Secure covert communication method based on cognitive radio

Abstract

The invention discloses a secure covert communication method based on cognitive radio. A cognitive radio framework of a delay-tolerant relay covert unmanned aerial vehicle (UAV) and a plurality of collusion eavesdroppers and listeners is researched. In the framework, a legal unmanned aerial vehicle serves as an air relay, and communication is achieved when a direct link between a ground transmitter and a receiver is blocked. Then, in consideration of the uncertainty of the positions of a plurality of eavesdropping nodes and monitoring nodes, a robust optimization problem is constructed. According to the method, the trajectory and power of the unmanned aerial vehicle and the transmitting power of a transmitting node are jointly optimized, a processable version is constructed by using a Pessak inequality, a Zensen inequality and a binary search method for extremely complex hidden constraints, and then an algorithm based on alternate optimization is proposed to solve the optimization problem. Low complexity is achieved, and an algorithm based on primal-dual search and an algorithm based on continuous convex approximation are designed for each sub-problem. A numerical result proves the effectiveness of the proposed algorithm.

Description

Technical Field

[0001] The present invention mainly relates to the field of wireless communications, and in particular to a secure covert communication method based on cognitive radio. Background Art

[0002] The statements in this section only provide background information related to the present disclosure, and these statements may constitute prior art. In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art.

[0003] Drones have attracted widespread attention due to their high probability of establishing links with ground nodes in high-rise urban areas in the presence of eavesdroppers. In the context of the current rapid development of technology, wired communication networks have gradually shown their limitations and cannot fully meet the diverse needs of contemporary society for communication services. The development of the new generation of wireless communication technology not only focuses on the continuous improvement of transmission speed, but more importantly, this technology must have energy-saving characteristics on the basis of ensuring communication security and stability. The system should be flexible and adaptable, and be able to provide differentiated service quality according to the personalized needs of different users. Such a wireless communication system can better adapt to the complex changes in modern society. At present, the application prospects of drone communication technology in 5G networks and future communication networks are broad, and the development momentum is rapid, such as the integration of drones and ground communication systems, and the role of drones in communication networks. However, drone communication technology also faces many challenges. For example, the literature Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

[0004] It is pointed out that the emergence and large-scale application of drones have broken the limitations of traditional land communications, greatly promoted the development of the Internet of Things and the integrated network of "air, land, and sea", and laid a strong foundation for the realization of the next-generation mobile communication network of "Internet of Everything". The existing communication systems designed based on drone trajectories are classified into base station drone communication systems, relay drone communication systems, and drone auxiliary communication systems for aerial users according to the different roles played by drones in wireless communication networks. For example, the typical base station drone proposed in the literature [Qingqing Wu, Yong Zeng, Rui Zhang. Joint trajectory and communication design for Multi-UAV enabled wireless networks [J]. IEEE Transactions on Wireless Communications, 2018, 17 (3): 2109-2121.] maximizes the system transmission rate by jointly optimizing the drone transmission power, drone flight trajectory and user scheduling coefficient. Reference [Ju-Hyung Lee, Ki-Hong Park, Young-Chai Ko, Mohamed-Slim Alouini. Throughput maximization of mixed FSO / RF UAV-aided mobile relaying with a buffer [J]. IEEE Transactions on Wireless Communications, 2021, 20 (1): 683-694.] By designing the flight trajectory of the relay UAV as an energy buffer, the signal transmission rate in the FSO (Free-Space Optical) link and the RF (Radio Frequency) link are matched to maximize the system transmission throughput. Reference [Weiran Luo, Yanyan Shen, Bo Yang, Shuqiang Wang, Xinping Guan. Joint 3-D trajectory and resource optimization in Multi-UAV-Enabled IoT networks with wireless power transfer [J]. IEEE Internet of Things Journal, 2021, 8 (10): 7833-7848.] studies the design of flight trajectories for aerial user UAVs in the Internet of Things to achieve maximum and minimum data collection.

[0005] The openness and broadcasting nature of wireless communications make signal transmissions easy to be intercepted and interfered with. Attackers can use these characteristics to steal transmitted data or interfere with signals, thereby stealing sensitive information or disrupting the normal operation of the network. The literature [Wang Di. Theoretical Research on Physical Layer Security of UAV Communication Systems [D]. Chongqing University of Posts and Telecommunications 2020.] explains that physical layer security is a supplementary security technology. Its core principle is to rely on the inherent unpredictability of wireless channels and use information theory as a basic architecture. By flexibly changing transmission strategies and parameters to cope with the random characteristics of physical media and the differences between legitimate channels, the security of information during transmission is ensured. In short, this method uses the natural characteristics of wireless channels to dynamically adjust the transmission mode to ensure the security of data transmission.

