CN114124705A - Resource allocation method based on max-min fairness for unmanned aerial vehicle-assisted backscatter communication system - Google Patents
Resource allocation method based on max-min fairness for unmanned aerial vehicle-assisted 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-assisted backscatter communication system, and belongs to the field of backscatter communication network resource allocation. The invention uses a Time Division Multiple Access (TDMA) transmission protocol, considers user fairness under the constraint conditions of backscattering equipment scheduling, reflection coefficient, unmanned aerial vehicle transmitting power and unmanned aerial vehicle flight track variable, and establishes an unmanned aerial vehicle auxiliary backscattering communication system resource allocation model based on max-min fairness. The original problem is converted into four sub-problems by using a block coordinate descent method (BCD), and then a first-order Taylor expansion equivalence conversion is carried out by using a sequential convex approximation algorithm (SCA) to obtain a convex optimization problem. And solving through an optimality equation of the convex optimization problem to obtain optimal backscattering equipment scheduling, 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 against system sum rate.
Description
Technical Field
The invention belongs to the field of backscattering communication network resource allocation, and particularly relates to a max-min fairness-based resource allocation method for an unmanned aerial vehicle-assisted backscattering communication system.
Background
The internet of things has become one of the emerging technologies for the next generation network development, which is recognized as the ultimate infrastructure to connect everything anywhere, but the problems of energy limitation and high cost make its deployment face some challenges, and the proposal of backscatter technology is used to solve these problems. When the coverage area of the traditional cellular base station cannot meet the requirement, the unmanned aerial vehicle can have the advantages of longer network life, reliable data collection and real-time data transmission according to the advantages of high maneuverability, flexible deployment, small restriction on terrain and the like. The unmanned Aerial vehicle is used as an Aerial Base Station (ABS), and the deployment position of the unmanned Aerial vehicle is determined according to the time-space distribution characteristics of the ground users. Khan et al, in the document "Backscatter-Enabled NOMA for Future 6G Systems: a New Optimization Framework under NOMA supported Backscatter for Future 6G Systems" IEEE comm.let t., vol.25, No.5, pp.1669-1672, May 2021, proposes a power domain non-orthogonal multiple access Optimization Framework supporting Backscatter, a New power domain non-orthogonal multiple access (PD-NOMA) environment Backscatter communication (AmBC) system Optimization Framework with incomplete Successive Interference Cancellation (SIC) decoding. And a Lagrange multiplier is iteratively calculated by a secondary gradient method, and the emission power of the source and the reflection coefficient of the backscatter tag are jointly optimized. However, no practical matter is considered here, and when the coverage requirement cannot be met by the conventional cellular base station, the drone can be used as an aerial base station to assist the backscatter communication system. Yu Zhan et al in the document "Hierarchical Deep Learning for Backscattering Data Collection With Multiple UAVs" IEEE Internet ranges j.vol.8, No.5, pp.3786-3800,1march1,2021, propose a multi-drone assisted Data Collection scenario where a drone may fly close to 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 unmanned aerial vehicle is reduced as much as possible, and the flight trajectory of the unmanned aerial vehicle is not considered in the article.
