CN114466390B - SWIPT system performance optimization method and system based on intelligent reflector assistance - Google Patents
SWIPT system performance optimization method and system based on intelligent reflector assistance Download PDFInfo
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Abstract
The invention discloses a SWIPT system performance optimization method and system based on intelligent reflecting surface assistance, which are characterized in that information is decoded and energy is collected through a power divider, RIS is introduced to compensate serious path loss and shadow fading, and IGS is used to compensate the loss caused by unavoidable asymmetric hardware distortion; the goal is to maximize the minimum achievable rate for the user and the weighted rate energy domain by jointly optimizing the active and passive beamforming vectors and the power allocation coefficients under the constraints of the AP power budget and the energy quality of service requirements for the user. The method adopts an asymmetric Gaussian signal scheme to compensate the influence of HWI and the interference among users, obviously improves the maximum and minimum rates, and solves the problem of performance optimization of the RIS-assisted SWIPT system with HWI.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a SWIPT system performance optimization method and system based on intelligent reflection surface assistance.
Background
Reconfigurable smart surfaces (RIS) are distinguished in new technologies by their unique low cost, low energy consumption, programmable and easy to deploy features. RIS is an artificial electromagnetic surface structure with programmable electromagnetic properties. The RIS is composed of a large number of passive reflective elements, each of which is a reconfigurable scatterer (e.g., positive Intrinsic Negative (PIN) diodes, varactor tuned resonators, liquid crystal, and microelectromechanical systems (MEMS) technology). By means of a central controller, each reflecting element can independently control the amplitude and phase of the incident electromagnetic wave (i.e. add different time delays to the incident wave). Unlike conventional massive MIMO, backscatter, amplification and conversion relay, etc., the presence of RIS provides a controllable, customizable link for the communication system, which is easy to deploy on the surface of various buildings.
In addition, energy co-transmission (Simultaneous Wireless Information and Power TRANSFER SWIPT) is attracting more and more attention in view of the ubiquitous wireless connection and stable, permanent energy demands in the future. Wireless power transmission is inefficient due to severe large-scale fading. Although beamforming gain brought by large-scale Multiple-Input Multiple-Output (MIMO) technology can meet the transmission rate and energy requirements of users, the technology also brings challenges of excessive energy consumption, increased complexity of transceivers and the like, and the cost performance of the beamforming gain is far lower than that of the RIS technology. However, due to factors such as ambient temperature, humidity, hardware aging, etc., hardware interrupts (Hardware Interrupt HWI) do exist in a wide range of wireless communication systems, such as phase noise, digital-to-analog converters/analog-to-digital converters, etc. Converter (DAC/ADC) quantization noise, power Amplifier (PA), low Noise Amplifier (LNA), low Pass Filter (LPF), and Automatic Gain Control (AGC) nonlinearity, in-phase/quadrature-phase imbalance (IQI)), and the like.
At present, relatively perfect hardware distortion models and corresponding compensation algorithms are not considered for relevant work in the research on information and energy simultaneous transmission, and energy users and information users in SWIPT systems are mostly modeled as distributed deployment types, so that discussion analysis on power split in mixed users is absent. In addition, for the problem of phase shift optimization of RIS, no effective method for solving the constant mode constraint and the discrete phase shift constraint exists in the current scientific research work.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a SWIPT system performance optimization method and system based on intelligent reflection surface assistance, which take the influence of asymmetric HWI in a transceiver into consideration, adopts an asymmetric Gaussian signal (IGS) scheme to compensate the influence of HWI and inter-user interference, and solves the problem of performance optimization of the SWIPT system with the HWI assistance in the prior art.
The invention adopts the following technical scheme:
A SWIPT system performance optimization method based on intelligent reflecting surface assistance comprises the following steps:
s1, a wireless access point AP transmits an asymmetric Gaussian IGS signal influenced by hardware loss HWI after wide linear transformation through a direct link and an indirect link passing through a reconfigurable intelligent surface RIS;
S2, enabling the asymmetric Gaussian IGS signals to reach user equipment through a channel, dividing the signals into two parts by the user equipment through a power divider, and respectively carrying out information decoding and energy collection;
S3, maximizing the minimum achievable rate of all user equipment in the step S2 by optimizing the power splitting coefficient of the power splitter in the step S2, the wide linear precoding vector of the wireless access point AP in the step S1 and the passive reflection coefficient of the reconfigurable intelligent surface RIS, and under the power constraint of the wireless access point AP, the phase shift constraint of the reflection element and the QoS constraint of the energy collection in the step S2;
S4, generating a feasible initial point of the wide linear precoding vector in the step S3 by using a maximum ratio transmission scheme, iterating the minimum achievable rate obtained in the step S3 to enable a transmission signal to be a common PGS, randomly initializing the value of the power splitting coefficient in the step S3 according to the minimum energy collection threshold value, and randomly generating a reflection coefficient initial value of the reconfigurable intelligent surface RIS in a feasible domain;
s5, dividing the variable into variable blocks according to the wide linear precoding vector, the coupling relation between the passive reflection coefficient of RIS and the power splitting coefficient and the influence weight on the optimization target And/> For a wide linear precoding vector of a wireless access point AP, θ is the passive reflection coefficient of the reconfigurable intelligent surface RIS,/>For the power splitting coefficient of the power splitter, according to the initial value of the reflection coefficient generated in the step S4, a double-layer alternating iterative optimization method is used for alternately optimizing the variable block/>And/>Until the objective function in the step S3 is converged, the user rate optimization of the SWIPT system is completed;
S6, optimizing the weighted rate energy domain maximization problem of the SWIPT system according to the double-layer alternate iteration optimization method and the corresponding decoupling method in the step S5 based on the total power constraint and the reflection coefficient constant modulus constraint of the wireless access point AP.
Specifically, in step S1, the wireless access point AP is equipped with M transmitting antennas to serve K single-antenna users, and the reconfigurable intelligent surface RIS is composed of N reflecting elements; the user set is expressed asThe channel information of all channels is known at the radio access point AP.
Specifically, step S1 specifically includes:
s101, modeling a direct channel from a wireless Access Point (AP) to a user as a Rayleigh channel according to the deployment characteristic of the RIS, and modeling a reflection channel passing through a reconfigurable intelligent surface RIS as a rice channel, wherein the reconfigurable intelligent surface RIS comprises discrete phase shift and continuous phase shift;
s102, performing wide linear precoding on user signals at a wireless Access Point (AP) to generate equivalent baseband IGS signals Generating a transmitting signal after passing through a radio frequency module with lossThe method comprises the following steps:
Where Λ 1 is the amplitude distortion at the AP end, Λ 2 is the rotation error at the AP end, D T is the additive distortion noise, which is the conjugate of the baseband signal.
