CN114124705B - Max-min fairness-based resource allocation method for unmanned aerial vehicle auxiliary backscatter communication system - Google Patents
Max-min fairness-based resource allocation method for unmanned aerial vehicle auxiliary backscatter communication system Download PDFInfo
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Abstract
The invention discloses a max-min fairness-based resource allocation method for an unmanned aerial vehicle auxiliary backscatter communication system, and belongs to the field of backscatter communication network resource allocation. According to the invention, a Time Division Multiple Access (TDMA) transmission protocol is used, under the constraint conditions of back scattering equipment scheduling, reflection coefficient, unmanned aerial vehicle transmitting power and unmanned aerial vehicle flight track variable, user fairness is considered, and an unmanned aerial vehicle auxiliary back scattering communication system resource allocation model based on max-min fairness is established. The original problem is converted into four sub-problems by using a block coordinate descent method (BCD), and then the first-order Taylor expansion equivalence conversion is performed into a convex optimization problem by using a continuous convex approximation algorithm (SCA). And solving an optimality equation of the convex optimization problem to obtain optimal back scattering equipment scheduling, an optimal reflection coefficient, optimal unmanned aerial vehicle transmitting power and optimal unmanned aerial vehicle flight track, and finally obtaining the minimum user rate. The algorithm has low complexity while being able to trade-off user minimum rate and system sum rate.
Description
Technical Field
The invention belongs to the field of resource allocation of a backscatter communication network, and particularly relates to a max-min fairness-based resource allocation method of an unmanned aerial vehicle auxiliary backscatter communication system.
Background
The internet of things has become one of the emerging technologies for the next generation network development, which is recognized as an ultimate infrastructure for connecting everything anytime anywhere, but the problems of energy limitation and high cost make its deployment face some challenges, and the back scattering technology has been proposed to solve these problems. When the coverage area of the traditional cellular base station can not meet the requirements, the unmanned aerial vehicle can be flexibly deployed according to the advantages of high mobility, small terrain restriction and the like, and particularly has longer network service life, reliable data collection and real-time data transmission. The unmanned aerial vehicle is used as an air base station (Aerial Base Station), which is called ABS for short, and the deployment position of the unmanned aerial vehicle is determined based on the space-time distribution characteristics of the ground users. U.S. Pat. No.5,1669-1672, may 2021, in document "backscattering-Enabled NOMA for Future 6G Systems:A New Optimization Framework under Imperfect SIC (NOMA-supported backscattering for future 6G systems: new optimization framework under imperfect continuous interference cancellation)" proposes a power domain non-orthogonal multiple access optimization framework supporting backscattering, a new power domain non-orthogonal multiple access (PD-NOMA) environmental backscattering communication (AmBC) system optimization framework with imperfect continuous interference cancellation (SIC) decoding. And (3) iteratively calculating Lagrangian multipliers by a sub-gradient method, and jointly optimizing the emission power of the source and the reflection coefficient of the backscatter tag. Practical problems are not considered herein and when conventional cellular base stations fail to meet coverage requirements, unmanned aerial vehicles may be utilized to act as air base stations to aid in backscatter communication systems. Yu Zhan et al in document "Hierarchical Deep Reinforcement Learning for Backscattering Data Collection With Multiple UAVs (hierarchical deep reinforcement learning for backscatter data acquisition of multi-frame drones)" IEEE Internet Things j.vol.8, no.5, pp.3786-3800,1march1,2021, propose a multi-drone assisted data collection scenario in which a drone may fly near a Backscatter Sensor Node (BSN) to activate it and then collect data. After the collection task is completed, the total flight time of the rechargeable drone is minimized, and the flight trajectory of the drone is not considered in this paper.
