Transceiver optimization method for irs-assisted multi-carrier mimo wireless power communication system

Abstract

The application provides a transceiver optimization method for an IRS-aided multi-carrier MIMO wireless power communication system, relates to the technical field of wireless power communication systems, and comprises the following steps: performing linear precoding or Tomlinson-Harashima nonlinear precoding processing on symbol vectors to be carried by different subcarriers, so as to obtain signal vectors corresponding to linear transceivers or nonlinear transceivers; based on the signal vectors, a receiving signal of the transceiver of the multi-carrier MIMO wireless power communication system is established; under the premise of satisfying given transmission power constraints and energy collection power constraints, a transceiver optimization problem with the sum of mean square errors of the receiving signal as an objective function is established and solved, and a locally optimal transceiver optimization scheme is obtained. The application effectively unifies linear transceivers and nonlinear transceivers, and the nonlinear transceiver can be applied to the linear transceiver case, so as to adapt to different use scenarios.

Description

Technical Field

[0001] This invention relates to the technical field of wireless power-carrying communication systems, and particularly to a transceiver optimization method for IRS-assisted multi-carrier MIMO wireless power-carrying communication systems. Background Technology

[0002] In the B5G and 6G era, reference [1] proposed that the Internet of Things (IoT) is a key technology with great development potential, because it can significantly improve people's quality of life and has many specific application scenarios, such as autonomous driving, implantable devices and smart healthcare. In the future, the use of a large number of IoT devices will consume more and more electricity. Therefore, how to extend the working cycle of IoT networks and improve energy efficiency is an urgent issue to be addressed.

[0003] Against this backdrop, wireless power transfer technology has attracted widespread attention from academia and industry. This technology enables the parallel transmission of information and energy, providing support for green communication in energy-constrained IoT networks. Reference [2] proposes power-splitting (PS) and time-switching (TS) as two feasible technical solutions for the simultaneous transmission of wireless information and energy. Power splitting is the receiver using a power divider to divide the received signal into two parts according to a certain ratio, one part for information decoding (ID) and the other part for energy harvesting (EH). Time-switching is the alternation of information decoding and energy harvesting in different time periods. However, for traditional wireless information and power transfer (SWIPT) systems, the received signal strength will be significantly reduced due to severe channel attenuation, which limits the EH and ID performance of SWIPT. In order to overcome this dilemma, reference [3] proposes that intelligent reflecting surface (IRS) technology has become a very promising solution in recent years. IRS improves the received signal strength by reconstructing a favorable wireless propagation environment. Reference [4] proposes that an IRS is a metal surface composed of a certain number of passive reflective elements, and the phase of each reflective element can be adjusted by a smart controller. Given the respective advantages of SWIPT and IRS in wireless communication, IRS-assisted SWIPT systems have become a research hotspot in academia and industry in recent years.

[0004] To date, references [5-8] have conducted relatively in-depth research on IRS-assisted PS-based SWIPT systems. Reference [5] proposes a joint optimization framework for IRS-assisted PS-based MIMO SWIPT IoT networks and maximizes the weighted sum rate under transmit power and EH constraints. Reference [6] studies a multi-user IRS-assisted PS-based MISO SWIPT system and maximizes the system's energy efficiency under transmit power constraints, EH constraints, and data rate constraints. Reference [7] studies an IRS-assisted cooperative non-orthogonal multiple access PS-based MISO SWIPT system and maximizes the system's fairness rate under quality of service (QoS) and transmit power constraints. Reference [8] designs a physical layer secure beamforming scheme for IRS-assisted PS-based NOMAMISO SWIPT networks and achieves the maximum sum rate under EH, interference power, and signal-to-interference-plus-noise ratio constraints. The aforementioned references [5-8] mainly use data rate as the system design criterion, therefore this is not suitable for characterizing the accuracy of signal recovery at the receiver. Furthermore, these works primarily focus on the design of linear transceivers for IRS-assisted SWIPT systems. References [9-10] propose that for traditional wireless information transfer (WIT) systems, combining Multiple-Input Multiple-Output (MIMO) with nonlinear Tomlinson-Harashimap coding (THP) or decision feedback equalization techniques can potentially improve communication performance. References [11-12] have applied THP to MIMO SWIPT and IRS-assisted MIMO systems, respectively. IRS-assisted MIMO SWIPT systems combine the advantages of IRS and SWIPT, but research on THP-based IRS-assisted MIMO SWIPT systems remains an open question. Therefore, how to apply THP to IRS-assisted MIMO SWIPT systems to improve communication performance and unify the design schemes of linear and nonlinear transceivers to adapt to different application scenarios are extremely important technical problems to be solved.

[0005] The document in question is:

[0006] [1]P.X.Nguyen et al.,"Backscatter-Assisted Data Offloading in OFDMA-Based Wireless-Powered Mobile Edge Computing for IoTNetworks,"in IEEEInternet Things J.,vol.8,no.11,pp.9233-9243,1June1,2021.

