A resource optimization method based on RIS-assisted wireless power backscattering communication system

Abstract

The present application relates to a kind of resource optimization method based on RIS auxiliary wireless energy-carrying backscattering communication system, belong to low-power Internet of Things technical field, the present application is aimed at the problem that transmission performance is poor caused by obstruction obstruction and channel perturbation, consider transmission time constraint, energy collection constraint, the maximum interference power constraint allowed by main receiver, channel uncertainty constraint and intelligent metasurface phase shift constraint, construct energy efficiency maximization resource management model based on bounded channel uncertainty, utilize worst criterion method and Cauchy inequality to convert original non-convex optimization problem containing parameter uncertainty into deterministic problem;By variable substitution method, continuous convex approximation method, block coordinate descent method and semi-positive definite programming method, the deterministic problem is converted into convex problem solution, the present application can effectively improve system total energy efficiency, while reducing main user interruption probability, improve signal propagation path.

Description

Technical Field

[0001] This invention belongs to the field of low-power Internet of Things (IoT) technology, specifically relating to a resource optimization method for a RIS-assisted wireless energy-carrying backscatter communication system. Background Technology

[0002] Backscatter communication networks are one of the key technologies for next-generation IoT low-power applications. With the widespread deployment of transmitting nodes in various IoT application scenarios, already scarce spectrum resources are becoming increasingly strained, leading to increased energy consumption. Simultaneously, due to the dynamic and random nature of the wireless environment, receiver signals may be blocked by obstacles, causing a sharp decline in the communication quality of traditional backscatter communication networks.

[0003] Furthermore, in practical communication systems, the communication link between transceivers suffers from quantization errors, feedback delays, and estimation errors, making it extremely difficult for base stations to obtain accurate channel state information. Since practical backscattering nodes are typically passive devices, algorithm design must not only improve system throughput but also consider energy harvesting from backscattering nodes and energy replenishment for the system. Therefore, energy efficiency optimization and resource management in RIS-assisted backscattering communication network scenarios with channel uncertainty are of great significance and application value. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a resource optimization method for a RIS-assisted wireless energy-carrying backscatter communication system. This method considers transmission time constraints, energy harvesting constraints, the maximum allowable interference power constraint for the primary user, channel uncertainty constraints, and RIS phase shift constraints, aiming to maximize system energy efficiency. A network model and a system model are established for the RIS-assisted backscatter communication network. The original non-convex optimization problem with parameter uncertainties is transformed into a deterministic non-convex optimization problem using the worst-case criterion method and Cauchy's inequality. The fractional programming problem is transformed into an equivalent problem in subtraction form using the Tinkelbach method. Finally, the non-convex problem is transformed into a convex problem for solution using variable substitution, continuous convex approximation, block coordinate descent, and semidefinite programming methods.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A resource optimization method based on a RIS-assisted wireless energy-carrying backscatter communication system, wherein the RIS-assisted wireless energy-carrying backscatter communication system includes: U main receivers, a RIS with N reflective elements, K pairs of secondary transmitters and secondary receivers, and a main transmitter; each secondary transmitter is equipped with a radio frequency energy harvesting module and a backscattering module for harvesting radio frequency energy and backscattered radio signals, respectively; the secondary transmitters and secondary receivers communicate via backscattering through the backscattering module or via active transmission through the radio frequency energy harvesting module; the RIS is deployed between the secondary transmitters and secondary receivers, and the channel gain of the secondary receivers is changed by adjusting the phase shift of the RIS. The method is characterized by the following steps:

[0007] S1: Initialize the parameters of the RIS-assisted wireless energy-carrying backscatter communication system;

[0008] S2: Based on channel uncertainty, a resource optimization model is constructed with the goal of maximizing the total energy efficiency of the RIS-assisted wireless energy-carrying backscatter communication system, taking into account the transmission time and energy harvesting constraints of the secondary transmitter, the maximum allowable interference power constraint of the primary receiver, the channel uncertainty constraint, and the phase shift constraint of RIS.

