Wireless charging intelligent reflecting surface assisted wireless information and energy simultaneous transmission system parameter optimization method

Abstract

The invention discloses a parameter optimization method of a wireless charging intelligent reflecting surface assisted wireless information and energy simultaneous transmission system, and belongs to the technical field of wireless communication. The invention aims to solve the problem that the existing wireless information and energy simultaneous transmission system parameter design cannot realize high-efficiency operation of wireless information and energy simultaneous transmission. Comprising the following steps: establishing a non-convex initial optimization function based on active beam forming w, intelligent reflecting surface passive beam forming theta and a user power division ratio rho by taking a maximum weighting rate sum as a target; meanwhile, setting constraint conditions; performing Lagrange dual transformation on the non-convex initial optimization function, extracting a signal-to-noise ratio item by introducing an auxiliary variable, and then introducing the auxiliary variable to perform secondary transformation to obtain a decoupled optimization function; and performing iterative solution on the decoupled optimization function by using an alternate optimization method until the difference value of the weighted rate sums obtained in adjacent iteration results meets a preset precision value, and outputting a parameter optimization value. The method is used for parameter optimization of the wireless information and energy simultaneous transmission system.

Description

Technical Field

[0001] The invention relates to a method for optimizing parameters of a wireless charging intelligent reflective surface-assisted wireless signal energy simultaneous transmission system, and belongs to the technical field of wireless communications. Background Art

[0002] In recent years, IoT devices have been increasingly used in various fields of society, such as medical care, manufacturing, transportation, and smart homes, and the number of IoT devices has exploded. However, in special scenarios (toxic outdoor scenarios, implanted in the human body, sensors embedded in walls), it is impossible to connect to the power grid and the battery cannot be replaced frequently; at the same time, for a large number of IoT devices deployed, it takes a lot of time and a lot of work to manually replace the battery or charge it. Wireless information and energy transmission, the full name of which is simultaneous transmission of wireless information and energy, combines wireless information transmission and wireless energy transmission, and uses the physical properties of radio frequency signals that can carry both information and energy. It is considered to be an effective way to solve the large-scale deployment of the Internet of Things.

[0003] Power splitting technology is an effective receiver framework for achieving wireless signal-energy transmission. It divides the received energy into energy flow and information flow, which are used for energy collection and information decoding respectively.

[0004] Since the transmission loss of RF signals is very large, wireless signal transmission usually operates at a low efficiency. In recent years, smart reflective surfaces have attracted extensive attention from academia and industry. They are composed of a large number of low-cost reflective units. By deploying smart reflective surfaces, the receiving power of the receiver can be greatly improved.

[0005] In the current academic research on intelligent reflective surfaces to assist wireless signal-energy transmission, the intelligent reflective surfaces are powered by fixed cables, which greatly limits the deployment scenarios of intelligent reflective surfaces and brings additional deployment costs. Using wireless energy transmission technology to enable intelligent reflective surfaces to absorb energy from wireless signals emitted by transmitters to maintain their continuous operation is an effective method. However, how to optimize the base station active beamforming, the intelligent reflective surface dependency matrix, and the power split ratio is still an open research problem. Summary of the invention

[0006] In view of the problem that the existing parameter design of wireless signal energy simultaneous transmission system cannot enable the wireless signal energy simultaneous transmission to operate efficiently, the present invention provides a method for optimizing the parameters of a wireless signal energy simultaneous transmission system assisted by a wireless charging intelligent reflective surface.

[0007] A method for optimizing parameters of a wireless charging intelligent reflective surface-assisted wireless signal transmission system of the present invention comprises:

[0008] Based on active beamforming w, passive beamforming of smart reflective surface Θ and user power split ratio ρ, a non-convex initial optimization function is established with the weighted rate and maximum as the goal; at the same time, the optimization constraints of the maximum transmission power of the base station, the minimum energy requirement of the wireless charging smart reflective surface, the minimum energy requirement of a single user, the phase shift matrix of the wireless charging smart reflective surface and the power split threshold are set;

[0009] The Lagrange dual transformation is used for the non-convex initial optimization function. After extracting the signal-to-noise ratio term by introducing auxiliary variables, the auxiliary variables are introduced for secondary transformation to obtain the decoupled optimization function.