[0007] [2]R.Zhang and C.K.Ho,"MIMO Broadcasting for Simultaneous WirelessInformation and Power Transfer,"in IEEE Trans.Wirel.Commun.,vol.12,no.5,pp.1989-2001,May 2013.

[0008] [3]C.Pan et al.,"Intelligent Reflecting SurfaceAided MIMOBroadcasting for Simultaneous Wireless Information andPower Transfer,"in IEEEJ.Sel.Areas Commun.,vol.38,no.8,pp.1719-1734,Aug.2020.

[0009] [4]S.Zhang and R.Zhang,"Capacity Characterization for IntelligentReflecting SurfaceAided MIMO Communication,"in IEEE J.Sel.Areas Commun.,vol.38,no.8,pp.1823-1838,Aug.2020.

[0010] [5]X.Xie et al.,"A Joint Optimization Framework for IRS-AssistedEnergy Self-Sustainable IoT Networks,"in IEEE Internet Things J.,vol.9,no.15,pp.

[0011] 13767-13779,1Aug.1,2022.

[0012] [6]J.Tang,Z.Peng,D.K.C.So,X.Zhang,K.-K.Wong and J.A.Chambers,"EnergyEfficiency Optimization for a Multiuser IRS-Aided MISO System With SWIPT,"inIEEE Trans.Commun.,vol.71,no.10,pp.5950-5962,Oct.2023.

[0013] [7]Z.Yang et al.,"User Fairness Optimization ofIRS-AssistedCooperative MISO-NOMA for ITS With SWIPT,"in IEEE Trans.Intell.Transp.Syst.,vol.25,no.7,pp.6861-6872,July 2024.

[0014] [8]R.Sun,W.Wang,L.Xu,N.Zhao,N.Al-Dhahir,and X.Wang,“Securebeamforming for IRS-assistedNOMA SWIPT networks,”IEEE Trans.Commun.,pp.1–1,2024.

[0015] [9]M.B.Shenouda and T.N.Davidson,"A framework for designing MIMOsystems with decision feedback equalization or Tomlinson-harashimaprecoding,"in IEEE J.Sel.Areas Commun.,vol.26,no.2,pp.401-411,February 2008.

[0016]

[10] AAD'Amico, "Tomlinson-Harashima Precoding in MIMO Systems: AUnifiedApproach to Transceiver Optimization Based on Multiplicative Schur-Convexity," in IEEE Trans.Signal Process., vol.56, no.8, pp.3662-3677, Aug.2008.

[0017]

[11] Q.Li, Q.Zhang and J.Qin, "Robust Tomlinson–Harashima Precoding WithGaussian Uncertainties for SWIPT in MIMO Broadcast Channels," in IEEETrans.Signal Process., vol.65, no.6, pp.1399-1411, 15March15, 2017.

[0018]

[12] S.Gong, C.Xing, Summary of the Invention

[0019] To address the issue that THP-based nonlinear transceivers have not yet been applied to IRS-assisted multicarrier MIMO systems to improve communication system performance, this invention proposes a transceiver optimization method for IRS-assisted multicarrier MIMO wireless power-carrying communication systems. This method effectively unifies the design schemes of linear and nonlinear transceivers to meet different application scenarios.

[0020] To achieve the above-mentioned technical effects, the technical solution of the present invention is as follows:

[0021] A transceiver optimization method for an IRS-assisted multi-carrier MIMO wireless power-carrying communication system includes the following steps:

[0022] S1. Perform linear precoding or Tomlinson-Harahima nonlinear precoding on the symbol vectors to be carried by different subcarriers to obtain the signal vectors corresponding to the nonlinear transceiver or the linear transceiver;

[0023] S2. Based on the signal vector, establish the received signal of the transceiver of the multi-carrier MIMO wireless power-carrying communication system;

[0024] S3. Under the premise of satisfying the given transmit power constraints and collect power constraints, establish a transceiver optimization problem with the objective function of minimizing the sum of mean square errors of the received signals;

[0025] S4. Solve the transceiver optimization problem to obtain a locally optimal transceiver optimization scheme.

[0026] Preferably, the multi-carrier MIMO wireless power-carrying communication system includes N... t A transmitter with one antenna, equipped with N r Receiver with one antenna and equipped with N s The system comprises a smart reflective surface IRS with passive reflective elements; wherein the total bandwidth of the multi-carrier MIMO wireless power-carrying communication system is W, which is evenly distributed among N subcarriers, and the equivalent frequency domain channel matrix of the k-th subcarrier is calculated as follows:

[0027]

[0028] Among them, H k This represents the equivalent frequency domain channel matrix of the k-th subcarrier. This represents the equivalent frequency domain channel matrix of the k-th subcarrier transmitter-receiver link. This represents the equivalent frequency domain channel matrix of the k-th subcarrier transmitter-smart reflector IRS link. Let represent the equivalent frequency domain channel matrix of the k-th subcarrier smart reflector surface IRS-receiver link; This represents the reflection coefficient matrix of the intelligent reflective surface IRS, where diag(.) represents the diagonal matrix, and φ m This represents the reflection coefficient of the m-th passive reflective element. φ m The phase adjustment range is [0, 2π).

