RIS-assisted MIMO system dual-time-scale transmission method and RIS-assisted MIMO system dual-time-scale transmission system

Abstract

The invention discloses a double-time-scale transmission method and system for an RIS-assisted MIMO system. The method comprises the following steps: step 1, constructing a single-cellular uplink RIS-assisted millimeter wave large-scale MIMO system model; 2, converting an RIS element phase shift vector problem into a multi-arm bandit problem, and finding out a correlation matrix closely related to each time slot phase shift vector; 3, based on an improved orthogonal matching pursuit algorithm, calculating a small time slot matrix of the current time slot by using information of all time slots before the current time slot, and finally obtaining a correlation matrix of the whole time period; step 4, according to the calculated correlation matrix, solving phase shift vectors of all time slots by using a CMAB-based phase shift algorithm; and 5, calculating an effective uplink matrix and a combined matrix according to the solved phase shift vector, and further solving a base station receiving signal. According to the method, the cost can be remarkably reduced, and the effective spectrum efficiency is improved.

Description

Technical Field

[0001] The invention relates to a dual-time-scale transmission method for a RIS-assisted MIMO system, and belongs to the technical field of wireless communications. Background Art

[0002] In recent years, smart reflecting surface (RIS)-assisted networks have attracted much attention due to their ability to improve network capacity. Smart radio environments can use smart reflecting surfaces (RIS) to manipulate the propagation of incident electromagnetic waves in a programmable manner, thereby actively changing the implementation of channels and turning wireless channels into a controllable system block that can be optimized to improve the overall performance of the system.

[0003] In order to fully utilize the advantages of the intelligent reflection surface (IRS), various joint beamforming design schemes have been proposed in the prior art. Active and passive beamforming joint design schemes have been proposed for single-cell multi-user multiple-input multiple-output (MIMO) or multiple-input multiple-output (MISO) systems, with the aim of minimizing the total transmit power or maximizing the spectrum efficiency. And by analyzing the ergodic capacity, a distributed RIS-assisted progressive optimal beamforming algorithm for large-scale antenna systems has been proposed. However, the above technical solutions all assume that the base station (BS) can provide instantaneous channel state information (I-CSI). In order to calculate the instantaneous channel state information (I-CSI) within the channel coherence interval, the pilot overhead will increase with the increase in the number of cells and users on the intelligent reflection surface.

[0004] In order to reduce the pilot overhead, a dual time-scale transmission scheme (TTTS) based on S-CSI is proposed to greatly reduce the pilot overhead, including the optimal phase shift design based on the ergodic spectral efficiency upper bound and statistical channel state information. Considering both single-user and multi-user cases, an efficient algorithm based on statistical channel state information CSI is designed to design the phase shift, and the phase shift and power allocation under large time scales are designed based on S-CSI. At the same time, the base station (BS) beamforming under small time scales is designed. However, I-CSI is not easy to obtain, especially at high speeds. When the user moves quickly, the time that I-CSI remains unchanged is relatively short, and the pilot overhead for calculating I-CSI occupies a large proportion of symbols, thereby reducing the spectrum efficiency. Summary of the invention

[0005] The technical problem to be solved by the present invention is that the prior art uses CSI to calculate channels and transmit signals, which has low efficiency and high cost.

[0006] In order to solve the above technical problems, the present invention provides a dual time scale transmission method of a RIS-assisted MIMO system, comprising the following steps:

[0007] Step 1: Build a single-cell uplink RIS-assisted millimeter-wave massive MIMO system model;

[0008] Step 2: Phase shift vector θ of the RIS element t The design problem is transformed into a multi-armed bandit problem, and the phase shift vector θ corresponding to each time slot is found. t The closely related correlation matrix R;

[0009] Step 3: Based on the improved orthogonal matching pursuit algorithm, the information of all time slots before the current time slot is used to calculate the mini-slot matrix of the current time slot. Finally, the correlation matrix R of the entire time period is obtained;

[0010] Step 4: According to the calculated correlation matrix R, the phase shift vector θ of all time slots is solved using the CMAB-based phase shift algorithm. t ;

[0011] Step 5: According to the obtained phase shift vector Θ t Calculate the effective upstream matrix and the combination matrix Further solve the base station received signal y(t) = w(t)G(t)diag(θ(t))h(t)s(t), represents the channel energy, is the effective upstream matrix of the transformation, Representation Matrix The transposed conjugate matrix of , diag(θ(t)) represents the diagonal matrix of the phase shift matrix θ(t), and G(t) is the channel vector from the smart reflection surface RIS to the base station BS.

[0012] In the aforementioned dual-time-scale transmission method of the RIS-assisted MIMO system, in step 1, the single-cell uplink intelligent reflection surface RIS-assisted millimeter-wave large-scale MIMO system includes a base station BS, the base station BS communicates with a single-antenna user through a RIS having N cellular units, and the base station BS and the RIS are respectively equipped with M=M x ×M y and N = N x ×N y Uniform planar antenna array of M elements x , M y Respectively represent the number of elements of the uniform antenna array at the base station side in the horizontal and vertical directions; N x , N y represent the number of elements of the uniform antenna array on the RIS side in the horizontal and vertical directions, respectively;

[0013] Let G(t) and h(t) represent the channel vector from the intelligent reflection surface RIS to the base station BS and the channel vector from the user to the RIS in the tth time slot respectively; the geometric millimeter wave channel model is used to describe the channel characteristics, y = [y 1 ,y 2,…,y K ] T represents the received signal; K represents the number of channels;

[0014] The channel vector G(t) from the intelligent reflection surface RIS to the base station BS is expressed as:

[0015]

[0016] Where, L is the number of paths; is the path gain of the lth path; a M (·) and a N (·) are the steering vectors of RIS and BS, respectively; denote the horizontal departure angle and vertical departure angle of the lth path on the RIS side, respectively; denote the horizontal arrival angle and vertical arrival angle of the lth path at the base station side respectively;

[0017] The channel vector h(t) from the user to the RIS is expressed as:

[0018]

