A Secure and Covert Communication Method Based on Cognitive Radio

Abstract

This invention discloses a secure covert communication method based on cognitive radio, investigating a delay-tolerant relay covert unmanned aerial vehicle (UAV) and a cognitive radio framework involving multiple colluding eavesdroppers and listeners. In this framework, a legitimate UAV acts as an airborne relay, enabling communication when the direct link between the ground transmitter and receiver is blocked. Subsequently, considering the uncertainty of the locations of multiple eavesdropping and listening nodes, a robust optimization problem is constructed. By jointly optimizing the UAV's trajectory and power, and the transmitter's transmission power, a tractable version is constructed using Pinsk inequalities, Jensen inequalities, and a binary search method for extremely complex covert constraints. Following this, an algorithm based on alternating optimization is proposed to solve the optimization problem. Achieving low complexity, algorithms based on primal-dual search and continuous convex approximation are designed for each subproblem. Numerical results demonstrate the effectiveness of our proposed algorithm.

Description

Technical Field

[0001] This invention mainly relates to the field of wireless communication, specifically a secure and covert communication method based on cognitive radio. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure, and these statements may constitute prior art. In the process of developing this invention, the inventors discovered at least the following problems in the prior art.

[0003] Drones have garnered widespread attention due to their high potential to establish links with ground nodes in high-rise urban areas, even in the presence of eavesdroppers. In the context of rapid technological advancements, wired communication networks are increasingly showing their limitations, failing to fully meet the diverse communication service demands of contemporary society. The development of next-generation wireless communication technologies not only focuses on continuously improving transmission speeds, but more importantly, it must ensure communication security and stability while also possessing energy-saving characteristics. The system should be flexible and adaptable, capable of providing differentiated service quality to meet the individual needs of different users. Only such wireless communication systems can better adapt to the complex changes of modern society. Currently, drone communication technology has broad application prospects and rapid development momentum in 5G networks and future communication networks, such as the integration of drones with 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" addresses this.

[0004] The emergence and large-scale application of UAVs have broken the limitations of traditional land communication, greatly promoted the development of the Internet of Things and the integrated "air, land, sea" network, and laid a strong foundation for the next generation of mobile communication networks that realize the "Internet of Everything". Existing communication systems based on UAV trajectory design are classified into base station UAV communication systems, relay UAV communication systems, and auxiliary communication systems for UAVs in the air, according to the different roles of UAVs in the wireless communication network. For example, the typical base station UAV 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 UAV transmission power, UAV 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.] designs the flight trajectory of a relay UAV as an energy buffer to achieve the matching of signal transmission rates on the FSO (Free-Space Optical) link and the RF (Radio Frequency) link, thereby maximizing the system's 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 acquisition.

[0005] The openness and broadcast nature of wireless communication make signal transmission easily intercepted and interfered with. Attackers can exploit 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. Research on Physical Layer Security Theory of Unmanned Aerial Vehicle Communication Systems [D]. Chongqing University of Posts and Telecommunications, 2020.] elucidates physical layer security as a supplementary security technology. Its core principle relies on the inherent unpredictability of wireless channels, using information theory as its foundational architecture. By flexibly changing transmission strategies and parameters, it addresses the randomness of the physical medium and the differences between legitimate channels, thus ensuring the security of information during transmission. In short, this method utilizes the natural characteristics of wireless channels to dynamically adjust the transmission mode, ensuring the security of data transmission.

[0006] Currently, the above content is mostly found in cognitive radio or simply considering covert communication, such as the literature H. Lei, J. Jiang, H. Yang, K.-H. Park, ISasari, 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. This paper provides the imprecise locations of the receiver and the eavesdropping point, and maximizes the communication rate of the entire communication process by jointly optimizing the transmission power of the UAV relay and the hovering position of the UAV. Our research explores using UAVs as aerial relay nodes to transmit signals when the direct communication path between the transmitter and receiver is blocked. To ensure the covertness of the communication, the UAV also plays a cooperative jamming role to suppress ground nodes attempting to monitor. Considering the potential changes in the location of malicious nodes, we constructed an optimization model designed to maximize the transmission rate by collaboratively optimizing the drone's flight path and output power.

