A UAV-assisted NOMA backscatter communication system and rate maximization method

Abstract

The present invention claims protection for a drone-assisted non-orthogonal multiple access (NOMA) backscatter communication system and a rate maximization method, which belongs to the field of wireless communication resource allocation. The method takes into account the drone transmit power and the backscatterer energy constraints, and maximizes the system sum rate by controlling the drone transmit power, the reflection coefficient of the backscatterer and the position of the drone. This method is mainly based on variable substitution, block coordinated descent (BCD), fractional programming and quadratic transformation methods, etc., and maximizes the system sum rate by converting non-convex problems into convex optimization problems for solution. The present invention has the advantages of low computational complexity, can ensure the energy constraint of the backscatterer, and improve the system sum rate.

Description

Technical Field

[0001] The present invention relates to the technical field of resource allocation of a drone-assisted NOMA backscatter communication system, and in particular to a drone-assisted NOMA backscatter communication system and a rate maximization method. Background Art

[0002] The development goals of the sixth-generation mobile communication system indicate that people are no longer content with human-to-human and human-to-object communications, but are exploring object-to-object communications. Therefore, in 6G and future communication systems, researchers both domestically and internationally are focusing on the Internet of Things (IoT). To achieve low cost, low energy consumption, low complexity, accommodate more users, and provide better communication quality, combining backscatter communication technology with NOMA has become a trend in future wireless communications and 6G development. Furthermore, drone-assisted communication has also attracted research attention due to its ease of deployment, high mobility, and good line-of-sight with ground users.

[0003] At present, through the study of backscatter communication resource allocation, it is found that there are three main systems considered in current research: traditional backscatter communication system, NOMA-assisted backscatter communication system and drone-assisted backscatter communication system. Most of the research related to resource allocation of the three systems is based on optimization indicators such as throughput and energy efficiency. In the research related to resource allocation of traditional backscatter communication systems, such as Xu Yongjun et al. published an article entitled "Optimal resource allocation for wireless powered multi-carrier backscatter communication networks" in "IEEE Wireless Communications Letters, 2020, 9(8):1191-1195." The authors only considered ground base stations. In practical problems, drones can also be considered as base stations to enhance mobility and use the NOMA protocol to increase the number of users. In the research related to resource allocation of NOMA-assisted backscatter communication systems, such as the article titled "Backscatter-enabled NOMA for Future 6G Systems: A New Optimization Framework Under Imperfect SIC" published by Li Xingwang et al. in IEEE Communications Letters, 2021, 25(5): 1669-1672., the authors only considered ground base stations. In practical problems, drones can be considered as base stations to establish a good line of sight with users. In the research related to resource allocation of drone-assisted backscatter communication systems, such as the article titled "Energy-efficient UAV backscatter communication with joint trajectory design and resource optimization" published by Yang Gang et al. in IEEE Transactions on Wireless Communications, 2020, 20(2): 926-941., the authors used a time division multiple access protocol. In order to increase the number of users, the NOMA protocol can be used.

[0004] There is little existing work on drone-assisted NOMA backscatter communication systems, so resource allocation for drone-assisted NOMA backscatter communication systems is a promising research direction. It is possible to consider optimizing the position of drones to maximize the system sum rate.

[0005] After searching, the application publication number CN112468205A is a backscatter security communication method suitable for unmanned aerial vehicles, including: determining the network model, network communication mode and protocol; simplifying the network model and discretizing the continuous time; calculating the received signal power of each backscatter device on the ground; calculating the energy that can be harvested by each backscatter device at any time, calculating the backscatter channel capacity, and calculating the eavesdropping channel capacity of each eavesdropper; defining the optimization goal as maximizing the fair throughput of the backscatter device, and obtaining the optimization goal expression and its constraints; simplifying the optimization goal problem, and solving it according to the optimization goal problem using the block coordinate descent method; including three parts: UAV flight trajectory design, device backscatter factor allocation and device time slot allocation, while taking into account the issues of energy harvesting and communication security of ground devices; in addition, while realizing the energy supply to multiple passive devices on the ground, it also ensures the fairness and security of data transmission of multiple devices.

