Secure communication methods for joint drone deployment, power allocation, and coalition game

Abstract

This invention discloses a secure communication method for joint UAV deployment, power allocation, and coalition game theory, belonging to the field of communication technology. The implementation method is as follows: the number of subcarrier channels of the UAV is set to the number of coalitions that can be formed. Ground nodes form non-overlapping coalitions, and nodes within the same coalition evenly allocate communication time slots, communicating with the UAV via TDMA. Communication nodes in a multi-node coalition select a node outside themselves as a cooperative jammer to send messages to ground eavesdroppers, improving coalition security and efficiency during communication. Under the constraint of UAV endurance, a two-layer optimization problem is constructed to improve the overall security and efficiency. In the upper-layer optimization problem, a stable coalition formation is obtained based on the TCFG algorithm. Under the stable coalition formation, the lower-layer non-convex optimization problem is solved to obtain the UAV deployment result and ground node power allocation result that maximize the overall security and efficiency, thus achieving the maximization of the overall security and efficiency.

Description

Technical Field

[0001] This invention relates to a secure communication method for joint UAV deployment, power allocation, and coalition game theory, belonging to the field of communication technology. Background Technology

[0002] The International Telecommunication Union (ITU) has proposed six typical scenarios for 6G (6th Generation Network) communication, among which drones play a crucial role in achieving global three-dimensional coverage. Compared to terrestrial networks, drone communication networks leverage the flexibility of drones, using them as relays or receivers for terrestrial IoT devices at altitudes where line-of-sight (LoS) communication is permitted, thereby reducing fading and distortion caused by shadows and signal congestion. Furthermore, in hotspot areas with poor infrastructure, such as forests, deserts, and oceans, traditional terrestrial base stations may not provide sufficient service. With the development of wireless transmission technology, secure transmission has become a pressing issue for 6G wireless communication. Drone communication networks harbor numerous intentional or unintentional eavesdroppers, posing a significant threat to data transmission security. Physical Layer Security (PLS) utilizes the characteristics of the physical layer, such as the uniqueness and reciprocity of physical channel information, to achieve information encryption. As an alternative to cryptographic-based communication security technologies, PLS eliminates the need for key design and distribution, avoiding complex algorithms and making it more suitable for future large-scale, decentralized 6G wireless communication. To further improve the PLS of communication systems, artificial noise (AN) has been widely studied as a cooperative interference technique. In reference [1] (see: A.Li, Q.Wu, and R.Zhang, “UAV-enabled cooperative jamming for improving secrecy of ground wiretapchannel,” IEEE Wireless Commun. Lett., vol.8, no.1, pp.181–184, Feb.2019), the UAV acts as a cooperative jammer, transmitting AN to counter eavesdropping attacks. In reference [2] (see: C.Han, L.Bai, T.Bai, and J.Choi, “Joint UAV deployment and power allocation for secure space-air-ground communications,” IEEE Trans. Commun., vol.70, no.10, pp.6804–6818, Oct.2022), the UAV acts as both a relay and a cooperative jammer, transmitting AN to jam eavesdroppers while sending and receiving confidential data, and optimizing power to improve communication security.In addition, reference [3] (see: H. Lei, H. Yang, K.-H. Park, ISasari, J. Jiang, and M.-S. Alouini, “Joint trajectory design and user scheduling for secure aerial underlay IoT systems,” IEEE Internet Things J., vol. 10, no. 15, pp. 13 637–13 648, Aug. 2023.) developed a dual-drone scenario, in which one drone transmits confidential data and the other drone sends AN to interfere with eavesdroppers. However, using only one drone as a cooperative jammer in a communication system would result in a waste of drone resources. Furthermore, when a drone acts as both a relay and a cooperative jammer, it would cause additional energy consumption, such as drone flight power consumption, antenna power consumption, and system electrostatic power consumption, and increase system complexity and the difficulty of performing the task. Therefore, it is particularly important to find a mutually beneficial cooperative jamming strategy in drone communication networks.

[0003] In UAV-assisted communication networks, the UAV's endurance problem can be solved by providing laser charging services to the UAV through a Low Altitude Platform (LAP). A special photovoltaic panel is installed on the UAV, which receives laser light and converts it into electrical energy. Reference [4] (see: A. Ranjha and G. Kaddoum, “URLLC-enabled by laser powered UAV relay: A quasi-optimal design of resource allocation, trajectory planning and energy harvesting,” IEEE Trans. Veh. Technol., vol. 71, no. 1, pp. 753–765, Jan. 2022) considers using a laser transmitter to charge the relay UAV in the air, enabling the UAV to complete flight using the collected laser energy. In addition, Reference [4] gives the expression for the received signal strength of the UAV collecting energy on the laser link and concludes that the laser energy collection is less affected by the transceiver distance than the radio frequency energy collection. Summary of the Invention

[0004] The technical problem to be solved by the secure communication method for joint UAV deployment, power allocation, and coalition game disclosed in this invention is as follows: the number of available subcarrier channels for UAV communication is set as the number of coalitions, each ground node is assigned to a coalition, and each coalition occupies one subcarrier channel for uplink communication; ground nodes in the same coalition communicate with the UAV in a time division multiple access (TDMA) manner and evenly allocate communication time slots; nodes in a multi-node coalition have dual identities: transmitting secure information as senders in communication time slots, and being selected as or not as cooperating jammers to transmit artificial noise (AN) in non-communication time slots; during the communication period, while ensuring the UAV's endurance, the deployment of UAVs, the power allocation of ground nodes, and coalition formation are jointly optimized to combat eavesdropping by ground radio frequency (RF) eavesdroppers and maximize the sum of the security rates of all ground nodes, i.e., the overall security rate.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] This invention discloses a secure communication method for joint UAV deployment, power allocation, and alliance game theory. A three-dimensional Cartesian coordinate system is established, with the ordinate of N ground nodes and a full-band RF eavesdropper set to 0. A UAV with M subcarrier channels hovers over N (N>M) dispersed ground nodes. While the ground nodes transmit secure information through the subcarrier channels, the eavesdropper launches an eavesdropping attack. During a time period T, the UAV's endurance is addressed through LAP laser charging, where special photovoltaic panels on the UAV receive laser light and convert it into electricity. The UAV's hovering position is strategically positioned to be as close as possible to the ground nodes, minimizing power loss from free-space transmission, while ensuring sufficient endurance. Since N>M, it's impossible for each node to completely occupy a UAV subcarrier channel during time period T. The number of subcarrier channels is set to the number of possible alliances, M. Ground nodes form M non-overlapping alliances, where nodes belonging to the same alliance communicate with the UAV using Time Division Multiple Access (TDMA) and evenly distribute communication time slots. A cooperative jamming strategy is constructed for secure uplink communication in a multi-node alliance. In this alliance, nodes in a communication time slot select one node within the alliance as a cooperative jammer to send an AN (Alternate Animation) to an eavesdropper, thereby increasing the sum of the security rates of ground nodes in the alliance over a time interval T, i.e., the alliance security sum rate. Channel models are established between ground nodes and UAVs and between ground nodes and eavesdroppers, and the security rate of communication between UAVs and ground nodes is analyzed. Under the constraint of UAV endurance, a two-level optimization problem is constructed to improve the security sum rate of all ground nodes, i.e., the overall security sum rate. In the upper-level optimization problem, all ground nodes form different alliances, and each node has the opportunity to change the alliance formation through "switching" or "exchanging" actions. In the lower-level optimization problem, the UAV simultaneously acts as a receiver for ground nodes in M ​​alliances. The UAV's hovering position is deployed, and the transmit power and cooperative jamming power of each ground node in the alliance are redistributed. The block coordinate descent (BCD) method is used to solve the underlying optimization problem, which is decomposed into two non-convex subproblems: Subproblem 1: Optimize UAV deployment given a power allocation for ground nodes; Subproblem 2: Optimize power allocation for ground nodes given a given UAV deployment. Auxiliary variables are introduced, and the successive convex approximation SCA method is used to transform these two subproblems into two convex function difference DC programming problems and a second-order cone SOC programming problem, respectively. Solving the two DC programming problems yields the UAV deployment result, and solving the SOC programming problem yields the ground node power allocation result. Iterative solutions to subproblems 1 and 2 ensure convergence of the underlying problem within a finite number of iterations, resulting in the UAV deployment result and ground node power allocation result that maximize overall security and efficiency.In the upper-level optimization problem, based on the two-step alliance formation game theory (TCFG) algorithm, the alliance formation is changed by the "switching" or "exchanging" actions of nodes until a stable alliance is obtained. Under the stable alliance formation, the lower-level non-convex optimization problem is solved to obtain the UAV deployment result and the power allocation result of the ground nodes that maximize the overall security and efficiency. The overall security and efficiency are maximized based on the UAV deployment result and the power allocation result of the ground nodes.