In the existing research on backscattering communication, the communication performance difference of backscattering equipment at different positions in a system is large, the serious fairness problem is faced, the scene of combining backscattering communication and an unmanned aerial vehicle is rarely considered, and in order to make up for the defects of the existing research, the invention provides an unmanned aerial vehicle auxiliary backscattering communication device and a resource allocation control method. The flight trajectory is optimized by using the flexible mobility and operability of the unmanned aerial vehicle, so that the distance between the unmanned aerial vehicle and the backscattering equipment is effectively shortened, and the throughput of a communication system is improved.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. The max-min fairness-based resource allocation method of the unmanned aerial vehicle-assisted backscatter communication system is capable of improving the 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:
the method comprises the following steps: establishing an unmanned aerial vehicle-assisted backscatter communication network system model which comprises an unmanned aerial vehicle, K backscatter devices and a backscatter receiver, wherein the max-min fairness-based resource allocation model is a non-convex optimization problem;
step two: initializing a reflection coefficient of a backscattering device, total transmitting power of an unmanned aerial vehicle and a flight path of the unmanned aerial vehicle, and introducing an auxiliary variable, wherein the proposed unmanned aerial vehicle backscattering communication network system model is a non-convex optimization problem containing a non-convex constraint function and a coupling variable, and an original non-convex optimization problem can be divided into four sub-problems by using an alternative optimization technology, such as a block coordinate descent method;
step three: the constraint conditions contained in the first sub-problem solution have binary constraints, binary variables are converted into continuous variables by using a scaling idea, and the variables initialized in the second step are substituted to obtain a backscatter device modulation value and a minimum speed value;
step four: substituting the backscatter device modulation value obtained in the third step, the unmanned aerial vehicle transmitting power initial value and the unmanned aerial vehicle flight trajectory initial value to obtain a reflection coefficient value, and updating a minimum speed value;
step five: substituting the backscatter device modulation value obtained in the third step, the reflection coefficient value obtained in the fourth step and the unmanned aerial vehicle flight trajectory initial value to obtain an unmanned aerial vehicle flight power value, and updating the minimum speed value;
step six: performing equivalent conversion from first-order Taylor expansion to a convex optimization problem by adopting a continuous convex approximation algorithm SCA, substituting the backscatter device modulation 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 to obtain an unmanned aerial vehicle flight trajectory, and updating a minimum speed value;
step seven: and performing alternate optimization on the four sub-problems, iteratively updating to solve the convex problem, setting a convergence threshold, judging whether the convergence threshold is met according to the convergence condition, and finally obtaining the optimal backscattering 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, the model of the unmanned aerial vehicle-assisted backscatter communication system based on max-min fairness in the first step is as follows:
the optimization problem P0, wherein
D represents the equivalent substitution coefficient, RkRepresents the rate, DmaxRepresents the maximum horizontal distance, beta, that the drone can fly in a time slotbrFor the channel gain of the backscatter device to the backscatter receiver, the expression is
(ε is an exponential distribution random variable with a mean value of 1), β0For the channel gain at a reference distance of 1 meter, K is the number of backscatter devices, N is the number of equal time slots, ak[n]Scheduling the kth backscatter device for drone at time slot n, Pn]For the transmit power of the drone at n time slots, q n]For the flight trajectory of the drone, wkFor the position of the kth backscatter device, H is the altitude of the drone, bk[n]As the reflection coefficient of the kth backscatter device at n time slots, betak[n]For the channel gain from the unmanned plane to the k-th backscattering device in the time slot n, the expression is
T is the running time of the unmanned aerial vehicle,
for mean power of drone, PmaxMaximum transmission power, η, for unmanned aerial vehiclekFor the kth backscatter device energy collection efficiency, σ2Is additive white Gaussian noise of the receiver, EkThe total energy used to power is collected for the kth backscatter device.
Further, the original problem is converted into four sub-problems in the second step, A, B, P, Q is used for respectively representing scheduling, reflection coefficient, unmanned plane transmitting power and unmanned plane flight track variable, and an auxiliary variable tau is introduced to enable the original problem to be converted into four sub-problems
As a function of A, B, P, Q;
(1) the first subproblem is as follows: backscatter device scheduling optimization, given B, P, Q, will be ak[n]The binary variable in the method is widened into a continuous variable, the binary variable is converted into the continuous variable by utilizing the zooming idea, the problem is a standard linear programming LP problem, and a linear programming interior point method is adopted for solving;
(2) a second sub-problem: and (4) optimizing the reflection coefficient, setting A, P, Q, optimizing the problem, writing an expression, and solving by using a convex optimization interior point algorithm.
(3) The third subproblem: optimizing the transmission power of the unmanned aerial vehicle, giving A, B, Q to solve the power P, writing an expression and obtaining the expression;
(4) the fourth sub-problem: unmanned aerial vehicle flight trajectory optimization, given A, B, P, a successive convex approximation algorithm can be used to optimize the flight trajectory of the unmanned aerial vehicle, resulting in an expression, and by applying the successive convex approximation algorithm, the sub-problem four is converted into a convex problem.