[0006] However, the protocol adopted by this patent is a time division multiple access protocol, that is, at most one backscatterer is called for data transmission in one time slot, and one orthogonal resource block is only allocated to one user, which limits the performance index of the system's throughput and cannot meet the needs of a large number of users accessing the system at the same time. In addition, this method considers the fairness of data transmission from multiple backscatterers and cannot guarantee the maximum throughput of the system. Therefore, it is not suitable for scenarios with the maximum system throughput. The protocol used in the drone-assisted NOMA backscatter communication system and rate maximization method is the NOMA protocol. In this method, all backscatterers transmit data to the drone simultaneously through power domain multiplexing. Compared with time division multiple access, this realizes more user connections and improves the efficiency of spectrum utilization. In addition, this method considers the maximization of the sum rate of the entire system, which can achieve the maximum rate transmission of the system. Summary of the Invention

[0007] The present invention aims to solve the above problems in the prior art. It proposes a drone-assisted NOMA backscatter communication system and a rate maximization method. The technical solution of the present invention is as follows:

[0008] A UAV-assisted NOMA backscatter communication system and rate maximization method, comprising the following steps:

[0009] Step 1) Set the number of backscatterers, rate decision threshold, maximum number of iterations, and initialization number of iterations;

[0010] Step 2) Establish an optimization problem, rewrite the objective function and constraints based on the BCD and quadratic transformation methods, and obtain two sub-problems P1 and P2. P1 is the reflection coefficient optimization problem, and P2 is the UAV position optimization problem.

[0011] Step 3) Initialize the position, reflection coefficient, and system sum rate of the UAV, solve subproblem P1, and calculate the reflection coefficient of each backscatterer based on the initial position of the UAV;

[0012] Step 4) Substitute the reflection coefficient obtained from sub-problem P1 into sub-problem P2 to update the position of the drone;

[0013] Step 5) Determine whether the sum rate update converges, calculate the updated sum rate value, if the absolute value of the difference between the updated sum rate and the previous sum rate is not greater than the sum rate decision threshold, the sum rate converges, the maximum sum rate value is given, and the method ends; if the absolute value of the difference between the updated sum rate and the previous sum rate is greater than the sum rate decision threshold, the newly calculated sum rate value is saved as the sum rate value at this time and go to step 3) to update the reflection coefficient until the sum rate meets the conditions and the maximum sum rate is given.

[0014] Furthermore, in step 1), the number of backscatterers N, the rate decision threshold ζ, and the maximum number of iterations l are set. max , initialize the number of iterations l = 0.

[0015] Furthermore, the step 2) establishes an optimization problem, specifically including:

[0016] N backscatterers are independently distributed in an area. The full-duplex UAV transmits the RF signal to all backscatterers in the downlink. Each backscatterer uses the energy collected from the RF signal to send its information back to the UAV through the uplink. The position of the UAV is (x u ,y u ), the position of the jth backscatterer is (x j ,y j ), the distance from the UAV to the jth backscatterer is Where H is the flight altitude of the UAV. Assuming that the UAV has complete knowledge of the channel state information CSI and considering the channel between the BD and the UAV as the line-of-sight LoS model, the channel power gain between the UAV and the backscatterer is Where β0 represents the channel power gain at a reference distance of 1m; the signal received by the backscatterer from the drone is divided into two parts, the first part of the signal Received by the energy harvester, the received energy is E j =η j (1-r j )P u h j , where P u is the UAV transmission power, x(n) is the signal transmitted by the UAV, η j represents the energy efficiency conversion coefficient of the jth backscatterer, r jrepresents the reflection coefficient of the jth backscatterer; the second part of the signal After being modulated by the backscatterer and reflected back to the drone, the reflected signal is where a j (n) is the information of the backscatterer itself; the decoding order is specified from the 1st backscatterer to the Nth backscatterer, then the rate of the jth backscatterer is Where α represents the residual coefficient of UAV self-interference, h uu is the UAV self-interference channel gain, σ 2 is the system noise; the sum rate of the system is Set up the optimization problem:

[0017]

[0018] Where C1 is the reflection coefficient constraint; C2 is the maximum transmit power constraint of the UAV; C3 is the energy constraint, indicating that the energy consumed by the backscatterer does not exceed the collected energy, where P c The power consumed by the backscatterer to maintain its own circuit operation.

