CN116546487A - Unmanned aerial vehicle safety communication method under assistance of intelligent reflecting surface - Google Patents
Unmanned aerial vehicle safety communication method under assistance of intelligent reflecting surface Download PDFInfo
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Abstract
The invention relates to an unmanned aerial vehicle safety communication method assisted by an intelligent reflecting surface, which comprises the following steps: constructing a downlink communication system consisting of an unmanned plane, an intelligent reflecting surface, M legal users and an active eavesdropper; setting a target problem P1 as a three-dimensional track of the unmanned aerial vehicle through joint optimization of user scheduling and intelligent reflection surface reflection phase shift, and maximizing the average confidentiality rate of the system; the reflection phase shift of the given intelligent reflection surface and the three-dimensional track of the unmanned aerial vehicle are input into a target problem P1 to be optimized to obtain optimal unmanned aerial vehicle user scheduling; inputting the optimal unmanned aerial vehicle user schedule and a given three-dimensional track into a target problem P1 to be optimized to obtain an intelligent reflection surface optimal reflection phase shift; inputting optimal user scheduling and optimal reflection phase shift of the intelligent reflection surface into a target problem P1 to be optimized to obtain an optimal three-dimensional track; the method maximizes the average confidentiality rate of the system and avoids interception of user information while ensuring the service quality of legal users.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle communication, in particular to an unmanned aerial vehicle safety communication method assisted by an intelligent reflecting surface.
Background
The unmanned aerial vehicle can well solve the problem and increase the information transmission capacity of a communication system in the face of increasing user quantity and wireless network capacity. The unmanned aerial vehicle (Unmanned Aerial Vehicle, UAV) has high mobility as an aerial communication platform, can provide effective and reliable coverage, and improves the energy efficiency of wireless communication. The method can adapt to various communication environments by adjusting the position, and can be deployed as a mobile base station, a mobile relay and an air user, thereby exhibiting the advantages which are not possessed by the traditional static base station. In particular, the unmanned aerial vehicle can shorten the communication distance with the ground user as an air base station, establish a Line-of-Sight (LOS) link, and provide efficient data service. Therefore, unmanned aerial vehicles offer a wide range of potential applications in the 5G and beyond communications field.
One of the key challenges faced by unmanned aerial vehicle communication networks is that, because the transmission of air-to-ground signals between unmanned aerial vehicle-ground nodes is blocked by obstacles such as high-rise buildings, non-line-of-sight paths may exist, resulting in reduced signal quality at the receiving end, which affects normal communication. Emerging smart reflective surface technologies can address this challenge by actively reconstructing the propagation environment. The drone can fly near the ground terminals and communicate with them over a line-of-sight link, thereby increasing the air-to-ground data rate. This increases the likelihood of an eavesdropper maliciously stealing the user information, since the apparent channel dominates in an air-to-ground system.
In the existing research, it has been proposed to maximize the average privacy rate of a single-user communication system by jointly optimizing the horizontal trajectory, transmit beam formation and smart reflective surface phase shift of the unmanned aerial vehicle. From the safety point of view, the track, the power control and the intelligent reflection surface phase shift of the unmanned aerial vehicle are jointly optimized under the worst condition caused by an eavesdropper, so that the maximum confidentiality rate of a single user is achieved. It has also been proposed to introduce an intelligent reflective surface as a relay into an unmanned aerial vehicle wireless information and energy synchronous transmission (SWIPT) network, to improve system performance while guaranteeing the energy requirements of the energy receiver. However, in the face of increasing network capacity, single-user environments have not been well suited for use in current environments, resulting in an increased risk of malicious eavesdropping of user information by an eavesdropper.
Disclosure of Invention
The invention aims to solve the technical problem of providing the unmanned aerial vehicle safety communication method under the assistance of the intelligent reflecting surface, which can maximize the average confidentiality rate of a system and avoid interception of user information by carrying out joint optimization on user scheduling, the three-dimensional track of the unmanned aerial vehicle and the reflection phase shift of the intelligent reflecting surface under the condition of ensuring the service quality of legal users.
The technical scheme adopted by the invention is that the unmanned aerial vehicle safety communication method assisted by the intelligent reflecting surface comprises the following steps:
s1, constructing a downlink communication system consisting of an unmanned plane, an intelligent reflecting surface, M legal users and an active eavesdropper; the intelligent reflecting surface is fixedly arranged on a building, the unmanned aerial vehicle communicates with an associated legal user through a link in each time slot, and the link is an unmanned aerial vehicle-legal user direct link or an unmanned aerial vehicle-intelligent reflecting surface-legal user indirect link;
s2, setting target problem P 1 The target problem P 1 The average confidentiality rate of the system is maximized for optimizing the user scheduling, the intelligent reflection surface reflection phase shift and the unmanned aerial vehicle three-dimensional track through combination;
s3, optionally setting reflection phase shift of the intelligent reflection surface and inputting the three-dimensional track of the unmanned aerial vehicle to the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization to obtain optimal unmanned aerial vehicle user scheduling;
s4, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3 and the three-dimensional track of the unmanned aerial vehicle arbitrarily set in the step S3 into the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization to obtain the optimal reflection phase shift of the intelligent reflection surface;
s5, adjusting the optimal unmanned aerial vehicle user obtained in the step S3The optimal reflection phase shift of the intelligent reflection surface obtained in the step S4 is input to the target problem P set in the step S2 1 In, by the target problem P 1 And performing iterative optimization to obtain the optimal three-dimensional track of the unmanned aerial vehicle.
