A cooperative optimization method for space-air-ground network secure transmission

Abstract

The application discloses a kind of for space-air-ground network security transmission collaborative optimization method, it is related to communication security technical field, including: collection network object data, transmission stage data, channel parameter, noise power, satellite transmission power, unmanned aerial vehicle group beam vector, code rate, redundancy rate and constraint data, based on network object data and transmission stage data generation transmission link data;According to channel parameter, noise power, satellite transmission power, unmanned aerial vehicle group beam vector and transmission link data obtain reachable rate, based on reachable rate, code rate and redundancy rate generation security performance data;According to security performance data, satellite transmission power, unmanned aerial vehicle group beam vector, code rate, redundancy rate and constraint data establish joint optimization problem;The application is under the condition of giving consideration to communication reliability and anti-interception ability, coordinates satellite and the transmission resource of unmanned aerial vehicle group, improves the security transmission efficiency of space-air-ground network.

Description

Technical Field

[0001] This invention relates to the field of communication security technology, and specifically to a collaborative optimization method for secure air-space-ground network transmission. Background Technology

[0002] Space-air-ground integrated communication leverages the wide coverage of satellites, the flexible deployment of UAVs, and the convenient access of ground terminals to form multi-layered collaborative transmission links in remote areas, emergency communications, wide-area monitoring, and mobile access missions. Satellite links are responsible for long-distance information delivery, UAV swarms undertake relay forwarding and space beam adjustment, and physical layer secure transmission reduces the risk of information being parsed by unauthorized nodes through rate redundancy, power configuration, and beam control.

[0003] Currently, in the process of secure downlink transmission from satellites to ground terminals via UAV swarms, there are usually problems such as the phased operation of legitimate two-hop links and eavesdropping links, significant differences in channel states at different receivers, and fragmented evaluation of connection reliability and confidentiality. This makes it difficult for power configuration and rate settings to simultaneously reflect legitimate reception quality, eavesdropping risk, and effective confidential transmission capabilities.

[0004] Secondly, there is a coupling relationship between satellite transmission power, UAV swarm forwarding power, beam direction, coding rate and redundancy rate. The security transmission optimization process under probabilistic constraints is prone to forming a non-convex solution structure. If only a single power or a single beam direction is adjusted, it is difficult to simultaneously enhance the reception of ground terminals and suppress eavesdropping links. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a collaborative optimization method for secure air-space-ground network transmission.

[0006] A collaborative optimization method for secure air-space-ground network transmission includes:

[0007] Collect network object data, transmission stage data, channel parameters, noise power, satellite transmission power, UAV swarm beam vector, codeword rate, redundancy rate and constraint data, and generate transmission link data based on network object data and transmission stage data;

[0008] The achievable rate is obtained based on channel parameters, noise power, satellite transmit power, UAV swarm beam vector, and transmission link data. Security performance data is then generated based on the achievable rate, codeword rate, and redundancy rate.

[0009] A joint optimization problem is established based on safety performance data, satellite transmission power, UAV swarm beam vector, codeword rate, redundancy rate, and constraint data. The joint optimization problem is then reconstructed to obtain reconstructed data.

[0010] By reconstructing data and alternately solving the joint optimization problem, satellite transmission power, UAV swarm transmission power, codeword rate, redundancy rate, and UAV swarm beam vector are generated.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0012] This invention establishes a correspondence between transmission link data and achievable rate and security performance data in a space-air-ground network security transmission environment where satellites, drone swarms using decoding and forwarding protocols, legitimate ground users, and eavesdroppers coexist. This allows the transmission status of legitimate double-hop links and eavesdropping links to simultaneously participate in reliability and confidentiality determination, thereby enabling connection failure probability, confidentiality failure probability, and system effective security rate to characterize secure transmission quality under the same constraint framework.

[0013] Furthermore, this invention also enables the first-order Marcum-Q constraint, beam decomposition parameters, and auxiliary variables to jointly constrain the satellite transmit power, UAV swarm transmit power, codeword rate, redundancy rate, and UAV swarm beam vector generation process by jointly optimizing the correspondence between the problem and the reconstructed data. This improves the effective security rate of the system and enhances the cooperative adaptation capability of the UAV swarm beam vector between ground legitimate user transmission and eavesdropping link suppression, while satisfying the connection failure probability threshold and the security failure probability threshold.

[0014] In summary, this invention, while taking into account both communication reliability and anti-eavesdropping capabilities, coordinates the transmission resources of satellites and UAV swarms, thereby improving the secure transmission efficiency of air-space-ground networks. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 The flowchart illustrates a collaborative optimization method for secure air-space-ground network transmission provided by this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 As shown in the figure, this embodiment discloses a collaborative optimization method for secure air-space-ground network transmission, the method comprising:

[0019] S11: Collect network object data, transmission stage data, channel parameters, noise power, satellite transmission power, UAV swarm beam vector, codeword rate, redundancy rate and constraint data, and generate transmission link data based on network object data and transmission stage data;

[0020] In one specific embodiment, network object data includes satellites, a swarm of drones using a decoding-forwarding protocol, legitimate ground users, and eavesdroppers. Transmission phase data includes satellite transmission phase and drone swarm forwarding phase. Channel parameters include equivalent channel parameters from satellite to drone swarm, channel parameters from satellite to eavesdropper, channel parameters from drone swarm to legitimate ground users, and channel parameters from drone swarm to eavesdropper. Noise power includes noise power at drone swarm, noise power at legitimate ground users, and noise power at eavesdropper. Satellite transmit power is the transmit power of the satellite when transmitting confidential information during the satellite transmission phase. Drone swarm beam vector is the beamforming vector used by the drone swarm when collaboratively forwarding confidential information during the drone swarm forwarding phase. Codeword rate is the total coding rate used by the satellite and drone swarm to transmit confidential information. Redundancy rate is the redundant coding rate used to resist decoding of the eavesdropping link. Constraint data includes connection failure probability threshold, confidentiality failure probability threshold, satellite transmit power constraint, drone swarm transmit power constraint, codeword rate constraint, and redundancy rate constraint.