Further, the feasible set of reflected phase shifts ψ is expressed as:
when the phase shift value of each reflective element of the reconfigurable intelligent surface RIS can be continuously adjusted, the viable set of reflective phase shifts ψ is expressed as:
where L is the accuracy of the reflected phase shift and m is the phase shift index.
Specifically, in step S2, the information decoding signal y k received by the kth user is:
Mutual information between information decoding signals y k and s k of kth user Is that
Wherein χ 1,k is the receiving end amplitude distortion, ρ k is the power splitting coefficient of the kth user, y r,k is the kth user received signal, d R,k is the user equipment end equivalent additive noise, χ 2,k is the receiving end rotation distortion, I 2 is the unit diagonal matrix of 2 x2, Γ k is the equivalent amplitude of the signal, w k is the wide linear precoding vector of the kth user, w j is the wide linear precoding vector of the jth user,Is equivalent noise;
the radio frequency power E k received by the kth user is:
Wherein, For the desired sign, xi k(wj, θ) is the equivalent amplitude of the received radio frequency signal,/>W j is the wide linear precoding vector for the j-th user;
The energy ω k collected by user k is:
Where U is the maximum energy that a user may collect when the diode rectifier circuit breaks down in reverse, and a and b are constants.
Specifically, in step S3, the optimization problem is specifically:
P1
s.t.ωk≥E-1(emin),k∈κU
[θ]n∈Ψ,n∈{1,…N}
0≤ρk≤1,k∈κU
Wherein, kappa U is the set where the user is located, For a wide linear precoding vector, [ θ ] n is the nth term in the reflection coefficient vector, RIS has N L reflection elements in total, P is the maximum power provided by the base station, E -1(emin) is the inverse of the energy harvesting function E (E min).
Specifically, in step S4, the feasible initial points w 1′,k and w' 2,k of the precoding vector are:
Wherein H k is an equivalent channel from the AP to the user k, H is conjugate transpose conversion, and 0 M is an all-zero vector of M dimensions.
Specifically, in step S5, PS coefficients are combined and precoding optimizedThe method comprises the following steps:
P2
ωk≥E-1(emin),k∈κU
0≤ρk≤1,k∈κU
Wherein γ is a real variable;
The joint PS coefficients and passive beamforming optimization are specifically:
P3
s.t.|[θ]n|≤1,n∈{1,…N}
s.t.rk′(θ,ρk)≥2γ,k∈κU
0≤ρk≤1,k∈κU
Wherein eta is a punishment coefficient, theta is a norm of the reflection coefficient vector, n is the index of the reflective element;
the achievable rates for any user always satisfy the following relationship:
Wherein, And/>Rate expressions in the (1+1) th and the (l) th iteration processes respectively;
the overall complexity of the alternate iterative optimization method is expressed as:
Wherein, For time complexity, N is the number of reflective elements, K is the number of users, M is the number of transmit antennas, and I 0、I1 and I 2 represent the number of iterations required for P2, P4, and P1 convergence, respectively.
Specifically, in step S6, the optimization problem of the ticket rate energy domain is:
P8
[θ]n∈Ψ,n∈{1,…N}
0≤ρk≤1,k∈κU
Wherein γ 1 is a real number optimization variable, r k' is a rate expression, k is a user index, and λ 2＝1-λ1 is a weight factor.
Another technical solution of the present invention is a RIS-assisted swit system performance optimization system for HWI comprising:
the wireless access point AP transmits an asymmetric Gaussian IGS signal influenced by hardware loss HWI after wide linear transformation through a direct link and an indirect link passing through a reconfigurable intelligent surface RIS;
The distribution module is used for enabling the asymmetric Gaussian IGS signals to reach the user equipment through the channel, and the user equipment divides the signals into two parts by utilizing the power divider to respectively perform information decoding and energy collection;
The processing module maximizes the minimum achievable rate of all user equipment in the distribution module by optimizing the power splitting coefficient of the power splitter in the distribution module, the wide linear precoding vector of the wireless access point AP in the transmission module and the passive reflection coefficient of the reconfigurable intelligent surface RIS;
The initialization module is used for generating a feasible initial point of the wide linear precoding vector in the processing module by using a maximum ratio transmission scheme, iterating the minimum achievable rate obtained by the processing module to enable a sending signal to be a common PGS, randomly initializing the value of a power splitting coefficient in the processing module according to the minimum energy collection threshold value, and randomly generating a reflection coefficient initial value of a reconfigurable intelligent surface RIS in a feasible domain;
The first optimization module divides the variable into variable blocks according to the coupling relation among the wide linear precoding vector, the passive reflection coefficient of RIS and the power splitting coefficient and the influence weight on the optimization target And/> For a wide linear precoding vector of a wireless access point AP, θ is the passive reflection coefficient of the reconfigurable intelligent surface RIS,/>For the power splitting coefficient of the power splitter, according to the initial value of the reflection coefficient generated by the initialization module, a double-layer alternate iterative optimization method is used for alternately optimizing the variable block/>And/>Until the objective function in the processing module converges, the user rate optimization of the SWIPT system is completed;
And the second optimization module optimizes the weighted rate energy domain maximization problem of the SWIPT system according to the double-layer alternate iteration optimization method of the first optimization module and a corresponding decoupling method based on the total power constraint and the reflection coefficient constant modulus constraint of the wireless access point AP.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention relates to a method for optimizing the performance of a RIS-assisted SWIPT system with HWI, which considers the asymmetric characteristics brought by hardware defects (such as IQI, phase noise, DAC/ADC quantization noise and the like) in a radio frequency module at a receiving and transmitting end, rather than modeling the asymmetric characteristics as additive noise related to transmitting power as a traditional model, adopts an IGS scheme to compensate signal asymmetry brought by the IQI, and maximizes a user rate by jointly optimizing an active wide linear precoding vector at an AP, a passive reflection coefficient at the RIS and a power division coefficient at a user by constraint of energy and total power at user equipment, and provides an iterative optimization algorithm TIAO which is more effective than the traditional alternative optimization algorithm according to a punishment-based method and an SCA technology. Furthermore, the invention can also be used to handle the case of RIS and split sliding systems with discrete phase shifts. Weighted rate energy domain optimization issues are also considered to improve both achievable rate and harvest energy. Through simulation, it is noted that the combination of RIS and IGS can effectively compensate for hardware distortion and interference and can significantly increase the maximum-minimum rate.