Further, the first sub-problem: backscatter device scheduling optimization, given B, P, Q, will be ak[n]The binary variable in (1) is relaxed into a continuous variable, and specifically comprises the following steps:
can be written as follows
And if all the constraints are put on the objective function, the constrained optimization problem is converted into an unconstrained optimization problem, a standard linear programming LP problem is obtained after scaling, the linear programming interior point method is adopted for solving, and an optimal solution is achieved through a series of iterations.
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, which is solved by convex optimization interior point algorithm.
Further, the solution is solved by using a convex optimization interior point algorithm, and an optimal solution is achieved through a series of iterations.
Further, the sub-problem three: drone transmit power optimization, given A, B, Q to solve for power P, the expression is written as follows:
further, the sub-problem four: the flight trajectory of the drone is optimized, given that A, B, P can use a successive convex approximation algorithm to optimize the flight trajectory of the drone, this sub-problem can be written in the form
||q[n+1]-q[n]||≤Dmax,n＝1,.....,N-1
q(1)＝q(N)
Objective function to variable q [ n ]]Not convex, but due to constraints
Left side of (c) with respect to | q [ n | ]]-wk||2Is a convex function, so the objective function is about | | q [ n |)]-wk||2The convex optimization problem of (2); according to the SCA algorithm, the first-order Taylor expansion of the convex function at any point is the global lower bound of the convex function, and q [ n ]]At q0[n]Performing a first-order Taylor expansion to obtain a lower bound;
wherein
To obtain Rk[n]Lower bound, writing the objective function in the form
||q[n+1]-q[n]||≤Dmax,n＝1,.....,N-1
q(1)＝q(N)
The fourth sub-problem has been transformed into a convex problem by applying a successive convex approximation algorithm.
Furthermore, the successive convex approximation algorithm is mainly used for solving a series of solutions of convex optimization problems similar to the original problem through iteration, and when the final convergence condition is met, the obtained solution can be approximately regarded as the solution of the original problem. Specifically, the value of the nth iteration point is found by using the substitution function found in claim 9, the target value is found by using the relation between the nth and the (n + 1) th values, and the above steps are repeated until the condition converges.
The invention has the following advantages and beneficial effects:
according to the unmanned aerial vehicle-assisted backscatter communication system resource allocation method based on max-min fairness under the conditions of scheduling, reflection coefficient, unmanned aerial vehicle transmitting power and unmanned aerial vehicle flight track constraint, the original non-convex problem comprises a non-convex constraint function and a coupling variable, the problem is not easy to solve, the original non-convex problem is converted into a convex optimization problem by using methods such as a block coordinate descent method and a continuous convex approximation technology, compared with a ground base station, the unmanned aerial vehicle base station has stronger adaptability to environmental changes, and therefore the unmanned aerial vehicle can be deployed in an area without infrastructure coverage to provide emergency communication connection. When natural disasters such as earthquakes, tsunamis, torrential floods and the like occur, the ground base station is often damaged, and communication services cannot be provided when communication infrastructures in disaster areas are damaged, so that the development of rescue actions is greatly hindered. The unmanned aerial vehicle base station is not limited by basic communication facilities of disaster areas, can rapidly provide large-scale reliable communication for disaster areas in a master-slave unmanned aerial vehicle mode, and provides a circular track initialization method to obtain the optimal flight track for establishing communication between the unmanned aerial vehicle and the backscattering equipment. Compared with other backscatter communication systems, the backscatter communication system has the advantages of low complexity and simple solution, adopts max-min fairness criterion for optimizing the target, improves the fairness of users, and has better feasibility and practicability.
