CN112243252B - Safety transmission enhancement method for relay network of unmanned aerial vehicle - Google Patents

Safety transmission enhancement method for relay network of unmanned aerial vehicle Download PDF

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CN112243252B
CN112243252B CN202010937439.2A CN202010937439A CN112243252B CN 112243252 B CN112243252 B CN 112243252B CN 202010937439 A CN202010937439 A CN 202010937439A CN 112243252 B CN112243252 B CN 112243252B
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唐晓
刘娜
张若南
王大伟
翟道森
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Northwestern Polytechnical University
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    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B17/30—Monitoring; Testing of propagation channels
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention belongs to the technical field of unmanned aerial vehicle communication, and discloses a safe transmission enhancing method for an unmanned aerial vehicle relay network, which comprises the steps of establishing a communication system from a ground source node to a destination node by taking an unmanned aerial vehicle as a relay and a channel model, and calculating the private rate of a transmission link from the source node to the destination node according to signals received by the unmanned aerial vehicle and the destination node in two time slots, namely a half-duplex mode; constructing an optimization model which takes the maximum privacy rate as a target function and designs the position constraint condition of the interference power unmanned aerial vehicle; traversing the destination node D under the condition that the position of the unmanned aerial vehicle is fixed, and optimizing an interference power distribution scheme by the coordinate position of the eavesdropper E so as to maximize the privacy rate; and obtaining an optimal position generation data set of the unmanned aerial vehicle by using an exhaustive search method, constructing and training a DNN model, and finding the optimal position of the unmanned aerial vehicle by using the high calculation efficiency of the DNN. The unmanned aerial vehicle is convenient to deploy and is not limited by complex terrains and obstacles; the communication applicability is strong, and the information transmission quality is high.

Description

Safety transmission enhancement method for relay network of unmanned aerial vehicle
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle communication, and particularly relates to a safe transmission enhancing method for an unmanned aerial vehicle relay network.
Background
At present: the rapid development of wireless communication technology brings various conveniences to people. Meanwhile, the concern on the security of wireless information is increasing. The wireless communication security problem is essentially the broadcast nature of wireless propagation. Therefore, how to secure wireless communication from the physical layer is a key to solve this problem. The physical layer security is established under an interception channel model, and under the condition that the interception channel model has lower performance compared with a legal channel, the model realizes perfect confidentiality in information theory.
With the rapid development and deployment of 5G networks, academic and industrial communities have shown great attention to Unmanned Aerial Vehicles (UAVs). The unmanned aerial vehicle has the advantages of low cost, convenience in deployment and the like, has higher flexibility and adaptability, and therefore has wide application in the military and civil fields. In particular, it may be used as a wireless sensor node, a relay station or a mobile base station, etc. Drones are commonly used to facilitate communications in complex and diverse environments. However, the reliability of the drone communication system may be reduced due to uncertainties in the flight environment, thereby affecting communication quality. Therefore, it is of great significance to improve network performance involving drones.
On the other hand, deep Neural Networks (DNNs) have also made significant progress while advancing various applications. Deep learning techniques have also been applied in wireless communication research, such as transmit power control and channel estimation, while resulting in significant performance gains. DNN enables us to formulate effective policies for communication systems based on data and knowledge. Furthermore, the use of a well-trained DNN model can significantly reduce the complexity of the practical implementation. Due to the significant advantages of DNN, deep learning has been widely used in drone communications to facilitate design and improve performance.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) Drones are commonly used to facilitate communications in complex and diverse environments. Considering a complex outdoor environment, due to uncertainties in the flight environment, for example: high-rise buildings may block the LOS communication link between the ground user and the BS, which may reduce the reliability of the drone communication system, thereby affecting communication quality.
(2) The existing physical layer security is in the traditional point-to-point information transmission technology, and the main security problem is that nodes at two ends of communication transceiving are intercepted. Compared with the traditional point-to-point information transmission technology, the cooperative relay network has a more serious safety problem, because the probability of eavesdropping confidential information by an eavesdropper is improved by the expansion of an information diffusion surface in the multi-path transmission process of the relay node.
(3) Existing security schemes lack consideration of the potential uncertainty of the actual network.
The difficulty in solving the above problems and defects is: the existing security mechanism is based on a key system, and the physical layer security is a brand new solution. In a scene considered by the invention, the unmanned aerial vehicle relay network adopts a physical layer safety mechanism and needs to reasonably distribute interference signals transmitted by a legal receiver. In addition, a deep neural network model is designed to improve the network performance.
The significance for solving the problems and the defects is as follows: the invention adopts a physical layer security scheme, which does not need a secret key and has lower complexity; considering that an unmanned aerial vehicle is taken as a network relay, and an amplification forwarding protocol of the unmanned aerial vehicle relay is utilized to research the private rate of legal transmission; also, it is considered that a legitimate receiver transmits an interference signal independent of the source signal to combat eavesdropping. Aiming at the problem, the problem is firstly decomposed into two sub-problems, and the problems of interference strategies and unmanned aerial vehicle position deployment are respectively solved. Then, on the basis of an effective binary search method, the problem of interference is solved, and then a DNN framework is used for solving unmanned aerial vehicle deployment. Finally, a simulation result is given, and the effectiveness of the proposed scheme is verified.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a safe transmission enhancing method for an unmanned aerial vehicle relay network.
The invention is realized in this way, a secure transmission enhancing method facing to the relay network of the unmanned aerial vehicle, the secure transmission enhancing method facing to the relay network of the unmanned aerial vehicle comprises:
establishing an unmanned aerial vehicle relay communication system, wherein the aim of the system is to jointly optimize interference power and the relay position of the unmanned aerial vehicle so as to maximize the privacy rate of the system;
establishing a channel model of a ground source node-destination node communication system taking an unmanned aerial vehicle as a relay and calculating the private rate of a transmission link from a source node to a destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in two time slot-half duplex modes;
constructing an optimization model which takes the maximum privacy rate as a target function and designs interference power and unmanned aerial vehicle position constraint conditions;
under the condition that the position of the unmanned aerial vehicle is fixed, traversing the coordinate positions of the destination node D and the eavesdropper E, and optimizing an interference power distribution scheme by using a binary search algorithm so as to maximize the privacy rate;
under the condition of optimal interference power, an exhaustive search method is used for obtaining the optimal position of the unmanned aerial vehicle, a data set is generated, a DNN model is constructed and trained, the DNN model is applied to a test set, the optimal position of the unmanned aerial vehicle is found by utilizing the high calculation efficiency of the DNN, and safe transmission is achieved at the maximum private rate.