[0010] The decoupled optimization function is iteratively solved using the alternating optimization method. Each iteration includes:

[0011] Optimize the active beamforming w: fix all variables except the active beamforming w in the decoupled optimization function, remove the irrelevant items of the active beamforming w, redescribe the optimization problem of the active beamforming w, use the SCA technology to transform the re-described optimization problem from a non-convex problem to a convex problem, and then perform data processing to obtain the optimized value of the active beamforming w;

[0012] Optimize the passive beamforming Θ of the smart reflector: fix other variables except the passive beamforming Θ of the smart reflector in the decoupled optimization function, remove irrelevant items except the passive beamforming Θ of the smart reflector, redescribe the optimization problem of the passive beamforming Θ of the smart reflector, transform the re-described optimization problem from a non-convex problem to a convex problem using the SCA technology, and then perform data processing to obtain the optimized value of the passive beamforming Θ of the smart reflector;

[0013] Optimize the user power split ratio ρ: fix other variables except the user power split ratio ρ in the decoupled optimization function, remove the irrelevant items of the user power split ratio ρ, re-describe the optimization problem of the user power split ratio ρ, and calculate the optimal value of the user power split ratio ρ;

[0014] The back calculation is repeated until the difference between the weighted rate sums obtained in the adjacent iteration results meets the preset accuracy value, and the final optimized value of the active beamforming w, the final optimized value of the passive beamforming Θ of the smart reflector, and the final optimized value of the user power division ratio ρ are obtained.

[0015] According to the parameter optimization method of the wireless charging intelligent reflective surface assisted wireless signal and energy simultaneous transmission system of the present invention, during the wireless signal and energy simultaneous transmission process, the wireless charging intelligent reflective surface absorbs energy from the wireless signal sent by the base station and stores it for the continuous operation of the wireless charging intelligent reflective surface.

[0016] According to the parameter optimization method of the wireless charging intelligent reflective surface assisted wireless signal energy simultaneous transmission system of the present invention, the non-convex initial optimization function is described as problem (P1):

[0017]

[0018] Where R sum is the weighted rate sum, K is the number of single-antenna users, w k is the active beamforming vector corresponding to the kth single-antenna user, M t is the number of base station antennas, w=[w1,w2,w3,…w K ], represents the channel matrix between the base station and the wireless charging intelligent reflective surface, is a complex number domain, N is the number of reflectors in the wireless charging intelligent reflective surface, P max is the maximum transmission power of the base station, η irs ∈(0,1) is the energy conversion efficiency of the smart reflector, I is the unit matrix, ε irs is the minimum transmission power of the smart reflector, η k is the energy conversion efficiency of the kth single antenna user; ρ k is the power split ratio of the kth single antenna user, ρ=[ρ1,ρ2,ρ3,…ρ K ],g k represents the equivalent channel from the base station to the kth single-antenna user, ε k is the minimum transmission power of the kth single-antenna user, β n is the amplitude reflection coefficient of the smart reflector of the nth reflector, θ n The smart reflection surface phase shift of the nth reflection element, n∈[1,N].

[0019] According to the wireless charging intelligent reflective surface assisted wireless signal energy simultaneous transmission system parameter optimization method of the present invention, the weighted rate and R sum The calculation method is:

[0020] The signal received by the kth single-antenna user is expressed as y k :

[0021]

[0022] In the formula is the channel vector from the wireless charging smart reflective surface to the kth single antenna user, is the channel vector from the base station to the kth single-antenna user, and x is the signal sent by the base station:

[0023]

[0024] In the formula represents the data symbol transmitted by the kth single-antenna user, represents the Gaussian white noise of the kth single-antenna user; n k The variance of

[0025] The signal received by the k-th single-antenna user information decoding circuit is expressed as

[0026]

[0027] The signal y received by the kth single-antenna user k Press k The ratio of is distributed to the information decoding circuit of the kth single antenna user, according to 1-ρ k The proportion of is distributed to the energy receiving circuit of the kth single antenna user; represents the signal processing noise caused by phase shift and nonlinearity; For z k The variance of

[0028] Calculate the signal-to-noise ratio Ψ of the kth single-antenna user k :

[0029]

[0030] The transmission rate R of the kth single-antenna user k for:

[0031] R k =log2(1+Ψ k ),

[0032] Thus, the weighted rate and R are obtained sum :

[0033]

[0034] Where υ k is the rate weight of the kth single-antenna user.