Preferably, in step S2, the target problem P 1 Expressed as:
P 1 :
C4:z[1]＝z init ,z[T]＝z final
wherein, a represents the user schedule,
α m [t]scheduling variable alpha representing association relation between unmanned aerial vehicle and legal user m in t time slot m [t]∈{0,1}，α m [t]=1 means that the drone serves the legitimate user m in the t slot, ±>Otherwise alpha m [t]=0; phi represents the reflection phase shift vector of the smart reflective surface, < >> Indicating the (n) th on the smart reflective surface in the t time slot x ,n y ) A phase shift of the individual reflective elements relative to the legitimate user m; q represents a horizontal trajectory vector of the unmanned aerial vehicle, +.>q[t]＝[x[t],y[t]] T Representing a horizontal trajectory of the unmanned aerial vehicle; h represents a vertical trajectory vector of the unmanned aerial vehicle,H[t]representing a vertical track of the unmanned aerial vehicle, wherein the three-dimensional track of the unmanned aerial vehicle consists of a horizontal track and a vertical track; r is R m [t]Representing the reachability and rate of terrestrial subscriber m in time slot t,/>Representing a set of M legitimate users,m=0 is denoted as an index of an eavesdropper, +.>Represents the total number of ground users, consisting of M legal users and one eavesdropper +.>R represents m,0 [t]Upper bound of R m,0 [t]Representing the achievable sum rate of eavesdroppers m=0 in the t time slot;the method comprises the steps that C1 represents constraint on user association scheduling, C2 represents constraint on ensuring that in each time slot, the unmanned aerial vehicle is associated with one legal user at most and performs information transmission, C3 represents constraint on initial and final positions of the unmanned aerial vehicle, C5 represents constraint on reflection phase shift of an intelligent reflection surface, and C6 and C7 represent track constraint of the unmanned aerial vehicle, wherein service quality of each legal user is guaranteed; q (Q) max Represents the maximum horizontal movement distance allowed by the unmanned aerial vehicle per time slot, Q max ＝V h τ，H max Indicating the maximum vertical movement distance, H, allowed by the drone per slot max ＝V z τ, the length of each time slot is τ seconds, and the maximum horizontal flight speed and the maximum vertical flight speed of the unmanned aerial vehicle in each time slot are V respectively h and Vz 。
Preferably, the specific process of step S3 includes the following steps:
s3.1, optionally setting reflection phase shift of the intelligent reflection surface and inputting three-dimensional track of the unmanned aerial vehicle to the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization conversion to target problem P 2 ：
P 2 :
C1, C2, C3; wherein,representing the average security rate of the system;
s3.2, target problem P 2 Conversion of binary relaxation into target problem Question of objective->Performing iterative optimization to obtain optimal unmanned aerial vehicle user callDegree.
Preferably, in step S4, the optimal unmanned aerial vehicle user schedule obtained in step S3 and the three-dimensional trajectory of the unmanned aerial vehicle arbitrarily set in step S3 are input to the target question P set in step S2 1 In the method, the optimal reflection phase shift of the intelligent reflection surface at the legal user m in the t time slot is obtained by iterative optimization of the target problem P1, and is as follows:
wherein ,
indicating the (n) th on the smart reflective surface in the t time slot x ,n y ) An optimal phase shift of the individual reflective elements relative to the user m; />Is a random scattering component subject to a circularly symmetric complex gaussian distribution of zero mean, unit variance; θ (1)[t] and ζ(1) [t]Vertical and horizontal AOAs, respectively denoted as drone-to-smart reflective surface in the t-slot; /> and />Represented as vertical AODs and horizontal AODs, respectively, from the smart reflective surface to the user.
