A method for secure communication of UAV-assisted multi-user based on hybrid DF and AF protocol

Abstract

The present application relates to a kind of based on the secure communication method of UAV assisted multi-user under hybrid DF and AF protocol, comprising: modeling the network structure of UAV assisted multi-user secure communication based on hybrid DF and AF protocol;Modeling the communication link model of UAV assisted relay;Modeling the power gain of communication link;Modeling the transmission power of UAV as relay and interference and the transmission power limit condition of sending end user;Modeling the channel bandwidth allocation and constraint model of UAV relay assisted based on hybrid DF and AF protocol;Modeling the secure transmission rate of sending end user;Modeling the secure transmission rate of receiving end user;Modeling the system minimum secure transmission rate maximization optimization model;Optimization model is solved using grey wolf optimization algorithm, and the best allocation result of UAV transmission power, the transmission power of receiving end user and channel bandwidth is obtained, and the best allocation result is substituted into system minimum secure transmission rate maximization function to obtain optimization sub-solution.This method is conducive to the realization of system minimum transmission rate maximization.

Description

Technical Field

[0001] This invention relates to the field of UAV-assisted technology, specifically to a secure communication method for multiple users assisted by UAVs based on a hybrid decode and forward (DF) and amplify and forward (AF) protocol. Background Technology

[0002] Unmanned Aerial Vehicle (UAV) communication in the field of wireless communication possesses unique advantages such as high flexibility, on-demand deployment, rapid networking, and line-of-sight information transmission, making UAV communication systems highly promising for various application needs. When UAVs act as relays, their inherent characteristics (such as high mobility and flexible deployment) can improve the coverage and connectivity of wireless communication systems. However, security is a major challenge for UAV-assisted relay communication systems, such as the confidentiality and eavesdropping threats posed by air-to-ground line-of-sight communication links. Physical layer security has been proposed, which utilizes the physical characteristics of wireless channels to achieve secure communication in wireless networks and investigates the amount of data securely transmitted to the intended receiver. Therefore, researching ways to improve the confidentiality of transmitted data in communication systems with UAV assistance is of great significance for increasing secure throughput.

[0003] In recent years, Physical Layer Security (PLS) has emerged as a more computationally efficient and resource-saving alternative in UAV wireless communication systems. The basic idea of ​​PLS ​​is to leverage the characteristics and impairments of wireless channels, including noise, fading, interference, dispersion, and diversity, to ensure successful data decoding by the target user while preventing eavesdroppers from doing so. Therefore, the primary design goal of PLS ​​is to increase the performance difference between legitimate receiver links and eavesdropper links through well-designed transmission schemes. Key challenges include passive and active eavesdropping. Specifically, on the one hand, information transmission is insecure and can be easily eavesdropped on by unwanted receivers, leading to the risk of information leakage. On the other hand, legitimate links are vulnerable to malicious interference attacks. Furthermore, the location of ground-based eavesdroppers is unknown, and there is the possibility of coordinated interference from aerial eavesdroppers. Summary of the Invention

[0004] The purpose of this invention is to provide a secure communication method for UAV-assisted multi-user communication based on a hybrid DF and AF protocol, which is beneficial for maximizing the minimum transmission rate of the system.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a UAV-assisted multi-user secure communication method based on hybrid DF and AF protocols, comprising the following steps:

[0006] S1: Model the network structure for secure communication between UAV-assisted multi-users based on hybrid DF and AF protocols, including: UAV, a transmitter composed of multiple users, and a receiver composed of a base station and multiple users capable of autonomous communication;

[0007] S2: Model the communication link of UAV-assisted relay;

[0008] S3: Model the power gain of the communication link;

[0009] S4: Model the transmit power of the UAV as a relay and jammer, as well as the limitations on the transmit power of the transmitting user;

[0010] S5: Modeling a UAV relay auxiliary channel bandwidth allocation and constraint model based on hybrid DF and AF protocols;

[0011] S6: Model the secure transmission rate of each user at the sending end;

[0012] S7: Model the secure transmission rate for each user at the receiving end;

[0013] S8: Modeling system minimum secure transmission rate maximization optimization model;

[0014] S9: The Grey Wolf optimization algorithm is used to solve the optimization model for maximizing the minimum secure transmission rate of the system. The optimal allocation results of UAV transmission power, receiver user transmission power and channel bandwidth are obtained. The optimal allocation results are then substituted into the minimum secure transmission rate maximization function of the system to obtain the optimized solution.

[0015] Furthermore, in step S1, a secure communication scenario involving multiple users assisted by a dual-antenna UAV is considered; when K users at the transmitting end send confidential information, the relay UAV receives the information, and simultaneously, the eavesdropper E at the transmitting end... Se Eavesdropping; when the relay UAV reaches the receiving end, the receiving base station Bs receives the confidential information, and then the base station sends the information to M users at the receiving end. The eavesdropper E at the receiving end... Re Real-time eavesdropping on two communications; to improve system security, a dual-antenna UAV is introduced, with one antenna R for transmitting and receiving information, and the other antenna J for emitting artificial noise as a jamming signal to interfere with the eavesdropper; the user actively provides the location of the UAV group, the initial and final positions of the UAVs are predetermined safe positions, and all UAVs fly at a fixed altitude H; in addition, the location of the eavesdropper can be detected from the local oscillator power unintentionally leaked from the radio frequency front end.