[0029] Preferably, the linear precoding or Tomlinson-Harahima nonlinear precoding process for the symbol vectors to be carried by different subcarriers includes:

[0030] Define the symbol vector of the k-th subcarrier as: L k ≤min{N t N r}, through the modulo operator MOD and the lower triangular feedback matrix By performing nonlinear processing on the symbol vectors to be carried by different subcarriers, the calculation expression for the signal vector corresponding to the nonlinear transceiver is obtained as follows:

[0031]

[0032] Among them, v k (n) represents the signal vector v k The nth element in Let v represent a complex vector. k The real and imaginary parts of all elements are restricted to... Within the region, the modulo operation expression is defined as:

[0033]

[0034] Among them, MOD M (.) represents the modulo operation, and x represents the input of the modulo operation. Represents the largest integer not exceeding z;

[0035] In a nonlinear transceiver, v k The calculation expression is:

[0036]

[0037] in, U represents a lower triangular matrix with unit diagonal elements. k =s k +i k Represents a valid signal vector; when B k When = 0, the nonlinear transceiver simplifies to a linear transceiver.

[0038] Preferably, the calculation expression for the received signal of the transceiver in the multi-carrier MIMO wireless power-carrying communication system based on the signal vector is as follows:

[0039]

[0040] Among them, y k Indicates receiving signal, This represents the frequency domain precoding matrix in the transmitter. The antenna noise vector is an additive white Gaussian noise vector.

[0041] Preferably, the receiver uses a power divider to divide the received signal into a β-proportional portion and a 1-β-proportional portion. The β-proportional portion of the received signal power is used for energy harvesting, and the frequency domain signal y sent to the energy harvester is... kEH The calculation expression is:

[0042]

[0043] The received signal power of the 1-β proportional portion is used for information decoding, and the frequency domain signal sent to the information decoder is... Represented as:

[0044]

[0045] in, Representing the decoding noise efficiency, it is an additive white Gaussian noise vector introduced by the digital circuitry in the information decoder.

[0046] Preferably, the transceiver optimization problem is:

[0047]

[0048] in, M represents the equalization matrix of the k-th subcarrier. k Let P represent the MSE matrix of the k-th subcarrier, Tr(.) represent the trace of the matrix, and P th E represents the system's maximum transmit power. in E represents the input power of the energy harvester. th This indicates the energy harvesting power requirements of the system design. This represents the average energy harvesting power threshold.

[0049] Preferably, the MSE matrix M of the k-th subcarrier k The calculation method is as follows:

[0050] frequency domain signal Equilibrium converted into an equalization signal The calculation expression is:

[0051]

[0052] Among them, based on equalization signal The MSE matrix of the k-th subcarrier is calculated as follows: Equalization signal Substituting the calculation expression into the equation, we can transform it into:

[0053]

[0054] in, The calculation expression is:

[0055]

[0056] in, Ψ represents the variance of the decoding noise. k (P k G k C k The calculation expression for ) is:

[0057]

[0058] in, This means it is equivalent to.

[0059] Preferably, the input power E of the energy harvester in The calculation method is as follows:

[0060]

[0061] in, The value represents the antenna noise variance, and the superscript H indicates the conjugate of the matrix.

[0062] Preferably, the average energy harvesting power threshold The calculation method is as follows:

[0063] Based on input power E in Calculate the harvesting power γ(E) of the energy harvester. in )for:

[0064]

[0065] Considering the energy harvesting power E required by the system design th Let the energy harvesting constraint be γ(E in )≥E th The energy harvesting constraints are transformed into equivalent constraints as follows:

[0066]

[0067] in, The calculation expression is as follows:

[0068]

[0069] Among them, E m E0 and E0 represent the saturation power and activation power of the energy harvester, respectively, while τ and ν characterize the circuit characteristics of the energy harvester.

[0070] Preferably, in S4, an alternating optimization algorithm is used to solve the transceiver optimization problem, including: sequentially solving {G} in the transceiver optimization problem in an alternating manner. k}、{C k}、{P kWe optimize β and Φ until the objective function converges, thus obtaining the transceiver optimization scheme.

[0071] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0072] This invention proposes a transceiver optimization method for an IRS-assisted multi-carrier MIMO wireless power-carrying communication system. First, linear precoding or Thomlinson-Harashima nonlinear precoding is performed on the symbol vectors to be carried by different subcarriers to obtain the signal vectors corresponding to the linear or nonlinear transceiver. Then, based on the signal vectors, the received signal of the transceiver in the multi-carrier MIMO wireless power-carrying communication system is established. By establishing and solving the transceiver optimization problem, a locally optimal transceiver optimization scheme is obtained, thus providing a unified design framework for linear or nonlinear transceivers to adapt to different application scenarios. Attached Figure Description

[0073] Figure 1 This is a flowchart illustrating a transceiver optimization method for an IRS-assisted multi-carrier MIMO wireless power-carrying communication system proposed in an embodiment of the present invention.