[0035] According to the parameter optimization method of the wireless charging intelligent reflective surface assisted wireless signal energy simultaneous transmission system of the present invention, the calculation method of the energy absorbed by the wireless charging intelligent reflective surface is:

[0036] The signal y received by the wireless charging intelligent reflective surface is obtained based on the signal x sent by the base station irs :

[0037] y irs =Fx+n irs ,

[0038] In the formula Gaussian white noise for wireless charging intelligent reflective surface, n irs The variance of

[0039] make

[0040] β n ∈[0,1],θ n ∈[0,2π);

[0041] Ignoring the influence of noise, the energy received by the wireless charging smart reflective surface for:

[0042]

[0043] Signal energy reflected by the wireless charging smart reflective surface for:

[0044]

[0045] Then the wireless charging intelligent reflective surface absorbs energy P irs for:

[0046]

[0047] According to the wireless charging intelligent reflective surface assisted wireless signal energy simultaneous transmission system parameter optimization method of the present invention, the signal received by the k-th single antenna user energy receiving circuit is expressed as:

[0048]

[0049] Then the energy P received by the kth single-antenna user is k for:

[0050]

[0051] According to the wireless charging intelligent reflective surface assisted wireless signal energy simultaneous transmission system parameter optimization method of the present invention, the method for obtaining the decoupled optimization function is:

[0052] Use Lagrange dual transformation for non-convex initial optimization function and introduce auxiliary variables In the formula is a set of real numbers, u=[u1,u2,…,u K ], extract the signal-to-noise ratio term, and reformulate problem (P1) as problem (P2):

[0053]

[0054] Where M1(u,w,θ,ρ) is:

[0055]

[0056] Then perform a secondary transformation and introduce auxiliary variables l=[l1,l2,…,l K ], re-describe problem (P2) as problem (P3) as a decoupled optimization function:

[0057]

[0058] Where M2(u,l,w,θ,ρ) is:

[0059]

[0060] in

[0061] u k The optimal value of and l k The optimal value of By solving and get:

[0062]

[0063] Beneficial effects of the present invention: The method of the present invention fully utilizes system resources by jointly optimizing the active beamforming of the base station, the passive beamforming of the intelligent reflector and the user power split ratio, improves the efficiency of wireless information transmission and wireless energy transmission, and can effectively improve the information transmission rate. At the same time, the method of the present invention has strong adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a principle block diagram of the wireless charging intelligent reflective surface assisted wireless signal energy simultaneous transmission system of the present invention;

[0065] Figure 2 It is a graph showing the relationship between the weighted rate and the number of iterations in the verification experiment. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0067] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0068] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0069] Specific implementation method 1. Combination Figure 1 As shown, the present invention provides a method for optimizing parameters of a wireless charging intelligent reflective surface assisted wireless signal energy simultaneous transmission system, comprising:

[0070] Based on active beamforming w, passive beamforming of smart reflective surface Θ and user power split ratio ρ, a non-convex initial optimization function is established with the weighted rate and maximum as the goal; at the same time, the optimization constraints of the maximum transmission power of the base station, the minimum energy requirement of the wireless charging smart reflective surface, the minimum energy requirement of a single user, the phase shift matrix of the wireless charging smart reflective surface and the power split threshold are set;

[0071] The Lagrange dual transformation is used for the non-convex initial optimization function. After extracting the signal-to-noise ratio term by introducing auxiliary variables, the auxiliary variables are introduced for secondary transformation to obtain the decoupled optimization function.

