u [ n ] For the The spatial three-dimensional coordinates of the drone in each time slot, x v [n], y v [n],0 For the The spatial three-dimensional coordinates of the target vehicle in each time slot.

[0051] In a further preferred embodiment, in step 2, the first optimization problem is expressed as:

[0052]

[0053]

[0054]

[0055] C3: q[1]= q Initial ,q N = q Finally

[0056]

[0057]

[0058] C6: R 2 [ n ] ≥ R th , ∀ n ∈ N

[0059] in R s [ n ] The information security rate of the system is:

[0060] R s [ n ]= R 1 [ n ] - R relay [ n ]

[0061] R 1 [ n ] The information rate of decoding information s1 for the target vehicle:

[0062] R 1 [ n ]= log 2 1+ c 1

[0063] is the signal-to-interference-noise ratio when decoding information s1:

[0064] c 1 = ϖ 2 P b G RV [ n ] 2 ⋅ H BR [ n ] 2 ϖ 2 G RV [ n ] 2 s R 2 + P b G BV [ n ] 2 + s V 2

[0065] R relay [ n ] is the information rate of the relay node:

[0066]

[0067] R 2 [ n ] The information rate of decoding information s2 for the target vehicle:

[0068] R 2 [ n ]= log 2 1+ c 2

[0069] is the signal-to-interference-noise ratio when decoding information s2:

[0070] c 2 = P b G BV [ n ] 2 ϖ 2 G RV [ n ] 2 s R 2 + s V 2

[0071] The total power of the system during communication:

[0072] P t = P b + P v + ϖ 2 P b H BR [n] 2 + P v G VR [n] 2 + s R 2

[0073] Constraint C1 specifies the transmission power range of the ground base station and the target vehicle. is the maximum transmission power specified; constraint C2 is the relay node amplification factor constraint; constraints C3 and C4 indicate that the initial and final positions of the drone have been determined, and the maximum displacement of the drone in adjacent time slots is specified according to the flight speed of the drone ,in is the initial position of the drone, The end position of the drone. is the interval between adjacent time slots; constraint C5 is the flight area allowed for the drone. The permitted flight area is set; constraint C6 stipulates the minimum threshold of the information rate of the target vehicle decoding information s2.

[0074] In a further preferred solution, in step 3, the process of performing fractional programming on the first optimization problem using the Dinkelbach algorithm to obtain the converted second optimization problem is specifically as follows:

[0075] For a given Q= q[ n ],∀ n ∈ N , introduce parameters The second optimization problem is:

[0076]

[0077]

[0078]

[0079] R 2 [ n ]≥ R t h ,∀ n ∈ N .

[0080] A further preferred solution is that in step 4, the DC planning and CCP algorithm are used to optimize and solve the second optimization problem to obtain the optimized relay node amplification factor, the transmission power of the ground base station and the transmission power of the target vehicle; in step 5, the firefly algorithm is used to process the UAV flight trajectory; in step 6, the DDPG algorithm is used to optimize and obtain the target flight trajectory of the UAV.

[0081] In addition, the present invention further proposes a secure communication system based on UAV-RIS based on the above method, comprising:

[0082] The model building module is used to build a two-hop signal model, wherein the first hop in the two-hop signal model includes a ground base station sending the first information to a relay node via a direct link and a reconfigurable smart surface auxiliary link, and the target vehicle sending an interference signal to the relay node; the second hop includes the relay node forwarding the first information to the target vehicle, and the ground base station sending new information;

[0083] The problem construction module constructs the first optimization problem with the transmission power of the ground base station, the transmission power of the target vehicle, the amplification factor of the relay node, and the flight trajectory of the UAV as parameters and the goal of maximizing the system's safety and energy efficiency;

[0084] The problem conversion module uses the Dinkelbach algorithm to perform fractional programming on the first optimization problem to obtain a converted second optimization problem;

[0085] The processing module optimizes the second optimization problem by using DC planning and CCP algorithm to obtain the optimized amplification factor of the relay node, the transmission power of the ground base station and the transmission power of the target vehicle;

[0086] The trajectory screening module processes the UAV flight trajectory using the firefly algorithm based on the optimized amplification factor of the relay node, the transmission power of the ground base station and the transmission power of the target vehicle based on the first optimization problem, and obtains the UAV flight trajectory that meets the first condition as a data set;

[0087] The optimal trajectory calculation module uses the data set as the experience pool sample and uses the DDPG algorithm to optimize the target flight trajectory of the UAV.

[0088] Beneficial effects:

[0089] The secure communication method based on UAV-RIS proposed in this paper constructs a two-hop signal model, uses the ground base station to send information to the relay node through a direct link and a reconfigurable intelligent surface (RIS) auxiliary link, and the target vehicle sends an interference signal to the relay node to reduce the risk of eavesdropping; then the relay node forwards the received information to the target vehicle, and the ground base station sends new information. The optimization problem is constructed with the ground base station transmission power, the target vehicle transmission power, the relay node amplification factor and the UAV flight trajectory as parameters to maximize the security energy efficiency of the system. The fractional programming is performed by the Dinkelbach algorithm, which is transformed into the second optimization problem, and the DC programming and CCP algorithm are used to solve it, and the optimized amplification factor and the ground base station and target vehicle transmission power are obtained. Finally, based on these optimization results, the firefly algorithm is used to process the UAV flight trajectory to generate a flight trajectory data set that meets the conditions, and the DDPG algorithm is used to further optimize the target flight trajectory of the UAV, thereby improving the security and energy efficiency of the communication system.