[0009] S3: The worst-case criterion method and Cauchy's inequality are used to transform the resource optimization model based on channel uncertainty into a deterministic problem model;

[0010] S4: Fix the phase shift θ of the RIS during the backscattering time of the nth RIS reflector unit during the kth transmitter. n,k The phase shift θ of the RIS in the nth RIS reflection unit during the active transmission time of the kth transmitter. n,0 The transmit power P of the kth transmitter k ST The deterministic problem model is transformed into a convex optimization problem using the variable substitution method to calculate the time α for the k-th transmitter to perform backscatter communication. k The reflection coefficient ρ of the kth transmitter k ;

[0011] S5: Fixed α k ρ k θ n,k and θ n,0 The deterministic problem model is transformed into a convex optimization problem using the continuous convex approximation method to calculate P. k ST ;

[0012] S6: Fixed α k ρ k and P k STThe semidefinite relaxation method is used to transform the deterministic problem model into a convex optimization problem to calculate the RIS phase shift matrix Θ;

[0013] S7: Determine whether the overall energy efficiency of the RIS-assisted wireless energy-carrying backscatter communication system has converged; if so, calculate and output the optimal overall energy efficiency η of the optimal RIS-assisted wireless energy-carrying backscatter communication system. * The optimal phase shift matrix Θ of RIS * Then end; otherwise, proceed to S8;

[0014] S8: Determine if the current iteration count is greater than the maximum iteration count; if so, output η. * and Θ * If the iteration ends, then proceed to the next iteration and return to S4.

[0015] Furthermore, the parameters of the RIS-assisted wireless energy-carrying backscatter communication system include: time frame length T, total transmit power P0 of the main transmitter, and noise variance of the receiver at the k-th transmitter. The noise variance at the k-th receiver end Circuit power consumption of the kth transmitter The maximum interference power threshold that the u-th master receiver can tolerate The joint channel estimation error radius from the k-th secondary transmitter to the u-th primary receiver Joint channel estimation error radius from the master transmitter to the kth secondary receiver Joint channel estimation error radius from the main transmitter to the kth secondary transmitter The total throughput R of the RIS-assisted wireless power-carrying backscatter communication system TOTAL(0) The total energy consumption E of the RIS-assisted wireless power-carrying backscatter communication system TOTAL(0) The total energy efficiency η and the maximum number of iterations D of the RIS-assisted wireless energy-carrying backscatter communication system max Convergence accuracy ω and number of iterations d.

[0016] Furthermore, the calculation of the time α for the k-th transmitter to perform backscatter communication... k The reflection coefficient ρ of the kth transmitter k include:

[0017]

[0018]

[0019]

[0020] C5:0<ρ k <1

[0021] Where ||·||2 represents the L2 norm, and Φ=[φ1,φ2,...,φ N ,1] T , where φ n , This represents the diagonal matrix of the nth reflection unit in the RIS. This represents the channel estimate for the joint channel from the primary transmitter to the k-th secondary transmitter. Let represent the equivalent joint channel gain of the direct link from the k-th transmitter to the k-th receiver and the link via RIS reflection to the k-th receiver. This represents the sum of interference and noise during the backscatter communication process of the k-th transmitter. This represents the throughput of the k-th transmitter during active transmission. Let B represent the deterministic total energy consumption of the RIS-assisted wireless energy-carrying backscatter communication system, and let B represent the bandwidth.

[0022] Furthermore, calculate the transmission power P of the k-th transmitter. k ST include:

[0023]

[0024]

[0025]

[0026] in, This represents the throughput of the k-th transmitter during backscatter communication. This represents the sum of interference and noise during the active transmission of the k-th transmitter. This represents the channel estimate from the k-th transmitter to the u-th master receiver.