[0007] However, in the study of 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 maximization of the confidentiality rate is discussed between a single listener and a single eavesdropper. This paper studies a cognitive wireless network model in which multiple ground surveillance personnel are located near the transmitter to monitor the communication process, while another group of ground eavesdroppers are located near the receiver to intercept sensitive information. The aim is to maximize the transmission rate by coordinating the optimization of the UAV's flight path and output power. Summary of the Invention

[0008] In view of the above problems, the purpose of this invention is to solve some of the problems in the prior art, or at least alleviate these problems.

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

[0010] A communication system model is constructed, which includes a transmitter S, a receiver D, multiple eavesdropping terminals E, and multiple listening terminals W. A drone R is set as a relay node in the communication network and flies along a specific trajectory to achieve communication between the blocked transmitter and receiver. For the eavesdropping terminals E and listening terminals W with uncertain locations in the communication network, the drone R is temporarily set as a jammer and a transmitter to achieve the security and concealment of the communication system.

[0011] Specifically, this model considers an airborne relay system where the direct link between S and D is blocked. In the presence of multiple master users and multiple malicious FD eavesdroppers cooperating with each other, S transmits information to cognitive user D with the help of airborne relay R. The drone R is equipped with both receiving and transmitting antennas, employing FD mode for reception and jamming. All nodes are single-antenna. Ground eavesdropping terminals W near S attempt to determine whether S transmits, while multiple ground-based FD eavesdroppers E near D attempt to eavesdrop on the information S sends to D, and also send jamming to reduce the reception quality of the legitimate link. Some ground nodes are also single-antenna, and the SIC at R considers residual self-interference channels.

[0012] With the goal of maximizing the secure transmission rate of the communication system, a system with a transmission power P is constructed. S UAV flight trajectory Q R and transmit power P RAn optimized mathematical model for variables; where P R For the transmit power of UAV R, P S This refers to the transmit power of the transmitting end;

[0013] Fractional decoupling is performed on the established optimization mathematical model to obtain the processed optimization mathematical model;

[0014] The optimized mathematical model is decoupled to obtain two subproblems concerning the transmission power and the UAV flight trajectory. For each non-convex subproblem, a convex approximation fitting method is used to transform it into a convex problem for solution.

[0015] An iterative algorithm is used to solve for the optimal transmission power and UAV flight trajectory, so as to achieve higher secure transmission efficiency while ensuring the concealment of the transmitting end.

[0016] The present invention also provides a communication system, including a transmitter S, a receiver D, multiple eavesdropping terminals E, multiple listening terminals W, and a drone R. The communication system uses the above-mentioned secure and covert communication method based on cognitive radio when communicating.

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

[0018] This invention addresses the realities of scenarios where direct communication between the transmitting and receiving ends is impossible due to obstacles, and where multiple eavesdroppers and listeners exist in uncertain locations. It proposes a scheme where a drone acts as a relay and jammer in stages, using cooperative communication to combat eavesdropping and listening. The goal is to maximize the system's secure transmission rate, and the transmit power P of the relay drone R in the airspace of the communication network is adjusted accordingly. R Transmit power P at the transmitting end S And the flight trajectory Q of the airspace relay drone R. R Dynamic planning and design are employed. This invention optimizes the best path and transmission power while ensuring the sender remains concealed, thereby maximizing the transmission rate. Attached Figure Description

[0019] Figure 1 This is a communication system model of the present invention;

[0020] Figure 2 The optimal flight path map for the drone;

[0021] Figure 3 Power graph of the drone at T=100s;

[0022] Figure 4 Power graph of the UAV at T=70s;

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

[0024] Figure 6 The graph shows the convergence of the algorithm's iterations, with T = 100s. Detailed Implementation

[0025] The present invention will be further described below with reference to the accompanying drawings. The embodiments of the present invention are only used to illustrate the present invention and not to limit the present invention. Various substitutions and modifications made based on ordinary technical knowledge and conventional means in the art without departing from the technical concept of the present invention should be included within the scope of the present invention.