Drawings
Fig. 1 is a model diagram of a preferred embodiment drone assisted backscatter communications system provided by the present invention;
FIG. 2 is a graph comparing the optimal trajectory of the UAV at different times of flight according to the present invention;
fig. 3 is a graph comparing the change in drone speed over time for a period of time T60 s in accordance with the present invention;
FIG. 4 is a graph comparing the average rate of max-min versus time for different algorithms in accordance with the present invention;
FIG. 5 shows a comparison of the present invention
A plot of the average rate of max-min versus time for the cases;
fig. 6 is a sequence diagram of the present invention comparing drone dispatch backscatter devices at a time period T-60 s;
FIG. 7 is a graph of the convergence performance of the present invention versus the average rate at different times of flight T max-min;
fig. 8 is a flow chart of the present invention providing the resource allocation of the preferred embodiment of the unmanned aerial vehicle-assisted backscatter communication system based on max-min fairness.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
this embodiment is a resource allocation method for an unmanned aerial vehicle-assisted backscatter communication system based on user rate fairness, and considers a ground backscatter device system with K ═ 6, where the backscatter devices are randomly and uniformly distributed at 70 × 70m2Within the geographic area 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 at [10,10 ]]. Assuming that the height H of the unmanned aerial vehicle is 10m, the maximum flying speed is 5m/s, t is 1s, the maximum transmitting power P is 3w and the average power
Is 10 dBm. Channel gain beta00.1, white Gaussian noise σ of the receiver2The value is-110 dBm, and the energy collection efficiency etakThe value is 0.8, and the total energy collected by the kth backscatter device for power supply is 0.26 multiplied by 10 at least-6J, the proposed algorithm has a convergence decision threshold epsilon of 10-4。
The first step, specifically, establishing the model of the unmanned aerial vehicle-assisted backscatter communication system based on the max-min fairness is as follows:
||q[n+1]-q[n]||≤Dmax,n＝1,.....,N-1
q[1]＝q[N]
wherein
D represents the equivalent substitution coefficient, RkRepresents the rate, DmaxRepresents the maximum horizontal distance, beta, that the drone can fly in a time slotbrFor the channel gain of the backscatter device to the backscatter receiver, the expression is
(ε is an exponential distribution random variable with a mean value of 1), β0For the channel gain at a reference distance of 1 meter, K is the number of backscatter devices, N is the number of equal time slots, ak[n]Scheduling the kth backscatter device for drone at time slot n, Pn]For the transmit power of the drone at n time slots, q n]For the flight trajectory of the drone, wkFor the position of the kth backscatter device, H is the altitude of the drone, bk[n]As the reflection coefficient of the kth backscatter device at n time slots, betak[n]For the channel gain from the unmanned plane to the k-th backscattering device in the time slot n, the expression is
T is the running time of the unmanned aerial vehicle,
for mean power of drone, PmaxMaximum transmission power, η, for unmanned aerial vehiclekFor the kth backscatter deviceEfficiency of energy collection, σ2Is additive white Gaussian noise of the receiver, EkCollecting total energy for powering a kth backscatter device;
in the second step, the original problem is converted into four subproblems, the scheduling, the reflection coefficient, the unmanned plane transmitting power and the unmanned plane flight track variable are respectively represented by A, B, P, Q, and an auxiliary variable tau is introduced to ensure that
As a function of A, B, P, Q;
(1) the first subproblem is as follows: backscatter device scheduling optimization, given B, P, Q, will be ak[n]The binary variable in (1) 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 have binary constraints, the binary variables are converted into continuous variables by using a scaling idea, the scaling idea is to put the constraints on an objective function for consideration, if all the constraints are put on the objective function, a 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 an optimal solution is achieved through a series of iterations;
(2) a second sub-problem: reflection coefficient optimization, given A, P, Q, the optimization problem can be written as the following form P2:
p2 is a convex optimization problem, solved using a convex optimization interior point algorithm, which is solved using a convex optimization interior point algorithm to arrive at an optimal solution through a series of iterations.
(3) The third subproblem: optimization of drone transmit power, given A, B, Q to solve for power P
P3 is a convex optimization problem, which is solved by convex optimization interior point algorithm.