Further, the wireless access point AP is equipped with M transmitting antennas to serve K single antenna users, and the RIS is composed of N reflecting elements; the user set is expressed asThe focus of the present application is to study IGS and RIS for the maximum performance gain that is brought about by the swit system with HWI, so it is assumed that the CSI of all channels at the AP is completely known, which can be obtained by various existing channel estimation methods.
Further, the user equipment can decode information and collect energy through the power splitter to meet different requirements. The appropriate gaussian signals s k -CN (0, 1) are made to represent the required data symbols, and in order to obtain the required IGS signals to compensate for hardware distortion, the signals are subjected to wide linear transformation, the IGS signals further promote the reachable rate of the user, reduce the influence caused by the interference of the user and the limitation of the equipment, can be used for compensating the I/Q imbalance at the base station, and are also beneficial to counteracting the influence of the interference signals at the information user receiver.
Further, a viable set of phase shift values is denoted by ψ, defining a diagonal matrix as the passive beamforming coefficient matrix of RIS, let β n =1 for simplicity; consider the RIS in the case of Continuous Phase Shift (CPS). In particular, to facilitate theoretical analysis under relatively ideal hardware conditions, each reflective element of the RIS may be continuously adjusted, or the phase shifter may have a high resolution, so that the set of reflection coefficients may be exactly approximated asTo be more practical, it is assumed that each reflective element of the RIS can only generate a discrete phase shift value of l=2 b, b representing the number of bits corresponding to the phase shift level, due to hardware and cost constraints.
Further, because of the broadcasting characteristics of the wireless channel, the user equipment can acquire energy from any electromagnetic wave meeting the specific microwave radiation specification, and thus, the received radio frequency power and the finally collected available power can be related together by adopting a nonlinear energy collection model, thereby being more suitable for practical application
Further, in order to balance the basic performance tradeoff between information transmission and energy collection, our goal is to maximize the minimum achievable rate, power splitting coefficient and reflection coefficient of the user with the power constraint at the AP and the phase shift constraint of the reflective element and the energy constraint of the user equipment by optimizing the precoding vector.
Further, compared with a special case where the IGS signal PGS signal has a lower degree of freedom and can be considered as the simplest IGS signal, the scheme where the initial value selects the maximum ratio transmission scheme and is set to PGS can reduce the complexity of the initialization calculation.
Further, in practice, the energy acquired by the user equipment and the achievable rate are determined primarily by the power splitting coefficient, followed by the active beamforming vector and the reflection coefficient of the RIS, and thus, considered asThe method has the highest priority in the optimization problem, and needs to be updated in time in the optimization process. In addition, find/>, by observing the user rate expression and collecting the energy expressionAnd/>The coupling relationship between theta is weaker and/>The coupling relation between the optimization variable and theta is strong, and the optimization variable is divided into two blocks, namely/>And/>The two blocks are then alternately optimized, which may increase the efficiency of the algorithm as compared to alternately optimizing each variable independently. However, the fact that whether the optimized variables converge before alternately optimizing the other variables must be considered. In particular, the transformation between optimization problems based on the SCA method is not equivalent and there must be a certain scaling error, so we need to iterate the loop optimization on the scaled optimization problem until the optimization variables converge, and then get a smooth solution of the original problem instead of a feasible solution. If this step is omitted, the simple alternate optimization method is inefficient and affects the user's rate.
Further, unlike the energy requirement of the fixed energy harvesting threshold in step S3, it is contemplated to maximize both the harvested energy and the achievable rate. For this, the problem of optimizing the weighted maximum minimum rate energy tuples is constructed.
In summary, the method of the invention adopts an asymmetric gaussian signal scheme to compensate the influence of HWI and the interference between users, solves the problem of performance optimization for the RIS-assisted SWIPT system with HWI, and obviously improves the maximum and minimum rates.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a diagram of a system model of the present invention;
FIG. 2 is a diagram of asymmetric hardware distortion of a transceiver in accordance with the present invention;
FIG. 3 is a convex hull diagram corresponding to the RIS phase shift set of the present invention;
FIG. 4 is a diagram of a simulation environment in accordance with the present invention;
FIG. 5 is a convergence trend chart of the present invention;
FIG. 6 is a graph of AP power budget versus maximum and minimum achievable rates for the present invention;
FIG. 7 is a graph of the weight rate energy domain of the present invention;
FIG. 8 is a graph of amplitude distortion coefficients versus maximum and minimum achievable rates for the present invention;
FIG. 9 is a graph of the number of reflective elements versus the maximum and minimum achievable rates of the present invention.
The symbols are defined as follows:
bold lower case letters (such as: ) Representing vectors, bold capital letters (e.g.:/> ) Representing a matrix, lower case letters (such as: x) represents a scalar. /(I)Representing an n×m complex matrix space, I N represents an n×n identity matrix. The circularly symmetric complex gaussian random variable with a mean of 0 and a variance of 1 is denoted as x-CN (0, 1). For scalar x,/>And/>Representing the real part of {. For vector/> Representation/>Conjugation of/>Representation/>Transpose of/>Representation/>Is a conjugate transpose of (a). /(I)Indicating the expected value of [.cndot ]. For square matrix/> Representation matrix/>Trace,/>Representation matrix/>Inverse of/(I)Representation matrix/>Is used for the treatment of the disease of the heart,Representation/> For matrix/>F norm of/>Furthermore,/>Represents a diagonal matrix whose diagonal terms are defined by vectors/>[ A ] m,n and elements in the m-th row and n-th column representing matrix A.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it will be understood that the terms "comprises" and "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The invention provides a SWIPT system performance optimization method based on intelligent reflecting surface assistance, which comprises the steps of simultaneously decoding information and collecting energy through a power divider, respectively providing infinite and finite discrete phase shift values for a phase shifter of RIS, introducing RIS to compensate serious path loss and shadow fading, and using IGS to compensate the loss caused by unavoidable asymmetric hardware distortion; the goal is to maximize the minimum achievable rate for a user and the rate energy domain with weight by jointly optimizing the active and passive beamforming vectors and the power allocation coefficients, subject to the constraints of the AP power budget and the user's energy quality of service (QoS) requirements.