[0006] However, at present, the above content is mostly seen in cognitive radio or simply considering covert communication, such as the literature H.Lei, J.Jiang, H.Yang, K.-H.Park, ISAnsari, G.Pan, and M.-S.Alouini, "Trajectory and power design for aerial CRNs with colluding eavesdroppers," doi:arXiv:2310.13931Oct2023, [Online]:https: / / doi.org / 10.48550 / arXiv.2310.13931. Given the inaccurate position of the receiving end and the eavesdropper, the communication rate of the entire communication process is maximized by jointly optimizing the transmission power of the drone relay and the hovering position of the drone. We explore the use of drones as aerial relay nodes to transmit signals when the direct communication path between the transmitter and the receiver is blocked. In order to ensure the confidentiality of the communication, the drone also plays the role of cooperative interference to suppress the ground nodes that attempt to monitor. Considering the location changes of potential malicious nodes, we construct an optimization model that aims to maximize the transmission rate by collaboratively optimizing the flight path and output power of the drones.

[0007] However, in the literature on covert communication, such as J. Jiang, H. Lei, K.-H. Park, G. Pan, and M.-S. Alouini, "Aerial relay to achieve covertness and security.," doi:arXiv:2406.06842Jun.2024, [Online]:https: / / arxiv.org / abs / 2406.06842., the maximum confidentiality rate discussed between a single listener and a single eavesdropper is discussed. This paper studies the model of cognitive wireless networks. At the same time, multiple ground monitors are located near the transmitter, trying to monitor the communication process, while another group of ground eavesdroppers are located near the receiver, trying to intercept sensitive information, aiming to maximize the transmission rate by collaboratively optimizing the flight path and output power of the drone. Summary of the invention

[0008] In view of the above problems, the object of the present invention is to solve part of the problems in the prior art, or at least alleviate these problems.

[0009] The technical solution adopted by the present invention is a secure covert communication method based on cognitive radio, comprising the following steps:

[0010] Build a communication system model: including a transmitter S, a receiver D, multiple eavesdroppers E, and multiple listening terminals W; set the drone R as a relay node of the communication network to fly along a specific trajectory to achieve communication between the blocked transmitter and receiver; for the eavesdroppers E and listening terminals W with uncertain locations in the communication network, the drone R is set as a jammer and transmitter in stages to achieve the security and concealment of the communication system;

[0011] Specifically, an aerial relay system is considered in this model, where the direct link between S and D is blocked. In the presence of multiple primary users and multiple malicious FD eavesdroppers who cooperate with each other, S transmits information to cognitive user D with the help of aerial relay R. UAV R is equipped with receiving antennas and transmitting antennas, and uses FD mode for reception and interference. All nodes are single antennas, and the ground eavesdropper W near S tries to decide whether S sends, while multiple FD eavesdroppers E on the ground near D want to eavesdrop on the information sent by S to D, and also send interference to reduce the reception quality of the legitimate link. Some ground nodes are also single antennas, and the SIC at R takes into account the residual self-interference channel.

[0012] With the goal of maximizing the secure transmission rate of the communication system, a transmission power P S 、UAV flight trajectory Q R And the transmission power P Ris the optimization mathematical model of variables; among them, P R is the transmission power of UAV R, P S is the transmission power of the transmitter;

[0013] Perform fractional decoupling on the established optimization mathematical model to obtain the processed optimization mathematical model;

[0014] The processed optimization mathematical model is decoupled to obtain two sub-problems about the transmission power and the flight trajectory of the UAV. For each non-convex sub-problem, a convex approximation fitting method is used to convert it into a convex problem for solution.

[0015] An iterative algorithm is used to solve the optimal transmission power and the flight trajectory of the UAV to ensure higher security transmission efficiency while ensuring the concealment of the transmitter.

[0016] The present invention also provides a communication system, including a sending end S, a receiving end D, multiple eavesdropping ends E, multiple monitoring ends W and a drone R. The communication system adopts the above-mentioned secure and covert communication method based on cognitive radio during communication.

