CN115002800A - Unmanned aerial vehicle-assisted NOMA (non-orthogonal multiple Access) backscattering communication system and rate maximization method - Google Patents

Abstract

The present invention claims to protect a non-orthogonal multiple access (NOMA) backscatter communication system and rate maximization method assisted by an unmanned aerial vehicle, belonging to the field of wireless communication resource allocation. The method takes into account the UAV launch power and backscatterer energy constraints, and maximizes the sum rate of the system by controlling the UAV launch power, the reflection coefficient of the backscatterer, and the UAV position. This method is mainly based on variable substitution, block coordinated descent (BCD), fractional programming and quadratic transformation methods, etc., and maximizes the system and speed by converting non-convex problems into convex optimization problems. The invention has the advantages of low computational complexity, guaranteeing the energy constraint of the backscatterer, and improving the system and speed.

Description

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

technical field

The invention relates to the technical field of resource allocation of a NOMA backscatter communication system assisted by an unmanned aerial vehicle, in particular to a NOMA backscatter communication system assisted by an unmanned aerial vehicle and a rate maximization method.

Background technique

From the perspective of the development goals of the sixth-generation mobile communication system, people are no longer satisfied with the communication between people and things, but further explore the communication between things and things. Therefore, in 6G and future communication systems, domestic and foreign Researchers will focus on the Internet of Things. For low cost, low energy consumption, low complexity, accommodating more users and better communication quality, the combination of backscatter communication technology and NOMA has become a trend of future wireless communication and 6G development. At the same time, UAV-assisted communication has also attracted the attention of researchers due to its ease of deployment, high mobility, and good line-of-sight with ground users.

At present, by studying the allocation of backscatter communication resources, it is found that there are three main systems considered in the current study: traditional backscatter communication systems, NOMA-assisted backscatter communication systems, and UAV-assisted backscatter communication systems. Most of the related researches on resource allocation of the three systems are optimized based on indicators such as throughput and energy efficiency. In the related research on resource allocation of traditional backscatter communication systems, for example, Xu Yongjun et al. published a paper entitled "Optimal resourceallocation for wireless powered multi-carrier" in "IEEE Wireless Communications Letters, 2020, 9(8): 1191-1195." In the article "backscatter communicationnetworks", the author only considers the base station on the ground. In practical problems, the drone can also be used as a base station to enhance mobility and use the NOMA protocol to increase the number of users. In the related research on resource allocation of backscatter communication systems assisted by NOMA, for example, Li Xingwang et al. published a paper entitled "Backscatter-enabled NOMA for Future 6G Systems: A New Optimization Framework Under Imperfect SIC", the author only considers the base station on the ground. In practical problems, the drone can be considered as a base station to establish a good line of sight with the user. In the related research on resource allocation of UAV-assisted backscatter communication system, for example, Yang Gang et al. published a paper entitled "Energy- "efficient UAV backscatter communication with joint trajectory design and resource optimization", the author uses the time division multiple access protocol, in order to increase the number of users, the NOMA protocol can be used.

For the UAV-assisted NOMA backscatter communication system, there are few existing works, so the resource allocation of the UAV-assisted NOMA backscatter communication system is a promising research direction, and optimization of the UAV's backscatter communication system can be considered. position to maximize the sum rate of the system.

After retrieval, the application publication number CN112468205A, a backscattering safety communication method suitable for unmanned aerial vehicles, includes: determining the network model, network communication method and protocol; simplifying the network model and discretizing the continuous time; The received signal power of the scattering device; find the energy that can be harvested by each backscattering device at any time, find the backscattering channel capacity, and find the eavesdropping channel capacity of each eavesdropper; define the optimization goal to maximize the fairness of the backscattering device Throughput, the optimization objective expression and its constraints are obtained; the optimization objective problem is simplified, and the block coordinate descent method is used to solve it according to the optimization objective problem; it includes three parts: UAV flight trajectory design, equipment backscatter factor allocation and equipment time slot allocation , while taking into account the issues of energy harvesting and communication security for ground equipment; in addition, while realizing the energy supply to multiple passive equipment on the ground, it also ensures the fairness and security of data transmission among multiple equipment.