(4) The fourth sub-problem: the flight trajectory of the drone is optimized, given that A, B, P can use a successive convex approximation algorithm to optimize the flight trajectory of the drone, this sub-problem can be written in the form
Objective function to variable q [ n ]]Not convex, but due to constraints
Left side of (c) with respect to | q [ n | ]]-wk||2Is a convex function, so the objective function is about | | q [ n |)]-wk||2The convex optimization problem of (1). According to the SCA algorithm, the first-order Taylor expansion of the convex function at any point is the global lower bound of the convex function, and q [ n ]]At q0[n]And performing a first-order Taylor expansion on the upper boundary to obtain a lower boundary.
Wherein
Thereby obtaining Rk[n]Lower bound, writing the objective function in the form
By applying the successive convex approximation algorithm, the sub-problem P4 has been transformed into a convex problem P5.
In the present embodiment, fig. 2 shows the optimal trajectory diagram of the drone at different flight times T, and when T is 10s, the trajectory of the drone is limited by the short distance. Along with T increase, the unmanned aerial vehicle utilizes self mobility self-adaptive expansion and adjusts a track path, and can be more close to the backscattering equipment on the ground. When 60s is being held at T, it can be clearly observed that the drone can stay above all backscatter devices and fixed flight for a period of time and the drone trajectory becomes closed loop, connecting all points directly above the backscatter device position. Thus, the best channel communication can be obtained, and the maximized minimum average rate can be obtained. It can also be observed that the trace sampling points around each backscatter device are denser than the sampling points far away from the backscatter device, which means that the speed of the drone is reduced when approaching the backscatter device, and more time is spent for transmitting more information with the backscatter device using the LOS channel. As can be seen from fig. 4, when T is 60s, an optimal line-of-sight (LoS) channel can be obtained for communication. When the drone is flying on top of each backscatter device, the speed will drop to zero. When T is 10 and 20s, the drone is flying at maximum speed, in order to avoid wasting time, and to get the best channel for information transmission as close to each backscatter device as possible within a limited time;
it can be seen from fig. 3 that the algorithm proposed herein is clearly superior to the other two algorithms, where algorithm 1 is compared: given a circular initial trajectory of the drone, the average rate of the receiver max-min tends to stabilize after reaching the peak, since the increase starts when T is smaller, the increasing circular trajectory ensures the maneuverability of the drone, leading to better results. The average rate of max-min increases with T and eventually becomes saturated when T is large enough. Comparison algorithm 2: the drone remains static so the receiver max-min average rate is independent of time T, since the channel between all nodes is unchanged. The unmanned aerial vehicle does not fully utilize the maneuverability and the distribution of nodes, so that the performance of the unmanned aerial vehicle is poorer than that of the proposed algorithm;
from FIG. 5, it can be seen that
The variation of the average max-min rate over time T in this case, it can be seen that the average max-min rates all increase as time T increases. Increasing from 5dBm to 15dBm, the average max-min rate also increases. When in use
At 15dBm there are performance gains of 19% and 39% for the max-min average rate compared to the other two schemes, respectively;
as can be seen from fig. 6, when T is 60s, it can be observed that only one backscatter device is scheduled by the drone per time slot based on the time division multiple access protocol, and the sequence of scheduling the backscatter devices is 4, 5, 6, 1,2, and 3. Fig. 7 shows the convergence performance of the proposed algorithm when T is 60, 80, 100 s. The algorithm converged within 6 iterations and the throughput increased significantly in the previous 3 iterations, the fast convergence of the algorithm was verified, and the throughputs of T60, 80, 100s finally converged to 1.1293, 1.1518, 1.1605bps/Hz, respectively.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, 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 an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (10)
1. A max-min fairness-based resource allocation method for an unmanned aerial vehicle-assisted backscatter communication system is characterized by comprising the following steps:
the method comprises the following steps: establishing an unmanned aerial vehicle-assisted backscatter communication network system model which comprises an unmanned aerial vehicle, K backscatter devices and a backscatter receiver, wherein the max-min fairness-based resource allocation model is a non-convex optimization problem;