[0007] The secure communication method for joint UAV deployment, power allocation, and coalition game theory disclosed in this invention includes the following steps:

[0008] Step 1: Establish a three-dimensional Cartesian coordinate system, construct an expression for the received signal strength of the UAV during energy harvesting via the laser link, and establish the conditions that must be met to ensure the UAV's endurance. Establish channel models between ground nodes and UAVs, and between ground nodes and eavesdroppers. In the presence of RF eavesdroppers and non-communication nodes sending ANs to eavesdroppers, calculate the secure communication rate between ground nodes and UAVs in each alliance, and establish constraint equations for the power of ground nodes in each alliance.

[0009] Step 1.1: Use E and U represent the sets of ground nodes, ground eavesdroppers, and drones, respectively. The location of the LAP is fixed and is represented as [x LAP ,y LAP ,z LAP The horizontal coordinate of the nth node is represented by q. n =[x n ,y n The horizontal coordinate of E is represented by q. E =[x E ,y E ], and their vertical coordinates are both 0. The horizontal and vertical coordinates of the drone are represented as q. U =[x U ,y U ] and z U .

[0010] Step 1.2: Construct the expression for the received signal strength of the UAV during energy harvesting via the laser link, and establish the endurance conditions required to ensure the UAV's continued operation. Considering that the limited onboard battery is insufficient to support the UAV's propulsion and communication within time period T, the LAP provides laser charging services for the UAV. Since the energy consumed by communication, baseband signal processing, and thermal noise in the RF link is much smaller, only the energy consumed by the UAV's propulsion is considered. The power consumed by the rotorcraft UAV in hovering state is expressed as...

[0011] P U,hov =Pb +P i (1)

[0012] In the formula P b and P i These are the airfoil power and the induced power, respectively. The LAP has a fixed emitter power P. L The intensity of the laser signal received by the drone is

[0013]

[0014] in Let denot be the distance between the UAV and the LAP, D be the initial size of the laser beam, χ be the optical efficiency of the joint transmitter-receiver, θ be the angular spread, A be the area of ​​the collecting lens, and κ be the attenuation coefficient of the medium. The energy harvesting module on the UAV has a constant laser power harvesting efficiency ω∈(0,1), and the laser power harvested by the UAV is expressed as:

[0015]

[0016] P h It is about d UL The function is monotonically decreasing. κ and θ are both very small constants, making laser power harvesting less affected by transceiver distance than RF power harvesting. The condition that the drone's endurance must meet is...

[0017] P h ≥P U,hov (4)

[0018] Step 1.3: Establish channel models between ground nodes and UAVs, and between ground nodes and eavesdroppers. In the presence of an RF eavesdropper, cooperative jamming nodes in non-transmission time slots send AN signals to the eavesdropper, calculating the secure communication rate between ground nodes and UAVs in each alliance. For multi-node alliances... Analyze the power allocation among nodes in the alliance. Let the nodes be... For communication nodes, nodes The cooperative interference node selected for node n. Using the LoS model as the air-to-ground communication model, the channel gain g between node n and the UAV is... U,m [n] follows the free-space path loss model. The channel gain g between node n and the eavesdropper... E,m [n] follows a free-space path loss model and a small-scale Rayleigh fading distribution. g U,m [n] and g E,m [n] are respectively represented as

[0019]

[0020] Where β0 is the channel gain with a reference distance of 1m between the ground node and the drone, and β1 is the channel gain with a reference distance of 1m between the ground node and the eavesdropper. It is a random variable with a unit mean, describing the exponential distribution of Rayleigh fading. This represents the distance between node n and the drone. This represents the distance between node n and the eavesdropper. Under LoS propagation conditions, the communication rate between node n and the drone is...

[0021]

[0022] Where p s,m [n] represents the transmit power of node n, N 0,U The noise power on the drone is represented by γ0 = β0 / N. 0,U The eavesdropping rate between node n and the ground eavesdropper is expressed as...

[0023]

[0024] Where N 0,E The noise power of the eavesdropper is represented by γ1 = β1 / N 0,E Node n′, acting as a cooperative disruptor of node n, sends a signal with power p to the eavesdropper. j,m For AN[n], according to Shannon's capacity formula, the eavesdropping rate between node n and the ground eavesdropper is expressed as:

[0025]

[0026] in This represents the channel gain between node n′ and the eavesdropper.

[0027] When node n′ sends a jamming signal to the eavesdropper, the communication security rate between node n and the drone is expressed as:

[0028]

[0029] Among them, the operator [·] + To make equation (10) less smooth at the zero value, remove [·]. + This does not affect the solution of the optimal value of equation (10) and can make equation (10) smooth at the zero value. Equation (10) is transformed into:

[0030]

[0031] According to formulas (9)(10)(11), the existence of cooperative interference is beneficial to suppressing the eavesdropping rate and improving the confidentiality performance of node n.

[0032] Establish the power constraint equations for the ground nodes in each alliance. Let the average power of each node be P. ave Peak power is P peak P ave ≤P peak For the m-th alliance set up Contains N m There are nodes, and the total available power is N. m ×T×P ave The sequence is reallocated within time interval T. and sequence Indicates alliance The set of transmit power and cooperative jamming power of the mid-node. The power constraint equation in is expressed as follows:

[0033]

[0034] Step Two: Use Let represent the set of all alliances. All ground nodes form M non-overlapping alliances. Alliances are categorized into three types based on the number of nodes: empty alliances, single-node alliances, and multi-node alliances. The maximum alliance secrecy rate is defined for each of these three types. With the objective of maximizing the overall secrecy rate, and under constraints such as UAV endurance, ground node power, and the maximum allowed number of nodes in each alliance, an optimization problem is established to maximize the overall secrecy rate.