However, the protocol used in this patent is a time division multiple access protocol, that is, at most one backscatterer is called in a time slot for data transmission, and one orthogonal resource block is only allocated to one user, which limits the performance index of the system throughput. It cannot meet the needs of massive users accessing the system at the same time. Moreover, this method considers the fairness of data transmission of multiple backscatterers, and cannot guarantee the maximum throughput of the system, so it is not suitable for the scenario with the maximum system throughput. The protocol used in the UAV-assisted NOMA backscatter communication system and rate maximization method is the NOMA protocol. In this method, all backscatterers transmit data to the UAV simultaneously through power domain multiplexing, compared with time division. address, which realizes more user connections and improves the efficiency of spectrum utilization, and the method considers the maximization of the sum rate of the entire system, which can realize the maximum rate transmission of the system.

SUMMARY OF THE INVENTION

The present invention aims to solve the above problems of the prior art. A UAV-assisted NOMA backscatter communication system and rate maximization method are proposed. The technical scheme of the present invention is as follows:

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

Step 1), set the number of backscatterers, the rate decision threshold, the maximum number of iterations, and the number of initialization iterations;

Step 2), establish an optimization problem, rewrite the objective function and constraints based on the BCD and quadratic transformation method, 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 and reflection coefficient and system and rate of the UAV, solve the sub-problem P1, and calculate the reflection coefficient of each backscatterer by the initial position of the UAV;

Step 4), substituting the reflection coefficient obtained by sub-problem P1 into sub-problem P2 to update the position of the drone;

Step 5), the judgment of the sum rate update convergence, calculate the updated sum rate value, if the absolute value of the difference between the updated sum rate and the last sum rate is not greater than the sum rate judgment threshold, the sum rate converges, and the maximum sum is given. Rate value, 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 judgment threshold, save the newly calculated sum rate value as the current sum rate value and go to step 3 ) to update the reflection coefficient until the sum rate satisfies the condition, giving the maximum sum rate.

Further, in the step 1), the number N of backscatterers, the rate decision threshold ζ, the maximum number of iterations l _max , and the number of initialization iterations l=0 are set.

Further, the step 2) establishes an optimization problem, which specifically includes:

其中H为无人机飞行高度；假设无人机完全了解信道状态信息CSI，并考虑BD和无人机之间的信道为视距LoS模型，无人机和反向散射器之间的信道功率增益为

其中β₀表示参考距离1m处的信道功率增益；反向散射器从无人机接收到的信号分为两部分，第一部分信号

由能量采集器接收，接收到的能量为E_j＝η_j(1-r_j)P_uh_j，其中P_u为无人机发射功率，x(n)为无人机发射的信号，η_j表示第j个反向散射器的能量效率转换系数，r_j表示第j个反向散射器的反射系数；第二部分信号

由反向散射器调制后反射回无人机，反射的信号为

其中a_j(n)是反向散射器自身的信息；规定解码顺序从第1个反向散射器到第N个反向散射器，则第j个反向散射器的速率为

其中α表示无人机自干扰残余系数，h_uu为无人机自干扰信道增益，σ²为系统噪声；系统的和速率为

建立优化问题：N backscatterers are distributed independently in an area, the full-duplex drone transmits the RF signal to all the backscatterers on the downlink, each backscatterer uses the energy it collects from the RF signal to Its information is sent back to the UAV via the uplink; the UAV's position is (x _u , y _u ), the position of the j-th backscatterer is (x _j , y _j ), and the UAV to the j-th The distance of the backscatterers is

where H is the flying height of the UAV; assuming that the UAV fully understands the channel state information CSI, and considers the channel between the BD and the UAV as a line-of-sight LoS model, the channel power between the UAV and the backscatterer Gain is

where β ₀ represents the channel power gain at a reference distance of 1m; the signal received by the backscatterer from the UAV 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 transmit power of the drone, x(n) is the signal transmitted by the drone, η _j represents the energy efficiency conversion coefficient of the j-th backscatterer, and r _j represents the reflection coefficient of the j-th backscatterer; the second part of the signal

Modulated by the backscatterer and then reflected back to the drone, the reflected signal is

where a _j (n) is the information of the backscatterer itself; if the decoding order is specified from the 1st backscatterer to the Nth backscatterer, the rate of the jth backscatterer is

where α is the residual coefficient of UAV self-jamming, _huu is the self-jamming channel gain of UAV, σ ² is the system noise; the sum rate of the system is

Create an optimization problem:

In the formula, 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, and P _c is the backscatterer to maintain its own circuit work. The power that needs to be consumed.