[0019] Furthermore, after obtaining the optimization problem in step 2), the optimization problem is divided into two sub-problems P1 and P2 based on BCD, specifically including:

[0020] First, given (x u ,y u ) and r j , the objective function in (1) is about P u is a monotonically increasing function, so P u =P max ; will h j After substitution, the fixed drone position (x u ,y u ), and the optimization of the logarithmic function log2(1+x) can be changed to optimize x, so the sub-problem P1 reflection coefficient optimization problem is obtained

[0021] P1:

[0022]

[0023] P max represents the maximum transmission power of the UAV, η j represents the energy efficiency conversion coefficient of the jth backscatterer. Fixed reflection coefficient r j And order We can get the sub-problem P2 drone position optimization problem; P2:

[0024]

[0025] Furthermore, solving the sub-problem P1 in step 3) specifically includes:

[0026] First initialize the position of the drone Reflection coefficient and rate R total (l) = 0; for subproblem P1, the objective function is about r j A monotonically increasing function of , the reflection coefficient can be obtained from the monotonicity:

[0027]

[0028] Furthermore, the step 4) solves the sub-problem P2, specifically including:

[0029] For subproblem P2, it is a nonlinear fractional programming problem. The quadratic transformation algorithm can be used to obtain the optimization problem

[0030]

[0031] Among them, {g1,...,g N} represents a set of auxiliary variables in the secondary transformation algorithm;

[0032] In (4), for a given g j ,j∈{1,...,N}, the optimization problem is:

[0033]

[0034] Among them, z j =(g j ) 2 ,j∈{1,...,N} represents a set of auxiliary variables. The position of the UAV can be obtained by using the first-order optimal condition of the objective function in (5):

[0035]

[0036] in, Represents the auxiliary variable in the quadratic transformation algorithm, used to update the position of the UAV for the l+1th time; is the auxiliary variable in (5), and its value is

[0037] Furthermore, in step 5), the updated system sum rate R is calculated total The values ​​are:

[0038]

[0039] Compare|R total (l+1)-R total(l)| and the size of the rate decision threshold ζ, where R total (l+1) is the sum rate of the system after l+1 iterations; if |R total (l+1)-R total (l)| is not greater than ζ, the sum rate converges, giving the maximum sum rate, and the method ends; if |R total (l+1)-R total (l)| is greater than ζ, the newly calculated sum rate is saved as the sum rate at this time and go to step 3) to update the reflection coefficient until the sum rate meets the conditions and the maximum sum rate is given.

[0040] The advantages and beneficial effects of the present invention are as follows:

[0041] The present invention considers drones flying in the air as both base stations and receivers. Drones are small in size, light in weight, and highly mobile, making them more cost-effective than traditional base stations in remote areas. The backscatterer provided by the present invention is a passive device that only collects energy through the signal transmitted by the drone and backscatters its own information. It has the advantages of low cost, low energy consumption, and low complexity. In addition, the present invention uses the NOMA protocol, and all backscatterers transmit data to the drone simultaneously through power domain multiplexing. Compared with OMA, it can accommodate more user connections, make full use of communication resources, and meet the needs of green communication. Compared with other schemes, the present invention proposes an iterative algorithm based on BCD and quadratic transformation method. In step 2), the optimization problem is first established. The problem is non-convex and difficult to solve directly. Therefore, BCD is used to decompose the original problem into two sub-problems: P1 reflection coefficient optimization problem and P2 drone position optimization problem; in step 3), the closed-form solution of the reflection coefficient can be obtained by using fractional programming and monotonicity; in step 4), the objective function of P2 is non-convex and difficult to solve directly. Therefore, the quadratic transformation algorithm is used to convexify the objective function and constraints, and variable substitution is used to further simplify the problem, and finally a convex problem (5) is obtained. The first-order optimal condition of the objective function is further used to obtain the updated drone position, which can fully approximate the optimal solution. Compared with other schemes, it can maximize the system sum rate on the basis of ensuring the energy constraint of the backscatterer, has a small number of convergence times, is easy to operate, and has strong practicality and feasibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The present invention provides a preferred embodiment of the drone-assisted NOMA backscatter communication system model;