[0035] Step 2.1: Use This represents the set of all alliances. All ground nodes form M non-overlapping alliances. Based on the number of nodes in an alliance, alliances are divided into three types: empty alliance, single-node alliance, and multi-node alliance. The maximum alliance secrecy rate is defined for empty alliance, single-node alliance, and multi-node alliance.

[0036] The empty alliance has no nodes, and its maximum alliance secrecy rate is always zero.

[0037] The single-node alliance There is only one node n in the alliance. During the time period T, this node completely occupies a subcarrier channel, but there are no cooperating interference nodes. The maximum alliance secrecy and efficiency of a single-node alliance are determined by g. U,m [n] and g E,m [n] determines. When g U,m [n]>g E,m [n], the maximum alliance secrecy and rate Conversely, if the maximum alliance secrecy rate is 0, then the optimal behavior for node n is not to transmit data.

[0038] The multi-node alliance There are two or more nodes in the network. Nodes in the same alliance allocate communication time slots equally and communicate with the drone using TDMA. Nodes in a communication time slot select one node within the alliance to act as a cooperative jammer and send an AN to the eavesdropper. Multi-node alliance The maximum alliance secrecy and efficiency are obtained by optimizing the power of drone deployment and ground nodes in step 3.2.

[0039] Step 2.2: With the goal of maximizing overall security and efficiency, and under the constraints of UAV endurance, ground node power, and the maximum number of nodes allowed by each alliance, establish an optimization problem to maximize overall security and efficiency.

[0040]

[0041] Among them, C max This represents the maximum number of nodes allowed in a consortium. Indicates the flight area of ​​the drone. and They represent The set of transmit power and cooperative jamming power of each node.

[0042] Step 3: Optimize the overall confidentiality and efficiency established in Step 2. This is equivalent to a two-level optimization problem. In the upper-level optimization problem, all ground nodes form M non-overlapping alliances, and each node has the opportunity to change the alliance formation through "switching" or "exchanging" actions. In the lower-level optimization problem, the UAV simultaneously acts as the receiver of the M alliances. The UAV's hovering position is deployed, and the transmit power and cooperative jamming power of each node in each alliance are redistributed. The BCD method is used to solve the lower-level optimization problem, decomposing it into two non-convex subproblems: Subproblem 1: Optimize UAV deployment given the power allocation of ground nodes; Subproblem 2: Optimize the power allocation of ground nodes given the UAV deployment. By introducing auxiliary variables and using the SCA method, these two subproblems are transformed into two DC programming problems and one SOC programming problem, respectively. The UAV deployment result is obtained by solving the two DC programming problems, and the power allocation result of the ground nodes is obtained by solving the SOC programming problem. Iteratively solving subproblems 1 and 2 ensures that the lower-level problem converges in a finite number of iterations, yielding the UAV deployment result and the power allocation result of the ground nodes that maximize the overall security and efficiency. In the upper-level optimization problem, based on the TCFG algorithm, a coalition game method is used to change the coalition formation through the "switching" or "exchanging" actions of nodes until a stable coalition formation is obtained. Under the stable coalition formation, the lower-level non-convex optimization problem is solved to obtain the UAV deployment result and the power allocation result of the ground nodes that maximize the overall security and efficiency. The overall security and efficiency are maximized based on the UAV deployment result and the power allocation result of the ground nodes.

[0043] Step 3.1: In the upper-level optimization problem, all ground nodes form M non-overlapping alliances. Each node has the opportunity to change the alliance formation through a "switch" or "exchange" action. Generate the initial alliance formation.

[0044] Step 3.1.1: The alliance formation optimization problem is expressed as...

[0045]

[0046] In the formula This indicates the maximum alliance secrecy and rate of success under the current alliance formation.

[0047] Step 3.1.2: Generate the initial alliance and calculate the maximum overall secrecy and rate under this alliance based on Step 3.2. Set flag-switch←1 and flag-exchange←1.

[0048] Step 3.2: In the underlying optimization problem, the UAV simultaneously acts as the receiver for M alliances. The UAV hovering positions are deployed, and the transmit power and cooperative jamming power of each node in each alliance are redistributed. The BCD method is used to solve the underlying optimization problem, decomposing it into two non-convex subproblems: Subproblem 1: Optimize UAV deployment given the ground node power allocation; Subproblem 2: Optimize ground node power allocation given the UAV deployment. Auxiliary variables are introduced, and SCA technology is used to transform these two subproblems into two DC programming problems and one SOC programming problem, respectively. Solving the two DC programming problems yields the UAV deployment result, and solving the SOC programming problem yields the ground node power allocation result. Iterative solutions to Subproblem 1 and Subproblem 2 ensure that the underlying problem converges in a finite number of iterations, resulting in the UAV deployment result and ground node power allocation result that maximize overall security and efficiency.

[0049] Step 3.2.1: Based on the overall confidentiality and rate maximization problem established in Step 2.2, and with a fixed alliance formation, the underlying optimization problem is expressed as follows:

[0050]

[0051] Using the BCD method The problem is broken down into two sub-problems: Sub-problem 1: Optimize UAV deployment given a power allocation for ground nodes; Sub-problem 2: Optimize power allocation for ground nodes given a UAV deployment. By introducing auxiliary variables and using the SCA method, these two sub-problems are transformed into two DC programming problems and one SOC programming problem, respectively. The UAV deployment result is obtained by solving the two DC programming problems, and the power allocation result for ground nodes is obtained by solving the SOC programming problem.

[0052] Step 3.2.2: Optimize UAV deployment given the power allocation of ground nodes. The optimization problem is expressed as follows:

[0053]

[0054] Where [x] m [n],y m [n]] and p s,m [n] represents the alliance. The horizontal coordinates and transmission power of node n. Since the objective function in equation (20) is non-convex and the constraints in equation (4) are non-convex, therefore This is a nonconvex optimization problem. To transform (20) into a solvable form, auxiliary variables are introduced. Let t relax the objective function in equation (20) and the constraints in equation (4), respectively. Use the SCA method to convert the objective function in equation (20) into a concave function. Convert the constraints in equation (4) into two equivalent constraints and write them as two convex function difference forms. Rewritten as

[0055]

[0056] (x U -x LAP ) 2 +(y U -y LAP ) 2 +(z U -z LAP ) 2 ≤t 2 , (twenty three)

[0057] (P b +P i )×(t·θ+D) 2 -P L ωAχe -κt ≤0, (24) (17)

[0059] The non-concavity of the objective function in equation (21) and the non-concavity of the constraints in equations (23) and (24) are handled by first-order Taylor expansion. It's about u m The convex function of [n], and with the help of a first-order Taylor expansion, is obtained in the (l+1)th iteration.

[0060]

[0061] in is u m [n] is the value at the l-th iteration. t in equations (23) and (24) 2 and e -κt It was expanded with the help of the first-order Taylor expansion to

[0062] t 2 ≥-(t (l) ) 2 +2tt (l) (26)

[0063] and

[0064] e -κt ≥e -κt(l) -κe -κt(l) (tt (l) (27)

[0065] question Rewritten as

[0066]

[0067] st (x U -x LAP ) 2 +(y U -y LAP ) 2 +(z U -z LAP ) 2 +(t (l) ) 2 -2tt (l) ≤0, (29)

[0068]

[0069] The objective function in equation (28) is concave with respect to u, and the constraints in equations (29) and (30) are concave with respect to {x}. U ,y U ,z U The region {u,t} is convex. Therefore, the interior point method can be used to solve the convex optimization problem. The results of the drone deployment were obtained.

