A dual RIS-assisted robust transmission method for backscatter communication

Abstract

The present invention relates to a dual-RIS-assisted backscatter communication robust transmission method, which belongs to the field of backscatter communication. The method includes: constructing a dual-RIS-assisted wireless power backscatter communication system model; considering the maximum transmission power constraint of the power station, the transmission power constraint of the backscatter device, the RIS phase shift constraint, the channel uncertainty constraint and the energy collection constraint, establishing a system energy efficiency maximization resource allocation model based on bounded channel uncertainty; using the S process and Cauchy inequality to convert the uncertainty non-convex optimization problem into a deterministic problem; using the Dinkelbach method to convert the fractional programming problem into a subtraction form; using the semi-definite relaxation method, the penalty concave-convex process method and the alternating optimization method to convert the non-convex optimization problem into an equivalent convex optimization problem. The present invention can effectively improve the total energy efficiency of the system while reducing the probability of communication interruption under the condition of channel uncertainty in the wireless power backscatter communication system.

Description

Technical Field

[0001] The invention belongs to the field of backscatter communication and relates to a double RIS-assisted backscatter communication robust transmission method. Background Art

[0002] Backscatter communication has been proposed as a potential technology for the next generation of low-power IoT, significantly reducing system energy consumption and enabling spectrum resource sharing. However, the transmission link in traditional backscatter communication systems can be blocked by environmental obstacles, thus degrading system performance. Therefore, reconfigurable smart surfaces have been introduced into backscatter communication systems. In a RIS-assisted backscatter communication system, backscattering devices can communicate with the help of RIS even if obstacles block the transmission path. Electromagnetic waves and wireless signals can be efficiently transmitted to their destinations through reconfigurable channels, effectively improving the capacity and coverage of wireless networks.

[0003] Most existing RIS-assisted wireless powered backscatter communication systems only consider the signal enhancement of the energy collection link by a single RIS, and do not consider the problem of poor signal transmission quality due to the information transmission link between the node and the user being blocked by obstacles. The method of the present invention considers deploying two RIS to assist in energy collection and information transmission, which can ensure both sufficient energy supply and reliable information transmission. Due to the influence of estimation errors, feedback delays and quantization errors in the actual physical channel, it is difficult to obtain true channel state information. The present invention takes into account imperfect CSI in the system design stage, which can make the system robust and perform well under various channel conditions. In addition, most of the existing RIS-assisted wireless powered backscatter communication system resource allocation methods study resource allocation problems with the optimization goal of maximizing system throughput or transmission rate. The method of the present invention considers a resource allocation method that maximizes the total energy efficiency of the system, achieving a trade-off between rate and energy consumption. Summary of the Invention

[0004] In view of this, the present invention aims to provide a robust transmission method for dual-RIS-assisted backscatter communication. Taking into account the power station transmit power constraints, backscatter device transmit power constraints, backscatter device throughput constraints, channel uncertainty constraints, RIS phase shift constraints, and energy harvesting constraints, a system model is established for a dual-RIS-assisted wireless powered backscatter communication system with maximizing system energy efficiency as the optimization goal. The uncertain non-convex optimization problem is converted into a deterministic problem using the S-process and Cauchy inequality; the fractional programming problem is converted into a subtractive form using the Dinkelbach method; and the non-convex optimization problem is converted into an equivalent convex optimization problem for solution using a semidefinite relaxation method, a penalized concave-convex process method, and an alternating optimization method.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A dual RIS-assisted backscatter communication robust transmission method is based on a dual reconfigurable smart surface-assisted wireless power backscatter communication system, which includes: K backscatter nodes, a a RIS of a reflective element a , a with N b RIS of a reflective element b , a power station with M antennas and a single-antenna information receiver; the direct link from the backscatter node to the information receiver is blocked by an obstacle; RIS a Deployed between the power station and the backscatter node, RIS b Deployed between the backscatter node and the information receiver, the RIS is used to change the channel gain by adjusting the phase shift of the RIS. The method includes the following steps:

[0007] S1: Construct a dual-RIS assisted wireless power backscatter communication system model; the parameters of the dual-RIS assisted wireless power backscatter communication system include the number of power station antennas M, RIS a Number of reflective elements N a RIS b Number of reflective elements N b , the number of backscatter devices K, the maximum transmission power threshold of the power station P max , the transmission power p of the kth backscatter device k , the minimum throughput threshold of the kth backscatter device The energy consumed by the kth backscattering device E k , energy collection time t0, active transmission time of the kth backscattering device t k , system transmission frame length T, power station through RIS a The upper bound of the cascade channel error to the backscatter node is ε 1,k , the upper bound of the channel error from the power station to the backscatter node ε 2,k , backscatter nodes through RIS b The upper bound of the cascade channel error to the information receiver is ε 3,k 、System total energy efficiency η EE ;

[0008] S2: Considering the power station transmit power constraint, backscatter device transmit power constraint, backscatter device throughput constraint, channel uncertainty constraint, RIS phase shift constraint and energy harvesting constraint, a robust resource allocation model for a dual-RIS-assisted wireless power backscatter communication system is established based on channel uncertainty and with the total energy efficiency maximized.

[0009] S3: Using Cauchy inequality, S-process, Dinkelbach method, semi-definite relaxation method, penalized concave-convex process and alternating optimization method, the original problem is transformed into an equivalent convex optimization subproblem;

[0010] S4: Use CVX to solve the convex optimization problem and obtain the optimal beamforming vector, device transmit power, RIS phase shift and time allocation, that is, the optimal resource allocation solution.

[0011] Furthermore, the total energy efficiency of the system is based on Calculate; where R sum represents the total system throughput, E sum Indicates the total energy consumption of the system.

[0012] Furthermore, the robust resource allocation model of the dual RIS-assisted wireless power backscatter communication system with the total energy efficiency maximization as the optimization goal is:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] C5:|v a,n | 2 =1,|v b,n | 2 =1

[0019]

[0020]

[0021] in, represents the total energy consumption of the system, ω k is the beamforming vector from the power station to the kth backscatter device, represents the energy consumed by the kth backscatter device, represents the energy collected by the kth backscattering device, 0≤ρ k ≤1 represents the energy conversion factor of the kth backscattering device, represents the power collected by the kth backscattering device, (·) H is the conjugate transpose of the matrix, v a =[v a,1 ,…,v a,N ] T RIS aThe phase shift vector, RIS a The phase of the nth reflector unit, h k represents the channel from the power station to the kth backscatter device, and represents the channel estimation value, Δh k represents the channel estimation error, Indicates that the power station passes through RIS a The cascade channel to the kth backscatter device, l k RIS a The channel to the kth backscatter device, f k Indicates power station to RIS a channel, and represents the channel estimation value, ΔH k represents the channel estimation error, represents the throughput of the kth backscatter device, g u,k Indicates that the kth backscatter device passes through RIS b channel to the information receiver, and represents the channel estimation value, Δg u,k represents the channel estimation error, v b =[v b,1 ,…,v b,N ] T RIS b The phase shift vector, RIS b The phase of the nth reflector unit, δ 2 is the background noise at the information receiver.

[0022] Furthermore, in the equivalent convex optimization subproblem, the resource allocation problem where the optimization variables are the beamforming vector and the backscatter device transmit power is expressed as:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] in, is the slack variable, Represents R k The lower bound of I MN represents the (M×N)×(M×N)-dimensional unit diagonal matrix, I M represents the M×M dimensional unit diagonal matrix,

[0031]

[0032]

[0033]

[0034]

[0035] Furthermore, in the equivalent convex optimization subproblem, the optimization variable is RIS a The phase-shifted resource allocation problem is expressed as:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] C 11 :|v a,n | 2 ≤1+a n+N

[0042] C 12 :a≥0

[0043] Where a=[a1,…,a 2N ] T represents the slack variable, λ (l) is the regularization factor.