[0019] Where P is the number of paths, β p ∈CN(0,1) is the p-th path gain, denote the horizontal and vertical arrival angles of the p-th path on the RIS side, respectively;

[0020] θ t represents the phase shift vector of RIS in the tth time slot, where the nth element θ n,t The modulus value satisfies |θ n,t |=1, let s(t) be the symbol transmitted by the user, the combination matrix of the base station in the tth time slot is w(t), and assuming that the direct channel between the base station and the user is blocked, the signal received by the base station in time slot t is:

[0021] y(t)=w(t)G(t)diag(θ(t))h(t)s(t)+n(t) (3)

[0022] Where n(t) is the Gaussian noise in the tth time slot;

[0023] When the phase shift vector θ is obtained t Then, a combination matrix is ​​designed in each time slot, as shown in formula (6). The user sends a pilot signal s=1, and the signal received by the base station in the tth time slot is:

[0024]

[0025] Among them, the effective uplink channel matrix becomes n(t) is the noise;

[0026] The effective uplink channel matrix is ​​transformed into:

[0027]

[0028] The combined matrix w(t) is expressed as formula (6):

[0029]

[0030] in, represents the channel energy, Representation Matrix The transposed conjugate matrix of .

[0031] In the aforementioned dual-time-scale transmission method of the RIS-assisted MIMO system, in step 2, the time slot phase shift vector θ is designed. t When Maximization, expressed as a problem As follows:

[0032]

[0033] st|θ n,t |=1,n=1,2,…,M. (8)

[0034] θ t represents the phase shift vector of RIS in the tth time slot, where the nth element is θ n,t ;

[0035] The problem Convert it into a multi-armed bandit problem to solve it. The solution process is:

[0036] Considering phase shift as action, channel energy As a bonus, the expression is:

[0037]

[0038] Among them, r(θ t ) represents the phase shift vector θ t The reward under, E(·) represents the energy;

[0039] make is a cascade channel, equation (9) is simplified to:

[0040]

[0041] Among them, the correlation matrix diag(h(t)) represents the diagonal matrix of the effective uplink channel matrix, Cascade channel.

[0042] In the aforementioned dual-time-scale transmission method of a RIS-assisted MIMO system, in step 3, in the tth time slot, the signal received by the base station is:

[0043]

[0044] t c,1 and t c,2 represents the start time slot and the end time slot in the cth coherence time. Assuming that the current time slot is in the Cth coherence time, equation (11) is transformed into:

[0045]

[0046] in and θ c are the received signal and the performed phase shift vector within the Cth coherence time, represents the cascaded channel in the cth coherence time, represents the noise in the cth coherence time, we can get

[0047]

[0048] where ∧ n yes The diagonal matrix of is a random variable with mean zero, is the noise matrix in the Cth coherence time, represents the transposed conjugate matrix, represents the concatenated channel matrix of the kth time slot in the cth coherence time;

[0049] According to formula (13), the calculation of the correlation matrix R is expressed as the problem

[0050]

[0051] in, ‖·‖ F represents the F-norm;

[0052] make Then there is

[0053]

[0054] in, is the KR product matrix operation, ⊙ is the Hadamard matrix operation; let the variable matrix R G =E(G H (t)G(t)), variable matrix R h =E(h * (t)h T(t)), then

[0055] R=E(G H (t)G(t))⊙E(h * (t)h T (t)) = R G ⊙R h (16)

[0056] Defining intermediate variables represents the Kronecker product operation, vec(·) is a vectorized operator, ∧ h is a diagonal matrix, m c is the signal of the cth coherence time, M c is the signal matrix of the cth coherence time, then the intermediate variable The computational problem is expressed as

[0057]

[0058] Let A=[A 1 ; A 2 ;…;A c ],m=[m 1 ;m 2 ;…;m c ], then the problem Transform into a problem

[0059]

[0060] For the problem Using OMP algorithm to solve intermediate variables Get the intermediate variable Then use the following formula to calculate the diagonal matrix

[0061]

[0062] Among them, unvec(·) is a matrix operator, Indicates that the intermediate variable Matrixization, Indicates that the intermediate variable After matrixing, the transformed result is transformed into a transposed conjugate matrix. At the same time, the diagonal matrix is ​​obtained After that, use Solving the change matrix 2

[0063] , and denote the discrete Fourier transform (DFT) vector on the RIS side and the transpose of the discrete Fourier transform (DFT) vector on the RIS side, respectively.

[0064] Similarly, given the matrix change matrix two The initial value of the change matrix is ​​solved according to the above formula (17) to formula (20): Solve the change matrix by Then iteratively calculate the change matrix of different time slots and change matrix 2 Until convergence, finally get the small phase shift matrix Finally, the correlation matrix R of the entire time period is obtained.

[0065] In the aforementioned dual-time-scale transmission method of a RIS-assisted MIMO system, in step 4, the phase shift vector of the t-th time slot is expressed as:

[0066]

[0067] Where k is a random variable uniformly distributed in [0, 1], ε and η are two constants; η represents the probability of selecting the optimal phase shift; ε represents the probability of selecting the optimal phase shift in the set S t The probability of selecting a phase shift with high spectral efficiency in random(S t ) represents a random selection set S t An element in sub Indicates that in the set S t Select a random variable in the subspace close to the main eigenvalue, θ opt is the phase shift that maximizes the received energy.

[0068] U R represents the combined matrix composed of the eigenvectors corresponding to the eigenvalues ​​of the correlation matrix R; the chord distance represents the distance between the phase shift vector and the correlation matrix R, which is defined as

[0069]

[0070] where u θ is the eigenvector of θ, that is f sub (S t ) is the problem The solution,

[0071]

[0072] For the problem First calculate the set S t The chord distance of each phase shift in the problem is then selected as the phase shift vector with the smallest chord distance. The solution;

[0073] rand(S t ) represents a random selection set S t An element of , Δ represents the phase shift, Δ opt is the phase shift that maximizes the received energy, which is the problem Solution:

[0074]

[0075] st|θ i |=1,i=1,2,…,M. (25)

[0076] Among them, θ represents the phase shift and st represents being restricted by, that is, the restriction condition.