[0017] The present invention has the following beneficial effects:

[0018] Aiming at the actual situation that there are multiple eavesdroppers and monitors with uncertain positions in the scenario where the transmitting end and the receiving end cannot communicate directly due to obstacles, the present invention proposes a scheme in which drones act as relays and jammers in stages to combat eavesdropping and monitoring in a collaborative communication manner. With the goal of maximizing the system's secure transmission rate, the transmission power P of the airspace relay drone R in the communication network is set to be maximally transmitted. R , the transmitting power P of the transmitter S And the flight trajectory Q of the airspace relay drone R R The present invention optimizes the best path and transmission power to ensure the maximum transmission rate while ensuring the concealment of the transmitting end. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A communication system model of the present invention;

[0020] Figure 2 The optimal flight trajectory map for the UAV;

[0021] Figure 3 is the power diagram of the UAV at T = 100s;

[0022] Figure 4 is the power diagram of the UAV at T = 70s;

[0023] Figure 5 To maximize the concealment rate graph, T = 100s;

[0024] Figure 6 It is the algorithm iteration convergence diagram, T = 100s. DETAILED DESCRIPTION

[0025] The present invention is further described below in conjunction with the accompanying drawings. The embodiments of the present invention are only used to illustrate the present invention rather than to limit the present invention. Without departing from the technical idea of ​​the present invention, various substitutions and changes are made according to common technical knowledge and customary means in the field, which should all be included in the scope of the present invention.

[0026] In order to utilize the high mobility and flexibility of drones to cope with complex communication environments, the present invention constructs a drone-assisted secure and covert communication system in a cognitive radio environment. On the basis of ensuring the security of the communication system, the present invention strives to improve the service quality of the communication system. A secure and covert communication method based on drone relay is proposed, and through theoretical analysis and simulation experiments, the flight path of the drone is planned, aiming to achieve the confidentiality of the communication system and maximize the energy efficiency of communication transmission. A secure and covert communication method based on drone relay includes the following steps:

[0027] Building a communication system model: Figure 1 As shown, it includes a sending end S, a receiving end D, multiple eavesdropping ends E and a listening end W; a relay drone R is set as a relay node of the communication network to fly along a specific trajectory to achieve communication between the blocked sending end and receiving end; for the eavesdropping end E and the listening end W with uncertain positions in the communication network, the security and concealment of the communication system are achieved by setting the relay drone R as a jammer and transmitter in stages.

[0028] With the goal of maximizing the secure transmission rate of the communication system, a transmission power P R , P S And the drone flight trajectory Q R is a mathematical model of the variables.

[0029] The established mathematical model is decoupled by fractions to obtain the processed optimized mathematical model.

[0030] The processed optimization mathematical model is decoupled to obtain three sub-problems about the transmission power and the flight trajectory of the UAV. For each non-convex sub-problem, a convex approximation fitting method is used to convert it into a convex problem for solution.

[0031] An iterative algorithm is used to solve the optimal transmission power and UAV flight trajectory to ensure higher security transmission efficiency while ensuring the concealment of the transmitter.

[0032] In order to solve the problem that the transmitter and receiver cannot communicate directly due to obstacles, and in complex situations where there are potential eavesdroppers, the present invention proposes a new strategy, which is to use drones to act as communication relays and jammers at different stages to collaboratively protect communications from eavesdropping. The goal of this strategy is to optimize the transmission rate of drones in the communication network and ensure the security of transmission. This includes dynamically adjusting and designing the transmission power of the drone relay, the transmission power of the transmitter, and the flight route of the drone. Through these optimization measures, the present invention aims to ensure the concealment of the communication process while improving the security of the communication system. This method not only improves the energy efficiency of secure transmission, but also is suitable for changeable and complex communication scenarios.

[0033] The phased setting includes a signal collection phase (i.e., the first phase) and a signal forwarding phase (i.e., the second phase); the processed optimization mathematical model performs a decoupling operation based on the BCD method, and the BCD method decouples the original optimization problem into a transmission power P by fixing the optimization variables. R , P S And the relay drone flight trajectory Q R sub-problem of .

[0034] Its working principle is:

[0035] Based on wireless communication technology, physical layer security principles, and the basic principles of covert communication, we have constructed a wireless communication system model with drones as relays. In this model, given that the location information of eavesdroppers and monitors cannot be accurately known, we must consider the problem-solving capabilities in practical applications, that is, robustness. Therefore, we transformed the original problem into a problem of how to effectively use drones for relay-assisted communication under the most unfavorable circumstances. In other words, we need to design a drone relay-assisted communication system model that can maintain communication security and reliability in the worst-case scenario in response to potential eavesdropping and monitoring threats, so as to ensure that the system's communication performance and security can be effectively guaranteed even in extreme situations.

[0036] Secondly, the mathematical theory derivation and problem solving analysis in the model building process were improved. In view of the non-convexity and high coupling of the optimization problem corresponding to the system model, an approximate fitting method based on the Block Coordinate Descent (BCD) algorithm and continuous convex approximation was used to decouple and transform the original optimization problem.