[0070] Step 3.2.3: Optimize power allocation for ground nodes given a drone deployment. For the alliance The optimization problem is represented as

[0071]

[0072] The objective function in equation (31) is a highly complex function containing multiple optimization variables and random variables. Considering the optimization problem... To improve robustness, the objective function in equation (31) is tightened, and the negative terms in equation (31) are taken to their maximum values, expressed as follows:

[0073]

[0074] The second term of the objective function in equation (32) contains random variables in both the numerator and denominator. Therefore, the objective function in equation (32) can be considered as a random variable. The function, for ease of solving, will be... Expressed as

[0075]

[0076] in Jensen's inequality is applied to handle the "-max" in the objective function of equation (32). Specifically, the calculation of equation (32) is performed using... upper bound and The lower bound of the function. When node n is the sender and node n′ is a cooperative jammer, find the maximum eavesdropping rate from node n to the eavesdropper. Since the function... The concavity of the material yields...

[0077]

[0078] for Its lower bound is based on Jensen's inequality and ln(1+e) x The concavity and convexity of ) are obtained

[0079]

[0080] in The parameter is An exponentially distributed random variable, and has

[0081]

[0082] Where ε is the Euler constant. Substituting equation (36) into equation (35), we get... The lower bound is

[0083]

[0084] According to formulas (33)(34)(35)(36)(37), when n′ is used as When there are cooperative interference nodes, the maximum eavesdropping rate from node n to the eavesdropper is:

[0085]

[0086] Therefore, the optimization problem Rephrased as

[0087]

[0088] The objective function in equation (39) is highly complex. To facilitate observation, variables are introduced to... Transform it into an equivalent optimization problem, denoted as:

[0089]

[0090] in and The non-convex constraints in equations (44), (45), and (46) are equivalently written as

[0091]

[0092] Linearizing the non-convex constraints in equations (47), (48), and (49) around the current point in the l-th iteration, we obtain...

[0093] Therefore, the optimization problem Rephrased as

[0094]

[0095] st(12),(13),(14),(41),(42),(43),(50),(51),(52)

[0096] Optimization problem It is an optimization problem involving SOC constraints, which is solved using CVX to obtain the power allocation results of the ground nodes.

[0097] Step 3.2.4: Utilize the optimization problems from Steps 3.2.2 and 3.2.3 and The deployment results of the UAVs and the power allocation results of the ground nodes are obtained separately, and the solution is iteratively obtained. and The process continues until convergence, ultimately yielding the results of the drone deployment and the power allocation for the ground nodes.

[0098] Step 3.3: In the upper-level optimization problem, based on the TCFG algorithm, the alliance game method is used to change the alliance formation through the "switching" or "exchanging" actions of nodes until a stable alliance is obtained. Under the stable alliance formation, the lower-level non-convex optimization problem is solved to obtain the UAV deployment result and the power allocation result of the ground nodes that maximize the overall security and efficiency. The overall security and efficiency are maximized based on the UAV deployment result and the power allocation result of the ground nodes.

[0099] Step 3.3.1: In the upper-level optimization problem, the framework and definition of the developed TCFG algorithm are as follows. The TCFG algorithm divides all nodes into M non-overlapping federations. The alliance formation is updated using two alliance rules: "switch partial order" and "exchange partial order". Each node follows the same alliance update rules and has its own alliance formation priority. After traversing all nodes, the alliance with the highest overall secrecy and rate becomes the updated alliance formation. Consider the nodes... Current alliance formation Move node n from Switch to The utility is expressed as

[0100]

[0101] in This refers to the alliance formed after node n moves. and They are based on and The maximum overall confidentiality and rate are obtained. Therefore, u m,m′ [n] describes the current alliance formation. The next node n from Switch to The contribution of the node. The definition of the switching partial order is as follows. If there is a node and Alliance The following conditions must be met:

[0102]

[0103] Remove node n from the alliance Switch to Consider two nodes and Current alliance formation The utility of a node exchange alliance is

[0104]

[0105] in It is an alliance formed after node exchanges. Therefore, v m,m′ [n,n′] describes the current alliance form. Two nodes in the exchange alliance and The contribution of the exchange partial order. The definition of the exchange partial order is as follows. If there are two nodes m′≠m satisfies the following condition:

[0106]

[0107] Swap the alliances to which nodes n and n′ currently belong.

[0108] Step 3.3.2: Traverse all nodes according to the switching partial order and swapping partial order rules. In each traversal, select the alliance formation corresponding to the maximum overall secrecy and rate as the new alliance formation, and then proceed to the next traversal until the overall secrecy and rate no longer increase. The specific operation is as follows: Traverse all nodes according to the switching partial order rules. The utility of the current alliance formation is... For nodes node Traversal of the selection alliance and from Switch to Use step 3.2 to calculate the maximum overall secrecy and rate under the new alliance formation. Compare and And store the result u m,m′ [n]. If it exists Satisfy u m,m′ If [n] > 0, find the largest u. m,m′ The corresponding alliance is formed at [n] and the current alliance is updated by setting flag-switch←1; otherwise, flag-switch←0 is set. Repeat the above steps until flag-switch←0. Traverse all nodes according to the described partial order swapping rule. The utility of the current alliance formation is... For nodes node Traversal selection Then exchange alliances with node n′. Calculate the maximum overall secrecy and rate of return under the new alliance formation using step 3.2. Compare and And store the result v m,m′ [n, n′]. If it exists Satisfy v m,m′ If [n,n′]>0, find the largest v. m,m′ The alliance corresponding to [n,n′] is formed and updated, setting flag-exchange←1; otherwise, flag-exchange←0 is set. The above steps are repeated until flag-exchange←0, resulting in a stable alliance. Under stable alliance formation, step 3.2 is used to solve the underlying non-convex optimization problem, obtaining the UAV deployment result and ground node power allocation result that maximize overall security and efficiency. Based on the UAV deployment result and ground node power allocation result, the overall security and efficiency are maximized.

[0109] Beneficial effects:

[0110] 1. This invention discloses a secure communication method for joint UAV deployment, power allocation, and coalition game theory, using... Let represent the set of all alliances. All ground nodes form M non-overlapping alliances. Alliances are categorized into three types based on the number of nodes: empty alliances, single-node alliances, and multi-node alliances. The maximum alliance secrecy rate is defined for each of these three types. With the objective of maximizing the overall secrecy rate, and under constraints such as UAV endurance, ground node power, and the maximum allowed number of nodes in each alliance, an optimization problem is established to maximize the overall secrecy rate. Optimizing the overall confidentiality and efficiency The problem is structured as a two-layer optimization problem. The upper-layer optimization problem is solved using a coalition game theory approach, while the lower-layer optimization problem is solved using a convex optimization approach. This yields the coalition formation result, UAV deployment result, and ground node power allocation result that maximize overall security and efficiency. Based on these coalition formation results, UAV deployment results, and ground node power allocation results, the overall security and efficiency are maximized.