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

First, given (x _u , y _u ) and r _j , the objective function in (1) is a monotonically increasing function of P _u , so there is P _u =P _max ; after substituting h _j into the fixed UAV position (x _u , y _u ), and the optimized logarithmic function log ₂ (1+x) can be changed to optimize x, so the sub-problem P1 reflection coefficient optimization problem is obtained

P1:

可以得到子问题P2无人机位置优化问题；P2： _Pmax represents the maximum transmit power of the UAV, and _ηj represents the energy efficiency conversion coefficient of the jth backscatterer. Fix the reflection coefficient r _j and let

The sub-problem P2 UAV position optimization problem can be obtained; P2:

Further, solving the sub-problem P1 in the step 3) specifically includes:

反射系数

和速率R_total(l)＝0；对于子问题P1，目标函数是关于r_j的单调递增函数，由单调性可得反射系数：First initialize the position of the drone

Reflection coefficient

The sum rate R _total (l)=0; for the sub-problem P1, the objective function is a monotonically increasing function of r _j , and the reflection coefficient can be obtained from the monotonicity:

Further, the step 4) solves the sub-problem P2, which specifically includes:

For subproblem P2, it is a nonlinear fractional programming problem, and the optimization problem can be obtained by using the quadratic transformation algorithm

Among them, {g ₁ ,...,g _N } represents a set of auxiliary variables in the quadratic transformation algorithm;

In (4), for a given g _j ,j∈{1,...,N}, the optimization problem can be obtained:

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

表示二次变换算法中的辅助变量，用来更新第l+1次无人机的位置；

是(5)中的辅助变量，其值是

in,

Represents the auxiliary variable in the quadratic transformation algorithm, which is used to update the position of the 1+1th UAV;

is the auxiliary variable in (5) whose value is

Further, in the described step 5), the value of the updated system and rate R _total is calculated as:

Compare |R _total (l+1)-R _total (l)| with the sum 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, the maximum sum rate is given, and the method ends; if |R _total (l+1)-R _total (l)| is greater than ζ, the The newly calculated sum rate is saved as the current sum rate and goes to step 3) to update the reflection coefficient until the sum rate satisfies the condition, giving the maximum sum rate.

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

The present invention considers the aerial flying drone as both a base station and a receiver, the drone is small in size, light in weight, strong in mobility, and more cost-effective than traditional base stations in remote areas. The backscatterer provided by the present invention is a passive device, which only collects energy through the signal emitted by the drone, and backscatters its own information, and 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 UAV at the same time 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 solutions, the present invention proposes an iterative algorithm based on BCD and quadratic transformation method. In step 2), an optimization problem is first established, which is non-convex and difficult to solve directly. The problem is decomposed into two sub-problems, the P1 reflection coefficient optimization problem and the P2 UAV 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 P2 The objective function is non-convex, and it is difficult to solve it directly. Therefore, the quadratic transformation algorithm is used to make the objective function and constraints convex, and variable substitution is used to further simplify the problem. Finally, the convex problem (5) is obtained, and the first-order optimization of the objective function is further used. The updated UAV position can fully approximate the optimal solution, and compared with other schemes, it can maximize the sum rate of the system on the basis of ensuring the energy constraint of the backscatterer, with fewer convergence times, easy to operate, and practical robustness and feasibility.

