A method for maximizing energy efficiency in an IRS-assisted MISO wireless power-carrying communication system

Abstract

This invention belongs to the field of wireless communication technology, specifically relating to an IRS-assisted method for maximizing the energy efficiency of a MISO wireless power-carrying communication system. The method includes: constructing a downlink channel model; constructing a mathematical model for maximizing system energy efficiency based on the downlink channel model; decoupling the base station beamforming and reflector phase shift parameters using an alternating iterative algorithm; transforming the original non-convex problem into a standard semidefinite programming form based on the decoupling results, and solving it using convex optimization tools; obtaining the optimal MISO wireless power-carrying communication system energy efficiency ratio when the beamforming vector and phase shift cause the total system energy efficiency to converge. This invention constructs a smart reflector-assisted MIMO wireless power-carrying communication system model and introduces an IRS system for communication assistance. It designs and analyzes an alternating optimization algorithm that jointly optimizes the transmit beamforming vector, the reflected beamforming vector, and the power allocation factor to maximize system energy efficiency.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a method for maximizing the energy efficiency of an IRS-assisted MISO wireless energy-carrying communication system. Background Technology

[0002] With the development of various communication technologies, the transmission rate of wireless networks has achieved a qualitative leap, such as massive MIMO and millimeter-wave communication technologies. However, the energy consumption and hardware cost of communication networks remain pressing issues. Compared to traditional 4G technology, while 5G communication improves information transmission efficiency, it also increases system energy consumption and network construction costs. In recent years, intelligent reflectors (IRS) have been proposed as a low-cost and energy-efficient method to address system energy efficiency issues and improve communication quality, attracting widespread attention. Unlike traditional reflector communication, intelligent reflectors can reflect signals by adjusting the phase shift of various components in real time through a controller. This reflected signal can both enhance the received power of the target user and weaken the received power of eavesdroppers, thereby improving system security. Therefore, research on intelligent reflectors is crucial.

[0003] In intelligent reflector communication systems, resource allocation can improve and enhance the transmission quality of user information by adjusting the base station's transmit beam and the phase shift of the reflector. First, energy efficiency, which simultaneously increases transmission rate and reduces power loss, is a crucial communication performance indicator. Second, the presence of eavesdroppers in real-world communication systems reduces user security in intelligent reflector systems. Finally, the randomness of wireless channels and severe channel attenuation result in weak energy and information reception by the receiver in a SWIPT system. Therefore, researching the maximization of the security energy efficiency ratio in IRS-assisted MISO wireless powered communication systems is of great significance. Summary of the Invention

[0004] This invention considers constraints such as user safety rate constraints, base station maximum transmit power, IRS beamforming matrix, and power allocation factor, and establishes a nonlinear, multivariate coupled resource allocation model with the objective of maximizing the safety and energy efficiency of a multi-input single-output cellular communication system. First, the Dinkelbach method is used to transform the fractional objective function into a form of subtraction of auxiliary variable parameters. Then, an alternating iterative algorithm is used to decouple the base station beamforming and reflector phase shift parameters. Finally, the original non-convex problem is transformed into a standard semidefinite programming form, and solved using convex optimization tools.

[0005] To achieve the above objectives, this invention proposes a method for maximizing the energy efficiency of an IRS-assisted MISO wireless power-carrying communication system, the method comprising:

[0006] S1: Construct a downlink channel model for an IRS-assisted MISO wireless power-carrying communication system;

[0007] S2: Construct a mathematical model based on maximizing system energy efficiency for the downlink channel model; construct a nonlinear, multivariable coupled energy efficiency-maximizing resource allocation algorithm for user security rate constraints, base station maximum transmit power constraints, continuous phase shift constraints, and power allocation factor constraints;

[0008] S3: The Dinkelbach method is used to transform the fractional objective function into a form of subtraction of auxiliary variable parameters, and an alternating iterative algorithm is used to decouple the base station beamforming and reflector phase shift parameters;

[0009] S4: Based on the decoupling results, the original non-convex problem is transformed into a standard semidefinite programming form and solved using convex optimization tools. When the beamforming vector and phase shift cause the total system energy efficiency to converge, the optimal energy efficiency ratio of the MISO wireless energy-carrying communication system is obtained.