[0044] Furthermore, in the equivalent convex optimization subproblem, the optimization variable is RIS b The phase-shifted resource allocation problem is expressed as:

[0045]

[0046]

[0047] C 13 :V b,(n,n)=1

[0048]

[0049] in,

[0050] Furthermore, in the equivalent convex optimization subproblem, the resource allocation problem with time as the optimization variable is expressed as:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] The beneficial effects of the present invention are that, compared with the method under perfect channel state information, the scheme of the present invention has higher energy efficiency and stronger robustness, thereby improving the robustness and energy efficiency of the wireless power backscatter communication system.

[0058] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0060] Figure 1 It is a system model diagram of the present invention;

[0061] Figure 2 Flow chart of the method of the present invention;

[0062] Figure 3 is the energy efficiency convergence diagram of the method of the present invention;

[0063] Figure 4 This is the robustness diagram of the method of the present invention. DETAILED DESCRIPTION

[0064] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways 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 illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0065] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0066] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships 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 direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0067] The present invention provides a dual RIS-assisted backscatter communication robust transmission method, such as Figure 2 Shown, including:

[0068] S1: Construct a dual RIS-assisted wireless power backscatter communication system model;

[0069] S2: Considering the power station transmit power constraint, backscatter device transmit power constraint, backscatter device throughput constraint, channel uncertainty constraint, RIS phase shift constraint and energy harvesting constraint, a robust resource allocation model for a dual-RIS-assisted wireless power backscatter communication system is established based on channel uncertainty and with the total energy efficiency maximized.

[0070] S3: Using Cauchy inequality, S-process, Dinkelbach method, semi-definite relaxation method, penalized concave-convex process method and alternating optimization method, the original problem is transformed into an equivalent convex optimization subproblem;

[0071] S4: Use CVX to solve the convex optimization problem and obtain the optimal beamforming vector, device transmit power, RIS phase shift and time allocation, that is, the optimal resource allocation solution.

[0072] In this embodiment, if Figure 1 As shown, the system consists of a power station with M antennas and a a RIS of reflective elements a , a containing N b RIS of reflective elements b , a single-antenna information receiver, K single-antenna backscattering devices. Divide the time block T into K+1 time slots, that is, where t k represents the active transmission time of the kth backscatter device, and t0 represents the energy collection time. In the energy collection phase, the power station transmits energy through the direct link and the RIS a The reflected link to the backscatter device sends an energy signal to the backscatter device. In the active transmission phase, since the direct link is blocked by obstacles, the backscatter device transmits energy signals to the backscatter device through the RIS. b The reflection link to the information receiver actively transmits the information signal to the information receiver. Define the backscatter device set RIS array element collection

[0073] In the energy collection phase, the signal collected by the kth backscattering device is

[0074]

[0075] in, represents the channel vector from the power station to the kth backscattering device, v a =[v a,1 ,…,v a,N ] T RIS a The phase shift vector, RIS a The phase of the nth reflector unit, Indicates that the power station passes through RIS a The cascade channel to the kth backscatter device, is the beamforming vector from the power station to the kth backscatter device, s k is the signal sent by the power station to the kth backscattering device, and satisfies E[|s k | 2 ]=1,n k Indicates that at the kth backscattering device, the mean is 0 and the variance is Additive white Gaussian noise.

[0076] According to the linear energy collection model, the energy collected at the kth backscattering device is

[0077]

[0078] Where 0≤ρ k ≤1 represents the energy conversion factor of the kth backscattering device.