[0021] Specifically, the steps for generating transmission link data based on network object data and transmission phase data include:

[0022] S111: Identify satellites, drone swarms using decoding and forwarding protocols, legitimate ground users, and eavesdroppers from network object data;

[0023] In one specific embodiment, the satellite is denoted as A drone swarm using a decoding-forwarding protocol is denoted as The number of drones in a drone swarm is denoted as Ground-based legitimate users are recorded as The eavesdropper recorded as ,in, The number of drones is obtained from the network object data. Satellites, drone swarms using decoding and forwarding protocols, legitimate ground users, and eavesdroppers are all determined by the object number and spatial location in the network object data.

[0024] S112: Determine the satellite transmission phase and the drone swarm relay phase from the transmission phase data;

[0025] In one specific embodiment, the satellite transmission phase is the phase in which the satellite sends confidential information to a swarm of drones using a decoding-forwarding protocol, and the drone swarm forwarding phase is the phase in which the drone swarm using the decoding-forwarding protocol decodes the confidential information and then forwards the confidential information to legitimate ground users. The confidential information symbol is denoted as... The confidential information symbols satisfy ;

[0026] in, Represents the mathematical expectation. The power is obtained by normalizing the output of the encoder at the transmitting end. The normalization method is to divide the original information symbol by the root mean square value of the original information symbol power, so that the average power of the normalized confidential information symbol is 1.

[0027] S113: Based on the satellite, the drone swarm using the decoding and forwarding protocol, the legitimate ground user, the eavesdropper, the satellite transmission phase, and the drone swarm forwarding phase, the satellite-to-drone swarm link, the drone swarm-to-legitimate ground user link, the satellite-to-eavesdropper link, and the drone swarm-to-eavesdropper link are obtained.

[0028] In one specific embodiment, during the satellite transmission phase, the satellite-to-drone swarm link is obtained based on the downlink transmission direction between the satellite and the drone swarm using the decoding and forwarding protocol, and the satellite-to-eavesdropper link is obtained based on the eavesdropping direction between the satellite and the eavesdropper. During the drone swarm forwarding phase, the drone swarm-to-legitimate ground user link is obtained based on the forwarding direction between the drone swarm using the decoding and forwarding protocol and the legitimate ground user, and the drone swarm-to-eavesdropper link is obtained based on the eavesdropping direction between the drone swarm using the decoding and forwarding protocol and the eavesdropper.

[0029] Specifically, the steps to obtain the satellite-to-drone swarm link, the drone swarm-to-legitimate ground user link, the satellite-to-eavesdropper link, and the drone swarm-to-eavesdropper link include:

[0030] S113.1: Based on the transmission direction of the satellite and the UAV swarm using the decoding and forwarding protocol during the satellite transmission phase, obtain the link from the satellite to the UAV swarm;

[0031] In one specific embodiment, the satellite-to-UAV swarm link is a line-of-sight dominant link, and the satellite-to-UAV swarm link is a line-of-sight dominant link. The channel parameters of the UAV are denoted as The equivalent channel parameters from the satellite to the UAV swarm are denoted as... ,in, Number the drone. When the distance difference between the satellite and each drone is less than a preset distance difference threshold, the following settings are executed: The preset distance difference threshold is determined based on experimental data from the deployment of the air-space-ground network.

[0032] S113.2: Based on the transmission direction of the UAV swarm and the ground legitimate user during the UAV swarm forwarding phase using the decoding and forwarding protocol, the link from the UAV swarm to the ground legitimate user is obtained;

[0033] In one specific embodiment, the link from the UAV swarm to the ground legitimate user is represented by a Ricean fading channel, and the channel parameters from the UAV swarm to the ground legitimate user include large-scale path loss values. Rice factor normalized value Line-of-sight channel components Non-line-of-sight channel components The channel parameters from the drone swarm to the legitimate ground user are represented as follows: ;

[0034] Among them, Rice factor normalized value The Rice factor of the link from the drone swarm to the legitimate ground user is obtained by dividing the Rice factor by the sum of the Rice factor and 1, with a value ranging from 0 to 1.

[0035] S113.3: Based on the targets eavesdropped on by the eavesdropper during the satellite transmission phase and the drone swarm relay phase, obtain the satellite-to-eavesdropper link and the drone swarm-to-eavesdropper link.

[0036] In one specific embodiment, the satellite-to-eavesdropper link uses the satellite-to-eavesdropper channel parameters. This indicates that the link from the drone swarm to the eavesdropper uses a Ricean fading channel representation, and the channel parameters from the drone swarm to the eavesdropper include large-scale path loss values. Rice factor normalized value Line-of-sight channel components Non-line-of-sight channel components The channel parameters from the drone swarm to the eavesdropper are expressed as follows: ;

[0037] Among them, Rice factor normalized value The Rice factor of the link from the drone swarm to the eavesdropper is obtained by dividing the Rice factor by the sum of the Rice factor and 1, and the value ranges from 0 to 1.