Description of drawings

Fig. 1 is the NOMA backscatter communication system model that the present invention provides the preferred embodiment UAV-assisted;

Fig. 2 is the influence of the UAV launch power of the present invention contrasting two kinds of schemes on system and speed;

Fig. 3 is that the present invention compares the influence of the UAV flying height of two kinds of schemes on system and speed;

Fig. 4 is the influence of the unmanned aerial vehicle self-interference residual coefficient of the present invention comparing two kinds of schemes on the system and rate;

FIG. 5 is a schematic flow chart of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

The technical scheme that the present invention solves the above-mentioned technical problems is:

As shown in Figure 5, a UAV-assisted NOMA backscatter communication system and rate maximization method, which includes the following steps:

Step 1), set the number of backscatterers, the rate decision threshold, the maximum number of iterations, and the number of initialization iterations;

Step 2), establish an optimization problem, rewrite the objective function and constraints based on the BCD and quadratic transformation method, 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 and reflection coefficient and system and rate of the UAV, solve the sub-problem P1, and calculate the reflection coefficient of each backscatterer by the initial position of the UAV;

Step 4), substituting the reflection coefficient obtained by sub-problem P1 into sub-problem P2 to update the position of the drone;

Step 5), the judgment of the sum rate update convergence, calculate the updated sum rate value, if the absolute value of the difference between the updated sum rate and the last sum rate is not greater than the sum rate judgment threshold, the sum rate converges, and the maximum sum is given. Rate value, 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 judgment threshold, save the newly calculated sum rate value as the current sum rate value and go to step 3 ) to update the reflection coefficient until the sum rate satisfies the condition, giving the maximum sum rate.

Further, as described in step 1), the number N of backscatterers, the rate decision threshold ζ, the maximum number of iterations l _max , and the number of initialization iterations l=0 are set.

Further, establishing the optimization problem in the step 2) specifically includes:

其中H为无人机飞行高度。无人机和反向散射器之间的信道功率增益为

其中β₀表示参考距离1m处的信道功率增益。反向散射器从无人机接收到的信号分为两部分，第一部分信号

由能量采集器接收，接收到的能量为E_j＝η_j(1-r_j)P_uh_j，其中P_u为无人机发射功率，x(n)为无人机发射的信号，η_j表示第j个反向散射器的能量效率转换系数，r_j表示第j个反向散射器的反射系数；第二部分信号

由反向散射器调制后反射回无人机，反射的信号为

其中a_j(n)是反向散射器自身的信息。规定解码顺序从第1个反向散射器到第N个反向散射器，则第j个反向散射器的速率为

其中α表示无人机自干扰残余系数，h_uu为无人机自干扰信道增益，σ²为系统噪声。系统的和速率为

建立优化问题：N backscatterers are distributed independently in an area, the full-duplex drone transmits the RF signal to all the backscatterers on the downlink, each backscatterer uses the energy it collects from the RF signal to Its information is sent back to the drone via the uplink. The position of the drone is (x _u , y _u ), the position of the j-th backscatterer is (x _j , y _j ), and the distance from the drone to the j-th backscatterer is

where H is the flying height of the drone. The channel power gain between the UAV and the backscatterer is

where β ₀ represents the channel power gain at a reference distance of 1 m. 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 transmit power of the drone, x(n) is the signal transmitted by the drone, η _j represents the energy efficiency conversion coefficient of the j-th backscatterer, and r _j represents the reflection coefficient of the j-th backscatterer; the second part of the signal

Modulated by the backscatterer and then reflected back to the drone, the reflected signal is

where a _j (n) is the information of the backscatter itself. The decoding order is specified from the 1st backscatterer to the Nth backscatterer, then the rate of the jth backscatterer is

where α is the residual coefficient of UAV self-jamming, _huu is the UAV self-jamming channel gain, and σ ² is the system noise. The sum rate of the system is

Create an optimization problem:

In the formula, 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, and P _c is the backscatterer to maintain its own circuit work. The power that needs to be consumed.