[0079] In the active transmission phase, the signal collected at the information receiver is expressed as

[0080]

[0081] Among them, p k represents the transmission power of the kth backscatter device, v b =[v b,1 ,…,v b,N ] T RIS b The phase shift vector, RIS b The phase of the nth reflector unit, Indicates that the kth backscatter device passes through RIS b Cascade channel to the information receiver, x k represents the information symbol sent by the kth backscattering device, n0 represents the information receiver with a mean of 0 and a variance of δ 2 Additive white Gaussian noise.

[0082] The throughput of the kth backscatter device is expressed as

[0083]

[0084] The total energy consumed by the system is expressed as

[0085]

[0086] in, represents the energy consumed by the kth backscatter device, The energy consumed by the kth backscatter device circuit.

[0087] In order to overcome the influence of channel uncertainty, the channel uncertainty factor is taken into account in the above optimization problem. According to the robust optimization theory, the channel uncertainty problem can be described as:

[0088]

[0089] and represents the channel estimation value, ΔH k , Δhk and Δg u,k represents the channel estimation error, ε 1,k , ε 2,k and ε 3,k represents the upper bound of the estimation error.

[0090] Based on the above channel uncertainty set, the power station transmit power constraint, backscatter device transmit power constraint, backscatter device throughput constraint, channel uncertainty constraint, RIS phase shift constraint and energy collection constraint are considered. By jointly optimizing the power station transmit beamforming vector, backscatter device transmit power, RIS a and RIS b The phase shift, energy collection time and active transmission time of the system are optimized to maximize the system energy efficiency. Therefore, the robust resource allocation problem of maximizing the system energy efficiency can be modeled as

[0091]

[0092] in, Represents the set of optimization variables. C1 represents the maximum transmit power constraint of the energy station; C2 represents the energy collection constraint; C3 represents the minimum throughput constraint; C4 represents the transmit power constraint of the backscatter device; C5 represents the RIS phase shift constraint; C6 represents the time constraint; and C7 represents the channel uncertainty constraint.

[0093] For constraints with uncertainty, based on the variable relaxation method, slack variables are introduced Constraint C2 can be transformed into:

[0094]

[0095]

[0096] Based on the first-order Taylor expansion, can be converted to:

[0097]

[0098] Among them, v a,(n) and ω k,(n) is the value of the nth iteration, Will and Substituting into formula (10) we can get:

[0099]

[0100] in,

[0101]

[0102] Therefore, formula (9) can be equivalently written as:

[0103]

[0104] Since Equation (12) contains multiple linear inequalities, the S-process can be used to transform Equation (12) into:

[0105]

[0106] in, is the slack variable, d k =2Re{q k}-s k , I MN represents the (M×N)×(M×N)-dimensional unit diagonal matrix, I M represents the M×M dimensional unit diagonal matrix.

[0107] Will Bring in R k Can get Based on the inequality ab≥-|a||b|, can be converted to:

[0108]

[0109] Among them, v b,i where v b The i-th element of . Using the Cauchy inequality, we can get the following inequality:

[0110]

[0111] Combining equations (14) and (15), we can obtain:

[0112]

[0113] According to the Dinkelbach method, the objective function in fractional form can be transformed into a subtraction form:

[0114]

[0115] Based on the above transformation, the following deterministic optimization problem can be obtained:

[0116]

[0117] in, The optimization problem (18) is still a non-convex optimization problem. There are still coupled variables in the objective function and the constraints, so it is still difficult to solve.

[0118] Based on the study of alternating optimization theory, the optimization problem (18) is decomposed into four sub-problems: 1) the optimization problem of the power station beamforming and backscattering equipment transmission power; 2) the RIS a Phase shift optimization problem; 3) RIS b Phase shift optimization problem; 4) Time optimization problem.

[0119] By fixing {v a ,v b ,t k ,t0}, we can get the following sub-optimization problem:

[0120]

[0121] The optimization problem (19) is a convex optimization problem and can be solved directly using the CVX toolbox.