[0038] S114: Generate transmission link data based on satellite-to-UAV swarm link, UAV swarm-to-legitimate ground user link, satellite-to-eavesdropper link, and UAV swarm-to-eavesdropper link.

[0039] In one specific embodiment, the satellite-to-UAV swarm link and the UAV swarm-to-legitimate ground user link are associated as legitimate two-hop links, and the satellite-to-eavesdropper link and the UAV swarm-to-eavesdropper link are associated as eavesdropping links. The legitimate two-hop links and eavesdropping links are written into the same link record according to the transmission stage, the sending object, and the receiving object to obtain transmission link data.

[0040] S12: Obtain the achievable rate based on channel parameters, noise power, satellite transmission power, UAV swarm beam vector and transmission link data, and generate security performance data based on the achievable rate, codeword rate and redundancy rate. The security performance data includes connection failure probability, confidentiality failure probability and system effective security rate.

[0041] In a specific embodiment, the signal received by the UAV swarm using a decoding-forwarding protocol during the satellite transmission phase is represented as follows: ;

[0042] in, This indicates the signal received by the drone swarm during the satellite transmission phase. Indicates satellite transmission power. This represents the equivalent channel parameters from the satellite to the drone swarm. Indicates length is A column vector whose elements are all 1. Symbols representing confidential information This represents the noise vector at the drone swarm.

[0043] The elements of the noise vector at the UAV swarm location follow a zero-mean complex Gaussian distribution with a variance equal to the noise power at the UAV swarm location. ;

[0044] The signal received by a legitimate ground user during the drone swarm relay phase is represented as follows: ,in, This indicates that legitimate ground users are receiving signals. This represents the channel parameters from the drone swarm to legitimate ground users. Represents the beam vector of the UAV swarm. This represents the noise at a legal ground user location, where the noise follows a zero-mean complex Gaussian distribution with a variance equal to the noise power at the legal ground user location. The signal received by the eavesdropper during the relay phase of the drone swarm is represented as ;

[0045] in, This indicates that the eavesdropper received the signal. This indicates the channel parameters from the drone swarm to the eavesdropper. Let the noise at the eavesdropper's location be a variable, and the noise at the eavesdropper's location follows a zero-mean complex Gaussian distribution with a variance equal to the noise power at the eavesdropper's location. .

[0046] Specifically, the steps to obtain the achievable rate include:

[0047] S121: Identify the satellite-to-UAV swarm link, the UAV swarm-to-legitimate ground user link, the satellite-to-eavesdropper link, and the UAV swarm-to-eavesdropper link from the transmission link data;

[0048] In one specific embodiment, the satellite-to-UAV swarm link and the UAV swarm-to-legitimate ground user link corresponding to the legitimate two-hop link are read from the transmission link data, and the satellite-to-eavesdropper link and the UAV swarm-to-eavesdropper link corresponding to the eavesdropping link are read. The legitimate two-hop link is used to calculate the reachability rate of the legitimate two-hop link, and the eavesdropping link is used to calculate the reachability rate of the eavesdropping link.

[0049] S122: Based on the satellite transmit power, channel parameters, noise power, and satellite-to-UAV swarm link, obtain the achievable rate of the satellite-to-UAV swarm link;

[0050] In one specific embodiment, the achievable rate of the satellite-to-UAV swarm link is expressed as: ,in, Indicates the achievable speed of the satellite-to-drone swarm link. Indicates satellite transmission power. Indicates the number of drones. This represents the equivalent channel parameters from the satellite to the drone swarm. The noise power at the drone swarm is represented by the satellite transmission power, which is obtained from S11. The number of drones is obtained from the network object data. The equivalent channel parameters from the satellite to the drone swarm are obtained from the channel parameters. The noise power at the drone swarm is obtained from the noise power.

[0051] S123: Based on the UAV swarm beam vector, channel parameters, noise power, and the link from the UAV swarm to the ground legal user, the achievable rate of the link from the UAV swarm to the ground legal user is obtained;

[0052] In one specific embodiment, the reachable rate of the link from the drone swarm to the ground-based legitimate user is expressed as: ,in, This indicates the reachable speed of the link from the drone swarm to the legitimate ground user. This represents the channel parameters from the drone swarm to legitimate ground users. Represents the beam vector of the UAV swarm. The noise power at the ground legitimate user is represented by the UAV swarm beam vector, which is obtained by S11. The channel parameters from the UAV swarm to the ground legitimate user are obtained by reading the channel parameters, and the noise power at the ground legitimate user is obtained by reading the noise power.

[0053] S124: The reachable rates of the legitimate two-hop link and the eavesdropping link are obtained by using the reachable rates of the satellite-to-UAV swarm link, the reachable rate of the UAV swarm to the legitimate ground user link, the satellite transmit power, the UAV swarm beam vector, the channel parameters, the noise power, the satellite-to-eavesdropper link, and the UAV swarm-to-eavesdropper link.