[0074] Figure 2 This is a schematic diagram of the structure of the multi-carrier MIMO wireless power-carrying communication system proposed in the embodiments of the present invention;

[0075] Figure 3 This diagram illustrates the average sumMSE versus transmit power distribution proposed in this embodiment of the invention.

[0076] Figure 4 This diagram illustrates the distribution of the average sumMSE contrast reflectance unit number proposed in this embodiment of the invention.

[0077] Figure 5 This diagram illustrates the average achievable rate versus transmission power distribution as presented in this embodiment of the invention. Detailed Implementation

[0078] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent.

[0079] It is understandable to those skilled in the art that some well-known details may be omitted from the accompanying drawings;

[0080] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0081] Example 1

[0082] like Figure 1As shown, this embodiment provides a transceiver optimization method for an IRS-assisted multi-carrier MIMO wireless power-carrying communication system, including the following steps:

[0083] S1. Perform linear precoding or Tomlinson-Harahima nonlinear precoding on the symbol vectors to be carried by different subcarriers to obtain the signal vectors corresponding to the nonlinear transceiver or the linear transceiver;

[0084] S2. Based on the signal vector, establish the received signal of the transceiver of the multi-carrier MIMO wireless power-carrying communication system;

[0085] S3. Under the premise of satisfying the given transmit power constraints and collect power constraints, establish a transceiver optimization problem with the objective function of minimizing the sum of mean square errors of the received signals;

[0086] S4. Solve the transceiver optimization problem to obtain a locally optimal transceiver optimization scheme.

[0087] See Figure 2 The multi-carrier MIMO wireless power-carrying communication system includes N... t A transmitter with one antenna, equipped with N r Receiver with one antenna and equipped with N s The system comprises a smart reflective surface IRS with passive reflective elements; wherein the total bandwidth of the multi-carrier MIMO wireless power-carrying communication system is W, and the total bandwidth is evenly distributed among N subcarriers.

[0088] The equivalent frequency domain channel between transceivers mainly consists of two parts: a direct link and a reflected link, where the reflected link is dynamically adjusted by the IRS controller. Therefore, the expression for calculating the equivalent frequency domain channel matrix of the k-th subcarrier is:

[0089]

[0090] in, This represents the equivalent frequency domain channel matrix of the k-th subcarrier. This represents the equivalent frequency domain channel matrix of the k-th subcarrier transmitter-receiver link. This represents the equivalent frequency domain channel matrix of the k-th subcarrier transmitter-smart reflector IRS link. Let represent the equivalent frequency domain channel matrix of the k-th subcarrier smart reflector surface IRS-receiver link; This represents the reflection coefficient matrix of the intelligent reflective surface IRS, where diag(.) represents the diagonal matrix, and φ m This represents the reflection coefficient of the m-th passive reflective element. φ m The phase adjustment range is [0, 2π).

[0091] The linear precoding or Tomlinson-Harahima nonlinear precoding process for the symbol vectors to be carried by different subcarriers includes:

[0092] Define the symbol vector of the k-th subcarrier as: L k ≤min{N t N r}, s k All elements are M-QAM modulated with a mean of 0 and a variance of 1. Before transmission, s k First, it is processed by the THP unit; the THP processing unit uses the modulo operator MOD and the lower triangular feedback matrix. Nonlinear processing is performed on the symbol vectors to be carried by different subcarriers, especially when B k When = 0, the nonlinear transceiver simplifies to a linear transceiver, and the expression for calculating the signal vector corresponding to the nonlinear transceiver is:

[0093] The calculation expression is:

[0094]

[0095] Among them, v k (n) represents the signal vector v k The nth element in Let v represent a complex vector. k The real and imaginary parts of all elements are restricted to... Within the region, the modulo operation expression is defined as:

[0096]

[0097] Among them, MOD M (.) represents the modulo operation, and x represents the input of the modulo operation. Represents the largest integer not exceeding z;

[0098] Based on the above analysis, in a nonlinear transceiver, v k The calculation expression is:

[0099]

[0100] in, U represents a lower triangular matrix with unit diagonal elements. k =s k +i k This represents a valid signal vector; if M is large enough, then we have Established. When B k When C = 0, the nonlinear transceiver simplifies to a linear transceiver, and for a linear transceiver, we have Ck =I Lk v k =u k =s k .

[0101] Next, a frequency domain precoding matrix is ​​used. Used to process v k The transmitted signal is obtained and then converted into a received signal after propagation through the wireless channel. Based on the signal vector, the calculation expression for the received signal of the transceiver in the multi-carrier MIMO wireless power-carrying communication system is established as follows:

[0102]

[0103] Among them, y k Indicates receiving signal, This represents the frequency domain precoding matrix in the transmitter. The antenna noise vector is an additive white Gaussian noise vector. This indicates that the expression follows a zero mean and a variance of . It is an independent complex Gaussian random distribution.