In the existing back scattering communication research, the communication performance difference of back scattering devices at different positions in a system is large, the serious fairness problem is faced, and the scene of combining back scattering communication with an unmanned aerial vehicle is rarely considered. The flight track is optimized by utilizing the flexible mobility and operability of the unmanned aerial vehicle, so that the distance between the unmanned aerial vehicle and the back scattering equipment is effectively shortened, and the throughput of a communication system is further improved.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. The resource allocation method based on max-min fairness of the unmanned aerial vehicle auxiliary backscatter communication system is improved in minimum user rate. The technical scheme of the invention is as follows:
a max-min fairness-based resource allocation method for an unmanned aerial vehicle assisted backscatter communication system comprises the following steps:
step one: establishing an unmanned aerial vehicle auxiliary back scattering communication network system model, wherein the unmanned aerial vehicle auxiliary back scattering communication network system model comprises an unmanned aerial vehicle, K back scattering devices and a back scattering receiver, and the max-min fairness-based resource allocation model is a non-convex optimization problem;
step two: initializing reflection coefficients of back scattering equipment, total emission power of an unmanned aerial vehicle and flight track of the unmanned aerial vehicle, introducing an auxiliary variable, and dividing the original non-convex optimization problem into four sub-problems by using an alternative optimization technology such as a block coordinate descent method, wherein the proposed unmanned aerial vehicle back scattering communication network system model is a non-convex optimization problem comprising a non-convex constraint function and a coupling variable;
step three: the constraint conditions contained in the first solving of the sub-problems are subjected to binary constraint, binary variables are converted into continuous variables by utilizing the idea of scaling, and the initialized variables in the second step are substituted to obtain a scheduling value and a minimum speed value of the back scattering equipment;
step four: substituting the back scattering equipment scheduling value, the unmanned aerial vehicle transmitting power initial value and the unmanned aerial vehicle flight track initial value obtained in the step three into a reflectance value, and updating a minimum speed value;
step five: substituting the back scattering equipment scheduling value obtained in the third step, the reflectance value obtained in the fourth step and the initial value of the unmanned aerial vehicle flight track to obtain an unmanned aerial vehicle flight power value, and updating the minimum speed value;
step six: performing first-order Taylor expansion equivalence conversion into convex optimization by adopting a continuous convex approximation algorithm SCA, substituting the back scattering equipment scheduling value obtained in the third step, the reflection coefficient value obtained in the fourth step and the unmanned aerial vehicle flight power value obtained in the fifth step into an unmanned aerial vehicle flight track, and updating the minimum speed value;
step seven: and (3) carrying out alternate optimization on the four sub-problems, carrying out iterative updating to solve the convex problem, setting a convergence threshold, judging whether the convergence threshold is met according to convergence conditions, and finally obtaining the optimal back scattering equipment scheduling, the optimal reflection coefficient, the optimal unmanned aerial vehicle transmitting power and the optimal unmanned aerial vehicle flight track corresponding to the optimal target value.
Further, in the first step, the unmanned aerial vehicle auxiliary backscatter communication system model based on max-min fairness is:
in the above-mentioned optimization problem P0, in whichD represents an equivalent substitution coefficient, R k Represent rate, D max Representing the maximum horizontal distance, beta, that the unmanned aerial vehicle can fly in one time slot br For the channel gain of the backscatter device to the backscatter receiver, the expression is +.>(ε is an exponentially distributed random variable with an average of 1), β 0 For the channel gain when the reference distance is 1 meter, K is the number of back scattering devices, N is the number of equal time slots, a k [n]For scheduling kth backscatter device for unmanned in slot n, P [ n ]]For the transmit power of the drone in n time slots, q [ n ]]Is the flight track of the unmanned plane, w k Is the position of the kth backscattering device, H is the altitude of the unmanned aerial vehicle, b k [n]Is at nReflection coefficient, beta, of the kth backscattering device k [n]For the channel gain from the drone to the kth backscatter device in time slot n, the expression +.>T is unmanned plane running time, < >>For the average power of unmanned aerial vehicle, P max Is the maximum transmitting power of the unmanned aerial vehicle, eta k Energy collection efficiency, σ, for kth backscatter device 2 For additive white Gaussian noise of the receiver E k The total energy for powering is collected for the kth backscatter device.
Furthermore, in the second step, the original problem is converted into four sub-problems, the scheduling, the reflection coefficient, the unmanned aerial vehicle transmitting power and the unmanned aerial vehicle flight path variable are respectively represented by A, B, P, Q, and an auxiliary variable tau is introduced to enableAs a function of A, B, P, Q;
(1) Sub-problem one: backscatter device scheduling optimization, given B, P, Q, will a k [n]The binary variable in the linear programming system is widened to be a continuous variable, the binary variable is converted to be the continuous variable by utilizing the idea of scaling, the linear programming system is a standard linear programming LP problem, and a linear programming interior point method is adopted for solving;
(2) Secondary problems: and optimizing the reflection coefficient, giving A, P, Q an optimization problem, writing an expression, and solving by using a convex optimization interior point algorithm.