[0016] Furthermore, the communication link model for UAV-assisted relays, which is modeled in step S2, classifies and processes the communication links in the UAV-assisted relay system as follows:

[0017] (1) Legitimate link: Se[k]-to-R, R-to-Bs and Bs-to-Re[m] indicate that the sending user sends confidential data to the UAV, the UAV relays the data to the receiving base station, and the base station transmits the information to the user;

[0018] (2) Eavesdropping link: Se[k]-to-E Se This indicates that an eavesdropper at the sending end is listening to the sending user; R-to-E Re This indicates that an eavesdropper at the receiving end is eavesdropping on the relay UAV; Bs-to-E Re This indicates that an eavesdropper is listening to the base station at the receiving end;

[0019] (3) Interference Link: J-to-E Se J-to-E Re J-to-Bs, J-to-Re[m] represent artificial noise emitted by the UAV; where, based on the node location, and assuming that all links are dominated by LOS links, Se[k]-to-E Se ,Bs-to-Re[m],Bs-to-E Re The link is considered a ground-to-ground channel; where Se[k]-to-R, J-to-E Se R-to-Bs, R-to-E Re J-to-E Re The J-to-Bs and J-to-Re[m] links are considered as air-to-ground channels.

[0020] Further, in step S3, the coordinates of the k-th user at the sending end are... The coordinates of the m-th user at the receiving end are The coordinates of the receiving base station are W. Bs =[x Bs ,y Bs [0, 0], where the coordinates of the eavesdroppers at the sending and receiving ends are respectively represented as... and Assuming the UAV remains hovering while receiving and transmitting data, its initial and final positions are determined by... and To represent; based on the free space path loss model, each link is modeled; then the power gain of the ground-to-ground channel is expressed as:

[0021]

[0022] Where β0 represents the channel power at a reference distance d0 = 1m, and Represent the power gain and distance from Se[k], respectively; h Bs,Re[m] and d Bs,Re[m] Let Bs and Re[m] represent the power gain and distance, respectively. and They represent from Bs to E respectively. Re Power gain and distance;

[0023] The power gain of the air-to-ground channel is expressed as:

[0024]

[0025] in, and Let S represent the power gain and distance of the UAV at its initial position relative to Se[k], respectively. and These represent the initial position of the UAV and E, respectively. Se Power gain and distance; and These represent the power gain and distance of the UAV at the termination position relative to Bs, respectively. and These represent the UAV at the termination position E. Re Power gain and distance.

[0026] Furthermore, in step S4, since the maximum transmit power constraint of UAV and Se[k] is related to the limitation of the device's real-time transmit power, the maximum transmit power constraint of UAV is as follows:

[0027] 0≤P i ≤P i max ,i∈{J,R}

[0028] Among them, P i ,P i max i∈{J,R} represent the transmit power and maximum transmit power of the dual-line UAV as interference and relay, respectively. The average power constraint is the restriction on the total power of the entire optimization process. Therefore, the power constraint of Se[k] is as follows:

[0029]

[0030] Among them, P Se[k] It is the transmission power of Se[k]. This represents the average transmit power of the transmitting end. Let P represent the maximum transmit power of Se[k]; where the receiver is capable of autonomous communication, transmitting the transmit power P of Bs. BsThe transmit power is fixed and greater than the maximum transmit power of UAV and Se[k]; the relationship between UAV and Se[k] is expressed as follows:

[0031] Further, in step S5, the data information from the transmitting end is forwarded to the receiving end with the assistance of a UAV, utilizing the UAV's time-of-flight difference to achieve half-duplex communication, and a sufficiently large buffer is provided for the UAV. In the entire system, the transmitting end transmits confidential data to the receiving end through a relay UAV based on a decoding-forwarding protocol. At the receiving end, the UAV transmits data to both the receiving end user and the receiving end eavesdropper through a relay base station based on an amplification-forwarding protocol. The entire system employs frequency division multiple access (FDMA), with user bandwidths of b1Hz and b2Hz for receiving and forwarding, respectively. The bandwidth applicable to the eavesdropper at the transmitting end is the same as the UAV's receiving bandwidth. At the receiving end, the bandwidth applicable to the base station and the receiving end user is the same as the UAV's forwarding bandwidth. It is assumed that the UAV and ground nodes remain stationary during transmission, and all ground nodes are equipped with a single antenna. The bandwidth is constrained by the total bandwidth as follows:

[0032]

[0033] Where B is the total channel bandwidth, and the sum of the sub-channel bandwidths of the number of users does not exceed the total bandwidth.

[0034] Further, step S6 specifically involves: since the UAV receives interference from other users in the cluster when receiving information from the kth user terminal, the bandwidth applicable to the UAV at the sending end is b1 for receiving data; based on the above assumption, the achievable transmission rate from the sending end to the UAV is given by the following formula:

[0035]

[0036] The achievable transmission rate between the sender and the eavesdropper is:

[0037]

[0038] Where, σ 2 Represents noise power; using the average achievable confidentiality rate as a metric for system security performance, the minimum secure transmission rate for K users at the transmitting end of the relay-assisted system enabled by UAV is:

[0039]

[0040] Where, [x] + =max(x,0) means that the value must be greater than zero.