Further, the safety transmission enhancement method facing the unmanned aerial vehicle relay network establishes an unmanned aerial vehicle relay communication system, and aims to jointly optimize interference power and an unmanned aerial vehicle relay position so as to maximize the privacy rate of the system; consider an unmanned aerial vehicle relay communication system consisting of a source node S, a destination node D, a UAV relay R, and an eavesdropper E, S sends a signal to the UAV relay, R amplifies the signal and forwards it to D.
Further, the safe transmission enhancing method for the unmanned aerial vehicle relay network establishes a channel model of a communication system from a ground source node to a destination node by taking the unmanned aerial vehicle as a relay, and calculates the private rate of a transmission link from the source node to the destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in two time slot-half duplex modes; channel model for a ground source node to destination node communication system:
Figure BDA0002672456210000031
where c is the speed of light, α G Is the path loss exponent, f, of the terrestrial communication link c Is the carrier frequency, d eg Is the distance, σ, between the eavesdropper and the ground user G Is the shadow fading variation of the channel;
the channel relevant with unmanned aerial vehicle contains line of sight LoS group and non-line of sight NLoS group, has the probability that the LOS is connected between ground user and the unmanned aerial vehicle:
Figure BDA0002672456210000041
Figure BDA0002672456210000042
where A and B are constants depending on the environment, (x) u ,y u ) Representing the position of the drone in the horizontal dimension, h represents the altitude of the drone, (x) g ,y g ) The probability of NLoS is P NLoS ＝1-P LoS The path loss models of the LoS link and the NLoS link are respectively as follows:
Figure BDA0002672456210000043
Figure BDA0002672456210000044
where d is the distance of transmission and where,
Figure BDA0002672456210000045
α L and alpha N Is the path loss exponent, η, of LoS and NLoS channels LoS And η NLoS Average excess loss for LoS and NLoS, respectively; the probability average path loss is obtained by averaging under LoS and NLoS conditions:
h ij ＝P LoS L LoS +P NLoS L NLoS ，i∈{R,D}，j∈{R,E,D}；
in a first time slot, S transmits a signal to an unmanned aerial vehicle relay R, and the signal is intercepted by an eavesdropper E; meanwhile, D emits artificial noise, so that an eavesdropper is confused; in the second time slot, S is in a silent state, the unmanned aerial vehicle relay amplifies the received signal and sends the signal to D, D is also eavesdropped by an eavesdropper, and z is used S And z J Representing confidential signals and cooperative interference signals from S and D, z S And z J Are all normalized powers, i.e. | z S | 2 =1 and | z J | 2 =1, where | represents the absolute value, the signals received at UAV and E at the first slot are:
Figure BDA0002672456210000046
Figure BDA0002672456210000047
wherein P is S And P D Are the transmit power from S and D, respectively, n R And
Figure BDA0002672456210000048
is a complex additive white Gaussian noise AWGN at R and D, following a mean of zero and variance of
Figure BDA0002672456210000049
Complex gaussian distribution of (a);
in the second time slot, R amplifies and forwards the received signal to D, P with an amplification factor beta R For a transmit power of R, β is expressed as:
Figure BDA0002672456210000051
then, the signals received at D and E are:
Figure BDA0002672456210000052
Figure BDA0002672456210000053
wherein n is D And
Figure BDA0002672456210000054
is complex additive white gaussian noise at D and E, D effectively removes this term and yields the received signal at D as:
Figure BDA0002672456210000055
and calculating the private rate of a transmission link from the source node to the destination node, and redefining the channel-to-noise ratio:
Figure BDA0002672456210000056
in the first time slot, the signal to interference plus noise ratio (SINR) of the eavesdropping link is as follows:
Figure BDA0002672456210000057
the instantaneous SINRs of D and E are:
Figure BDA0002672456210000058
Figure BDA0002672456210000059
the eavesdropper adopts a maximum ratio combining MRC method, and the signal-to-interference-and-noise ratio SINR at the eavesdropping node E is as follows:
Figure BDA0002672456210000061
in a relay system based on physical layer security, the privacy rate is obtained as follows:
Figure BDA0002672456210000062
further, the method for enhancing the safety transmission of the relay network facing the unmanned aerial vehicle establishes an optimization model which takes the maximum privacy rate as an objective function, designs interference power and unmanned aerial vehicle position constraint conditions:
Figure BDA0002672456210000063
wherein (x) u ,y u ) Indicating the horizontal position of the drone in the considered area a.
Further, the method for enhancing the safety transmission of the relay network facing the unmanned aerial vehicle determines that the coordinate positions of the target node D and the eavesdropper E are traversed under the condition that the position of the unmanned aerial vehicle is fixed, and optimizes an interference power distribution scheme by using a binary search algorithm so as to maximize the privacy rate: under the condition that fixed unmanned aerial vehicle places, interference power's optimization problem as follows:
Figure BDA0002672456210000064
further, the safe transmission enhancing method for the relay network of the unmanned aerial vehicle utilizes a binary search algorithm to solve the obtained power, namely the power distribution scheme of the current position of the fixed unmanned aerial vehicle, and the optimal interference power is
Figure BDA0002672456210000065
The method comprises the following steps: initialization: setting P D Given as P min And P max In which P is min =0; defining a sufficiently small threshold epsilon;
step two: order to
Figure BDA0002672456210000071
R S Given by the second step, if
Figure BDA0002672456210000072
Then P is max ＝P * Else P min ＝P * ；
Step three: if P max -P min If is less than epsilon, the optimal interference power P is obtained * Completing the power distribution strategy of the unmanned aerial vehicle relay communication system; otherwise, repeating the step two.