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

First given (x _u , y _u ) and r _j , the objective function in (1) is a monotonically increasing function with respect to P _u , so there is P _u =P _max . After substituting h _j into the fixed UAV position (x _u , y _u ), and optimizing the logarithmic function log ₂ (1+x) can be changed to optimize x, so the sub-problem P1 reflection coefficient optimization problem is obtained

P1:

可以得到子问题P2无人机位置优化问题P2： _Pmax represents the maximum transmit power of the UAV, and _ηj represents the energy efficiency conversion coefficient of the jth backscatterer. Fix the reflection coefficient r _j and let

The sub-problem P2 UAV position optimization problem P2 can be obtained:

反射系数

和速率R_total(l)＝0。对于子问题P1，目标函数是关于r_j的单调递增函数，由单调性可得反射系数：Further, in the step 3), the sub-problem P1 is solved. First initialize the position of the drone

Reflection coefficient

and rate R _total (l)=0. For subproblem P1, the objective function is a monotonically increasing function of r _j , and the reflection coefficient can be obtained from the monotonicity:

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

Among them, {g ₁ ,...,g _N } represents a set of auxiliary variables in the quadratic transformation algorithm.

In (4), for a given g _j ,j∈{1,...,N}, the optimization problem can be obtained:

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

表示二次变换算法中的辅助变量，用来更新第l+1次无人机的位置；

是(5)中的辅助变量，其值是

in,

Represents the auxiliary variable in the quadratic transformation algorithm, which is used to update the position of the 1+1th UAV;

is the auxiliary variable in (5) whose value is

Further, in the described step 5), the value of the updated system and rate R _total is calculated as:

Compare |R _total (l+1)-R _total (l)| with the sum 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, the maximum sum rate is given, and the method ends; if |R _total (l+1)-R _total (l)| is greater than ζ, the The newly calculated sum rate is saved as the current sum rate and goes to step 3) to update the reflection coefficient until the sum rate satisfies the condition, giving the maximum sum rate.

The invention discloses a NOMA backscatter communication system assisted by unmanned aerial vehicle and a rate maximization method, including: setting the number of backscatterers, a rate judgment threshold, the maximum number of iterations, and the number of initialization iterations; establishing an optimization problem based on BCD And the quadratic transformation method rewrites the objective function and constraints, and obtains two sub-problems P1 and P2, P1 is the reflection coefficient optimization problem, P2 is the UAV position optimization problem; initialize the UAV position, reflection coefficient and system and rate, solve Sub-problem P1, calculate the reflection coefficient of each backscatterer from the initial position of the UAV; substitute the reflection coefficient obtained from sub-problem P1 into sub-problem P2 to update the position of the UAV; and judge the convergence of the rate update, calculate the update If the absolute value of the difference between the updated sum rate and the previous sum rate is not greater than the sum rate judgment threshold, the sum rate converges, and the maximum sum rate value is given, and the method ends; if the updated sum rate is the same as the previous sum rate The absolute value of the difference between the first sum rate is greater than the sum rate decision threshold, then save the newly calculated sum rate value as the current sum rate value and go to step 3) to update the reflection coefficient until the sum rate satisfies the condition, giving get the maximum sum rate. The present invention considers the aerial flying drone as both a base station and a receiver, the drone is small in size, light in weight, strong in mobility, and more cost-effective than traditional base stations in remote areas. The backscatterer provided by the present invention is a passive device, which only collects energy through the signal emitted by the drone, and backscatters its own information, and has the advantages of low cost, low energy consumption and low complexity. In addition, the present invention can accommodate more user connections by using the NOMA protocol, can make full use of communication resources, and meet the requirements of green communication. The present invention proposes an iterative algorithm based on the BCD and quadratic transformation methods. The constraints and functions are convexized by methods such as variable substitution and fractional programming, which can fully approximate the optimal solution, and can guarantee the inverse performance compared with other schemes. The sum rate of the system is maximized on the basis of the energy constraint of the scatterer, the number of convergence is small, the operation is convenient, and the practicability and feasibility are strong.

This embodiment is a UAV-assisted NOMA backscatter communication system and a rate maximization method. A UAV-assisted NOMA backscatter communication system includes a full-duplex UAV and N backscattering The device is randomly deployed in a square area of 30m × 30m. When the reference distance is 1m, the channel power gain β ₀ =0.1, the energy efficiency conversion coefficient of the backscatterer η = 0.6, the power consumed by the backscatterer to maintain its own circuit work P _c =0.25μW, additive white Gaussian noise σ ² =-90dBm, UAV self-interference residual coefficient α=-100dB.