The invention discloses an SWIPT system performance optimization method based on intelligent reflecting surface assistance, which comprises the following steps:
S1, a wireless Access Point (AP) transmits a signal influenced by hardware interrupt (HWI) after wide linear transformation to user equipment through a direct link and an indirect link passing through a Reconfigurable Intelligent Surface (RIS);
referring to fig. 1, consider a RIS-assisted multi-user swit system in which an AP is equipped with M transmit antennas serving K single antenna users, the RIS consisting of N reflective elements. The user set is expressed as In particular, each user can decode information and collect energy simultaneously through the power splitter; in the invention, a quasi-static flat fading channel model is adopted, and the channel coefficient is kept unchanged in each channel coherence time. To characterize the maximum performance gain that IGS brings to a swit system with HWI, the channel information of all channels is completely known at the AP.
S101, modeling a direct channel from an AP to a user as a Rayleigh channel according to the deployment characteristic of RIS, modeling a reflection channel passing through the RIS as a rice channel, and considering that the RIS has two conditions of discrete phase shift and continuous phase shift in practical application;
Let the And/>Representing the direct channel between AP and user k and the equivalent channel from RIS to user k, respectively,/>Representing the channel between the RIS and the AP; diagonal matrix/>Representing the passive beamforming coefficients of RIS, β n ε [0,1] and θ n ε [0,2 pi ] represent the reflection amplitude and phase of the nth element, respectively.
For the sake of simplicity the device is designed to be,The channel between AP and user k is denoted as:
Wherein, And/>Representing the path loss of the AP-RIS-user k link and the direct link between AP and user k, respectively, channel G is modeled with a Rician fading channel model and written as:
Where G NLOS is a non-line of sight (NLOS) component, K R is a Rician factor, and G LOS is a line of sight (LOS) component written as:
Where λ represents the wavelength, d AP and d RIS are the distances (m x,my) and (n x,ny) between the antennas and the reflective elements at the AP and RIS, respectively, representing the coordinates θ 1,n and Φ 1,n of the mth antenna in the AP antenna array and the nth reflective element in the RIS, respectively, representing the azimuth and elevation angles of view with respect to the nth reflective element, and θ 1,m and Φ 1,m representing the azimuth and elevation angles of view with respect to the mth transmitting antenna; the following two cases are considered for the reflection phase shift of the RIS:
(a) Under relatively ideal hardware conditions, each reflective element of the RIS is continuously tunable, or the phase shifter has a high resolution, so the set of reflection coefficients closely approximates:
(b) Because of hardware and cost constraints, each reflective element of the RIS can only generate l=2 b discrete phase shift values, b representing the number of bits corresponding to the phase shift level.
For simplicity, the discrete phase shift values are uniformly quantized over [0,2 pi ], and thus the discrete value set for each reflective element is given by:
S102, pre-coding a user signal at an AP, and generating a transmitting signal after passing through a radio frequency module with loss;
to obtain the desired IGS signal to compensate for hardware distortion, the signal is subjected to a wide linear transformation, and the transmit signal vector for an ideal communication system is as follows:
Wherein, And/>Is a broad range of linear precoding vectors,/>The covariance and pseudo-covariance matrices of (1) are:
Wherein, Representation X has an asymmetric character. As shown in fig. 2, the amplitude of the I branch and the Q branch of the signal are different due to the I/Q imbalance in the mixer, and the phase difference is no longer/>In the present invention, the I branch is considered ideal and all errors occur in the Q branch.
The equivalent quadrature unbalanced baseband transmit signal is represented as:
Wherein, Representing additive distortion noise due to nonlinearity at AP DAC, LPF, PA,/>And/>Expressed as:
S2, transmitting signals reach a receiving end through a channel, and user equipment divides the signals into two parts by utilizing a power divider according to own requirements to respectively perform information decoding and energy collection;
S201, user equipment decodes part of the received signals;
The received signal for the kth user is expressed as:
Wherein, Is gaussian noise which is independently and uniformly distributed.
To harvest energy and decode information simultaneously, the user employs a power splitting receiver architecture, as shown in FIG. 2, by which the signal y r,k is split intoAnd/>The two parts correspond to the information decoding and energy collecting processes respectively. Similarly, considering the I/Q imbalance and additional hardware distortion of the UE, the information decoded signal received by the kth user is expressed as:
wherein the additional hardware distortion generated by a non-ideal LNA, LPF, power splitter, etc. for the kth user is modeled as In addition,/>And/>Expressed as:
scalar g k Represents the amplitude and phase mismatch, g k =1 and/>, respectively, of each mixerIndicating that the UE has perfect I/Q balance.
The definition of w k＝{w1,k,w2,k is that,Then, the augmented form of the signal received for information decoding, further representing the kth user, is represented as:
Wherein the method comprises the steps of
Mutual information between y k and s k of the kth user is expressed as
Wherein,
S202, the user equipment collects energy of part of received signals;
Due to the broadcast nature of the wireless channel, the user equipment may extract energy from any electromagnetic wave that meets the specifications of a particular microwave radiation. Thus, the radio frequency power received by the kth user is
Wherein the method comprises the steps of
In practical application, the nonlinear logarithmic energy collection model is more suitable for accurately simulating the actual energy collection process due to the saturation behavior of the rectifying circuit. The energy collected by user k is expressed as
Where U is a constant, representing the maximum energy that a user may collect when the diode rectifier circuit breaks down in reverse, and a and b are also constants, depending on the actual circuit parameters, such as circuit sensitivity and current leakage U, a, b, all obtained by fitting experimental measurements.
S3, constructing a maximum minimization problem of the user rate according to the rate in the step S2 and the collected energy expression, and proposing TIAO algorithm in order to ensure basic performance trade-off between information transmission and energy collection; the optimization objective is to maximize the minimum achievable rate, power splitting coefficient and reflection coefficient of the user under the power constraint at the AP, the phase shift constraint of the reflection element and the energy collection QoS constraint by optimizing the wide linear precoding vector; the optimization problem is expressed as:
s.t.ωk≥E-1(emin),k∈κU (19)
[θ]n∈Ψ,n∈{1,…N} (20)
0≤ρk≤1,k∈κU (22)
Wherein, Is an inverse function of ψ (x), e min > 0 is the minimum radio frequency power requirement, P is the maximum power budget of the AP, and ψ represents the feasible set of phase shifts.