Further, the safe transmission enhancing method for the relay network of the unmanned aerial vehicle obtains the optimal position of the unmanned aerial vehicle by using an exhaustive search method under the condition of optimal interference power, generates a data set, constructs and trains a DNN model, applies the DNN model to a test set, finds the optimal position of the unmanned aerial vehicle by using the high calculation efficiency of the DNN, maximizes the private rate and realizes safe transmission;
obtaining the optimal interference power and obtaining the optimization problem of unmanned plane position deployment as follows:
Figure BDA0002672456210000073
further, the unmanned aerial vehicle relay network-oriented secure transmission enhancement method adopts a neural network model with a plurality of hidden layers and a large amount of training data to learn network characteristics so as to solve the optimization problem of unmanned aerial vehicle position deployment and realize a DNN-based unmanned aerial vehicle deployment scheme; constructing a fully-connected DNN model with an input layer, two hidden layers and an output layer, learning the input/output relationship by training DNN, applying the model to a test set, finding the optimal position of an unmanned aerial vehicle by utilizing the high calculation efficiency of DNN, maximizing the privacy speed, realizing the safe transmission of the system, considering a system positioned in the range of 200 m x 200 m, wherein the fixed height h of the unmanned aerial vehicle is 150 m, and a source node has a fixed position (0, 0), firstly, fixing the positions of S, D and ETraversing the horizontal position of the unmanned aerial vehicle, and obtaining the optimal interference power P by adopting a binary search algorithm * Under the condition of optimal interference power, the maximum privacy rate corresponds to the optimal position of the unmanned aerial vehicle; then, through traversing the coordinate positions of D and E, finding the optimal positions of the unmanned aerial vehicles corresponding to different D and E, and constructing a data set;
in the input layer of the DNN model, coordinates (x) of a target node and an eavesdropper are set d ,y d ) And (x) e ,y e ) Reshaped into a 4 x 1 data sample, denoted
Figure BDA0002672456210000074
Coordinates (x) of optimum position of drone u ,y u ) As a tag output, is represented as
Figure BDA0002672456210000075
Wherein i ∈ {1,2, ·, N }, and Q = [ Q ] - [ Q ] 1 ,q 2 ,···,q N ]As input for DNN, q i Each item in the (1) corresponds to an input neuron, and the optimal position U = [ U ] of the unmanned aerial vehicle is taken 1 ,u 2 ,···,u N ]As output of the DNN model, u i Each item in the table corresponds to an output neuron, and finally, the well-trained DNN model is used for a test set, so that the optimal position of the unmanned aerial vehicle can be quickly and effectively found, and the problem of optimizing the position of the unmanned aerial vehicle can be solved.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
establishing an unmanned aerial vehicle relay communication system, wherein the aim of the system is to jointly optimize interference power and the relay position of the unmanned aerial vehicle so as to maximize the privacy rate of the system;
establishing a channel model of a ground source node-destination node communication system taking an unmanned aerial vehicle as a relay and calculating the private rate of a transmission link from a source node to a destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in two time slot-half duplex modes;
constructing an optimization model which takes the maximum privacy rate as a target function and designs interference power and unmanned aerial vehicle position constraint conditions;
under the condition that the position of the unmanned aerial vehicle is fixed, traversing the coordinate positions of the destination node D and the eavesdropper E, and optimizing an interference power distribution scheme by utilizing a binary search algorithm so as to maximize the private rate;
under the condition of optimal interference power, an exhaustive search method is used for obtaining the optimal position of the unmanned aerial vehicle, a data set is generated, a DNN model is constructed and trained, the DNN model is applied to a test set, the optimal position of the unmanned aerial vehicle is found by utilizing the high calculation efficiency of the DNN, and safe transmission is achieved at the maximum private rate.
Another object of the present invention is to provide a secure transmission enhancing system for a relay network of an unmanned aerial vehicle, which implements the secure transmission enhancing method for a relay network of an unmanned aerial vehicle, the secure transmission enhancing system for a relay network of an unmanned aerial vehicle comprising:
the unmanned aerial vehicle relay communication system establishing module is used for establishing an unmanned aerial vehicle relay communication system, and aims to jointly optimize interference power and an unmanned aerial vehicle relay position so as to maximize the privacy rate of the system;
the system comprises a channel model and time slot establishing module, a data transmission module and a data transmission module, wherein the channel model and time slot establishing module is used for establishing a channel model of a ground source node to destination node communication system taking an unmanned aerial vehicle as a relay and calculating the private rate of a transmission link from a source node to a destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in two time slot-half duplex modes;
the target function construction module is used for constructing an optimization model which takes the maximum privacy rate as a target function, designs interference power and unmanned aerial vehicle position constraint conditions;
the privacy rate maximization module is used for traversing the coordinate positions of the destination node D and the eavesdropper E under the condition that the position of the unmanned aerial vehicle is fixed, and optimizing an interference power distribution scheme by utilizing a binary search algorithm so as to maximize the privacy rate;
and the safe transmission module is used for obtaining the optimal position of the unmanned aerial vehicle by using an exhaustive search method under the condition of optimal interference power, generating a data set, constructing and training a DNN (digital noise network) model, applying the DNN model to a test set, finding the optimal position of the unmanned aerial vehicle by using the high calculation efficiency of the DNN, and realizing safe transmission at the maximized private rate.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention relates to a safe transmission enhancing method for an unmanned aerial vehicle relay network, which comprises the steps of firstly, carrying out power distribution on interference signals transmitted by a receiver under the condition that the position of an unmanned aerial vehicle is fixed so as to realize the maximized privacy rate; aiming at the problem of unmanned aerial vehicle position deployment, a DNN model is constructed under the optimal interference power and is solved, so that the optimal communication effect is ensured, and the reliability of communication is ensured; according with the practical situation. The unmanned aerial vehicle is convenient to deploy, flexible to move and not limited by complex terrains and obstacles; the cost is low, and the reliability is high; the practical communication applicability is strong, and the information transmission quality is high.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a flowchart of a method for enhancing secure transmission for an unmanned aerial vehicle relay network according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a secure transmission enhancement system for an unmanned aerial vehicle relay network according to an embodiment of the present invention;
in fig. 2: 1. an unmanned aerial vehicle relay communication system establishing module; 2. a channel model and time slot establishing module; 3. an objective function construction module; 4. a privacy rate maximization module; 5. and a safe transmission module.
Fig. 3 is a schematic diagram of a relationship between a privacy rate and an eavesdropper position in an unmanned aerial vehicle relay network according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a relationship between a privacy rate and a relay power of an unmanned aerial vehicle relay network provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Aiming at the problems in the prior art, the invention provides a method and a system for enhancing safety transmission of an unmanned aerial vehicle relay network, and the invention is described in detail with reference to the attached drawings.
