Method and system for optimizing sensing and communication integrated system based on reconfigurable intelligent surface

Abstract

The invention discloses an optimization method and system of a sensing and communication integrated system based on a reconfigurable intelligent surface. The method comprises the following steps: constructing a receiving signal model of the sensing and communication integrated system; performing whitening preprocessing on the signal received by the sensing and communication integrated system; performing interference cancellation processing on the pre-processed signal by adopting a maximum ratio combining technology, and designing a minimum mean square error estimator based on communication constellation structure information; and utilizing a minimum mean square error estimator to carry out sensing parameter estimation on the signal after interference offset processing, and completing optimization. According to the method, colored interference noise is converted into white noise through whitening preprocessing, optimal receiving of communication signals is achieved through the maximum ratio combining technology, and the communication performance is effectively improved; according to the method, the structure information of the communication constellation is fully utilized, the MMSE estimator is designed based on the Bayesian estimation theory, and the precision of sensing parameter estimation is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of wireless communication and radar sensing, and particularly to an optimization method and system for a communication-perception integrated system based on a reconfigurable intelligent surface. Background Art

[0002] With the development of 5G / 6G mobile communication systems and the popularization of Internet of Things applications, wireless communication networks need to simultaneously meet the requirements of high-quality communication services and environmental perception. In traditional solutions, communication and perception systems are often designed and deployed independently, which not only causes waste of spectrum resources, but also increases the complexity and cost of the system. Relevant research shows that the millimeter-wave band has the potential of both high communication capacity and high perception accuracy. The existing communication-perception integrated systems mainly have the following problems:

[0003] 1) The perception signal causes serious interference to the communication signal, reducing the communication quality;

[0004] 2) The communication signal affects the target parameter estimation, reducing the perception accuracy;

[0005] 3) The system resource utilization efficiency is not high, and it is difficult to ensure both communication and perception performance simultaneously;

[0006] 4) Under complex channel conditions, there is a lack of effective interference cancellation and parameter estimation methods.

[0007] With the development of wireless communication technology, the industry trend is towards the deep integration of communication and perception functions. Especially in scenarios such as autonomous driving, smart cities, and industrial Internet of Things, the demand for systems with both high-speed communication and accurate environmental perception capabilities is increasing day by day. Therefore, developing effective communication-perception integrated systems and their optimization methods has important theoretical significance and application value. Summary of the Invention

[0008] To solve the problem of mutual interference existing when an existing millimeter-wave radar and communication system operate concurrently, the present invention provides an optimization method and system for a communication-perception integrated system based on a reconfigurable intelligent surface. This method realizes the effective separation and processing of perception and communication signals on the premise of ensuring communication performance.

[0009] To achieve the above object, the present invention provides an optimization method for a communication-perception integrated system based on a reconfigurable intelligent surface, and the steps include:

[0010] Constructing a received signal model of the communication-perception integrated system;

[0011] Performing whitening preprocessing on the signals received by the communication-perception integrated system;

[0012] The interference cancellation process is performed on the preprocessed signal using the maximum ratio combining technique;

[0013] Design a minimum mean square error estimator based on the communication constellation structure information;

[0014] Use the minimum mean square error estimator to estimate the sensing parameters of the signal after the interference cancellation process, and complete the optimization.

[0015] Preferably, the constructed received signal model includes:

[0016] y(n) = (h c + h rc ΦG)s(n) + α(n)b(θ)a H (θ)fx(n) + z(n)

[0017] where, (h c + h rc ΦG)s(n) is the communication signal term, α(n)b(θ)a H (θ)fx(n) is the sensing signal term, z(n) is the additive noise, h c is the direct channel between the user equipment and the base station, h rc is the channel between the user equipment and the RIS, Φ is the RIS phase adjustment matrix, G is the channel between the RIS and the base station, s(n) is the user transmitted signal, α(n) is the target reflection coefficient, b(θ) is the transmit steering vector, a(θ) is the receive steering vector, f is the transmit beamforming vector, and x(n) is the nth sample of the radar transmitted signal.