[0104] For energy harvesting, all antennas are configured to use a uniform energy harvesting ratio β∈(0,1) for power division. The receiver uses a power divider to divide the received signal into a β-proportional portion and a 1-β-proportional portion. The β-proportional portion of the received signal power is used for energy harvesting, and the frequency domain signal sent to the energy harvester is... The calculation expression is:

[0105]

[0106] According to Pasvald's theorem, the input power of the energy harvester is... Through derivation, the input power E of the energy harvester can be obtained. in The calculation method is as follows:

[0107]

[0108] in, The value represents the antenna noise variance, and the superscript H indicates the conjugate of the matrix.

[0109] The average energy harvesting power threshold The calculation method is as follows:

[0110] Based on input power E in A nonlinear EH model is used to calculate the harvesting power γ(E) of the energy harvester. in )for:

[0111]

[0112] Considering the energy harvesting requirements E of the system design th Let the energy harvesting constraint be γ(E in )≥E th The energy harvesting constraints are transformed into equivalent constraints as follows:

[0113]

[0114] in, The calculation expression is as follows:

[0115]

[0116] Among them, E m E0 and E0 represent the saturation power and activation power of the energy harvester, respectively, while τ and ν characterize the circuit characteristics of the energy harvester.

[0117] The received signal power of the 1-β proportional portion is used for information decoding, and the frequency domain signal sent to the information decoder is... Represented as:

[0118]

[0119] in, The decoding noise vector represents the additive white Gaussian noise vector introduced by the digital circuitry in the information decoder. This indicates that the expression follows a zero mean and a variance of . It is an independent complex Gaussian random distribution.

[0120] MSE matrix M of the k-th subcarrier k The calculation method is as follows:

[0121] frequency domain signal Equilibrium converted into an equalization signal The calculation expression is:

[0122]

[0123] Among them, based on equalization signal The MSE matrix of the k-th subcarrier is calculated as follows: Equalization signal Substituting the calculation expression into the equation, we can transform it into:

[0124]

[0125] in, The calculation expression is:

[0126]

[0127] in, Ψ represents the second variance. k (P k G k C k The calculation expression for ) is:

[0128]

[0129] in, This means it is equivalent to.

[0130] The problem of establishing a transceiver optimization is as follows:

[0131]

[0132] in, M represents the equalization matrix of the k-th subcarrier. k Let P represent the MSE matrix of the k-th subcarrier, Tr(.) represent the trace of the matrix, and P th E represents the system's maximum transmit power. in E represents the input power of the energy harvester. th This indicates the energy harvesting requirements of the system design. This represents the average energy harvesting power threshold.

[0133] In S4, This is a highly nonconvex optimization problem because the complex coupling relationships between different optimization variables exist in the objective function and constraint C2. Furthermore, the unit modulus constraint in constraint C4 adds an additional challenge to solving this problem.

[0134] Therefore, the alternating optimization (AO) algorithm is used for nonconvex problems. The solution process includes: sequentially solving {G} in the transceiver optimization problem using an alternating approach. k}、{C k}、{P k Optimize β and Φ until the objective function converges to obtain the transceiver optimization scheme. The specific optimization method is as follows:

[0135] For matrix {G k The optimizations include:

[0136] Due to {G k} Only appears in the question In the objective function, this means {G} k The optimization of} can be simplified into an unconstrained optimization problem, let Regarding G kSince the first derivative is zero, the optimal closed-form expression for the equilibrium matrix can be derived:

[0137]

[0138] Substituting equation (1.12) into equation (1.10), and using the matrix inversion lemma, we can further obtain:

[0139]

[0140] in, as well as

[0141] For matrix {C k The optimizations include:

[0142] As mentioned above, for linear transceivers, Therefore, the following mainly derives the {C} of THP. k The optimal closed-form expression for} can be obtained from equation (1.13):

[0143]

[0144] in, represent The i-th column. From equation (51), we know that [M k ] i,i Only depends on The i-th column means that the MSE of different symbols can be minimized independently. Let T k The Cholesky decomposition is represented as We can obtain:

[0145]

[0146] Among them, Q k It is a lower triangular matrix. It is a lower triangular matrix on the unit diagonal, and has If [M] k ] i,i The minimum value can be achieved if and only if the summation term on the right-hand side of the second equal sign in equation (1.15) is zero, which requires that:

[0147]

[0148] Then we can obtain the optimal solution. expression:

[0149]

[0150] Substituting equation (1.16) into equation (1.13), the MSE matrix of THP can be rewritten as follows: Therefore, we can conclude that:

[0151]

[0152] For matrix {P k The joint optimization of} and β proportions includes:

[0153] For a given {G k}, {C k} and Φ, {P k The joint optimization problem of} and β can be described as follows:

[0154]

[0155] Using the following proposition, we can This can be further simplified into a scalar optimization problem.

[0156] Proposition 1: For a given β, the problem The optimal precoding matrix satisfies the following structure:

[0157]

[0158] in, Depend on front L k The eigenvectors corresponding to large eigenvalues ​​constitute the structure. z k,i The transmit power allocated to the i-th symbol of the k-th subcarrier. It is a unitary matrix, and it makes the MSE matrix of THP... The diagonal elements are all equal to:

[0159]

[0160] Where, λ k,i yes The i-th largest eigenvalue.