[0041] Further, step S7 specifically involves: the UAV transmitting data to the base station using bandwidth b2 at the receiving end. The received signal-to-dryness ratio is expressed as: Then UAV transmits data to E Re The received SINR is represented as: Therefore, the received SINR of the base station transmitting data to Re[m] in the applicable bandwidth b2 is given by the following formula: The receiving base station transmits data to E within the applicable bandwidth b2. Re The received SINR is given by the following formula: in, Represents the receiver's connection to UAV and E Re The main network interference and noise power; according to Shannon's formula and AF relay characteristics, the achievable rate of UAV to Re[m] via Bs based on the AF protocol is given by the following formula: UAV transmits data to E via Bs based on the AF protocol. Re The achievable speed is given by the following formula: Based on the above, the minimum secure transmission rate between UAV and Re[m] can be derived as follows: Where, [x] + =max(x,0) means that the value must be greater than zero.

[0042] Further, step S8 specifically involves: in the UAV-assisted relay system, by jointly optimizing the transmit power of the UAV's dual-antenna relay and jamming functions, the transmit power of the transmitting user, and the bandwidth allocation under the hybrid DF and AF protocols, taking into account power and bandwidth constraints, maximizing the minimum secure transmission rate. To achieve the goal, we determine the optimal secure communication method, namely:

[0043] Further, step S9 specifically involves: using the Grey Wolf optimization algorithm to solve the system's minimum secure transmission rate maximization optimization model, obtaining the transmit power of the UAV's dual-antenna relay and jamming functions, the transmit power of the transmitting user, and the optimal allocation result of the channel bandwidth under the hybrid DF and AF protocols, and substituting the optimal allocation result into the system's minimum secure transmission rate maximization function to obtain the system's secure transmission rate; specifically including the following steps:

[0044] S91: To optimize the minimum secure transmission rate maximization function of the system, a method combining the penalty function method and the method of defining the feasible solution range of the gray wolf during the initialization phase and retaining only feasible solutions within the range during iterative updates is adopted to solve this problem; the feasible solution range of each gray wolf is initialized, and the upper and lower bounds of the position and the measures for exceeding the bounds are specified; thus, the position vector X of the i-th gray wolf is obtained. i (t)=[Β i(t),Ρse i (t),Ρu i (t)];

[0045] S92: Calculate the objective function value based on the fitness function; transform the nonlinear problem with inequality constraints on the objective function into an unconstrained problem using the penalty function method. The fitness function consists of the objective function and the penalty function, and its expression is:

[0046] F(X i (t))=f obj (X i (t))-σ p f p (X i (t))

[0047] Where is f obj (X) Objective function, σ p f represents the penalty coefficient. p (X) represents the penalty function, which includes the following 5 formulas:

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] Where max(·,·) means taking the larger of the two values;

[0054] S93: Based on the constructed fitness function, substitute each individual vector to calculate the fitness value; generate I gray wolves based on the feasible solution, and then calculate the position vector of each gray wolf according to the objective function. The higher the fitness of the gray wolf, the better the objective function value; at the iteration number t, save the top three gray wolves with the best fitness and name them α, β, and δ respectively, represented by the following formula:

[0055] X α (t)=X i (t),if fit i (t)≥fit α (t)

[0056] X β (t)=X i (t),if fit i (t)<fit α (t),fiti (t)≥fit β (t)

[0057] X δ (t)=X i (t),if fit i (t)<fit α (t),fit i (t)<fit β (t),fit i (t)≥fit δ (t)

[0058] Led by α, β, and δ wolves, update the positions at time t+1 and select new α, β, and δ wolves to lead the next position update;

[0059] S94: Determine if the termination condition has been met. If yes, update the termination position and output the best individual as the suboptimal solution; otherwise, perform position updates under the leadership of α, β, and δ wolves as follows:

[0060] D α =|C1·X α (t)-X i (t)|,X α,i (t)=X α (t)-Α1·D α C1 = 2r2, A1 = 2a·r1-a

[0061] D β =|C1·X β (t)-X i (t)|,X β,i (t)=X β (t)-Α1·D β C2 = 2r2, A2 = 2a·r1-a

[0062] D δ =|C1·X δ (t)-X i (t)|,X δ,i (t)=X δ (t)-Α1·D δ C3 = 2r2, A3 = 2a·r1-a

[0063] Among them, D α D β D δ Let X represent the positional components between α, β, and δ wolves and the i-th wolf, respectively. α,i (t),X β,i (t),X δ,i(t) represents the positional components between the i-th wolves led by α, β, and δ wolves, respectively, where A1, A2, A3 and C1, C2, C3 are coefficient vectors, a represents a random vector that linearly decreases from 2 to 0 during the entire iteration process, and r1 and r2 are random vectors in [0, 1].

[0064] S95: By adjusting the coefficient vectors A and C, the algorithm avoids getting trapped in local optima; the updated position of gray wolf i under the simultaneous guidance of α, β, and δ wolves is shown below:

[0065]

[0066] Finally, the global optimal position and objective function value in the gray wolf pack are given by the following equation: The system's secure transmission rate is obtained by substituting the global optimal position and objective function value into the system's minimum secure transmission rate maximization function.