[0037] Finally, the present invention adopts an iterative method to process each sub-problem through a continuous cycle process, so that the system's secure transmission rate gradually approaches a specific threshold. This threshold represents the goal pursued by the present invention, that is, the maximum system secure transmission rate. In other words, through continuous iterative optimization, we gradually stabilize the system's secure transmission rate to a fixed value, which corresponds to the highest system secure transmission energy efficiency level we expect to achieve. The specific steps are:

[0038] S1: Build a communication system model.

[0039] Figure 1 This is a schematic diagram of the model of the entire solution, which includes a sender S, a receiver D, multiple eavesdropping terminals E, and multiple listening terminals W. Here, U 1 ,U 2 ,W 1 ,W 2 ,W 3 and E represent the horizontal coordinates of the two main users, three listeners, and the eavesdropping end in the communication network. The relay drone R flies at a fixed altitude H. Definition as well as They represent the horizontal coordinates of the eavesdropping end E and the monitoring end W estimated by the relay drone through the airborne radar, as well as It means that the estimated errors made by the relay drone satisfy ||Δq E ||≤r E and ||Δq W ||≤r W , where r E represents the maximum error in estimating the location of the eavesdropping end, q E ,q W They represent eavesdropping and monitoring, respectively. The channels between the relay drone R and the transmitter and receiver are defined as legal links, and the channels associated with the eavesdropping and monitoring ends are defined as illegal links.

[0040] Among them, the relay drone R, as the aerial relay device of the communication network, adopts a phased communication method, dividing the entire communication process into two stages: signal collection and signal forwarding. At the same time, the relay drone R also acts as a jammer to interfere and suppress the listening end during the signal collection stage.

[0041] S2: With the goal of maximizing the secure transmission rate of the communication system, a structure based on the transmission power P of the UAV and the transmitter is constructed. R , P S And the drone flight trajectory Q R is a mathematical model of the variables.

[0042] The signal-to-interference-noise ratios of the relay drone R in the signal collection phase and the receiving end D in the signal forwarding phase are:

[0043]

[0044] Where P S (n) They represent the transmission power of the transmitter S and the transmission power of the interference signal of the drone R in the signal collection phase, respectively. SR (n), h RR and h RD (n) respectively represent the channel gain from the transmitter S to the relay drone R, and the self-interference channel generated when the relay drone R works in full-duplex mode (the channel gain of this channel is determined by the size of the self-interference elimination residual, and satisfies the normal distribution with a mean of 0 and a variance of ψ, that is, it satisfies the relationship E(h RR 2 )=ψ, where the size of ψ represents the size of the self-interference cancellation residual) and the channel gain from the relay drone R to the receiving end D. σ 2 Indicates the power of additive Gaussian white noise. n represents the nth time slot, N 1 , N 2 Represent the uplink and downlink time slots respectively.

[0045] The signal-to-interference-to-noise ratio of the eavesdropping end can be expressed as:

[0046]

[0047] In the formula, Represents the channel gain between the relay drone R and the eavesdropping end E. R is the drone location, The location center of the eavesdropping node, The radius within which the eavesdropper's location is uncertain.

[0048] According to the above, the average achievable transmission rate of the communication system can be expressed as:

[0049]

[0050] In the formula It represents the instantaneous achievable transmission rate from S to R in the nth time slot during the signal collection phase. sec 2 (n) = [R D (n)-R E (n)] + represents the instantaneous safe transmission rate achievable from R to D in the nth time slot during the signal forwarding phase, where are the instantaneous achievable transmission rates from R to D in the nth time slot and from R to E in the nth 0th time slot. B is the channel coefficient, is the channel coefficient from the i-th eavesdropper to the received signal, is the power of the ith eavesdropper, and represents the channel coefficient between the i-th eavesdropper and the j-th eavesdropper and drone, K is the number of eavesdroppers; N represents the total uplink and downlink time slots.

[0051] For the average achievable transmission rate R ave In order to eliminate the operator E(·) and the position estimation error, we take its lower bound as follows:

[0052]

[0053] In the formula, The lower bound is obtained by linearly expanding the convex function expression using Jensen's inequality.

[0054] in β 0 represents the channel gain per unit distance, H represents the fixed altitude at which R flies, and it is not difficult to know through the trigonometric inequality q R ,q D and UAV, the location of the receiving signal node and the i-th UAV, is a Gaussian random variable.

[0055] According to the modeling of the UAV flight trajectory in the previous article, v R (n) can be expressed as

[0056] ||q R (n)-q R (n-1)||≤v max , n∈N 1

[0057] ||q R (n)-q R (n-1)||≤v max , n∈N 2

[0058] v R (n) and v max represents the speed and maximum speed of the UAV in the nth time slot, q R (n) and q R (n-1) represents the position of the drone at the nth time slot and the n-1th time slot.