Preferably, the specific process of step S5 includes the following steps:
step one, optimizing a horizontal track of the unmanned aerial vehicle:
s5.1, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3, the optimal reflection phase shift of the intelligent reflection surface obtained in the step S4 and the vertical track of any given unmanned aerial vehicle into the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization conversion to unmannedNon-convex target problem P of horizontal trajectory of machine 3 ：
S5.2, the reachable sum rate R to legal user m in t time slot m [t]And (3) performing iterative optimization: from the following components
Wherein B represents system bandwidth, P represents transmitting power and sigma of unmanned aerial vehicle 2 Is Gaussian white noise power, ψ m [t]Phase shift matrix denoted unmanned plane-smart reflective surface-legal subscriber link +.> wherein ,
N x and Ny The number of reflecting elements of the intelligent reflecting surface in the x direction and the y direction are respectively represented; toward R m [t]Introducing a relaxation variable-> and ηQ [t]To replace g respectively m[t] and d(1) [t]And satisfies the constraint: /> and ηQ [t]≥(d (1) [t]) 2 Obtaining the reachable sum rate R of legal user m in t time slot m [t]Redefined as:
wherein ,/> For->Performing first-order Taylor expansion to obtain +.>Lower bound of->Said->As a function of the concavity of the curve, wherein ,
s5.3, pair R m,0 [t]Upper bound of (2)And (3) performing iterative optimization: by->Introducing a relaxation variable v Q[t] and />To replace (d) 0 [t]) 2 and (d(1) [t]) 2 And respectively meet constraint v Q [t]≤(d 0 [t]) 2 and />Reachability and rate of eavesdropper in t time slot +.>Redefined as:the right side of the newly introduced inequality constraint pertains to q [ t ]]Is convex, and is transformed into a concave function by first-order taylor expansion at the first iteration:
(d 0 [t]) 2 ≥q lb1 [t]＝(d 0 [t] (l) ) 2 +2(q[t] (l) -u 0 ) T (q[t]-q[t] (l) )，
(d (1) [t]) 2 ≥q lb2 [t]＝(d (1) [t] (l) ) 2 +2(q[t] (l) -q I ) T (q[t]-q[t] (l) )；
s5.4, optimizing the concave function obtained according to the step S5.2 and the step S5.3, and solving the problem P of non-convex targets of the horizontal track of the unmanned aerial vehicle obtained in the step S5.1 3 Conversion to convex problem P 4 ： wherein ,/>Problem of convexion P 4 Performing iterative optimization to obtain an optimal horizontal track of the unmanned aerial vehicle;
step two, optimizing the vertical track of the unmanned aerial vehicle:
s5.5, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3, the optimal reflection phase shift of the intelligent reflection surface obtained in the step S4 and the vertical track of any given unmanned aerial vehicle into the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization and converting into an optimization problem P5 of the vertical trajectory of the unmanned aerial vehicle:the optimization problem P 5 Is a non-convex optimization problem;
s5.6, para-youProblem P of chemical conversion 5 Performing convex optimization conversion to obtain a vertical track optimization problem P of the unmanned aerial vehicle 6 ：P 6 :
s.t.C4,C7
wherein ,
and ηH [t]Are respectively (d) m [t]) 2 and (dm [t]) 2 At iteration I for q [ t ]]Performing first-order Taylor expansion to obtain upper bound, H lb1[t] and Hlb2 [t]Respectively is v H[t] and />At iteration I for q [ t ]]Performing first-order Taylor expansion to obtain an upper bound; vertical trajectory optimization problem P for unmanned aerial vehicle 6 Performing laminationAnd (5) optimizing the generation to obtain the optimal vertical track of the unmanned aerial vehicle.
The beneficial effects of the invention are as follows: by adopting the unmanned aerial vehicle safety communication method under the assistance of the intelligent reflecting surface, the method adopts the algorithm based on alternate optimization and continuous convex approximation, and the user scheduling, the reflection phase shift of the intelligent reflecting surface and the three-dimensional track of the unmanned aerial vehicle are jointly optimized, so that the average confidentiality rate of the system is maximized while the service quality of legal users is ensured, and the user information is prevented from being eavesdropped.
Drawings
Fig. 1 is a schematic structural diagram of a downlink communication system constructed in accordance with the present invention;
fig. 2 is a schematic view of a horizontal direction of an information transmission angle between a drone and a user in the present invention;
FIG. 3 is a schematic view of the vertical direction of the information transmission angle between the unmanned aerial vehicle and the user in the present invention;
FIG. 4 is a schematic representation of average privacy ratio versus number of reflective elements obtainable by the system with the aid of intelligent reflective surfaces in accordance with the present invention;
FIG. 5 is a graph of a comparison of performance obtained in the present invention using the method of the present invention under four transmission schemes;
FIG. 6 is a comparison of security of a smart reflective surface under different phase control strategies in accordance with the present invention;
FIG. 7 is a graph showing the relationship between the average security rate of the system and the transmitting power of the unmanned aerial vehicle under different phase control strategies;
fig. 8 is a graph showing the effect of the path loss index of the smart reflective surface to the terrestrial user channel on the security level of the system according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings in combination with specific embodiments to enable one skilled in the art to practice the invention by reference to the specification, the scope of the invention being limited to the specific embodiments.