[0161] Proof: Given β, if constraint C2 is satisfied, then the problem... This can be transformed into minimizing the sum MSE of the received signal under a given transmit power. Substituting equation (1.19) into equation (1.18) yields:

[0162]

[0163] Furthermore, substituting equations (1.19) and (1.21) into... This leads to an equivalent scalar optimization problem:

[0164]

[0165] Wherein, F0 is defined as:

[0166]

[0167] as well as

[0168] However It remains a non-convex optimization problem, which can be transformed into an approximately convex form using the SCA technique. Introducing auxiliary variable {s k,i} and {p k,i}, we can obtain the following equivalent problem:

[0169]

[0170] Where F is defined as:

[0171]

[0172] The convexity of F can be verified by proving that its Hessian matrix is ​​positive semi-definite. Furthermore, the problem... The constraints C3, C5, and C6 are affine, C7 is convex, and the only non-convex term is C8. To obtain the problem... The approximate convex form can be represented by a first-order Taylor expansion of C8. For a convex function f(x), its first-order Taylor expansion is: in Let f(·) represent the gradient of f(·). Therefore, we can obtain:

[0173]

[0174] in, and This is the optimal solution in the nth iteration of the SCA method. Due to the problem... It is a convex problem, and a detailed proof can be found in Example 2 below. Therefore, it can be solved using existing convex optimization toolboxes such as CVX.

[0175] Based on the above analysis, the problem The solution process is shown in Algorithm 1:

[0176]

[0177] Optimization of the reflection coefficient matrix Φ includes:

[0178] For a given {P k}、{G k} and {C k The optimization problem concerning the reflection coefficient matrix Φ can be expressed as:

[0179]

[0180] It should be noted that the objective function and C2 are non-convex with respect to Φ, and the unit modulus constraint of C4 is also non-convex. To address these challenges, [the following is a separate, unrelated section:] The problem is broken down into a series of subproblems, and then relaxation techniques are used to solve these subproblems.

[0181] Let r k,m and R respectively k The m-th column and T k In the m-th row, the channel matrix of equation (1.1) can be rewritten in the following equivalent form:

[0182]

[0183] make T represents k P k The m-th row, therefore we can Further rewritten as

[0184]

[0185] J k,m and K k,m Defined as

[0186]

[0187] The derivation in equation (1.25) applies... It can be observed that φ m It did not appear in J k,m and K k,m Based on the above analysis and discarding some irrelevant terms, It can be decomposed into a series of subproblems, and the m-th reflection coefficient can be optimized by solving the following problem:

[0188]

[0189] in, Represents T k P k C k The m-th line, and The current problem The only non-convex constraint is C 11 , will C 11 Relaxation is |φ m|≤1 can yield:

[0190]

[0191] Obviously It is about φ m This is a convex problem that can be solved using CVX. For each iteration of the AO algorithm, a series of... Complete the optimization of the reflection coefficient matrix. If the final optimized Φ ＊ Satisfy constraint C 11 This indicates that relaxation is tight, which means Φ ＊ yes A local optimal solution; if not satisfied, then it is necessary to... Normalization was performed. Additionally, φ... m , Normalization will not violate the EH constraint because the collected energy will increase with the increase of the reflection coefficient amplitude.

[0192] Based on the above analysis, the process of the AO algorithm is summarized in Algorithm 2:

[0193]

[0194]

[0195] Furthermore, based on the system parameter settings in Table 1, simulation experiments were conducted using the transceiver optimization algorithm for the IRS-assisted multi-carrier MIMO wireless power-carrying communication system proposed in this embodiment. All simulation results were obtained after 2000 independent channel implementations. To verify the effectiveness of the algorithm proposed in this embodiment, corresponding simulations were also performed for schemes without IRS and random phase IRS, which served as control groups.

[0196] Table 1 System Simulation Parameters

[0197]

[0198] Figure 3 This demonstrates the relationship between average sum MSE performance and transmit power, where N s =20,E th= -5dBm; It can be seen that as the transmit power increases, the average sum MSE for all transceiver algorithms continuously decreases. Regardless of whether it's THP or a linear transceiver, all schemes employing IRS outperform those without, and the scheme proposed in this embodiment always achieves the minimum sum MSE performance. Furthermore, for the same transceiver algorithm, THP consistently outperforms the linear transceiver in sum MSE performance. It is worth noting that using the transceiver design proposed in this embodiment, the performance difference between THP and the linear transceiver is more significant than with other schemes, further verifying that THP has a more pronounced performance advantage in the IRS-assisted MIMOSWIPT system.

[0199] Figure 4 This demonstrates the relationship between average sum MSE performance and the number of reflective elements, where P th =28dBm, E th = -5dBm; It can be seen that, regardless of whether it's THP or a linear transceiver, the average sumMSE performance of the transceiver algorithm proposed in this embodiment is always better than other schemes. For the same transceiver algorithm, THP's performance is always better than that of the linear transceiver. Furthermore, under the premise of using the transceiver scheme proposed in this embodiment, as the number of reflection units increases, the performance of both the linear transceiver and THP also continuously improves. This is because: a larger number of reflection units can improve the quality of the wireless channel, which is very beneficial to the communication performance of the MIMO system.