[0090] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0092] Figure 1 A flow chart of a secure communication method based on UAV-RIS provided by the present invention;

[0093] Figure 2 It is a schematic diagram of the structure of the direct link and the indirect link in the present invention;

[0094] Figure 3 It is a relationship diagram between safety energy efficiency and ground base station transmission power under different vehicle transmission powers in the present invention;

[0095] Figure 4 A three-dimensional diagram of the information security rate of the system of the present invention, the transmission power of the ground base station and the transmission power of the target vehicle;

[0096] Figure 5 A structural schematic diagram of a secure communication system based on UAV-RIS provided by the present invention. DETAILED DESCRIPTION

[0097] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0098] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0099] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In addition, in the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0100] In an embodiment of the present invention, there is provided Figure 1 A secure communication method based on UAV-RIS is shown, the method comprising steps S101 to S106:

[0101] S101, constructing a two-hop signal model, wherein the first hop in the two-hop signal model includes a ground base station sending first information to a relay node via a direct link and a human-machine reconfigurable intelligent surface auxiliary link, and a target vehicle sending an interference signal to the relay node; the second hop in the two-hop signal model includes the relay node forwarding the first information to the target vehicle, and the ground base station sending new information to the target vehicle.

[0102] Among them, after the relay node forwards the first information to the target vehicle, the target vehicle processes the two simultaneously received information streams through non-orthogonal multiple access (NOMA) and serial interference cancellation (SIC). In detail, at the transmitting end of the relay node, a non-orthogonal transmission method is used to superimpose the signals of different users, and different signal powers are allocated to each user through power multiplexing technology; at the receiving end of the target vehicle, SIC technology is used to make step-by-step judgments and eliminate interference on user signals according to the signal power, thereby achieving accurate reception of multiple information streams. It is possible to access more users within limited spectrum resources, improve spectrum efficiency and the number of connections, and ensure that the receiving end can accurately separate and demodulate multiple user signals, providing a more reliable and efficient solution for vehicle communications.

[0103] The steps to construct a two-hop signal model are as follows:

[0104] (1) Based on the reconfigurable smart surface, the phase matrix of the passive reflective elements on the UAV is configured.

[0105] Specific:

[0106] have A reconfigurable intelligent surface (RIS) with passive reflective elements is mounted on a drone to enhance ground information transmission. is the number of rows of the array composed of passive reflective elements, is the number of columns of the array composed of passive reflective elements; the phase matrix of the passive reflective element array Θ[n] It can be expressed as:

[0107] I [ n ]=diag( e j ϑ 1 [ n ] , e j ϑ 2 [ n ] ,..., e j ϑ M [ n ] )∈ C M × M

[0108] in ϑ i [n]∈[0,2π),∀i∈M Indicates The phase of a passive reflective element; Indicates time slots; is the imaginary number symbol;

[0109] In this embodiment, the phase shift of the reconfigurable smart surface (RIS) is divided into a first jump phase shift and a second jump phase shift, which are respectively expressed as:

[0110]

[0111] I 2 [n]=diag( e j ϑ 1 2 [n] , e j ϑ 2 2 [n] ,..., e j ϑ M 2 [n] )∈ C M×M

[0112] in I 1 [n] is the first jump phase shift matrix, I 2 [n] is the second hop phase shift matrix, ϑ i 1 [n]∈[0,2π),∀i∈M Indicates The first phase shift of a passive reflector element is ϑ i 2 [n]∈[0,2π),∀i∈M Indicates The second hop phase shift of a passive reflective element.

[0113] (2) Based on the three-dimensional Cartesian coordinate system, describe the position coordinates of the UAV, target vehicle, ground base station and relay node.

[0114] Specific:

[0115] In the three-dimensional Cartesian coordinate system, the positions of the ground base station and the relay node are fixed, and the coordinates of the ground base station are , the relay node coordinates are ; The UAV flight time T contains N time slots; In the system model considered, the target vehicle and the UAV are mobile, The position of the drone in each time slot is expressed as: q[n] = x u [ n ], y u [ n ], z u [ n ] , the position of the target vehicle is expressed as: L[n]= x v [n], y v [n],0 .

[0116] (3) According to the phase matrix and the position coordinates, the ground base station is described to send the first information to the relay node via the direct link and the auxiliary link of the reconfigurable smart surface, and the vehicle sends an interference signal to the relay node.

[0117] Specific:

[0118] For the first-hop communication, the ground base station first sends the first information to the relay node through a direct link and an indirect link assisted by the Reconfigurable Smart Surface (RIS) on the UAV. Since the ground relay node is untrustworthy, the target vehicle also sends a jamming signal to the relay node to reduce eavesdropping.

[0119] In the time slots, the signal received by the relay node in the first-hop communication can be expressed as:

[0120] s R n = P b H BR [ n ] x s1 n + P v G VR n x I n + n R n (1)

[0121] in is the signal received by the relay node in the first-hop communication, is the transmission power of the ground base station, is the transmission power of the target vehicle, n R [n] is the noise (additive white Gaussian noise, AWGN) that the relay node receives when receiving information, and is subject to n R [n]∼CN(0, s R 2 ) ; x s1 [n] The first information s1 sent by the ground base station, x I [n] The interference signal sent to the target vehicle satisfies E x s1 [n] 2 =E x I [n] 2 =1 .

[0122] H BR [ n ] Represents the channel vector between the ground base station and the relay node:

[0123] H BR [ n ]= h BR [ n ]+ h UR H [ n ] I 1 [ n ] h BU [ n ]

[0124] represents the direct link between the ground base station and the relay node, and there is h BR n = x 0 d BR [n] d 0 - α 1 h ̃ BR [n] ,in is the reference distance set The path loss at is the communication attenuation coefficient, d BR [n] For the time slot, the distance between the ground base station and the relay node, and the distance between points ab are calculated as follows: ; h ̃ BR [n] is a complex number that follows an independent and identically distributed cyclic complex Gaussian distribution, that is, h ̃ BR [n]∼CN(0,1) .