[0067] Compared with existing technologies, the present invention has the following advantages: The present invention provides a UAV-assisted multi-user secure communication method based on a hybrid DF and AF protocol to solve the problem of secure communication with multiple users and multiple relay nodes, where multiple eavesdropping occurs. Under the constraints of simultaneously satisfying UAV transmit power, user transmit power, and channel bandwidth, this method models the joint allocation problem of UAV transmit power, transmitting power of the transmitting end user, and channel bandwidth as an optimization problem, and models the minimum secure transmission rate as an optimization problem. By solving this problem, the minimum secure transmission rate of the system is maximized. Attached Figure Description

[0068] Figure 1 This is a flowchart illustrating the method implementation of an embodiment of the present invention;

[0069] Figure 2 This is a schematic diagram of the network structure for secure communication between UAV-assisted multi-users based on a hybrid DF and AF protocol in an embodiment of the present invention;

[0070] Figure 3 This is a communication link diagram of the UAV-assisted relay system in an embodiment of the present invention;

[0071] Figure 4 This is a hybrid DF and AF protocol for UAV-assisted relay in this embodiment of the invention;

[0072] Figure 5 This is a flowchart illustrating the implementation of the GWO algorithm in this embodiment of the invention. Detailed Implementation

[0073] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0074] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0075] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0076] This invention addresses the problem of multiple eavesdropping in multi-user, multi-relay node secure communication using UAV relay-assisted systems. It proposes a UAV-assisted multi-user secure communication method based on a hybrid DF and AF protocol. This method introduces a dual-antenna UAV that emits artificial noise to interfere with ground-based eavesdroppers. Specifically, this invention considers a dual-antenna UAV, a transmitting end consisting of multiple users, and a receiving end consisting of a base station and multiple users capable of autonomous communication. When the transmitting end sends confidential information, the relay UAV receives the information, and simultaneously, eavesdroppers at the transmitting end will eavesdrop. When the relay UAV reaches the receiving end, the base station at the receiving end receives the confidential information and then sends the information to the receiving end users. Eavesdroppers at the receiving end will eavesdrop on the communication twice in real time. To improve system security, a dual-antenna UAV is introduced. One antenna is used for both transmitting and receiving information, while the other emits artificial noise as a jamming signal to interfere with eavesdroppers. The transmitting end consists of K ground users. Let Se[k] be the denoting element; the receiving end consists of one base station and M ground users. Let Re[m] be the eavesdropper E at the sending end. Se Located near the user group, the eavesdropper E at the receiving end Re Located within the range of the base station, it can be used for two eavesdropping operations.

[0077] like Figure 1 As shown, this embodiment provides a UAV-assisted multi-user secure communication method based on a hybrid DF and AF protocol, characterized by the following steps:

[0078] S1: Model a network structure for secure communication between UAV-assisted multi-users based on a hybrid DF and AF protocol. Its main components include: UAVs, a transmitter composed of multiple users, and a receiver composed of a base station and multiple users capable of autonomous communication.

[0079] like Figure 2As shown, consider a secure communication system for multiple users assisted by a dual-antenna UAV; when K users at the transmitting end send confidential information, the relay UAV receives the information, and simultaneously, an eavesdropper E at the transmitting end... Se The information will be eavesdropped on; when the relay UAV reaches the receiving end, the receiving base station Bs receives the confidential information, and then the base station sends the information to M users at the receiving end, and the eavesdropper E at the receiving end... Re The system will eavesdrop on two communications in real time. To improve the system's security, a dual-antenna UAV is introduced. One antenna, R, is used to send and receive information, while the other antenna, J, is used to emit artificial noise as a jamming signal to interfere with the eavesdropper. The user actively provides the location of the UAV group. The initial and final positions of the UAVs are predetermined safe positions, and all UAVs fly at a fixed altitude H, which can be considered the minimum altitude to avoid collisions with infrastructure obstacles. In addition, the location of the eavesdropper can be detected from the power of the local oscillator unintentionally leaked from the radio frequency front end.

[0080] S2: Model the communication link model for UAV-assisted relays.

[0081] like Figure 3 As shown in the model, the communication link model for UAV-assisted relays classifies and processes the communication links in the UAV-assisted relay system as follows:

[0082] (1) Legitimate link: Se[k]-to-R, R-to-Bs and Bs-to-Re[m] indicate that the sending user sends confidential data to the UAV, the UAV relays the data to the receiving base station, and the base station transmits the information to the user.

[0083] (2) Eavesdropping link: Se[k]-to-E Se This indicates that an eavesdropper at the sending end is listening to the sending user; R-to-E Re This indicates that an eavesdropper at the receiving end is eavesdropping on the relay UAV; Bs-to-E Re This indicates that an eavesdropper is listening to the base station at the receiving end.

[0084] (3) Interference Link: J-to-E Se J-to-E Re J-to-Bs, J-to-Re[m] represent artificial noise emitted by the UAV; where, based on the node location, and assuming that all links are dominated by LOS links, Se[k]-to-E Se ,Bs-to-Re[m],Bs-to-E Re The link can be viewed as a ground-to-ground (G2G) channel; where Se[k]-to-R, J-to-E Se R-to-Bs, R-to-ERe J-to-E Re J-to-Bs and J-to-Re[m] links can be considered as air-to-ground (A2G) channels.

[0085] S3: Model the power gain of the communication link.