[0054] In a specific embodiment, the reachable rate of a legitimate two-hop link is expressed as:

[0055]

[0056] in, This represents the reachable rate of a legitimate two-hop link. Indicates the achievable speed of the satellite-to-drone swarm link. This indicates the reachable speed of the link from the drone swarm to the legitimate ground user;

[0057] The achievable rate of an eavesdropping link is expressed as:

[0058]

[0059] In the formula, This indicates the achievable speed of the eavesdropping link. Indicates satellite transmission power. This indicates the channel parameters from the satellite to the eavesdropper. This indicates the channel parameters from the drone swarm to the eavesdropper. Represents the beam vector of the UAV swarm. This indicates the noise power at the eavesdropper's location.

[0060] Specifically, the steps for generating security performance data include:

[0061] S125: Determine the reachability of the legitimate two-hop link and the reachability of the eavesdropping link from the reachability rates;

[0062] In one specific embodiment, the reachable rate of a legitimate two-hop link is read from the reachable rate. And the achievable rate of the eavesdropping link The system determines connection failures by comparing the reachable rate and codeword rate of a legitimate double-hop link, and determines security failures by comparing the reachable rate and redundancy rate of an eavesdropping link.

[0063] S126: Based on the reachable rate and codeword rate of the legitimate two-hop link, obtain the connection failure probability; based on the reachable rate and redundancy rate of the eavesdropping link, obtain the confidentiality failure probability.

[0064] In a specific embodiment, the connection failure probability is expressed as:

[0065]

[0066] Indicates the probability of connection failure. This represents the reachable rate of a legitimate two-hop link. Indicates codeword rate, This represents the line-of-sight channel component in the channel from the UAV swarm to the legitimate ground user.

[0067] The probability of failing to maintain secrecy is expressed as:

[0068]

[0069] in, Indicates the probability of failure to maintain secrecy. This indicates the achievable speed of the eavesdropping link. Indicates redundancy rate. This represents the line-of-sight channel component in the channel from the drone swarm to the eavesdropper.

[0070] S127: Obtain the connection success probability from the connection failure probability, and obtain the effective information rate based on the codeword rate and redundancy rate;

[0071] In a specific embodiment, the probability of a successful connection is expressed as: ,in, The probability of connection failure is represented by the effective information rate. ,in, Indicates codeword rate, This indicates the redundancy rate, which is less than or equal to the codeword rate.

[0072] S128: Obtain the system's effective security rate based on the connection success probability and the effective information rate, and generate security performance data based on the connection failure probability, the confidentiality failure probability, and the system's effective security rate.

[0073] In one specific embodiment, the system's effective safe rate is expressed as: ,in, Indicates the system's effective safe rate. Indicates the probability of connection failure. Indicates codeword rate, The redundancy rate is represented; the connection failure probability, the security failure probability, and the effective security rate of the system are correlated according to the same group of satellite transmission power, UAV swarm beam vector, codeword rate, and redundancy rate to obtain security performance data.

[0074] S13: Based on the safety performance data, satellite transmission power, UAV swarm beam vector, codeword rate, redundancy rate and constraint data, establish a joint optimization problem, perform equivalent reconstruction on the joint optimization problem to obtain reconstructed data, the reconstructed data including first-order Marcum-Q constraints, beam decomposition parameters and auxiliary variables;

[0075] In one specific embodiment, the security performance data includes the connection failure probability, the confidentiality failure probability, and the system's effective security rate. The constraint data includes a connection failure probability threshold, a confidentiality failure probability threshold, satellite transmission power constraints, UAV swarm transmission power constraints, codeword rate constraints, redundancy rate constraints, a convergence threshold, initial values ​​of Lagrange multipliers, and initial values ​​of penalty factors. The connection failure probability threshold is denoted as... The probability threshold for failure to maintain secrecy is denoted as The maximum transmission power of the satellite is denoted as The maximum transmission power of the drone swarm is denoted as The maximum codeword rate is denoted as The convergence threshold is determined based on historical iterative experimental data of the system's effective safety rate, and the initial values ​​of the Lagrange multipliers and the penalty factor are determined based on experimental data of the constraint optimization solution.

[0076] Specifically, the steps to establish a joint optimization problem include:

[0077] S131: Determine the connection failure probability, confidentiality failure probability, and effective security rate of the system from the security performance data;

[0078] In one specific embodiment, the connection failure probability, security failure probability, and effective security rate of the same set of satellite transmit power, UAV swarm beam vector, codeword rate, and redundancy rate are read from the security performance data. The connection failure probability is denoted as... The probability of failure to maintain secrecy is denoted as The system's effective safe rate is denoted as .

[0079] S132: Determine the connection failure probability threshold, the security failure probability threshold, the satellite transmission power constraint, the UAV swarm transmission power constraint, the codeword rate constraint, and the redundancy rate constraint from the constraint data.

[0080] In one specific embodiment, a connection failure probability threshold is used to limit the upper limit of the connection failure probability, a security failure probability threshold is used to limit the upper limit of the security failure probability, a satellite transmit power constraint is used to limit the satellite transmit power to not exceed the maximum satellite transmit power, a UAV swarm transmit power constraint is used to limit the square of the modulus of the UAV swarm beam vector to not exceed the maximum UAV swarm transmit power, a codeword rate constraint is used to limit the codeword rate to not exceed the maximum codeword rate, and a redundancy rate constraint is used to limit the redundancy rate to not exceed the codeword rate.