[0125] G VR [n] Represents the channel vector from the target vehicle to the relay node:

[0126] G VR [n]= h RV [n]+ h UR H [n] I 1 [n] h VU [n]⋅ e j 2 πn f RUV

[0127] in h UR [n] represents the link from the drone to the relay node, h UR H [n] express h UR [n] The conjugate transpose of h BU [n] represents the link from the ground base station to the UAV, and h BU [n]∈ C M ×1 、 h UR [n]∈ C M ×1 It follows a Rician distribution and can be modeled as:

[0128]

[0129] h UR [ n ]= x 0 d UR [ n ] - α 2 K Rc 1+ K Rc h ̄ UR L [ n ]+ 1 1+ K Rc h ̄ UR N [ n ] (2)

[0130] in Indicates the distance from the ground base station to the drone. Indicates the distance from the drone to the relay node, is the open space attenuation coefficient, h ̄ BU N [ n ] represents the non-line-of-sight component between the ground base station and the UAV, h ̄ UR N [ n ] represents the non-line-of-sight component between the UAV and the relay node, h ̄ BU L [ n ] represents the line-of-sight component between the ground base station and the UAV, h ̄ UR L [ n ] Represents the line-of-sight component between the UAV and the relay node.

[0131] For those equipped with The reconfigurable smart surface with passive reflective elements is modeled as a URA array, and its antenna array response is expressed as:

[0132] a RIS i i n , f i n = a x i i n , f i n ⊗ a y i i n , f i n = 1, e j 2 π d x l cos i i n cos f i n ,..., e j 2 π M x - 1 d x l cos i i n cos f i n T ⊗ 1, e j 2 π d y l cos i i [ n ] sin f i [ n ] ,..., e j 2 π M y - 1 d y l cos i i [ n ] sin f i [ n ] T (3)

[0133] in are the pitch angle and azimuth angle from node i to the UAV, is the spacing between adjacent passive reflective elements in the same row and column on the reconfigurable smart surface, ,in represents the wavelength. Therefore It can be expressed as:

[0134] h ̄ BU L n = a RIS i BU n , f BU n = a x i BU n , f BU n ⊗ a y i BU n , f BU n = 1, e j 2 π d x l cos i BU n cos f BU n ,..., e j 2 π M x - 1 d x l cos i BU n cos f BU n T ⊗ 1, e j 2 π d y l cos i BU [ n ] sin f BU [ n ] ,..., e j 2 π M y - 1 d y l cos i BU [ n ] sin f BU [ n ] T ,

[0135] ,

[0136] in:

[0137]

[0138] cos i UR [ n ] cos f UR [ n ]= x u [ n ] - x R d UR [ n ] , cos i UR [ n ] sin f UR [ n ]= y u [ n ] - y R d UR [ n ] (4)

[0139] The communication link between the target vehicle-UAV-relay node is:

[0140] h VUR [ n ]= h UR H [ n ] I 1 [ n ] h VU [ n ]⋅ e j 2 πn f RUV (5)

[0141] in h VU [n] is the direct link between the target vehicle and the UAV, h VU [n]∈ C M×1 It obeys Rician decay and can be expressed as:

[0142]

[0143] e 3 = x 0 d VU [ n ] - α 2 (6)

[0144] in h ̄ VU N [n] is the non-line-of-sight component between the target vehicle and the UAV, h ̄ VU L [ n ] is the line-of-sight component between the target vehicle and the UAV:

[0145] h ̄ VU L [ n ]= a RIS i VU [ n ], f VU [ n ] = a x i VU n , f VU n ⊗ a y i VU n , f VU n = 1, e j 2 π d x l cos i VU n cos f VU n ,..., e j 2 π M x - 1 d x l cos i VU n cos f VU n T ⊗ 1, e j 2 π d y l cos i VU [ n ] sin f VU [ n ] ,..., e j 2 π M y - 1 d y l cos i VU [ n ] sin f VU [ n ] T ,

[0146]

[0147] cos i VU [ n ] sin f VU [ n ]= y u [ n ]- y v [ n ] d VU [ n ] (7)

[0148] is the Rice factor, d VU [ n ] Indicates the distance from the target vehicle to the UAV, is the Doppler frequency shift between the target vehicle-UAV-relay node:

[0149] (8)

[0150] in represents the speed of the target vehicle, represents the speed of the drone, and there are ψ V = x u [ n ]- x v [ n ] d VU [ n ] , ψ R = x u [ n ]- x R d UR [ n ] .

[0151] Likewise, h RV [ n ] is the direct link between the relay node and the target vehicle, which is modeled as follows when considering the Doppler shift error:

[0152] h RV [ n ]= x 0 d RV [ n ] - α 1 e j 2 πn f RV (9)

[0153] in d RV [ n ] is the distance between the relay node and the target vehicle, is the Doppler frequency shift between the relay node and the target vehicle:

[0154] , ψ RV = x v [ n ]- x R d RV [ n ] .

[0155] Therefore, the final channel vector from the target vehicle to the relay node is G VR [ n ] and the channel vector between the ground base station and the relay node H BR [ n ] Can be expressed as:

[0156] G VR [ n ]= h RV [ n ]+ h VUR [ n ]

[0157] H BR [ n ]= h BR [ n ]+ h UR H [ n ] Θ 1 [ n ] h BU [ n ] (10)

[0158] According to Shannon's formula, in the nth time slot, the information rate of the relay node can be expressed as:

[0159] (11)

[0160] (4) Based on the phase matrix and position coordinates, describe that the relay node forwards the first information to the target vehicle and the ground base station sends new information.