[0077] In the aforementioned dual-time-scale transmission method for RIS-assisted MIMO system, in step 4, a phase shift design algorithm based on the Riemann conjugate gradient method is used to solve the problem The steps are as follows, where ∈ 1 is the convergence threshold:

[0078] Step 1: Input the calculated vector matrix And set the coordinates of the starting point on the complex circular manifold θ 0 =1, k=0;

[0079] Step 2: Calculate the Riemann gradient gradf(θ according to formula (27) (k) );

[0080] Step 3: Calculate the Riemann conjugate direction d according to formula (26) k ;

[0081] Step 4: Select the step size α according to the Armijo backtracking line search rule k ;

[0082] Step 5: From k←k+1, update point θ according to formula (28);

[0083] Step 6: Calculate the gradient ||gradf(θ (k) )|| 2 The modulus of ||gradf(θ (k) )|| 2 >∈ 1 ,repeat

[0084] Step2-Step 6; until ‖gradf(x k )‖ 2 ≤∈ 1 ;

[0085] Step 7: Output the phase shift θ that maximizes the received energyopt ;

[0086]

[0087] Among them, β k is the Polak-Ribiere parameter, gradf(θ (k) ) is the function f on the manifold Upper θ (k) The Riemann gradient of a point is:

[0088]

[0089] in, is a vector transfer operation, and the calculation formula is

[0090]

[0091] In the aforementioned dual-time-scale transmission method of the RIS-assisted MIMO system, in step 4, the phase shift vector θ is obtained based on the phase shift algorithm of CMAB. t The steps are as follows, where ∈ 2 is the convergence threshold:

[0092] Step 1: Let the starting signal y l =1, starting position θ l =1, l = 1, 2, ..., t, initialize the starting time slot change matrix Starting time slot change matrix 2 The initial iteration number k=1; I represents the identity matrix whose elements are all 1;

[0093] Step 2: According to the formula and Calculate the signal matrix M of the cth coherence time c and the phase shift vector Θ of the cth coherence time c ;

[0094] Step 3: Given the change matrix of the kth time slot The initial value is converted into the intermediate variable of the kth time slot according to formula (19) The solution problem is to use the OMP algorithm to solve the intermediate variables of the kth time slot.

[0095] Step 4: Solve the diagonal matrix according to formula (20) Then according to the formula Solve the change matrix of the kth time slot 2

[0096] Step 5: The method is the same as Step 4. Given the change matrix of the kth time slot, Initial value, solve the change matrix of the kth time slot

[0097] Step 6: From k←k+1, solve the change matrix 2 of the k+1th time slot With the change matrix

[0098] Step 7: Calculate the gradients separately If the modulus of or Repeat Step 3-Step 6 until satisfied or Until now; record the change matrix 2 of the kth time slot With the change matrix

[0099] Step 8: Calculate the formula based on the small slot matrix Calculate the output mini-slot matrix

[0100] Step 9: Obtain the designed phase shift vector θ according to equation (21) t , and output.

[0101] In the aforementioned dual-time-scale transmission method of the RIS-assisted MIMO system, in step 5, according to the obtained phase shift vector θ t Calculate the effective upstream matrix And the combination matrix w(t) is used to further solve the base station received signal y(t). The steps are as follows:

[0102] According to formula (29), the effective uplink matrix is ​​calculated

[0103]

[0104] Calculate the combination matrix w(t) according to formula (6);

[0105]

[0106] in, represents the channel energy, Representation Matrix The transposed conjugate matrix of ;

[0107] The base station received signal y(t) is calculated according to formula (30):

[0108] y(t)=w(t)G(t)diag(0(t))h(t)s(t) (30).

[0109] A computer device / equipment / system comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0110] A computer-readable storage medium stores a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0111] A computer program product comprises a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0112] The beneficial effects achieved by the present invention are as follows: the method of the present invention transforms the phase shift design problem into a MAB problem, and uses an online learning process to obtain the phase shift without the need for CSI; and solves the effective uplink channel matrix based on the phase shift, designs a combination matrix at a small time scale, reduces interference, and improves the system spectrum efficiency. The method of the present invention can significantly improve the spectrum efficiency and can significantly reduce the cost of channel calculation without the need for CSI. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] Figure 1 is a schematic diagram of a dual time scale transmission structure provided by Embodiment 1 of the present invention;

[0114] Figure 2 is a graph showing the relationship between ESE performance and the number of time slots under different schemes of Embodiment 1 of the present invention;

[0115] Figure 3 is a graph showing the relationship between ESE performance and signal-to-noise ratio under different schemes of Example 1 of the present invention;

[0116] Figure 4 This is a graph showing the relationship between the ESE performance and the number of channel paths under different schemes according to Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0117] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0118] Example 1

[0119] This embodiment considers a single-cell uplink RIS-assisted millimeter-wave massive MIMO system and proposes a dual-time-scale transmission method (TTTs) for a RIS-assisted MIMO system based on context MAB. Different from other dual-time-scale transmission schemes, the RIS element phase shift is designed based on MAB and does not require CSI.

[0120] On the large time scale of the TTTs scheme, this embodiment transforms the RIS element phase shift design problem into a MAB problem, regards the phase shift as an action, and uses the channel energy as a reward. A learning process is used to obtain the phase shift, and the reward is related to the covariance channel matrix (CCM) of the cascade channel. During the learning process, the covariance channel matrix (CCM) is learned by an improved orthogonal matching pursuit algorithm, and an improved greedy algorithm is used to design a phase shift with high spectral efficiency. In addition, a manifold optimization algorithm is used to obtain the optimal phase shift.

[0121] On the small time scale of the TTTs scheme, a combination is designed based on the effective uplink channel matrix to reduce interference and improve spectrum efficiency. The simulation results also verify the good performance of the scheme.

[0122] A dual-time-scale transmission method for a RIS-assisted MIMO system comprises the following steps:

[0123] Step 1: Build a single-cell uplink RIS (intelligent reflector)-assisted millimeter-wave massive MIMO system model and plan the overall solution.