[0086] The coordinates of the kth user at the sending end are The coordinates of the m-th user at the receiving end are The coordinates of the receiving base station are W. Bs =[x Bs ,y Bs [0, 0], where the coordinates of the eavesdroppers at the sending and receiving ends are respectively represented as... and Assuming the UAV remains hovering while receiving and transmitting data, its initial and final positions are determined by... and To represent this; modeling each link according to the free space path loss model; then the power gain of the G2G channel can be written as:

[0087]

[0088] Where β0 represents the channel power at a reference distance d0 = 1m, and Represent the power gain and distance from Se[k], respectively; h Bs,Re[m] and d Bs,Re[m] Let Bs and Re[m] represent the power gain and distance, respectively. and They represent from Bs to E respectively. Re Power gain and distance.

[0089] The power gain of the A2G channel can be written as:

[0090]

[0091] in, and Let S represent the power gain and distance of the UAV at its initial position relative to Se[k], respectively. and These represent the initial position of the UAV and E, respectively. Se Power gain and distance; and These represent the power gain and distance of the UAV at the termination position relative to Bs, respectively. and These represent the UAV at the termination position E. Re Power gain and distance.

[0092] S4: Model the transmit power of the UAV as a relay and jammer, as well as the transmit power constraints of the transmitting user.

[0093] Since the maximum transmit power constraints of UAV and Se[k] are related to the real-time transmit power limitations of the device, the maximum transmit power constraints of UAV are as follows:

[0094] 0≤P i ≤P i max ,i∈{J,R}

[0095] Among them, P i ,P i max i∈{J,R} represent the transmit power and maximum transmit power of the dual-line UAV as interference and relay, respectively. The average power constraint is actually a limitation on the total power of the entire optimization process, which also corresponds to the energy-saving considerations in the system. Therefore, the power constraint of Se[k] is as follows:

[0096]

[0097] Among them, P Se[k] It is the transmission power of Se[k]. This represents the average transmit power of the transmitting end. Let P represent the maximum transmit power of Se[k]; where the receiver is capable of autonomous communication, transmitting the transmit power P of Bs. Bs The power is fixed and greater than the maximum transmit power of UAV and Se[k]; the relationship between UAV and Se[k] can be expressed as follows:

[0098] S5: Modeling a UAV relay auxiliary channel bandwidth allocation and constraint model based on hybrid DF and AF protocols.

[0099] like Figure 4As shown, data from the transmitting end is relayed to the receiving end via UAV-assisted relay, utilizing the UAV's time-of-flight difference to achieve half-duplex communication, and a sufficiently large buffer is provided for the UAV. In the entire system, the transmitting end transmits confidential data to the receiving end via a relay UAV based on the Decode and Forward (DF) protocol. At the receiving end, the UAV transmits data to both the receiving user and the receiving eavesdropper via a relay base station based on the Amplify and Forward (AF) protocol. The entire system employs Frequency Division Multiple Access (FDMA), with users using bandwidths b1Hz for receiving and b2Hz for forwarding. The bandwidth applicable to the eavesdropper at the transmitting end is the same as the UAV's receiving bandwidth. At the receiving end, the bandwidth applicable to the eavesdropper, the base station, and the receiving user is the same as the UAV's forwarding bandwidth. It is assumed that the UAV and ground nodes remain stationary during transmission, and all ground nodes are equipped with a single antenna. The bandwidth is constrained by the total bandwidth as follows: Where B is the total channel bandwidth, and the sum of the sub-channel bandwidths of the number of users does not exceed the total bandwidth.

[0100] S6: Model the secure transmission rate for each user at the sending end.

[0101] Since the UAV receives interference from other users in the cluster when receiving information from the kth user, the bandwidth applicable to the UAV at the sending end is b1 for receiving data. Based on the above assumptions, the achievable transmission rate from the sending end to the UAV is given by the following formula:

[0102]

[0103] The achievable transmission rate between the sender and the eavesdropper is:

[0104]

[0105] Where, σ 2 Represents noise power; using the average achievable confidentiality rate as a metric for system security performance, the minimum secure transmission rate for K users at the transmitting end of the relay-assisted system enabled by UAV is:

[0106] (in bps)

[0107] Where, [x] + =max(x,0) means that the value must be greater than zero.

[0108] S7: Model the secure transmission rate for each user at the receiving end.

[0109] The signal-to-interference and noise ratio (SINR) of a UAV transmitting data to a base station using bandwidth b2 at the receiving end can be expressed as: Then UAV transmits data to E Re The received SINR can be expressed as: Therefore, the received SINR of the base station transmitting data to Re[m] in the applicable bandwidth b2 is given by the following formula: The receiving base station transmits data to E within the applicable bandwidth b2. Re The received SINR is given by the following formula: in, Represents the receiver's connection to UAV and E Re The main network interference and noise power; according to Shannon's formula and AF relay characteristics, the achievable rate of UAV to Re[m] via Bs based on the AF protocol is given by the following formula: UAV transmits data to E via Bs based on the AF protocol. Re The achievable speed is given by the following formula: Based on the above, the minimum secure transmission rate between UAV and Re[m] can be derived as follows: Where, [x] + =max(x,0) means that the value must be greater than zero.

[0110] S8: Modeling system minimum secure transmission rate maximization optimization model.