The optimization problem P1 is non-convex due to non-concave objective functions and non-convex constraints (19), (20). Complex couplings between variables make solving very difficult.
For this purpose, a two-layer iterative alternating optimization algorithm (TIAO) is proposed based on the SCA technique. Specifically, at the inner layer, each variable needs to iterate to a stable point, and the outer layers alternate to optimize the variables until a specific convergence condition is met. In practice, the amount of energy acquired by the UE and the achievable rate are determined primarily by the power splitting coefficient, followed by the active beamforming vector and the reflection coefficient of the RIS, and thus are considered to beThe highest priority is given to the optimization problem P1 and needs to be updated in time during the optimization process. Furthermore, by observing (15) and (16), it was found/>And θ,/>The coupling relationship between them is relatively weak. /(I)
Inspired by the above, the optimization variable is divided into two blocks, i.eAnd/>The two variable blocks are then alternately optimized, which may increase the efficiency of the algorithm as compared to alternately optimizing each variable independently.
Next, the specific steps for optimizing these two variables will be described as follows:
s301, combining PS coefficients and pre-coding optimization;
Initial points for any given fixed feasible phase shift vector $θ and power splitting coefficient Optimizing the variable block/>, by solving the following problemsThe following are provided:
0≤ρk≤1,k∈κU (27)
However, due to the complex non-convex constraints (24) and (25), P2 is still non-convex. The main idea of the SCA technique is to obtain a stable point of the original non-convex problem by iteratively solving a series of convex optimization problems similar to the original problem. Thus, the following concave lower bound approximation is obtained based on the SCA technique: at the feasible iteration point Obtaining a concave lower bound approximation expression/>, of (24)And is represented as
Wherein the method comprises the steps of
Both sides of inequality (25) are convex, so the energy constraint does not meet the standard expression for convex optimization, requiring the development of a concave or linear approximation on the left side of (25). By applying the following inequality
(25) At the position ofThe first order linear approximation at this point is expressed as
Wherein the method comprises the steps of
Thus, in the first iteration, a feasible initial point is givenFirst+1st feasible point of problemThe following convex optimization problem is solved:
(31),(26),(27) (35)
however, the fact is considered whether the variable being optimized converges before alternately optimizing the other variables. In particular, since the transition between optimization problems P3 and P2 is not equivalent and there must be some scaling error, an iterative loop of P3 is required until Convergence and then a smooth locally optimal solution for P2 is obtained instead of a feasible solution. If this step is omitted, the result of the alternate optimization will become unstable, making P1 difficult to converge.
S302, combining PS coefficients with passive beam forming optimization;
for any given feasible precoding vector And the initial point of the power split coefficient, variable block/>The optimization is performed by solving the following problems, specifically:
s.t.rk′(θ,ρk)≥2γ,k∈κU (37)
|[θ]n|＝1,n∈{1,…NL} (39)
0≤ρk≤1,k∈κU (40)
In most of the current related work at present, there is no effective way to solve the challenges presented by the constant modulus constraint in (39). Some studies solve this problem by alternately optimizing the reflectance of each reflective element, which also greatly increases the complexity of the algorithm, and others relax the unit-mode constraint to a convex-set constraint, which makes the result relatively inaccurate. Therefore, an effective penalty-based solution is proposed to solve this problem.
Equivalent conversion of P4 to
s.t.|[θ]n|≤1,,n∈{1,…NL} (42)
(37)，(38)，(40) (43)
At this time, the non-convex set where the phase shift value is located becomes the convex hull corresponding thereto. Since the objective function of P5 is convex, a first order approximation is used at θ (n) to obtain a linear lower bound for the objective function, i.e., F (θ, γ). Ltoreq.γ+η (2Re ((θ (n))Hθ)-||θ(n)||2). The magnitude of the penalty factor severely affects the magnitude of γ in the objective function when the penalty term is too large, the focus of the overall optimization problem shifts to find a viable reflection coefficient, ignoring improvements to γ.
Wherein the method comprises the steps of
The radio frequency power received by the kth user is linearly approximated as
Wherein the method comprises the steps of
Referring to fig. 3, where solid lines or points represent feasible sets of reflection coefficients that may be actually achieved, the shaded portions represent corresponding convex hulls.
From visual observation, it was found that when F (θ, γ) is maximized, the values of [ θ ] n, N ε {1, … N } will gradually trend towards the limit points of the convex hull boundary, replacing (42) with the discrete phase shifts 2-bit and 1-bit, respectively:
S4, in order to solve the optimization problem set forth in the S3, a feasible initial value selection scheme is given as follows;
For most iterative optimizations, a good initial point is important, which can speed up the convergence of the algorithm. Next, a method of finding an initial point is given. Because of severe large-scale fading, the energy QoS constraint is the most sensitive of all constraints, the Maximum Ratio Transmission (MRT) scheme is used first to generate a viable starting point for the precoding vector, i.e
And randomly initializing the value of the power splitting coefficient rho k 'according to the size of e min, obtaining the result of the 0 th iteration based on w 1′,k,w′2,k,ρk' and by solving the following feasibility problem
(26)(27) (51)
RIS has the significance of improving the system in that changing the existing communication link, even if the phase of the incident wave is unchanged, brings a certain gain to the WPT. Therefore, the initial value of the reflection coefficient is not strictly required, and the reflection coefficient is only required to be obtained in a feasible set.
S5, ensuring feasibility of the algorithm in the step S3, and analyzing convergence and complexity of the method according to the invention;
The feasible variable block of P3 in the first iteration is On the basis, a convex proxy function/>, of the original rate function is constructedIt partially preserves the geometric features of the original expression, i.e., the two function gradients are equal. Obtaining the next iteration feasible solution/>, by solving for P3The achievable rates of any user always satisfy the following relationship
Variable block by successive iterations of P3Eventually converging to a locally optimal solution for P2. Likewise, the variable blocks (ρ k, θ) will eventually converge to a locally optimal solution by cycling through P5.
Pseudocode algorithm 1 summarizes the specific details of the proposed alternating iterative optimization algorithm. And adjusting the size of the threshold delta and epsilon or T 1,T2 according to the dimension of the variable and the number of the constraint to balance the weight of each variable, thereby obtaining better objective function gain. The computational complexity of the solution P2, determined by the number of deterministic optimization variables and secondary and linear constraints, is
The computational complexity of solving for P4 is
Thus, the overall complexity of algorithm 1 is expressed as
Where I 0、I1 and I 2 represent the number of iterations required for P2, P4, and P1 to converge, respectively.