The invention relates to an unmanned aerial vehicle safety communication method assisted by an intelligent reflecting surface, which comprises the following steps:
s1, as shown in FIG. 1, constructing a downlink communication system consisting of an unmanned plane, an intelligent reflecting surface, M legal users and an active eavesdropper; the intelligent reflecting surface is fixedly arranged on a building, the unmanned aerial vehicle communicates with an associated legal user through a link in each time slot, and the link is an unmanned aerial vehicle-legal user direct link or an unmanned aerial vehicle-intelligent reflecting surface-legal user indirect link;
in FIG. 1, z [ t ]]＝[q[t] T ,H[t]]Representing a three-dimensional trajectory of the drone within a t-slot, wherein,and H [ t ]]Respectively representing the horizontal track and the vertical track of the unmanned plane; for convenience of description, ++>The individual legitimate users and eavesdroppers are collectively referred to as terrestrial users-> wherein />Expressed as a set of legitimate users, m=0 expressed as an index of an eavesdropper; the horizontal position of the ground user m is denoted +.>The intelligent reflecting surface is fixedly arranged on the surface of a building, and the horizontal position of the intelligent reflecting surface is expressed as +.>The vertical position is denoted as H I The method comprises the steps of carrying out a first treatment on the surface of the Setting the flight period of the unmanned aerial vehicle as T, the length of each time slot as tau seconds, and the maximum horizontal flight speed and the maximum vertical flight speed of the unmanned aerial vehicle in each time slot as V respectively h and Vz The method comprises the steps of carrying out a first treatment on the surface of the For every legal user->The reflection phase shift of the smart reflective surface in the t-slot is denoted +.>
In fig. 1, since the range channel is dominant in the space-to-ground channel, the method of the present invention will consider the direct link of the drone to the ground user in addition to the indirect link due to passive reflection by the smart reflective surface; in the t time slot, the direct channel from the unmanned aerial vehicle to the ground user m is expressed as wherein β0 Channel gain at reference distance d=1m; d, d m [t]Is the distance from the drone to the ground user m in time slot t, specifically denoted +.>In the t time slot, the channel from the unmanned aerial vehicle to the intelligent reflecting surface is expressed as:
wherein ,
and />Respectively representing the phase shift of the unmanned aerial vehicle relative to the origin of the intelligent reflecting surface in the x dimension and the y dimension in the t time slot; d, d (1) [t]The distance from the unmanned plane to the intelligent reflecting surface in the t time slot is expressed as +.>As shown in FIG. 2, the relationship between the unmanned plane and the intelligent reflecting surface in angle and position can be used +.> and />To describe, θ (1)[t] and ζ(1) [t]The vertical AOAs and the horizontal AOAs are respectively expressed as unmanned aerial vehicle to intelligent reflecting surface in t time slots;
s2, setting a target problem P1, wherein the target problem P1 is the average confidentiality rate of a maximized system by jointly optimizing user scheduling, intelligent reflection surface reflection phase shift and unmanned aerial vehicle three-dimensional track; the specific process of setting the target problem P1 is:
obtaining legal users in t time slots according to shannon formulaThe achievable rate of (2):
wherein ,αm [t]E {0,1} means unmanned and legitimate user +.>Scheduling variables of the association relationship; alpha m [t]=1 means that the drone serves legal users in t slots +.>Otherwise alpha m [t]=0. B is the system bandwidth, P is the transmit power of the unmanned aerial vehicle, sigma 2 Is gaussian white noise power. Psi m [t]Phase shift matrix expressed as unmanned plane-intelligent reflecting surface-legal user link, specifically expressed as wherein ,
since small scale fading between smart reflective surfaces and eavesdroppers is difficult to obtain, R is obtained by solving the Jasen inequality m,0 [t]Upper bound of (2)I.e. consider the achievable sum rate of the eavesdropper m=0 in the worst case as:
wherein ,Ψm,0 [t]Phase shift matrix for unmanned plane-intelligent reflecting surface-eavesdropper link, specifically expressed as +.>
Optimizing user scheduling by federationReflective phase shift of intelligent reflective surfaceHorizontal track of unmanned aerial vehicle->And vertical trajectory of unmanned aerial vehicle->The following optimization problems are established: />Wherein C1 is constraint on user association scheduling, C2 ensures that in each time slot, the unmanned aerial vehicle is associated with one legal user at most and performs information transmission, C3 ensures service quality of each legal user, C4 is initial and final position constraint of the unmanned aerial vehicle, C5 is constraint on reflection phase shift of an intelligent reflecting surface, C6 and C7 are track constraint of the unmanned aerial vehicle, wherein Q is as follows max ＝V h τ is the maximum horizontal movement distance allowed by the drone per slot, H max ＝V z τ is the maximum vertical travel distance allowed for the drone per slot;
s3, optionally setting reflection phase shift of the intelligent reflection surface and inputting the three-dimensional track of the unmanned aerial vehicle to the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization to obtain optimal unmanned aerial vehicle user scheduling; the specific process is as follows:
s3.1, optionally setting reflection phase shift of the intelligent reflection surface and inputting three-dimensional track of the unmanned aerial vehicle to the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization conversion to target problem P 2 ：