[0059] In order to ensure the concealment of the communication process, the concealment quality control parameter ε constraint is set, that is:

[0060]

[0061] In the formula, where h SW 、h RW (n) represents the channel gain between the transmitter S and the listener W and the channel gain between the relay drone R and the listener W. I represents the time slot when transmitting the signal, and represents the channel coefficient from the monitoring node to the signal transmission point and the drone, and M represents the number of monitoring nodes. and about is monotonically increasing, so the constraint can be simplified to where γ max Can be obtained by dichotomy. SW (n) describes the signal-to-interference-to-noise ratio achieved by the listening end W in the nth time slot, Indicates the degree of demand for concealment quality during communication.

[0062] because There is an estimation error in the position of the listening end. We use the triangle inequality to eliminate the estimation error and obtain The upper bound of It satisfies the following size relationship:

[0063]

[0064] In the formula, They represent the channel coefficients from the monitoring node to the signal sending point and the UAV respectively.

[0065] in as well as r W is the radius of position uncertainty, η is the unknown coefficient in large-scale fading, and q S The locations where the signals are sent respectively.

[0066] About cognitive radio:

[0067]

[0068] in, is the interference power of the eavesdropping end, represents the probability density function, h RU and represents the channel coefficient from the primary user to the drone to the i-th eavesdropper, They represent the distances from the primary user to the UAV and the i-th eavesdropping point respectively.

[0069]

[0070] Thus, a complete optimization problem is constructed, that is, the optimization mathematical model is:

[0071]

[0072] stC1.R ave ≤ω

[0073]

[0074] C3.q R (N) = q F

[0075] C4.q R (1) = q I

[0076]

[0077] C7.||q R (n)-q R (n-1)||≤v max ,n∈N 1

[0078] C8.||q R (n)-q R (n-1)||≤v max ,n∈N 2

[0079]

[0080] In the above formula, P R , P S Respectively represent the transmission power of the UAV and the transmitter, Q R represents the flight trajectory of the drone, R ave represents the average safe achievable rate, ω represents the maximum average safe achievable rate, q R (N) and q R (1) represents the position of the drone in the first time slot and the last time slot, represents the maximum instantaneous transmission power of the transmitter S, represents the maximum instantaneous transmission power of UAV R, q F With q I They represent the flight position of the drone R at the initial moment and the flight position at the end moment, respectively, max Defines the maximum flight speed of the drone at any time; N 1 , N 2 Respectively represent the uplink and downlink time slots; P S (n) represents the transmission power of the transmitter S during the signal collection phase; PR (n) represents the transmission power of UAV R during the signal forwarding stage; and represents the channel coefficient between the drone and the two primary users, The power of the i-th UAV is defined, represents the distance between the UAV and the primary user, β 0 represents the channel gain, Γ is the threshold in cognitive radio, r represents the radius of the uncertain eavesdropping location, i represents the i-th time slot, represents the transmission power of the first primary user, constraint C2 is a hidden constraint, γ SW (n) describes the signal-to-interference-to-noise ratio achieved by the listening end W in the nth time slot, It indicates the demand for concealment quality during communication; C3~C4 limit the initial and final positions of the UAV flight; C5~C6 constrain the maximum instantaneous transmission power of the UAV and the transmitter during operation; C7~C8 limit the maximum speed of the UAV flight; C9~C10 ensure the power constraint of cognitive radio.

[0081] The UAV relay covert communication trajectory scheme proposed in the present invention involves a nonlinear optimization problem with highly correlated multiple parameters. The process of solving this problem will follow the following steps.

[0082] S3: Decouple the established mathematical model to obtain a processed optimized mathematical model.

[0083] The fractional optimization problem P1 (i.e., the optimization mathematical model) is fractionally decoupled based on the Dinkelbach method. The optimization problem P2 after decoupling the fraction based on the Dinkelbach method is:

[0084]

[0085] ω 1 is the slack variable when optimizing power, Respectively represent the uplink and downlink transmission rates.

[0086] S4: For the processed optimization mathematical model, decoupling operation is performed based on the BCD method to obtain two sub-problems about the UAV's transmission power and the UAV's flight trajectory. For each non-convex sub-problem, the convex approximation fitting method is used to convert it into a convex problem for solution.

[0087] Based on the Block Coordinate Descent (BCD) algorithm, problem P2 is decoupled and the transmission power P is obtained. R , P S And the drone flight trajectory Q RTwo sub-problems.