[0200] Figure 5 This demonstrates the relationship between average achievable rate and transmit power, where P th =28dBm, E th = -5dBm; It can be observed that the achievable rate of all schemes increases with increasing transmit power. Regardless of whether it's a linear transceiver or THP, the scheme proposed in this embodiment achieves optimal performance. For the same transceiver scheme, THP always outperforms the linear transceiver. The reason is that the design goal of this embodiment is to minimize sumMSE rather than maximize information rate. For THP, its optimal precoding matrix not only minimizes the sumMSE of each subcarrier but also maximizes the achievable rate of each subcarrier. For a linear transceiver, its optimal precoding matrix can only minimize the sumMSE of each subcarrier, which leads to the performance difference between the linear transceiver and THP.

[0201] Simulation results show that the AO algorithm proposed in this embodiment can achieve a smaller sumMSE compared with the traditional benchmark scheme. Furthermore, THP consistently outperforms the linear transceiver in both sum MSE and average reachable rate, further demonstrating THP's performance advantage.

[0202] In this embodiment, a transceiver optimization scheme for applying a nonlinear / linear transceiver to a smart reflector-assisted multi-carrier MIMO wireless power-carrying communication system is first designed, including a transceiver model, a transceiver optimization algorithm, and simulation results. Then, an IRS-assisted multi-carrier MIMO based on THP is first modeled. The SWIPT system, based on this, can be simplified to a linear transceiver model simply by changing the transmitter's feedback matrix to an all-zero matrix. The transmitter model is characterized by employing THP technology, where THP processing includes modulo operations, a feedback matrix, and a precoding matrix. When the feedback matrix is ​​an all-zero matrix, the transceiver model is equivalent to a linear transceiver model. The receiver model is characterized by using a power divider to split the signal into two parts at a certain ratio: one part for information decoding and the other for energy harvesting. The energy harvester is characterized by its nonlinear properties, which better characterize the physical features of a real energy harvesting model and has strong practical significance. This embodiment also optimizes the transmit precoding matrix, transmit feedback matrix, receive equalization matrix, power allocation ratio, and reflection coefficient matrix of the multi-carrier MIMOSWIPT system to minimize the sum of the received signal's MSE while satisfying given transmit power and harvesting power constraints. Because the established optimization problem is highly nonconvex, an alternating optimization algorithm is proposed for solving it, mainly including the following two steps: First, for a given IRS reflection coefficient matrix, the original optimization problem is transformed into a joint scalar optimization problem concerning power allocation and power splitting ratio using the optimal closed-form expressions of the precoding matrix, feedback matrix, and equalization matrix. This problem is then solved using the successive convex approximation (SCA) technique. Next, using the precoding matrix, feedback matrix, equalization matrix, and power allocation ratio obtained in the previous step, the optimization problem concerning the reflection coefficient matrix is ​​decomposed into a series of subproblems. By solving these subproblems sequentially, the optimization of the reflection coefficient matrix is ​​completed. The alternating iteration of these two steps can find the local optimum of the system design. Simulation results show that, regardless of whether it is a nonlinear or linear transceiver, compared to schemes without IRS and random phase IRS schemes, the transceiver design proposed in this embodiment can significantly improve the system's sunMSE performance.

[0203] Example 2

[0204] This embodiment provides a detailed proof that problem P5 is a convex problem, as follows:

[0205] Since constraints C3, C5, and C6 of problem P5 are affine, and constraints C7 and C9 are convex, to prove that problem P5 is convex, it suffices to prove that the Hessian matrix of the objective function F is positive semi-definite.

[0206] For THP, we can get Therefore, it is only necessary to prove the Hessian matrix. It is positive semidefinite, that is:

[0207]

[0208] in, It can be proven And for have:

[0209]

[0210] therefore, It can be represented as:

[0211]

[0212] in, t=1,…,L k .because Therefore, it is only necessary to prove:

[0213]

[0214] Since the following equation always holds true:

[0215]

[0216] Therefore, equations (1.31) and (1.28) can be easily proven.

[0217] The same method can be used to prove this for linear transceivers. It is convex. Specifically, it can be proven that the Hessian matrix... Among them have:

[0218]

[0219] It can be easily obtained using (1.33). It is a convex function.

[0220] In summary, It is a convex problem.