[0081] S133: Determine the target conditions based on the system's effective safety rate, and determine the optimization variables based on the satellite transmit power, UAV swarm beam vector, codeword rate, and redundancy rate;

[0082] In one specific embodiment, maximizing the system's effective safe rate is taken as the objective condition, and the satellite transmission power is... UAV swarm beam vector Code word rate and redundancy rate As optimization variables, satellite transmit power and UAV swarm beam vector are used to influence the reachability rate of legitimate two-hop links and the reachability rate of eavesdropping links, codeword rate is used to influence the connection failure probability, and redundancy rate is used to influence the security failure probability.

[0083] S134: Establish a joint optimization problem based on the objective conditions, optimization variables, connection failure probability, security failure probability, connection failure probability threshold, security failure probability threshold, satellite transmission power constraint, UAV swarm transmission power constraint, codeword rate constraint, and redundancy rate constraint.

[0084] In a specific embodiment, the joint optimization problem is expressed as:

[0085]

[0086] The constraints are expressed as follows:

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] in, Indicates the system's effective safe rate. Indicates the probability of connection failure. Indicates the probability of failure to maintain secrecy. This represents the threshold for the probability of connection failure. This represents the threshold for the probability of failing to maintain secrecy. Indicates satellite transmission power. Indicates the satellite's maximum transmission power. Represents the beam vector of the UAV swarm. Indicates the transmission power of the drone swarm. Indicates the maximum transmission power of the drone swarm. Indicates codeword rate, Indicates redundancy rate. This indicates the maximum codeword rate.

[0093] Specifically, the steps for equivalent reconstruction of the joint optimization problem include:

[0094] S135: Based on the connection failure probability and the confidentiality failure probability, the probability constraints in the joint optimization problem are determinized to obtain the first-order Marcum-Q constraint;

[0095] In a specific embodiment, the effective power of the received signal under Ricean fading channel follows a non-central chi-square distribution with 2 degrees of freedom. The connection failure probability constraint and the security failure probability constraint are transformed into first-order Marcum-Q constraints. The first-order Marcum-Q constraint corresponding to the connection failure probability is expressed as:

[0096]

[0097] The first-order Marcum-Q constraint corresponding to the probability of secrecy failure is expressed as:

[0098]

[0099] in, This represents the first-order Marcum-Q function. This represents the line-of-sight channel component in the channel from the UAV swarm to the legitimate ground user. This represents the line-of-sight channel component in the channel from the drone swarm to the eavesdropper. Indicates the noise power at the legal ground user location. This indicates the noise power at the eavesdropper's location. This indicates the channel parameters from the satellite to the eavesdropper. and The large-scale path loss and Rice factor normalized values ​​of the link from the UAV swarm to the ground legitimate user are obtained by substituting them into the non-centralized chi-square standardization process. and The large-scale path loss value and Rice factor normalized value of the link from the drone swarm to the eavesdropper are obtained by substituting them into the non-central chi-square standardization process.

[0100] It should be noted that: when the large-scale path loss value of the link from the drone swarm to the legitimate ground user is... Rice factor normalization value hour, , ;

[0101] When the large-scale path loss value of the link from the drone swarm to the eavesdropper is Rice factor normalization value hour, , .

[0102] S136: Power direction decomposition is performed based on the beam vector of the UAV swarm to obtain beam decomposition parameters, which include the UAV swarm transmit power and the unity norm beam direction vector.

[0103] In a specific embodiment, the UAV swarm beam vector is decomposed into the UAV swarm transmit power and the unity-norm beam direction vector, as follows:

[0104]

[0105]

[0106] in, Indicates the transmission power of the drone swarm. Represents the unit norm beam direction vector. It is obtained from the squared magnitude of the beam vector of the UAV swarm, that is The unit norm beam direction vector is obtained by dividing the UAV swarm beam vector by the magnitude of the UAV swarm beam vector, i.e. The squared modulus of the unit norm beam direction vector is 1.

[0107] It should be noted that when the initial UAV swarm beam vector is a zero vector, the unit norm beam direction vector is initialized based on the line-of-sight channel component in the UAV swarm to the ground legal user channel and then normalized. The normalization process is to divide the initialized direction vector by the modulus of the initialized direction vector.

[0108] S137: Auxiliary variables are obtained based on code word rate and UAV swarm transmission power;

[0109] In a specific embodiment, the auxiliary variable is denoted as The auxiliary variables are obtained from the codeword rate and the UAV swarm transmission power, and are expressed as:

[0110]

[0111] in, Represents auxiliary variables. Indicates codeword rate, The auxiliary variable represents the transmit power of the UAV swarm, and its value is not less than 0. The codeword rate is represented by the inverse expression of the auxiliary variable and the UAV swarm transmit power, as follows:

[0112]

[0113] S138: Based on the first-order Marcum-Q constraint, beam decomposition parameters, and auxiliary variables, the reconstructed data is obtained.

[0114] In a specific embodiment, the first-order Marcum-Q constraint, the UAV swarm transmit power, the unity-norm beam direction vector, and the auxiliary variables are written into the same reconstruction record to obtain reconstruction data. The reconstruction data is used to transform the joint optimization problem into an alternating solution problem with regard to the auxiliary variables and the unity-norm beam direction vector.

[0115] S14: By reconstructing data and alternately solving the joint optimization problem, satellite transmission power, UAV swarm transmission power, codeword rate, redundancy rate, and UAV swarm beam vector are generated.