[0111] In UAV-assisted relay systems, the minimum secure transmission rate is maximized by jointly optimizing the transmit power of the UAV's dual-antenna relay and jamming functions, the transmit power of the transmitting user, and the bandwidth allocation under hybrid DF and AF protocols, taking into account power and bandwidth constraints. To achieve the goal, we determine the optimal secure communication method, namely:

[0112] S9: The Grey Wolf Optimizer (GWO) algorithm is used to solve the optimization model for maximizing the minimum secure transmission rate of the system. The optimal allocation results of UAV transmit power, receiver user transmit power and channel bandwidth are obtained, and the optimal allocation results are substituted into the minimum secure transmission rate maximization function of the system to obtain the optimized solution.

[0113] Specifically, the Grey Wolf (GWO) optimization algorithm is used to solve the minimum secure transmission rate maximization optimization model of the system. This yields the transmit power of the UAV's dual-antenna relay and jamming functions, the transmit power of the transmitting user, and the optimal allocation of channel bandwidth under the hybrid DF and AF protocols. The optimal allocation result is then substituted into the minimum secure transmission rate maximization function to obtain the system's secure transmission rate. In this embodiment, the GWO implementation process is as follows: Figure 5 As shown, the specific steps include:

[0114] S91: To optimize the minimum secure transmission rate maximization function of the system, a method combining the penalty function method and the method of defining the feasible solution range of the gray wolf during the initialization phase and retaining only feasible solutions within the range during iterative updates is adopted to solve this problem; the feasible solution range of each gray wolf is initialized, and the upper and lower bounds of the position and the measures for exceeding the bounds are specified; thus, the position vector X of the i-th gray wolf is obtained. i (t)=[Β i (t),Ρse i (t),Ρu i (t)].

[0115] S92: Calculate the objective function value based on the fitness function; transform the nonlinear problem with inequality constraints on the objective function into an unconstrained problem using the penalty function method. The fitness function consists of the objective function and the penalty function, and its expression is:

[0116] F(X i (t))=f obj (X i (t))-σ p f p (X i (t))

[0117] Where is f obj (X) Objective function, σ p f represents the penalty coefficient. p (X) represents the penalty function, which includes 5 formulas as shown below:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] Here, max(·,·) means taking the larger of the two values.

[0124] S93: Based on the constructed fitness function, substitute each individual vector to calculate the fitness value; generate I gray wolves based on the feasible solution, and then calculate the position vector of each gray wolf according to the objective function. The higher the fitness of the gray wolf, the better the objective function value; at the iteration number t, save the top three gray wolves with the best fitness and name them α, β, and δ respectively, represented by the following formula:

[0125] X α (t)=X i (t),if fit i (t)≥fit α (t)

[0126] X β (t)=X i (t),if fit i (t)<fit α (t),fit i (t)≥fit β (t)

[0127] X δ (t)=X i (t),if fit i (t)<fit α (t),fit i (t)<fit β (t),fit i (t)≥fit δ (t)

[0128] Led by α, β, and δ wolves, update the positions at time t+1 and select new α, β, and δ wolves to lead the next position update.

[0129] S94: Determine if the termination condition has been met. If yes, update the termination position and output the best individual as the suboptimal solution; otherwise, perform position updates under the leadership of α, β, and δ wolves as follows:

[0130] D α =|C1·X α (t)-X i (t)|,X α,i (t)=X α (t)-Α1·D α C1 = 2r2, A1 = 2a·r1-a

[0131] D β =|C1·X β (t)-X i (t)|,X β,i (t)=X β (t)-Α1·Dβ C2 = 2r2, A2 = 2a·r1-a

[0132] D δ =|C1·X δ (t)-X i (t)|,X δ,i (t)=X δ (t)-Α1·D δ C3 = 2r2, A3 = 2a·r1-a

[0133] Among them, D α D β D δ Let X represent the positional components between α, β, and δ wolves and the i-th wolf, respectively. α,i (t),X β,i (t),X δ,i (t) represents the positional components between wolves i led by wolves α, β, and δ, respectively, where A1, A2, A3 and C1, C2, C3 are coefficient vectors, a represents a random vector that linearly decreases from 2 to 0 during the entire iteration process, and r1 and r2 are random vectors in [0, 1].

[0134] S95: By adjusting the coefficient vectors A and C, the algorithm avoids getting trapped in local optima; the updated position of gray wolf i under the simultaneous guidance of α, β, and δ wolves is shown below:

[0135]

[0136] Finally, the global optimal position and objective function value in the gray wolf pack are given by the following equation: The system's secure transmission rate is obtained by substituting the global optimal position and objective function value into the system's minimum secure transmission rate maximization function.

[0137] This invention addresses the problem of multiple eavesdropping in multi-user, multi-relay node secure communication using UAV relays. It proposes a UAV-assisted multi-user secure communication method based on a hybrid DF and AF protocol. This method introduces a dual-antenna UAV to emit artificial noise to interfere with ground eavesdroppers. To maximize the minimum secure transmission rate, this method jointly optimizes the transmit power of the UAV, the transmit power of the transmitting user, and the channel bandwidth allocation ratio. Compared with fixed transmit power, fixed bandwidth allocation ratio, the Grey Wolf optimization algorithm, and the Firefly optimization algorithm, this method improves the minimum transmission rate and enhances system performance. However, since the problem is a mixed-integer nonlinear programming problem, direct solution is not suitable. Therefore, a penalty function method is used, combined with defining the feasible solution range of the Grey Wolf algorithm during initialization and retaining only feasible solutions within this range during iterative updates. Finally, the Grey Wolf optimization algorithm is used for position updates to obtain a suboptimal solution to the problem.

