CN115002800A - Unmanned aerial vehicle-assisted NOMA (non-orthogonal multiple Access) backscattering communication system and rate maximization method - Google Patents
Unmanned aerial vehicle-assisted NOMA (non-orthogonal multiple Access) backscattering communication system and rate maximization method Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle-assisted Non-orthogonal Multiple Access (NOMA) backscattering communication system and a rate maximization method, and belongs to the field of wireless communication resource allocation. The method maximizes the sum rate of the system by controlling the drone transmit power, the backscatter reflectance, and the drone's position, taking into account the drone transmit power and the backscatter energy constraints. The method is mainly based on variable substitution, block coordinated reduction (BCD), fractional programming, quadratic transformation methods and the like, and the system and the speed are improved to the maximum extent by converting a non-convex problem into a convex optimization problem to solve. The invention has the advantages of low calculation complexity, energy constraint of the reverse diffuser, and system and speed improvement.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle-assisted NOMA backscatter communication system resource allocation, in particular to an unmanned aerial vehicle-assisted NOMA backscatter communication system and a rate maximization method.
Background
From the development goal of the sixth generation mobile communication system, people no longer satisfy the communication between people and things, but further explore the communication between things and things, so that researchers at home and abroad pay more attention to the internet of things in the 6G and future communication systems. Combining backscatter communication technology with NOMA has become a trend in future wireless communications and 6G developments for low cost, low power consumption, low complexity, accommodating more users, and better communication quality. Meanwhile, the unmanned aerial vehicle auxiliary communication also draws attention of researchers due to the fact that the unmanned aerial vehicle is easy to deploy, high in mobility and good in sight distance with ground users.
Currently, as a result of research on backscattering communication resource allocation, there are three main systems considered in the current research: conventional backscatter communications systems, NOMA-assisted backscatter communications systems, and drone-assisted backscatter communications systems. Resource allocation related researches of the three systems are mostly optimized based on indexes such as throughput, energy efficiency and the like. In the research related to resource allocation of the conventional backscattering communication system, for example, Xu Yongjun et al published in IEEE Wireless Communications Letters,2020,9(8): 1191-. In the research related to resource allocation of NOMA assisted Backscatter communication system, for example, Li Xingwang et al published in IEEE Communications Letters,2021,25(5): 1669-. In research related to resource allocation in unmanned-vehicle-assisted backscatter communication systems, such as the article entitled "Energy-efficiency UAV backscattering communication with joint object design and resource optimization" published by Yang Gang et al in IEEE Transactions on Wireless Communications,2020,20(2):926 941, the authors employ a time division multiple access protocol, which may be used to increase the number of users.
For unmanned aerial vehicle assisted NOMA backscatter communications systems, there is less work currently, and therefore resource allocation for unmanned aerial vehicle assisted NOMA backscatter communications systems is a promising direction of research, and optimization of the position of an unmanned aerial vehicle can be considered to maximize the sum rate of the system.
Through retrieval, application publication No. CN112468205A, a backscatter secure communication method suitable for an unmanned aerial vehicle includes: determining a network model, a network communication mode and a protocol; simplifying the network model, and discretizing the continuous time; solving the received signal power of each backscattering device on the ground; solving the energy which can be harvested by each backscattering device at any time, solving the backscattering channel capacity, and solving the eavesdropping channel capacity of each eavesdropper; defining an optimization target as maximizing the fair throughput of the backscattering equipment to obtain an optimization target expression and the constraint thereof; simplifying the optimization target problem, and solving the optimization target problem by adopting a block coordinate descent method; the method comprises three parts of unmanned aerial vehicle flight path design, equipment backscattering factor distribution and equipment time slot distribution, and simultaneously considers the problems of ground equipment harvesting energy and communication safety; in addition, the fairness and the safety of data transmission of a plurality of devices are guaranteed while the energy supply of a plurality of passive devices on the ground is realized.
However, the protocol adopted in the patent is a time division multiple access protocol, that is, at most one reverse scatterer is called in one time slot for data transmission, and one orthogonal resource block is only allocated to one user, which limits the performance index of the throughput of the system and cannot meet the requirement that a large number of users access the system at the same time. In addition, the method considers the fairness of data transmission of a plurality of reverse scatterers, and cannot ensure the maximum throughput of the system, so that the method is not suitable for the scene with the maximum system throughput. The protocol used in the drone-assisted NOMA backscatter communications system and rate maximization method is the NOMA protocol, in which all the backscatter transmitters transmit data to the drone simultaneously through power domain multiplexing, which, in contrast to time division multiple access, enables more user connections, improves the efficiency of spectrum utilization, and which considers the sum rate maximization of the entire system, enables the maximum rate transmission of the system.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A drone assisted NOMA backscatter communications system and rate maximization method are presented. The technical scheme of the invention is as follows:
a drone-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.