C1, C2, C3; wherein,representing the average security rate of the system;
s3.2, since constraint C1 is a binary constraint, problem P is caused 2 Still non-convex problem, for target problem P 2 Conversion of binary relaxation into target problem Question of objective->Performing iterative optimization to obtain optimal unmanned aerial vehicle user scheduling; problem->Is a standard convex optimization problem that can be realized by existing optimization tools, such as CVX 68]And the like can obtain effective solutions;
s4, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3 and the three-dimensional track of the unmanned aerial vehicle arbitrarily set in the step S3 into the target problem P set in the step S2 1 In the method, the target problem P1 is subjected to iterative optimization to obtain the optimal reflection phase shift of the intelligent reflection surface: wherein ,
indicating the (n) th on the smart reflective surface in the t time slot x ,n y ) An optimal phase shift of the individual reflective elements relative to the user m; />Is a random scattering component subject to a circularly symmetric complex gaussian distribution of zero mean, unit variance; θ (1)[t] and ζ(1) [t]Vertical and horizontal AOAs, respectively denoted as drone-to-smart reflective surface in the t-slot; /> and />Vertical and horizontal AODs, respectively, denoted smart reflective surface to user;
s5, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3 and the optimal reflection phase shift of the intelligent reflection surface obtained in the step S4 into the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization to obtain an optimal three-dimensional track of the unmanned aerial vehicle; the specific process is as follows:
step one, optimizing a horizontal track of the unmanned aerial vehicle:
s5.1, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3, the optimal reflection phase shift of the intelligent reflection surface obtained in the step S4 and the vertical track of any given unmanned aerial vehicle into the step S2Targeting problem P 1 In the method, the target problem P1 is subjected to iterative optimization and is converted into a non-convex target problem P of the horizontal track of the unmanned aerial vehicle 3 ：Due to the objective function->And constraint C3 regarding the horizontal trajectory q [ t ] of the drone]Neither convex nor concave, thus problem P 3 Is still a non-convex problem, so R is required m[t] and />Respectively optimizing to solve the problem of non-convexity;
s5.2, the reachable sum rate R to legal user m in t time slot m [t]And (3) performing iterative optimization: from the following components
Wherein B represents system bandwidth, P represents transmitting power and sigma of unmanned aerial vehicle 2 Is Gaussian white noise power, ψ m [t]Phase shift matrix denoted unmanned plane-smart reflective surface-legal subscriber link +.> wherein ,
N x and Ny The number of reflecting elements of the intelligent reflecting surface in the x direction and the y direction are respectively represented; r in the formula m [t]With respect to q [ t ]]Is not convex, so toward R m [t]Introducing a relaxation variable-> and ηQ [t]To replace g respectively m[t] and d(1) [t]And satisfies the constraint: and ηQ [t]≥(d (1) [t]) 2 Obtaining the reachable sum rate R of legal user m in t time slot m [t]Redefined as:
wherein ,/> For->Performing first-order Taylor expansion to obtain +.>Lower bound of->Said->As a function of the concavity of the curve, wherein ,
s5.3, pair R m,0 [t]Upper bound of (2)And (3) performing iterative optimization: from the following components
In the formula->With respect to q [ t ]]Is not convex, so that a relaxation variable v is introduced Q[t] and />To replace (d) 0 [t]) 2 and (d(1) [t]) 2 And respectively meet constraint v Q [t]≤(d 0 [t]) 2 and />Reachability and rate of eavesdropper in t time slot +.>Redefined as: is related to the upsilon Q[t] and />To the right of the newly introduced inequality constraint with respect to q t]Is convex, and is transformed into a concave function by first-order taylor expansion at the first iteration: (d) 0 [t]) 2 ≥q lb1 [t]＝(d 0 [t] (l) ) 2 +2(q[t] (l) -u 0 ) T (q[t]-q[t] (l) )，(d( 1 )[t]) 2 ≥q lb2 [t]＝(d( 1 )[t] (l) ) 2 +2(q[t] (l) -q I ) T (q[t]-q[t] (l) )；
S5.4, optimizing the concave function obtained according to the step S5.2 and the step S5.3, and solving the problem P of non-convex targets of the horizontal track of the unmanned aerial vehicle obtained in the step S5.1 3 Conversion to convex problem P 4 ： wherein ,/>Problem of convexion P 4 Performing iterative optimization to obtain an optimal horizontal track of the unmanned aerial vehicle;
step two, optimizing the vertical track of the unmanned aerial vehicle:
s5.5, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3, the optimal reflection phase shift of the intelligent reflection surface obtained in the step S4 and the vertical track of any given unmanned aerial vehicle into the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization and converting into an optimization problem P of a vertical track of the unmanned aerial vehicle 5 ：Because of->And constraint C3 is neither convex nor non-convex, thus problem P 5 Still a non-convex optimization problem;
s5.6 due to R m[t] and can use the problem P 3 The same transformation concept of the optimization problem P is subjected to convex optimization 5 Performing convex optimization conversion to obtain a vertical track optimization problem P of the unmanned aerial vehicle 6 ：
P 6 :
s.t.C4,C7
wherein ,
and ηH [t]Are respectively (d) m [t]) 2 and (dm [t]) 2 At iteration I for q [ t ]]Performing first-order Taylor expansion to obtain upper bound, H lb1[t] and Hlb2 [t]Respectively is v H[t] and />At iteration I for q [ t ]]Performing first-order Taylor expansion to obtain an upper bound; vertical trajectory optimization problem P for unmanned aerial vehicle 6 And performing iterative optimization to obtain the optimal vertical track of the unmanned aerial vehicle.