[0116] In a specific embodiment, the joint optimization problem is solved alternately by outer iteration and inner iteration. The outer iteration fixes the unit norm beam direction vector and updates auxiliary variables, satellite transmit power, UAV swarm transmit power, codeword rate and redundancy rate. The inner iteration fixes auxiliary variables and updates the unit norm beam direction vector. The outer iteration and inner iteration are executed alternately until the change in the effective safety rate of the system meets the convergence threshold.

[0117] Specifically, the steps of alternately solving the joint optimization problem by reconstructing data include:

[0118] S141: Determine the first-order Marcum-Q constraints, beam decomposition parameters, and auxiliary variables from the reconstructed data;

[0119] In a specific embodiment, the first-order Marcum-Q constraint corresponding to the connection failure probability, the first-order Marcum-Q constraint corresponding to the security failure probability, the UAV swarm transmit power, the unit norm beam direction vector, and auxiliary variables are read from the reconstructed data, and the unit norm beam direction vector is used as the fixed direction of the current outer iteration.

[0120] S142: The unit norm beam direction vector in the fixed beam decomposition parameters, and the auxiliary variables, satellite transmit power, UAV swarm transmit power, codeword rate and redundancy rate are updated according to the first order Marcum-Q constraint;

[0121] In one specific embodiment, a fixed unit norm beam direction vector is used. Based on the constraint that the reachable rate of the satellite-to-UAV swarm link equals the codeword rate, the satellite transmit power is obtained:

[0122]

[0123] in, Indicates satellite transmission power. Represents auxiliary variables. Indicates the transmission power of the drone swarm. Indicates the noise power at the drone swarm. Indicates the number of drones. This represents the equivalent channel parameters from the satellite to the drone swarm.

[0124] Based on the satellite's maximum transmission power, the UAV swarm's maximum transmission power, and the maximum codeword rate, the UAV swarm's transmission power is obtained:

[0125]

[0126] in, This indicates that when the auxiliary variable is The transmit power of the drone swarm at that time Indicates the maximum transmission power of the drone swarm. Indicates the satellite's maximum transmission power. This indicates the maximum codeword rate.

[0127] The codeword rate is obtained based on auxiliary variables and the transmit power of the UAV swarm:

[0128]

[0129] Based on the first-order Marcum-Q constraint corresponding to the secrecy failure probability, the redundancy rate is obtained:

[0130]

[0131] in, To meet nonnegative numbers, , The search interval is obtained by performing a one-dimensional binary search on the first-order Marcum-Q function, with the upper endpoint of the search interval being gradually expanded until the function value is no greater than the probability threshold of the secrecy failure.

[0132] It should be noted that the feasible values ​​of the auxiliary variables are jointly limited by the first-order Marcum-Q constraints corresponding to the connection failure probability, the satellite transmission power constraints, the UAV swarm transmission power constraints, the codeword rate constraints, and the redundancy rate constraints. A one-dimensional traversal or one-dimensional search is performed on the feasible values ​​of the auxiliary variables to calculate the system effective safety rate corresponding to different auxiliary variables, and the value of the auxiliary variable with the largest system effective safety rate is used as the updated value of the auxiliary variable.

[0133] S143: Fix auxiliary variables and update the unit norm beam direction vector according to the first-order Marcum-Q constraint;

[0134] In a specific embodiment, after fixing the auxiliary variables, the feasible region of the unity norm beam direction vector is a complex unit spherical manifold, which is represented as:

[0135]

[0136] in, Represents a complex unit spherical manifold. Represents the unit norm beam direction vector. Indicates length is The complex vector space.

[0137] Specifically, the steps for updating the unit norm beam direction vector based on the first-order Marcum-Q constraint include:

[0138] S143.1: Incorporate the connection failure probability threshold constraint and the secrecy failure probability threshold constraint into the augmented Lagrange objective function;

[0139] In a specific embodiment, a connection failure probability threshold constraint function and a confidentiality failure probability threshold constraint function are constructed, expressed as follows:

[0140]

[0141]

[0142] in, This represents a threshold constraint function representing the probability of connection failure. The threshold constraint function represents the probability of security failure. When both constraint functions are not greater than 0, the corresponding unit norm beam direction vector satisfies the connection failure probability threshold constraint and the security failure probability threshold constraint.

[0143] The augmented Lagrange objective function is expressed as:

[0144]

[0145] in, This represents the augmented Lagrange objective function. This represents the Lagrange multiplier corresponding to the threshold constraint for connection failure probability. This represents the Lagrange multiplier corresponding to the threshold constraint on the probability of secrecy failure. Indicates the penalty factor. Represents the real number Pick The initial values ​​of the Lagrange multipliers and the initial values ​​of the penalty factors are read from the constraint data.

[0146] S143.2: Obtain the Riemann gradient based on the projection gradient of the augmented Lagrange objective function onto the complex unit spherical manifold;

[0147] In a specific embodiment, the complex unit spherical manifold is in the unit norm beam direction vector. The tangent space at point is represented as:

[0148]

[0149] in, Represents the complex unit spherical manifold in The tangent space at the point, This represents a complex direction vector in the tangent space. Indicates taking the real part, This indicates the conjugate transpose.

[0150] Projecting the Euclidean gradient of the augmented Lagrangian objective function with respect to the unit norm beam direction vector onto the tangent space yields the Riemann gradient:

[0151]

[0152] in, Represents the Riemann gradient. The Euclidean gradient of the augmented Lagrange objective function with respect to the unit norm beam direction vector is expressed as follows: the Euclidean gradient is obtained by taking the derivatives of the system effective security rate, the connection failure probability threshold constraint function, and the secrecy failure probability threshold constraint function in the augmented Lagrange objective function and then summing them.