Further, the number N of the reverse scatterers, the rate decision threshold ζ and the maximum iteration number l are set in the step 1) max The number of initialization iterations l is 0.
Further, the step 2) of establishing an optimization problem specifically includes:
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 a decoding order from the 1 st to the Nth inverse scatterer, the j-th inverse scatterer has a rate of
Where α represents the unmanned aerial vehicle 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.
Further, after the optimization problem is obtained in step 2), the optimization problem is divided into two sub-problems P1 and P2 based on BCD, which specifically include:
first given (x) u ,y u ) And r j The objective function in (1) is with respect to P u Is monotonically increasing function of, therefore, 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 be changed to optimize x, thus obtaining the sub-problem P1 reflection coefficient optimization problem
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:
further, the solving of the sub-problem P1 in step 3) specifically includes:
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:
further, the solving of the sub-problem P2 in step 4) specifically includes:
for the subproblem P2, which is a nonlinear fractional programming problem, the quadratic transformation algorithm can be used to obtain an optimization problem
Wherein, { g 1 ,...,g N Represents a set of auxiliary variables in the quadratic transformation algorithm;
in (4), forAt a given g j And j belongs to { 1., N }, and an optimization problem can be obtained:
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 an auxiliary variable in a quadratic transformation algorithm and used for updating the position of the (l +1) th unmanned aerial vehicle;
is an auxiliary variable in (5) whose value is
Further, in the step 5), an updated system and rate R are calculated total The values of (A) are:
compare | 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 iteration 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) If | is greater than ζ, will be newThe calculated sum rate is saved as the sum rate at that time and transferred to step 3) to update the reflection coefficient until the sum rate satisfies the condition, giving the maximum sum rate.
The invention has the following advantages and beneficial effects:
the unmanned aerial vehicle flying in the air is taken as a base station and a receiver, the unmanned aerial vehicle is small in size, light in weight and strong in mobility, and the unmanned aerial vehicle has higher cost performance than a traditional base station in a remote area. The back scatterer provided by the invention is a passive device, collects energy only through signals transmitted by the unmanned aerial vehicle, and backscatters self information, and has the advantages of low cost, low energy consumption and low complexity. In addition, the invention uses NOMA protocol, all the reverse scatterers transmit data to the unmanned aerial vehicle through power domain multiplexing, compared with OMA, more user connections can be accommodated, communication resources are fully utilized, and the requirement of green communication is met. Compared with other schemes, the iterative algorithm based on the BCD and the quadratic transformation method is provided, in the step 2), an optimization problem is firstly established, the problem is non-convex and difficult to directly solve, and therefore the original problem is decomposed into two sub-problems of a P1 reflection coefficient optimization problem and a P2 unmanned aerial vehicle position optimization problem by the BCD; in the step 3), closed-form solution of the reflection coefficient can be obtained by utilizing the fractional programming and monotonicity; in step 4), the target function of P2 is non-convex, which makes direct solution difficult, so that the quadratic transformation algorithm is adopted to make the target function and constraint convex, and the variable substitution is used to further simplify the problem, finally obtaining the convex problem (5), further using the first-order optimal condition of the target function to obtain the updated position of the unmanned aerial vehicle, which can fully approach the optimal solution, and compared with other schemes, the sum rate of the system can be maximized on the basis of ensuring the energy constraint of the back diffuser, the convergence times are few, the operation is convenient, and the practicability and feasibility are strong.
Drawings
FIG. 1 is a NOMA backscatter communications system model with the present invention providing the assistance of a preferred embodiment drone;
fig. 2 is a diagram of the present invention comparing the effect of drone transmit power on system and rate for two schemes;
FIG. 3 is a graph of the present invention comparing the effect of flying height of a drone on system and velocity for two solutions;
fig. 4 is a diagram comparing the influence of the self-interference residual coefficient of the drone on the system and the rate according to the two schemes;
FIG. 5 is a schematic flow diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly in the following 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:
as shown in fig. 5, a drone assisted NOMA backscatter communications system and method of rate maximization, 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.
Further, in step 1), the number N of the reverse scatterers, the rate decision threshold ζ and the maximum iteration number l are set max The number of initialization iterations l is 0.
Further, the establishing of the optimization problem in the step 2) specifically includes:
the N backscatter fans are independently distributed in an area, and the full-duplex drone transmits a radio frequency signal to all of the backscatter fans in the downlink, each backscatter fan using 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 unmanned aerial vehicle flying height. Channel power gain between drone and backscatter spread is
Wherein beta is 0 The channel power gain at the reference distance of 1m is shown. The signal received by the backscatter spread from the drone is divided 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 jth inverse diffuser j Representing the reflection coefficient of the jth inverse diffuser; the second part 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 a decoding order from the 1 st to the Nth inverse scatterer, the j-th inverse scatterer has a rate of
Where α represents the unmanned aerial vehicle 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.