[0111] 2. The secure communication method for joint UAV deployment, power allocation and alliance game disclosed in this invention occupies one subcarrier channel of the UAV in the entire time slot. The nodes in each alliance communicate with the UAV in the TDMA mode and allocate the time slots equally, which reduces the complexity of the optimization problem.

[0112] 3. The secure communication method for joint UAV deployment, power allocation and alliance game disclosed in this invention constructs a cooperative interference strategy for secure uplink communication of nodes in a multi-node alliance. Nodes in the communication time slot of the multi-node alliance select one node in the same alliance as a cooperative jammer to send AN to the eavesdropper, thereby improving the alliance's security and efficiency, and thus improving the overall security and efficiency.

[0113] 4. This invention discloses a secure communication method for joint deployment, power allocation, and alliance game among drones. The drones use lasers for in-flight charging. Compared with radio frequency charging, this method is less affected by the transmission distance of the transceiver and is more suitable for wireless communication scenarios with long transmission distances. Attached Figure Description

[0114] Figure 1 This is a schematic diagram of the three-dimensional Cartesian coordinate system and each channel in step one of the present invention;

[0115] Figure 2 This is a flowchart of the secure communication method for joint UAV deployment, power allocation, and coalition game disclosed in this invention;

[0116] Figure 3 This shows the deployment of ground nodes, eavesdroppers, LAP, and the initial drone in this example;

[0117] Figure 4 The initial alliance is formed randomly in this example;

[0118] Figure 5 This is the optimized drone deployment obtained in this example;

[0119] Figure 6 This is the power allocation of the ground nodes obtained based on the initial alliance optimization in this example;

[0120] Figure 7 This is the stable alliance formation obtained using the TCFG algorithm in this example. Detailed Implementation

[0121] To better illustrate the purpose and advantages of the present invention, the invention will be further described below in conjunction with the accompanying drawings and examples.

[0122] Example 1:

[0123] To verify the feasibility of the method, based on Figure 1 The channel shown is used to establish a three-dimensional Cartesian coordinate system, and the coordinates of the ground nodes, the eavesdropper, the UAV, and the LAP are defined. The 15 ground nodes are randomly distributed in a region with horizontal coordinates of [-500 500 -500 500]m. The eavesdropper's coordinates are (500, 500, 0)m, the UAV's initial coordinates are (0, 0, 400)m, the UAV's deployable area is [-500 500 100 -500 500 900]m, and the LAP's coordinates are (0, 0, 1000)m.

[0124] like Figure 2 As shown in the example, the secure communication method for joint UAV deployment, power allocation, and coalition game disclosed in this example has the following specific implementation steps:

[0125] Step 1: Establish a three-dimensional Cartesian coordinate system, construct an expression for the received signal strength of the UAV during energy harvesting via the laser link, and establish the conditions that must be met to ensure the UAV's endurance. Establish channel models between ground nodes and UAVs, and between ground nodes and eavesdroppers. In the presence of RF eavesdroppers and non-communication nodes sending ANs to eavesdroppers, calculate the secure communication rate between ground nodes and UAVs in each alliance, and establish constraint equations for the power of ground nodes in each alliance.

[0126] The power consumed by a rotary-wing drone in hovering mode is expressed as follows:

[0127] P U,hov =P b +P i (58)

[0128] LAP has a fixed transmit power P L The laser power collected by the drone is expressed as

[0129]

[0130] The conditions that a drone needs to meet for extended battery life are:

[0131] P h ≥P U,hov (60)

[0132] Channel gain g between node n and the UAV U,m [n] follows the free space path loss model, expressed as

[0133]

[0134] Channel gain g between node n and the eavesdropper E,m [n] follows a free-space path loss model and a small-scale Rayleigh fading distribution, denoted as:

[0135]

[0136] Under LoS propagation conditions, the data rate for communication between node n and the drone is:

[0137]

[0138] When node n′ acts as a cooperative interferer to node n, it sends a signal with power p to the eavesdropper. j,m In an AN of [n], the eavesdropping rate between node n and the eavesdropper is expressed as:

[0139]

[0140] The secure communication rate between node n and the drone is expressed as:

[0141]

[0142] For the alliance The power constraint of the ground node is expressed as

[0143]

[0144] Step 2: With the goal of maximizing overall security and efficiency, and under the constraints of drone endurance, ground node power, and the maximum number of nodes allowed by each alliance, establish an optimization problem to maximize overall security and efficiency.

[0145]

[0146] Step 3: Optimize the problem This problem can be viewed as a two-level optimization problem. In the upper-level optimization problem, all ground nodes form M non-overlapping alliances, and each node has the opportunity to change the alliance formation through "switching" or "exchanging" actions. In the lower-level optimization problem, the UAV simultaneously acts as the receiver of the M alliances. The UAV's hovering position is deployed, and the transmit power and cooperative jamming power of each node in each alliance are redistributed. The BCD method is used to solve the lower-level optimization problem, which is decomposed into two non-convex subproblems: Subproblem 1: Optimize UAV deployment given the power allocation of ground nodes; Subproblem 2: Optimize the power allocation of ground nodes given the UAV deployment. By introducing auxiliary variables and using SCA technology, these two subproblems are transformed into two DC programming problems and one SOC programming problem, respectively. The UAV deployment result is obtained by solving the two DC programming problems, and the power allocation result of the ground nodes is obtained by solving the SOC programming problem. Iteratively solving subproblems 1 and 2 ensures that the lower-level problem converges in a finite number of iterations, yielding the UAV deployment result and the power allocation result of the ground nodes that maximize the overall security and efficiency. In the upper-level optimization problem, based on the TCFG algorithm, a coalition game method is used to change the coalition formation through the "switching" or "exchanging" actions of nodes until a stable coalition formation is obtained. Under the stable coalition formation, the lower-level non-convex optimization problem is solved to obtain the UAV deployment result and the power allocation result of the ground nodes that maximize the overall security and efficiency. The overall security and efficiency are maximized based on the UAV deployment result and the power allocation result of the ground nodes.

[0147] Step 3.1: In the upper-level optimization problem, all ground nodes form M non-overlapping alliances. Each node has the opportunity to change the alliance formation through a "switch" or "exchange" action. Generate the initial alliance formation and set flag-switch←1 and flag-exchange←1. The alliance formation optimization problem is represented as follows:

[0148]

[0149] Step 3.2: In the underlying optimization problem, the UAV simultaneously acts as the receiver for M alliances. The UAV hovering positions are deployed, and the transmit power and cooperative jamming power of each node in each alliance are redistributed. The BCD method is used to solve the underlying optimization problem, decomposing it into two non-convex subproblems: Subproblem 1: Optimize UAV deployment given the ground node power allocation; Subproblem 2: Optimize ground node power allocation given the UAV deployment. Auxiliary variables are introduced, and SCA technology is used to transform these two subproblems into two DC programming problems and one SOC programming problem, respectively. Solving the two DC programming problems yields the UAV deployment result, and solving the SOC programming problem yields the ground node power allocation result. Iterative solutions to Subproblem 1 and Subproblem 2 ensure that the underlying problem converges in a finite number of iterations, resulting in the UAV deployment result and ground node power allocation result that maximize overall security and efficiency.