[0122] By fixing {ω k ,p k ,v b ,t k ,t0}, we can get the following sub-optimization problem:

[0123]

[0124] Due to the existence of the unit module constraint, problem (20) is a non-convex optimization problem. Constraints It can be equivalent to 1≤|v a,n | 2 ≤1. Using the first-order Taylor inequality, the non-convex part 1≤|v a,n | 2 Convert to Based on the method of penalizing the concave-convex process, problem (20) can be transformed into:

[0125]

[0126] where a=[a1,…,a 2N ] T represents the slack variable, λ (l) represents the regularization factor. Problem (21) is a semi-positive definite programming problem and can be solved using the CVX toolbox.

[0127] By fixing {ω k ,p k ,v b ,t k ,t0}, we can get the following sub-optimization problem:

[0128]

[0129] definition and Can get definition V b ≥0 and rank(V b )=1. Using the semi-positive definite relaxation method to deal with the rank-one constraint, problem (22) is converted to:

[0130]

[0131] If the obtained optimal phase shift satisfies rank(V b )=1, the optimal phase shift can be obtained by eigenvalue decomposition Otherwise, a Gaussian randomization method is used to obtain an approximate solution.

[0132] By fixing {ω k ,p k ,v a ,v b}, we can get the following sub-optimization problem:

[0133]

[0134] Problem (24) is a linear programming problem and can be solved using linear programming tools.

[0135] The application effect of the present invention is described in detail below with reference to simulation.

[0136] 1) Simulation conditions

[0137] This section verifies the convergence and effectiveness of the proposed method through simulation results. Assume that there is a power station located at (0,0,0) in the system, RIS a Located at (10,0,15), RIS b At (30,0,10), the backscatter device is located in a circle with a radius of 5 meters centered at (20,0,0), and the information receiver is located at (40,0,0). The channel model includes large-scale fading and small-scale fading. Large-scale fading is PL = -30-10αlog 10 (d) dB, where α is the path fading factor and d is the link distance. Small-scale fading follows the Rayleigh distribution. Define the normalized uncertainty upper bound and And σ H =σ h =σ g =0.1. Other simulation parameters are shown in Table 1.

[0138] Table 1

[0139]