As shown in fig. 1, the method for enhancing secure transmission for the relay network of the unmanned aerial vehicle provided by the invention comprises the following steps:
s101: establishing an unmanned aerial vehicle relay communication system, wherein the aim of the system is to jointly optimize interference power and the relay position of the unmanned aerial vehicle so as to maximize the privacy rate of the system;
s102: establishing a channel model of a ground source node-destination node communication system taking an unmanned aerial vehicle as a relay and calculating the private rate of a transmission link from a source node to a destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in two time slot-half duplex modes;
s103: constructing an optimization model which takes the maximum privacy rate as an objective function and designs interference power and unmanned aerial vehicle position constraint conditions;
s104: under the condition that the position of the unmanned aerial vehicle is fixed, traversing the coordinate positions of the destination node D and the eavesdropper E, and optimizing an interference power distribution scheme by utilizing a binary search algorithm so as to maximize the private rate;
s105: under the condition of optimal interference power, an exhaustive search method is used for obtaining the optimal position of the unmanned aerial vehicle, a data set is generated, a DNN model is constructed and trained, the DNN model is applied to a test set, the optimal position of the unmanned aerial vehicle is found by utilizing the high calculation efficiency of the DNN, and safe transmission is achieved at the maximum private rate.
A person skilled in the art can also use other steps to implement the method for enhancing secure transmission for an unmanned aerial vehicle relay network provided by the present invention, and the method for enhancing secure transmission for an unmanned aerial vehicle relay network provided by the present invention shown in fig. 1 is only a specific embodiment.
As shown in fig. 2, the system for enhancing secure transmission for the relay network of the unmanned aerial vehicle provided by the present invention includes:
the unmanned aerial vehicle relay communication system establishing module 1 is used for establishing an unmanned aerial vehicle relay communication system, and aims to jointly optimize interference power and an unmanned aerial vehicle relay position so as to maximize the privacy rate of the system;
the channel model and time slot establishing module 2 is used for establishing a channel model of a communication system from a ground source node to a destination node by taking the unmanned aerial vehicle as a relay and calculating the private rate of a transmission link from the source node to the destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in a two time slot-half duplex mode;
the objective function construction module 3 is used for constructing an optimization model which takes the maximum privacy rate as an objective function, designs interference power and unmanned aerial vehicle position constraint conditions;
the privacy rate maximization module 4 is used for traversing the coordinate positions of the destination node D and the eavesdropper E under the condition that the position of the unmanned aerial vehicle is fixed, and optimizing an interference power distribution scheme by utilizing a binary search algorithm so as to maximize the privacy rate;
and the safe transmission module 5 is used for obtaining the optimal position of the unmanned aerial vehicle by using an exhaustive search method under the condition of optimal interference power, generating a data set, constructing and training a DNN (digital noise network) model, applying the DNN model to a test set, finding the optimal position of the unmanned aerial vehicle by using the high calculation efficiency of the DNN, and realizing safe transmission at the maximum private rate.
The technical solution of the present invention is further described below with reference to the accompanying drawings.
The method for enhancing the safety transmission facing the unmanned aerial vehicle relay network specifically comprises the following implementation steps:
the first step is as follows: establishing an unmanned aerial vehicle relay communication system, wherein the aim of the system is to jointly optimize interference power and the relay position of the unmanned aerial vehicle so as to maximize the privacy rate of the system; consider an unmanned aerial vehicle relay communication system consisting of a source node (S), a destination node (D), a UAV relay (R) and an eavesdropper (E). S sends a signal to the UAV relay, which then amplifies and forwards the signal to D. For simplicity, the present invention contemplates that all network nodes are equipped with one antenna. In addition, considering the condition that no direct link exists between the S and the D, the optimization goal of the unmanned aerial vehicle relay communication system is to achieve the maximization of the privacy rate through jointly optimizing interference power distribution and unmanned aerial vehicle position deployment, and safe transmission is achieved.
The second step is that: establishing a channel model of a ground source node-destination node communication system taking an unmanned aerial vehicle as a relay and calculating the private rate of a transmission link from a source node to a destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in a two-time slot-half-duplex mode; channel model for a ground source node to destination node communication system:
Figure BDA0002672456210000121
where c is the speed of light, α G Is the path loss exponent, f, of the terrestrial communication link c Is the carrier frequency, d eg Is the distance, σ, between the eavesdropper and the ground user G Is the shadow fading variation of the channel;
the channels associated with drones include line-of-sight (LoS) groups and non-line-of-sight (NLoS) groups. Probability of having LOS connection between ground user and unmanned aerial vehicle
Figure BDA0002672456210000122
Figure BDA0002672456210000123
Where A and B are constants that depend on the environment, (x) u ,y u ) Representing the position of the drone in the horizontal dimension, h represents the height of the drone, (x) g ,y g ) Indicating the location of the terrestrial user. Furthermore, the probability of NLoS is P NLoS ＝1-P LoS . In addition, the path loss models of the LoS and NLoS links are respectively
Figure BDA0002672456210000124
Figure BDA0002672456210000125
Where d is the distance of transmission and where,
Figure BDA0002672456210000126
further, α L And alpha N Is the path loss exponent for LoS and NLoS channels. Eta LoS And η NLoS The average penalty for LoS and NLoS, respectively. The invention considers that the probability average path loss is obtained by averaging under LoS and NLoS conditions
h ij ＝P LoS L LoS +P NLoS L NLoS ，i∈{R,D}，j∈{R,E,D}；
In the first time slot, S transmits its signal to the drone relay (R), which is also eavesdropped by the eavesdropper (E). At the same time, D emits artificial noise, confusing the eavesdropper. In the second time slot, S is in a silent state, the unmanned aerial vehicle relay amplifies the received signal and sends the signal to D, and D is also eavesdropped by an eavesdropper. By z in the invention S And z J Representing the confidential signal and the cooperative interference signal from S and D. z is a radical of S And z J Are all normalized powers, i.e. | z S | 2 =1 and | z J | 2 =1, where | · | represents an absolute value. Thus, in the first time slot, the signals received at UAV and E are
Figure BDA0002672456210000131
Figure BDA0002672456210000132
Wherein P is S And P D Are the transmit power from S and D, respectively, n R And
Figure BDA0002672456210000133
is complex Additive White Gaussian Noise (AWGN) at R and D, following a mean of zero and variance of
Figure BDA0002672456210000134
Complex gaussian distribution of (a).