[0026] To leverage the high maneuverability and flexibility of UAVs to address complex communication environments, this invention constructs a UAV-assisted secure and covert communication system in a cognitive radio environment. While ensuring the security of the communication system, this invention aims to improve the quality of service. It proposes a secure and covert communication method based on UAV relay, and through theoretical analysis and simulation experiments, plans the UAV's flight path to achieve both the covertness of the communication system and maximize the energy efficiency of communication transmission. A secure and covert communication method based on UAV relay includes the following steps:

[0027] Constructing a communication system model: such as Figure 1 As shown, it includes a transmitter S, a receiver D, multiple eavesdropping terminals E, and a listening terminal W; a relay drone R is set as a relay node of the communication network and flies along a specific trajectory to realize communication between the blocked transmitter and receiver; in response to the eavesdropping terminals E and the listening terminal W with uncertain locations in the communication network, the relay drone R is set as a jammer and a transmitter in stages to achieve the security and concealment of the communication system.

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

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

[0030] The optimized mathematical model is decoupled to obtain three sub-problems concerning the transmission power and the UAV flight trajectory. 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 find the optimal transmission power and UAV flight trajectory to achieve higher secure transmission efficiency while ensuring the transmitter remains concealed.

[0032] To address the problem of obstacles preventing direct communication between the transmitter and receiver, and in complex situations involving potential eavesdroppers, this invention proposes a novel strategy: utilizing drones as communication relays and jamming devices at different stages to collaboratively protect communications from eavesdropping. The goal of this strategy is to optimize the transmission rate of drones within the communication network, ensuring transmission security. This includes dynamically adjusting and designing the transmission power of the drone relay, the transmission power of the transmitter, and the drone's flight path. Through these optimization measures, this invention aims to guarantee the stealth of the communication process while improving the security of the communication system. This method not only improves the energy efficiency of secure transmission but is also applicable to variable and complex communication scenarios.

[0033] The phased setup includes a signal acquisition phase (i.e., the first phase) and a signal forwarding phase (i.e., the second phase); the processed optimization mathematical model is decoupled based on the BCD method, which decouples the original optimization problem into a problem concerning the transmit power P by fixing the optimization variables. R P S And the flight trajectory Q of the relay drone R Sub-problems.

[0034] Its working principle is as follows:

[0035] Based on wireless communication technology, physical layer security principles, and the fundamental principles of covert communication, we constructed a wireless communication system model using drones as relay-assisted systems. In this model, given the inability to accurately ascertain the locations of eavesdroppers and listeners, we must consider the robustness required for handling real-world problems. Therefore, we transformed the original problem into how to effectively utilize drones for relay-assisted communication under the worst-case scenario. In other words, we need to design a drone-based relay-assisted communication system model that can maintain communication security and reliability even in the worst-case scenario, ensuring effective protection of the system's communication performance and security even in extreme situations.

[0036] Secondly, the mathematical theoretical 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, the approximate fitting method based on the Block Coordinate Descent (BCD) algorithm and continuous convex approximation was adopted to decouple and transform the original optimization problem.

[0037] Ultimately, this invention adopts an iterative method, processing each sub-problem through a continuous loop, gradually bringing the system's secure transmission rate closer to a specific threshold. This threshold represents the goal pursued by this invention: maximizing the system's 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 desired level of system secure transmission energy efficiency. The specific steps are as follows:

[0038] S1: Construct a communication system model.

[0039] Figure 1 This is a schematic diagram of the entire scheme, including a transmitter S, a receiver D, multiple eavesdropping terminals E, and multiple listening terminals W. Here, U1, U2, W1, W2, W3, and E represent the horizontal coordinates of the two main users, three listeners, and the eavesdropping terminals in the communication network, respectively. The relay drone R flies at a fixed altitude H. Definitions... as well as These represent the horizontal coordinates of the eavesdropping terminal E and the listening terminal W, estimated by the relay drone using its onboard radar. as well as The errors estimated by the relay UAV satisfy ||Δq||. E ||≤r E and ||Δq W ||≤r W , where r E q represents the maximum error in estimating the location of the eavesdropping device. E q W These represent eavesdropping and listening, respectively. The channels between the relay drone R and the transmitter and receiver are defined as legitimate links, while the channels associated with the eavesdropping and listening ends are defined as illegitimate links.