[0153] S143.3: Update the unit norm beam direction vector based on the Riemann gradient, and update the Lagrange multipliers and penalty factors according to the violation of the connection failure probability threshold constraint and the secrecy failure probability threshold constraint.

[0154] In one specific embodiment, in the first In the next inner iteration, the unity norm beam direction vector is updated as follows:

[0155]

[0156]

[0157] in, Indicates the first The unit norm beam direction vector of the next inner iteration Indicates the first The step size of the next inner iteration is obtained by backtracking. This represents the update result of the unit norm beam direction vector before normalization. Indicates the first The unit norm beam direction vector of the next inner iteration.

[0158] The violation rates of the connection failure probability threshold constraint and the confidentiality failure probability threshold constraint are respectively and The Lagrange multipliers are updated as follows:

[0159]

[0160]

[0161] in, Indicates the first The Lagrange multipliers corresponding to the connection failure probability threshold constraints in the next inner iteration Indicates the first The Lagrange multiplier corresponding to the security failure probability threshold constraint in the next inner iteration.

[0162] It should be noted that: when the first The total violation of the innermost iteration is not less than that of the first iteration. When the product of the comprehensive violation amount and the decrease ratio value of the inner iteration is multiplied, the penalty factor is increased proportionally; when the... The comprehensive violation of the innermost iteration is less than that of the first iteration. When multiplying the comprehensive violation amount and the decrease ratio value in the next inner iteration, the penalty factor remains unchanged, and the decrease ratio value is read from the constraint data, with a value range of 0 to 1.

[0163] S144: Based on the UAV swarm transmit power and the unit norm beam direction vector, obtain the UAV swarm beam vector, and generate the satellite transmit power, UAV swarm transmit power, codeword rate, redundancy rate, and UAV swarm beam vector.

[0164] In one specific embodiment, in the first After the outermost iteration is completed, the first iteration is calculated. The system's effective safety rate in the second outer iteration and the first The absolute difference between the effective safety rates of the system in the next outer iteration is used to obtain the change in the effective safety rate of the system. When the change in the effective safety rate of the system is less than or equal to the convergence threshold, the alternating solution of the joint optimization problem is stopped, and the satellite transmission power, UAV swarm transmission power, codeword rate, redundancy rate and unity norm beam direction vector corresponding to the current outer iteration are used as the output basis.

[0165] The beam vector representation of a drone swarm is as follows:

[0166]

[0167] in, This represents the output beam vector of the UAV swarm. This indicates the output transmission power of the drone swarm. This represents the output unit norm beam direction vector; it also represents the output satellite transmit power. Output of drone swarm transmission power Output codeword rate Output redundancy rate and the output UAV swarm beam vector As a result of collaborative optimization.

[0168] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A collaborative optimization method for secure air-space-ground network transmission, characterized in that, The method includes: Collect network object data, transmission stage data, channel parameters, noise power, satellite transmission power, UAV swarm beam vector, codeword rate, redundancy rate and constraint data, and generate transmission link data based on network object data and transmission stage data; The achievable rate is obtained based on channel parameters, noise power, satellite transmit power, UAV swarm beam vector, and transmission link data. Security performance data is then generated based on the achievable rate, codeword rate, and redundancy rate. A joint optimization problem is established based on safety performance data, satellite transmission power, UAV swarm beam vector, codeword rate, redundancy rate, and constraint data. The joint optimization problem is then reconstructed to obtain reconstructed data. By reconstructing data and alternately solving the joint optimization problem, satellite transmission power, UAV swarm transmission power, codeword rate, redundancy rate, and UAV swarm beam vector are generated.

2. The collaborative optimization method for secure air-space-ground network transmission according to claim 1, characterized in that, The security performance data includes connection failure probability, confidentiality failure probability, and system effective security rate. The reconstruction data includes first-order Marcum-Q constraints, beam decomposition parameters, and auxiliary variables.

3. The method for coordinated optimization of satellite power and UAV swarm beam in an integrated air-space-ground network according to claim 2, characterized in that, The steps for generating transmission link data based on network object data and transmission phase data include: Identify satellites, drone swarms using decoding and forwarding protocols, legitimate ground users, and eavesdroppers from network object data; The satellite transmission phase and the drone swarm relay phase are determined from the data from the transmission phase; Based on the satellite, the drone swarm using the decoding and forwarding protocol, the legitimate ground user, the eavesdropper, the satellite transmission phase, and the drone swarm forwarding phase, the satellite-to-drone swarm link, the drone swarm-to-legitimate ground user link, the satellite-to-eavesdropper link, and the drone swarm-to-eavesdropper link are obtained. Transmission link data is generated based on the satellite-to-drone swarm link, the drone swarm-to-legitimate ground user link, the satellite-to-eavesdropper link, and the drone swarm-to-eavesdropper link.