[0018] Preferably, the method for performing the whitening preprocessing includes:

[0019] Calculate the covariance matrix of the interference plus noise term:

[0020] R c = E[gx(n)x H (n)g H + σ 2 I = gg H + σ 2 I

[0021] where, g = b(θ)a H (θ) is the combination of the steering vectors; σ represents the standard deviation of the noise; E represents the expectation operator; I represents the identity matrix; H represents the matrix transpose symbol;

[0022] Construct the whitening matrix:

[0023]

[0024] Perform whitening processing on the received signal to obtain:

[0025] Qy(n)=Q(h c +h rc ΦG)s(n)+Qz c (n).

[0026] Preferably, the method using the maximum ratio combining technique includes:

[0027] Design a combiner:

[0028] w = Q(h c +h rc ΦG)

[0029] The signal after MRC processing is:

[0030]

[0031] Let H c = h c +h rc ΦG, and the signal-to-noise ratio at the receiving end is expressed as:

[0032]

[0033] where P s represents the average power of the transmitted symbol; w represents the MRC combiner vector; H c represents the combined channel matrix.

[0034] Preferably, the method for designing the minimum mean square error estimator includes:

[0035] Set the communication symbols to be uniformly distributed in the constellation point set;

[0036] Construct the MMSE estimator of the target reflection coefficient based on the Bayesian estimation theory;

[0037] Obtain the final estimator expression through probability decomposition and integral calculation.

[0038] Preferably, the final estimator expression:

[0039]

[0040] where p represents the probability density function; A represents the symbol constellation point set; i represents the index of the constellation point; s i represents the i-th constellation point in each constellation point set; y represents the received signal vector; α represents the target reflection coefficient.

[0041] Preferably, the method for optimizing the received signal s(n) includes:

[0042]

[0043] Among them, P s is the transmit signal power constraint, and is the communication constellation point set.

[0044] The present invention also provides an optimization system for a perception-communication integrated system based on a reconfigurable intelligent surface, which is used to implement the above method, including: a construction module, a processing module, a cancellation module, a design module, and an optimization module;

[0045] The construction module is used to construct a received signal model of the perception-communication integrated system;

[0046] The processing module is used to perform whitening preprocessing on the signals received by the perception-communication integrated system;

[0047] The cancellation module is used to perform interference cancellation processing on the preprocessed signals by using the maximum ratio combining technique;

[0048] The design module is used to design a minimum mean square error estimator based on the communication constellation structure information;

[0049] The optimization module is used to perform perception parameter estimation on the signals after interference cancellation processing by using the minimum mean square error estimator to complete the optimization.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] The present invention converts colored interference noise into white noise through whitening preprocessing, and uses the maximum ratio combining technique to achieve the optimal reception of communication signals, effectively improving communication performance; the present invention makes full use of the structure information of the communication constellation, designs an MMSE estimator based on Bayesian estimation theory, and significantly improves the accuracy of perception parameter estimation. Description of the Drawings

[0052] In order to more clearly illustrate the technical solutions of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0053] Figure 1 is a schematic diagram of the system structure of the application scenario of the embodiment of the present invention;

[0054] Figure 2 is a schematic diagram of the relationship curve between the base station received signal-to-noise ratio (SNR) and the number of RIS elements in the embodiment of the present invention;

[0055] Figure 3Schematic diagram of the relationship curve between the base station received signal-to-noise ratio (SNR) and the communication signal power under 8PSK modulation in the embodiments of the present invention;

[0056] Figure 4 Schematic diagram of the influence of different PSK (phase shift keying) modulation methods on the base station received SNR in the embodiments of the present invention;

[0057] Figure 5 Schematic diagram of the sensing performance curve of the RIS-assisted ISAC system in the embodiments of the present invention. Detailed implementation manners

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0060] Embodiment 1

[0061] This embodiment provides an optimization method for a sensing and communication integrated system based on a reconfigurable intelligent surface. The method is applied to a system including a base station, a reconfigurable intelligent surface (RIS) communication user equipment, and a target, as Figure 1 shown; the figure shows the signal propagation paths, including the direct channel from the user equipment to the base station, the reflected channel from the user equipment through the RIS to the base station, and the path where the radar signal transmitted by the base station is reflected back to the base station by the target. The specific steps include:

[0062] S1. Construct a received signal model for the sensing and communication integrated system.