[0043] Figure 2 This paper compares the effects of the UAV transmission power on the system and rate of the two schemes;

[0044] Figure 3This paper compares the effects of the flight altitude of the UAV on the system and speed of the two solutions;

[0045] Figure 4 The present invention compares the effects of the UAV self-interference residual coefficient on the system and rate of the two schemes;

[0046] Figure 5 It is a schematic flow diagram of the present invention. DETAILED DESCRIPTION

[0047] The following will describe the technical solutions in the embodiments of the present invention in detail with reference to the accompanying drawings. The described embodiments are only a part of the embodiments of the present invention.

[0048] The technical solution of the present invention to solve the above technical problems is:

[0049] like Figure 5 As shown, a UAV-assisted NOMA backscatter communication system and rate maximization method include the following steps:

[0050] Step 1) Set the number of backscatterers, rate decision threshold, maximum number of iterations, and initialization number of iterations;

[0051] Step 2) Establish an optimization problem, rewrite the objective function and constraints based on the BCD and quadratic transformation methods, and obtain two sub-problems P1 and P2. P1 is the reflection coefficient optimization problem, and P2 is the UAV position optimization problem.

[0052] Step 3) Initialize the position, reflection coefficient, and system sum rate of the UAV, solve subproblem P1, and calculate the reflection coefficient of each backscatterer based on the initial position of the UAV;

[0053] Step 4) Substitute the reflection coefficient obtained from sub-problem P1 into sub-problem P2 to update the position of the drone;

[0054] Step 5) Determine whether the sum rate update converges, calculate the updated sum rate value, if the absolute value of the difference between the updated sum rate and the previous sum rate is not greater than the sum rate decision threshold, the sum rate converges, the maximum sum rate value is given, and the method ends; if the absolute value of the difference between the updated sum rate and the previous sum rate is greater than the sum rate decision threshold, the newly calculated sum rate value is saved as the sum rate value at this time and go to step 3) to update the reflection coefficient until the sum rate meets the conditions and the maximum sum rate is given.

[0055] Furthermore, in step 1), the number of backscatterers N, the rate decision threshold ζ, and the maximum number of iterations l are set. max , initialize the number of iterations l = 0.

[0056] Furthermore, establishing the optimization problem in step 2) specifically includes:

[0057] N backscatterers are independently distributed in an area. The full-duplex UAV transmits the RF signal to all backscatterers in the downlink. Each backscatterer uses the energy collected from the RF signal to send its information back to the UAV via the uplink. The position of the UAV is (x u ,y u ), the position of the jth backscatterer is (x j ,y j ), the distance from the UAV to the jth backscatterer is Where H is the flight altitude of the UAV. The channel power gain between the UAV and the backscatterer is Where β0 represents the channel power gain at a reference distance of 1m. The signal received by the backscatterer from the drone is divided into two parts. The first part of the signal Received by the energy harvester, the received energy is E j =η j (1-r j )P u h j , where P u is the UAV transmission power, x(n) is the signal transmitted by the UAV, η j represents the energy efficiency conversion coefficient of the jth backscatterer, r j represents the reflection coefficient of the jth backscatterer; the second part of the signal After being modulated by the backscatterer and reflected back to the drone, the reflected signal is where a j (n) is the information of the backscatterer itself. The decoding order is specified from the 1st backscatterer to the Nth backscatterer, then the rate of the jth backscatterer is Where α represents the residual coefficient of UAV self-interference, h uu is the UAV self-interference channel gain, σ 2 is the system noise. The sum rate of the system is Set up the optimization problem:

[0058]

[0059] Where C1 is the reflection coefficient constraint; C2 is the maximum transmit power constraint of the UAV; C3 is the energy constraint, indicating that the energy consumed by the backscatterer does not exceed the collected energy, where P c The power consumed by the backscatterer to maintain its own circuit operation.