In the present invention, problem P 1 Is decomposed into three sub-problems and then solved alternately by designing an algorithm based on alternating iterations and successive convex approximations. Specifically, in the first iteration, the process of solving four sub-problems is: first step to solve the sub-problemBy giving intelligenceReflective phase shift of reflective surface and three-dimensional trajectory { ψ } of unmanned aerial vehicle (l) ,Q (l) ,H (l) Solving for optimal user schedule A (l+1) For the next iteration; the second step solves the sub-problem P 4 Given an optimal user schedule, a reflection phase shift of an optimal smart reflective surface, and an arbitrary vertical trajectory of the drone { a } (l+1) ,Ψ (l) ,H (l) Iterative solution of optimal horizontal track Q of unmanned aerial vehicle by using continuous convex approximation algorithm (l+1) The method comprises the steps of carrying out a first treatment on the surface of the Third step of solving the sub-problem P 6 Given an optimal user schedule, an optimal reflected phase shift of the smart reflective surface, and a horizontal trajectory { A) of any drone (l+1) ,Ψ (l+1) ,Q (l+1) Iterative solution of optimal vertical trajectory H of unmanned aerial vehicle by using continuous convex approximation algorithm (l+1) The method comprises the steps of carrying out a first treatment on the surface of the Finally, the reflection phase shift of the intelligent reflection surface is repeatedly updated in an iterative mode until convergence, namely the threshold value is smaller than epsilon 3 。
In order to verify the feasibility and effectiveness of the method, the method is subjected to simulation test, and the method is concretely as follows:
the horizontal coordinates of legal users and eavesdroppers are respectively set as [200,100;270,50;230,60] T And [100,40 ]]The coordinates of the intelligent reflecting surface are [200,0,10 ]]The initial position and the final position of the flight track of the unmanned aerial vehicle are respectively set as [0,80,80 ]]And [450,70,80 ]]The maximum horizontal flight speed and the maximum vertical flight speed of the unmanned aerial vehicle in each time slot are V respectively h =40m/s and V z =3m/s, the unmanned plane has a transmit power of p=1w, a slot number of t=50s, each slot length of τ=1s, and a noise power of σ 2 = -80dBm, the channel gain at reference distance is β 0 -20dB, path loss coefficient η=2.3, threshold epsilon 1 ＝ε 2 ＝10 -4 and ε3 ＝10 -8 。
The results of the simulation test are shown in fig. 4 to 8:
FIG. 4 illustrates an iterative process of the proposed algorithm in the present invention; the result shows that the system with the assistance of the intelligent reflecting surface can obtain higher average confidentiality, and the number of reflecting elements is in direct proportion to the average confidentiality.
Referring to fig. 5, the performance of four schemes is demonstrated: (1) the design proposed by the method of the present invention; (2) not performing unmanned aerial vehicle track optimization design; (3) design without consideration of user scheduling; (4) a transmission design without intelligent reflecting surface; the results show that for any transmission design with the aid of a smart reflective surface, the average privacy ratio increases with the number of reflective elements of the smart reflective surface, and the proposed design performs best among the four transmission designs.
Referring to FIG. 6, security of an Intelligent Reflective Surface (IRS) under different phase control strategies is illustrated; the result shows that when the intelligent reflecting surface adopts optimal or random phase control, the average confidentiality of legal users increases along with the increase of the number of reflecting elements, and the optimal phase control can obtain better confidentiality benefit than the random phase control; in addition, significant performance gains are obtained by the scheme of optimal phase control of the reflective surface compared to transmission schemes without intelligent reflective surface.
Referring to fig. 7, a relationship between the average security rate of the system and the transmit power of the unmanned aerial vehicle under different phase control strategies is shown; the result shows that the confidentiality of the system under the three conditions can be enhanced along with the increase of the transmitting power of the unmanned aerial vehicle; the confidentiality degree under the assistance of the intelligent reflecting surface is much better than that of the case without the intelligent reflecting surface, so that the benefit brought by the intelligent reflecting surface to the safety network communication is fully reflected; in addition, the security degree of the intelligent reflecting surface under the optimal phase control strategy is better than that of the intelligent reflecting surface under the random phase control strategy.
Referring to fig. 8, the effect of the path loss index of the smart reflective surface to the terrestrial user channel on the system privacy level is studied; when the path loss index increases from 2.1 to 2.9, the security of the system aided by the smart reflective surface is deteriorated, because the propagation loss to the wireless link becomes larger as the path loss index increases, resulting in deterioration of the security of the system as a whole; the confidentiality of the intelligent reflecting surface is still much better than the case without the intelligent reflecting surface, and the confidentiality degree of the intelligent reflecting surface adopting the optimal phase control strategy is better than the case adopting the random phase control strategy.
Specifically, with the assistance of an intelligent radiation surface, the invention carries out safety design aiming at an unmanned aerial vehicle communication system, and provides an algorithm based on alternate optimization and continuous convex approximation; simulation results show that: compared with other reference schemes, the algorithm can obtain better average confidentiality rate of the system, and the huge advantage of the intelligent reflecting surface in the design of safe transmission is presented.