4. The method for coordinated optimization of satellite power and UAV swarm beam in an integrated air-space-ground network according to claim 3, characterized in that, The steps to obtain the satellite-to-drone swarm link, the drone swarm-to-legitimate ground user link, the satellite-to-eavesdropper link, and the drone swarm-to-eavesdropper link include: Based on the transmission direction of the satellite and the UAV swarm using the decoding and forwarding protocol during the satellite transmission phase, the satellite-to-UAV swarm link is obtained; Based on the transmission direction of the UAV swarm and the ground legitimate user during the UAV swarm forwarding phase using the decoding and forwarding protocol, the link from the UAV swarm to the ground legitimate user is obtained; Based on the targets eavesdropped on by the eavesdropper during the satellite transmission phase and the drone swarm relay phase, the links from the satellite to the eavesdropper and from the drone swarm to the eavesdropper are obtained.

5. The method for coordinated optimization of satellite power and UAV swarm beam in an integrated air-space-ground network according to claim 4, characterized in that, The steps to obtain the achievable rate include: Identify the satellite-to-drone swarm link, the drone swarm-to-legitimate ground user link, the satellite-to-eavesdropper link, and the drone swarm-to-eavesdropper link from the transmission link data; Based on the satellite transmit power, channel parameters, noise power, and satellite-to-UAV swarm link, the achievable rate of the satellite-to-UAV swarm link is obtained; Based on the UAV swarm beam vector, channel parameters, noise power, and the link from the UAV swarm to the ground legitimate user, the achievable rate of the link from the UAV swarm to the ground legitimate user is obtained; The reachable rates of the legitimate two-hop link and the eavesdropping link are obtained by using the reachable rates of the satellite-to-UAV swarm link, the reachable rate of the UAV swarm-to-legitimate ground user link, the satellite transmit power, the UAV swarm beam vector, channel parameters, noise power, the satellite-to-eavesdropper link, and the UAV swarm-to-eavesdropper link.

6. The method for coordinated optimization of satellite power and UAV swarm beam in an integrated air-space-ground network according to claim 5, characterized in that, The steps for generating security performance data include: Determine the reachability of the legitimate two-hop link and the reachability of the eavesdropping link from the reachability rates; The connection failure probability is obtained based on the reachable rate and codeword rate of the legitimate two-hop link, and the confidentiality failure probability is obtained based on the reachable rate and redundancy rate of the eavesdropping link. The connection success probability is obtained by calculating the connection failure probability, and the effective information rate is obtained based on the codeword rate and redundancy rate. The effective security rate of the system is obtained based on the probability of successful connection and the effective information rate. Security performance data is generated based on the probability of connection failure, the probability of confidentiality failure, and the effective security rate of the system.

7. The method for coordinated optimization of satellite power and UAV swarm beam in an integrated air-space-ground network according to claim 6, characterized in that, The steps to establish a joint optimization problem include: Determine the probability of connection failure, the probability of confidentiality failure, and the effective security rate of the system from security performance data; Determine the connection failure probability threshold, the security failure probability threshold, the satellite transmission power constraint, the UAV swarm transmission power constraint, the codeword rate constraint, and the redundancy rate constraint from the constraint data. The target conditions are determined based on the system's effective safety rate, and the optimization variables are determined based on the satellite transmission power, UAV swarm beam vector, codeword rate, and redundancy rate. A joint optimization problem is established based on the objective conditions, optimization variables, connection failure probability, security failure probability, connection failure probability threshold, security failure probability threshold, satellite transmission power constraint, UAV swarm transmission power constraint, codeword rate constraint, and redundancy rate constraint.

8. The method for coordinated optimization of satellite power and UAV swarm beam in an integrated air-space-ground network according to claim 7, characterized in that, The steps for equivalent reconstruction of a joint optimization problem include: Based on the connection failure probability and the confidentiality failure probability, the probability constraints in the joint optimization problem are determinized to obtain the first-order Marcum-Q constraint. Power direction decomposition is performed based on the beam vector of the UAV swarm to obtain beam decomposition parameters, which include the UAV swarm transmit power and the unity norm beam direction vector. Auxiliary variables are obtained based on code word rate and drone swarm transmission power; Based on the first-order Marcum-Q constraint, beam decomposition parameters, and auxiliary variables, the reconstructed data is obtained.

9. The method for coordinated optimization of satellite power and UAV swarm beam in an integrated air-space-ground network according to claim 8, characterized in that, The steps of alternately solving the joint optimization problem by reconstructing data include: The first-order Marcum-Q constraints, beam decomposition parameters, and auxiliary variables are determined from the reconstructed data. The unity-norm beam direction vector in the fixed beam decomposition parameters is updated with auxiliary variables, satellite transmit power, UAV swarm transmit power, codeword rate, and redundancy rate according to the first-order Marcum-Q constraint. With auxiliary variables fixed, update the unit norm beam direction vector according to the first-order Marcum-Q constraint; The UAV swarm beam vector is obtained based on the UAV swarm transmit power and the unit norm beam direction vector, and the satellite transmit power, UAV swarm transmit power, codeword rate, redundancy rate and UAV swarm beam vector are generated.

10. The method for coordinated optimization of satellite power and UAV swarm beam in an integrated air-space-ground network according to claim 9, characterized in that, The steps for updating the unit norm beam direction vector based on the first-order Marcum-Q constraint include: The connection failure probability threshold constraint and the secrecy failure probability threshold constraint are incorporated into the augmented Lagrange objective function; The Riemann gradient is obtained by projecting the augmented Lagrange objective function onto the complex unit spherical manifold. The unit norm beam direction vector is updated based on the Riemann gradient, and the Lagrange multipliers and penalty factors are updated based on the violation of the connection failure probability threshold constraint and the secrecy failure probability threshold constraint.

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