[0060] After obtaining the optimization problem in step 2), the optimization problem is divided into two sub-problems P1 and P2 based on BCD. The specific process includes:

[0061] First, given (x u ,y u ) and r j , the objective function in (1) is about P u is a monotonically increasing function, so P u =P max . j After substitution, the fixed drone position (x u ,y u ), and the optimization of the logarithmic function log2(1+x) can be changed to optimize x, so the sub-problem P1 reflection coefficient optimization problem is obtained

[0062] P1:

[0063]

[0064] P max represents the maximum transmission power of the UAV, η j represents the energy efficiency conversion coefficient of the jth backscatterer. Fixed reflection coefficient r j And order We can get the sub-problem P2 drone position optimization problem P2:

[0065]

[0066] Furthermore, in step 3), the sub-problem P1 is solved. First, the position of the drone is initialized. Reflection coefficient and rate R total (l) = 0. For subproblem P1, the objective function is about r j A monotonically increasing function of , the reflection coefficient can be obtained from the monotonicity:

[0067]

[0068] Furthermore, in step 4), the sub-problem P2 is solved. For the sub-problem P2, it is a nonlinear fractional programming problem, and the optimization problem can be obtained by using the quadratic transformation algorithm.

[0069]

[0070] Among them, {g1,...,g N} represents a set of auxiliary variables in the quadratic transformation algorithm.

[0071] In (4), for a given g j ,j∈{1,...,N}, the optimization problem is:

[0072]

[0073] Among them, z j =(g j ) 2 ,j∈{1,...,N} represents a set of auxiliary variables. The position of the UAV can be obtained by using the first-order optimal condition of the objective function in (5):

[0074]

[0075] in, Represents the auxiliary variable in the quadratic transformation algorithm, used to update the position of the UAV for the l+1th time; is the auxiliary variable in (5), and its value is

[0076] Furthermore, in step 5), the updated system sum rate R is calculated total The values ​​are:

[0077]

[0078] Compare|R total (l+1)-R total (l)| and the size of the rate decision threshold ζ, where R total (l+1) is the sum rate of the system after l+1 iterations; if |R total (l+1)-R total (l)| is not greater than ζ, the sum rate converges, giving the maximum sum rate, and the method ends; if |R total (l+1)-R total (l)| is greater than ζ, the newly calculated sum rate is saved as the sum rate at this time and go to step 3) to update the reflection coefficient until the sum rate meets the conditions and the maximum sum rate is given.

[0079] The present invention discloses a UAV-assisted NOMA backscatter communication system and rate maximization method, comprising: setting the number of backscatterers, a rate decision threshold, a maximum number of iterations, and an initialization number of iterations; establishing an optimization problem, rewriting the objective function and constraints based on a BCD and quadratic transformation method, and obtaining two subproblems P1 and P2, where P1 is a reflection coefficient optimization problem and P2 is a UAV position optimization problem; initializing the UAV's position, reflection coefficient, and system sum rate, solving subproblem P1, and calculating the reflection coefficient of each backscatterer based on the UAV's initial position; substituting the reflection coefficient obtained from subproblem P1 into subproblem P2 to update the UAV's position; judging the convergence of the sum rate update, calculating an updated sum rate value, and if the absolute value of the difference between the updated sum rate and the previous sum rate is not greater than the sum rate decision threshold, the sum rate converges, a maximum sum rate value is given, and the method ends; if the absolute value of the difference between the updated sum rate and the previous sum rate is greater than the sum rate decision threshold, the newly calculated sum rate value is saved as the sum rate value at that time and the process goes to step 3) to update the reflection coefficient until the sum rate meets the condition and the maximum sum rate is given. The present invention considers that drones flying in the air can be used as both base stations and receivers. Drones are small in size, light in weight, and highly mobile, making them more cost-effective than traditional base stations in remote areas. The backscatterer provided by the present invention is a passive device that only collects energy through the signal transmitted by the drone and backscatters its own information. It has the advantages of low cost, low energy consumption, and low complexity. In addition, the present invention uses the NOMA protocol to accommodate more user connections, can make full use of communication resources, and meet the needs of green communications. The present invention proposes an iterative algorithm based on BCD and quadratic transformation methods, which convexifies constraints and functions through methods such as variable substitution and fractional programming, can fully approximate the optimal solution, and compared with other solutions, can maximize the system sum rate on the basis of ensuring the energy constraint of the backscatterer, has a small number of convergence times, is easy to operate, and has strong practicality and feasibility.