[0124] Step 2: Phase shift vector θ of the RIS element t The design problem is transformed into a multi-armed bandit problem, and the phase shift vector θ corresponding to each time slot is found. t The closely related correlation matrix R;

[0125] Step 3: Based on the improved orthogonal matching pursuit algorithm, the information of all time slots before the current time slot is used to calculate the mini-slot matrix of the current time slot. Finally, the correlation matrix R of the entire time period is obtained;

[0126] Step 4: According to the calculated correlation matrix R, the phase shift vector θ of all time slots is solved using the CMAB-based phase shift algorithm. t ;

[0127] Step 5: According to the obtained phase shift vector Θ t Calculate the effective upstream matrix and the combination matrix Further solve the base station received signal y(t) = w(t)G(t)diag(θ(t))h(t)s(t), represents the channel energy, is the effective upstream matrix of the transformation, Representation Matrix The transposed conjugate matrix of , diag(θ(t)) represents the diagonal matrix of the phase shift matrix θ(t), and G(t) is the channel vector from the smart reflection surface RIS to the base station BS.

[0128] In step 1, a single-cell uplink intelligent reflector RIS-assisted millimeter-wave massive MIMO system is constructed, which includes a base station BS. The base station BS communicates with a single-antenna user through a RIS with N cellular units. The base station BS and the intelligent reflector RIS are respectively equipped with M = M x ×M y and N = N x ×N y Uniform Planar Antenna (UPA) array of M elements x , M y Respectively represent the number of elements of the uniform antenna array at the base station side in the horizontal and vertical directions; N x , N y represent the number of elements of the uniform antenna array on the RIS side in the horizontal and vertical directions, respectively;

[0129] Let G(t) and h(t) represent the channel vector from the intelligent reflection surface RIS to the base station BS and the channel vector from the user to the RIS in the tth time slot respectively; the geometric millimeter wave channel model is used to describe the channel characteristics, y = [y 1 ,y 2 ,…,y K ] T Indicates receiving signal;

[0130] The channel vector G(t) from the intelligent reflection surface RIS to the base station BS is expressed as:

[0131]

[0132] Where, L is the number of paths; is the path gain of the lth path; a M (·) and a N (·) are the steering vectors of RIS and BS, respectively; denote the horizontal departure angle and vertical departure angle of the lth path on the RIS side, respectively; They represent the horizontal arrival angle and vertical arrival angle of the lth path at the base station side respectively. represents a complex Gaussian distribution with mean 0, statistical independence of the real and imaginary parts, and variance 1 / 2 each.

[0133] Similarly, the channel vector h(t) from the user to the RIS is expressed as:

[0134]

[0135] Where P is the number of paths, is the pth path gain, denote the horizontal and vertical arrival angles of the pth path on the RIS side, respectively.

[0136] θ trepresents the phase shift vector of RIS in the tth time slot, where the nth element θ n,t The modulus value satisfies |θ n,t |=1, let s(t) be the symbol transmitted by the user, the combination matrix of the base station in the tth time slot is w(t), and assuming that the direct channel between the base station and the user is blocked, the signal received by the base station in time slot t is:

[0137] y(t)=w(t)G(t)diag(θ(t))h(t)s(t)+n(t) (3)

[0138] where n(t) is the Gaussian noise in the t-th time slot.

[0139] Assume that the statistical channel state information CSI remains unchanged within T coherence time blocks, and the instantaneous CSI remains unchanged within a coherence time. Each coherence time includes Tc time slots. The phase shift of RIS changes once in each time slot. The structure is as follows: Figure 1 shown.

[0140] When the phase shift vector θ is obtained t Then, a combination matrix is ​​designed in a small time scale (in each time slot), as shown in equation (6). The user sends a pilot signal s=1, and the signal received by the base station in the tth time slot is:

[0141]

[0142] Among them, the effective uplink channel matrix becomes n(t) is noise, whose elements are Gaussian variables with zero mean and unit variance. Considering equation (4), the effective uplink channel matrix is The least squares criterion can be used to estimate, and the effective uplink channel matrix can be transformed into:

[0143]

[0144] The combined matrix w(t) is expressed as formula (6):

[0145]

[0146] in, represents the channel energy, Representation Matrix The transposed conjugate matrix of .

[0147] In step 2, the RIS element phase shift vector θ t The design problem is transformed into a multi-armed bandit problem, finding the phase shift vector θ t The closely related correlation matrix R is as follows:

[0148] First, according to Shannon's formula and equation (4), we know that the spectrum efficiency (ESE) is affected by the channel energy. Therefore, when designing the time slot phase shift vector θ t When Maximizing to improve spectral efficiency can be formulated as the problem As shown below:

[0149]

[0150] st|θ n，t |=1,n=1,2,…,M. (8)

[0151] θ t represents the phase shift vector of RIS in the tth time slot, where the nth element is θ n,t ;

[0152] When using traditional algorithms to solve problems When solving the problem in the above example, CSI is required, which will cost a lot of pilot overhead.

[0153] Therefore, in order to reduce system overhead, the problem Convert it into a multi-armed bandit problem to solve it. The process is:

[0154] Considering phase shift as action, channel energy As a bonus, the expression is:

[0155]

[0156] Among them, r(θ t ) represents the phase shift θ t The reward under, E(·) represents the energy;

[0157] make For the cascade channel, equation (9) can be simplified as:

[0158]

[0159] Among them, the correlation matrix diag(h(t)) represents the diagonal matrix of the effective uplink channel matrix, is a cascade channel;

[0160] According to formula (10), the correlation matrix R is related to the selected action phase shift vector θ t It is very important to obtain the phase shift vector θ t The correlation matrix needs to be calculated.