In this embodiment, FIG. 1 is a model of a NOMA backscatter communication system assisted by an unmanned aerial vehicle according to an embodiment of the present invention. FIG. 2 is a comparison diagram of the influence of the UAV transmit power on the system and the rate obtained in the average position scheme and the random position scheme and the method of this embodiment. FIG. 3 is a comparison diagram of the effects of the flying height of the UAV on the system and the speed obtained by the average position scheme and the random position scheme and the method of this embodiment. FIG. 4 is a comparison diagram of the influence of the UAV self-interference residual coefficient on the system and the rate obtained in the average position scheme and the random position scheme and the method of this embodiment. It can be seen from Figure 2 that, compared with the two comparison schemes, the system and rate obtained by the proposed scheme increase with the increase of the maximum transmit power of the UAV, and in different power ranges and rates are higher than the two comparisons. Program. It can be seen from Figure 3 that, compared with the two comparison schemes, the system and speed obtained by the proposed scheme decrease with the increase of the flying height of the UAV, and the speed in different altitude intervals is higher than that of the two comparison schemes. . It can be seen from Figure 4 that, compared with the two comparison schemes, the system and rate obtained by the proposed scheme decrease with the increase of the residual coefficient of UAV self-interference, and the rate and rate at different altitudes are higher than those of the two comparison schemes. Program.

It should also be noted that the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device comprising a series of elements includes not only those elements, but also Other elements not expressly listed, or which are inherent to such a process, method, article of manufacture, or apparatus are also included. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article of manufacture, or device that includes the element.

The above embodiments should be understood as only for illustrating the present invention and not for limiting the protection scope of the present invention. After reading the contents of the description of the present invention, the skilled person can 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 (7)

1. An unmanned-plane-assisted NOMA backscatter communications system and rate maximization method, comprising the steps of:

step 1), setting the number of reverse scatterers, a rate decision threshold, the maximum iteration times and the initialization iteration times;

step 2), establishing an optimization problem, and rewriting a target function and constraint based on a BCD and quadratic transformation method to obtain two sub-problems P1 and P2, wherein P1 is a reflection coefficient optimization problem, and P2 is an unmanned aerial vehicle position optimization problem;

step 3), initializing the position, the reflection coefficient, the system and the speed of the unmanned aerial vehicle, solving a subproblem P1, and calculating the reflection coefficient of each reverse scatterer according to the initial position of the unmanned aerial vehicle;

step 4), substituting the reflection coefficient obtained by the sub-problem P1 into the sub-problem P2 to update the position of the unmanned aerial vehicle;

step 5), judging the rate updating convergence, calculating an updated rate value, if the absolute value of the difference between the updated rate and the last rate is not more than a rate decision threshold, and the rate is converged, giving the maximum rate value, and ending the method; if the absolute value of the difference between the updated sum rate and the last sum rate is greater than the sum rate decision threshold, the newly calculated sum rate value is saved as the current sum rate value, and the step 3) is carried out to update the reflection coefficient until the sum rate meets the condition, and the maximum sum rate is given.

2. The drone-assisted NOMA backscatter communication system and rate maximization method of claim 1, wherein in step 1) the number N of backscatter diffusers is set, andrate decision threshold ζ, maximum number of iterations l _max The number of initialization iterations l is 0.

3. The drone-assisted NOMA backscatter communication system and rate maximization method of claim 2, wherein said step 2) of establishing an optimization problem specifically comprises:

n backscatter diffusers are distributed independently in an area, a full-duplex drone transmits a radio frequency signal to all backscatter diffusers in the downlink, each backscatter diffuser uses its energy collected from the radio frequency signal to send its information back to the drone over the uplink; the position of the unmanned plane is (x) _u ,y _u ) The j-th inverse diffuser is located at (x) _j ,y _j ) The distance from the drone to the jth reverse diffuser is

Wherein H is the flying height of the unmanned aerial vehicle; assuming that the drone has full knowledge of the channel state information, CSI, and considering the channel between BD and drone as the line-of-sight, LoS, model, the channel power gain between drone and backscatter is