[0080] This embodiment is a drone-assisted NOMA backscatter communication system and rate maximization method. In a drone-assisted NOMA backscatter communication system, a full-duplex drone and N backscatterers are randomly deployed in a 30m×30m square area. The channel power gain β0 is 0.1 when the reference distance is 1m, the energy efficiency conversion coefficient η of the backscatterer is 0.6, and the power P consumed by the backscatterer to maintain its own circuit operation is c =0.25μW, additive white Gaussian noise σ 2 =-90dBm, UAV self-interference residual coefficient α =-100dB.

[0081] In this embodiment, Figure 1 The present invention provides an embodiment of a drone-assisted NOMA backscatter communication system model. Figure 2 This is a comparison chart of the effects of the UAV transmission power on the system and rate obtained in the average position scheme, the random position scheme, and the method of this embodiment. Figure 3 This is a comparison chart of the effects of the average position scheme, the random position scheme, and the method of this embodiment on the flight altitude of the UAV on the system and rate. Figure 4 This is a comparison chart of the effects of the UAV self-interference residual coefficient on the system and rate obtained by the average position scheme, random position scheme and the method of this embodiment. Figure 2 It can be seen that compared with the two comparison schemes, the system sum rate of the proposed scheme increases with the increase of the maximum transmission power of the UAV, and is higher than the two comparison schemes in different power ranges. Figure 3 It can be seen that compared with the two comparison schemes, the system sum rate of the proposed scheme decreases as the flight altitude of the UAV increases, and the sum rate in different altitude ranges is higher than the two comparison schemes. Figure 4 It can be seen that compared with the two comparative schemes, the system sum rate obtained by the proposed scheme decreases with the increase of the UAV self-interference residual coefficient, and the sum rate is higher than the two comparative schemes in different height ranges.