[0088] Regarding the transmission power P R , P S Subproblem P2.1 is:

[0089]

[0090] C3:P S |h SW | 2 ≤(P J R |h RW | 2 )γ max +σ 2 γ max

[0091]

[0092] The sub-problem P3 about the flight trajectory of the drone is:

[0093]

[0094] C4:q R (1) = q I

[0095] C5:q R (N) = q F

[0096] C6:||q R (n)-q R (n-1)||≤v max ,n∈N 1

[0097] C7:||q R (n)-q R (n-1)||≤v max ,n∈N 2

[0098]

[0099] ω 2 Represents the slack variables used when optimizing the UAV trajectory.

[0100] The obtained sub-problems are converted into convex optimization problems. It can be seen that both sub-problems are non-convex optimization problems. The following are the specific steps to convert them into convex optimization problems. It can be seen that both sub-problems are non-convex optimization problems. The following are the specific steps to convert them into convex optimization problems.

[0101] The subproblem P2.1 is solved by introducing slack variables and transforming it into a convex problem through continuous convex approximation:

[0102]

[0103] Among them, ω 1 is a slack variable, A(n) represents the part after convex approximation, which is specifically expressed as

[0104]

[0105] In the above formula, ω 1 is the slack variable, B is the bandwidth, represent the instantaneous achievable rate of each time slot in the first and second phases of communication respectively; at this time, the sub-problem P2.1 is about P R , P S is a standard convex optimization problem that can be solved by the interior point method.

[0106] The subproblem P3 is solved by introducing slack variables and converting it into a convex problem through continuous convex approximation (i.e., introducing slack variables ω 2 Then, the non-convex part of the constraint is expanded by a first-order Taylor expansion.

[0107] For , in order to solve the strong coupling, the constraint becomes:

[0108]

[0109] For subproblem P3, introduce the slack variable ω 2 , a(n), b(n), c(n), d(n) and e(n), the first-order Taylor expansion of the non-convex part of the constraint can be obtained. The subproblem P3.1 solves the optimization problem by introducing slack variables and continuous convex approximation:

[0110]

[0111] C4:q R (1) = q I

[0112] C5:q R (N) = q F

[0113] C6:||q R (n)-q R (n-1)||≤v max ,n∈N 1

[0114] C7:||q R (n)-q R (n-1)||≤vmax ,n∈N 2

[0115]

[0116] C9:a(n)≥||q S -q R || 2 +H 2

[0117] C10:b(n)≥||q R -q D || 2 +H 2

[0118]

[0119] C12:d(n)≤||q R -q U || 2 +H 2

[0120] C13:e(n)≥||q R -q W || 2 +H 2

[0121] In the above formula, ω 2 , a(n), b(n), c(n), d(n) and e(n) are slack variables introduced by the equivalent problem P3. a(n), b(n), c(n), d(n) and e(n) are the first-order Taylor expansions of the non-convex part.

[0122] a(n)≥||q S -q R || 2 +H 2

[0123] b(n)≥||q R -q D || 2 +H 2

[0124]

[0125] d(n)≤||q R -q U || 2 +H 2

[0126] e(n)≥||q R -q W ||2 +H 2

[0127] in, The expression of

[0128] The Taylor expansion for the velocity constraint is:

[0129]

[0130] For hidden constraints:

[0131]

[0132] ψ and ζ are Gaussian random variables, and represents the channel coefficient between the ith eavesdropper and other eavesdroppers and the receiving signal, and l represents a constant, which makes the unknown variable become a constant.

[0133] To ensure the quality of service (QOS) of the primary user, the average interference caused by S and Em must be limited to within the interference temperature (IT) threshold Γr.

[0134] The subproblem P3.1 is about optimizing the variable Q R This is a standard convex optimization problem that can be solved using the interior point method.

[0135] Based on the above transformation, the sub-problem is to optimize the variable Q R This is a standard convex optimization problem that can be solved using the interior point method.

[0136] S5: Use iterative algorithm to solve the optimal stage transmission power and UAV flight trajectory.

[0137] Design an iterative algorithm flow and connect the obtained convex optimization problems in series. The specific steps are shown in Table 1.

[0138] Table 1 Iterative algorithm

[0139]

[0140] Proof of algorithm convergence:

[0141] 1. In the first step of the algorithm, problem P2 is a problem about P R , P S So we can get the maximization problem:

[0142]

[0143] Similarly, in the third step of the algorithm, the above relationship is also satisfied, so we can get that the function must be a non-decreasing function and the function There is an upper bound, so the function can be made to Approximate a fixed constant, which is the optimal safe transmission energy efficiency of the entire system.