[0161] Specific:

[0162] For the second-hop communication, the relay node forwards the first information s1 received in the first hop to the target vehicle, and the ground base station sends new information s2 to the target vehicle.

[0163] The new information s2 sent by the ground base station is expressed as x s2 [n] , and satisfies E x s2 [n] 2 =1 .

[0164] The target vehicle's second-hop receiving information can be expressed as:

[0165] s V [ n ]= ϖ G RV [ n ] s R [ n ]+ P b G BV [ n ] x s 2 [ n ]+ n V [ n ] (12)

[0166] in is the amplification factor of the relay node, n V [n] is additive noise, subject to n V [n] ∼ CN(0, σ V 2 ) .

[0167] G BV [ n ] is the channel vector from the ground base station to the UAV and then to the target vehicle:

[0168] G BV [ n ]= h VU H [ n ] Θ 2 [ n ] h BU [ n ] ⋅ e j 2 πn f BUV (13)

[0169] in is the Doppler frequency shift between the ground base station-UAV-target vehicle:

[0170] f BUV = 1 λ V D ψ V + V U ψ B , ψ B = x u [ n ] - x B d BU [ n ] (14)

[0171] is the channel vector from the relay node to the target vehicle:

[0172] G RV n = h RV n + h RUV n = h RV [ n ]+ h VU H [ n ] Θ 2 [ n ] h UR [ n ] ⋅ e j 2 πn f RUV (15)

[0173] in h VU H [ n ] for h VU [n] The conjugate transpose of .

[0174] From formula (12), we can see that the target vehicle receives the information in the second hop ( s V [n] ) contains the interference signal sent by the target vehicle during the first hop communication. Since the interference signal is sent by the target vehicle itself, the received signal s V [n] After this interference signal can be eliminated, s V [n] Can be rewritten as:

[0175] s V n = ϖ G RV n × P b H BR [ n ] x s 1 n + n R n + P b G BV n x s 2 n + n V n = ϖ P b G RV [ n ] H BR [ n ] x s 1 [ n ] ⏟ signal x s 1 + P b G BV [ n ] x s 2 [ n ] ⏟ signal x s 2 + ϖ G RV [ n ] n R [ n ]+ n V [ n ] ⏟ noise (16)

[0176] For the target vehicle, since two messages s1 and s2 are received at the same time, the target vehicle uses non-orthogonal multiple access (NOMA) technology to decode the message. Based on the successive interference cancellation (SIC) technology, the target vehicle treats the message s2 as interference noise when decoding the message s1, so the signal-to-noise ratio when decoding the message s1 is Can represent:

[0177] γ 1 = ϖ 2 P b G RV [ n ] 2 ⋅ H BR [ n ] 2 ϖ 2 G RV [ n ] 2 σ R 2 + P b G BV [ n ] 2 + σ V 2 (17)

[0178] After successfully decoding information s1, the target vehicle uses continuous interference elimination to remove the information and then decodes the signal-to-interference-to-noise ratio of s2. for:

[0179] γ 2 = P b G BV [ n ] 2 ϖ 2 G RV [ n ] 2 σ R 2 + σ V 2 (18)

[0180] Therefore, the information rates of the target vehicle decoding information s1 and s2 can be expressed as:

[0181] R 1 [ n ]= log 2 1+ γ 1 , R 2 [ n ]= log 2 1+ γ 2 (19)

[0182] The information security rate of the system is:

[0183] R s [ n ]= R 1 [ n ] - R relay [ n ] (20)

[0184] S102, based on the transmission power of the ground base station , the target vehicle's transmission power , the amplification factor of the relay node and the flight trajectory of the UAV as parameters, and the first optimization problem is constructed with the goal of maximizing the system safety energy efficiency.

[0185] Specific:

[0186] In order to balance information security and power consumption, in this invention, we jointly optimize the transmission power of the ground base station, the transmission power of the target vehicle, the amplification factor of the relay node and the flight trajectory of the UAV. Q= q[n], ∀ n ∈ N , thereby maximizing the safe energy efficiency of the system, the first optimization problem can be formulated as:

[0187]

[0188]

[0189]

[0190] C3: q[1]= q Initial ,q N = q Finally

[0191]

[0192]

[0193] C6: R 2 [ n ] ≥ R th , ∀ n ∈ N (twenty one)

[0194] in The total power of the system during communication:

[0195] P t = P b + P v + ϖ 2 P b H BR [n] 2 + P v G VR [n] 2 + σ R 2

[0196] Constraint C1 specifies the transmission power range of the ground base station and the target vehicle. is the maximum transmission power specified. Constraint C2 is the relay node amplification factor constraint. Constraints C3 and C4 indicate that the initial and final positions of the drone have been determined, and the maximum displacement of the drone in adjacent time slots is specified according to the flight speed of the drone. ,in is the initial position of the drone, The end position of the drone. is the interval between adjacent time slots. Constraint C5 is the flight area allowed for the drone. The constraint C6 specifies the minimum threshold of the information rate of the target vehicle decoding information s2.

[0197] S103, using the Dinkelbach algorithm to perform fractional programming on the first optimization problem to obtain a converted second optimization problem.