After the optimization problem is obtained in the step 2), the specific process of dividing the optimization problem into two sub-problems P1 and P2 based on BCD comprises the following steps:
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 . 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 be changed to optimize x, thus obtaining the sub-problem P1 reflection coefficient optimization problem
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 make an order
A sub-problem P2 drone position optimization problem P2 can be obtained:
further, the sub-problem P1 is solved in the step 3). 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:
further, in the step 4), a sub-problem P2 is solved. For the subproblem P2, which is a nonlinear fractional programming problem, the quadratic transformation algorithm can be used to obtain an optimization problem
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:
wherein z is j ＝(g j ) 2 J ∈ { 1.,. N } represents oneThe auxiliary variables are grouped. And (5) obtaining the position of the unmanned aerial vehicle by using the first-order optimal condition of the objective function in the step (5):
wherein,
representing an auxiliary variable in a quadratic transformation algorithm and used for updating the position of the (l +1) th unmanned aerial vehicle;
is an auxiliary variable in (5) whose value is
Further, in the step 5), an updated system and rate R are 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 iteration l +1 times; if | R total (l+1)-R total (l) If | is not greater than ζ, and the rate converges, giving the maximum sum rate, 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.
The invention discloses an unmanned aerial vehicle assisted NOMA backscattering communication system and a speed maximization method, which comprise the following steps: setting the number of the reverse scatterers, a rate decision threshold, the maximum iteration times and the initialization iteration times; establishing an optimization problem, and rewriting a target function and constraint based on a BCD and quadratic transformation method to obtain two subproblems P1 and P2, wherein P1 is a reflection coefficient optimization problem, and P2 is an unmanned aerial vehicle position optimization problem; initializing the position, reflection coefficient, system and 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; substituting the reflection coefficient obtained by the sub-problem P1 into the sub-problem P2 to update the position of the unmanned aerial vehicle; judging the convergence of the sum rate, calculating an updated sum rate value, if the absolute value of the difference between the updated sum rate and the last sum rate is not more than the sum rate judgment threshold, and the sum rate is converged, giving out the maximum sum 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. The unmanned aerial vehicle flying in the air is taken as a base station and a receiver, the unmanned aerial vehicle is small in size, light in weight and strong in mobility, and the unmanned aerial vehicle has higher cost performance than a traditional base station in a remote area. The back scatterer provided by the invention is a passive device, collects energy only through signals transmitted by the unmanned aerial vehicle, and backscatters own information, and has the advantages of low cost, low energy consumption and low complexity. In addition, the invention can accommodate more user connections by using the NOMA protocol, can fully utilize communication resources and conforms to the requirement of green communication. The iterative algorithm based on the BCD and the quadratic transformation method is provided, constraints and functions are emphasized through methods such as variable replacement and fractional planning, the optimal solution can be fully approximated, the sum rate of the system can be maximized on the basis of ensuring the energy constraint of a reverse scatterer compared with other schemes, the convergence frequency is low, the operation is convenient, and the practicability and the feasibility are high.
This embodiment is a drone-assisted NOMA backscatter communications system and method of rate maximization, comprising a full-duplex drone and N backscatter receivers randomly deployed in a 30m x 30m square area in a drone-assisted NOMA backscatter communications system. Reference toChannel power gain beta at a distance of 1m 0 0.1, the energy efficiency conversion coefficient eta of the reverse scatterer is 0.6, and the reverse scatterer maintains the power P consumed by the self circuit to work c 0.25 μ W, additive white gaussian noise σ 2 -90dBm, unmanned aerial vehicle self-interference residual coefficient α -100 dB.
In this embodiment, fig. 1 provides an embodiment of a model of a drone-assisted NOMA backscatter communications system. Fig. 2 is a graph comparing the effect of the drone transmitting power on the system and speed obtained in the mean position scheme and the random position scheme with the method of the present embodiment. Fig. 3 is a graph comparing the influence of the flying height of the drone on the system and the speed obtained by the method of the present embodiment on the average position scheme and the random position scheme. Fig. 4 is a graph comparing the influence of the self-interference residual coefficient of the unmanned aerial vehicle on the system and the velocity obtained by the method of the embodiment on the average position scheme and the random position scheme. As can be seen from fig. 2, compared to the two comparison schemes, the proposed scheme results in a system and a speed that increase with the maximum transmit power of the drone, and is higher than the two comparison schemes at different power intervals and speeds. As can be seen from fig. 3, compared to the two comparison schemes, the proposed scheme results in a system and a velocity that decrease as the flying height of the drone increases, and that are higher at different altitude intervals and velocities than the two comparison schemes. As can be seen from fig. 4, compared to the two comparison schemes, the proposed scheme results in a system and a speed that decrease as the self-interference residual coefficient of the drone increases, and both are higher at different altitude intervals and speeds than the two comparison schemes.
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 identical elements in the 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 (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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