[0144] Figure 2 The optimal flight path of the relay drone is shown in Table 1. Figure 2 It can be clearly seen that the flight trajectory of the drone has different characteristics under different total flight times. In the signal collection stage, the trajectory of the relay drone starts from the starting point and successfully performs the following actions: approaching the transmitter, hovering, and leaving. In this process, the drone is as close to the listening end as possible to increase the amount of signals collected, thereby enhancing the effect of interference. In the signal forwarding stage, the trajectory of the relay drone tries to get close to and hover near the receiving end while avoiding being detected by the eavesdropping end to improve the efficiency of forwarding information. Finally, the drone flies to the preset end point and completes the communication task. In addition, the trajectory of the drone also presents different characteristics with different total flight times. A notable feature is that as the flight time increases, the trajectory of the drone tends to be closer to the transmitter, and even hovers. This is because, in the signal collection stage, a higher transmission rate can be obtained through friendly interference, and the transmission rate of the entire system depends not only on the signal collection stage. Therefore, when the total flight time is short, the drone will tend to optimize the signal forwarding stage with a lower transmission rate. When the flight time increases, the drone will try to increase the signal amount in the signal collection stage as much as possible while meeting the requirements of the signal forwarding stage. In short, the flight trajectory of the drone is the optimization result under the requirements and time constraints of different stages.

[0145] Figure 3 and Figure 4 and the power diagram of the UAV at T = 70s and T = 100s;

[0146] Figure 5 Maximize concealment rate graph;

[0147] Figure 6 The algorithm iteration convergence diagram, the simulation verifies the convergence of the scheme of the present invention. It can be seen from the simulation diagram that the effectiveness of the algorithm proposed in the scheme of the present invention.

[0148] The present invention aims to ensure that the concealed information of the transmitter is not detected, and to increase the maximum confidentiality rate of the communication system as much as possible under the constraints of cognitive radio, so as to ensure the security and reliability of drone relay communication. During the transmission process, the complex scenario of drone relay communication is considered, and drone relay is used to realize data transmission when the transmitter and the receiver cannot communicate directly due to obstacles. On this basis, the drone phased relay strategy is considered, so that the drone can also serve as a friendly jammer, further improving the concealment performance of the system. By calculating the error detection probability of illegal monitoring in the worst case, and taking it as a concealment constraint, the drone trajectory and power are optimized to maximize the confidentiality rate of the drone. Subsequently, the corresponding non-convex optimization problem is established and effectively solved by the proposed algorithm.

[0149] Compared with fixed trajectory design or simply considering partial optimization design of the scheme, the present invention proposes a scheme for jointly optimizing the transmission power and the flight trajectory of the relay drone, which has a higher confidentiality rate while ensuring the concealment of the transmitter. The simulation results also show the correctness and effectiveness of the above scheme and algorithm.

[0150] The present invention can be specifically applied in the fields of post-disaster reconstruction, secret communications, etc., to improve the security and reliability of communications, reduce the energy consumption of relay drones, increase system security capacity, and provide a wider range of service coverage, etc. This will help improve production efficiency, strengthen emergency response, and improve decision-making, thereby achieving higher benefits and efficiency in various fields.

[0151] A computer-readable storage medium storing a set of computer programs. When the program is executed by a processor, it will perform a series of operations to implement the process of a confidential communication method based on drone relay.

Claims

1. A secure covert communication method based on cognitive radio, characterized in that: The following steps are involved: Build a communication system model: including a transmitter S, a receiver D, multiple eavesdroppers E, and multiple listening terminals W; set the drone R as a relay node of the communication network to fly along a specific trajectory to achieve communication between the blocked transmitter and receiver; for the eavesdroppers E and listening terminals W with uncertain locations in the communication network, the drone R is set as a jammer and transmitter in stages to achieve the security and concealment of the communication system; With the goal of maximizing the secure transmission rate of the communication system, a transmission power P S 、UAV flight trajectory Q R And the transmission power P R It is a mathematical model for the optimization of variables; Among them, P R is the transmission power of UAV R, P S is the transmission power of the transmitter; Perform fractional decoupling on the established optimization mathematical model to obtain the processed optimization mathematical model; The processed optimization mathematical model is decoupled to obtain two sub-problems about the transmission power and the flight trajectory of the UAV. For each non-convex sub-problem, a convex approximation fitting method is used to convert it into a convex problem for solution. An iterative algorithm is used to solve the optimal transmission power and the flight trajectory of the UAV to ensure higher security transmission efficiency while ensuring the concealment of the transmitter.

2. According to claim 1, a secure covert communication method based on cognitive radio is characterized in that: The drone R is stagedly set as a jammer and a transmitter mainly to set the drone R to the signal collection stage and the signal forwarding stage.