step two: initializing a reflection coefficient of a backscattering device, total transmitting power of an unmanned aerial vehicle and a flight path of the unmanned aerial vehicle, and introducing an auxiliary variable, wherein the proposed unmanned aerial vehicle backscattering communication network system model is a non-convex optimization problem containing a non-convex constraint function and a coupling variable, and an original non-convex optimization problem can be divided into four sub-problems by using an alternative optimization technology, such as a block coordinate descent method;
step three: the constraint conditions contained in the first sub-problem solution have binary constraints, binary variables are converted into continuous variables by using a scaling idea, and the variables initialized in the second step are substituted to obtain a backscatter device modulation value and a minimum speed value;
step four: substituting the backscatter device modulation value obtained in the third step, the unmanned aerial vehicle transmitting power initial value and the unmanned aerial vehicle flight trajectory initial value to obtain a reflection coefficient value, and updating a minimum speed value;
step five: substituting the backscatter device modulation value obtained in the third step, the reflection coefficient value obtained in the fourth step and the unmanned aerial vehicle flight trajectory initial value to obtain an unmanned aerial vehicle flight power value, and updating the minimum speed value;
step six: performing equivalent conversion from first-order Taylor expansion to a convex optimization problem by adopting a continuous convex approximation algorithm SCA, substituting the backscatter device modulation 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 to obtain an unmanned aerial vehicle flight trajectory, and updating a minimum speed value;
step seven: and performing alternate optimization on the four sub-problems, iteratively updating to solve the convex problem, setting a convergence threshold, judging whether the convergence threshold is met according to the convergence condition, and finally obtaining the optimal backscattering 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 method for resource allocation based on max-min fairness for unmanned aerial vehicle-assisted backscatter communication system according to claim 1, wherein the model of the unmanned aerial vehicle-assisted backscatter communication system based on max-min fairness in the first step is:
||q[n+1]-q[n]||≤Dmax,n＝1,.....,N-1
q[1]＝q[N]
the optimization problem P0, wherein
D represents the equivalent substitution coefficient, RkRepresents the rate, DmaxRepresents the maximum horizontal distance, beta, that the drone can fly in a time slotbrFor the channel gain of the backscatter device to the backscatter receiver, the expression is
(ε is an exponential distribution random variable with a mean value of 1), β0For the channel gain at a reference distance of 1 meter, K is the number of backscatter devices, N is the number of equal time slots, ak[n]Scheduling the kth backscatter device for drone at time slot n, Pn]For the transmit power of the drone at n time slots, q n]For the flight trajectory of the drone, wkFor the position of the kth backscatter device, H is the altitude of the drone, bk[n]As the reflection coefficient of the kth backscatter device at n time slots, betak[n]For the channel gain from the unmanned plane to the k-th backscattering device in the time slot n, the expression is
T is the running time of the unmanned aerial vehicle,
for mean power of drone, PmaxMaximum transmission power, η, for unmanned aerial vehiclekFor the kth backscatter device energy collection efficiency, σ2Is additive white Gaussian noise of the receiver, EkThe total energy used to power is collected for the kth backscatter device.
3. The max-min fairness based resource allocation method for the unmanned aerial vehicle-assisted backscatter communication system according to claim 1, wherein the original problem in the second step is converted into four subproblems, the scheduling, the reflection coefficient, the unmanned aerial vehicle emission power and the unmanned aerial vehicle flight path variable are respectively represented by A, B, P, Q, and an auxiliary variable τ is introduced to enable the auxiliary variable τ to be introduced to enable the auxiliary variable τ to be used for enabling the auxiliary variable τ to be used for realizing the scheduling, the reflection coefficient, the unmanned aerial vehicle emission power and the unmanned aerial vehicle flight path variable to be used for realizing the fair resource allocation
As a function of A, B, P, Q;
(1) the first subproblem is as follows: backscatter device scheduling optimization, given B, P, Q, will be ak[n]The binary variable in the method is widened into a continuous variable, the binary variable is converted into the continuous variable by utilizing the zooming idea, the problem is a standard linear programming LP problem, and a linear programming interior point method is adopted for solving;
(2) a second sub-problem: and (4) optimizing the reflection coefficient, setting A, P, Q, optimizing the problem, writing an expression, and solving by using a convex optimization interior point algorithm.