[0138] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0139] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0140] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0141] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0142] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A secure communication method for UAV-assisted multi-users based on a hybrid DF and AF protocol, characterized in that, Includes the following steps: S1: Model the network structure for secure communication between UAV-assisted multi-users based on hybrid DF and AF protocols, including: UAV, a transmitter composed of multiple users, and a receiver composed of a base station and multiple users capable of autonomous communication; S2: Model the communication link of UAV-assisted relay; S3: Model the power gain of the communication link; S4: Model the transmit power of the UAV as a relay and jammer, as well as the limitations on the transmit power of the transmitting user; S5: Modeling a UAV relay auxiliary channel bandwidth allocation and constraint model based on hybrid DF and AF protocols; S6: Model the secure transmission rate for each user at the sending end; S7: Model the secure transmission rate for each user at the receiving end; S8: Modeling system minimum secure transmission rate maximization optimization model; S9: The Grey Wolf optimization algorithm is used to solve the optimization model for maximizing the minimum secure transmission rate of the system. The optimal allocation results of UAV transmission power, receiver user transmission power and channel bandwidth are obtained. The optimal allocation results are then substituted into the function for maximizing the minimum secure transmission rate of the system to obtain the optimized solution. In step S3, the sending end... The coordinates of each user are The receiving end The coordinates of each user are Then the coordinates of the receiving base station are The coordinates of the eavesdroppers at the sending and receiving ends are respectively represented as... and Assume that the UAV remains in a hovering state while receiving and transmitting data, and its initial and final positions are determined by... and To represent; based on the free space path loss model, each link is modeled; then the power gain of the ground-to-ground channel is expressed as: in, Indicates reference distance Channel power at that location, and They represent from The power gain and distance achieved; and They represent from arrive Power gain and distance; and They represent from arrive Power gain and distance; The power gain of the air-to-ground channel is expressed as: in, and These represent the initial position and... Power gain and distance; and These represent the initial position and... Power gain and distance; and These respectively indicate the UAV at the termination position and Power gain and distance; and These represent the UAV at the termination position. Power gain and distance; Step S6 specifically involves: because the UAV receives the... When sending information to a single user, interference from other users may occur; for UAVs, the applicable bandwidth at the sending end is... Used for receiving data; based on the above assumptions, the achievable transmission rate from the transmitter to the UAV is given by the following formula: The achievable transmission rate between the sender and the eavesdropper is: in, Represents noise power; average achievable security ratio is used as a metric for system security performance, and the relay auxiliary system enabled by the UAV is used at the transmitting end. The minimum secure transmission rate for each user is: in, This indicates that the value must be greater than zero; Specifically, step S7 involves: the UAV applying bandwidth at the receiving end. The signal-to-dryness ratio (SDR) of data transmitted to the base station is expressed as: Then the UAV transmits data to The received SINR is represented as: Therefore from within applicable bandwidth Transmit data to The received SINR is given by the following formula: ,from within applicable bandwidth Transmit data to The received SINR is given by the following formula: ;in, Represents the receiver's connection to UAV and The main network interference and noise power; according to Shannon's formula and AF relay characteristics, UAVs transmit data via AF-based protocols. Towards The achievable speed is given by the following formula: UAVs use the AF protocol. Towards The achievable speed is given by the following formula: From the above, we can deduce that UAV and Minimum secure transmission rate between: ,in, This indicates that the value must be greater than zero; Step S8 specifically involves: In the UAV-assisted relay system, by jointly optimizing the transmission power of the UAV's dual-antenna relay and jamming functions, the transmission power of the transmitting user, and the bandwidth allocation under the hybrid DF and AF protocols, and considering power and bandwidth constraints, maximizing the minimum secure transmission rate. To achieve the goal, we determine the optimal secure communication method, namely: .

2. The secure communication method for UAV-assisted multi-users based on a hybrid DF and AF protocol according to claim 1, characterized in that, In step S1, consider a secure communication scenario involving multiple users assisted by a dual-antenna UAV; when the transmitting end When a user sends confidential information, a relay UAV receives the information, while an eavesdropper at the sending end simultaneously... Eavesdropping; when the relay UAV flies to the receiving end, the receiving base station The base station receives confidential information and then sends the information to the receiving end. One user, the eavesdropper at the receiving end. Real-time eavesdropping on two communications; to improve system security, a dual-antenna UAV is introduced, with one antenna R for transmitting and receiving information, and the other antenna J for emitting artificial noise as a jamming signal to interfere with the eavesdropper; assume the user actively provides the locations of the UAV group, the initial and final positions of the UAVs are predetermined safe locations, and all UAVs are at a fixed altitude. Flight; in addition, the location of the eavesdropper was detected from the local oscillator power unintentionally leaked from the radio frequency front end.