[0010] Preferably, the downlink channel model of the IRS-assisted MISO wireless powered communication system includes: a single-antenna user, an eavesdropper, a multi-antenna base station, and a smart reflector; the system includes a downlink transmission mode, in which the receiver receives direct link signals and indirect link signals; the direct link refers to the base station directly transmitting the signal to the user receiver; the indirect link refers to the base station's transmitted signal being reflected to the receiver via the smart reflector.

[0011] Furthermore, the received signals from the user and the eavesdropper are as follows:

[0012]

[0013]

[0014] in, This indicates that the user receives the signal used for information decoding, where ρ represents the power allocation factor, and h... BU h represents the channel coefficient from the base station to the user. IU Let φ represent the channel coefficients from the smart reflector to the user, φ represent the channel matrix from the smart reflector to the eavesdropper, x represent the transmitted signal from the base station, and y represent the transmitted signal from the base station. Eve The signal received by the eavesdropper, h BE h represents the channel coefficient from the base station to the eavesdropper. IE Let n represent the channel matrix from the smart reflector to the eavesdropper. u Represents user noise power, n e This indicates the noise power of the eavesdropper.

[0015] The preferred expression for the energy-efficient resource allocation algorithm is:

[0016]

[0017] stR S ≥R min ,

[0018]

[0019] ||w|| 2 +Tr(V)≤P max ,

[0020] 0 < ρ < 1,

[0021]

[0022] Among them, R U P represents the user's information rate. t (w) represents the total power consumption of the system, w represents the beamforming vector matrix of the base station, v represents artificial noise, θ represents the phase shift of the intelligent reflector unit, ρ represents the power allocation factor, and R S R represents the user's confidentiality rate. min The minimum confidentiality rate for legitimate users, h u E represents the user's channel matrix. min Let Tr(V) represent the minimum energy received by the user, and let P represent the trace of matrix V. max θ represents the maximum transmit power of the base station. n This represents the phase shift of the nth reflecting unit of the smart reflector, where N represents the number of reflecting units in the smart reflector.

[0023] Preferably, the process of decoupling the base station beamforming and reflector phase shift parameters using an alternating iterative algorithm includes:

[0024] Step 1: Fix the phase shift matrix Φ and power allocation factor ρ of the smart reflector, and calculate the beamforming vectors w and v;

[0025] Step 2: Fix the beamforming vectors w and v and the power distribution factor ρ, and calculate the phase shift matrix Φ of the reflector surface;

[0026] Step 3: Fix the beamforming vectors w and v and the phase shift matrix Φ of the reflector, calculate the power distribution factor ρ, and obtain the optimal parameters w, v, Φ and ρ.

[0027] Furthermore, the process of calculating the beamforming vectors w and v includes: transforming the optimization objective into problem P2 given the phase shift Φ and power allocation factor ρ; defining the system energy efficiency η, transforming problem P2 into problem P3 using the Dinkelbach method, and obtaining the optimal beamforming vector w based on problem P3; defining W = ww HProblem P3 is transformed into problem P4. A semidefinite relaxation method is used to constrain problem P4, and the optimal W value is obtained by using the convex optimization toolbox.

[0028] Furthermore, calculating the power allocation factor ρ includes: transforming the original problem P1 into problem P7 based on the beamforming vectors w and v and the phase shift matrix Φ of the reflector, and simplifying problem 7; calculating the optimal value of the simplified problem 7, which is the optimal power allocation factor; the expression for the simplified problem 7 is:

[0029]

[0030]

[0031] ρTr(G U W)≥E min ,

[0032] 0 < ρ < 1.

[0033] γ U (w)=w H G U w

[0034]

[0035] γ E (w)=w H G E w

[0036]

[0037] in, Indicated by the intelligent reflection unit parameter q H Construct (N+1)×1 auxiliary variables, V U The signal-to-noise ratio of the user is represented by Tr(V), the trace of matrix V is represented by P. BS P represents the power consumption of the base station hardware. IRS P represents the hardware power consumption of the smart reflective surface. U E represents the power consumption of the mobile user terminal hardware, ρ represents the power allocation factor, and E represents the power consumption of the mobile user terminal hardware. min G represents the minimum energy received by the user. U This represents the ratio of the user channel transmission matrix to noise. Let V represent the m-th element of the phase shift matrix, Q represent the diagonal matrix format of the phase shift matrix, and V represent the element of the m-th element of the phase shift matrix. E The signal-to-noise ratio of the eavesdropper is represented, and Q(n,n) represents the nth element on the diagonal when the phase shift matrix is ​​a diagonal matrix.

