An intelligent reflecting surface assisted backscatter communication method and system

Abstract

The present invention discloses an intelligent reflecting surface (RIS)-aided backscatter communication method and system, belonging to the field of wireless communication technologies. The method includes the following steps: 1) establishing an RIS-aided backscatter communication network model based on an actual phase shift model; 2) establishing a transmit power optimization problem for a carrier emitter (CE) considering the received signal-to-noise ratio constraint, the energy harvesting constraint, the tag reflection coefficient constraint, and the phase shift amplitude constraint of the RIS reflection coefficient; 3) using the maximum ratio transmission (MRT) algorithm for transmit beamforming and obtaining the optimal solutions for the RIS phase shift and the tag reflection coefficient based on the alternating optimization (AO) algorithm. The present invention considers the mapping relationship between the amplitude and the phase shift of the RIS reflection coefficient, can balance the influence of both on the system performance, designs the phase shift according to the actual parameters of the RIS, and can effectively improve the system energy efficiency in practical use compared with the algorithm based on the ideal phase shift model.

Description

Technical Field

[0001] The present invention relates to a RIS-assisted backscatter communication method and system, belonging to the field of wireless communication technology. Background Art

[0002] With the development of communication technology, more and more wireless devices are connected to the Internet of Things, and the problem of energy supply for a large number of nodes needs to be solved urgently. The backscatter communication technology has emerged. The backscatter communication device can modulate information by using the carrier wave emitted by the radio frequency source and reflect it to the receiver, so as to complete the data transmission with extremely low energy consumption, and such devices can absorb the energy of the carrier wave signal to complete their own energy supply. However, the backscatter communication still has the disadvantage of short effective communication distance, and the cost of extending the communication distance is usually to increase the transmission power of the radio frequency source.

[0003] Each reflection unit of the reconfigurable intelligent surface (RIS) has adjustable electromagnetic characteristics, which can provide a supplementary link for wireless communication, thus achieving the effect of improving the channel state. Therefore, combining RIS with backscatter communication technology can better meet the energy supply and communication requirements of Internet of Things devices.

[0004] Currently, most of the research on RIS is based on the ideal phase shift model, that is, it is considered that the phase of the RIS reflection coefficient can be adjusted arbitrarily while the amplitude is constant at 1, which is difficult to achieve in practical applications. Summary of the Invention

[0005] In view of this, the present invention discloses a RIS-assisted backscatter communication method and system, which considers the correlation between the amplitude and phase shift of the RIS reflection coefficient, can design the phase shift according to the actual parameters of the RIS, and can effectively improve the system energy efficiency in practical use compared with the algorithm based on the ideal phase shift model.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A RIS-assisted backscatter communication method, the method comprising:

[0008] Step 1: Establish a reconfigurable intelligent surface (RIS)-assisted backscatter communication network model based on the actual phase shift model:

[0009] Step 2: Consider the received signal-to-noise ratio constraint, the energy harvesting constraint, the tag reflection coefficient constraint, and the phase shift amplitude constraint of the RIS reflection coefficient, and establish a carrier emitter (CE) transmission power optimization problem;

[0010] Step 3: Solve the CE transmission power optimization problem established in Step 2 to obtain the optimal RIS phase shift and the tag reflection coefficient;

[0011] Step 4: Use the optimal RIS phase shift and tag reflection coefficient obtained in Step 3 as the parameters of the RIS-assisted backscatter communication network model to achieve RIS-assisted backscatter communication.

[0012] As a further optimization scheme of the present invention, in Step 1, the RIS-assisted backscatter communication network model includes a CE with M antennas, a single-antenna tag, a single-antenna receiver, and a RIS with N reflection units; the channels between the CE and the tag and between the tag and the receiver both include an auxiliary link reflected by the RIS.