(3) Sub-problem three: optimizing the transmitting power of the unmanned aerial vehicle, giving A, B, Q to solve the power P, writing an expression, and obtaining the expression;
(4) Sub-problem four: the unmanned aerial vehicle flight trajectory is optimized, given A, B, P the unmanned aerial vehicle flight trajectory can be optimized using a continuous convex approximation algorithm, an expression is derived, by applying the continuous convex approximation algorithm,
the sub-problem four is converted into a convex problem.
Further, the sub-problem one: backscatter device scheduling optimization, given B, P, Q, will a k [n]The binary variable in (a) is relaxed to be a continuous variable, and specifically comprises:
can be written in the following form
The constraint condition contained in the first solution of the sub-problem is binary constraint, binary variable is converted into continuous variable by utilizing the scaling idea, the scaling idea is to put the constraint on the objective function for consideration, if all the constraint is put on the objective function, the constraint optimization problem is converted into unconstrained optimization problem, a standard linear programming LP problem is obtained after scaling, the solution is achieved by adopting a linear programming interior point method, and the optimal solution is achieved through a series of iteration.
Further, the sub-problem two: the reflection coefficient optimization, given A, P, Q, optimization problem can be written as follows
This is a convex optimization problem solved by convex optimization interior point algorithm.
Further, the convex optimization interior point algorithm is used for solving, and the optimal solution is achieved through a series of iteration.
Further, the sub-problem three: unmanned aerial vehicle transmit power optimization, given A, B, Q to solve for power P, writes the expression as follows:
further, the sub-problem four: unmanned aerial vehicle flight trajectory optimization, given A, B, P can use continuous convex approximation algorithm to optimize unmanned aerial vehicle flight trajectory, this sub-problem can be written as follows
||q[n+1]-q[n]≤D max ,n＝1,.....,N-1
q(1)＝q(N)
Objective function for variable q [ n ]]Is not convex, but due to constraintsIs relative to the left side of q n]-w k || 2 Is a convex function, so the objective function is related to q n]-w k || 2 Convex optimization problem of (a); from the SCA algorithm, it is known that the first-order Taylor expansion of the convex function at any point is its global lower bound, with respect to q [ n ]]At q 0 [n]Performing first-order Taylor expansion on the upper part to obtain a lower bound of the lower part;
wherein the method comprises the steps ofObtain R k [n]Is written as the following form
q[n+1]-q[n]||≤D max ,n＝1,.....,N-1
q(1)＝q(N)
The fourth sub-problem has been converted to a convex problem by applying a successive convex approximation algorithm.
Furthermore, the continuous convex approximation algorithm is mainly to solve a series of solutions of convex optimization problems similar to the original problems through iteration, and when the final convergence condition is met, the obtained solutions can be approximately regarded as the solutions of the original problems. And obtaining the value of the nth iteration point, obtaining a target value by utilizing the relation between the nth iteration point and the n+1st iteration point, and repeating the steps until the condition is converged.
The invention has the advantages and beneficial effects as follows:
according to the unmanned aerial vehicle auxiliary backscatter communication system resource allocation method based on max-min fairness under the constraint conditions of scheduling, reflection coefficients, unmanned aerial vehicle transmitting power and unmanned aerial vehicle flight tracks, the original non-convex problem comprises a non-convex constraint function and a coupling variable, the original non-convex problem is not easy to solve, the original non-convex problem is converted into a convex optimization problem by utilizing methods such as a block coordinate descent method, a continuous convex approximation technology and the like, and compared with a ground base station, the unmanned aerial vehicle base station has stronger adaptability to environmental changes, so that the unmanned aerial vehicle can be deployed in an area without infrastructure coverage to provide emergency communication connection. When natural disasters such as earthquake, tsunami, mountain floods and the like occur, ground base stations are often destroyed, communication infrastructure in disaster areas is damaged, communication services cannot be provided, and rescue actions are greatly hindered. The unmanned aerial vehicle base station is not limited by the infrastructure of disaster areas, can rapidly provide large-scale reliable communication for disaster areas in a master-slave unmanned aerial vehicle mode, and meanwhile, provides a circular track initialization method to obtain an optimal flight track for establishing communication between the unmanned aerial vehicle and the backscatter equipment. Compared with other back scattering communication systems, the invention has the advantages of low complexity and simple solution, adopts max-min fairness criterion for optimization targets, improves fairness of users, and has better feasibility and practicability.