3. The secure communication method for UAV-assisted multi-users based on a hybrid DF and AF protocol according to claim 1, characterized in that, The communication link model for UAV-assisted relays, which is modeled in step S2, is classified and processed as follows: (1) Legitimate link: , and This means that the sending user sends confidential data to the UAV, the UAV relays it to the receiving base station, and the base station transmits the information to the user. (2) Eavesdropping link: This indicates that an eavesdropper is listening to the user at the sending end. This indicates that an eavesdropper at the receiving end is eavesdropping on the relay UAV; This indicates that an eavesdropper is listening to the base station at the receiving end; (3) Interference links: , , , This indicates that the UAV is emitting artificial noise; where, based on the node location, and assuming that all links have a LOS link as the dominant link, , , The link is considered a ground-to-ground channel; where, , , , , , , The link is considered an air-to-ground channel.

4. The secure communication method for UAV-assisted multi-users based on a hybrid DF and AF protocol according to claim 1, characterized in that, In step S4, because the UAV and The maximum transmit power constraint is related to the real-time transmit power limit of the equipment. The maximum transmit power constraint for UAVs is as follows: in, Let represent the transmit power and maximum transmit power of the dual-line UAV acting as interference and relay, respectively. The average power constraint is a limitation on the total power throughout the entire optimization process. The power constraints are as follows: in, yes The transmission power, This represents the average transmit power of the transmitting end. express The maximum transmit power; wherein, the receiver is capable of autonomous communication, and will Transmission power Fixed, and greater than UAV and Maximum transmit power; UAV and The relationship is represented as .

5. A secure communication method for UAV-assisted multi-users based on a hybrid DF and AF protocol according to claim 4, characterized in that, In step S5, the data information from the transmitting end is forwarded to the receiving end with the assistance of a UAV relay, utilizing the UAV's time-of-flight difference to achieve half-duplex communication, and a sufficiently large buffer is provided for the UAV. Throughout the system, the transmitting end transmits confidential data to the receiving end via a relay UAV based on a decoding-forwarding protocol. At the receiving end, the UAV transmits data to both the receiving user and the eavesdropper via a relay base station based on an amplification-forwarding protocol. The entire system employs frequency division multiple access, with bandwidths allocated for receiving and forwarding by the user respectively. and The bandwidth applicable to the eavesdropper at the transmitting end is the same as the receiving bandwidth of the UAV. At the receiving end, the bandwidth applicable to the base station and the receiving user is the same as the forwarding bandwidth of the UAV. It is assumed that the UAV and ground nodes remain stationary during transmission, and that all ground nodes are equipped with a single antenna. The bandwidth is constrained by the total bandwidth as follows: in, The total bandwidth of the channel is denoted by , and the sum of the sub-channel bandwidths of the number of users shall not exceed the total bandwidth.

6. The secure communication method for UAV-assisted multi-users based on a hybrid DF and AF protocol according to claim 1, characterized in that, Step S9 specifically involves: using the Grey Wolf optimization algorithm to solve the system's minimum secure transmission rate maximization optimization model, obtaining the transmit power of the UAV's dual-antenna relay and jamming functions, the transmit power of the transmitting user, and the optimal allocation result of the channel bandwidth under the hybrid DF and AF protocols. The optimal allocation result is then substituted into the system's minimum secure transmission rate maximization function to obtain the system's secure transmission rate. This specifically includes the following steps: S91: To optimize the minimum secure transmission rate maximization function of the system, a combination of the penalty function method and a method that defines the feasible solution range of the gray wolves during the initialization phase and retains only feasible solutions within the range during iterative updates is used to solve the optimization problem; the feasible solution range of each gray wolf is initialized, and the upper and lower bounds of its position and the measures for exceeding the bounds are specified; thus, the first... Sekiro's position vector ; S92: Calculate the objective function value based on the fitness function; transform the nonlinear problem with inequality constraints on the objective function into an unconstrained problem using the penalty function method. The fitness function consists of the objective function and the penalty function, and its expression is: Among them, is objective function Indicates the penalty coefficient. The penalty function is represented by the following five formulas: in, This indicates that the larger of the two values ​​should be selected. S93: Based on the constructed fitness function, substitute each individual vector to calculate the fitness value; generate a feasible solution based on the fitness function. Only gray wolves are used, and then the position vector of each gray wolf is calculated according to the objective function. The higher the fitness of the gray wolf, the better the objective function value; in the number of iterations... At that time, the three gray wolves with the best survival rate were named accordingly. It is expressed by the following formula: exist Under the leadership of wolves, updates Next position and select the new Wolf, lead the next position update; S94: Determine if the termination condition has been met. If so, update the termination position and output the best individual as the second-best solution; otherwise, proceed to... Under the wolf's leadership, the positions were updated as follows: in, They represent Wolves and the First Positional components between wolves, vectors They represent in Led by wolves The positional components among the wolves, where... and It is a coefficient vector. This represents a random vector that linearly decreases from 2 to 0 during the entire iteration process. and It is a random vector in [0, 1]; S95: By adjusting the coefficient vector and To avoid the algorithm getting trapped in local optima, then Gray Wolf... exist The wolf also led to the updated location, as shown below: Finally, the global optimal position and objective function value in the gray wolf pack are given by the following equation: The system's secure transmission rate is obtained by substituting the global optimal position and objective function value into the system's minimum secure transmission rate maximization function.

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