[0198] Specific:

[0199] For a given Q= q[ n ],∀ n ∈ N , the first optimization problem is transformed into:

[0200]

[0201]

[0202]

[0203] (twenty two)

[0204] Using Dinkelbach for fractional programming, introducing parameters Formula 22 is transformed into the second optimization problem:

[0205]

[0206]

[0207]

[0208] R 2 [ n ]≥ R t h ,∀ n ∈ N (twenty three)

[0209] S104, using DC planning (Difference of Convex Functions) and CCP algorithm (Convex-Concave Procedure) to optimize and solve the second optimization problem, and obtain the optimized relay node amplification factor, the transmission power of the ground base station and the transmission power of the target vehicle; DC planning and CCP algorithm are both existing technologies.

[0210] Specific:

[0211] definition R s [n]= R 1 [n] - R relay [n]= f 1 - f 2 - f 3 , R 1 n > R relay [n] ,in:

[0212] (twenty four)

[0213] power The optimization problem can be expressed as:

[0214]

[0215]

[0216] R 2 [ n ]≥ R t h ,∀ n ∈ N (25)

[0217] This optimization problem belongs to DC planning problem. Since the function All of them are concave functions, so the CCP method is used to transform the DC function into a convex optimization sub-problem by constructing a convex function, find any two points with the same gradient of the constructed convex function and reduce their distance to make the objective function converge, and obtain the optimized relay node amplification factor, the transmission power of the ground base station and the transmission power of the target vehicle. The details are as follows:

[0218] make According to the properties of concave functions, the functions are , Convex function. By Perform a Taylor expansion to linearly approximate its function value, that is:

[0219] (26)

[0220] in For function The rth iteration is at a feasible point The gradient at can be expressed as:

[0221] ∇ H P b r = - f 1 ' = 1 ln 2 ⋅ ϖ 2 G RV [ n ] 2 ⋅ H BR [ n ] 2 + G BV [ n ] 2 ϖ 2 P b r G RV [ n ] 2 ⋅ H BR [ n ] 2 + ϖ 2 G RV [ n ] 2 σ R 2 + P b r G BV [ n ] 2 + σ V 2 (27)

[0222] Therefore, for the ground base station transmission power The optimization subproblem can be refactored as:

[0223]

[0224]

[0225] P b G BV [ n ] 2 ϖ 2 G RV [ n ] 2 σ R 2 + σ V 2 ≥ 2 R t h - 1,∀ n ∈ N (28)

[0226] For the target vehicle transmission power , information rate R 1 [ n ] is a constant, and the second-order derivative of the information rate of the relay node satisfies ,therefore About After ignoring the constant term in the objective function, the target vehicle transmits power The optimization subproblem can be expressed as:

[0227]

[0228] (29)

[0229] (30)

[0230] in yes for At feasible point The first-order derivative at can be expressed as:

[0231] ∇ R relay P v r = - 1 ln 2 ⋅ P b H BR [ n ] 2 G VR [ n ] 2 P v G VR [ n ] 2 + σ R 2 (31)

[0232] for , is a constant term, we define .about The optimization subproblem can be expressed as:

[0233]

[0234]

[0235] P b G BV [ n ] 2 ς G RV [ n ] 2 σ R 2 + σ V 2 ≥ 2 R t h - 1,∀ n ∈ N (32)

[0236] It is found that the objective function can be found in the variable The tangent line of any point in the domain is found, so the objective function is smooth. We use Newton's method with faster convergence speed to solve it. Definition:

[0237] By calculation Update variables ,in:

[0238]

[0239]

[0240] (33)

[0241] The relay node amplification factor, the ground base station transmission power and the target vehicle transmission power are thus optimized.

[0242] S105, based on the optimized relay node amplification factor, the transmission power of the ground base station, and the transmission power of the target vehicle, for the first optimization problem, the firefly algorithm is used to process the flight trajectory of the UAV, and a plurality of flight trajectories that meet the first condition are obtained as a data set. The first condition is a constraint condition on the flight trajectory of the UAV in the first optimization problem. The firefly algorithm is a prior art.

[0243] Specific:

[0244] In the mathematical model of the firefly algorithm, its brightness can be expressed as:

[0245] (34)

[0246] in is the initial brightness of the firefly, is the loss factor, and d is the distance between fireflies. When low-brightness fireflies are attracted, their attraction can be expressed as:

[0247] (35)

[0248] in is the initial attraction when the distance between the two fireflies is 0. The attraction is an important part of the distance the fireflies move after being attracted. When , the coordinates of the low-brightness firefly moving in the x-axis direction can be expressed as:

[0249] (36)

[0250] in is the step length, is a random number that follows a Gaussian distribution, which can avoid falling into a local optimum during optimization. There is also a special case where the moving coordinates of the brightest firefly in the firefly population also satisfy equation (36), except that .

[0251] Brightness settings of fireflies at different locations Z i (d)= R s i [n] P t , R s i [n] is the information security rate corresponding to the position of the i-th firefly. The attractiveness of the firefly is set to:

[0252] (37)

[0253] in , are the i-th firefly and the j-th firefly.

[0254] S106, using the data set as an experience pool sample, and optimizing the first optimization problem using the DDPG algorithm to obtain the target flight trajectory of the UAV. The DDPG algorithm is a prior art.

[0255] Specific:

[0256] Configure environmental information according to the locations of ground base stations, relay nodes, target vehicles, and signal models; configure state information according to the three-dimensional coordinates of the drone; configure action information according to the position changes of the drone in each time period; and configure the reward function according to the information security rate.

[0257] The environment refers to the vehicle network downlink scenario assisted by the reconfigurable intelligent surface of the drone. It mainly uses the DDPG algorithm to optimize the drone trajectory based on the location of the ground base station, relay node, target vehicle and signal model to maximize the channel safety rate of the system.