[0161] In step 3, the mini-slot matrix of the current time slot is calculated by using the information of all time slots before the current time slot based on the improved orthogonal matching pursuit algorithm. And use the mini-slot matrix Select the combined arm of the current time slot. The details are as follows:

[0162] In the tth time slot, the signal received by the base station is:

[0163]

[0164] Cascade Channel It remains unchanged within a coherence time, t c,1 and t c,2 represents the start time slot and the end time slot in the cth coherence time;

[0165] Assuming that the current time slot is within the Cth coherence time, considering all previous time slots, equation (11) can be transformed into:

[0166]

[0168] in and are the received signal and the executed phase shift vector within the Cth coherence time, respectively. represents the cascaded channel in the cth coherence time, represents the noise in the cth coherence time, we can get

[0169]

[0170] where ∧ n yes The diagonal matrix of is a random variable with mean zero, is the noise matrix in the Cth coherence time, represents the transposed conjugate matrix, represents the concatenated channel matrix of the kth time slot in the cth coherence time;

[0171] According to formula (13), the calculation problem of matrix R is expressed as

[0172]

[0173] in, ||·|| F represents the F-norm;

[0174] make Then there is

[0175]

[0176] in, is the KR product matrix operation, ⊙ is the Hadamard matrix operation;

[0177] Let the variable matrix R G =E(G H (t)G(t)) and the variable matrix R h =E(h * (t)h T (t)), then

[0178] R=E(G H (t)G(t))☉E(h * (t)h T (t)) = R G ⊙R h (16)

[0179] Therefore, the calculation problem of the matrix R can be transformed into the calculation of the variable matrix R G , variable matrix 2 R h Make an estimate.

[0180] D BS and D RIS Denote the discrete Fourier transform (DFT) vectors of the BS side and the RIS side respectively, and we can get h k (t) = D RIS λ h(t) , where ∧ G(t) ,λ h(t) are G(t) and h k (t) is represented by the angular domain. From the channel model constructed previously, we know that the angular domain ∧G (t) and angle domain is sparse, and an alternating iterative optimization algorithm is used to estimate the variable matrix R G and variable matrix R h , the process is as follows:

[0181] When a variable matrix R is given G After initialization, estimate the variable matrix R h , which is equivalent to solving the variable matrix R h The effective uplink channel h in each time slot k The angular range λ of (t) h(t) , the proof is as follows:

[0182]

[0183] make

[0184] Defining intermediate variables m c =vec(Mc );

[0185] represents the Kronecker product operation, vec(·) is a vectorized operator, ∧ h is a diagonal matrix, m c is the signal of the cth coherence time, M c is the signal matrix of the cth coherence time, then the intermediate variable The computational problem can be expressed as:

[0186]

[0187] Let A=[A 1 ; A 2 ;…;A c ],m=[m 1 ;m 2 ;…;m c ], then the problem Can be transformed into a problem

[0188]

[0189] For the problem Using OMP algorithm to solve intermediate variables Since the diagonal matrix is a Hermitian matrix, and we get the intermediate variable The diagonal matrix can then be calculated using the following formula

[0190]

[0191] Among them, unvec(·) is a matrix operator, Indicates that the intermediate variable Matrixization, Indicates that the intermediate variable After matrixing, the transformed result is transformed into a transposed conjugate matrix.

[0192] At the same time, we get the diagonal matrix After that, use Solving the change matrix 2 approximation, and Respectively represent the discrete Fourier transform (DFT) vector on the RIS side and the transpose of the discrete Fourier transform (DFT) vector on the RIS side. Similarly, given the matrix transformation matrix 2 The initial value of the change matrix is ​​solved according to the above formula (17) to formula (20): Solve the change matrix by Then iteratively calculate the change matrix of different time slots and change matrix 2 Until convergence, finally get the small phase shift matrix Finally, the correlation matrix R of the entire time period is obtained.

[0193] In step 4, according to the calculated correlation matrix R, the phase shift vector θ of all time slots is solved using the CMAB-based phase shift algorithm. t ;include:

[0194] The improved ∈-greedy algorithm uses 1-e -ηt The probability of selecting the arm that maximizes the spectral efficiency is e -ηt The probability of selecting a random phase shift is , where η is a constant, and when randomly selecting a phase shift, M random phase shifts are selected, which is defined as the set S t , used for exploration, the improvement lies in that when randomly selecting a phase shift, a phase shift vector close to the main eigenvector is selected with probability ∈, so that the random selection action can bring higher spectral efficiency.

[0195] Thus, the phase shift vector of the tth time slot is expressed as:

[0196]

[0197] Among them, k is a random variable uniformly distributed in [0, 1], ε and η are two constants; η represents the probability of selecting the optimal phase shift; the larger η is, the more actions are selected to explore; ε represents the probability of selecting the optimal phase shift in the set S t The probability of selecting a phase shift with high spectral efficiency in random(S t ) represents a random selection set S t An element in sub Indicates that in the set S t Select a random variable close to the subspace corresponding to the main eigenvalue in the θ to achieve higher spectral efficiency. opt is the phase shift that maximizes the received energy.

[0198] Defining λ i represents the number of f in the i-th time slot sub (S t ) corresponds to the maximum eigenvalue, and select the eigenvalue greater than The eigenvalue of ρ is a constant, U R represents the combined matrix composed of the eigenvectors corresponding to the eigenvalues ​​of the correlation matrix R; the chord distance represents the distance between the phase shift vector and the correlation matrix R, which is defined as

[0199]

[0200] where u θis the eigenvector of θ, that is f sub (S t ) is the problem The solution,

[0201]

[0202] For the problem First calculate the set S t The chord distance of each phase shift in the problem is then selected as the phase shift vector with the smallest chord distance. solution, high spectral efficiency can be achieved at this time.

[0203] random(S t ) represents a random selection set S t An element of , θ represents the phase shift, θ opt is the phase shift that maximizes the received energy, which is the problem Solution:

[0204]

[0205] st|θ i |=1,i=1,2,…,M. (25)

[0206] Among them, θ represents the phase shift and st represents being restricted by, that is, the restriction condition.