[0040] Among them, the relay UAV R, as an airborne relay device in the communication network, adopts a phased communication method, dividing the entire communication process into two stages: signal collection and signal forwarding. Simultaneously, during the signal collection stage, the relay UAV R also functions as a jammer to suppress the listening end.

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

[0042] The signal-to-interference-plus-noise ratios (SIRs) of the relay UAV R during the signal collection phase and the receiver D during the signal forwarding phase are as follows:

[0043]

[0044] In the formula, P S (n) h represents the transmit power of the transmitter S during the signal collection phase and the transmit power of the UAV R interference signal, respectively. SR (n), h RR and h RD (n) represent the channel gain from the transmitter S to the relay UAV R, and the self-interference channel generated when the relay UAV R operates in full-duplex mode, respectively (the channel gain of this channel is determined by the magnitude of the self-interference cancellation residue, and satisfies a normal distribution with mean 0 and variance ψ, i.e., it satisfies the relation E(h) RR 2 ) = ψ, where the magnitude of ψ represents the magnitude of the self-interference cancellation residue and the channel gain from the relay UAV R to the receiver D. σ 2 This represents the power of the additive white Gaussian noise. n represents the nth time slot, and N1 and N2 represent the uplink and downlink time slots, respectively.

[0045] The signal-to-interference-plus-noise ratio (SIR) of the eavesdropping device can be expressed as:

[0046]

[0047] In the formula, q represents the channel gain between the relay drone R and the eavesdropping device E. R Location of the drone. The location center of the eavesdropping node, The radius represents the radius at which the location of the eavesdropper is uncertain.

[0048] Based on the above, the average achievable transmission rate of this communication system can be expressed as:

[0049]

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

[0051] For the average achievable transmission rate R ave To eliminate the operator E(·) and position estimation error present in it, we now take its lower bound, as shown below.

[0052]

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

[0054] in β0 represents the channel gain per unit distance, and H represents the fixed altitude at which R flies. It is easy to see from the triangle inequality that... q R q D and The location of the receiving signal node and the i-th drone. It is a Gaussian random variable.

[0055] Based on the modeling of the drone's flight trajectory described above, v R (n) can be represented as

[0056] ||q R (n)-q R (n-1)||≤v max , n∈N1

[0057] ||q R (n)-q R (n-1)||≤v max , n∈N2

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

[0059] To ensure the secrecy of the communication process, a secrecy quality control parameter ε is set as a constraint, namely:

[0060]

[0061] In the formula, Where h SW h RW (n) represent the channel gain between the transmitter S and the receiver W, and the channel gain between the relay UAV R and the receiver W, respectively. I represents the time slot during signal transmission. and This represents the channel coefficients from the monitoring node to the transmitting signal point and the drone, where M represents the number of monitoring nodes. It can be seen that... and about It is monotonically increasing, so the constraint can be simplified using the bisection method. Where γ max It can be obtained using the binary search method. γ SW (n) describes the signal-to-interference-plus-noise ratio (SIR) achieved by the monitoring end W in the nth time slot. This indicates the required level of concealment quality during communication.

[0062] because There is an estimation error in the location of the listening end. This error is eliminated using the triangle inequality to obtain the correct location. The upper realm They satisfy the following size relationship:

[0063]

[0064] In the formula, These represent the channel coefficients from the listening node to the transmitting signal point and the drone, respectively.

[0065] in as well as r W Let be the radius whose location is uncertain, η be the unknown coefficient during large-scale fading, and q be the radius whose location is uncertain. S The locations where the signals are sent.

[0066] About cognitive radio:

[0067]

[0068] in, This refers to the interference power of the eavesdropping device. Let h represent the probability density function. RU and This represents the channel coefficients from the main user and the drone to the i-th eavesdropper. and represent the distances from the main user to the drone and the i-th eavesdropping point, respectively.