[0027] Furthermore, the calculation of the RIS phase shift matrix Θ includes:

[0028]

[0029]

[0030]

[0031]

[0032] C6:Rank(Θ)=1

[0033] Furthermore, determining whether the overall energy efficiency of the RIS-assisted wireless energy-carrying backscatter communication system converges includes:

[0034] The RIS-assisted wireless energy-carrying backscatter communication system converges when the total energy efficiency of the system satisfies |η(d)-η(d-1)|≤ω during the d-th iteration; otherwise, it does not converge.

[0035] The present invention has at least the following beneficial effects

[0036] Compared with methods under perfect channel state information, the method provided by this invention has higher energy efficiency and stronger robustness, thus improving the robustness and throughput of backscatter communication networks.

[0037] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0038] Figure 1 This is a system model diagram of the present invention;

[0039] Figure 2 This is a flowchart of the method of the present invention;

[0040] Figure 3 This is the energy efficiency convergence diagram of the method of the present invention;

[0041] Figure 4 This is a robustness diagram of the method of the present invention. Detailed Implementation

[0042] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0043] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0044] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0045] like Figure 1 As shown, this invention provides a resource optimization method based on a RIS-assisted wireless energy-carrying backscatter communication system. Considering the downlink transmission scenario of the RIS-assisted wireless energy-carrying backscatter communication system, the RIS-assisted wireless energy-carrying backscatter communication system includes: U main receivers, a RIS with N reflective elements, K pairs of secondary transmitters and secondary receivers, and a main transmitter. Each secondary transmitter is equipped with a radio frequency energy harvesting module and a backscattering module for harvesting radio frequency energy and backscattered radio signals, respectively. The secondary transmitters and secondary receivers communicate via backscattering through the backscattering module or via active transmission through the radio frequency energy harvesting module. The RIS is deployed between the secondary transmitters and secondary receivers, and the channel gain of the secondary receivers is changed by adjusting the phase shift of the RIS. All devices have single-antenna transceivers, and the active transmission and backscattering communication of the secondary transmitters do not occur simultaneously.

[0046] The RIS is deployed between the secondary transmitter and the secondary receiver to improve the performance of the secondary user. By adjusting the phase shift of the RIS, the channel gain of the secondary receiver is changed, thereby enhancing the useful signal received by the secondary receiver.

[0047] S1: Initialize the parameters of the RIS-assisted wireless energy-carrying backscatter communication system;

[0048] Preferably, the parameters of the RIS-assisted wireless energy-carrying backscatter communication system include: time frame length T, total transmit power P0 of the main transmitter, and noise variance of the receiver at the k-th transmitter. The noise variance at the k-th receiver end Circuit power consumption of the kth transmitter The maximum interference power threshold that the u-th master receiver can tolerate The joint channel estimation error radius from the k-th secondary transmitter to the u-th primary receiver Joint channel estimation error radius from the master transmitter to the kth secondary receiver Joint channel estimation error radius from the main transmitter to the kth secondary transmitter The total throughput R of the RIS-assisted wireless power-carrying backscatter communication system TOTAL(0) The total energy consumption E of the RIS-assisted wireless power-carrying backscatter communication system TOTAL(0) The total energy efficiency η and the maximum number of iterations D of the RIS-assisted wireless energy-carrying backscatter communication system max Convergence accuracy ω and number of iterations d.

[0049] S2: Based on channel uncertainty, a resource optimization model is constructed with the goal of maximizing the total energy efficiency of the RIS-assisted wireless energy-carrying backscatter communication system, taking into account the transmission time and energy harvesting constraints of the secondary transmitter, the maximum allowable interference power constraint of the primary receiver, the channel uncertainty constraint, and the phase shift constraint of RIS.