In the second time slot, R amplifies and forwards the received signal to D with an amplification factor β. P R Is the transmit power of R. Thus, β can be represented as
Figure BDA0002672456210000135
Then, the signals received at D, E are
Figure BDA0002672456210000136
Figure BDA0002672456210000137
Wherein n is D And
Figure BDA0002672456210000138
is complex additive white gaussian noise at D and E. Due to interference signal z J From D, which is well understood. Then, D can effectively remove the term, and can obtain the received signal at D as
Figure BDA0002672456210000141
Calculating the private rate of a transmission link from a source node to a destination node: for simplicity, the present invention redefines the channel to noise ratio:
Figure BDA0002672456210000142
in the first time slot, the signal to interference plus noise ratio (SINR) of the eavesdropping link is:
Figure BDA0002672456210000143
the instantaneous SINRs of D and E are:
Figure BDA0002672456210000144
Figure BDA0002672456210000145
in order to obtain the best eavesdropping performance, the eavesdropper employs a Maximum Ratio Combining (MRC) method. Then, the signal to interference and noise ratio (SINR) at the eavesdropping node E is
Figure BDA0002672456210000146
In a relay system based on physical layer security, the achievable privacy rate is obtained as follows:
Figure BDA0002672456210000147
the third step: constructing an optimization model which takes the maximum privacy rate as a target function, designs interference power and unmanned aerial vehicle position constraint conditions:
Figure BDA0002672456210000151
wherein R is S In the second step, (x) u ,y u ) Indicating the horizontal position of the drone in the considered area a. Although this problem appears to be simple, it is rather cumbersome to solve due to the non-convexity of the objective function and the feasible region. In the following steps, the problem is broken down into two sub-problems. The outer layer is the deployment optimization of the unmanned aerial vehicle position, and the inner layer is the optimization of the interference power distribution under the fixed unmanned aerial vehicle position.
The fourth step: determining the coordinate positions of a traversal target node D and an eavesdropper E under the condition that the position of the unmanned aerial vehicle is fixed, and optimizing an interference power distribution scheme by using a binary search algorithm so as to maximize the privacy rate: under the condition that fixed unmanned aerial vehicle places, the optimization problem of interference power is as follows:
Figure BDA0002672456210000152
there is a fundamental tradeoff to this problem that needs to be addressed carefully. If the interference power is too small, it will not sufficiently interfere with the eavesdropper. Conversely, if the interference power is too large, it will not be able to effectively relay the source information due to the limited total power of the relay, thereby also affecting the security of the communication system. Therefore, to meet the requirements of both aspects, we consider optimizing the interference power.
To maximize the privacy rate of the interference power, we first analyze the nature of the privacy rate. For the optimization problem, the power obtained by solving the binary search algorithm, namely the power distribution scheme of the current fixed unmanned aerial vehicle position, is utilized, and the optimal interference power is
Figure BDA0002672456210000153
Binary search algorithm
The method comprises the following steps: initialization: setting P D Given as P min And P max In which P is min =0; defining a sufficiently small threshold epsilon;
step two: order to
Figure BDA0002672456210000154
R S Given by the second step if
Figure BDA0002672456210000155
Then P is max ＝P * Else P min ＝P * ；
Step three: if P max -P min If is less than epsilon, the optimal interference power P is obtained * Completing a power distribution strategy of the unmanned aerial vehicle relay communication system; otherwise, repeating the step two. In addition, a cost factor is introduced, the objective function of the problem in the form of a fraction is converted into a subtraction formula with power as cost, the problem is converted into a convex optimization problem, and the Dinkelbach algorithm is adopted to carry out loop iteration on the converted problem to obtain the optimal solution of the original power optimization problem.
The fifth step: under the condition of optimal interference power, an exhaustive search method is used for obtaining the optimal position of the unmanned aerial vehicle, a data set is generated, a DNN model is constructed and trained, the DNN model is applied to a test set, the optimal position of the unmanned aerial vehicle is found by utilizing the high calculation efficiency of the DNN, and safe transmission is achieved at the maximum private rate.
On the basis of the fourth step, the optimal interference power is obtained, and the optimization problem of unmanned aerial vehicle position deployment is obtained as follows:
Figure BDA0002672456210000161
and the interference power constraint in the optimization problem is obtained by solving the optimization problem in the fourth step, namely, the optimization problem of the position of the unmanned aerial vehicle is solved under the condition of the optimal interference power. Because certain difficulties exist in solving the problems and no available scheme with high computational efficiency exists at present, in the part, the invention adopts a neural network model with a plurality of hidden layers and a large amount of training data to learn network characteristics and then solves the optimization problem of unmanned aerial vehicle position deployment, thereby realizing a DNN-based optimization methodUnmanned aerial vehicle deploys the scheme. A DNN model with an input layer, two hidden layers and an output layer and complete connection is constructed, the DNN is trained to learn the input/output relation, the DNN model is applied to a test set, the optimal position of the unmanned aerial vehicle is found by utilizing the high computing efficiency of the DNN, the privacy rate is maximized, and the safe transmission of the system is realized. Consider a system that is located within a 200 m x 200 m range. The fixed height h of the unmanned aerial vehicle is 150 meters, and the source node has a fixed position (0, 0). Firstly, fixing the positions of S, D and E, traversing the horizontal position of the unmanned aerial vehicle, and obtaining the optimal interference power P by adopting a binary search algorithm * And under the condition of the optimal interference power, the maximum privacy rate corresponds to the optimal position of the unmanned aerial vehicle. And then, finding the optimal positions of the unmanned aerial vehicles corresponding to different D and E by traversing the coordinate positions of the D and E, thereby constructing a data set. Since the binary search algorithm in the fourth step can be effectively implemented, data collection can be conveniently performed. In the input layer of the DNN model, coordinates (x) of a target node and an eavesdropper are set d ,y d ) And (x) e ,y e ) Reshaped into a 4 × 1 data sample, which can be represented as
Figure BDA0002672456210000171
Coordinates (x) of optimum position of unmanned aerial vehicle u ,y u ) As a tag output, can be expressed as
Figure BDA0002672456210000172
Where i ∈ {1,2, ·, N }. Q = [ Q ] 1 ,q 2 ,···,q N ]As input for DNN, q i One for each input neuron. Get the optimal position U of unmanned aerial vehicle = [ U = 1 ,u 2 ,···,u N ]As output of the DNN model, u i One for each output neuron. And finally, using the well-trained DNN model in a test set, quickly and effectively finding the optimal position of the unmanned aerial vehicle, and solving the problem of position optimization of the unmanned aerial vehicle so as to realize the highest privacy rate. In addition, the unmanned aerial vehicle position deployment problem can be solved by using a continuous convex approximation (SCA) method under the condition of optimal interference powerThe continuous convex approximation method is a method for solving a non-convex optimization problem, can carry out global approximation on an original non-convex expression, converts the non-convex problem into a convex optimization problem by carrying out relaxation and first-order Taylor expansion on a non-convex target function and a constraint condition, and can solve an optimal solution of position optimization in the process under a continuous convex approximation algorithm framework to find the optimal position of the unmanned aerial vehicle.