[0150] Consider handling the situation once the alliance is established. The underlying non-convex optimization problem is represented as

[0151]

[0152] Subproblem 1: Optimize UAV deployment given the power allocation of ground nodes. The optimization problem is expressed as follows:

[0153]

[0154] (x U -x LAP ) 2 +(y U -y LAP ) 2 +(z U -z LAP ) 2 +(t (l) ) 2 -2tt (l) ≤0, (76)

[0155]

[0156] Solving convex optimization problems using the interior point method The results of the drone deployment were obtained.

[0157] Sub-problem 2: Optimize power allocation for ground nodes given a drone deployment, in order to form a coalition For example, the optimization problem is represented as:

[0158]

[0159] Solving optimization problems using CVX The power allocation results of the ground nodes are obtained.

[0160] Step 3.3: In the upper-level optimization problem, based on the TCFG algorithm, the alliance game method is used to change the alliance formation through the "switching" or "exchanging" actions of nodes until a stable alliance is obtained. Under the stable alliance formation, the lower-level non-convex optimization problem is solved to obtain the UAV deployment result and the power allocation result of the ground nodes that maximize the overall security and efficiency. The overall security and efficiency are maximized based on the UAV deployment result and the power allocation result of the ground nodes.

[0161] Traverse all nodes according to the switching and swapping partial orders. In each traversal, select the alliance formation corresponding to the maximum overall secrecy and rate as the new alliance formation, and then proceed to the next traversal until the overall secrecy and rate no longer increase. The specific operation is as follows: Traverse all nodes according to the switching partial order rules. The utility of the current alliance formation is... For nodes node Traversal of the selection alliance and from Switch to Use step 3.2 to calculate the maximum overall secrecy and rate under the new alliance formation. Compare and And store the result u m,m′ [n]. If it exists Satisfy u m,m′ If [n] > 0, find the largest u. m,m′ The corresponding alliance is formed at [n] and the current alliance is updated by setting flag-switch←1; otherwise, flag-switch←0 is set. Repeat the above steps until flag-switch←0. Traverse all nodes according to the described partial order swapping rule. The utility of the current alliance formation is... For nodes node Traversal selection Then exchange alliances with node n′. Calculate the maximum overall secrecy and rate of return under the new alliance formation using step 3.2. Compare and And store the result v m,m′ [n, n′]. If it exists Satisfy v m,m′ If [n,n′]>0, find the largest v. m,m′The alliance corresponding to [n,n′] is formed and updated, setting flag-exchange←1; otherwise, flag-exchange←0 is set. The above steps are repeated until flag-exchange←0, resulting in a stable alliance. Under stable alliance formation, step 3.2 is used to solve the underlying non-convex optimization problem, obtaining the UAV deployment result and ground node power allocation result that maximize overall security and efficiency. Based on the UAV deployment result and ground node power allocation result, the overall security and efficiency are maximized.

[0162] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is used to explain the present invention. It is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A secure communication method for joint UAV deployment, power allocation, and coalition game theory, characterized by: Includes the following steps, Step 1: Establish a three-dimensional Cartesian coordinate system, construct an expression for the received signal strength of the UAV during energy harvesting on the laser link, and construct the conditions that must be met to ensure the UAV's endurance; establish a channel model between ground nodes and UAVs and a channel model between N ground nodes and eavesdroppers; in the presence of RF eavesdroppers and artificial noise AN sent by non-communication nodes to eavesdroppers, calculate the secure communication rate between ground nodes and UAVs in each alliance, and establish constraint equations for the power of ground nodes in each alliance; Step Two: Use Let M be the set of all alliances; all ground nodes form M non-overlapping alliances; alliances are classified into three types based on the number of nodes in each alliance: empty alliance, single-node alliance, and multi-node alliance; and the maximum alliance secrecy rate is defined for empty alliance, single-node alliance, and multi-node alliance. With the objective of maximizing the overall secrecy rate, and under constraints of UAV endurance, ground node power, and the maximum allowed number of nodes in each alliance, an optimization problem is established to maximize the overall secrecy rate. Step 3: Optimize the overall confidentiality and efficiency established in Step 2. This is equivalent to a two-level optimization problem. In the upper-level optimization problem, all ground nodes form M non-overlapping alliances, and each node has the opportunity to change the alliance formation through "switching" or "exchanging" actions. In the lower-level optimization problem, the UAV simultaneously acts as the receiver of the M alliances. The UAV's hovering position is deployed, and the transmit power and cooperative interference power of each node in each alliance are redistributed. The block coordinate descent (BCD) method is used to solve the lower-level optimization problem, which is decomposed into two non-convex subproblems: Subproblem 1: Optimize UAV deployment given the power allocation of ground nodes; Subproblem 2: Optimize the power allocation of ground nodes given the UAV deployment. By introducing auxiliary variables and using the successive convex approximation (SCA) method, these two subproblems are transformed into two convex function difference DC programming problems and a second-order cone SOC programming problem, respectively. The UAV deployment result is obtained by solving the two DC programming problems, and the power allocation result of the ground nodes is obtained by solving the SOC programming problem. Iteratively solving subproblems one and two allows the underlying problem to converge in a finite number of iterations, yielding the UAV deployment result and the power allocation result of the ground nodes that maximize overall security and efficiency. In the upper-level optimization problem, based on the two-step alliance formation game theory (TCFG) algorithm, the alliance formation is changed by the "switching" or "exchanging" actions of nodes until a stable alliance is obtained. Under the stable alliance formation, the lower-level non-convex optimization problem is solved to obtain the UAV deployment result and the power allocation result of the ground nodes that maximize the overall security and efficiency. The overall security and efficiency are maximized based on the UAV deployment result and the power allocation result of the ground nodes.