[0258] In the state configuration, the optimization variable of the DDPG algorithm is the drone trajectory. The flight time of the drone is discretized, that is, it is assumed that the drone remains stationary in adjacent time periods. Therefore, the three-dimensional coordinates of the drone in all time periods are used as the state information of DDPG, that is:

[0259] S= q[ n ]= x u [ n ], y u [ n ], z u [ n ] ,∀ n ∈ N (38)

[0260] In the action configuration, when the flight time T is divided into N time periods, the trajectory of the drone is discretized into N three-dimensional coordinates. We divide the movement area of ​​the drone along the x, y, and z axes. In adjacent time periods, the drone can only move one unit distance along each coordinate axis, and its movement distance must satisfy the constraint q[ n ] - q[ n - 1] 2 ≤ V U δ 2 Therefore, the actions of the DDPG algorithm can be defined as:

[0261] (39)

[0262] In the configuration of the reward function, the information security rate of the current period is calculated based on the action and state information selected by DDPG. When the drone exceeds the specified area or does not meet the rate constraint, its reward is set to a negative number, which can be expressed as:

[0263] r [ n ]= - 0.5× R s n , exceed X×Y×Z R s [ n ], at X×Y×Z inside (40)

[0264] Therefore, the total reward of the DDPG algorithm is the sum of all period rewards, that is: R= ∑ n =1 N r [ n ] .

[0265] A random noise is added to the actor network, and an action is selected based on the current state. The action interacts with the environment to generate the next state and the current reward. The critic network calculates the value function based on the action through the Bellman equation, that is: ,in is the reward for the current action and state, is the discount factor, is the value of the target network of the critic.

[0266] The actor network and the critic network are updated by gradient ascent and minimization of the error function, respectively. The gradient and error function are expressed as:

[0267]

[0268] L U = E [ r + τma x a ' Q ' ( s ' , a ' ) - Q ( s , a ) ] 2 (41)

[0269] in, is the action selection strategy of the actor.

[0270] Then, the target network of the actor network and the critic network is updated through soft update to control the update speed of the target network. Specifically, it is expressed as:

[0271]

[0272] (42)

[0273] Finally, the optimized target flight trajectory of the UAV is obtained.

[0274] After obtaining the target flight trajectory of the drone, the target flight trajectory is simulated, and the simulation results are used to verify whether the target flight trajectory meets the preset conditions. That is, the simulation model is used to verify whether the target flight trajectory meets the safety indicators, which can facilitate R&D personnel to make corrections and references.

[0275] The effect of the present invention is described below in conjunction with the simulation of the comparative example, wherein the simulation parameters of the comparative example are: the coordinates of the ground base station and the relay node are set to (0,0,0) and (90,36,0). According to the road traffic safety law, the speed of vehicles in urban areas cannot exceed 50km / h under normal circumstances. Therefore, in this embodiment, the driving speed of the target vehicle is set to 10m / s. The flight speed of the drone is 20m / s.

[0276] Figure 3 Demonstrated safe energy efficiency (SEE) vs. ground base station transmit power The figure shows the relationship between the transmission power of four different target vehicles. The performance comparison of safety energy efficiency (SEE) is shown in Figure =0.1W, it can be seen that the SEE of the system increases with the increase of the base station transmission power, but when the power increases to a certain value (18dBm), the SEE decreases with the increase of the base station transmission power. This shows that the security energy efficiency cannot be improved by simply increasing the base station transmission power, because when the base station transmission power increases, the signal strength received by the relay node of the first-hop communication will also increase, which means that its eavesdropping performance will also be enhanced. In addition, when the interference power of the target vehicle increases, a higher base station transmission power is also required to ensure the stability of the security energy efficiency, for example, When increasing from 0.3W to 0.6W, the base station power needs to be increased from 15dBm to 18dBm under the same SEE.

[0277] Figure 4 Demonstrates the system's information security rate and ground base station transmission power , target vehicle transmission power This figure aims to analyze the relationship between the information security rate and the transmission power of the ground base station and the target vehicle. In order to clearly illustrate the changing relationship, the security rate is defined as The vertical The curve is the safe rate and the base station transmission power The relationship between the different powers The security speed of the following systems is less than zero, that is, when the target vehicle does not send interference signals, it is easier for the relay node to eavesdrop and cause information leakage. The red dots in the figure are the values ​​of the two powers corresponding to the maximum security rate, when the maximum information security rate can reach 3.18bps / Hz.

[0278] According to the above Figure 3 and Figure 4The problem summarized in this paper is to construct a two-hop signal model, and use the ground base station to send information to the relay node through a direct link and a reconfigurable intelligent surface (RIS) auxiliary link. At the same time, the target vehicle sends an interference signal to the relay node to reduce the risk of eavesdropping; then the relay node forwards the received information to the target vehicle, and the ground base station resends the new information. With the ground base station transmission power, the target vehicle transmission power, the relay node amplification factor and the UAV flight trajectory as parameters, the optimization problem is constructed to maximize the safety and energy efficiency of the system. Fractional programming is performed through the Dinkelbach algorithm, which is converted into the second optimization problem, and the DC programming and CCP algorithm are used to solve it to obtain the optimized amplification factor and transmission power. Finally, based on these optimization results, the firefly algorithm is used to process the UAV flight trajectory to generate a flight trajectory data set that meets the conditions, and the DDPG algorithm is used to further optimize the target flight trajectory of the UAV, thereby improving the security and energy efficiency of the communication system.