The technical effects of the present invention will be described in detail with reference to experiments.
Fig. 3 shows a schematic diagram of a relationship between security performance of the unmanned aerial vehicle relay network communication system and a position of an eavesdropper, and it can be seen from fig. 3 that, under different eavesdropper position conditions, because an interference condition is satisfied, a privacy rate of the system changes with the change of the position, and increases and then decreases with the change of the position. In contrast, with both the direct transmission without relaying and without interference, the privacy rate decreases when close to the eavesdropper and increases when far away from the eavesdropper. Numerically, the private rate of the proposed scheme is 1.7dB higher than that of the two schemes of direct transmission without relay and interference, and the safe transmission of the network is strictly ensured.
Fig. 4 shows a schematic diagram of a relationship between the security performance and the relay power of the unmanned aerial vehicle relay network communication system, and it can be seen from fig. 4 that the privacy rate of the system under the scheme of the present invention is increased compared with the scheme without interference due to the requirement of the fixed position condition of the eavesdropper, and the privacy rate of the scheme without interference is lower than the result of the scheme of the present invention by 0.8dB at most in numerical view. In the direct transmission scheme without the relay, the privacy rate is 0.35dB and remains unchanged because of the absence of the relay power. The result is synthesized, so that the scheme provided by the invention has obvious advantages in safety performance compared with the reference, and the safety transmission of the existing unmanned aerial vehicle relay network is enhanced.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus of the present invention and its modules may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, or software executed by various types of processors, or a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims (6)

1. A secure transmission enhancement method for an unmanned aerial vehicle relay network is characterized by comprising the following steps:
establishing an unmanned aerial vehicle relay communication system, wherein the aim of the system is to jointly optimize interference power and the relay position of the unmanned aerial vehicle so as to maximize the privacy rate of the system;
establishing a channel model of a ground source node-destination node communication system taking an unmanned aerial vehicle as a relay and calculating the private rate of a transmission link from a source node to a destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in two time slot-half duplex modes;
constructing an optimization model which takes the maximum privacy rate as an objective function and designs interference power and unmanned aerial vehicle position constraint conditions;
under the condition that the position of the unmanned aerial vehicle is fixed, traversing the coordinate positions of the destination node D and the eavesdropper E, and optimizing an interference power distribution scheme by using a binary search algorithm so as to maximize the privacy rate;
under the condition of optimal interference power, obtaining the optimal position of the unmanned aerial vehicle by using an exhaustive search method, generating a data set, constructing and training a DNN model, applying the DNN model to a test set, finding the optimal position of the unmanned aerial vehicle by using the high calculation efficiency of DNN, and realizing safe transmission at the maximized private rate;
the safe transmission enhancement method for the unmanned aerial vehicle relay network establishes a channel model of a communication system from a ground source node to a destination node by taking an unmanned aerial vehicle as a relay and calculates the private rate of a transmission link from the source node to the destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in two time slot-half duplex modes; channel model for a ground source node to destination node communication system:
Figure FDA0003702033710000011
where c is the speed of light, α G Is the path loss exponent, f, of the terrestrial communication link c Is the carrier frequency, d eg Is the distance, σ, between the eavesdropper and the ground user G Is the shadow fading variation of the channel;
the channel relevant with unmanned aerial vehicle contains line of sight LoS group and non-line of sight NLoS group, has the probability that the LOS is connected between ground user and the unmanned aerial vehicle:
Figure FDA0003702033710000021
Figure FDA0003702033710000022
where A and B are constants depending on the environment, (x) u ,y u ) Indicating the position of the drone in the horizontal dimension, h indicates noneHeight of human machine (x) g ,y g ) The probability of NLoS is P NLoS ＝1-P LoS The path loss models of the LoS link and the NLoS link are respectively as follows:
Figure FDA0003702033710000023
Figure FDA0003702033710000024
where d is the distance of transmission and where,
Figure FDA0003702033710000025
α L and alpha N Is the path loss exponent, η, of LoS and NLoS channels LoS And η NLoS Average excess loss for LoS and NLoS, respectively; the probability average path loss is obtained by averaging under LoS and NLoS conditions:
h ij ＝P LoS L LoS +P NLoS L NLoS ，i∈{R,D}，j∈{R,E,D}；
in a first time slot, S transmits a signal to an unmanned aerial vehicle relay R, and the signal is intercepted by an eavesdropper E; meanwhile, D emits artificial noise, so that an eavesdropper is confused; in the second time slot, S is in a silent state, the unmanned aerial vehicle relay amplifies the received signal and sends the signal to D, D is also eavesdropped by an eavesdropper, and z is used S And z J Representing confidential and cooperative interference signals from S and D, z S And z J Are all normalized powers, i.e. | z S | 2 =1 and | z J | 2 =1, where | · | represents an absolute value, the signals received at UAV and E at the first slot are:
Figure FDA0003702033710000026
Figure FDA0003702033710000027
wherein P is S And P D Are the transmit power from S and D, respectively, n R And
Figure FDA0003702033710000028
is a complex additive white Gaussian noise AWGN at R and D, following a mean of zero and a variance of
Figure FDA0003702033710000029
Complex gaussian distribution of (a);
in the second time slot, R amplifies and forwards the received signal to D, P with an amplification factor beta R For a transmit power of R, β is expressed as:
Figure FDA0003702033710000031
then, the signals received at D and E are:
Figure FDA0003702033710000032
Figure FDA0003702033710000033
wherein n is D And
Figure FDA0003702033710000034
is complex additive white gaussian noise at D and E, D effectively removes the interfering signal term and gets the received signal at D as:
Figure FDA0003702033710000035
calculating the private rate of a transmission link from the source node to the destination node, and redefining a channel noise ratio:
Figure FDA0003702033710000036
in the first time slot, the signal to interference plus noise ratio (SINR) of the wiretap link is as follows:
Figure FDA0003702033710000037
the instantaneous SINRs of D and E are:
Figure FDA0003702033710000038
Figure FDA0003702033710000039
the eavesdropper adopts a maximum ratio combining MRC method, and the signal-to-interference-and-noise ratio (SINR) at the eavesdropping node E is as follows:
Figure FDA0003702033710000041
in a relay system based on physical layer security, the privacy rate is obtained as follows:
Figure FDA0003702033710000042
the safe transmission enhancement method for the relay network of the unmanned aerial vehicle is characterized in that an optimization model which takes the maximum privacy rate as a target function and designs interference power and unmanned aerial vehicle position constraint conditions is established:
Figure FDA0003702033710000043
Figure FDA0003702033710000044
(x u ,y u )∈Α；
wherein (x) u ,y u ) Representing the horizontal position of the drone in the considered area a;
the safety transmission enhancement method for the relay network of the unmanned aerial vehicle determines that the coordinate positions of a target node D and an eavesdropper E are traversed under the condition that the position of the unmanned aerial vehicle is fixed, and optimizes an interference power distribution scheme by using a binary search algorithm so as to maximize the privacy rate: under the condition that fixed unmanned aerial vehicle places, the optimization problem of interference power is as follows:
Figure FDA0003702033710000045
Figure FDA0003702033710000046
the safe transmission enhancing method for the relay network of the unmanned aerial vehicle utilizes the power obtained by the solution of the binary search algorithm, namely the power distribution scheme of the current fixed unmanned aerial vehicle position, and the optimal interference power is
Figure FDA0003702033710000047
The method comprises the following steps: initialization: setting P D Given as P min And P max In which P is min =0; defining a sufficiently small threshold epsilon;
step two: order to
Figure FDA0003702033710000051
R S Given by the second step if
Figure FDA0003702033710000052
Then P is max ＝P * Else P min ＝P * ；
Step three: if P max -P min If is less than epsilon, the optimal interference power P is obtained * Completing a power distribution strategy of the unmanned aerial vehicle relay communication system; otherwise, repeating the step two.
2. The secure transmission enhancement method for the drone-oriented relay network according to claim 1, wherein the secure transmission enhancement method for the drone-oriented relay network establishes a drone relay communication system with the goal of maximizing the privacy rate of the system by jointly optimizing the interference power and the drone relay position; consider an unmanned aerial vehicle relay communication system consisting of a source node S, a destination node D, a UAV relay R, and an eavesdropper E, S sending a signal to the UAV relay, R amplifying and forwarding the signal to D.
3. The method for enhancing secure transmission of the relay network of the unmanned aerial vehicle as claimed in claim 1, wherein the method for enhancing secure transmission of the relay network of the unmanned aerial vehicle uses an exhaustive search method to obtain an optimal position of the unmanned aerial vehicle under the condition of optimal interference power, generates a data set, constructs and trains a DNN model, applies the DNN model to a test set, and finds the optimal position of the unmanned aerial vehicle by using high calculation efficiency of DNN to maximize a private rate and realize secure transmission;
obtaining the optimal interference power and obtaining the optimization problem of unmanned plane position deployment as follows:
Figure FDA0003702033710000053
s.t.(x u ,y u )∈Α
Figure FDA0003702033710000054
4. the method for enhancing secure transmission of the relay network of the unmanned aerial vehicle as claimed in claim 3, wherein the method for enhancing secure transmission of the relay network of the unmanned aerial vehicle adopts a neural network model learning network feature with a plurality of hidden layers and a large amount of training data to solve an optimization problem of unmanned aerial vehicle position deployment, so as to realize a DNN-based unmanned aerial vehicle deployment scheme; the method comprises the steps of constructing a fully-connected DNN model with an input layer, two hidden layers and an output layer, learning an input/output relation through training DNN, applying the model to a test set, finding the optimal position of an unmanned aerial vehicle by utilizing the high calculation efficiency of the DNN, maximizing the privacy speed, realizing the safe transmission of the system, considering a system located in the range of 200 m x 200 m, wherein the fixed height h of the unmanned aerial vehicle is 150 m, a source node has a fixed position (0, 0), firstly, fixing the positions of S, D and E, traversing the horizontal position of the unmanned aerial vehicle, and obtaining the optimal interference power P by adopting a binary search algorithm * Under the condition of optimal interference power, the maximum privacy rate corresponds to the optimal position of the unmanned aerial vehicle; then, through traversing the coordinate positions of D and E, finding the optimal positions of the unmanned aerial vehicles corresponding to different D and E, and constructing a data set;
in the input layer of the DNN model, the coordinates (x) of the target node and the eavesdropper are determined d ,y d ) And (x) e ,y e ) Reshaped into a 4 × 1 data sample, denoted
Figure FDA0003702033710000061
Coordinates (x) of optimum position of drone u ,y u ) As a tag output, is represented as
Figure FDA0003702033710000062
Where i ∈ {1,2, \8230;, N }, and Q = [ Q ] - [ Q ] 1 ,q 2 ,…,q N ]As input for DNN, q i Each item in (1) corresponds to an input neuron, and the optimal position U = [ U ] of the unmanned aerial vehicle is taken 1 ,u 2 ,…,u N ]As output of the DNN model, u i Each item in the method corresponds to an output neuron, and finally, the well-trained DNN model is used for a test set, so that the optimal position of the unmanned aerial vehicle can be quickly and effectively found, and the problem of position optimization of the unmanned aerial vehicle can be solved.
5. A computer arrangement, characterized in that it comprises a memory and a processor, said memory storing a computer program that, when executed by said processor, causes said processor to carry out the secure transmission enhancement method towards a drone relay network according to any one of claims 1 to 4.