S6, weighting a rate energy domain;
Unlike the energy requirement of the fixed energy harvesting threshold in step S3, we consider maximizing both the harvesting energy and the achievable rate. The second optimization objective is to maximize the minimum user rate energy area among all users. The weighted optimization problem of maximizing the minimum rate energy tuple is expressed as:
(20)，(21)，(22) (57)
where lambda 2＝1-λ1 represents a threshold factor, lambda e is a constant factor determined by the order of magnitude of the energy received by the user equipment, and by adjusting the size of lambda 1 from 0 to 1, the complete rate energy domain is obtained. A certain point on the boundary of the rate energy domain region represents the minimum achievable rate and the maximum possible value of the minimum harvest energy for the same user. By introducing the auxiliary variable gamma 1, the above optimization problem is equivalently expressed as:
P7 clearly demonstrates the principle of the weighted rate energy domain optimization problem, namely using a threshold factor to relate the user's rate and energy QoS requirements and adjusting the weights to obtain pareto limits. Unlike the optimization problem in the third section, however, the right side of the constraint (60) is no longer convex due to the mutual coupling of γ 1 and ρ k. To decouple the variables gamma 1 and ρ k, a simple variable substitution is first introduced And arbitrary constant/>A standard quadratic function is then constructed as follows:
the following inequality is obtained for any non-negative variable x k
Will not be negative variableSubstituting the inequality to obtain
Thus, during one iteration, the convex upper bound on the right side of the constraint (60) is given by:
the optimization problem of the ticket rate energy domain is rewritten as:
Also, TIAO algorithm can solve this optimization problem.
In still another embodiment of the present invention, a system for optimizing performance of an RIS-assisted swit system with HWI is provided, where the system can be used to implement the above-mentioned intelligent reflection plane-assisted swit system performance optimization method, and specifically, the system for optimizing performance of an RIS-assisted swit system with HWI includes a sending module, an allocating module, a processing module, an initializing module, a first optimizing module, and a second optimizing module.
The wireless access point AP transmits an asymmetric Gaussian IGS signal influenced by hardware loss HWI after wide linear transformation through a direct link and an indirect link passing through a reconfigurable intelligent surface RIS;
The distribution module is used for enabling the asymmetric Gaussian IGS signals to reach the user equipment through the channel, and the user equipment divides the signals into two parts by utilizing the power divider to respectively perform information decoding and energy collection;
The processing module maximizes the minimum achievable rate of all user equipment in the distribution module by optimizing the power splitting coefficient of the power splitter in the distribution module, the wide linear precoding vector of the wireless access point AP in the transmission module and the passive reflection coefficient of the reconfigurable intelligent surface RIS;
The initialization module is used for generating a feasible initial point of the wide linear precoding vector in the processing module by using a maximum ratio transmission scheme, iterating the minimum achievable rate obtained by the processing module to enable a sending signal to be a common PGS, randomly initializing the value of a power splitting coefficient in the processing module according to the minimum energy collection threshold value, and randomly generating a reflection coefficient initial value of a reconfigurable intelligent surface RIS in a feasible domain;
The first optimization module divides the variable into variable blocks according to the coupling relation among the wide linear precoding vector, the passive reflection coefficient of RIS and the power splitting coefficient and the influence weight on the optimization target And/> For a wide linear precoding vector of a wireless access point AP, θ is the passive reflection coefficient of the reconfigurable intelligent surface RIS,/>For the power splitting coefficient of the power splitter, according to the initial value of the reflection coefficient generated by the initialization module, a double-layer alternate iterative optimization method is used for alternately optimizing the variable block/>And/>Until the objective function in the processing module converges, the user rate optimization of the SWIPT system is completed;
And the second optimization module optimizes the SWIPT system according to the double-layer alternate iterative optimization method of the first optimization module and the corresponding decoupling method, maximizes the weight rate energy domain on the premise of meeting the total power constraint and the reflection coefficient constant modulus constraint of the wireless access point AP, and completes the optimization of the weight rate energy domain of the SWIPT system.
Simulation verification
The advantages of IGS in the proposed system were analyzed by monte carlo simulation according to a 3GPPUrbanMicro (UMi) scene model at a 2.5GHz carrier frequency. The path loss coefficient β R,k as a function of distance is expressed as:
Where C AP-RIS＝GAP and C RIS-Uk＝GRIS are the path losses of the AP-RIS link and the RIS-user link at a reference distance of 1 meter, d AP-RIS and d RIS-Uk represent the distance between the AP and the RIS, and the distance between the RIS and the kth user, α 1 =2.2 and α 2 =3.67 represent the respective path loss indices of the channels of the AP-RIS link and the RIS-user link.
Let C AP-RIS＝GAP+GRIS-35.95,CRIS-Uk＝GRIS+GUk -33.05 units be dB, where G AP＝GRIS＝5dBi,GUk =0 dBi denote the antenna gains at AP, RIS and user, respectively. The parameter settings of the RIS-user link and the AP-user link are set to be the same.
In addition, bandwidth B C = 10MHz, noise power density at-150 dBm/Hz, for simplicity,And is also provided withη＝10-3。
Unless otherwise indicated, the setting of e min =6.5 microwatts (μw), a AP＝0.6IM,θBS＝5°IM,gk =0.6,a＝1500，b＝0.0022，U＝0.024。
Furthermore, let the small-scale fading channel model of the AP-user link and the RIS-user link follow the rayleigh distribution, and the Rician factor of the AP-RIS link is set to 3.
Referring to fig. 4, it is assumed that users are randomly distributed within the coverage of an AP. The coordinates of the AP are set to (d x, 0), the RIS is deployed in the y-z plane, and the reference element coordinates of the RIS are denoted as (d y, 0). As the signal energy loss is severe due to the large-scale fading, d x =3 meters and d y =6 meters are set. It is worth mentioning that the model and algorithm are easily extended to telematics systems or to separate SWIPT systems.