[0063] First, the received signal of the system needs to be processed, and its signal expression is:

[0064] y(n) = (h c + h rc ΦG)s(n)+α((n)b(θ)a H (θ)fx(n)+z(n)

[0065] where z(n) is the additive noise, h c is the direct channel between the user equipment and the base station, h rcThe channel between the user equipment and the RIS is \(h\), the RIS phase adjustment matrix is \(\varPhi\), the channel between the RIS and the base station is \(G\), the user transmitted signal is \(s(n)\), the target reflection coefficient is \(\alpha(n)\), the transmit steering vector is \(b(\theta)\), the receive steering vector is \(a(\theta)\), the transmit beamforming vector is \(f\), and the \(n\)-th sample of the radar transmitted signal is \(x(n)\); \(H\) represents the matrix transpose symbol.

[0066] Among them, the sensing part is:

[0067] y s (n) = \(\alpha(n)b(\theta)a\) H (\theta)fx(n)+z1(n)

[0068] The communication part is:

[0069] y c (n) = (h c +h rc \(\varPhi G)s(n)+z2(n)\)

[0070] The above \(z1(n)\) and \(z2(n)\) are the additive noises of the corresponding parts respectively.

[0071] The first step is to detect the communication symbol. When detecting the communication symbol, other signals (including the target echo and noise) are regarded as unwanted interference signals. Therefore, the model can be expressed as:

[0072] y(n) = (h c +h rc \(\varPhi G)s(n)+z\) c (n)

[0073] where, \(z\) c (n) = \(\alpha(n)b(\theta)a\) H (\theta)fx(n)+z(n).

[0074] Preprocess the sensing signal term \(\alpha(n)b(\theta)a\) H (\theta)fx(n). Since \(\alpha(n)b(\theta)a\) H (\theta)fx(n) is a complex Gaussian random variable, this term is the product of a complex Gaussian random variable and a deterministic term, and the additive noise \(z(n)\) follows a complex Gaussian distribution. After analysis, it is obtained that:

[0075]

[0076] where, \(I\) represents the identity matrix.

[0077] S2. Perform whitening preprocessing on the signals received by the integrated sensing and communication system.

[0078] First, calculate the covariance matrix of the interference plus noise term. Define \(g = b(\theta)a\)H (θ), then the interference plus noise term z c (n) has a covariance matrix of:

[0079] R c = E[gx(n)x H (n)g H +σ 2 I = gg H +σ 2 I

[0080] where g = b(θ)a H (θ) is a combination of steering vectors; σ represents the standard deviation of the noise; E represents the expected value operator.

[0081] After that, the colored noise matrix z c (n) needs to be converted into a white noise matrix, and whitening processing is required. The whitening matrix Q needs to satisfy:

[0082] E[Qz c (n)(Qz c (n)) H = QR c Q H = I

[0083] It can be shown that the whitening matrix is where Rc represents the covariance matrix.

[0084] The expression of the whitened received signal is:

[0085] Qy(n) = Q(h c + h rc ΦG)s(n)+Qz c (n).

[0086] S3. Use the maximum ratio combining technique to perform interference cancellation processing on the preprocessed signal.

[0087] To maximize the reception performance of the user transmitted signal (communication signal) s(n), maximum ratio combining (MRC) processing is used. Select the combiner w = Q(h c + h rc ΦG).

[0088] The signal after MRC processing is:

[0089]

[0090] Let H c = h c + h rc ΦG, then the signal-to-noise ratio at the receiver can be expressed as:

[0091]

[0092] Among them, P s represents the average power of the transmitted symbol; w represents the MRC combiner vector; H c represents the combined channel matrix.