[0279] Based on the same inventive concept, an embodiment of the present invention provides a secure communication system based on UAV-RIS, such as Figure 5 As shown, including:

[0280] The model building module 201 is used to build a two-hop signal model, wherein the first hop in the two-hop signal model includes the ground base station sending the first information to the relay node via the direct link and the reconfigurable intelligent surface auxiliary link, and the target vehicle sending an interference signal to the relay node; the second hop includes the relay node forwarding the first information to the target vehicle, and the ground base station sending new information;

[0281] The problem construction module 202 constructs a first optimization problem with the transmission power of the ground base station, the transmission power of the target vehicle, the amplification factor of the relay node, and the flight trajectory of the UAV as parameters and the goal of maximizing the safety energy efficiency of the system;

[0282] A problem conversion module 203 performs fractional programming on the first optimization problem using a Dinkelbach algorithm to obtain a converted second optimization problem;

[0283] Processing module 204, using DC planning and CCP algorithm to optimize the second optimization problem, to obtain optimized amplification factor of the relay node, transmission power of the ground base station and transmission power of the target vehicle;

[0284] The trajectory screening module 205 processes the UAV flight trajectory using the firefly algorithm based on the optimized amplification factor of the relay node, the transmission power of the ground base station and the transmission power of the target vehicle and the first optimization problem, and obtains the UAV flight trajectory that meets the first condition as a data set;

[0285] The optimal trajectory calculation module 206 uses the data set as an experience pool sample and optimizes the target flight trajectory of the UAV using the DDPG algorithm.

[0286] Based on the same inventive concept, this embodiment provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a secure communication method based on UAV-RIS when executing the computer program.

[0287] Based on the same inventive concept, this embodiment provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, a secure communication method based on UAV-RIS is implemented.

[0288] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A secure communication method based on UAV-RIS, characterized in that: The following steps are involved: Step 1: construct a two-hop signal model: the first hop in the two-hop signal model includes the ground base station sending the first information to the relay node via the direct link and the UAV reconfigurable intelligent surface auxiliary link, and the target vehicle sends an interference signal to the relay node via the direct link and the UAV reconfigurable intelligent surface auxiliary link; the second hop in the two-hop signal model includes the relay node forwarding the information received in the first hop to the target vehicle, and the ground base station sending new information to the target vehicle; In step 1, at the nth time slot, the signal s received by the relay node in the first hop communication R [n] is: Where P b is the transmission power of the ground base station, P v is the transmission power of the target vehicle, n R [n] is the noise received by the relay node when receiving information, subject to n R [n] Variance of the distribution, x s1 [n] is the first information s1 sent by the ground base station, x I [n] is the interference signal sent by the target vehicle; H BR [n] represents the channel vector between the ground base station and the relay node; G VR [n] represents the channel vector from the target vehicle to the relay node; In the nth time slot, the target vehicle in the second-hop communication receives the signal s V [n] is: in is the amplification factor of the relay node, n V [n] is additive noise, obeying n V [n] Variance of the distribution, x s2 [n] is the new information s2 sent by the ground base station, G BV [n] is the channel vector from the ground base station to the UAV and then to the target vehicle, G RV [n] is the channel vector from the relay node to the target vehicle; When decoding the first information s1 sent by the ground base station, the target vehicle regards the new information s2 sent by the ground base station as interference noise. After decoding the first information s1, the target vehicle removes the information using continuous interference elimination, and then decodes the new information s2. The channel vector H between the ground base station and the relay node BR [n] is: where h BR [n] represents the direct link between the ground base station and the relay node, h UR [n] represents the link from the drone to the relay node, Indicates h UR The conjugate transpose of [n], h BU [n] represents the link from the ground base station to the UAV; Θ1[n] is the first-hop phase shift matrix of the UAV reconfigurable smart surface: represents the first jump phase shift of the i-th passive reflective element in the UAV reconfigurable smart surface; the UAV reconfigurable smart surface is composed of M = M x ×M y passive reflective elements, M x is the number of rows of the array composed of passive reflective elements, M y is the number of columns of the array composed of passive reflective elements, j is the sign of an imaginary number; The channel vector G from the target vehicle to the relay node VR [n] is: where h RV [n] is the direct link between the relay node and the target vehicle, h VU [n] is the direct link between the target vehicle and the UAV, f RUV is the Doppler frequency shift between the target vehicle-UAV-relay node; The channel vector G from the relay node to the target vehicle RV [n] is: in h VU [n], Θ2[n] is the second-hop phase shift matrix of the reconfigurable intelligent surface of the UAV: represents the second hop phase shift of the i-th passive reflective element in the UAV reconfigurable smart surface; The channel vector G from the ground base station to the drone and then to the target vehicle BV [n] is: where f BUV is the Doppler frequency shift between the ground base station-UAV-target vehicle; Step 2: Taking the transmission power of the ground base station, the transmission power of the target vehicle, the amplification factor of the relay node, and the flight trajectory of the UAV as parameters, the first optimization problem is constructed with the goal of maximizing the system safety energy efficiency; Step 3: Perform fractional programming on the first optimization problem to obtain a converted second optimization problem; Step 4: Optimize and solve the second optimization problem to obtain the optimized relay node amplification factor, the transmission power of the ground base station and the transmission power of the target vehicle; Step 5: Based on the optimized relay node amplification factor, the transmission power of the ground base station, and the transmission power of the target vehicle, the optimization algorithm is used to process the flight trajectory of the UAV for the first optimization problem, and a number of flight trajectories that meet the first condition are obtained as a data set; the first condition is a constraint condition on the flight trajectory of the UAV in the first optimization problem; Step 6: Use the data set obtained in step 5 as the experience pool sample and use the optimization algorithm to optimize the target flight trajectory of the UAV.