[0072] The decoupled optimization function is iteratively solved using the alternating optimization method. Each iteration includes:

[0073] Optimize the active beamforming w: fix all variables except the active beamforming w in the decoupled optimization function, remove the irrelevant items of the active beamforming w, redescribe the optimization problem of the active beamforming w, use the SCA technology to transform the re-described optimization problem from a non-convex problem to a convex problem, and then perform data processing to obtain the optimized value of the active beamforming w;

[0074] Optimize the passive beamforming Θ of the smart reflector: fix other variables except the passive beamforming Θ of the smart reflector in the decoupled optimization function, remove irrelevant items except the passive beamforming Θ of the smart reflector, redescribe the optimization problem of the passive beamforming Θ of the smart reflector, transform the re-described optimization problem from a non-convex problem to a convex problem using the SCA technology, and then perform data processing to obtain the optimized value of the passive beamforming Θ of the smart reflector;

[0075] Optimize the user power split ratio ρ: fix other variables except the user power split ratio ρ in the decoupled optimization function, remove the irrelevant items of the user power split ratio ρ, re-describe the optimization problem of the user power split ratio ρ, and calculate the optimal value of the user power split ratio ρ;

[0076] The back calculation is repeated until the difference between the weighted rate sums obtained in the adjacent iteration results meets the preset accuracy value, and the final optimized value of the active beamforming w, the final optimized value of the passive beamforming Θ of the smart reflector, and the final optimized value of the user power division ratio ρ are obtained.

[0077] During the wireless signal transmission process, the wireless charging intelligent reflective surface absorbs energy from the wireless signal sent by the base station and stores it for the continuous operation of the wireless charging intelligent reflective surface.

[0078] The wireless signal transmission system constructed in this embodiment is as follows: Figure 1 As shown, the system is equipped with M t An access point with 4 antennas, a wireless charging smart reflective surface with N reflective elements, and K single-antenna users.

[0079] Furthermore, in the MISO system of wireless charging intelligent reflective surface assisted wireless signal transmission in this embodiment, the active beam forming of the access point is jointly optimized. Smart reflector passive beamforming θ and user power separation ratio The goal is to maximize the weighted sum of rates.

[0080] The non-convex initial optimization function is described as problem (P1):

[0081]

[0082] Where R sum is the weighted rate sum, K is the number of single-antenna users, w k is the active beamforming vector corresponding to the kth single-antenna user, M t is the number of base station antennas, w=[w1,w2,w3,…w K ], represents the channel matrix between the base station and the wireless charging intelligent reflective surface, is a complex number domain, N is the number of reflectors in the wireless charging intelligent reflective surface, P max is the maximum transmission power of the base station, η irs ∈(0,1) is the energy conversion efficiency of the smart reflector, I is the unit matrix, ε irs is the minimum transmission power of the smart reflector, η k is the energy conversion efficiency of the kth single antenna user; ρ k is the power split ratio of the kth single antenna user, ρ=[ρ1,ρ2,ρ3,…ρ K ],g k represents the equivalent channel from the base station to the kth single-antenna user, ε k is the minimum transmission power of the kth single-antenna user, β n is the amplitude reflection coefficient of the smart reflector of the nth reflector, θ n The smart reflection surface phase shift of the nth reflection element, n∈[1,N].

[0083] In problem (P1), constraint one constrains the maximum transmit power of the access point; constraint two ensures the minimum energy requirement of the smart reflector, constraint three ensures the minimum energy requirement of the user, constraint four represents the constraint of the smart reflector phase shift matrix, i.e., the passive beamforming θ of the smart reflector; constraint five limits the upper and lower limits of the power division ratio.

[0084] In this embodiment, the weighted rate and R sum The calculation method is:

[0085] The signal received by the kth single-antenna user is expressed as y k :

[0086]

[0087] In the formula is the channel vector from the wireless charging smart reflective surface to the kth single antenna user, is the channel vector from the base station to the kth single-antenna user, and x is the signal sent by the base station:

[0088]

[0089] In the formula represents the data symbol transmitted by the kth single-antenna user, represents the Gaussian white noise of the kth single-antenna user; n k The variance of

[0090] The signal received by the k-th single-antenna user information decoding circuit is expressed as

[0091]

[0092] The signal y received by the kth single-antenna user k Press k The ratio of is distributed to the information decoding circuit of the kth single antenna user, according to 1-ρ k The proportion of is distributed to the energy receiving circuit of the kth single antenna user; represents the signal processing noise caused by phase shift and nonlinearity; For z k The variance of

[0093] Calculate the signal-to-noise ratio Ψ of the kth single-antenna user k :

[0094]

[0095] The transmission rate R of the kth single-antenna user k for:

[0096] R k =log2(1+Ψ k ),

[0097] Thus, the weighted rate and R are obtained sum :

[0098]

[0099] Where υ k is the rate weight of the kth single-antenna user.