Wherein beta is ₀ Represents the channel power gain at a reference distance of 1 m; the signal received by the backscatter receiver from the drone is split into two parts, the first part signal

Received by the energy harvester, the received energy is E _j ＝η _j (1-r _j )P _u h _j In which P is _u For the transmitted power of the drone, x (n) for the signal transmitted by the drone, η _j Represents the energy efficiency conversion coefficient, r, of the j-th inverse diffuser _j Representing the reflection coefficient of the jth inverse diffuser; second partial signal

Modulated by a backscatter diffuser and reflected back to the drone, the reflected signal being

Wherein a is _j (n) is the information of the inverse diffuser itself; defining the decoding order from the 1 st to the Nth inverse scatterer, the jth inverse scatterer has a rate of

Where α represents the drone self-interference residual coefficient, h _uu For unmanned aerial vehicle self-interference channel gain, σ ² Is the system noise; the sum rate of the system is

Establishing an optimization problem:

where C1 is the reflectance constraint; c2 is maximum transmit power constraint for the drone; c3 is an energy constraint indicating that the energy consumed by the back diffuser does not exceed the energy collected, where P _c The power consumed to maintain the own circuitry for the opposing diffuser to operate.

4. The UAV-assisted NOMA backscatter communication system and rate maximization method of claim 3, wherein after the optimization problem is obtained in step 2), the optimization problem is divided into two sub-problems P1 and P2 based on BCD, specifically comprising:

first given (x) _u ,y _u ) And r _j The objective function in (1) is with respect to P _u Is a monotonically increasing function of, and thus has P _u ＝P _max (ii) a H is to be _j Fix the drone position after substitution (x) _u ,y _u ) And optimizing the logarithmic function log ₂ (1+ x) can instead be optimized for x, thus resulting in the inverse of the sub-problem P1Problem of optimization of radiation coefficients

P1：

P _max Representing the maximum transmitted power, η, of the drone _j Representing the energy efficiency conversion factor of the jth inverse diffuser. Fixed reflection coefficient r _j And order

A subproblem P2 unmanned plane position optimization problem can be obtained;

P2：

5. the UAV-assisted NOMA backscatter communication system and the rate maximization method of claim 4, wherein solving the sub-problem P1 in step 3) specifically comprises:

first initializing the position of the drone

Coefficient of reflection

And rate R _total (l) 0; for the sub-problem P1, the objective function is for r _j The reflection coefficient is obtained from monotonicity as follows:

6. the drone-assisted NOMA backscatter communication system and rate maximization method of claim 5, wherein said step 4) of solving a sub-problem P2 specifically comprises:

for the subproblem P2, which is a nonlinear fractional programming problem, the quadratic transformation algorithm can be used to obtain an optimization problem

s.t.C1:

Wherein, { g ₁ ,...,g _N Represents a set of auxiliary variables in the quadratic transformation algorithm;

in (4), for a given g _j J ∈ { 1., N }, an optimization problem can be obtained:

s.t.C1:

wherein z is _j ＝(g _j ) ² J ∈ { 1., N } represents a set of auxiliary variables, and the position of the drone can be obtained by using the first-order optimal condition of the objective function in (5):

wherein,

representing auxiliary variables in quadratic transformation algorithmsAmount to update the position of the drone;

is the auxiliary variable in (5) whose value is

7. Unmanned aerial vehicle-assisted NOMA backscatter communications system and rate maximization method according to claim 6, wherein in step 5) an updated system and rate R is calculated _total The values of (A) are:

comparison of | R _total (l+1)-R _total (l) I and the magnitude of the sum rate decision threshold ζ, where R _total (l +1) is the sum rate of the system after iterating for l +1 times; if | R _total (l+1)-R _total (l) | is not greater than ζ, and the rate converges, giving the maximum rate of sum, and the method ends; if | R _total (l+1)-R _total (l) And if the | is larger than zeta, saving the newly calculated sum rate as the current sum rate, and transferring to the step 3) to update the reflection coefficient until the sum rate meets the condition, so as to give the maximum sum rate.

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