[0207] For the problem Use the complex circle manifold optimization algorithm to solve The details are as follows:

[0208] 1) The unit module constraint forms a product of M complex circular manifolds, using It means that the recovery is popular yes The embedded submanifold of i |=1 represents a complex circular manifold;

[0209] 2) The manifold optimization algorithm is iterative, that is, given a point in the kth iteration Then point θ k The tangent space at is defined as in represents the real part operation, represents the Hadamard product operation, {·} * represents the conjugate operation;

[0210] 3) On the manifold The current point θ on (k) Select a descent direction Make the search direction always on the manifold and conform to the geometric shape of the manifold;

[0211] 4) Using the Riemann conjugate gradient method (RCG), the search direction d k Defined as:

[0212]

[0213] Among them, β k is the Polak-Ribiere parameter, gradf(θ (k) ) is the function f on the manifold Upper θ (k) The Riemann gradient of a point is:

[0214]

[0215] in, is a vector transfer operation, and the calculation formula is

[0216]

[0217] Finally, in the selected descending direction d k Afterwards, use the retraction operation In the manifold Find the next iteration point θ (k+1) .

[0218] In step 4, the phase shift design algorithm based on the Riemann conjugate gradient method solves the problem The steps are as follows, where ∈ 1 is the convergence threshold:

[0219] Step 1: Input the calculated vector matrix And set the coordinates of the starting point on the complex circular manifold θ 0 =1, k=0;

[0220] Step 2: Calculate the Riemann gradient gradf(θ according to formula (27) (k) );

[0221] Step 3: Calculate the Riemann conjugate direction d according to formula (26) k ;

[0222] Step 4: Select the step size α according to the Armijo backtracking line search rule k ;

[0223] Step 5: From k←k+1, update point θ according to formula (28);

[0224] Step 6: Calculate the gradient ||gradf(θ (k) )||2 The modulus of ||gradf(θ (k) )|| 2 >∈ 1 ,repeat

[0225] Step2-Step 6; until ‖gradf(x k )‖ 2 ≤∈ 1 ;

[0226] Step 7: Output the phase shift θ that maximizes the received energy opt .

[0227] In step 4, the phase shift vector θ is obtained based on the CMAB phase shift algorithm. t The steps are as follows, where ∈ 2 is the convergence threshold:

[0228] Step 1: Let the starting signal y l =1, starting position θ l =1, l = 1, 2, ..., t, initialize the starting time slot change matrix Starting time slot change matrix 2 The initial iteration number k=1; I represents the identity matrix whose elements are all 1;

[0229] Step 2: According to the formula and Calculate the signal matrix M of the cth coherence time c and the phase shift vector Θ of the cth coherence time c ;

[0230] Step 3: Given the change matrix of the kth time slot The initial value is converted into the intermediate variable of the kth time slot according to formula (19) The solution problem is to use the OMP algorithm to solve the intermediate variables of the kth time slot.

[0231] Step 4: Solve the diagonal matrix according to formula (20) Then according to the formula Solve the change matrix of the kth time slot 2

[0232] Step 5: The method is the same as Step 4. Given the change matrix of the kth time slot, Initial value, solve the change matrix of the kth time slot

[0233] Step 6: From k←k+1, solve the change matrix 2 of the k+1th time slot With the change matrix

[0234] Step 7: Calculate the gradients separately If the modulus of or Repeat Step 3-Step 6 until satisfied or Until now; record the change matrix 2 of the kth time slot With the change matrix

[0235] Step 8: Calculate the formula based on the small slot matrix Calculate the output mini-slot matrix

[0236] Step 9: Obtain the designed phase shift vector θ according to equation (21) t , and output.

[0237] Step 5: According to the obtained phase shift vector θ t Calculate the effective upstream matrix And the combination matrix w(t) is used to further solve the base station received signal y(t). The steps are as follows:

[0238] According to formula (29), the effective uplink matrix is ​​calculated

[0239]

[0240] Calculate the combination matrix w(t) according to formula (6);

[0241] The base station received signal y(t) is calculated according to formula (30):

[0242] y(t)=w(t)G(t)diag(θ(t))h(t)s(t) (30).

[0243] In the CMAB process, the present invention uses the information of all previous time slots to calculate the matrix R, designs the phase shift on this basis, and gives the corresponding solution. The simulation results verify that the proposed CMAB-based TTTS can significantly reduce the overhead and improve the effective spectrum efficiency (ESE).

[0244] The method in the embodiment of the present invention is simulated and verified as follows in conjunction with specific implementation methods.

[0245] The performance of the proposed scheme is verified by simulation. The millimeter wave with a frequency of 28 GHz and a wavelength of 10.7 mm is selected. It is calculated that there are 550 coherence times in the set time T. Consider a channel model in which both the BS and the RIS are equipped with an 8×8 antenna ULA array to provide services to a single-antenna user. In the proposed scheme, ε is set to 0.5, ∈ 1 Set to 0.01, ∈ 2 is set to 0.01. The proposed CMAB-based scheme is compared with the LRT-CCM scheme and the Random design scheme. In a time slot, since some symbols are used to transmit the pilot matrix, the effective spectral efficiency (ESE) of the system in t time slots is:

[0246]

[0247] Wherein, Ts=100 represents the number of symbols in each time slot, and τ=1 represents the number of pilot transmission symbols in each time slot.

[0248] Figure 2 The ESE of the CMAB scheme in each time slot is shown. The signal-to-noise ratio is 30dB. C (the number of time slots in a coherent interval) is set to 3 and 6, corresponding to speeds of 3.57m / s and 1.78m / s respectively (the time length of a time slot is 0.5ms). As the number of time slots increases, the ESE based on the MAB scheme first increases and then converges. The ESE when C=6 converges faster than the ESE when C=3. This is because the larger the C, the more time slots there are in a coherent time, and the more accurate the CCM calculation is, resulting in a higher ESE.

[0249] Figure 3 The ESE of different schemes under different signal-to-noise ratios are shown. We consider two cases, one is P=L=2 (number of channel paths) and the other is P=L=4. C is set to 3, which corresponds to a speed of about 3.57m / s. The CMAB-based scheme achieves higher ESE compared to the LRT-CCM and random schemes. The random scheme randomly designs phase shifts, resulting in the lowest spectral efficiency. The LRT-CCM scheme spends many time slots to calculate CCM, and the phase shifts in these time slots are independent of the channel, so the spectral efficiency is lower than that of the proposed scheme. It can also be seen that when P=L=2, the ESE of each scheme is smaller than the ESE when P=L=4.