Drawings
FIG. 1 is a model diagram of a unmanned aerial vehicle-assisted backscatter communication system in accordance with a preferred embodiment of the present invention;
FIG. 2 is a graph of the present invention versus the optimal trajectory of a drone at different times of flight T;
fig. 3 is a graph of the present invention versus speed of the drone over time for a period of t=60 s;
FIG. 4 is a graph of the present invention versus the average max-min rate over time for different algorithms;
FIG. 5 is a graph of the present invention versus the differenceA graph of the average max-min rate over time in this case;
fig. 6 is a sequence diagram of the present invention versus a drone scheduling backscatter device for a time period of t=60 s;
FIG. 7 is a graph of convergence performance of the present invention versus average rate at different times of flight Tmax-min;
fig. 8 is a flow chart of a preferred embodiment of a unmanned assisted backscatter communication system resource allocation based on max-min fairness provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and specifically described below with reference to the drawings in the embodiments of the present invention. The described embodiments are only a few embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the embodiment is a resource allocation method of an unmanned aerial vehicle assisted backscatter communication system based on user rate fairness, which considers a ground backscatter device system with k=6, and the backscatter devices are randomly and uniformly distributed in 70×70m 2 Is within a geographic region of (a). The backscatter devices are located at [ -18, 12 respectively]，[-20，-18]，[8，-26]，[30，-10]，[10，6]，[6，32]m, the receiver is located [10,10]. Assuming that the unmanned plane height H is 10m, the maximum flying speed is 5m/s, t=1s, and the maximum transmitting power P is 3w and the average powerIs 10dBm. Channel gain beta 0 0.1, gaussian white noise sigma of receiver 2 The value is-110 dBm, and the energy collection efficiency eta k The value is 0.8, and the kth backscattering device receivesThe total energy collected for power supply is 0.26×10 at minimum -6 J, convergence decision threshold epsilon takes on 10 in the proposed algorithm -4 。
The first step, specifically, the unmanned aerial vehicle auxiliary backscatter communication system model based on max-min fairness is established as follows:
||q[n+1]-q[n]||≤D max ,n＝1,.....,N-1
q[1]＝q[N]
wherein the method comprises the steps ofD represents an equivalent substitution coefficient, R k Represent rate, D max Representing the maximum horizontal distance, beta, that the unmanned aerial vehicle can fly in one time slot br For the channel gain of the backscatter device to the backscatter receiver, the expression is +.>(ε is an exponentially distributed random variable with an average of 1), β 0 For the channel gain when the reference distance is 1 meter, K is the number of back scattering devices, N is the number of equal time slots, a k [n]For scheduling kth backscatter device for unmanned in slot n, P [ n ]]For the transmit power of the drone in n time slots, q [ n ]]Is the flight track of the unmanned plane, w k Is the position of the kth backscattering device, H is the altitude of the unmanned aerial vehicle, b k [n]To the reflection coefficient, beta, of the kth backscatter device in n time slots k [n]For channel gain from the drone to the kth backscatter device in time slot n, the expression isT is unmanned plane running time, < >>For the average power of unmanned aerial vehicle, P max Is the maximum transmitting power of the unmanned aerial vehicle, eta k Energy collection efficiency, σ, for kth backscatter device 2 For additive white Gaussian noise of the receiver E k Collecting the total energy for powering the kth backscatter device;
in the second step, the original problem is converted into four sub-problems, the scheduling, the reflection coefficient, the unmanned aerial vehicle transmitting power and the unmanned aerial vehicle flight track variables are respectively represented by A, B, P, Q, and an auxiliary variable tau is introduced to enableAs a function of A, B, P, Q;
(1) Sub-problem one: backscatter device scheduling optimization, given B, P, Q, will a k [n]The binary variable of (c) is relaxed to a continuous variable, which can be written as the following form P1:
the constraint conditions contained in the solution of the sub-problem P1 are subjected to binary constraint, binary variables are converted into continuous variables by utilizing the scaling thought, the scaling thought is to put the constraint on an objective function for consideration, if all the constraint is put on the objective function, the constraint optimization problem is converted into an unconstrained optimization problem, a standard linear programming LP problem is obtained after scaling, the solution is carried out by adopting a linear programming interior point method, and the optimal solution is achieved through a series of iteration;
(2) Secondary problems: the reflection coefficient is optimized, given A, P, Q, the optimization problem can be written in the form P2 as follows:
p2 is a convex optimization problem, and is solved by a convex optimization interior point algorithm, wherein the convex optimization interior point algorithm is used for solving, and the optimal solution is achieved through a series of iteration.