[0038] The beneficial effects of this invention are:

[0039] This invention constructs a MIMO wireless energy-carrying communication system model assisted by a smart reflector and introduces an IRS system to assist in communication. It designs and analyzes an alternating optimization algorithm that jointly optimizes the transmit beamforming vector, the reflected beamforming vector, and the power allocation factor to maximize system energy efficiency. This invention employs SWIPT technology based on power allocation, solving the technical problem of requiring two different receivers for the energy receiver and the signal receiver. This allows users to simultaneously receive signals and store energy, and can utilize their stored energy to compensate for communication energy consumption to a certain extent, further improving the energy efficiency of the communication system. Simultaneously, this invention deploys a smart reflector into the communication system, thereby enabling reasonable configuration of the communication environment and achieving higher energy efficiency. Attached Figure Description

[0040] Figure 1 This is a model diagram of the IRS-assisted MISO wireless communication system of the present invention;

[0041] Figure 2 This is a flowchart of the algorithm of the present invention;

[0042] Figure 3 This is a schematic diagram showing the specific location of the IRS-assisted MISO wireless communication system of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] A method for maximizing energy efficiency in an IRS-assisted MISO wireless power-carrying communication system, such as... Figure 2 As shown, the method includes:

[0045] S1: Construct a downlink channel model for an IRS-assisted MISO wireless power-carrying communication system;

[0046] like Figure 1 As shown, this invention considers an IRS-assisted MISO wireless energy-carrying secure communication network model, consisting of a multi-antenna base station, a smart reflector, a single-antenna user, and an eavesdropper. The base station contains M antennas, the reflector contains N reflector elements, the single-antenna eavesdropper only needs to decode information, while the receiving user can perform signal decoding and energy harvesting.

[0047] Assuming the base station and the smart reflector, the user, and the eavesdropper, the channel coefficients between the smart reflector and the user, and the eavesdropper are respectively: Furthermore, all channels follow a small-scale Rayleigh fading model. Assume each channel has a unit bandwidth. Define the phase shift matrix. in This represents the increase and phase shift of the reflection coefficient of the i-th unit of the IRS. For convenience, β i The value is typically 1, indicating that the maximum reflection gain is obtained.

[0048] To protect data transmission from eavesdropping and to improve the user's acquisition efficiency, the BS sends the information signal and the AN signal together to the IRS. Therefore, the signal transmitted from the BS can be represented as: in This represents the base station transmit beamforming vector. Let E(ss) be the signal that user k expects to receive, and let E(ss) be the signal that user k expects to receive. H ) = 1, It is a pseudo-random AN vector generated by BS. Assume that v is modeled as a random vector with CSCG distributions, i.e., it satisfies

[0049] S2: Construct a mathematical model based on maximizing system energy efficiency for the downlink channel model; construct a nonlinear, multivariable coupled energy efficiency-maximizing resource allocation algorithm for user security rate constraints, base station maximum transmit power constraints, continuous phase shift constraints, and power allocation factor constraints.

[0050] At the user's location, using a power allocation strategy, the signals received by the user and the eavesdropper are as follows:

[0051]

[0052]

[0053] in, This indicates that the user receives the signal used for information decoding, where ρ represents the power allocation factor, and h... BU h represents the channel coefficient from the base station to the user. IU Let φ represent the channel coefficients from the smart reflector to the user, φ represent the channel matrix from the smart reflector to the eavesdropper, x represent the transmitted signal from the base station, and y represent the transmitted signal from the base station. Eve The signal received by the eavesdropper, h BE h represents the channel coefficient from the base station to the eavesdropper. IE Let n represent the channel matrix from the smart reflector to the eavesdropper. u Represents user noise power, n e This indicates the noise power of the eavesdropper.

[0054] R S The achievable confidentiality rate for the user is denoted as R. S =RU -R E If you remember The information rates for the user and the eavesdropper are respectively:

[0055]

[0056]

[0057] The energy efficiency maximization method for IRS-assisted MISO wireless power-carrying communication systems is characterized by establishing a mathematical model based on system energy efficiency maximization for the downlink channel model. Therefore, the resource allocation problem for energy efficiency maximization can be expressed as:

[0058]

[0059] stR S ≥R min ,

[0060]

[0061] ||w|| 2 +Tr(V)≤P max ,

[0062] 0 < ρ < 1,

[0063]

[0064] Among them, R min It is the minimum speed. E min This represents the minimum energy received by the user. The total energy received by the system is represented as: P t =ζ||w|| 2 +Tr(V)+P BS +P IRS +P U Where ζ represents the reciprocal of the drain efficiency of the transmit power amplifier, ζ∈[0,1] and is usually taken as ζ=1, P BS For base station hardware power consumption, P U For mobile user terminal hardware power consumption, P IRS =NP n (b) represents the hardware power consumption of the intelligent reflective surface, P n (b) represents the power consumption of each bit-bit resolution unit. This paper does not consider resolution optimization. max That is the maximum transmission power of the base station.