[0093] Through the above processing, the signal-to-noise ratio (SNR) at the receiving end is significantly improved. To verify the influence of the number of RIS elements on the SNR, in this embodiment, the relationship curve between the SNR received by the base station and the number of RIS elements shown in Figure 2 is obtained through simulation. It can be seen that as the number of RIS elements increases, the SNR shows a significant upward trend, verifying the key role of RIS in enhancing the signal reception performance.

[0094] S4. Design a minimum mean square error estimator based on the communication constellation structure information.

[0095] After completing the communication optimization design, it is necessary to design a sensing parameter estimator. The design process of the estimator Vp(s i ) based on the MMSE criterion specifically includes:

[0096]

[0097] Among them, is the constellation size.

[0098] According to the Bayesian estimation theory, the MMSE estimator of α is the mean of its posterior probability density function where α represents the target reflection coefficient; y represents the received signal vector.

[0099] Through probability decomposition and integral calculation, the final estimator expression can be obtained:

[0100]

[0101] Among them, p represents the probability density function; A represents the symbol constellation point set; i represents the index of the constellation point; s i represents the i-th constellation point in each constellation point set.

[0102] Among them, the relevant probability density function is:

[0103]

[0104] Among them, N represents the number of base station receiving antennas; y α represents the combined vector of the residual signal and the target reflection coefficient; C represents the joint covariance matrix of the received signal and the target reflection coefficient under the given communication symbol condition.

[0105] In the above formula, The given conditional covariance matrix is:

[0106]

[0107] The marginal probability density function is:

[0108]

[0109] S5. Use the minimum mean square error estimator to estimate the sensing parameters of the signal after interference cancellation processing to complete the optimization.

[0110] Based on the Cauchy - Schwarz inequality, we have:

[0111]

[0112] The equality holds if and only if w and H c are in the same direction, that is, w = kQH c ; where k is any non - zero constant. Substituting this optimal solution, the maximum value of SNR can be obtained as:

[0113]

[0114] The optimization problem of s(n) can be expressed as:

[0115]

[0116] where represents the communication constellation point set.

[0117] The objective function reflects the received power of the signal after passing through the channel, and the constraint conditions include the transmit power constraint and the constellation point constraint. This problem can be solved by the exhaustive method, and this processing can achieve the best communication performance.

[0118] To evaluate the sensing performance, in this embodiment, through Figure 5 the sensing performance curve of the RIS - assisted ISAC system is shown. The results show that under the action of the optimized MMSE estimator, the estimation error of the target reflection coefficient is significantly reduced, especially in the low SNR region, it can still maintain high accuracy, which fully verifies the advantages of the present invention in sensing parameter estimation.

[0119] Embodiment 2

[0120] In this embodiment, the method of the present invention is adopted to optimize the output signal of the integrated sensing and communication system, as shown in Table 1:

[0121] Table 1

[0122]

[0123] First, the received signal y(n) is taken as the input, and then the interference noise term z is calculatedc The covariance matrix R of (n) c . Next, the algorithm constructs a whitening matrix for converting colored noise into white noise. After whitening, in this embodiment, the maximum ratio combining (MRC) combiner w = Q(h c + h rc ΦG) is calculated, and MRC processing is performed to maximize the receiving performance of the communication signal. Finally, the communication symbol s(n) is optimized, and the optimization result S opt is output.

[0124] In this embodiment, the optimal MMSE estimation method of the present invention is also used for sensing parameter estimation, as shown in Table 2.

[0125] Table 2

[0126]

[0127] First, the received signal y(n) is used as the input, and the probability distribution of each symbol in the symbol constellation point set A is initialized to a uniform distribution, that is Then, the covariance matrix Rc is calculated, and the MMSE estimation value of the target reflection coefficient α is calculated according to the formula. This estimator makes full use of the structural information of the communication constellation and can achieve high-precision parameter estimation under complex channel conditions. Finally, the algorithm outputs the MMSE estimation value

[0128] This embodiment further analyzes the influence of the communication signal power on the system performance. Figure 3 The curve showing the relationship between the base station received SNR and the communication signal power under 8PSK modulation is presented. The simulation results show that as the communication signal power increases, the SNR gradually increases, but tends to saturate in the high-power region, reflecting the optimization potential of the system in power allocation.