[0069]

[0070] Thus, the 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∈N1

[0078] C8.||q R (n)-q R (n-1)||≤v max ,n∈N2

[0079]

[0080] In the above formula, P R P S Q represents the transmission power of the UAV and the transmitter, respectively. R R represents the flight path of the drone. ave q represents the average safe reach rate, ω represents the maximum average safe reach rate, and q represents the maximum safe reach rate. R (N) and q R (1) indicates the position of the UAV in the first and last time slots. This represents the maximum instantaneous transmit power of the transmitter S. q represents the maximum instantaneous transmit power of the UAV R. F With q I Let v represent the initial and final flight positions of the UAV R, respectively. max The maximum flight speed of the UAV at any given time is defined; N1 and N2 represent the uplink and downlink time slots, respectively; P S (n) represents the transmit power of the transmitter S during the signal collection phase; P R (n) represents the transmit power of UAV R during the signal relay phase; and This represents the channel coefficients between the drone and the two main users. The power of the i-th drone is defined. Let represent the distance between the drone and the main user, β0 represent the channel gain, Γ be the threshold in cognitive radio, r represent the radius at which the eavesdropping location is uncertain, and i represent the i-th time slot. This represents the transmission power of the first primary user, constraint C2 is a hidden constraint, and γ SW (n) describes the signal-to-interference-plus-noise ratio (SIR) achieved by the monitoring end W in the nth time slot. C3-C4 indicate the required stealth quality during communication; C5-C6 limit the initial and final positions of the UAV; C7-C8 constrain the maximum instantaneous transmit power of the UAV and the transmitter during operation; C9-C10 ensure the power constraints of cognitive radio.

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

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

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

[0084]

[0085] ω1 is a slack variable when optimizing power. These represent the uplink and downlink transmission rates, respectively.

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

[0087] The Block Coordinate Descent (BCD) algorithm is used to decouple problem P2, yielding information about the transmission power P. R P S And the drone flight trajectory Q R The two sub-problems.

[0088] Regarding the transmission power P R P S The subproblem P2.1 is:

[0089]

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

[0091]

[0092] The sub-problem P3 concerning the drone's flight trajectory 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∈N1

[0097] C7:||q R (n)-q R (n-1)||≤v max ,n∈N2

[0098]

[0099] ω2 represents the slack variable used when optimizing the drone trajectory.

[0100] The resulting subproblems are transformed into convex optimization problems. It is known that both subproblems are non-convex optimization problems. The specific steps for transforming them into convex optimization problems are as follows.

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

[0102]

[0103] Where ω1 is the slack variable, and A(n) represents the part after the convex approximation, specifically expressed as:

[0104]

[0105] In the above formula, ω1 is the slack variable, and B is the bandwidth. Let P and P represent the instantaneous achievable rates of each time slot in the first and second phases of communication, respectively; at this point, the subproblem P2.1 is about P.R P S The standard convex optimization problem can be solved using the interior point method.

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

[0107] To address the strong coupling, the constraints become:

[0108]

[0109] For subproblem P3, by introducing slack variables ω2, a(n), b(n), c(n), d(n), and e(n), a first-order Taylor expansion of the non-convex part of the constraints can be obtained. Subproblem P3.1 is solved 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∈N1

[0114] C7:||q R (n)-q R (n-1)||≤v max ,n∈N2

[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)≤||qR -q U || 2 +H 2

[0120] C13:e(n)≥||q R -q W || 2 +H 2

[0121] In the above equation, ω2, 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 specifically expressed as the first-order Taylor expansion over 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,

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

[0129]

[0130] For hidden constraints:

[0131]

[0132] ψ and ζ are Gaussian random variables. and Let represent the channel coefficients of the i-th eavesdropper and other eavesdroppers and receivers, and l represent a constant, such that the unknown variable becomes a constant.

[0133] To ensure the quality of service (QoS) for primary users, 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 the optimization variable Q. R The standard convex optimization problem can be solved using the interior point method.

[0135] Based on the above transformation, the sub-problem is about the optimization variable Q. R The standard convex optimization problem can be solved using the interior point method.

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

[0137] The iterative algorithm flow is designed to connect the resulting convex optimization problems sequentially. See Table 1 for specific steps.

[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 The problem is to maximize the sum of all possible values, therefore we can obtain:

[0142]

[0143] Similarly, the above relationship is also satisfied in the third step of the algorithm. Therefore, we can derive the function throughout the entire program algorithm process. It must be a non-decreasing function and the function It has an upper bound, so the function can be improved through continuous iteration of the program. Approximate a fixed constant, which is the optimal secure energy transmission efficiency of the entire system.