On the premise of ensuring the service quality of legal users, the invention jointly optimizes the user scheduling, the three-dimensional track of the unmanned aerial vehicle and the reflection phase shift of the intelligent reflecting surface, and maximizes the average confidentiality rate of the system. The problem is a mixed integer non-convex optimization problem, and a better suboptimal solution is found through an alternate optimization and continuous convex approximation algorithm, so that the service quality of a user is met, the confidentiality of a system is improved, and eavesdropping is avoided.
According to the invention, by utilizing the advantages of the intelligent reflecting surface, a double-hop link is established, and two unmanned aerial vehicle communication modes are generated: direct communication and indirect communication. By jointly optimizing the three-dimensional track of the unmanned aerial vehicle, the user scheduling and the reflection phase shift of the intelligent reflection surface improve the flexibility of the system, cover more user groups and realize safe and efficient information transmission.
Claims (5)
1. The unmanned aerial vehicle safety communication method assisted by the intelligent reflecting surface is characterized by comprising the following steps of: the method comprises the following steps:
s1, constructing a downlink communication system consisting of an unmanned plane, an intelligent reflecting surface, M legal users and an active eavesdropper; the intelligent reflecting surface is fixedly arranged on a building, the unmanned aerial vehicle communicates with an associated legal user through a link in each time slot, and the link is an unmanned aerial vehicle-legal user direct link or an unmanned aerial vehicle-intelligent reflecting surface-legal user indirect link;
s2, setting target problem P 1 The target problem P 1 Maximizing average secrecy of a system by jointly optimizing user scheduling, smart reflective surface reflective phase shift, and unmanned aerial vehicle three-dimensional trajectoryA rate;
s3, optionally setting reflection phase shift of the intelligent reflection surface, inputting the three-dimensional track of the unmanned aerial vehicle into the target problem P1 set in the step S2, and obtaining the target problem P 1 Performing iterative optimization to obtain optimal unmanned aerial vehicle user scheduling;
s4, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3 and the three-dimensional track of the unmanned aerial vehicle arbitrarily set in the step S3 into the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization to obtain the optimal reflection phase shift of the intelligent reflection surface;
s5, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3 and the optimal reflection phase shift of the intelligent reflection surface obtained in the step S4 into the target problem P set in the step S2 1 In, by the target problem P 1 And performing iterative optimization to obtain the optimal three-dimensional track of the unmanned aerial vehicle.
2. The unmanned aerial vehicle safety communication method with the aid of the intelligent reflecting surface according to claim 1, wherein: in step S2, the target problem P 1 Expressed as:wherein a represents user schedule, ++>α m [t]Scheduling variable alpha representing association relation between unmanned aerial vehicle and legal user m in t time slot m [t]∈{0,1}，α m [t]=1 means that the drone serves the legitimate user m in the t slot, ±>Otherwise alpha m [t]=0; phi represents the reflection phase shift vector of the smart reflective surface, < >> Indicating the (n) th on the smart reflective surface in the t time slot x ,n y ) A phase shift of the individual reflective elements relative to the legitimate user m; q represents a horizontal trajectory vector of the unmanned aerial vehicle,q[t]＝[x[t],y[t]] T representing a horizontal trajectory of the unmanned aerial vehicle; h represents a vertical trajectory vector of the unmanned aerial vehicle,H[t]representing a vertical track of the unmanned aerial vehicle, wherein the three-dimensional track of the unmanned aerial vehicle consists of a horizontal track and a vertical track; r is R m [t]Representing the reachability and rate of terrestrial subscriber m in time slot t,/> Representing a set of M legitimate users,m=0 is denoted as an index of an eavesdropper, +.>Represents the total number of ground users, consisting of M legal users and one eavesdropper +.> R represents m,0 [t]Upper bound of R m,0 [t]Representing the achievable sum rate of eavesdroppers m=0 in the t time slot; the method comprises the steps that C1 represents constraint on user association scheduling, C2 represents constraint on ensuring that in each time slot, the unmanned aerial vehicle is associated with one legal user at most and performs information transmission, C3 represents constraint on initial and final positions of the unmanned aerial vehicle, C5 represents constraint on reflection phase shift of an intelligent reflection surface, and C6 and C7 represent track constraint of the unmanned aerial vehicle, wherein service quality of each legal user is guaranteed; q (Q) max Represents the maximum horizontal movement distance allowed by the unmanned aerial vehicle per time slot, Q max ＝V h τ，H max Indicating the maximum vertical movement distance, H, allowed by the drone per slot max ＝V z τ, the length of each time slot is τ seconds, and the maximum horizontal flight speed and the maximum vertical flight speed of the unmanned aerial vehicle in each time slot are V respectively h and Vz 。
3. The unmanned aerial vehicle safety communication method with the aid of the intelligent reflecting surface according to claim 2, wherein: the specific process of step S3 includes the following steps:
s3.1, arbitrarily setting the reflection phase shift of the intelligent reflection surface, inputting the three-dimensional track of the unmanned aerial vehicle into the target problem P1 set in the step S2, and obtaining the target problem P 1 Performing iterative optimization conversion to target problem P 2 ： wherein ,/>Representing the average security rate of the system;
s3.2, target problem P 2 Conversion of binary relaxation into target problem Question of objective->And performing iterative optimization to obtain optimal unmanned aerial vehicle user scheduling.