(3) The third subproblem: optimizing the transmission power of the unmanned aerial vehicle, giving A, B, Q to solve the power P, writing an expression and obtaining the expression;
(4) the fourth sub-problem: unmanned aerial vehicle flight trajectory optimization, given A, B, P, a successive convex approximation algorithm can be used to optimize the flight trajectory of the unmanned aerial vehicle, resulting in an expression, and by applying the successive convex approximation algorithm, the sub-problem four is converted into a convex problem.
4. The method for max-min fairness based resource allocation in an unmanned-aerial-vehicle-assisted backscatter communication system according to claim 3, wherein the sub-problems are as follows: backscatter device scheduling optimization, given B, P, Q, will be ak[n]The binary variable in (1) is relaxed into a continuous variable, and specifically comprises the following steps:
can be written as follows
And the constraint conditions contained in the first sub-problem solution have binary constraints, and binary variables are converted into continuous variables by using a scaling idea, which is a standard linear programming LP problem and is solved by adopting a linear programming interior point method.
5. The max-min fairness based resource allocation method for the unmanned aerial vehicle-assisted backscatter communication system according to claim 4, wherein the binary variables are converted into continuous variables by using a scaling idea, the scaling idea is to put constraints on an objective function for consideration, if all the constraints are put on the objective function, a constraint optimization problem is converted into an unconstrained optimization problem, a standard linear programming LP problem is obtained after scaling, the linear programming LP problem is solved by using a linear programming interior point method, and an 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 two is: the reflection coefficient optimization, given A, P, Q, optimization problem can be written as follows
This is a convex optimization problem, which is solved by convex optimization interior point algorithm.
7. The max-min fairness based resource allocation method for an unmanned aerial vehicle-assisted backscatter communication system of claim 6, wherein the solution is performed by a convex optimization interior point algorithm, and an optimal solution is achieved through a series of iterations.
8. The method of claim 6, wherein the sub-problem is three: drone transmit power optimization, given A, B, Q to solve for power P, the expression is written as follows:
9. the unmanned-assisted backscatter communication system of claim 6, wherein the sub-problem is four: the flight trajectory of the drone is optimized, given that A, B, P can use a successive convex approximation algorithm to optimize the flight trajectory of the drone, this sub-problem can be written in the form
||q[n+1]-q[n]||≤Dmax,n＝1,.....,N-1
q(1)＝q(N)
Objective function to variable q [ n ]]Not convex, but due to constraints
Left side of (c) with respect to | q [ n | ]]-wk||2Is a convex function, so the objective function is about | | q [ n |)]-wk||2The convex optimization problem of (2); according to the SCA algorithm, the first-order Taylor expansion of the convex function at any point is the global lower bound of the convex function, and q [ n ]]At q0[n]Performing a first-order Taylor expansion to obtain a lower bound;
wherein
To obtain Rk[n]Lower bound, writing the objective function in the form
||q[n+1]-q[n]||≤Dmax,n＝1,.....,N-1
q(1)＝q(N)
The fourth sub-problem has been transformed into a convex problem by applying a successive convex approximation algorithm.
10. The max-min fairness-based resource allocation method for the unmanned aerial vehicle-assisted backscatter communication system according to claim 9, wherein the successive convex approximation algorithm is mainly used for solving a series of solutions of convex optimization problems similar to an original problem through iteration, when a final convergence condition is met, the obtained solutions can obtain the solution of the original problem, the value of an nth iteration point is obtained through the obtained substitution function, a target value is obtained through the relation between the nth value and the (n + 1) th value, and the steps are repeated until the condition converges.
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