3. The secure covert communication method based on cognitive radio according to claim 1, characterized in that: The optimization mathematical model is: s.t.C1.R ave ≤ω C3.q R (N)=q F C4.q R (1)=q I C7.||q R (n)-q R (n-1)||≤v max ,n∈N1 C8.||q R (n)-q R (n-1)||≤v max ,n∈N2 In the above formula, P R , P S Respectively represent the transmission power of the UAV and the transmitter, Q R represents the flight trajectory of the drone, R ave represents the average safe achievable rate, ω represents the maximum average safe achievable rate, q R (N) and q R (1) represents the position of the drone in the first time slot and the last time slot, represents the maximum instantaneous transmission power of the transmitter S, represents the maximum instantaneous transmission power of UAV R, q F With q I They represent the flight position of the drone R at the initial moment and the flight position at the end moment, respectively, max It defines the maximum flight speed of the drone at any time; N1 and N2 represent the uplink and downlink time slots respectively; P S (n) represents the transmission power of the transmitter S during the signal collection phase; P R (n) represents the transmission power of UAV R during the signal forwarding stage; and represents the channel coefficient between the drone and the two primary users, The power of the i-th UAV is defined, represents the distance between the UAV and the primary user, η represents the unknown coefficient in large-scale fading, β0 represents the channel gain, Γ is the threshold in cognitive radio, r represents the radius of the uncertain eavesdropping location, i represents the i-th time slot, represents the transmission power of the first primary user, constraint C2 is a hidden constraint, γ SW (n) describes the monitoring end W in the n The signal-to-interference-noise ratio achieved in a time slot is It indicates the demand for concealment quality during communication; C3~C4 limit the initial and final positions of the UAV flight; C5~C6 constrain the maximum instantaneous transmission power of the UAV and the transmitter during operation; C7~C8 limit the maximum speed of the UAV flight; C9~C10 ensure the power constraint of cognitive radio.

4. The secure covert communication method based on cognitive radio according to claim 3, characterized in that: The established optimization mathematical model is subjected to fractional decoupling to obtain the processed optimization mathematical model P2, which is expressed as: ω1 is the slack variable, It means that the power of the drone exists as noise at this moment, h RW With h SW They represent the channel coefficients for eavesdropping, drones and transmission power, respectively. They represent the instantaneous achievable rate of each time slot in the first and second phases of communication respectively.

5. The secure covert communication method based on cognitive radio according to claim 4, characterized in that: Based on the BCD algorithm, problem P2 is decoupled and two sub-problems are obtained: About the transmission power P R , P S Subproblem P2.1 is: C3:P S |h SW | 2 ≤(P J R |h RW | 2 )γ max +σ 2 γ max Subproblem P3 about the flight trajectory of the drone is: C4:q R (1)=q I C5:q R (N)=q F C6:||q R (n)-q R (n-1)||≤v max ,n∈N1 C7:||q R (n)-q R (n-1)||≤v max ,n∈N2 Where ω2 represents the slack variable.

6. The secure covert communication method based on cognitive radio according to claim 5, characterized in that: The subproblem P2.1 is solved by introducing slack variables and transforming it into a convex problem through continuous convex approximation: Among them, ω1 is the slack variable, A(n) represents the part after convex approximation, which is specifically expressed as B is the bandwidth, P E Represents the power of the eavesdropper.

7. The secure covert communication method based on cognitive radio according to claim 5, characterized in that: For subproblem P3, the optimization problem is solved by introducing slack variables ω2, a(n), b(n), c(n), d(n) and e(n), and continuous convex approximation: C4:q R (1)=q I C5:q R (N)=q F C6:||q R (n)-q R (n-1)||≤v max ,n∈N1 C7:||q R (n)-q R (n-1)||≤v max ,n∈N2 C9:a(n)≥||q S -q R || 2 +H 2 C10:b(n)≥||q R -q D || 2 +H 2 C12:d(n)≤||q R -q U || 2 +H 2 C13:e(n)≥||q R -q W || 2 +H 2 In the above formula, ω2, a(n), b(n), c(n), d(n) and e(n) are the slack variables introduced by the equivalent problem P3, and a(n), b(n), c(n), d(n) and e(n) are the first-order Taylor expansions of the non-convex part. a(n)≥||q S -q R || 2 +H 2 b(n)≥||q R -q D || 2 +H 2 d(n)≤||q R -q U || 2 +H 2 e(n)≥||q R -q W || 2 +H 2 in, ψ and ζ are Gaussian random variables, and represents the channel coefficient between the i-th eavesdropper and other eavesdroppers and the receiving signal, and l represents a constant.

8. A communication system, comprising a transmitting end S, a receiving end D, a plurality of eavesdropping ends E, a plurality of monitoring ends W and a drone R, characterized in that: The communication system uses the secure covert communication method based on cognitive radio as described in any one of claims 1 to 7 during communication.

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