[0144] Figure 2 The optimal flight path for the relay drone shown is obtained by optimizing the process in Table 1. Figure 2It is evident that the drone's flight trajectory exhibits different characteristics depending on the total flight time. During the signal acquisition phase, the relay drone's trajectory starts from the origin and successfully executes the following actions: approaching the transmitter, hovering, and departing. During this process, the drone tries to get as close as possible to the listening end to increase the amount of signal collected, thereby enhancing the jamming effect. During the signal relay phase, the relay drone's trajectory, while avoiding detection by the eavesdropping end, tries to get as close as possible to and hover near the receiver to improve the efficiency of information relay. Finally, the drone flies to the preset destination, completing the communication task. Furthermore, the drone's trajectory also exhibits different characteristics with different total flight times. A significant feature is that as flight time increases, the drone's trajectory tends to approach the transmitter more closely, even exhibiting hovering and lingering. This is because, during the signal acquisition phase, friendly interference can achieve a higher transmission rate, and the overall system transmission rate depends not only on the signal acquisition phase. Therefore, when the total flight time is short, the drone tends to optimize the signal relay phase, which has a lower transmission rate. However, as the flight time increases, the drone will maximize the signal quantity during the signal acquisition phase while still meeting the requirements of the signal relay phase. In short, the flight trajectory of a drone is the result of optimization under different stage requirements and time constraints.

[0145] Figure 3 and Figure 4 Power graphs of the drone at T=70s and T=100s;

[0146] Figure 5 Maximize the concealment rate map;

[0147] Figure 6 The algorithm's iterative convergence graph and simulation verification demonstrate the convergence of the proposed solution. The simulation graph shows the effectiveness of the algorithm proposed in this invention.

[0148] This invention aims to maximize the security and reliability of UAV relay communication by ensuring that the concealed information at the transmitting end remains undetected, while adhering to the constraints of cognitive radio. During transmission, considering the complex scenarios of UAV relay communication, UAV relay is used to transmit data when the transmitting and receiving ends cannot communicate directly due to obstacles. Furthermore, a staged UAV relay strategy is considered, enabling the UAV to simultaneously act as a friendly jammer, further enhancing the system's concealment performance. The worst-case false detection probability of illegal eavesdropping is calculated and used as a concealment constraint to optimize the UAV trajectory and power, aiming to maximize the UAV's security rate. Subsequently, the corresponding non-convex optimization problem is established and effectively solved by the proposed algorithm.

[0149] Compared to fixed trajectory designs or simply considering partial optimization of the scheme, this invention proposes a scheme for jointly optimizing transmission power and relay UAV flight trajectory, which achieves higher security rates while ensuring the concealment of the transmitting end. Simulation results also demonstrate the correctness and effectiveness of the above scheme and algorithm.

[0150] This invention can be specifically applied to fields such as post-disaster reconstruction and secure communications, improving communication security and reliability, reducing the energy consumption of relay drones, increasing system security capacity, and providing broader service coverage. This will help improve production efficiency, strengthen emergency response, and improve decision-making, thereby achieving greater benefits and efficiency in various fields.

[0151] A computer-readable storage medium containing a computer program. When executed by a processor, the program performs a series of operations to implement a secure communication method based on unmanned aerial vehicle (UAV) relay.