Referring to fig. 5, the convergence of algorithm 1 is plotted. In the illustration, igs+pgs indicates that the AP adopts IGS and PGS transmission schemes, respectively, ris+igs and ris+pgs indicate that the phase shift at the RIS is continuously adjustable, and AP adopts IGS and PGS transmission schemes, respectively, (xbit) indicates that the reflective element at the RIS is a xbit phase shifter. As can be seen from the figure, under different system settings and transmission schemes, the maximum-minimum achievable rate of the user increases rapidly during the first few iterations (i.e. 0-4) and gradually converges to a stable value in about 20 iterations. As a theoretical analysis, PGS-based schemes converge faster than IGS-based schemes due to the additional degrees of freedom of IGS.
Referring to fig. 6, when the PS scheme is used at the receiving end, the diagram depicts the maximum and minimum achievable rates versus the AP transmit power, where m=10, n=50, k=4. It can be seen that the IGS scheme in the present system provides an additional degree of freedom compared to the PGS scheme, which compensates for the effects of distortion, thus resulting in an increase of 0.25 bit rate. The efficiency improvement by RIS is evident, as the power increases, the impact of RIS on the system gradually decreases, which also laterally demonstrates the importance of RIS in improving energy efficiency. At the same time, discrete phase shift levels can result in a loss of system performance as compared to the continuous phase shift case. As the level of phase shift increases, the maximum and minimum rates of the user increase further, approaching the case of continuous phase shift.
Referring to fig. 7, the scheme of igs combined with RIS is apparent for the expansion of the rate energy domain, where the intersection of the curve with the x-axis represents the highest transmission rate that the system can achieve when all the energy is used for rate transmission. The intersection of the curve with the y-axis represents the highest energy that can be collected at the user device when all signals are used to collect energy.
Furthermore, as λ 2 increases, the greater the signal power allocated to the user equipment, the more serious the relative interference to the information gathering process, and thus, from the trend of the same color curve in fig. 7, the more pronounced IGS than PGS, the more the advantage is expanded by deploying RIS.
Referring to fig. 8, for simplicity, the amplitude distortion of the transmitting and receiving ends is set to be the same. Fig. 8 depicts the relationship between maximum-minimum achievable rate and amplitude distortion, p=35 dbm, m=10, n=50, k=4. As the amplitude distortion at the transceiver increases, the maximum-minimum achievable rate decreases significantly. Meanwhile, the IGS is adopted for transmission, so that adverse effects caused by amplitude distortion can be compensated, and the performance is more remarkable under high-amplitude distortion. When there is no amplitude distortion, i.e., g k =1, the gain of the IGS scheme is very small, but the IGS scheme is always better than PGS, which is a special case of IGS.
Referring to fig. 9, the graph plots the maximum-minimum achievable rate against the number of reflective elements, where m=10, p=35 dBm, k=4, g k =0.6. As the reflective element increases, the aperture of the RIS increases and the energy carried by the reflected beam increases. Thus, the user can allocate more signal energy to improve the signal-to-noise ratio. Figure 9 clearly demonstrates the superiority of IGS over PGS.
In summary, according to the intelligent reflection surface-assisted SWIPT system performance optimization method and system, in the SWIPT system with limited hardware, the performance of the IGS used by the intelligent reflection surface-assisted SWIPT system is obviously superior to that of the traditional PGS, and the arrangement of the RIS further expands the advantages.
Claims (9)
1. The SWIPT system performance optimization method based on the intelligent reflecting surface assistance is characterized by comprising the following steps of:
s1, a wireless access point AP transmits an asymmetric Gaussian IGS signal influenced by hardware loss HWI after wide linear transformation through a direct link and an indirect link passing through a reconfigurable intelligent surface RIS;
S2, enabling the asymmetric Gaussian IGS signals to reach user equipment through a channel, dividing the signals into two parts by the user equipment through a power divider, and respectively carrying out information decoding and energy collection;
S3, maximizing the minimum achievable rate of all user equipment in the step S2 by optimizing the power splitting coefficient of the power splitter in the step S2, the wide linear precoding vector of the wireless access point AP and the passive reflection coefficient of the reconfigurable intelligent surface RIS in the step S1, wherein the optimization problem is specifically as follows:
s.t.ωk≥E-1(emin),k∈κU
[θ]n∈Ψ,n∈{1,…N}
0≤ρk≤1,k∈κU
Wherein, kappa U is the set where the user is located, For a wide linear precoding vector, [ θ ] n is the nth term in the reflection coefficient vector, RIS has N L reflection elements in total, P is the maximum power provided by the base station, E -1(emin) is the inverse of the energy harvesting function E (E min);
S4, generating a feasible initial point of the wide linear precoding vector in the step S3 by using a maximum ratio transmission scheme, iterating the minimum achievable rate obtained in the step S3 to enable a transmission signal to be a common PGS, randomly initializing the value of the power splitting coefficient in the step S3 according to the minimum energy collection threshold value, and randomly generating a reflection coefficient initial value of the reconfigurable intelligent surface RIS in a feasible domain;
s5, dividing the variable into variable blocks according to the wide linear precoding vector, the coupling relation between the passive reflection coefficient of RIS and the power splitting coefficient and the influence weight on the optimization target And/> The method comprises the steps that a wireless Access Point (AP) is sent to a set of wide linear precoding vectors of all users, and theta is a passive reflection coefficient of a Reconfigurable Intelligent Surface (RIS)/>For the power splitting coefficient set of all users, according to the initial value of the reflection coefficient generated in the step S4, a double-layer alternate iterative optimization method is used for alternately optimizing the variable block/>And/>Until the objective function in the step S3 is converged, the user rate optimization of the SWIPT system is completed;
S6, optimizing the weighted rate energy domain maximization problem of the SWIPT system according to the double-layer alternate iteration optimization method and the decoupling method based on the total power constraint and the reflection coefficient constant modulus constraint of the wireless access point AP.
2. The method for optimizing the performance of a SWIPT system based on the assistance of an intelligent reflecting surface according to claim 1, wherein in step S1, a wireless access point AP is equipped with M transmitting antennas to serve K single antenna users, and a reconfigurable intelligent surface RIS is composed of N reflecting elements; the user set is expressed asThe channel information of all channels is known at the radio access point AP.