[0129] In addition, to verify the influence of different modulation methods on the communication performance, this embodiment compares the SNR performance under multiple PSK modulation schemes (such as QPSK, 8PSK, 16PSK), and the results are as Figure 4 shown. 8PSK modulation shows high SNR stability under complex channel conditions, verifying the adaptability of the method of the present invention to different modulation methods.

[0130] Embodiment 3

[0131] This embodiment also provides an optimization system for a perception-communication integrated system based on a reconfigurable intelligent surface, including: a construction module, a processing module, a cancellation module, a design module, and an optimization module; the construction module is used to construct a received signal model of the perception-communication integrated system, the processing module is used to perform whitening preprocessing on the signals received by the perception-communication integrated system; the cancellation module is used to perform interference cancellation processing on the preprocessed signals by using the maximum ratio combining technique; the design module is used to design a minimum mean square error estimator based on communication constellation structure information; the optimization module is used to use the minimum mean square error estimator to perform perception parameter estimation on the signals after interference cancellation processing to complete the optimization.

[0132] Next, this embodiment will be used to illustrate in detail how the present invention solves technical problems in actual work.

[0133] This embodiment is applied to a system including a base station, a reconfigurable intelligent surface (RIS) communication user equipment, and a target, as Figure 1 shown; this figure shows the signal propagation paths, including the direct channel from the user equipment to the base station, the reflected channel from the user equipment through the RIS to the base station, and the path where the radar signal transmitted by the base station is reflected back to the base station by the target.

[0134] Use the construction module to construct a received signal model of the perception-communication integrated system.

[0135] First, the signals received by the system need to be processed, and its signal expression is:

[0136] y(n) = (h c + h rc ΦG)s(n) + α(n)b(θ)a H (θ)fx(n) + z(n)

[0137] Among them, z(n) is additive noise, h c is the direct channel between the user equipment and the base station, h rc is the channel between the user equipment and the RIS, Φ is the RIS phase adjustment matrix, G is the channel between the RIS and the base station, s(n) is the user transmitted signal, α(n) is the target reflection coefficient, b(θ) is the transmit steering vector, a(θ) is the receive steering vector, f is the transmit beamforming vector, x(n) is the nth sample of the radar transmitted signal; H represents the matrix transpose symbol.

[0138] Among them, the perception part is:

[0139] y s (n) = α(n)b(θ)a H (θ)fx(n) + z1(n)

[0140] The communication part is:

[0141] y c (n) = (h c + h rc ΦG)s(n) + z2(n)

[0142] The above z1(n) and z2(n) are the additive noises of the corresponding parts respectively.

[0143] The first step is to detect the communication symbol. When detecting the communication symbol, other signals (including the target echo and noise) are regarded as unwanted interference signals. Therefore, the model can be expressed as:

[0144] y(n) = (h c + h rc ΦG)s(n) + z c (n)

[0145] where z c (n) = α(n)b(θ)a H (θ)fx(n) + z(n).

[0146] Preprocess the sensed signal term α(n)b(θ)a H (θ)fx(n). Since α(n)b(θ)a H (θ)fx(n) is a complex Gaussian random variable, this term is the product of a complex Gaussian random variable and a deterministic term, and the additive noise z(n) follows a complex Gaussian distribution. After analysis, we get:

[0147]

[0148] where I represents the identity matrix.

[0149] After that, the processing module performs whitening preprocessing on the signals received by the integrated sensing and communication system.

[0150] First, calculate the covariance matrix of the interference plus noise term. Define g = b(θ)a H (θ). Then the covariance matrix of the interference plus noise term z c (n) is:

[0151] R c = E[gx(n)x H (n)g H + σ 2 I = gg H + σ 2 I

[0152] where g = b(θ)a H (θ) is the combination of steering vectors; σ represents the standard deviation of the noise; E represents the expectation operator.