Claims

1. A secure and covert communication method based on cognitive radio, characterized in that, Includes the following steps: A communication system model is constructed, which includes a transmitter S, a receiver D, multiple eavesdropping terminals E, and multiple listening terminals W. A drone R is set as a relay node in the communication network and flies along a specific trajectory to achieve communication between the blocked transmitter and receiver. For the eavesdropping terminals E and listening terminals W with uncertain locations in the communication network, the drone R is temporarily set as a jammer and a transmitter to achieve the security and concealment of the communication system. With the goal of maximizing the secure transmission rate of the communication system, a system with a transmission power P is constructed. S UAV flight trajectory Q R and transmit power P R Optimization mathematical model for variables; Among them, P R For the transmit power of UAV R, P S This refers to the transmit power of the transmitting end; The optimized mathematical model is as follows: 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 Q represents the transmission power of the UAV and the transmitter, respectively. R R represents the flight path of the drone. ave q represents the average safe reach rate, ω represents the maximum average safe reach rate, and q represents the maximum safe reach rate. R (N) and q R (1) indicates the position of the UAV in the first and last time slots. This represents the maximum instantaneous transmit power of the transmitter S. q represents the maximum instantaneous transmit power of the UAV R. F With q I Let v represent the initial and final flight positions of the UAV R, respectively. max The maximum flight speed of the UAV at any given time is defined; N1 and N2 represent the uplink and downlink time slots, respectively; P S (n) represents the transmit power of the transmitter S during the signal collection phase; P R (n) represents the transmit power of UAV R during the signal relay phase; and This represents the channel coefficients between the drone and the two main users. The power of the i-th drone is defined. Let represent the distance between the drone and the main user, η represent the unknown coefficient during large-scale fading, β0 represent the channel gain, Γ is the threshold in cognitive radio, r represent the radius at which the eavesdropping location is uncertain, and i represent the i-th time slot. This represents the transmission power of the first primary user, constraint C2 is a hidden constraint, and γ SW (n) describes the signal-to-interference-plus-noise ratio (SIR) achieved by the monitoring end W in the nth time slot. C3-C4 indicate the required stealth quality during communication; C5-C6 limit the initial and final positions of the UAV; C7-C8 constrain the maximum instantaneous transmit power of the UAV and the transmitter during operation; C9-C10 ensure the power constraints of cognitive radio. Fractional decoupling is performed on the established optimization mathematical model to obtain the processed optimization mathematical model; The optimized mathematical model is decoupled to obtain two subproblems concerning the transmission power and the UAV flight trajectory. For each non-convex subproblem, a convex approximation fitting method is used to transform it into a convex problem for solution. An iterative algorithm is used to solve for the optimal transmission power and UAV flight trajectory, so as to achieve higher secure transmission efficiency while ensuring the concealment of the transmitting end.

2. The secure and covert communication method based on cognitive radio according to claim 1, characterized in that: The phased configuration of the UAV R as an jammer and transmitter mainly involves setting the UAV R as a signal collection phase and a signal forwarding phase.

3. The secure and covert communication method based on cognitive radio according to claim 1, characterized in that: The established optimization mathematical model is decoupled fractionally to obtain the processed optimization mathematical model P2, which is expressed as: ω1 is a slack variable. This indicates that the drone's power at this moment exists as noise, h RW with h SW These represent the channel coefficients for eavesdropping with drones and for transmission power, respectively. γ represents the instantaneous achievable rate of each time slot in the first and second phases of communication, respectively. max This represents the covert communication threshold obtained using the binary search method.

4. The secure and covert communication method based on cognitive radio according to claim 3, characterized in that: Based on the BCD algorithm, problem P2 is decoupled to obtain two subproblems: Regarding the transmission power P R P S The subproblem P2.1 is: C3:P S |h SW | 2 ≤(P J R |h RW | 2 )γ max +σ 2 γ max The sub-problem P3 regarding the drone's flight trajectory 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.

5. The secure and covert communication method based on cognitive radio according to claim 4, characterized in that: The subproblem P2.1 is solved by introducing slack variables and transforming it into a convex problem through continuous convex approximation: Where ω1 is the slack variable, and A(n) represents the part after the convex approximation, specifically expressed as: B represents bandwidth, P E This indicates the power of the eavesdropper.

6. The secure and covert communication method based on cognitive radio according to claim 4, 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 by 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 C11:c(n)≤||q Ei -q R || 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 equation, ω2, a(n), b(n), c(n), d(n), and e(n) are slack variables introduced by the equivalent problem P3, and a(n), b(n), c(n), d(n), and e(n) are the specific expressions of the first-order Taylor expansion over the non-convex part. a(n)≥||q S -q R || 2 +H 2 b(n)≥||q R -q D || 2 +H 2 c(n)≤||q Ei -q R || 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 Let represent the channel coefficients at the points where the i-th eavesdropper interacts with other eavesdroppers and the receiving signal, and l represent a constant.

7. A communication system comprising a transmitter S, a receiver D, multiple eavesdropping terminals E, multiple listening terminals W, and a drone R, characterized in that: The communication system uses the secure and covert communication method based on cognitive radio as described in any one of claims 1-6 when communicating.

Images