[0013] As a further optimization scheme of the present invention, in Step 2, the specific expression of the CE transmit power optimization problem P1 is:

[0014] P1:

[0015] s.t. C1:

[0016] C2: (1 - α)η|(h CT + h IT ΦH CI )w| 2 ≥ E min ,

[0017] C3: 0 ≤ α ≤ 1,

[0018] C4:

[0019] C5:

[0020] where C1 represents the minimum received signal-to-noise ratio constraint, γ min is the minimum signal-to-noise ratio of the tag signal at the receiver; C2 represents the tag energy harvesting constraint, E min is the minimum energy required to ensure the normal operation of the tag; C3 represents the tag reflection coefficient constraint; C4 represents the RIS reflection coefficient constraint, is the reflection coefficient of the nth reflection unit in the RIS; C5 represents the RIS phase shift constraint; are the channel gains from the CE to the tag, from the CE to the RIS, and from the RIS to the tag, respectively; is the reflection coefficient matrix of the RIS; is the transmit beamforming vector of the CE; α is the reflection coefficient of the tag; are the channel gains from the RIS to the receiver, from the tag to the receiver, and from the tag to the RIS, respectively; is the variance of the additive white Gaussian noise at the receiver; η is the energy conversion efficiency of the tag; β n(·) is the amplitude gain of the nth reflection unit in the RIS; θ n is the phase shift of the nth reflection unit in the RIS.

[0021] As a further optimization scheme of the present invention, the transmit beamforming vector of the CE is optimally designed using the maximum ratio transmission (MRT) algorithm to obtain the optimal transmit beamforming vector, and the specific expression is:

[0022]

[0023] where P is the transmit power of the CE;

[0024] Substituting the optimal transmit beamforming vector into P1, the optimization problem P2 is obtained, and the specific expression is:

[0025]

[0026] As a further optimization scheme of the present invention, the solution method for the optimization problem P2 is: using the AO algorithm to alternately optimize the RIS phase shift and the tag reflection coefficient until the set convergence condition is met.

[0027] As a further optimization scheme of the present invention, using the AO algorithm to optimize the RIS phase shift specifically means: only optimizing the phase shift of one reflection unit in the RIS each time, and keeping other reflection units unchanged during this optimization process until the optimization of all reflection units is completed.

[0028] As a further optimization scheme of the present invention, the specific expression of the phase shift optimization problem P3 of the nth reflection unit in the RIS is:

[0029] P3:

[0030] s.t. -π ≤ θ n ≤ π,

[0031] where f1(θ n ) is the function composed of all terms containing θ TR in |(h IR + h TI )| 2 , and b1 is the sum of all constants in |(h n + h TR + h IR Φh TI )| 2 that are independent of θ n ; f2(θ n ) is the function composed of all terms containing θ CT in |(h IT + h CI )| 2 , and b2 is the sum of all constants in |(h nA function composed of terms, where b2 is |(h CT +h IT ΦH CI )| 2 The sum of all constants independent of θ n in it; The expressions of f1(θ n ) and f2(θ n ) are respectively:

[0032]

[0033]

[0034] Where G1 n，n Represents the element in the n-th row and n-th column of matrix G1; G1 n，m Represents the element in the n-th row and m-th column of matrix G1; Represents the vector The n-th element.

[0035] As a further optimization scheme of the present invention, the solution method of P3 is:

[0036] S1: According to the values of f1(θ n ) and f2(θ n ) and the change trend of θ n , respectively determine their trust regions D1 and D2;

[0037] S2: Take the trust region D of P3 as D = D1 U D2;

[0038] S3: Use the exhaustive method to determine the optimal solution on the trust region D with the phase shift accuracy of the RIS as the step size

[0039] As a further optimization scheme of the present invention, the optimization method of the tag reflection coefficient is:

[0040] Step1: Determine the upper and lower bounds of the tag reflection coefficient, and the specific expressions are:

[0041]

[0042] Step2: The optimized tag reflection coefficient is:

[0043]

[0044] A RIS-assisted backscatter communication system based on the above method, including:

[0045] The network model construction module is used to establish a RIS-assisted backscatter communication network model based on the actual phase shift model: The CE transmits a carrier signal to the tag. The tag absorbs part of the signal energy by adjusting the reflection coefficient to maintain its own operation, and uses the remaining signal to send information to the receiver. The channels between the CE and the tag and between the tag and the receiver both include auxiliary links reflected by the RIS. The reflection coefficient of the RIS reflection unit satisfies the actual phase shift model;

[0046] The transmit power optimization problem construction module is used to establish the CE transmit power optimization problem: With the minimization of the CE transmit power as the objective, the CE transmit beamforming vector, the RIS reflection coefficient matrix, and the tag reflection coefficient are used as optimization variables, considering the received signal-to-noise ratio constraint, the energy harvesting constraint, the tag reflection coefficient constraint, and the phase shift amplitude constraint of the RIS reflection coefficient, and the optimization problem is formulated;

[0047] The optimization problem solving module is used to solve the CE transmit power optimization problem: The MRT algorithm is used for transmit beamforming to simplify the original problem, and the RIS phase shift optimization and the adjustment of the tag reflection coefficient are alternately performed based on the AO algorithm, and the solution result is fed back to the network model construction module.

[0048] Beneficial effects: The present invention combines the backscatter communication technology with the RIS technology, which not only solves the energy supply problem of Internet of Things devices but also extends the communication distance of backscatter communication. The present invention considers the mapping relationship between the amplitude and phase shift of the RIS reflection coefficient, can balance the influence of both on the system performance, and designs the phase shift according to the actual parameters of the RIS. Compared with the algorithm based on the ideal phase shift model, it can effectively improve the system energy efficiency in actual use. Description of the Drawings

[0049] Figure 1 It is a flowchart of the RIS-assisted backscatter communication method in an embodiment;

[0050] Figure 2 It is a system model diagram of the RIS-assisted backscatter communication in an embodiment;

[0051] Figure 3 It is a schematic diagram of the trusted domain inspection method in an embodiment. Detailed Embodiments

[0052] The present invention will be further described below in conjunction with the drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0053] In one embodiment, as Figure 1 shown, a RIS-assisted backscatter communication method includes the following steps:

[0054] Step 1, establish a RIS-assisted backscatter communication network model based on the actual phase shift model: The channels between the CE and the tag and between the tag and the receiver both include the auxiliary links reflected by the RIS;

[0055] Step 2, considering the received signal-to-noise ratio constraint, energy harvesting constraint, tag reflection coefficient constraint, and phase shift amplitude constraint of the RIS reflection coefficient, establish a CE transmit power optimization problem;

[0056] Step 3, use the MRT algorithm for optimal transmit beamforming to simplify the original optimization problem;

[0057] Step 4, use the AO algorithm for RIS phase shift optimization: Iteratively optimize the phase shift of a single reflection unit while keeping other reflection units unchanged;

[0058] Step 5, derive the upper and lower bounds of the tag reflection coefficient according to the constraints it satisfies and make adjustments;

[0059] Step 6, alternately perform RIS phase shift optimization and tag reflection coefficient adjustment until the objective function value converges.

[0060] The above backscatter communication method can be applied to wireless Internet of Things devices. This method considers the mapping relationship between the amplitude and phase shift of the RIS reflection coefficient, can balance the influence of both on the system performance, and designs the phase shift according to the actual parameters of the RIS. Compared with the algorithm based on the ideal phase shift model, it can effectively improve the system energy efficiency in actual use.

[0061] In this embodiment, as Figure 2 shown, the RIS-assisted backscatter communication network model based on the actual phase shift model in Step 1 includes a CE with M antennas, a tag with a single antenna, a receiver with a single antenna, and a RIS with N reflection units; The channels between the CE and the tag and between the tag and the receiver both include the auxiliary links reflected by the RIS.