[0153] After that, the colored noise matrix z c (n) needs to be converted into a white noise matrix, and whitening processing is required. The whitening matrix Q needs to satisfy:

[0154] E[Qz c (n)(Qz c (n)) H = QR c Q H = I

[0155] It can be proved that the whitening matrix is where Rc represents the covariance matrix.

[0156] The expression of the received signal after whitening is:

[0157] Qy(n) = Q(h c +h rc ΦG)s(n)+Qz c (n).

[0158] The cancellation module uses the maximum ratio combining technique to perform interference cancellation processing on the preprocessed signal.

[0159] To maximize the receiving performance of the user's transmitted signal (communication signal) s(n), maximum ratio combining (MRC) processing is adopted. The combiner w = Q(h c +h rc ΦG) is selected.

[0160] The signal after MRC processing is:

[0161]

[0162] Let H c = h c +h rc ΦG, then the signal-to-noise ratio at the receiving end can be expressed as:

[0163]

[0164] where P s represents the average power of the transmitted symbol; w represents the MRC combiner vector; H c represents the comprehensive channel matrix.

[0165] Through the above processing, the signal-to-noise ratio (SNR) at the receiving end is significantly improved. To verify the influence of the number of RIS elements on the SNR, in this embodiment, the relationship curve between the base station received SNR and the number of RIS elements shown in Figure 2 is obtained through simulation. It can be seen that as the number of RIS elements increases, the SNR shows a significant upward trend, verifying the key role of RIS in enhancing the signal receiving performance.

[0166] The design module designs a minimum mean square error estimator based on the communication constellation structure information.

[0167] After completing the communication optimization design, it is necessary to design a sensing parameter estimator. The estimator Vp(s i ) based on the MMSE criterion, and the design process specifically includes:

[0168]

[0169] Among them, is the constellation size.

[0170] According to Bayesian estimation theory, the MMSE estimator of α is the mean of its posterior probability density function where α represents the target reflection coefficient; y represents the received signal vector.

[0171] Through probability decomposition and integral calculation, the final estimator expression can be obtained:

[0172]

[0173] where p represents the probability density function; A represents the symbol constellation point set; i represents the index of the constellation point; s i represents the i-th constellation point in each constellation point set.

[0174] Among them, the relevant probability density function is:

[0175]

[0176] where N represents the number of base station receiving antennas; y α represents the combined vector of the residual signal and the target reflection coefficient; C represents the joint covariance matrix of the received signal and the target reflection coefficient under the given communication symbol condition.

[0177] In the above formula, the given conditional covariance matrix is:

[0178]

[0179] The marginal probability density function is:

[0180]

[0181] Finally, the optimization module uses the minimum mean square error estimator to perform sensing parameter estimation on the signal after interference cancellation processing to complete the optimization.

[0182] Based on the Cauchy - Schwarz inequality, it can be obtained that:

[0183]

[0184] if and only if w and H c take the equal sign when in the same direction, that is, w = kQH c ; where k is any non-zero constant. Substituting this optimal solution gives the maximum value of SNR as:

[0185]

[0186] The optimization problem of s(n) can be expressed as:

[0187]

[0188] where represents the communication constellation point set.

[0189] The objective function reflects the received power of the signal after passing through the channel. The constraint conditions include the transmit power constraint and the constellation point constraint. This problem can be solved by the exhaustive method, and this processing can achieve the best communication performance.

[0190] To evaluate the sensing performance, this embodiment passes Figure 5 shows the sensing performance curve of the RIS-assisted ISAC system. The results show that under the action of the optimized MMSE estimator, the estimation error of the target reflection coefficient is significantly reduced, and high precision can still be maintained especially in the low SNR region, fully verifying the advantages of the present invention in sensing parameter estimation.