[0221] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A transceiver optimization method for an IRS-assisted multi-carrier MIMO wireless power-carrying communication system, characterized in that, Includes the following steps: S1. Perform linear precoding or Tomlinson-Harashima nonlinear precoding on the symbol vectors to be carried by different subcarriers to obtain the signal vectors corresponding to the nonlinear transceiver or linear transceiver; the multi-carrier MIMO wireless power-carrying communication system includes equipment The transmitter with the antenna is equipped with Receiver and equipment for root antenna The intelligent reflective surface IRS of passive reflective units; wherein, the total bandwidth of the multi-carrier MIMO wireless power-carrying communication system is W The total bandwidth is evenly distributed to The subcarrier, the The expression for calculating the equivalent frequency domain channel matrix of each subcarrier is: （1.1） in, Indicates the first The equivalent frequency domain channel matrix of each subcarrier. Indicates the first The equivalent frequency domain channel matrix of a subcarrier transmitter-receiver link Indicates the first Equivalent frequency domain channel matrix of a subcarrier transmitter-smart reflector IRS link Indicates the first Equivalent frequency domain channel matrix of a subcarrier smart reflector surface IRS-receiver link; This represents the reflection coefficient matrix of the intelligent reflective surface IRS. Represents a diagonal matrix. Indicates the first The reflection coefficient of a passive reflective element. , The phase adjustment range is ; The linear precoding or Tomlinson-Harahima nonlinear precoding process for the symbol vectors to be carried by different subcarriers includes: Definition of the first The symbol vector of each subcarrier is , By using the modulo operator MOD and the lower triangular feedback matrix By performing nonlinear processing on the symbol vectors to be carried by different subcarriers, the calculation expression for the signal vector corresponding to the nonlinear transceiver is obtained as follows: in, Represents signal vector The first in n One element, To represent a complex vector, The real and imaginary parts of all elements are restricted to... Within the region, the modulo operation expression is defined as: in, This represents the modulo operation, where x represents the input to the modulo operation. Representing no more than The largest integer; In nonlinear transceivers The calculation expression is: （1.2） in, This represents a lower triangular matrix with unit diagonal elements. Represents a valid signal vector; when At that time, the nonlinear transceiver is simplified to a linear transceiver. , ; S2. Based on the signal vector, establish the received signal of the transceiver in the multi-carrier MIMO wireless power-carrying communication system; the calculation expression for establishing the received signal of the transceiver in the multi-carrier MIMO wireless power-carrying communication system based on the signal vector is as follows: （1.3） in, Indicates receiving signal, This represents the frequency domain precoding matrix in the transmitter. This represents the antenna noise vector, which is an additive white Gaussian noise vector. The receiver divides the received signal into power segments using a power divider. The received signal power of the proportional part and 1- The received signal power of the proportional portion, The received signal power of the proportional portion is used for energy harvesting, and the frequency domain signal sent to the energy harvester is... The calculation expression is: （1.4） 1- The received signal power of the proportional portion is used for information decoding, and the frequency domain signal sent to the information decoder is... Represented as: （1.8） in, This represents the decoding noise vector, which is an additive white Gaussian noise vector introduced by the digital circuitry in the information decoder. S3. Under the premise of satisfying the given transmit power constraints and collect power constraints, establish a transceiver optimization problem with the objective function of minimizing the sum of mean square errors of the received signals; The problem of establishing a transceiver optimization is as follows: in, Representing the The equalization matrix for each subcarrier, Indicates the first The MSE matrix of each subcarrier, Represents the trace of the matrix. Represents the system's maximum transmit power. This indicates the input power of the energy harvester. This indicates the energy harvesting power requirements of the system design. Indicates the average energy harvesting power threshold; S4. Solve the transceiver optimization problem to obtain a locally optimal transceiver optimization scheme.

2. The transceiver optimization method for an IRS-assisted multi-carrier MIMO wireless power-carrying communication system according to claim 1, characterized in that, The first MSE matrix of subcarriers The calculation method is as follows: frequency domain signal Equilibrium converted into an equalization signal The calculation expression is: （1.9） Among them, based on equalization signal Calculate the first The MSE matrix of each subcarrier is Equalization signal Substituting the calculation expression into the equation, we get: （1.10） in, The calculation expression is: in, Indicates the variance of decoding noise. The calculation expression is: （1.11） in, This means it is equivalent to.

3. The transceiver optimization method for an IRS-assisted multi-carrier MIMO wireless power-carrying communication system according to claim 2, characterized in that, The input power of the energy harvester The calculation method is as follows: (1.5) in, Indicates antenna noise variance, superscript H A conjugate device for a matrix.

4. The transceiver optimization method for an IRS-assisted multi-carrier MIMO wireless power-carrying communication system according to claim 3, characterized in that, The average energy harvesting power threshold The calculation method is as follows: Based on input power Calculate the harvesting power of the energy harvester for: （1.6） Considering the energy harvesting requirements of the system design Let the energy harvesting constraints be The energy harvesting constraints are transformed into equivalent constraints as follows: （1.7） in, The calculation expression is as follows: in, and These represent the saturation power and activation power of the energy harvester, respectively. and The circuit characteristics of the energy harvester were described.

5. The transceiver optimization method for an IRS-assisted multi-carrier MIMO wireless power-carrying communication system according to claim 4, characterized in that, In S4, an alternating optimization algorithm is used to solve the transceiver optimization problem, including: sequentially solving the transceiver optimization problem in an alternating manner. , , and Optimize until the objective function converges to obtain the transceiver optimization scheme.

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