[0250] Figure 4 The ESE of different schemes under different number of paths is shown. At SNR of 30dB, the ESE of the CMAB-based scheme is higher than that of other schemes. The ESE of the CMAB-based scheme increases with the increase of SNR. This is because the channel energy increases with the increase of the number of paths.

[0251] A computer device / equipment / system comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0252] A computer-readable storage medium stores a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0253] A computer program product comprises a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0254] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0255] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0256] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0257] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0258] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A dual time scale transmission method for a RIS-assisted MIMO system, characterized in that: The following steps are involved: Step 1: Build a single-cell uplink RIS-assisted millimeter-wave massive MIMO system model; Step 2: Phase shift vector θ of the RIS element t The design problem is transformed into a multi-armed bandit problem, and the phase shift vector θ of each time slot is found. t The closely related correlation matrix R; Step 3: Based on the improved orthogonal matching pursuit algorithm, the information of all time slots before the current time slot is used to calculate the mini-slot matrix of the current time slot. Finally, the correlation matrix R of the entire time period is obtained; Step 4: According to the calculated correlation matrix R, the phase shift vector θ of all time slots is solved using the CMAB-based phase shift algorithm. t ; Step 5: According to the obtained phase shift vector Θ t Calculate the effective upstream matrix and the combination matrix Further solve the base station received signal y(t) = w(t)G(t)diag(θ(t))h(t)s(t), represents the channel energy, is the effective upstream matrix of the transformation, Representation Matrix The transposed conjugate matrix of , diag(θ(t)) represents the diagonal matrix of the phase shift matrix θ(t), and G(t) is the channel vector from the smart reflection surface RIS to the base station BS.

2. A dual time scale transmission method for a RIS-assisted MIMO system according to claim 1, characterized in that: In step 1, the single-cell uplink intelligent reflector RIS-assisted millimeter-wave massive MIMO system includes a base station BS, the base station BS communicates with a single-antenna user through a RIS with N cellular units, and the base station BS and the RIS are respectively equipped with M=M x ×M y and N = N x ×N y Uniform planar antenna array of elements, M x , M y Respectively represent the number of elements of the uniform antenna array at the base station side in the horizontal and vertical directions; N x , N y Respectively represent the number of elements of the uniform antenna array on the RIS side in the horizontal and vertical directions; Let G(t) and h(t) represent the channel vector from the intelligent reflection surface RIS to the base station BS and the channel vector from the user to the RIS in the tth time slot respectively; the geometric millimeter wave channel model is used to describe the channel characteristics, y = [y1, y2, …, y K ] T represents the received signal; K represents the number of channels; The channel vector G(t) from the intelligent reflection surface RIS to the base station BS is expressed as: Where, L is the number of paths; is the path gain of the lth path; a M (·) and a N (·) are the steering vectors of RIS and BS, respectively; denote the horizontal departure angle and vertical departure angle of the lth path on the RIS side, respectively; denote the horizontal arrival angle and vertical arrival angle of the lth path at the base station side respectively; The channel vector h(t) from the user to the RIS is expressed as: Where P is the number of paths, β p is the pth path gain, denote the horizontal and vertical arrival angles of the p-th path on the RIS side, respectively; θ t represents the phase shift vector of RIS in the tth time slot, where the nth element θ n,t The modulus value satisfies |θ n,t |=1, let s(t) be the symbol transmitted by the user, the combination matrix of the base station in the tth time slot is w(t), and assuming that the direct channel between the base station and the user is blocked, the signal received by the base station in time slot t is: y(t)=w(t)G(t)diag(θ(t))h(t)s(t)+n(t) (3) Where n(t) is the Gaussian noise in the tth time slot; When the phase shift vector θ is obtained t Then, a combination matrix is ​​designed in each time slot, as shown in formula (6). The user sends a pilot signal s=1, and the signal received by the base station in the tth time slot is: Among them, the effective uplink channel matrix becomes n(t) is the noise; The effective uplink channel matrix is ​​transformed into: The combined matrix w(t) is expressed as formula (6): in, represents the channel energy, Representation Matrix The transposed conjugate matrix of .

3. A dual time scale transmission method for RIS-assisted MIMO system according to claim 2, characterized in that: In step 2, the time slot phase shift vector θ is designed. t When Maximization, expressed as a problem As follows: s.t.|θ n,t |=1,n=1,2,…,M. (8) The problem Convert it into a multi-armed bandit problem to solve it. The solution process is: Considering phase shift as action, channel energy As a bonus, the expression is: Among them, r(θ t ) represents the phase shift vector θ t The reward under, E(·) represents the energy; make is a cascade channel, equation (9) is simplified to: Among them, the correlation matrix diag(h(t)) represents the diagonal matrix of the effective uplink channel matrix, Cascade channel.

4. The dual time scale transmission method of a RIS-assisted MIMO system according to claim 3, characterized in that: In step 3, in the tth time slot, the signal received by the base station is: t c,1 and t c,2 represents the start time slot and the end time slot in the cth coherence time. Assuming that the current time slot is in the Cth coherence time, equation (11) is transformed into: in and θ c are the received signal and the executed phase shift vector within the Cth coherence time, respectively. represents the cascaded channel in the cth coherence time, represents the noise in the cth coherence time, we can get where ∧ n yes The diagonal matrix of is a random variable with mean zero, is the noise matrix within the Cth coherence time, represents the transposed conjugate matrix, represents the concatenated channel matrix of the kth time slot in the cth coherence time; According to formula (13), the calculation of the correlation matrix R is expressed as the problem in, ||·|| F represents the F-norm; make Then there is in, is the KR product matrix operation, ⊙ is the Hadamard matrix operation; let the variable matrix R G =E(G H (t)G(t)), variable matrix R h =E(h * (t)h T (t)), then R=E(G H (t)G(t))☉E(h*(t)h T (t))=R G ☉·R h (16) Defining intermediate variables m c =vec(M c ), represents the Kronecker product operation, vec(·) is the vectorized operator, ∧ h is a diagonal matrix, m c is the signal of the cth coherence time, M c is the signal matrix of the cth coherence time, then the intermediate variable The computational problem is expressed as Let A = [A1; A2; ...; A c ], m=[m1;m2;…;m c ] Question Transform into a problem For the problem Using OMP algorithm to solve intermediate variables Get the intermediate variable Then use the following formula to calculate the diagonal matrix Among them, unvec(·) is a matrix operator, Indicates that the intermediate variable Matrixization, Indicates that the intermediate variable After matrixing, the transformed result is transformed into a transposed conjugate matrix. At the same time, the diagonal matrix is ​​obtained After that, use Solving the change matrix 2 , and They represent the discrete Fourier transform vector on the RIS side and the transpose of the discrete Fourier transform vector on the RIS side respectively. Similarly, given the matrix change matrix two The initial value of the change matrix is ​​solved according to the above formula (17) to formula (20): Solve the change matrix by Then iteratively calculate the change matrix of different time slots and change matrix 2 Until convergence, finally get the small phase shift matrix Finally, the correlation matrix R of the entire time period is obtained.