(3) Sub-problem three: unmanned aerial vehicle transmit power optimization, given A, B, Q to solve for power P
P3 is a convex optimization problem, and is solved by a convex optimization interior point algorithm.
(4) Sub-problem four: unmanned aerial vehicle flight trajectory optimization, given A, B, P can use continuous convex approximation algorithm to optimize unmanned aerial vehicle flight trajectory, this sub-problem can be written as follows
Objective function for variable q [ n ]]Is not convex, but due to constraintsIs relative to the left side of q n]-w k || 2 Is a convex function, so the objective function is related to q n]-w k || 2 Convex optimization problem of (c). From the SCA algorithm, it is known that the first-order Taylor expansion of the convex function at any point is its global lower bound, with respect to q [ n ]]At q 0 [n]The first-order taylor expansion is performed on the upper layer to obtain the lower bound of the upper layer.
Wherein the method comprises the steps ofThereby obtaining R k [n]Is written as the following form
The sub-problem P4 has been converted to a convex problem P5 by applying a successive convex approximation algorithm.
In this embodiment, fig. 2 shows an optimal trajectory diagram of the drone at different times of flight T, when t=10s, the trajectory of the drone is limited by the short distance. As T increases, the drone exploits its mobility adaptation expansion and adjusts the trajectory path, more closely to the ground's backscatter equipment. At t=60 s, it can be clearly observed that the drone can stay above all the backscatter devices and fly for a fixed period of time and the drone trajectory becomes closed loop, connecting all points directly above the backscatter device location. In this way, optimal channel communication is achieved and a maximized minimum average rate is achieved. It can also be observed that the track sampling points around each backscatter device are denser than the sampling points far from the backscatter device, which represents a reduced speed when the drone approaches the backscatter device, taking more time to use LOS channels for more information transmission with the backscatter device. As can be seen from fig. 4, when t=60 s, an optimal line of sight transmission (LoS) channel can be obtained for communication. As the drone flies on top of each backscatter device, the speed will drop to zero. When t=10 and 20s, the drone is flying at maximum speed, in order to avoid wasting time, as close as possible to each backscatter device in a limited time, obtaining the best channel for information transmission;
it can be seen from fig. 3 that the algorithm presented herein is significantly better than the other two algorithms, where the comparison algorithm 1: given the circular initial trajectory of the drone, the average rate of the receiver max-min tends to stabilize after reaching the peak, since the gradually increasing circular trajectory guarantees the maneuverability of the drone when T is small, leading to better results. The max-min average rate increases with T and eventually becomes saturated when T is sufficiently large. Comparison algorithm 2: the drone remains stationary so the receiver max-min average rate is independent of time T, since the channel between all nodes is unchanged. Unmanned aerial vehicles also do not fully utilize their mobility and node distribution, resulting in their performance being poor compared to the proposed algorithm;
as can be seen from fig. 5, at different pointsIn the case of a change in the max-min average rate over time T, it can be seen that the max-min average rate increases with increasing time T. Increasing from 5dBm to 15dBm, the max-min average rate also increases. When (when)At 15dBm, the max-min average rate has 19% and 39% performance gain respectively compared with the other two schemes;
as can be seen from fig. 6, it can be observed that under the time division multiple access protocol, only one backscatter device is scheduled per time slot for the drone, in the order of 4, 5, 6, 1,2, 3, respectively, when t=60 s. Fig. 7 shows the convergence performance of the proposed algorithm when t=60, 80, 100 s. The algorithm converged within 6 iterations and throughput increased significantly in the first 3 iterations, verifying the fast convergence of the algorithm, and the throughput of t=60, 80, 100s eventually converged to 1.1293,1.1518,1.1605bps/Hz, respectively.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The above examples should be understood as illustrative only and not limiting the scope of the invention. Various changes and modifications to the present invention may be made by one skilled in the art after reading the teachings herein, and such equivalent changes and modifications are intended to fall within the scope of the invention as defined in the appended claims.