[0050] Preferably, the resource optimization model based on channel uncertainty includes:

[0051] The objective is to maximize the overall energy efficiency of a RIS-assisted wireless energy-carrying backscatter communication system, considering the transmission time and energy harvesting constraints of the secondary transmitters, the maximum allowable interference power constraint of the primary receiver, channel uncertainty constraints, and RIS phase shift constraints. Therefore, under perfect channel state information, the transmit power P of the k-th secondary transmitter is jointly optimized. k ST The time α for the k-th transmitter to perform backscatter communication k The phase shift θ of the nth RIS reflector unit during the backscattering time of the kth transmitter. n,k The phase shift θ of the RIS in the nth RIS reflection unit during the active transmission time of the kth transmitter. n,0 The reflection coefficient ρ of the kth transmitter k The problem of maximizing energy efficiency in resource allocation can be described as follows:

[0052]

[0053]

[0054]

[0055]

[0056] C4:|θ n,k |=1,|θ n,0 |=1

[0057] C5:0<ρ k <1

[0058] in, This represents the total throughput of the RIS-assisted wireless energy-carrying backscatter communication system. Let represent the transmission rate containing perturbation parameters of the k-th transmitter during backscatter communication. Let represent the signal-to-interference-plus-noise ratio (SIR) of the k-th transmitter during backscatter communication, including perturbation parameters. This represents the interference from the main transmitter to the k-th secondary transmitter. Let represent the equivalent joint channel gain of the direct link from the main transmitter to the k-th secondary receiver and the link via RIS reflection to the k-th secondary receiver. This represents the equivalent joint channel gain of the direct link from the master transmitter to the k-th master receiver and the link via RIS reflection to the k-th master receiver. Let represent the equivalent joint channel gain of the direct link from the k-th transmitter to the k-th receiver and the link via RIS reflection to the k-th receiver. This represents the transmission rate of the k-th transmitter during the active transmission process. This represents the signal-to-interference-plus-noise ratio (SIR) of the k-th transmitter during active transmission. This indicates that the k-th transmitter received interference from other receivers during active transmission. This represents the total energy consumption of a RIS-assisted wireless energy-carrying backscatter communication system. P represents the energy collected by the k-th transmitter. k EH This represents the actual power collected by the k-th transmitter under nonlinear energy harvesting. C1 represents the input power of the k-th transmitter, C2 is the transmission time constraint of the transmitter, C3 is the maximum interference power constraint allowed by the main receiver, C4 is the energy harvesting constraint of the transmitter, C5 is the phase shift constraint of the RIS, and C6 is the backscattering coefficient constraint.

[0059] To overcome the impact of channel uncertainty, the channel uncertainty factor is taken into account in the above optimization problem. According to robust optimization theory, the channel uncertainty problem can be described as follows:

[0060]

[0061] in, and Let represent the channel estimates from the main transmitter to the k-th secondary transmitter, from the main transmitter to the k-th secondary receiver, from the k-th secondary transmitter to the u-th main receiver, from the main transmitter to the RIS and then to the k-th secondary transmitter, from the main transmitter to the RIS and then to the k-th secondary receiver, and from the k-th secondary transmitter to the RIS and then to the u-th main receiver, respectively. and This indicates the corresponding estimation error. and This represents the radius of the corresponding estimation error. Ω PT-ST Ω PT-SR and Ω ST-PR Therefore and The set of channel uncertainty regions with radius .

[0062] Substituting the above channel uncertainty problem model into the equivalent joint channel G k,u,1 G k,2 and G k,3 It can be obtained

[0063]

[0064] in, and Let ΔG represent the channel estimates for the joint channels from the k-th transmitter to the u-th master receiver, from the master transmitter to the k-th receiver, and from the master transmitter to the k-th transmitter, respectively. k,u,1 ΔG k,2 and ΔG k,3 This represents the corresponding channel estimation error; therefore, we have

[0065]

[0066] in, and Let represent the radii of the joint channel estimation errors from the k-th transmitter to the u-th master receiver, from the master transmitter to the k-th receiver, and from the master transmitter to the k-th transmitter, respectively. Therefore, the robust energy efficiency optimization problem, i.e., the resource optimization model based on the channel uncertainty problem, can be expressed as:

[0067]

[0068]

[0069]

[0070]

[0071] C4:|θ n,k |=1,|θ n,0 |=1

[0072] C5:0<ρ k <1

[0073]

[0074]

[0075] Here, C6 represents the channel uncertainty set constraint. Due to the coupled optimization variables in the objective function and the presence of channel uncertainty in the constraints, this problem is a very difficult non-convex optimization problem to solve.