[0065] S3: The Dinkelbach method is used to transform the fractional objective function into a form of subtraction of auxiliary variable parameters, and an alternating iterative algorithm is used to decouple the base station beamforming and reflector phase shift parameters.

[0066] Since there is a strong coupling relationship between the beamforming vector of the base station and the phase shift of the smart reflector, an alternating iterative algorithm is used to solve the problem. The basic idea is as follows: 1) First, fix the phase shift matrix Φ of the smart reflector and the power allocation factor ρ to find the beamforming vectors w and v; 2) Then, fix the beamforming vectors w, v and the power allocation factor ρ to find the phase shift matrix Φ of the reflector; 3) First, fix the beamforming vectors w, v and the phase shift matrix Φ of the reflector to find the power allocation factor ρ.

[0067] Step 1: First, fix the phase shift matrix Φ of the smart reflector and the power distribution factor ρ to calculate the beamforming vectors w and v.

[0068] Given the phase shift Φ and the power distribution factor ρ, the original problem can be reformulated as:

[0069]

[0070]

[0071]

[0072] ||w|| 2 +Tr(V)≤P max .

[0073] γ U (w)=w H G U w

[0074]

[0075] γ E (w)=w H G E w

[0076]

[0077] in, Indicated by the intelligent reflection unit parameter q H Construct (N+1)×1 auxiliary variables, V U The signal-to-noise ratio of the user is represented by Tr(V), the trace of matrix V is represented by P. BS P represents the power consumption of the base station hardware. IRS P represents the hardware power consumption of the smart reflective surface. U E represents the power consumption of the mobile user terminal hardware, ρ represents the power allocation factor, and E represents the power consumption of the mobile user terminal hardware. minG represents the minimum energy received by the user. U This represents the ratio of the user channel transmission matrix to noise. Let V represent the m-th element of the phase shift matrix, Q represent the diagonal matrix format of the phase shift matrix, and V represent the element of the m-th element of the phase shift matrix. E The signal-to-noise ratio of the eavesdropper is represented, and Q(n,n) represents the nth element on the diagonal when the phase shift matrix is ​​a diagonal matrix.

[0078] Since problem P2 is still nonconvex, based on the Dinkelbach method, the fractional objective function is equivalently transformed into a parameter subtraction form. Defining the system energy efficiency η, problem P2 can then be transformed into:

[0079]

[0080]

[0081]

[0082] ||w|| 2 +Tr(V)≤P max .

[0083] Given η, the optimal beamforming vector w can be obtained from problem (3).

[0084] Define W = ww H The above equation can be transformed into a standard SDP problem, as shown below:

[0085]

[0086]

[0087] ρTr(G U W)≥E min ,

[0088] Tr(W) + Tr(V) ≤ P max ,

[0089] Rank(W) = 1.

[0090] Here, Tr(W) represents the trace of matrix A, and Rank(W) represents the rank of matrix A. Since the constraint that Rank(W) = 1 makes the above problem difficult to solve feasiblely, a semidefinite relaxation method is used to handle this constraint. Then, the above problem can be directly solved using the convex optimization toolbox to obtain the optimal value of W.

[0091] Step 2: Fix the beamforming vectors w and v and the power distribution factor ρ to calculate the phase shift matrix Φ of the reflector.

[0092] Based on the w and v obtained from the above equation, the phase shift matrix Φ is solved. Let... but:

[0093]

[0094]

[0095] Introducing auxiliary variables At this point, the original problem P1 can be rewritten as:

[0096]

[0097]

[0098] ρTr(G U W)≥E min ,

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] By introducing And if Rank(Q) = 1, then problem P5 can be converted into the standard SDP form:

[0105]

[0106]

[0107] ρTr(QV U )≥E min ,

[0108]

[0109] Rank(Q) = 1.