[0140] 2) Simulation results

[0141] In this embodiment, Figure 3 The energy efficiency convergence diagram of the iterative method in this example is given. Figure 4 The robustness diagram of the iterative method of this example is given. Figure 3 It shows that the method of the present invention can quickly achieve convergence, thereby proving that the method of the present invention can well guarantee the communication quality of the system and has real-time performance. Figure 4 The results show that as the normalized channel error increases, the probability of meeting the requirements for the proposed method is 100%, while the probability of meeting the requirements for other methods decreases as the normalized channel error increases. The average outage probability of the proposed method is 7.62% lower than that of the non-robust method, demonstrating that the proposed method has strong robustness. Figure 3 and Figure 4 The experimental results show that the method of the present invention not only ensures real-time performance but also guarantees service quality and has strong robustness.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A dual-RIS-assisted backscatter communication robust transmission method, based on a dual-reconfigurable smart surface-assisted wireless powered backscatter communication system, comprising: K backscatter nodes, one with N a RIS of a reflective element a , a with N b RIS of a reflective element b , a power station with M antennas and a single-antenna information receiver; the direct link from the backscatter node to the information receiver is blocked by an obstacle; RIS a Deployed between the power station and the backscatter node, RIS b The method is deployed between the backscatter node and the information receiver, and changes the channel gain by adjusting the phase shift of the RIS. The method is characterized in that: S1: Construct a dual-RIS assisted wireless power backscatter communication system model; the parameters of the dual-RIS assisted wireless power backscatter communication system include the number of power station antennas M, RIS a Number of reflective elements N a RIS b Number of reflective elements N b , the number of backscatter devices K, the maximum transmission power threshold of the power station P max , the transmission power p of the kth backscatter device k , the minimum throughput threshold of the kth backscatter device The energy consumed by the kth backscattering device E k , energy collection time t0, active transmission time of the kth backscattering device t k , system transmission frame length T, power station through RIS a The upper bound of the cascade channel error to the backscatter node is ε 1,k , the upper bound of the channel error from the power station to the backscatter node ε 2,k , backscatter nodes through RIS b The upper bound of the cascade channel error to the information receiver is ε 3,k 、System total energy efficiency η EE ; S2: Considering the power station transmit power constraint, backscatter device transmit power constraint, backscatter device throughput constraint, channel uncertainty constraint, RIS phase shift constraint and energy harvesting constraint, a robust resource allocation model for a dual-RIS-assisted wireless power backscatter communication system is established based on channel uncertainty and with the total energy efficiency maximized. S3: Using Cauchy inequality, S-process, Dinkelbach method, semi-definite relaxation method, penalized concave-convex process and alternating optimization method, the original problem is transformed into an equivalent convex optimization subproblem; S4: Use CVX to solve the convex optimization problem and obtain the optimal beamforming vector, device transmit power, RIS phase shift, and time allocation, that is, the optimal resource allocation solution; The total energy efficiency of the system is based on Calculate; where R sum represents the total system throughput, E sum Indicates the total energy consumption of the system; The robust resource allocation model of the dual RIS-assisted wireless power backscatter communication system with the optimization goal of maximizing total energy efficiency is: C5:|v a,n | 2 =1,|v b,n | 2 =1 in, represents the total energy consumption of the system, ω k is the beamforming vector from the power station to the kth backscatter device, represents the energy consumed by the kth backscatter device, represents the energy collected by the kth backscattering device, ρ k represents the energy conversion factor of the kth backscattering device, 0≤ρ k ≤1; represents the power collected by the kth backscattering device, (·) H is the conjugate transpose of the matrix, v a =[v a,1 ,…v a,n ,…,v a,N ] T RIS a The phase shift vector, RIS a The phase of the nth reflector unit, h k represents the channel from the power station to the kth backscatter device, and represents the channel estimation value, Δh k represents the channel estimation error, Indicates that the power station passes through RIS a The cascade channel to the kth backscatter device, l k RIS a The channel to the kth backscatter device, f k Indicates power station to RIS a channel, and represents the channel estimation value, ΔH k represents the channel estimation error, represents the throughput of the kth backscatter device, g u,k Indicates that the kth backscatter device passes through RIS b channel to the information receiver, and represents the channel estimation value, Δg u,k represents the channel estimation error, v b =[v b,1 ,…,v b,n ,…v b,N ] T RIS b The phase shift vector, RIS b The phase of the nth reflector unit, δ 2 is the background noise at the information receiver.

2. The dual RIS-assisted backscatter communication robust transmission method according to claim 1, characterized in that: In the equivalent convex optimization subproblem, the resource allocation problem with the optimization variables being the beamforming vector and the backscatter device transmit power is expressed as: in, is the slack variable, Represents R k The lower bound of I MN represents the (M×N)×(M×N)-dimensional unit diagonal matrix, I M represents the M×M dimensional unit diagonal matrix, d k =2Re{q k }-s k , 3. The dual RIS-assisted backscatter communication robust transmission method according to claim 1, characterized in that: In the equivalent convex optimization subproblem, the optimization variable is RIS a The phase-shifted resource allocation problem is expressed as: C 11 :|in a,n | 2 ≤1+a n+N C 12 :a≥0 Where a=[a1,…,a 2N ] T represents the slack variable, λ (l) is the regularization factor.

4. The dual RIS-assisted backscatter communication robust transmission method according to claim 1, characterized in that: In the equivalent convex optimization subproblem, the optimization variable is RIS b The phase-shifted resource allocation problem is expressed as: C 13 :V b,(n,n) =1 in, 5. The dual RIS-assisted backscatter communication robust transmission method according to claim 1, characterized in that: In the equivalent convex optimization subproblem, the resource allocation problem with time as the optimization variable is expressed as:

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