5. A dual time scale transmission method for a RIS-assisted MIMO system according to claim 4, characterized in that: In step 4, the phase shift vector of the tth time slot is expressed as: Where k is a random variable uniformly distributed in [0, 1], η represents the probability of selecting the optimal phase shift; ε represents the probability of selecting the optimal phase shift in the set S t The probability of selecting a phase shift with high spectral efficiency in random(S t ) represents a random selection set S t An element in sub Indicates that in the set S t Select a random variable in the subspace close to the main eigenvalue, θ opt is the phase shift that maximizes the received energy. U R represents the combined matrix composed of the eigenvectors corresponding to the eigenvalues ​​of the correlation matrix R; the chord distance represents the distance between the phase shift vector and the correlation matrix R, which is defined as where u θ is the eigenvector of the phase shift θ, i.e. f sub (S t ) is the solution to problem P5, For the problem First calculate the set S t The chord distance of each phase shift in the problem is then selected as the phase shift vector with the smallest chord distance. The solution; rand(S t ) represents a random selection set S t An element of opt is the phase shift that maximizes the received energy, which is the problem Solution: st|θ i |=1,i=1,2,…,M. (25)where θ represents the phase shift and st represents the constraint, i.e., the constraint condition.

6. A dual-time-scale transmission method for a RIS-assisted MIMO system according to claim 5, characterized in that: In step 4, the phase shift design algorithm based on the Riemann conjugate gradient method solves the problem The steps are as follows, where ∈1 is the convergence threshold: Step 1: Input the calculated vector matrix And set the coordinates of the starting point on the complex circular manifold θ0 = 1, k = 0; Step 2: Calculate the Riemann gradient gradf(θ according to formula (27) (k) ); Step 3: Calculate the Riemann conjugate direction d according to formula (26) k ; Step 4: Select the step size α according to the Armijo backtracking line search rule k ; Step 5: From k←k+1, update point θ according to formula (28); Step 6: Calculate the gradient ||gradf(θ (k) )||2, if ||gradf(θ (k) )||2>∈1, repeat Step2-Step 6; until ‖gradf(x k )‖2≤∈1; Step 7: Output the phase shift θ that maximizes the received energy opt ; Among them, β k is the Polak-Ribiere parameter, gradf(θ (k) ) is the function f on the manifold Upper θ (k) The Riemann gradient of a point is: in, is a vector transfer operation, and the calculation formula is 7. A dual-time-scale transmission method for a RIS-assisted MIMO system according to claim 6, characterized in that: In step 4, the phase shift vector θ is obtained based on the CMAB phase shift algorithm. t The steps are as follows, where ∈2 is the convergence threshold: Step 1: Let the starting signal y l =1, starting position θ l =1, l = 1, 2, ..., t, initialize the starting time slot change matrix Starting time slot change matrix 2 The initial iteration number k=1; I represents the identity matrix whose elements are all 1; Step 2: According to the formula and Calculate the signal matrix M of the cth coherence time c and the phase shift vector Θ of the cth coherence time c ;in, and are the received signal and the phase shift vector performed within the Cth coherence time, t c,1 and t c,2 represents the start and end time slots in the cth coherence time, represents the signal received by the channel at the beginning of the cth coherence time, represents the signal received by the cascade channel at the end of the cth coherence time, represents the phase shift of the cascaded channel at the beginning of the cth coherence time, represents the phase shift of the cascaded channel at the end of the cth coherence time. Step 3: Given the change matrix of the kth time slot The initial value is converted into the intermediate variable of the kth time slot according to formula (19) The solution problem is to use the OMP algorithm to solve the intermediate variables of the kth time slot Step 4: Solve the diagonal matrix according to formula (20) Then according to the formula Solve the change matrix of the kth time slot 2 Step 5: The method is the same as Step 4. Given the change matrix of the kth time slot, Initial value, solve the change matrix of the kth time slot Step 6: From k←k+1, solve the change matrix 2 of the k+1th time slot With the change matrix Step 7: Calculate the gradients separately If the modulus of or Repeat Step 3-Step 6 until you meet or Until now; record the change matrix 2 of the kth time slot With the change matrix Step 8: Calculate the formula based on the small slot matrix Calculate the output mini-slot matrix Step 9: Obtain the designed phase shift vector θ according to equation (21) t , and output.

8. The dual-time-scale transmission method of a RIS-assisted MIMO system according to claim 6, characterized in that: In step 5, according to the obtained phase shift vector θ t Calculate the effective upstream matrix And the combination matrix w(t) is used to further solve the base station received signal y(t). The steps are as follows: According to formula (29), the effective uplink matrix is ​​calculated Calculate the combination matrix w(t) according to formula (6); in, represents the channel energy, Representation Matrix The transposed conjugate matrix of ; The base station received signal y(t) is calculated according to formula (30): y(t)=w(t)G(t)diag(0(t))h(t)s(t) (30).

9. A computer device / equipment / system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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