[0062] The CE transmits a carrier signal to the tag. The tag absorbs part of the signal energy by adjusting the reflection coefficient to maintain its own operation, and uses the remaining signal to send information to the receiver. The signal received by the tag is expressed as:

[0063] y T =(h CT +h IT ΦH CI )ws(t), (1) where, are the channel gains from the CE to the tag, from the CE to the RIS, and from the RIS to the tag respectively; is the reflection coefficient matrix of the RIS; is the transmission beamforming vector of the CE; s(t) is the continuous wave signal transmitted by the CE and satisfies

[0064] Then the energy collected by the tag is expressed as:

[0065] E = (1 - α)η|(h CT + H IT ΦH CI )w| 2 , (2)

[0066] where α ∈ [0, 1] is the reflection coefficient of the tag; η ∈ [0, 1] is the energy conversion efficiency of the tag.

[0067] Specifically, the reflection coefficient matrix Φ of the RIS = diag(v H ), where is the reflection coefficient vector of the RIS; the amplitude and phase of the reflection coefficient of each reflection unit satisfy the following relationship:

[0068]

[0069] where β n (·) ∈ [0, 1] is the amplitude gain of the nth reflection unit in the RIS and is a function of θ n ; θ n ∈ [-π, π] is the phase shift of the nth reflection unit in the RIS; β min ≥ 0 is the minimum amplitude gain; φ ≥ 0 is the phase shift when the minimum amplitude gain is obtained plus k ≥ 0 is the steepness of the function curve; β min , φ, k are constants related to the specific implementation circuit of the RIS and can be obtained through actual measurement.

[0070] In one embodiment, in step 1, the receiver simultaneously receives the original signal transmitted by the CE, the signal modulated by the tag, and the noise; the receiver removes the DC component brought by the CE original signal before processing the signal. Therefore, the signal-to-noise ratio of the tag signal at the receiver is:

[0071]

[0072] where are the channel gains from the CE to the receiver, from the RIS to the receiver, from the tag to the receiver, and from the tag to the RIS respectively; is the variance of the additive white Gaussian noise.

[0073] In one embodiment, in step 2, the optimization problem aims to minimize the transmission power of the CE, and the optimization variables are the CE transmission beamforming vector, the RIS reflection coefficient matrix, and the tag reflection coefficient. The specific expression is:

[0074]

[0075] Among them, C1 represents the minimum received signal-to-noise ratio constraint, and γ min is the minimum signal-to-noise ratio of the tag signal at the receiver; C2 represents the tag energy harvesting constraint, and E min is the minimum energy required to ensure the normal operation of the tag; C3 represents the reflection coefficient constraint of the tag; C4 represents the reflection coefficient constraint of the RIS, where is the reflection coefficient of the nth reflection unit in the RIS; C5 represents the phase shift constraint of the RIS.

[0076] In one embodiment, the transmit beamforming vector in step 3 is designed using the MRT algorithm, and its expression is:

[0077]

[0078] where P = ||w|| 2 , which is the transmit power of the CE.

[0079] Substituting the expression of the reflection beamforming vector into problem (P1), it is simplified to:

[0080]

[0081] In one embodiment, problem (P2) is solved by alternately optimizing the phase shift of the RIS and the reflection coefficient of the tag using the AO algorithm. Among them, the phase shift optimization of the RIS also adopts the AO algorithm: iteratively optimize all reflection units in the RIS, and only optimize the phase shift of one reflection unit each time, while keeping other reflection units unchanged during the process; the phase shift optimization problem of the nth reflection unit in the RIS is:

[0082]

[0083] where f1(θ n ) is the function composed of all terms containing θ TR in |(h IR +h TI )| 2 , and b1 is the sum of all constants in |(h n +h TR +h IR Φh TI )| 2 that are independent of θ n ; f2(θ n ) is the function composed of all terms containing θ CT in |(h IT +h CI )| 2 , and b2 is the sum of all constants in |(h nA function composed of terms, where b2 is |(h CT +h IT ΦH CI )| 2 The sum of all constants independent of θ n in it; The expressions of f1(θ n ) and f2(θ n ) are respectively:

[0084]

[0085]

[0086] Where G1 n，n Represents the element in the n-th row and n-th column of matrix G1; G1 n，m Represents the element in the n-th row and m-th column of matrix G1; Represents the vector The n-th element.