[0076] S3: Using the worst-case criterion method and Cauchy's inequality, the resource optimization model based on channel uncertainty is transformed into a deterministic problem model:

[0077] Considering channel uncertainty constraints, based on Cauchy's inequality, C2 can be equivalently transformed as follows:

[0078]

[0079] Based on the worst-case criterion, C3 is rewritten as

[0080]

[0081] Similar to Can be rewritten as

[0082]

[0083] At this point, all uncertain constraints have been transformed into deterministic constraints. Therefore, based on the worst-case criterion method, the objective function can be transformed into maximizing the minimum energy efficiency of the system under the channel estimation error.

[0084] Based on the above transformation, similar to the uncertainty constraint transformation method, the objective function can be rewritten as follows:

[0085]

[0086] in, This represents the throughput of the k-th transmitter during backscatter communication. This represents the sum of interference and noise during the backscatter communication of the k-th transmitter. Let represent the throughput of the k-th transmitter during active transmission, where This represents the signal-to-interference-plus-noise ratio (SIR) of the k-th transmitter during active transmission. This represents the sum of interference and noise during the active transmission of the k-th transmitter. Let represent the deterministic total energy consumption of the RIS-assisted wireless energy-carrying backscatter communication system. Therefore, the optimization problem can be transformed into...

[0087]

[0088]

[0089] Processing the fractional objective function using Tinkelbach

[0090]

[0091]

[0092] At this point, the optimization problem has been transformed into a deterministic problem.

[0093] However, due to the objective function, and Due to the influence of [various factors], this problem remains a multivariate coupled nonconvex optimization problem. Therefore, an alternating iterative method based on block coordinate descent is adopted for solving it.

[0094] S4: Fix the phase shift θ of the RIS during the backscattering time of the nth RIS reflector unit during the kth transmitter. n,k The phase shift θ of the RIS in the nth RIS reflection unit during the active transmission time of the kth transmitter. n,0 The transmit power P of the kth transmitter k ST The deterministic problem model is transformed into a convex optimization problem using the variable substitution method to calculate the time α for the k-th transmitter to perform backscatter communication. k The reflection coefficient ρ of the kth transmitter k ;

[0095] Get information about variable α k and ρ k Sub-optimization problem

[0096]

[0097]

[0098] Due to α k With ρ k Coupling; the above problem is non-convex; using the variable substitution method, an auxiliary variable A is introduced. k =α k ρ k The problem can be transformed into

[0099]

[0100]

[0101] Due to constraints and 1-ρ in the expression k With α k There is a coupling relationship, and the transformed problem is still non-convex. An auxiliary variable u is introduced. k=1 / (1-ρ) k In this case, the problem is a convex optimization problem, which can be solved using the CVX toolbox.

[0102] S5: Fixed α k ρ k θ n,k and θ n,0 The deterministic problem model is transformed into a convex optimization problem using the continuous convex approximation method to calculate P. k ST ;

[0103] Next, fix the optimization variable α. k ρ k θ n,k and θ n,0 Get information about variable P k ST Sub-optimization problem

[0104]

[0105]

[0106] By applying the continuous convex approximation to the rate function of the active transmission phase in the above problem, its rate can be approximated as:

[0107]

[0108] in, and It is an auxiliary variable.

[0109] when When the equality holds, the inequality is true; therefore, the rate function during the active transmission phase is approximately:

[0110]

[0111] in, As an auxiliary variable, The initial value is the same as the initial value corresponding to the system parameter initialization; therefore, the problem can be transformed into...

[0112]

[0113]

[0114] At this point, the problem described above is a convex optimization problem, which can be solved using the CVX toolbox.