3. The method for optimizing the performance of a swit system based on intelligent reflector assistance according to claim 1, wherein step S1 is specifically:
s101, modeling a direct channel from a wireless Access Point (AP) to a user as a Rayleigh channel according to the deployment characteristic of the RIS, and modeling a reflection channel passing through a reconfigurable intelligent surface RIS as a rice channel, wherein the reconfigurable intelligent surface RIS comprises discrete phase shift and continuous phase shift;
S102, performing wide linear precoding on a user signal at a wireless Access Point (AP) to generate an equivalent baseband (IGS) signal x, and generating a transmitting signal x AP after passing through a radio frequency module with loss, wherein the method specifically comprises the following steps:
xAP＝Λ1x+Λ2x*+dT
Where Λ 1 is the amplitude distortion at the AP end, Λ 2 is the rotation error at the AP end, x * is the conjugate of the baseband signal, and d T is the additive distortion noise.
4. A method of intelligent reflector-assisted SWIPT system performance optimization as claimed in claim 3 wherein the viable set of reflected phase shifts ψ is expressed as:
when the phase shift value of each reflective element of the reconfigurable intelligent surface RIS can be continuously adjusted, the viable set of reflective phase shifts ψ is expressed as:
where L is the accuracy of the reflected phase shift and m is the phase shift index.
5. The intelligent reflector-assisted swit system performance optimization method according to claim 1, wherein in step S2, the information decoding signal y k received by the kth user is:
The rate expression r' k(wκ,θ,ρk between the information decoding signals y k and s k of the kth user) is
Wherein χ 1,k is the amplitude distortion of the receiving end, ρ k is the power splitting coefficient of the kth user, y r,k is the receiving signal of the kth user, d R,k is the equivalent additive noise of the user equipment end, χ 2,k is the rotation distortion of the receiving end, I 2 is the unit diagonal matrix of 2 x 2, Γ k is the equivalent amplitude of the signal, w k is the wide linear precoding vector of the kth user,Is equivalent noise;
Radio frequency power ω k(w,θ,ρk received by the kth user) is:
Wherein, For the desired sign, xi k(wj, θ) is the equivalent amplitude of the received radio frequency signal,/>For equivalent transmitting symbol, w j is the wide linear precoding vector of the j-th user, and the symbol is matrix tracing symbol;
The energy ω k collected by user k is:
Where U is the maximum energy that a user may collect when the diode rectifier circuit breaks down in reverse, and a and b are constants.
6. The intelligent reflector-assisted SWIPT system performance optimization method according to claim 1, wherein in step S4, the feasible initial points w '1,k and w' 2,k of the precoding vector are:
Wherein H k is an equivalent channel from the AP to the user k, H is conjugate transpose conversion, and 0 M is an all-zero vector of M dimensions.
7. The intelligent reflector-assisted SWIPT system performance optimization method as claimed in claim 1, wherein in step S5, PS coefficients are combined with precoding optimizationThe method comprises the following steps:
ωk≥E-1(emin),k∈κU
0≤ρk≤1,k∈κU
Wherein γ is a real variable;
The joint PS coefficients and passive beamforming optimization are specifically:
s.t.|[θ]n|≤1,n∈{1,…N}
s.t.r′k(θ,ρk)≥2γ,k∈κU
0≤ρk≤1,k∈κU
Wherein eta is a punishment coefficient, theta is a norm of the reflection coefficient vector, n is the index of the reflective element;
the achievable rates for any user always satisfy the following relationship:
Wherein, And/>Rate expressions in the (1+1) th and the (l) th iteration processes respectively;
the overall complexity of the alternate iterative optimization method is expressed as:
Wherein, For time complexity, N is the number of reflective elements, K is the number of users, M is the number of transmit antennas, and I 0、I1 and I 2 represent the number of iterations required for P2, P4, and P1 convergence, respectively.
8. The intelligent reflector-assisted SWIPT system performance optimization method according to claim 1, wherein in step S6, the optimization problem of the weight rate energy domain is:
[θ]n∈Ψ,n∈{1,…N}
0≤ρk≤1,k∈κU
Wherein γ 1 is a real number optimization variable, r' k is a rate expression, k is a user index, and λ 2＝1-λ1 is a weight factor.
9. A RIS-assisted swit system performance optimization system for a HWI comprising:
the wireless access point AP transmits an asymmetric Gaussian IGS signal influenced by hardware loss HWI after wide linear transformation through a direct link and an indirect link passing through a reconfigurable intelligent surface RIS;
The distribution module is used for enabling the asymmetric Gaussian IGS signals to reach the user equipment through the channel, and the user equipment divides the signals into two parts by utilizing the power divider to respectively perform information decoding and energy collection;
the processing module maximizes the minimum achievable rate of all user equipment in the distribution module by optimizing the power splitting coefficient of the power splitter in the distribution module and the wide linear precoding vector of the wireless access point AP and the passive reflection coefficient of the reconfigurable intelligent surface RIS in the transmission module, and the optimization problem is specifically as follows:
s.t.ωk≥E-1(emin),k∈κU
[θ]n∈Ψ,n∈{1,…N}
0≤ρk≤1,k∈κU
Wherein, kappa U is the set where the user is located, For a wide linear precoding vector, [ θ ] n is the nth term in the reflection coefficient vector, RIS has N L reflection elements in total, P is the maximum power provided by the base station, E -1(emin) is the inverse of the energy harvesting function E (E min);
The initialization module is used for generating a feasible initial point of the wide linear precoding vector in the processing module by using a maximum ratio transmission scheme, iterating the minimum achievable rate obtained by the processing module to enable a sending signal to be a common PGS, randomly initializing the value of a power splitting coefficient in the processing module according to the minimum energy collection threshold value, and randomly generating a reflection coefficient initial value of a reconfigurable intelligent surface RIS in a feasible domain;
The first optimization module divides the variable into variable blocks according to the coupling relation among the wide linear precoding vector, the passive reflection coefficient of RIS and the power splitting coefficient and the influence weight on the optimization target And/> The method comprises the steps that a wireless Access Point (AP) is sent to a set of wide linear precoding vectors of all users, and theta is a passive reflection coefficient of a Reconfigurable Intelligent Surface (RIS)/>For the power splitting coefficient set of all users, according to the initial value of the reflection coefficient generated by the initialization module, a double-layer alternate iterative optimization method is used for alternately optimizing the variable block/>And/>Until the objective function in the processing module converges, the user rate optimization of the SWIPT system is completed;
And the second optimization module optimizes the SWIPT system according to the double-layer alternate iterative optimization method of the first optimization module and the corresponding decoupling method, maximizes the weight rate energy domain on the premise of meeting the total power constraint and the reflection coefficient constant modulus constraint of the wireless access point AP, and completes the optimization of the weight rate energy domain of the SWIPT system.
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