[0087] In one embodiment, the solution to problem (P3) is:

[0088] S1: Determine their trust regions D1 and D2 respectively according to the change trends of the values of the functions f1(θ n ) and f2(θ n ) with the variable θ n ;

[0089] S2: Take the trust region D of problem (P3) as D = D1 ∪ D2;

[0090] S3: Use the exhaustive method to determine the optimal solution on the trust region D with the phase shift accuracy of the RIS as the step size

[0091] Specifically, the investigation of the trust regions of f1(θ n ) and f2(θ n ) is similar. Taking f1(θ n ) as an example, the change trend of its function value is mainly determined by β n (θ n ) and cos(arg a1 n -θ n ). The values in the trust region should make both as large as possible; where cos(arga1 n -θ n ) is the cosine function, and its maximum point is θ n ′ = arg a1 n . Therefore, the trust region should start from θ n ′ and be obtained in the direction that makes β n (θ n ) increase.

[0092] Specifically, as Figure 3 shown, in this example, the parameter φ of the actual phase shift model is 0. Therefore, according to the value of θ n ′ within the range of β n (θ n ), at different positions on the function image of the function f1(θ n ), the confidence intervals of the function f1(θ

[0093] ① If then the confidence interval is

[0094] ② If then the confidence interval is

[0095] ③ If then the confidence interval is

[0096] In one embodiment, the method for optimizing the tag reflection coefficient is as follows:

[0097] First, determine the upper and lower bounds of the tag reflection coefficient as:

[0098]

[0099] Furthermore, the formula for adjusting the tag reflection coefficient is obtained as:

[0100]

[0101] In one embodiment, based on the above method, an RIS-assisted backscatter communication system is further provided, including:

[0102] A network model construction module, configured to establish an RIS-assisted backscatter communication network model based on the actual phase shift model: The CE transmits a carrier signal to the tag. The tag absorbs part of the signal energy by adjusting the reflection coefficient to maintain its own operation, and uses the remaining signal to send information to the receiver; The channels between the CE and the tag and between the tag and the receiver both include auxiliary links reflected by the RIS; The reflection coefficient of the RIS reflection unit satisfies the actual phase shift model.

[0103] A transmit power optimization problem construction module, configured to establish a CE transmit power optimization problem: With the minimization of the CE transmit power as the objective, and with the CE transmit beamforming vector, the RIS reflection coefficient matrix, and the tag reflection coefficient as the optimization variables, considering the received signal-to-noise ratio constraint, the energy harvesting constraint, the tag reflection coefficient constraint, and the phase shift amplitude constraint of the RIS reflection coefficient, the optimization problem is formulated.

[0104] An optimization problem solving module is used to solve the CE transmit power optimization problem: The MRT algorithm is used for transmit beamforming to simplify the original problem, and based on the AO algorithm, the RIS phase shift optimization and the adjustment of the tag reflection coefficient are alternately performed.

[0105] In the above system, the data processing flow of each module is the same as that in the method, and will not be described repeatedly here.

[0106] Based on the same technical solution, the present invention also discloses a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to execute the above RIS-assisted backscatter communication method.

[0107] Based on the same technical solution, the present invention also discloses a computing device including one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the above RIS-assisted backscatter communication method.

[0108] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0109] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the function specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0110] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the function in the processFigure 1 one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple processes and / or the functions specified in one block or multiple blocks.

[0112] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included within the scope of the claims of the present invention pending approval of the application.