2. A secure communication method based on UAV-RIS according to claim 1, characterized in that: Direct link between ground base station and relay node ξ0 is the path loss at the set reference distance d0, α1 is the communication attenuation coefficient, d BR [n] is the distance between the ground base station and the relay node in the nth time slot; is a complex number that follows an independent and identically distributed cyclic complex Gaussian distribution; The link h from the drone to the relay node UR [n], the link from the ground base station to the drone h BU [n] Specifically: where d BU [n] represents the distance from the ground base station to the drone, d UR [n] represents the distance from the drone to the relay node, α2 is the air-to-ground attenuation coefficient, represents the non-line-of-sight component between the ground base station and the UAV, represents the non-line-of-sight component between the UAV and the relay node, represents the line-of-sight component between the ground base station and the UAV, represents the line-of-sight component between the UAV and the relay node; K Rc is the Rice factor.

3. A secure communication method based on UAV-RIS according to claim 2, characterized in that: Direct link between relay node and target vehicle d RV [n] is the distance between the relay node and the target vehicle, f RV is the Doppler frequency shift between the relay node and the target vehicle; Direct link between target vehicle and UAV h VU [n] Specifically: where d VU [n] is the distance from the target vehicle to the UAV, is the non-line-of-sight component between the target vehicle and the UAV, is the line-of-sight component between the target vehicle and the UAV.

4. A secure communication method based on UAV-RIS according to claim 3, characterized in that: Line-of-sight component between ground base station and drone for: Line-of-sight component between the drone and the relay node for: The line-of-sight component between the target vehicle and the UAV for: where d x is the spacing between adjacent passive reflective elements in the same row on the reconfigurable smart surface, d y is the spacing between adjacent passive reflective elements in the same column on the reconfigurable smart surface, λ represents the wavelength, and d BU [n] is the distance from the ground base station to the drone, d UR [n] is the distance from the drone to the relay node, d VU [n] is the distance from the target vehicle to the UAV; (x B ,y B ,0) is the spatial three-dimensional coordinate of the ground base station, (x R ,y R ,0) is the spatial three-dimensional coordinate of the relay node, (x u [n],y u [n],z u [n]) is the spatial three-dimensional coordinate of the UAV in the nth time slot, (x v [n],y v [n],0) is the spatial three-dimensional coordinate of the target vehicle in the nth time slot.

5. A secure communication method based on UAV-RIS according to claim 3, characterized in that: In step 2, the first optimization problem is expressed as: Where R s [n] is the information security rate of the system: R s [n]=R1[n]-R relay [n] R1[n] is the information rate of the target vehicle decoding information s1: R1[n]=log2(1+γ1) γ1 is the signal-to-interference-noise ratio when decoding information s1: R relay [n] is the information rate of the relay node: R2[n] is the information rate of the target vehicle decoding information s2: R2[n]=log2(1+γ2) γ2 is the signal-to-interference-noise ratio when decoding information s2: P t The total power of the system during communication: Constraint C1 specifies the transmission power range of the ground base station and the target vehicle, P max is the maximum transmission power specified; constraint C2 is the relay node amplification factor constraint; constraints C3 and C4 indicate that the initial and final positions of the drone have been determined, and the maximum displacement V of the drone in adjacent time slots is specified according to the flight speed of the drone U δ, where q Initial is the initial position of the UAV, q Finally is the termination position of the UAV, δ is the interval between adjacent time slots; Constraint C5 is the allowed flight area of ​​the UAV, and X×Y×Z is the set allowed flight area; constraint C6 stipulates the minimum threshold of the information rate of the target vehicle decoding information s2.

6. A secure communication method based on UAV-RIS according to claim 5, characterized in that: In step 3, the Dinkelbach algorithm is used to perform fractional programming on the first optimization problem to obtain the converted second optimization problem. Specifically, the process is as follows: For a given Introducing parameter η s The second optimization problem is:

7. A secure communication method based on UAV-RIS according to claim 6, characterized in that: In step 4, the DC planning and CCP algorithm are used to optimize and solve the second optimization problem to obtain the optimized relay node amplification factor, the transmission power of the ground base station and the transmission power of the target vehicle; in step 5, the firefly algorithm is used to process the UAV flight trajectory; in step 6, the DDPG algorithm is used to optimize and obtain the target flight trajectory of the UAV.

8. A secure communication system based on the method of claim 7, characterized in that: include: The model building module is used to build a two-hop signal model, wherein the first hop in the two-hop signal model includes a ground base station sending the first information to a relay node via a direct link and a reconfigurable smart surface auxiliary link, and the target vehicle sending an interference signal to the relay node; the second hop includes the relay node forwarding the first information to the target vehicle, and the ground base station sending new information; The problem construction module constructs the first optimization problem with the transmission power of the ground base station, the transmission power of the target vehicle, the amplification factor of the relay node, and the flight trajectory of the UAV as parameters and the goal of maximizing the system's safety and energy efficiency; The problem conversion module uses the Dinkelbach algorithm to perform fractional programming on the first optimization problem to obtain a converted second optimization problem; The processing module optimizes the second optimization problem by using DC planning and CCP algorithm to obtain the optimized amplification factor of the relay node, the transmission power of the ground base station and the transmission power of the target vehicle; The trajectory screening module processes the UAV flight trajectory using the firefly algorithm based on the optimized amplification factor of the relay node, the transmission power of the ground base station and the transmission power of the target vehicle based on the first optimization problem, and obtains the UAV flight trajectory that meets the first condition as a data set; The optimal trajectory calculation module uses the data set as the experience pool sample and uses the DDPG algorithm to optimize the target flight trajectory of the UAV.

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