2. The secure communication method for joint UAV deployment, power allocation, and coalition game as described in claim 1, characterized in that: The implementation method for step one is as follows: Step 1.1: Use E and U represent the sets of ground nodes, ground eavesdroppers, and drones, respectively; the location of LAP is fixed, represented as [x LAP ,y LAP ,z LAP The horizontal coordinate of the nth node is represented by q. n =[x n ,y n The horizontal coordinate of E is represented by q. E =[x E ,y E ], and their vertical coordinates are all 0; the horizontal and vertical coordinates of the drone are represented as q. U =[x U ,y U ] and z U ; Step 1.2: Construct the expression for the received signal strength of the UAV during energy harvesting via the laser link, and establish the endurance conditions required to ensure the UAV's continued operation; the power consumed by the rotorcraft UAV in hovering state is expressed as... P U,hov =P b +P i (1) In the formula P b and P i These are the airfoil power and the induced power, respectively; LAP has a fixed emitter power P. L The intensity of the laser signal received by the drone is in Let denot be the distance between the UAV and the LAP, D be the initial size of the laser beam, χ be the optical efficiency of the joint transmitter-receiver, θ be the angular spread, A be the area of ​​the collecting lens, and κ be the attenuation coefficient of the medium. The energy harvesting module on the UAV has a constant laser power harvesting efficiency ω∈(0,1), and the laser power harvested by the UAV is expressed as: P h It is about d UL The function is monotonically decreasing; κ and θ are both very small constant values, making the laser power harvesting less affected by the transceiver distance than the RF power harvesting; the condition that the drone's endurance needs to meet is... P h ≥P U,hov . (4) Step 1.3: Establish channel models between ground nodes and UAVs, and between ground nodes and eavesdroppers; in the presence of RF eavesdroppers, cooperative interference nodes in non-transmission time slots send ANs to the eavesdroppers, and calculate the secure communication rate between ground nodes and UAVs in each alliance; for multi-node alliances... Analyze the power allocation among nodes in the alliance; assume nodes... For communication nodes, nodes n′≠n is the cooperative interference node selected by node n; the line-of-sight LoS model is used as the air-to-ground communication model, and the channel gain g between node n and the UAV is... U,m [n] follows the free-space path loss model; the channel gain g between node n and the eavesdropper. E,m [n] follows a free-space path loss model and a small-scale Rayleigh fading distribution; g U,m [n] and g E,m [n] are respectively represented as Where β0 is the channel gain with a reference distance of 1m between the ground node and the drone, and β1 is the channel gain with a reference distance of 1m between the ground node and the eavesdropper; It is a random variable with a unit mean, describing the exponential distribution of Rayleigh fading; This represents the distance between node n and the drone. Let represent the distance between node n and the eavesdropper; under LoS propagation conditions, the communication rate between node n and the drone is . Where p s,m [n] represents the transmit power of node n, N 0,U The noise power on the drone is represented by γ0 = β0 / N. 0,U The eavesdropping rate between node n and the ground eavesdropper is expressed as: Where N 0,E The noise power of the eavesdropper is represented by γ1 = β1 / N 0,E Node n′, acting as a cooperative interferer to node n, sends a signal with power p to the eavesdropper. j,m For AN[n], according to Shannon's capacity formula, the eavesdropping rate between node n and the ground eavesdropper is expressed as: in This represents the channel gain between node n′ and the eavesdropper; When node n′ sends a jamming signal to the eavesdropper, the communication security rate between node n and the drone is expressed as: Among them, the operator [·] + To make equation (10) less smooth at the zero value, remove [·]. + This does not affect the solution of the optimal value of equation (10) and can make equation (10) smooth at the zero value. Equation (10) is transformed into: According to formulas (9)(10)(11), the existence of cooperative interference is beneficial to suppressing the eavesdropping rate and improving the confidentiality performance of node n; Establish the power constraint equations for the ground nodes in each alliance; let the average power of each node be P. ave Peak power is P peak P ave ≤P peak For the m-th alliance set up Contains N m There are nodes, and the total available power is N. m ×T×P ave The sequence is reallocated within time period T; and sequence Indicates alliance The set of transmit power and cooperative jamming power of the mid-node; The power constraint equation in is expressed as follows:

3. The secure communication method for joint UAV deployment, power allocation, and coalition game as described in claim 2, characterized in that: The second step is implemented as follows: Step 2.1: Use This represents the set of all alliances; all ground nodes form M non-overlapping alliances; alliances are classified into three types based on the number of nodes in the alliance: empty alliance, single-node alliance, and multi-node alliance, and the maximum alliance secrecy rate is defined for empty alliance, single-node alliance, and multi-node alliance. The empty alliance has no nodes, and its maximum alliance secrecy rate is always zero. The single-node alliance There is only one node n in the single-node alliance. During the time period T, this node completely occupies a subcarrier channel, but there are no cooperating interference nodes. The maximum alliance secrecy and rate of a single-node alliance are determined by g. U,m [n] and g E,m [n] determines; when g U,m [n]>g E,m [n], the maximum alliance secrecy and rate Conversely, if the maximum confidentiality rate of the alliance is 0, then the optimal behavior of node n is not to transmit data. The multi-node alliance There are two or more nodes in the network; nodes in the same alliance allocate communication time slots equally and communicate with the UAV using Time Division Multiple Access (TDMA); nodes in the same communication time slot select one node within the alliance as a cooperative jammer to send an AN to the eavesdropper; multi-node alliance. The maximum alliance secrecy and efficiency are obtained by optimizing the power of UAV deployment and ground nodes in step 3.2; Step 2.2: With the goal of maximizing overall security and efficiency, and under the constraints of UAV endurance, ground node power, and the maximum number of nodes allowed by each alliance, establish an optimization problem to maximize overall security and efficiency. (4)，(12)，(13)，(14) Among them, C max This represents the maximum number of nodes allowed in a consortium. Indicates the flight area of ​​the drone. and They represent The set of transmit power and cooperative jamming power of each node.

4. The secure communication method for joint UAV deployment, power allocation, and coalition game as described in claim 3, characterized in that: The method for implementing step three is as follows: Step 3.1: In the upper-level optimization problem, all ground nodes form M non-overlapping alliances, and each node has the opportunity to change the alliance formation through "switching" or "exchanging" actions; generate the initial alliance formation; Step 3.2: In the underlying optimization problem, the UAV simultaneously acts as the receiver for M alliances. The UAV hovering positions are deployed, and the transmit power and cooperative interference power of each node in each alliance are redistributed. The BCD method is used to solve the underlying optimization problem, which is decomposed into two non-convex subproblems: Subproblem 1: Optimize UAV deployment given the power allocation of ground nodes; Subproblem 2: Optimize the power allocation of ground nodes given the UAV deployment. Auxiliary variables are introduced, and SCA technology is used to transform these two subproblems into two DC programming problems and one SOC programming problem, respectively. The UAV deployment result is obtained by solving the two DC programming problems, and the power allocation result of the ground nodes is obtained by solving the SOC programming problem. Iteratively solving subproblems one and two allows the underlying problem to converge in a finite number of iterations, yielding the UAV deployment result and ground node power allocation result that maximizes overall security and efficiency. Step 3.3: In the upper-level optimization problem, based on the TCFG algorithm, the alliance game method is used to change the alliance formation through the "switching" or "exchanging" actions of nodes until a stable alliance is obtained. Under the stable alliance formation, the lower-level non-convex optimization problem is solved to obtain the UAV deployment result and the power allocation result of the ground nodes that maximize the overall security and efficiency. The overall security and efficiency are maximized based on the UAV deployment result and the power allocation result of the ground nodes.

5. The secure communication method for joint UAV deployment, power allocation, and coalition game as described in claim 4, characterized in that: Step 3.1 is implemented as follows: Step 3.1.1: The alliance formation optimization problem is expressed as... st(16), In the formula This indicates the maximum alliance secrecy and security rate under the current alliance formation. Step 3.1.2: Generate the initial alliance formation and calculate the maximum overall confidentiality and rate under the alliance formation according to Step 3.2; set flag-switch←1 and flag-exchange←1.