4. A method of unmanned aerial vehicle secure communication with the aid of an intelligent reflective surface according to claim 3, wherein: in step S4, the optimal unmanned aerial vehicle user schedule obtained in step S3 and the three-dimensional trajectory of the unmanned aerial vehicle arbitrarily set in step S3 are input into the target problem P1 set in step S2, and the target problem P1 is subjected to iterative optimization to obtain the optimal reflection phase shift of the intelligent reflection surface at the legal user m in the t time slot, which is: wherein ,/> Indicating the (n) th on the smart reflective surface in the t time slot x ,n y ) An optimal phase shift of the individual reflective elements relative to the user m; />Is a random scattering component subject to a circularly symmetric complex gaussian distribution of zero mean, unit variance; θ (1)[t] and ζ(1) [t]Vertical and horizontal AOAs, respectively denoted as drone-to-smart reflective surface in the t-slot; /> and />Represented as vertical AODs and horizontal AODs, respectively, from the smart reflective surface to the user.
5. The unmanned aerial vehicle safety communication method with the aid of the intelligent reflecting surface according to claim 4, wherein: the specific process of step S5 includes the following steps:
step one, optimizing a horizontal track of the unmanned aerial vehicle:
s5.1, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3, the optimal reflection phase shift of the intelligent reflection surface obtained in the step S4 and the vertical track of any given unmanned aerial vehicle into the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization and converting into non-convex target problem P of horizontal track of unmanned aerial vehicle 3 ：
S5.2, the reachable sum rate R to legal user m in t time slot m [t]And (3) performing iterative optimization: from the following components Wherein B represents system bandwidth, P represents transmitting power and sigma of unmanned aerial vehicle 2 Is Gaussian white noise power, ψ m [t]Represented as a phase shift matrix of the drone-smart reflective surface-legal user link, wherein ,N x and Ny The number of reflecting elements of the intelligent reflecting surface in the x direction and the y direction are respectively represented; toward R m [t]Introducing a relaxation variable-> and ηQ [t]To replace g respectively m[t] and d(1) [t]And is full ofFoot constraint: /> and ηQ [t]≥(d (1) [t]) 2 Obtaining the reachable sum rate R of legal user m in t time slot m [t]Redefined as: wherein ,/> For->Performing first-order Taylor expansion to obtain +.>Lower bound of->Said->As a function of the concavity of the curve, wherein ,
s5.3, pair R m,0 [t]Upper bound of (2)And (3) performing iterative optimization: by->Introducing a relaxation variable v Q[t] and />To replace (d) 0 [t]) 2 and (d(1) [t]) 2 And respectively meet constraint v Q [t]≤(d 0 [t]) 2 and />Reachability and rate of eavesdropper in t time slot +.>Redefined as:the right side of the newly introduced inequality constraint pertains to q [ t ]]Is convex, and is transformed into a concave function by first-order taylor expansion at the first iteration:
(d 0 [t]) 2 ≥q lb1 [t]＝(d 0 [t] (l) ) 2 +2(q[t] (l) -u 0 ) T (q[t]-q[t] (l) )，
(d (1) [t]) 2 ≥q lb2 [t]＝(d (1) [t] (l) ) 2 +2(q[t] (l) -q I ) T (q[t]-q[t] (l) )；
s5.4, optimizing the concave function obtained according to the step S5.2 and the step S5.3, and solving the problem P of non-convex targets of the horizontal track of the unmanned aerial vehicle obtained in the step S5.1 3 Conversion to convex problem P 4 ： wherein ,/>Problem of convexion P 4 Performing iterative optimization to obtain an optimal horizontal track of the unmanned aerial vehicle;
step two, optimizing the vertical track of the unmanned aerial vehicle:
s5.5, inputting the optimal unmanned aerial vehicle user schedule obtained in the step S3, the optimal reflection phase shift of the intelligent reflection surface obtained in the step S4 and the vertical track of any given unmanned aerial vehicle into the target problem P set in the step S2 1 In, by the target problem P 1 Performing iterative optimization and converting into an optimization problem P of a vertical track of the unmanned aerial vehicle 5 ：The optimization problem P 5 Is a non-convex optimization problem;
s5.6, problem P of optimization 5 Performing convex optimization conversion to obtain a vertical track optimization problem P of the unmanned aerial vehicle 6 ：
wherein ,/> and ηH [t]Are respectively (d) m [t]) 2 and (dm [t]) 2 At iteration I for q [ t ]]Performing first-order Taylor expansion to obtain upper bound, H lb1[t] and Hlb2 [t]Respectively is v H[t] and />At iteration I for q [ t ]]Performing first-order Taylor expansion to obtain an upper bound; vertical trajectory optimization problem P for unmanned aerial vehicle 6 And performing iterative optimization to obtain the optimal vertical track of the unmanned aerial vehicle.
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