[0082] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0083] The above embodiments should be understood as merely illustrating the present invention and not as limiting the scope of protection of the present invention. After reading the contents of the present invention, technicians may make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A UAV-assisted NOMA backscatter communication system and rate maximization method, characterized in that: The following steps are involved: Step 1) Set the number of backscatterers, rate decision threshold, maximum number of iterations, and initialization number of iterations; Step 2) Establish an optimization problem, rewrite the objective function and constraints based on the BCD and quadratic transformation methods, and obtain two sub-problems P1 and P2. P1 is the reflection coefficient optimization problem, and P2 is the UAV position optimization problem. Step 3) Initialize the position, reflection coefficient, and system sum rate of the UAV, solve subproblem P1, and calculate the reflection coefficient of each backscatterer based on the initial position of the UAV; Step 4) Substitute the reflection coefficient obtained from sub-problem P1 into sub-problem P2 to update the position of the drone; Step 5) Determine whether the sum rate update converges, calculate the updated sum rate value, and if the absolute value of the difference between the updated sum rate and the previous sum rate is not greater than the sum rate decision threshold, the sum rate converges, the maximum sum rate value is given, and the method ends; If the absolute value of the difference between the updated sum rate and the previous sum rate is greater than the sum rate decision threshold, the newly calculated sum rate value is saved as the sum rate value at this time and the process goes to step 3) to update the reflection coefficient until the sum rate meets the conditions and the maximum sum rate is obtained. The step 2) establishes an optimization problem, specifically including: N backscatterers are independently distributed in an area. The full-duplex UAV transmits the RF signal to all backscatterers in the downlink. Each backscatterer uses the energy collected from the RF signal to send its information back to the UAV through the uplink. The position of the UAV is (x u ,y u ), the position of the jth backscatterer is (x j ,y j ), the distance from the UAV to the jth backscatterer is Where H is the flight altitude of the UAV. Assuming that the UAV has complete knowledge of the channel state information CSI and considering the channel between the BD and the UAV as the line-of-sight LoS model, the channel power gain between the UAV and the backscatterer is Where β0 represents the channel power gain at a reference distance of 1m; the signal received by the backscatterer from the drone is divided into two parts, the first part of the signal Received by the energy harvester, the received energy is E j =η j (1-r j )P u h j , where P u is the UAV transmission power, x(n) is the signal transmitted by the UAV, η j represents the energy efficiency conversion coefficient of the jth backscatterer, r j represents the reflection coefficient of the jth backscatterer; the second part of the signal After being modulated by the backscatterer and reflected back to the drone, the reflected signal is where a j (n) is the information of the backscatterer itself; the decoding order is specified from the 1st backscatterer to the Nth backscatterer, then the rate of the jth backscatterer is Where α represents the residual coefficient of UAV self-interference, h uu is the UAV self-interference channel gain, σ 2 is the system noise; the sum rate of the system is Set up the optimization problem: Where C1 is the reflection coefficient constraint; C2 is the maximum transmit power constraint of the UAV; C3 is the energy constraint, indicating that the energy consumed by the backscatterer does not exceed the collected energy, where P c The power consumed by the backscatterer to maintain its own circuit operation; After obtaining the optimization problem in step 2), the optimization problem is divided into two sub-problems P1 and P2 based on BCD, specifically including: First, given (x u ,y u ) and r j , the objective function in (1) is about P u is a monotonically increasing function, so P u =P max ; will h j After substitution, the fixed drone position (x u ,y u ), and the optimization of the logarithmic function log2(1+x) can be changed to optimize x, so the sub-problem P1 reflection coefficient optimization problem is obtained P1： P max represents the maximum transmission power of the UAV, η j represents the energy efficiency conversion coefficient of the jth backscatterer; the fixed reflection coefficient r j And order We can get the sub-problem P2 UAV position optimization problem; Solving the sub-problem P1 in step 3) specifically includes: First initialize the position of the drone Reflection coefficient and rate R total (l) = 0; for subproblem P1, the objective function is about r j A monotonically increasing function of , the reflection coefficient can be obtained from the monotonicity: The step 4) solves the sub-problem P2, specifically including: For subproblem P2, it is a nonlinear fractional programming problem. The quadratic transformation algorithm can be used to obtain the optimization problem Among them, {g1,...,g N } represents a set of auxiliary variables in the secondary transformation algorithm; In (4), for a given g j ,j∈{1,...,N}, the optimization problem is: Among them, z j =(g j ) 2 ,j∈{1,...,N} represents a set of auxiliary variables. The position of the UAV can be obtained by using the first-order optimal condition of the objective function in (5): in, Represents the auxiliary variable in the quadratic transformation algorithm, used to update the position of the drone; is the auxiliary variable in (5), and its value is In step 5), the updated system sum rate R is calculated. total The values ​​are: Compare|R total (l+1)-R total (l)| and the size of the rate decision threshold ζ, where R total (l+1) is the sum rate of the system after l+1 iterations; if |R total (l+1)-R total (l)| is not greater than ζ, the sum rate converges, giving the maximum sum rate, and the method ends; if |R total (l+1)-R total (l)| is greater than ζ, the newly calculated sum rate is saved as the sum rate at this time and go to step 3) to update the reflection coefficient until the sum rate meets the conditions and the maximum sum rate is given.

2. The UAV-assisted NOMA backscatter communication system and rate maximization method according to claim 1, characterized in that: In step 1), the number of backscatterers N, the rate decision threshold ζ, and the maximum number of iterations l are set. max , initialize the number of iterations l = 0.

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