[0191] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. An optimization method for an integrated sensing and communication system based on reconfigurable intelligent surfaces, characterized in that the steps Including: Construct a received signal model for the integrated sensing and communication system; Perform whitening preprocessing on the signals received by the integrated sensing and communication system; Use the maximum ratio combining technique to perform interference cancellation on the preprocessed signals; Design a minimum mean square error estimator based on communication constellation structure information; Use the minimum mean square error estimator to estimate the sensing parameters of the signals after interference cancellation processing to complete the optimization.

2. The optimization method of the integrated sensing and communication system based on reconfigurable intelligent surfaces according to claim 1, wherein The constructed received signal model includes: y(n) = (h c + h rc ΦG)s(n) + α(n)b(θ)a H (θ)fx(n) + z(n) Among them, (h c + h rc ΦG)s(n) is the communication signal term, α(n)b(θ)a H (θ)fx(n) is the sensing signal term, z(n) is the additive noise, h c is the direct channel between the user equipment and the base station, h rc is the channel between the user equipment and the RIS, Φ is the RIS phase adjustment matrix, G is the channel between the RIS and the base station, s(n) is the user transmitted signal, α(n) is the target reflection coefficient, b(θ) is the transmit steering vector, a(θ) is the receive steering vector, f is the transmit beamforming vector, and x(n) is the nth sample of the radar transmitted signal.

3. The optimization method of the integrated sensing and communication system based on reconfigurable intelligent surface according to claim 2, wherein, The method for performing the whitening preprocessing includes: Calculate the covariance matrix of the interference plus noise term; R c = E[gx(n)x H (n)g H + σ 2 I = gg H + σ 2 I where g = b(θ)a H (θ) is a combination of steering vectors; σ represents the standard deviation of noise; E represents the expected value operator; I represents the identity matrix; H represents the matrix transpose symbol; Construct a whitening matrix; Perform whitening processing on the received signals to obtain: Qy(n) = Q(h c + h rc ΦG)s(n) + Qz c (n).

4. The optimization method of the integrated sensing and communication system based on reconfigurable intelligent surface according to claim 3, characterized in that, The method for using the maximum ratio combining technique includes: Design a combiner; w = Q(h c + h rc ΦG) The signal after MRC processing is: Let H c = h c + h rc ΦG, the signal-to-noise ratio at the receiving end is expressed as: where, P s represents the average power of the transmitted symbols; w represents the MRC combiner vector; H c represents the combined channel matrix.

5. The optimization method of the integrated sensing and communication system based on reconfigurable intelligent surfaces according to claim 4, wherein, The method for designing the minimum mean square error estimator includes: Set the communication symbols to be uniformly distributed in the constellation point set; Construct the MMSE estimator of the target reflection coefficient based on Bayesian estimation theory; Obtain the final estimator expression through probability decomposition and integration calculation.

6. The optimization method of the integrated sensing and communication system based on reconfigurable intelligent surface according to claim 5, characterized in that The final estimator expression: Among them, p represents the probability density function; A represents the symbol constellation point set; i represents the index of the constellation point; s i represents the i-th constellation point in each constellation point set; y represents the received signal vector; α represents the target reflection coefficient.

7. The optimization method of the integrated sensing and communication system based on reconfigurable intelligent surface according to claim 6, characterized in that The method for optimizing the received signal s(n) includes: Among them, P s is the transmission signal power constraint, and is the communication constellation point set.

8. An optimization system for an integrated sensing and communication system based on a reconfigurable intelligent surface, the system being used to implement the method according to any one of claims 1-7, characterized in that, Including: A construction module, a processing module, a cancellation module, a design module, and an optimization module; The construction module is used to construct a received signal model for the integrated sensing and communication system; The processing module is used to perform whitening preprocessing on the signals received by the integrated sensing and communication system; The cancellation module is used to perform interference cancellation on the preprocessed signals using the maximum ratio combining technique; The design module is used to design a minimum mean square error estimator based on communication constellation structure information; The optimization module is used to use the minimum mean square error estimator to estimate the sensing parameters of the signals after interference cancellation processing to complete the optimization.

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