[0110] Step 3: First, fix the beamforming vectors w and v and the phase shift matrix Φ of the reflector and calculate the power distribution factor ρ.

[0111] When the beamforming vectors w and v and the phase shift matrix Φ of the reflector are fixed, the original problem P1 can be rewritten as follows:

[0112]

[0113]

[0114]

[0115] 0 < ρ < 1.

[0116] It can be simplified to:

[0117]

[0118]

[0119] ρTr(G U W)≥E min ,

[0120] 0 < ρ < 1.

[0121] γ U (w)=w H G U w

[0122]

[0123] γ E (w)=w H G E w

[0124]

[0125] Where ρ represents the power allocation factor, Tr(A) represents the trace of matrix A, ζ represents the reciprocal of the drain efficiency of the transmit power amplifier, w represents the beamforming vector matrix of the base station, V represents the variance of artificial noise, and P BS P represents the power consumption of the base station hardware. IRS P represents the hardware power consumption of the smart reflective surface. U E represents the power consumption of the mobile user terminal hardware. min γ represents the minimum energy received by the user. U (w) represents the user's signal-to-noise ratio, h u The channel matrix of the user, h e The channel matrix representing the eavesdropper. Represents user noise, γ E (w) represents the signal-to-noise ratio of the eavesdropper.

[0126] The optimization problem regarding reflected beamforming also becomes a convex problem. Problem P6, like problem P4, obtains its optimal solution using the same method. However, after relaxing the rank-one constraint using SDR, the resulting solution is not a rank-one solution, such as Rank(Q) ≠ 1. Therefore, a rank-one solution can be obtained through singular value decomposition. Let Fi be the objective function value for the i-th iteration of problems P4, P6, and P8. i1 F i 2 and F i 3 The energy efficiency value of the i-th iteration is represented by η. i Therefore, the energy efficiency maximization algorithm based on alternating iteration can be summarized as shown in Table 1.

[0127] Table 1. Energy efficiency maximization algorithm based on alternating iteration

[0128]

[0129] like Figure 3 As shown, assume the base station has 2 antennas, the smart reflector has 4 array sources, and the system uses unit bandwidth. The path loss from the base station to the user and the eavesdropper is 2 and 3, respectively; the path loss from the base station to the reflector, and from the reflector to the user and the eavesdropper are 2.2, 2.2, and 3, respectively. Assume the base station and the smart reflector are on the same horizontal plane, as are the user and the eavesdropper. The vertical distance between the two horizontal planes is 4 meters, the distance from the base station to the reflector is 10 meters, and the horizontal distance from the base station to the eavesdropper is 8 meters.

[0130] Other simulation parameters are given in Table 2:

[0131] Table 2 Parameter Configuration Table

[0132]

[0133] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for maximizing energy efficiency of an IRS-assisted MISO wireless power communication system, the method comprising: include: S1: Construct a downlink channel model for an IRS-assisted MISO wireless power-carrying communication system; S2: Construct a mathematical model based on maximizing system energy efficiency for the downlink channel model; construct a nonlinear, multivariable coupled energy efficiency-maximizing resource allocation algorithm to address user security rate constraints, base station maximum transmit power constraints, continuous phase shift constraints, and power allocation factor constraints; the expression for the energy efficiency-maximizing resource allocation problem is: ； in, Indicates the user's information rate. This represents the total power consumption of the system. This represents the beamforming vector matrix of the base station. Indicates artificial noise. This indicates the phase shift of the intelligent reflective surface's reflective unit. Indicates the power allocation factor. Indicates the user's confidentiality rate. Indicates the minimum confidentiality rate for legitimate users. The channel matrix representing the user, This represents the minimum energy received by the user. Representation matrix traces, This indicates the maximum transmit power of the base station. This represents the phase shift of the nth reflecting unit of the smart reflector, where N represents the number of reflecting units in the smart reflector. S3: The Dinkelbach method is used to transform the fractional objective function into a form of subtraction of auxiliary variable parameters, and an alternating iterative algorithm is used to decouple the base station beamforming and reflector phase shift parameters; The process of decoupling base station beamforming and reflector phase shift parameters using an alternating iterative algorithm includes: Step 1: Fix the phase shift matrix of the smart reflector and power allocation factor Calculate the beamforming vector and ; Step 2: Fix the beamforming vector , and power allocation factor Calculate the phase shift matrix of the reflecting surface. ; Calculate the phase shift matrix of the reflecting surface Includes: constructing auxiliary variables Based on the auxiliary variables, the original problem P1 is transformed into problem P5, where the expression for problem P5 is: ； Introduction and Problem P5 can then be transformed into the standard SDP form, which is problem P6. Solving problem P6 yields the phase shift matrix of the reflecting surface. The expression for question P6 is: ； in, Indicated by intelligent reflection unit parameters Construct (N+1)×1 auxiliary variables, Indicates the user's signal-to-noise ratio. Describe the trace of matrix V. Indicates the power consumption of the base station hardware. This indicates the hardware power consumption of the smart reflective surface. Indicates the power consumption of the mobile user terminal hardware. This represents the minimum energy received by the user. This represents the ratio of the user channel transmission matrix to noise. This represents the m-th element of the phase shift matrix. The diagonal matrix format representing the phase shift matrix. Indicates the signal-to-noise ratio of the eavesdropper. This represents the nth element on the diagonal when the phase shift matrix is ​​a diagonal matrix; Step 3: Fix the beamforming vector , Phase shift matrix of the reflecting surface Calculate the power allocation factor To obtain the optimal parameters , , as well as ; S4: Based on the decoupling results, the original non-convex problem is transformed into a standard semidefinite programming form and solved using convex optimization tools. When the beamforming vector and phase shift cause the total system energy efficiency to converge, the optimal energy efficiency ratio of the MISO wireless energy-carrying communication system is obtained.