[0115] S6: Fixed α k ρ k and P k STThe semidefinite relaxation method is used to transform the deterministic problem model into a convex optimization problem to calculate the RIS phase shift matrix Θ;

[0116] Fixed optimization variable α k ρ k and P k ST Get information about θ n,k and θ n,0 The sub-optimization problem. Define Θ = Φ H Φ, and satisfying Rank(Θ)=1, the problem is transformed into using the positive semidefinite relaxation method.

[0117]

[0118]

[0119]

[0120]

[0121] C6:Rank(Θ)=1

[0122] By relaxing constraint C6 using the positive semidefinite relaxation technique, the above problem is transformed into a convex optimization problem, which can be solved using the CVX toolbox. A unique solution to Θ is constructed using Gaussian randomization. If the solution obtained for the above problem is Θ... * If Rank(Θ) * If ) = 1, then eigenvalue decomposition is used to obtain If Rank(Θ) * If ≠1, then an approximate solution to the problem can be obtained using the Gaussian randomization method.

[0123] Therefore, iterative robust energy efficiency optimization algorithms such as Figure 2 As shown.

[0124] The application effects of this invention will be described in detail below with reference to simulation.

[0125] Simulation conditions

[0126] Assume the path loss model is Γ(d)=Γ0(d) i / d0) -α Where Γ0 = -30 dBm represents the path loss at a reference distance d0 = 1 m, d i Let represent the distance between any two devices, α = 3 represent the path loss exponent, and small-scale fading follows Rayleigh fading. The distance from the main transmitter to the RIS is 6m; the main receiver is located at (0,9); the RIS is located at (3,3), and the secondary transmitter-receiver pairs are randomly distributed within a circle centered at (1,0) with a radius of 1m. Other simulation parameters are given in Table 1.

[0127] Table 1 Simulation Parameters

[0128]

[0129] Simulation results

[0130] In this embodiment, Figure 3 An energy efficiency convergence graph of the iterative method in this example is provided. Figure 4 Robustness diagrams for the iterative method in this example are provided. Figure 3 The method of the present invention can achieve convergence quickly, thus proving that the method of the present invention can effectively guarantee the communication quality of the main receiver user and has real-time performance. Figure 4 This shows that as the channel uncertainty upper bound (ΔG) increases... k,u,1 As ΔG increases, the interrupt probability of the master receiver user also increases in all methods, but at the same ΔG... k,u,1 Under these conditions, the actual interference power received by the main receiver of the method of the present invention is minimal and lower than the interference power threshold, thus proving that the method of the present invention has strong robustness. Figure 3 and Figure 4 Experimental results show that the method of the present invention ensures both real-time performance and service quality for the main receiver user, and has strong robustness.

[0131] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for resource optimization based on RIS-aided wireless power backscattering communication system, the RIS-aided wireless power backscattering communication system comprising: U main receivers, an RIS with N reflecting elements, K pairs of secondary transmitter-secondary receivers, a primary transmitter; Each secondary transmitter is equipped with a radio frequency energy harvesting module and a backscatter module for collecting radio frequency energy and backscattering radio signals, respectively; the secondary transmitter and the secondary receiver communicate through the backscatter module for backscatter communication or through the radio frequency energy harvesting module for active transmission communication; The RIS is deployed between the secondary transmitter and the secondary receiver, and the channel gain of the secondary receiver is changed by adjusting the phase shift of the RIS, characterized in that the method comprises the following steps: S1: initializing the parameters of the RIS-assisted wireless power-carrying backscatter communication system; S2: based on channel uncertainty, according to the transmission time constraint and the energy harvesting constraint of the secondary transmitter, the maximum interference power constraint allowed by the primary receiver, the channel uncertainty constraint and the phase shift constraint of the RIS, an optimization model based on channel uncertainty is constructed to maximize the total energy efficiency of the RIS-assisted wireless power-carrying backscatter communication system as the optimization objective; S3: using the worst criterion method and Cauchy inequality to convert the resource optimization model based on channel uncertainty into a deterministic problem model; S4: the phase shift θ of the nth RIS reflecting unit in the RIS within the backscattering time of the kth secondary transmitter n,k , the phase shift θ of the nth RIS reflecting unit in the RIS within the active transmission time of the kth secondary transmitter n,0 , and the transmission power of the kth secondary transmitter The time α of the kth secondary transmitter for backscattering communication is calculated by using the variable substitution method to convert the deterministic problem model into a convex optimization problem k , and the reflection coefficient ρ of the kth secondary transmitter k ; S5: Fixing a k , p k , q n,k , and q n,0 , the deterministic problem model is converted into a convex optimization problem by using the successive convex approximation method to obtain S6: Fixing a k , p k and The RIS phase shift matrix Θ is calculated by transforming the deterministic problem model into a convex optimization problem using the positive semi-definite relaxation method. S7: judging whether the total energy efficiency of the RIS-assisted wireless power backscattering communication system converges; if yes, calculating and outputting the optimal total energy efficiency η of the RIS-assisted wireless power backscattering communication system * and the optimal phase shift matrix Θ of the RIS * and then ending; otherwise, entering S8; S8: determine whether the current iteration number is greater than the maximum iteration number; if yes, output η * and Θ * and end, otherwise, update the current iteration number d, and then enter the next iteration, return to S4.