[0100] The calculation method of energy absorbed by the wireless charging intelligent reflective surface is:

[0101] The signal y received by the wireless charging intelligent reflective surface is obtained based on the signal x sent by the base station irs :

[0102] y irs =Fx+n irs ,

[0103] In the formula Gaussian white noise for wireless charging intelligent reflective surface, n irs The variance of

[0104] make

[0105] β n ∈[0,1],θ n ∈[0,2π);

[0106] Since the noise is very small compared to the signal strength, it can be ignored. Ignoring the influence of noise, the energy received by the wireless charging smart reflective surface for:

[0107]

[0108] Signal energy reflected by the wireless charging smart reflective surface for:

[0109]

[0110] Then the wireless charging intelligent reflective surface absorbs energy P irs for:

[0111]

[0112] The signal received by the kth single-antenna user energy receiving circuit is expressed as

[0113]

[0114] Then the energy P received by the kth single-antenna user is k for:

[0115]

[0116] Furthermore, the variable coupling in problem (P1) leads to a non-convex problem in the logarithmic sum, which makes the original problem difficult to solve. Therefore, the following optimization algorithm is proposed:

[0117] The method to obtain the decoupled optimization function is:

[0118] Use Lagrange dual transformation for non-convex initial optimization function and introduce auxiliary variables In the formula is a set of real numbers, u=[u1,u2,…,u K ], extract the signal-to-noise ratio term, and reformulate problem (P1) as problem (P2):

[0119]

[0120] Where M1(u,w,θ,ρ) is:

[0121]

[0122] Through the above operation, the signal-to-noise ratio term is extracted from the logarithm. However, the variables are still coupled together, and the problem is still non-convex. A secondary transformation is performed to introduce auxiliary variables l=[l1,l2,…,l K ], re-describe problem (P2) as problem (P3) as a decoupled optimization function:

[0123]

[0124] Where M2(u,l,w,θ,ρ) is:

[0125]

[0126] in At this point, the optimization variables have been decoupled and the problem is convex for any variable.

[0127] The variables u and l only appear in the objective function and not in the constraint variables, so u k The optimal value of and l k The optimal value of By solving and get:

[0128]

[0129] Going further, we use the alternating optimization method to solve problem (P3):

[0130] 1) The process of optimizing the active beamforming w includes:

[0131] In problem (P3), fix u, l, Θ, ρ, optimize w, and describe problem (P3) as problem (P4):

[0132]

[0133] Removing the terms in M2(u,l,w,Θ,ρ) that are not related to w, problem (P4) can be formulated as an optimization problem (P5) for active beamforming w:

[0134]

[0135] in

[0136] In problem (P5), constraints 2 and 3 are non-convex. The subproblem of optimizing w is still difficult to solve. Problem (P5) is transformed from a non-convex problem to a convex problem using SCA technology:

[0137] The second and third constraints in problem (P5) can be rewritten as:

[0138]

[0139] Taylor expansion of the above two equations yields:

[0140]

[0141] Where A=F H (I-θ H θ)F, Given the active beamforming vector of the kth single-antenna user, Then the second and third constraints in problem (P5) can be rewritten as:

[0142]

[0143] In the formula

[0144] definition Further describe problem (P5) as problem (P6):

[0145]

[0146] in

[0147] Problem (P6) is a convex problem, and the optimal value of active beamforming w is obtained by solving it using the CVX toolkit.