6. A secure transmission enhancement system for a relay network of an unmanned aerial vehicle, which implements the secure transmission enhancement method for the relay network of an unmanned aerial vehicle according to any one of claims 1 to 4, wherein the secure transmission enhancement system for the relay network of an unmanned aerial vehicle comprises:
the unmanned aerial vehicle relay communication system establishing module is used for establishing an unmanned aerial vehicle relay communication system, and aims to jointly optimize interference power and an unmanned aerial vehicle relay position so as to maximize the privacy rate of the system;
the system comprises a channel model and time slot establishing module, a data transmission module and a data transmission module, wherein the channel model and time slot establishing module is used for establishing a channel model of a ground source node to destination node communication system taking an unmanned aerial vehicle as a relay and calculating the private rate of a transmission link from a source node to a destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in two time slot-half duplex modes;
the target function construction module is used for constructing an optimization model which takes the maximum privacy rate as a target function, designs interference power and unmanned aerial vehicle position constraint conditions;
the privacy rate maximization module is used for traversing coordinate positions of the target node D and the eavesdropper E under the condition that the position of the unmanned aerial vehicle is fixed, and optimizing an interference power distribution scheme by utilizing a binary search algorithm so as to maximize the privacy rate;
the safe transmission module is used for obtaining the optimal position of the unmanned aerial vehicle by using an exhaustive search method under the condition of optimal interference power, generating a data set, constructing and training a DNN (digital noise network) model, applying the DNN model to a test set, finding the optimal position of the unmanned aerial vehicle by using the high calculation efficiency of the DNN, and realizing safe transmission by maximizing the private rate;
establishing a channel model of a ground source node-destination node communication system taking an unmanned aerial vehicle as a relay and calculating the private rate of a transmission link from a source node to a destination node according to a receiving signal of the unmanned aerial vehicle and a receiving signal of the destination node in two time slot-half duplex modes; channel model for a ground source node to destination node communication system:
Figure FDA0003702033710000071
where c is the speed of light, α G Is the path loss exponent, f, of the terrestrial communication link c Is the carrier frequency, d eg Is the distance, σ, between the eavesdropper and the ground user G Is the shadow fading variable of the channel;
the channel relevant with unmanned aerial vehicle contains sight distance LoS group and non-sight distance NLoS group, has the probability that the LOS is connected between ground user and the unmanned aerial vehicle:
Figure FDA0003702033710000072
Figure FDA0003702033710000073
where A and B are constants that depend on the environment, (x) u ,y u ) Representing the position of the drone in the horizontal dimension, h represents the altitude of the drone, (x) g ,y g ) Representing the position of the ground user with a probability P of NLoS NLoS ＝1-P LoS The path loss models of the LoS link and the NLoS link are respectively as follows:
Figure FDA0003702033710000074
Figure FDA0003702033710000075
where d is the distance of transmission and where,
Figure FDA0003702033710000081
α L and alpha N Is the path loss exponent, η, of LoS and NLoS channels LoS And η NLoS Average excess loss for LoS and NLoS, respectively; the probability average path loss is obtained by averaging under LoS and NLoS conditions:
h ij ＝P LoS L LoS +P NLoS L NLoS ，i∈{R,D}，j∈{R,E,D}；
in a first time slot, S transmits a signal to an unmanned aerial vehicle relay R, and the signal is intercepted by an eavesdropper E; meanwhile, D emits artificial noise, so that an eavesdropper is confused; in the second time slot, S is in a silent state, the unmanned aerial vehicle relay amplifies the received signal and sends the signal to D, D is also eavesdropped by an eavesdropper, and z is used S And z J Representing confidential signals and cooperative interference signals from S and D, z S And z J Are all normalized powers, i.e. | z S | 2 =1 and | z J | 2 =1, where | · | represents an absolute value, the signals received at UAV and E at the first slot are:
Figure FDA0003702033710000082
Figure FDA0003702033710000083
wherein P is S And P D Are the transmit power from S and D, respectively, n R And
Figure FDA0003702033710000084
is a complex additive white Gaussian noise AWGN at R and D, following a mean of zero and variance of
Figure FDA0003702033710000085
Complex gaussian distribution of (a);
in the second time slot, R amplifies and forwards the received signal to D, P with an amplification factor beta R For a transmit power of R, β is expressed as:
Figure FDA0003702033710000086
then, the signals received at D and E are:
Figure FDA0003702033710000087
Figure FDA0003702033710000088
wherein n is D And
Figure FDA0003702033710000091
is complex additive white gaussian noise at D and E, D effectively removes the term and yields the received signal at D as:
Figure FDA0003702033710000092
calculating the private rate of a transmission link from the source node to the destination node, and redefining a channel noise ratio:
Figure FDA0003702033710000093
in the first time slot, the signal to interference plus noise ratio (SINR) of the eavesdropping link is as follows:
Figure FDA0003702033710000094
the instantaneous SINRs of D and E are:
Figure FDA0003702033710000095
Figure FDA0003702033710000096
the eavesdropper adopts a maximum ratio combining MRC method, and the signal-to-interference-and-noise ratio SINR at the eavesdropping node E is as follows:
Figure FDA0003702033710000097
in a relay system based on physical layer security, the privacy rate is obtained as follows:
Figure FDA0003702033710000098
constructing an optimization model which takes the maximum privacy rate as a target function, designs interference power and unmanned aerial vehicle position constraint conditions:
Figure FDA0003702033710000101
Figure FDA0003702033710000102
(x u ,y u )∈Α；
wherein (x) u ,y u ) Representing the horizontal position of the drone in the considered zone a;
determining the coordinate positions of a traversal target node D and an eavesdropper E under the condition that the position of the unmanned aerial vehicle is fixed, and optimizing an interference power distribution scheme by using a binary search algorithm so as to maximize the privacy rate: under the condition that fixed unmanned aerial vehicle places, the optimization problem of interference power is as follows:
Figure FDA0003702033710000103
Figure FDA0003702033710000104
solving the obtained power, namely the power distribution scheme of the current fixed unmanned aerial vehicle position by utilizing a binary search algorithm, wherein the optimal interference power is
Figure FDA0003702033710000105
The method comprises the following steps: initialization: setting P D Given as P min And P max In which P is min =0; defining a sufficiently small threshold epsilon;
step two: order to
Figure FDA0003702033710000106
R S Given by the second step, if
Figure FDA0003702033710000107
Then P is max ＝P * Otherwise P min ＝P * ；
Step three: if P max -P min If is less than epsilon, the optimal interference power P is obtained * Completing the power distribution strategy of the unmanned aerial vehicle relay communication system; otherwise, repeating the step two.
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