2. The method of Claim 1, wherein, The parameters of the RIS-assisted wireless power backscatter communication system include: time frame length T, total transmit power of the primary transmitter P0, noise variance of the kth secondary transmitter receiving end Noise variance of the kth secondary transmitter receiving end Circuit energy consumption of the kth secondary transmitter Maximum interference power threshold tolerated by the u th primary receiver Joint channel estimation error radius of the kth secondary transmitter to the u th primary receiver Joint channel estimation error radius of the primary transmitter to the kth secondary receiver Joint channel estimation error radius of the primary transmitter to the kth secondary transmitter Total throughput R of the RIS-assisted wireless power backscatter communication system TOTAL(0) Total energy consumption E of the RIS-assisted wireless power backscatter communication system TOTAL(0) Total energy efficiency η of the RIS-assisted wireless power backscatter communication system, maximum iteration number D max Convergence accuracy ω and iteration number d.

3. The method of Claim 2, wherein, said calculating a time α for the kth secondary transmitter to perform backscatter communication k and a reflection coefficient p of the kth secondary transmitter k comprising: C5: 0 < p k <1 where ||·||2 denotes the two-norm, Φ = [φ1, φ2,..., φN] denotes the phase shift matrix of the RIS, and H = [h1, h2,..., hN] denotes the channel matrix of the RIS. N ,1] T where denotes the diagonal matrix of the nth reflection unit of the RIS, denotes the channel estimation value of the joint channel from the primary transmitter to the kth secondary transmitter, denotes the equivalent joint channel gain of the direct link from the kth secondary transmitter to the kth secondary receiver and the link reflected to the kth secondary receiver through the RIS, denotes the sum of interference and noise in the process of backscattering communication by the kth secondary transmitter, denotes the throughput in the process of active transmission by the kth secondary transmitter, denotes the deterministic total energy consumption of the RIS-assisted wireless power backscattering communication system, and B denotes the bandwidth.

4. The method of Claim 3, wherein, transmit power of the kth secondary transmitter comprising: wherein, represents the throughput of the kth secondary transmitter during the backscatter communication process, represents the sum of interference plus noise of the kth secondary transmitter during the active transmission process, represents the channel estimate from the kth secondary transmitter to the u th primary receiver.

5. The method of Claim 4, wherein, The calculation of the RIS phase shift matrix Θ includes: C6: Rank(Θ)=1.

6. The method of Claim 5, wherein, The judgment of whether the total energy efficiency of the RIS-assisted wireless power-carrying backscatter communication system converges includes: The total energy efficiency of the RIS-aided wireless power backscatter communication system at the dth iteration satisfies |η(d) - η(d - 1)|≤ω, then it converges, otherwise it does not converge, where,

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