6. The secure communication method for joint UAV deployment, power allocation, and coalition game as described in claim 5, characterized in that: Step 3.2 is implemented as follows: Step 3.2.1: Based on the overall confidentiality and rate maximization problem established in Step 2.2, and with a fixed alliance formation, the underlying optimization problem is expressed as follows: st(4),(12),(13),(14),(17) Using the BCD method The problem is decomposed into two sub-problems: Sub-problem 1: Optimize UAV deployment given the power allocation of ground nodes; Sub-problem 2: Optimize the power allocation of ground nodes given the UAV deployment. By introducing auxiliary variables and using the SCA method, these two sub-problems are transformed into two DC planning problems and one SOC planning problem, respectively. The UAV deployment result is obtained by solving the two DC planning problems, and the power allocation result of the ground nodes is obtained by solving the SOC planning problem. Step 3.2.2: Optimize UAV deployment given the power allocation of ground nodes. The optimization problem is expressed as follows: st(4), (17) Where [x] m [n],y m [n]] and p s,m [n] represents the alliance. The horizontal coordinates and transmission power of node n; since the objective function in equation (20) is non-convex and the constraints in equation (4) are non-convex, therefore This is a nonconvex optimization problem; in order to transform (20) into a solvable form, auxiliary variables are introduced. The objective function in equation (20) and the constraints in equation (4) are relaxed by t and t respectively; the objective function in equation (20) is converted into a concave function by using the SCA method; the constraints in equation (4) are converted into two equivalent constraints and written as two convex function difference forms; Rewritten as (x U -x LAP ) 2 +(y U -y LAP ) 2 +(z U -z LAP ) 2 ≤t 2 , (23) (P b +P i )×(t·θ+D) 2 -P L ωAχe -κt ≤0, (24) (17) The non-concavity of the objective function in equation (21) and the non-concavity of the constraints in equations (23) and (24) are handled by first-order Taylor expansion; It's about u m The convex function of [n], and with the help of a first-order Taylor expansion, is obtained in the (l+1)th iteration. in is u m [n] is the value at the l-th iteration; t in equations (23) and (24) 2 and e -κt It was expanded with the help of the first-order Taylor expansion to t 2 ≥-(t (l) ) 2 +2tt (l) , (26) and question Rewritten as s.t.(x U -x LAP ) 2 +(y U -y LAP ) 2 +(z U -z LAP ) 2 +(t (l) ) 2 -2tt (l) ≤0, (29) (17)，(26) The objective function in equation (28) is concave with respect to u, and the constraints in equations (29) and (30) are concave with respect to {x}. U ,y U ,z U The inequality {u,t} is convex; therefore, the interior point method can be used to solve the convex optimization problem. Obtain the drone deployment results; Step 3.2.3: Optimize power allocation for ground nodes given a drone deployment; for alliances The optimization problem is represented as The objective function in equation (31) is a highly complex function containing multiple optimization variables and random variables; considering the optimization problem To improve robustness, the objective function in equation (31) is tightened, and the negative terms in equation (31) are taken to their maximum values, expressed as follows: The second term of the objective function in equation (32) contains random variables in both the numerator and denominator. Therefore, the objective function in equation (32) can be considered as a random variable. The function, for ease of solving, will be... Expressed as in Jensen's inequality is applied to handle the "-max" in the objective function of equation (32); specifically, the calculation of equation (32) is performed using the inequality. upper bound and The lower bound of the function; when node n is the sender and node n′ is a cooperative jammer, find the maximum eavesdropping rate from node n to the eavesdropper; since the function The concavity of the material yields... for Its lower bound is based on Jensen's inequality and ln(1+e) x The concavity and convexity of ) are obtained in The parameter is An exponentially distributed random variable, and has Where ε is the Euler constant; substituting equation (36) into equation (35), we get The lower bound is According to formulas (33)(34)(35)(36)(37), when n′ is used as When there are cooperative interference nodes, the maximum eavesdropping rate from node n to the eavesdropper is: Therefore, the optimization problem Rephrased as The objective function in equation (39) is highly complex; to facilitate observation, variables are introduced to... Transform it into an equivalent optimization problem, denoted as: in and The non-convex constraints in equations (44), (45), and (46) are equivalently written as Linearizing the non-convex constraints in equations (47), (48), and (49) around the current point in the l-th iteration, we obtain... Therefore, the optimization problem Rephrased as st(12),(13),(14),(41),(42),(43),(50),(51),(52) Optimization problem This is an optimization problem involving SOC constraints, which is solved using CVX to obtain the power allocation results for the ground nodes; Step 3.2.4: Utilize the optimization problems from Steps 3.2.2 and 3.2.3 and The deployment results of the UAVs and the power allocation results of the ground nodes are obtained separately, and the solution is iteratively obtained. and The process continues until convergence, ultimately yielding the results of the drone deployment and the power allocation for the ground nodes.

7. The secure communication method for joint UAV deployment, power allocation, and coalition game as described in claim 6, characterized in that: Step 3.3 is implemented as follows: Step 3.3.1: In the upper-level optimization problem, the framework and definition of the developed TCFG algorithm are as follows; the TCFG algorithm divides all nodes into M non-overlapping federations. The alliance formation is updated using two alliance rules: "switch partial order" and "exchange partial order". Each node follows the same alliance update rules and has its own alliance formation priority. After traversing all nodes, the alliance with the highest overall secrecy and rate becomes the updated alliance formation. Consider the nodes... Current alliance formation Move node n from Switch to The utility is expressed as in The alliance is formed after node n moves; and They are based on and The maximum overall confidentiality and rate are obtained; therefore, u m,m′ [n] describes the current alliance formation. The next node n from Switch to The contribution; the definition of the switching partial order is as follows; if there is a node and Alliance m≠m′ satisfies the following condition: Remove node n from the alliance Switch to Consider two nodes and m′≠m, the current alliance is formed The utility of a node exchange alliance is in It is an alliance formed after node swaps; therefore, v m,m′ [n,n′] describes the current alliance form. Two nodes in the exchange alliance and The contribution; the definition of the exchange partial order is as follows; if there are two nodes m′≠m satisfies the following condition: Swap the alliances to which nodes n and n′ currently belong; Step 3.3.2: Traverse all nodes according to the switching partial order and swapping partial order rules; in each traversal, select the alliance formation corresponding to the maximum overall secrecy and rate as the new alliance formation, and then perform the next traversal until the overall secrecy and rate no longer increase; the specific operation is as follows: traverse all nodes according to the switching partial order rules; the utility of the current alliance formation is For nodes node Traversal of the selection alliance m≠m′, and from Switch to Use step 3.2 to calculate the maximum overall secrecy and rate under the new alliance formation. Compare and And store the result u m,m′ [n]; if it exists m′≠m satisfies u m,m′ If [n] > 0, find the largest u. m,m′ The corresponding alliance is formed at [n] and the current alliance is updated by setting flag-switch←1; otherwise, flag-switch←0 is set; repeat the above steps until flag-switch←0; traverse all nodes according to the swap partial order rule; the utility of the current alliance formation is For nodes node Traversal selection m′≠m and exchange alliances with node n′; use step 3.2 to calculate the maximum overall secrecy and rate under the new alliance formation. Compare and And store the result v m,m′ [n,n′]; if it exists m′≠m satisfies v m,m′ If [n,n′]>0, find the largest v. m,m′ When an alliance is formed corresponding to [n,n′], the current alliance is updated, and flag-exchange←1 is set; otherwise, flag-exchange←0 is set. Repeat the above steps until flag-exchange←0, which results in a stable alliance. Under the stable alliance, use step 3.2 to solve the underlying non-convex optimization problem to obtain the UAV deployment result and the power allocation result of the ground node that maximizes the overall security and efficiency. Maximize the overall security and efficiency based on the UAV deployment result and the power allocation result of the ground node.

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