Claims (8)
1. The max-min fairness-based resource allocation method for the unmanned aerial vehicle auxiliary backscatter communication system is characterized by comprising the following steps of:
step one: establishing an unmanned aerial vehicle auxiliary backscatter communication network system model, wherein the model is as follows:
||q[n+1]-q[n]||≤D max ,n＝1,.....,N-1
q[1]＝q[N]
in the above-mentioned optimization problem P0, in whichD represents an equivalent substitution coefficient, R k Represent rate, D max Representing the maximum horizontal distance, beta, that the unmanned aerial vehicle can fly in one time slot br For the channel gain of the backscatter device to the backscatter receiver, the expression is +.>Epsilon is an exponentially distributed random variable with a mean value of 1, beta 0 For the channel gain when the reference distance is 1 meter, K is the number of back scattering devices, N is the number of equal time slots, a k [n]For scheduling kth backscatter device for unmanned in slot n, P [ n ]]For the transmit power of the drone in n time slots, q [ n ]]Is the flight track of the unmanned plane, w k Is the position of the kth backscattering device, H is the altitude of the unmanned aerial vehicle, b k [n]To the reflection coefficient, beta, of the kth backscatter device in n time slots k [n]For channel gain from the drone to the kth backscatter device in time slot n, the expression isT is unmanned plane running time, < >>For the average power of unmanned aerial vehicle, P max Is the maximum transmitting power of the unmanned aerial vehicle, eta k Energy collection efficiency, σ, for kth backscatter device 2 For additive white Gaussian noise of the receiver E k Collecting the total energy for powering the kth backscatter device;
the resource allocation model based on max-min fairness is a non-convex optimization problem;
step two: initializing reflection coefficient of back scattering equipment, total transmitting power of unmanned aerial vehicle, unmanned aerial vehicle flight track, introducing an auxiliary variable, providing unmanned aerial vehicle back scattering communication network system model which is a non-convex optimization problem containing non-convex constraint function and coupling variable, dividing original non-convex optimization problem into four sub-problems by using block coordinate descent method, respectively using A, B, P, Q to respectively represent scheduling, reflection coefficient, unmanned aerial vehicle transmitting power and unmanned aerial vehicle flight track variable, introducing an auxiliary variable tau to makeAs a function of A, B, P, Q;
(1) Sub-problem one: backscatter device scheduling optimization, given B, P, Q, will a k [n]The binary variable in the linear programming system is widened to be a continuous variable, the binary variable is converted to be the continuous variable by utilizing the idea of scaling, the linear programming system is a standard linear programming LP problem, and a linear programming interior point method is adopted for solving;
(2) Secondary problems: optimizing the reflection coefficient, giving A, P, Q an optimization problem, writing an expression, and solving by using a convex optimization interior point algorithm;
(3) Sub-problem three: optimizing the transmitting power of the unmanned aerial vehicle, giving A, B, Q to solve the power P, writing an expression, and obtaining the expression;
(4) Sub-problem four: the unmanned aerial vehicle flight trajectory is optimized, given A, B, P the unmanned aerial vehicle flight trajectory can be optimized using a continuous convex approximation algorithm, an expression is derived, by applying the continuous convex approximation algorithm,
the fourth sub-problem is converted into a convex problem;
step three: the constraint conditions contained in the first solving of the sub-problems are subjected to binary constraint, binary variables are converted into continuous variables by utilizing the idea of scaling, and the initialized variables in the second step are substituted to obtain a scheduling value and a minimum speed value of the back scattering equipment;
step four: substituting the back scattering equipment scheduling value, the unmanned aerial vehicle transmitting power initial value and the unmanned aerial vehicle flight track initial value obtained in the step three into a reflectance value, and updating a minimum speed value;
step five: substituting the back scattering equipment scheduling value obtained in the third step, the reflectance value obtained in the fourth step and the initial value of the unmanned aerial vehicle flight track to obtain an unmanned aerial vehicle flight power value, and updating the minimum speed value;
step six: performing first-order Taylor expansion equivalence conversion into convex optimization by adopting a continuous convex approximation algorithm SCA, substituting the back scattering equipment scheduling value obtained in the third step, the reflection coefficient value obtained in the fourth step and the unmanned aerial vehicle flight power value obtained in the fifth step into an unmanned aerial vehicle flight track, and updating the minimum speed value;
step seven: and (3) carrying out alternate optimization on the four sub-problems, carrying out iterative updating to solve the convex problem, setting a convergence threshold, judging whether the convergence threshold is met according to convergence conditions, and finally obtaining the optimal back scattering equipment scheduling, the optimal reflection coefficient, the optimal unmanned aerial vehicle transmitting power and the optimal unmanned aerial vehicle flight track corresponding to the optimal target value.