[0148] 2) The process of optimizing the passive beamforming θ of the smart reflector includes:

[0149] Fix u, l, w, ρ in problem (P3), optimize Θ, and describe problem (P3) as problem (P7):

[0150]

[0151] Will Substitute into M3(w,Θ,ρ,u,l) and remove the items that are not related to Θ. The optimization objective in problem (P7) is described as M5(Θ):

[0152]

[0153] in

[0154]

[0155] definition Rewrite constraint 1 in problem (P7) as:

[0156]

[0157] Similarly, definition and Rewrite the constraint in problem (P7) as:

[0158]

[0159] definition Λ=(X 1,1 ,…,X N,N ) T ,ξ k =(T k1,1 ,…,T kN,N ) T , r=Tr(Q) / N, and Describe problem (P7) as problem (P8):

[0160]

[0161] in

[0162] Taylor expansion of constraint 2 in problem (P8) yields:

[0163]

[0164] In the formula For the corresponding A given value of

[0165] Rewrite the constraint in problem (P8) as:

[0166]

[0167] in

[0168] Then, problem (P8) is transformed into a convex problem and the optimal value of Θ for passive beamforming of the smart reflector is obtained by solving it using the CVX toolkit.

[0169] 3) The process of optimizing the user power division ratio ρ includes:

[0170] In problem (P3), fix u, l, w, Θ, optimize ρ, remove the items in the optimization objective that are not related to ρ, and describe problem (P3) as problem (P9):

[0171]

[0172] In problem (P9), constraint 1 restricts ρ k Upper limit:

[0173]

[0174] In the formula is k The upper limit of

[0175] when Problem (P9) has a solution, and the optimization objective increases with ρ k Monotonically decreasing, we get ρ k The optimized value of:

[0176]

[0177] The iterative process of this embodiment can be described as:

[0178] The number of iterations is represented as T, the preset precision value is ζ, and the upper right subscript of the corresponding parameter represents the number of iterations;

[0179] Initialization: T = 0, ζ > 0, w (0) , Θ (0) ,ρ (0) ,

[0180] Repeat until

[0181] Update u by formula (1) (T+1) ,

[0182] Update l through formula (2)(T+1) ,

[0183] Update w by solving problem (P6) (T+1) ,

[0184] Update Θ by solving problem (P8) (T+1) ,

[0185] Update ρ by formula (3) (T+1) ,

[0186] Output: w (*) , Θ (*) ,ρ (*) .

[0187] Output w (*) , Θ (*) ,ρ (*) The final optimized value output for the final iteration.

[0188] Verification experiment:

[0189] To verify the convergence of the method of the present invention, the following simulation conditions are set: the base station position is (0m, 0m), the intelligent reflector position is (4m, 1m), and 4 single-antenna users are randomly distributed in a circle with a center of (4m, 1m) and a radius of 1m. It is assumed that all channels are Ruili channels. The number of base station antennas is 16, and the maximum transmission power of the base station is 10W. -50dBm, The minimum energy requirement of the smart reflector and the user is 0.5mW, and the energy conversion efficiency is 0.7.

[0190] The experimental results show that Figure 2 The relationship between the weighted rate and the number of iterations is shown in FIG. 1 , from which it can be seen that as the number of iterations increases, the weighted rate and convergence that the method of the present invention can achieve. After 15 rounds of iterations, the algorithm converges, proving the effectiveness of the method of the present invention.

[0191] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that the features described in conjunction with a single embodiment may be used in other described embodiments.