2. The method for maximizing energy efficiency in an IRS-assisted MISO wireless power-carrying communication system according to claim 1, characterized in that, The downlink channel model of the IRS-assisted MISO wireless power-carrying communication system includes: a single-antenna user, an eavesdropper, a multi-antenna base station, and a smart reflector. The system employs downlink transmission, and its receiver receives both direct link and indirect link signals. The direct link refers to the base station directly transmitting signals to the user receiver. The indirect link refers to the base station's transmitted signals being reflected to the receiver via the smart reflector.

3. The method for maximizing energy efficiency in an IRS-assisted MISO wireless power-carrying communication system according to claim 2, characterized in that, The signals received by the user and the eavesdropper are as follows: ； ； in, This indicates the signal received by the user. Indicates the power allocation factor. This represents the channel coefficient from the base station to the user. This represents the channel coefficient from the smart reflector to the user. This represents the channel matrix from the smart reflector to the eavesdropper. This indicates the base station's transmitted signal. This indicates the signal received by the eavesdropper. This represents the channel coefficient from the base station to the eavesdropper. This represents the channel matrix from the smart reflector to the eavesdropper. Indicates user noise power. This indicates the noise power of the eavesdropper.

4. The method for maximizing energy efficiency in an IRS-assisted MISO wireless power-carrying communication system according to claim 1, characterized in that, Calculate beamforming vector and The process includes: at a given phase shift and power allocation factor In this case, the optimization objective is transformed into problem P2; the system energy efficiency is defined. Problem P2 is transformed into problem P3 using the Dinkelbach method, and the optimal beamforming vector is obtained based on problem P3. ;definition Problem P3 is transformed into problem P4. A semidefinite relaxation method is used to constrain problem P4, and the optimal solution is obtained using the convex optimization toolbox. value.

5. The method for maximizing energy efficiency in an IRS-assisted MISO wireless power-carrying communication system according to claim 1, characterized in that, Calculate the power allocation factor Includes: based on beamforming vector , Phase shift matrix of the reflecting surface Transform the original problem P1 into problem P7, and simplify problem 7; calculate the optimal value of the simplified problem 7, which is the optimal power allocation factor; the simplified expression for problem 7 is: ； ； ； ； ； in, These are intermediate parameters. This represents the ratio of the user's channel transmission matrix to noise. This represents the ratio of the eavesdropper's channel transmission matrix to noise. Indicates the power allocation factor. Representation matrix traces, It represents the reciprocal of the drain efficiency of the transmit power amplifier. This represents the beamforming vector matrix of the base station. The variance of artificial noise is represented. Indicates the power consumption of the base station hardware. This indicates the hardware power consumption of the smart reflective surface. Indicates the power consumption of the mobile user terminal hardware. This represents the minimum energy received by the user. Indicates the user's signal-to-noise ratio. The channel matrix representing the user, The channel matrix representing the eavesdropper. Indicates user noise. This indicates the signal-to-noise ratio of the eavesdropper.

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