2. The unmanned aerial vehicle assisted backscatter communication system of claim 1, wherein the sub-problem one: backscatter device scheduling optimization, given B, P, Q, will a k [n]The binary variable in (a) is relaxed to be a continuous variable, and specifically comprises:
can be written in the following form
The constraint condition contained in the first solution of the sub-problem is binary constraint, the binary variable is converted into a continuous variable by utilizing the idea of scaling, the linear programming LP problem is a standard linear programming LP problem, and the solution is carried out by adopting a linear programming interior point method.
3. The resource allocation method based on max-min fairness of the unmanned aerial vehicle assisted backscatter communication system according to claim 2, wherein the scaling concept is to convert binary variables into continuous variables, the scaling concept is to put constraints on objective functions to consider, if all constraints are put on objective functions, the constraint optimization problem is converted into unconstrained optimization problem, a standard linear programming LP problem is obtained after scaling, the linear programming interior point method is adopted to solve, and the optimal solution is achieved through a series of iterations.
4. The unmanned aerial vehicle assisted backscatter communication system of claim 2, wherein the sub-problem two: the reflection coefficient optimization, given A, P, Q, optimization problem can be written as follows
This is a convex optimization problem solved by convex optimization interior point algorithm.
5. The resource allocation method based on max-min fairness for the unmanned aerial vehicle assisted backscatter communication system of claim 4, wherein the solution is performed by a convex optimization interior point algorithm, and the optimal solution is achieved through a series of iterations.
6. The unmanned aerial vehicle assisted backscatter communication system of claim 4, wherein the sub-problem three: unmanned aerial vehicle transmit power optimization, given A, B, Q to solve for power P, writes the expression as follows:
7. the unmanned aerial vehicle assisted backscatter communication system of claim 6, wherein the sub-problem four: unmanned aerial vehicle flight trajectory optimization, given A, B, P can use continuous convex approximation algorithm to optimize unmanned aerial vehicle flight trajectory, this sub-problem can be written as follows
||q[n+1]-q[n||≤D max ,n＝1,.....,N-1
q(1)＝q(N)
Objective function for variable q [ n ]]Is not convex, but due to constraintsIs relative to the left side of q n]-w k || 2 Is a convex function, so the objective function is related to q n]-w k || 2 Convex optimization problem of (a); from the SCA algorithm, it is known that the first-order Taylor expansion of the convex function at any point is its global lower bound, with respect to q [ n ]]At q 0 [n]Performing first-order Taylor expansion on the upper part to obtain a lower bound of the lower part;
wherein the method comprises the steps ofObtain R k [n]Is written as the following form
||q[n+1]-q[n]||≤D max ,n＝1,.....,N-1
q(1)＝q(N)
The fourth sub-problem has been converted to a convex problem by applying a successive convex approximation algorithm.
8. The max-min fairness-based resource allocation method of unmanned aerial vehicle assisted backscatter communication system according to claim 7, wherein the continuous convex approximation algorithm is mainly to solve a series of solutions of convex optimization problems similar to the original problems through iteration, when the final convergence condition is established, the solution obtained at this time can obtain the solution of the original problems, the value of the nth iteration point is obtained by using the obtained substitution function, the target value is obtained by using the relation between the nth iteration point and the n+1th iteration point, and the above steps are repeated until the condition converges.
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