Claims

1. A RIS-assisted backscatter communication method, characterized in that, The method includes: Step 1: Establish an intelligent reflecting surface (RIS)-aided backscatter communication network model based on an actual phase shift model; Step 2: Considering the received signal-to-noise ratio constraint, energy harvesting constraint, tag reflection coefficient constraint, and phase shift amplitude constraint of the RIS reflection coefficient, establish a carrier emitter (CE) transmit power optimization problem; Step 3: Solve the CE transmit power optimization problem established in Step 2 to obtain the optimal RIS phase shift and tag reflection coefficient; Step 4: Use the optimal RIS phase shift and tag reflection coefficient obtained in Step 3 as the parameters of the RIS-aided backscatter communication network model to achieve RIS-aided backscatter communication; In Step 1, the RIS-aided backscatter communication network model includes a CE with M antennas, a tag with a single antenna, a receiver with a single antenna, and an RIS with N reflecting elements; the channels between the CE and the tag and between the tag and the receiver both include auxiliary links reflected by the RIS; In Step 2, the specific expression of the CE transmit power optimization problem P1 is: C2: (1 - α)η|(h CT + h IT ΦH CI )w| 2 ≥ E min , C3:0≤α≤1, where, C1 represents the minimum received signal-to-noise ratio constraint, γ min is the minimum signal-to-noise ratio of the tag signal at the receiver; C2 represents the tag energy harvesting constraint, E min is the minimum energy required to ensure the normal operation of the tag; C3 represents the tag reflection coefficient constraint; C4 represents the RIS reflection coefficient constraint, is the reflection coefficient of the nth reflection unit in the RIS; C5 represents the RIS phase shift constraint; are the channel gains from the CE to the tag, from the CE to the RIS, and from the RIS to the tag, respectively; is the reflection coefficient matrix of the RIS; is the transmit beamforming vector of the CE; α is the reflection coefficient of the tag; are the channel gains from the RIS to the receiver, from the tag to the receiver, and from the tag to the RIS, respectively; is the variance of the additive white Gaussian noise at the receiver; η is the energy conversion efficiency of the tag; β n (·) is the amplitude gain of the nth reflection unit in the RIS; θ n is the phase shift of the nth reflection unit in the RIS; The transmit beamforming vector of the CE is optimally designed using the maximum ratio transmission (MRT) algorithm to obtain the optimal transmit beamforming vector, and the specific expression is: where P is the transmit power of the CE; Substitute the optimal transmit beamforming vector into P1 to obtain the optimization problem P2, and the specific expression is: The solution method of the optimization problem P2 is: use the alternating optimization (AO) algorithm to alternately optimize the RIS phase shift and the tag reflection coefficient until the set convergence condition is met; Use the AO algorithm to optimize the RIS phase shift. Specifically: each time, only optimize the phase shift of one reflecting element in the RIS, and keep the other reflecting elements unchanged during this optimization process until all reflecting elements are optimized: The specific expression of the phase shift optimization problem P3 of the nth reflecting element in the RIS is: s.t. -π ≤ θ n ≤ π, Where f1(θ n ) is |(h TR +h IR Φh TI ) 2 All the θ n The function composed of terms, b1 is |(h TR +h IR Φh TI )| 2 All the n The sum of irrelevant constants; f2(θ n ) is |(h CT +h IT ΦH CI )| 2 All the θ n The function composed of terms, b2 is |(h CT +h IT ΦH CI )| 2 All the n The sum of irrelevant constants; f1(θ n ),f2(θ n ) are: Among them Denote the element at the n-th row and n-th column of matrix G1; G1 n,m Denote the element at the n-th row and m-th column of matrix G1; Denote the vector The n-th element; The solution method of P3 is: S1: Determine their respective confidence regions D1 and D2 according to the changing trends of the values of f1(θ n ), f2(θ n ) with respect to θ n ; S2: Take the trust region D of P3 as D = D1 ∪ D2; S3: On the trusted domain, D uses the exhaustive search method with the phase shift accuracy of the RIS as the step size to determine the optimal solution The optimization method of the tag reflection coefficient is: Step 1: Determine the upper and lower bounds of the tag reflection coefficient, and the specific expression is: Step 2: The optimized tag reflection coefficient is:

2. An RIS-assisted backscatter communication system based on the method described in claim 1, characterized in that, including: A network model construction module for establishing an RIS-aided backscatter communication network model based on an actual phase shift model; A transmit power optimization problem construction module for establishing a CE transmit power optimization problem; An optimization problem solution module for solving the CE transmit power optimization problem and feeding the solution result back to the network model construction module.

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