Claims

1. A method for optimizing parameters of a wireless charging intelligent reflective surface assisted wireless signal transmission system, characterized in that: include: Based on active beamforming w, passive beamforming of smart reflective surface Θ and user power split ratio ρ, a non-convex initial optimization function is established with the weighted rate and maximum as the goal; at the same time, the optimization constraints of the maximum transmission power of the base station, the minimum energy requirement of the wireless charging smart reflective surface, the minimum energy requirement of a single user, the phase shift matrix of the wireless charging smart reflective surface and the power split threshold are set; The Lagrange dual transformation is used for the non-convex initial optimization function. After extracting the signal-to-noise ratio term by introducing auxiliary variables, the auxiliary variables are introduced for secondary transformation to obtain the decoupled optimization function. The decoupled optimization function is iteratively solved using the alternating optimization method. Each iteration includes: Optimize the active beamforming w: fix all variables except the active beamforming w in the decoupled optimization function, remove the irrelevant items of the active beamforming w, redescribe the optimization problem of the active beamforming w, use the SCA technology to transform the re-described optimization problem from a non-convex problem to a convex problem, and then perform data processing to obtain the optimized value of the active beamforming w; Optimize the passive beamforming Θ of the smart reflector: fix other variables except the passive beamforming Θ of the smart reflector in the decoupled optimization function, remove irrelevant items except the passive beamforming Θ of the smart reflector, redescribe the optimization problem of the passive beamforming Θ of the smart reflector, transform the re-described optimization problem from a non-convex problem to a convex problem using the SCA technology, and then perform data processing to obtain the optimized value of the passive beamforming Θ of the smart reflector; Optimize the user power split ratio ρ: fix other variables except the user power split ratio ρ in the decoupled optimization function, remove the irrelevant items of the user power split ratio ρ, re-describe the optimization problem of the user power split ratio ρ, and calculate the optimal value of the user power split ratio ρ; The back calculation is repeated until the difference between the weighted rate sums obtained in the adjacent iteration results meets the preset accuracy value, and the final optimized value of the active beamforming w, the final optimized value of the passive beamforming Θ of the smart reflector, and the final optimized value of the user power division ratio ρ are obtained.

2. The method for optimizing parameters of a wireless charging intelligent reflective surface assisted wireless signal transmission system according to claim 1, characterized in that: During the wireless signal transmission process, the wireless charging intelligent reflective surface absorbs energy from the wireless signal sent by the base station and stores it for the continuous operation of the wireless charging intelligent reflective surface.

3. The method for optimizing parameters of a wireless charging intelligent reflective surface assisted wireless signal transmission system according to claim 2, characterized in that: The non-convex initial optimization function is described as problem (P1): Where R sum is the weighted rate sum, K is the number of single-antenna users, w k is the active beamforming vector corresponding to the kth single-antenna user, M t is the number of base station antennas, w=[w1,w2,w3,…w K ], represents the channel matrix between the base station and the wireless charging intelligent reflective surface, is a complex number domain, N is the number of reflectors in the wireless charging intelligent reflective surface, P max is the maximum transmission power of the base station, η irs ∈(0,1) is the energy conversion efficiency of the smart reflector, I is the unit matrix, ε irs is the minimum transmission power of the smart reflector, η k is the energy conversion efficiency of the kth single antenna user; ρ k is the power split ratio of the kth single antenna user, ρ=[ρ1,ρ2,ρ3,…ρ K ],g k represents the equivalent channel from the base station to the kth single-antenna user, ε k is the minimum transmission power of the kth single-antenna user, β n is the amplitude reflection coefficient of the smart reflector of the nth reflector, θ n The smart reflection surface phase shift of the nth reflection element, n∈[1,N].

4. The method for optimizing parameters of a wireless charging intelligent reflective surface assisted wireless signal transmission system according to claim 3, characterized in that: Weighted Rate and R sum The calculation method is: The signal received by the kth single-antenna user is expressed as y k : In the formula is the channel vector from the wireless charging smart reflective surface to the kth single antenna user, is the channel vector from the base station to the kth single-antenna user, and x is the signal sent by the base station: In the formula represents the data symbol transmitted by the kth single-antenna user, represents the Gaussian white noise of the kth single-antenna user; n k The variance of The signal received by the k-th single-antenna user information decoding circuit is expressed as The signal y received by the kth single-antenna user k Press k The ratio of is distributed to the information decoding circuit of the kth single antenna user, according to 1-ρ k The proportion of is distributed to the energy receiving circuit of the kth single antenna user; represents the signal processing noise caused by phase shift and nonlinearity; For z k The variance of Calculate the signal-to-noise ratio Ψ of the kth single-antenna user k : The transmission rate R of the kth single-antenna user k for: R k =log2(1+Ψ k ), Thus, the weighted rate and R are obtained sum : Where υ k is the rate weight of the kth single-antenna user.

5. The method for optimizing parameters of a wireless charging intelligent reflective surface assisted wireless signal transmission system according to claim 4, characterized in that: The calculation method of energy absorbed by the wireless charging intelligent reflective surface is: The signal y received by the wireless charging intelligent reflective surface is obtained based on the signal x sent by the base station irs : y irs =Fx+n irs , In the formula Gaussian white noise for wireless charging intelligent reflective surface, n irs The variance of make b n ∈[0,1],θ n ∈[0,2π); Ignoring the influence of noise, the energy received by the wireless charging smart reflective surface for: Signal energy reflected by the wireless charging smart reflective surface for: Then the wireless charging intelligent reflective surface absorbs energy P irs for:

6. The method for optimizing parameters of a wireless charging intelligent reflective surface assisted wireless signal transmission system according to claim 5, characterized in that: The signal received by the kth single-antenna user energy receiving circuit is expressed as Then the energy P received by the kth single-antenna user is k for:

7. The method for optimizing parameters of a wireless charging intelligent reflective surface assisted wireless signal transmission system according to claim 6, characterized in that: The method to obtain the decoupled optimization function is: Use Lagrange dual transformation for non-convex initial optimization function and introduce auxiliary variables In the formula is a set of real numbers, u=[u1,u2,…,u K ], extract the signal-to-noise ratio term, and reformulate problem (P1) as problem (P2): Where M1(u,w,θ,ρ) is: Then perform a secondary transformation and introduce auxiliary variables l=[l1,l2,…,l K ], re-describe problem (P2) as problem (P3) as a decoupled optimization function: Where M2(u,l,w,θ,ρ) is: in u k The optimal value of and l k The optimal value of By solving and get:

8. The method for optimizing parameters of a wireless charging intelligent reflective surface assisted wireless signal transmission system according to claim 7, characterized in that: The process of optimizing the active beamforming w includes: In problem (P3), fix u, l, Θ, ρ, optimize w, and describe problem (P3) as problem (P4): Removing the terms in M2(u,l,w,Θ,ρ) that are not related to w, problem (P4) can be formulated as an optimization problem (P5) for active beamforming w: in The problem (P5) is transformed from a non-convex problem to a convex problem using SCA technology: The second and third constraints in problem (P5) can be rewritten as: Taylor expansion of the above two equations yields: Where A=F H (I-θ H θ)F, Given the active beamforming vector of the kth single-antenna user, Then the second and third constraints in problem (P5) can be rewritten as: In the formula definition Further describe problem (P5) as problem (P6): in Problem (P6) is a convex problem, and the optimal value of active beamforming w is obtained by solving it using the CVX toolkit.

9. The method for optimizing parameters of a wireless charging intelligent reflective surface-assisted wireless signal transmission system according to claim 8, characterized in that: The process of optimizing the passive beamforming Θ of the smart reflector includes: Fix u, l, w, ρ in problem (P3), optimize Θ, and describe problem (P3) as problem (P7): Will Substitute into M3(w,Θ,ρ,u,l) and remove the items that are not related to Θ. The optimization objective in problem (P7) is described as M5(Θ): in definition Rewrite constraint 1 in problem (P7) as: Similarly, definition and Rewrite the constraint in problem (P7) as: definition Λ=(X 1,1 ,…,X N,N ) T ,ξ k =(T k1,1 ,…,T kN,N ) T , r=Tr(Q) / N, and Describe problem (P7) as problem (P8): in Taylor expansion of constraint 2 in problem (P8) yields: In the formula For the corresponding A given value of Rewrite the constraint in problem (P8) as: in Then, problem (P8) is transformed into a convex problem and the optimal value of Θ for passive beamforming of the smart reflector is obtained by solving it using the CVX toolkit.

10. The method for optimizing parameters of a wireless charging intelligent reflective surface assisted wireless signal transmission system according to claim 9, characterized in that: The process of optimizing the user power division ratio ρ includes: In problem (P3), fix u, l, w, Θ, optimize ρ, remove the items in the optimization objective that are not related to ρ, and describe problem (P3) as problem (P9): In problem (P9), constraint 1 restricts ρ k Upper limit: In the formula is k The upper limit of when Problem (P9) has a solution, and the optimization objective increases with ρ k Monotonically decreasing, we get ρ k The optimized value of:

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