RIS-assisted location awareness-based communication and sensing integration method

Abstract

The invention provides a RIS-assisted location awareness-based communication and sensing integration method, which is used for further improving the calculation efficiency of atomic norms and improving the accuracy of parameter estimation by utilizing priori knowledge between a base station and a reconfigurable intelligent surface. By incorporating priori knowledge into the scheme design, the calculation complexity can be significantly reduced, and the accuracy of channel estimation and positioning can be ensured at the same time. The method has the advantages that super-resolution channel estimation can be achieved, and the performance of the method is superior to that of an existing orthogonal matching pursuit method. The comparison of various performance indexes shows that the method provided by the invention has obvious advantages. The advantage is crucial for realizing accurate positioning service in a complex environment.

Description

Technical Field

[0001] The invention relates to a synaesthesia integration method based on position perception under the assistance of RIS, and belongs to the technical field of antenna array signal processing. Background Art

[0002] With the rapid advancement of wireless communication technology, the sixth generation of communication systems has received widespread attention. The sixth generation aims to surpass the performance of the current fifth generation communication system and meet the needs of future communications by achieving higher data throughput, lower transmission latency and higher connection density. To achieve these performance indicators, the sixth generation is expected to utilize the terahertz band, which is known for its wide bandwidth and the potential to provide extremely high data rates and capacity. Although the terahertz band offers great potential advantages, the signals in this band suffer from large path losses during propagation, which limits the scope and efficiency of its practical applications. To address this challenge, multiple-input multiple-output technology has been considered to improve system performance. Multiple-input multiple-output technology can effectively increase the spatial multiplexing rate of signals by deploying multiple transmit and receive antennas, thereby enhancing the spectral efficiency of the system. However, in the terahertz band, even with the use of large-scale multiple-input multiple-output technology, the system is still highly dependent on line-of-sight propagation. In complex wireless environments, such as non-line-of-sight scenarios commonly seen in urban landscapes, it is extremely difficult to obtain a stable line-of-sight propagation path. This dependence on line-of-sight paths exacerbates the application difficulties of terahertz communication in real environments.

[0003] The above issues are issues that should be considered and resolved in channel estimation and positioning in scenarios based on planar passive reconfigurable smart surfaces. Summary of the invention

[0004] In the scenario based on the passive reconfigurable smart surface of the array, the location information of the base station and the reconfigurable smart surface is further introduced as prior knowledge into the channel estimation process of the passive reconfigurable smart surface. This strategy not only reduces the computational complexity, but also enhances the accuracy of channel estimation, and also provides a solution for the positioning of the base station and the user. The technical solution of the present invention is:

[0005] A RIS-assisted synaesthesia integration method based on location perception, characterized in that it includes the following steps:

[0006] S1. Based on the Saleh-Valenzuela channel model, a channel model from the user to the base station through the smart metasurface is constructed. By using the prior knowledge between the base station and the reconfigurable smart surface, the channel model from the user to the base station through the smart metasurface can be decomposed into two sub-channel estimation problems, thereby solving the sub-channel between the user and the reconfigurable smart metasurface separately.

[0007] S2. The channel matrix solution problem of the sub-channel from the user to the base station through the smart metasurface can be transformed into an atomic norm minimization problem. Then, one parameter is solved in the first stage, and the parameter is eliminated in the second stage and other parameters are solved.

[0008] S3. After obtaining the parameters of each channel, the phase control matrix of the reconfigurable intelligent metasurface for a single user can be obtained through the incident angle information and reflection angle information of the reconfigurable intelligent metasurface;

[0009] S4. After obtaining the two sub-channel matrices and the phase control matrix, they can be combined to construct a channel matrix from the user to the base station through the intelligent metasurface. Then, according to the obtained path gain, they are substituted into the single slope model to obtain the three-dimensional coordinates of the user and the base station with the reconfigurable intelligent metasurface as the origin, and then the relative position of the user and the base station can be obtained.

[0010] Furthermore, step S1 specifically comprises:

[0011] S11. Consider a hybrid reconfigurable intelligent metasurface-assisted MIMO system, which includes a base station, a reconfigurable intelligent metasurface and multiple users. The base station and the users are respectively configured with N array elements. B and N M The uniform linear array of the reconfigurable smart surface is configured with N array elements. R The uniform array is configured, and the spacing between the array elements of each array is half a wavelength. It is assumed that there is a control link between the reconfigurable smart surface and the base station, and the direct path between the user and the base station does not exist due to being blocked.

[0012] S12. When performing channel modeling, the SV channel model is used to describe the channel characteristics between mobile users and reconfigurable smart surfaces. This model uses the angle of arrival, angle of departure, and corresponding path loss as basic parameters. Given that the goal is to achieve channel estimation and positioning of mobile users, this model pays special attention to the contribution of the direct path and ignores other multipath effects. In the current research scenario, since only the direct path is considered, the corresponding delay is zero, so the path gain can be simplified to a real number. Based on this assumption, the channel model expression between the mobile user and the reconfigurable smart surface can be derived as follows:

[0013]

[0014] where [θ M,R ] k represents the transmission angle of the kth user, and denote the azimuth and elevation angles of the kth user incident on the reconfigurable smart surface, [ρ M,R ] krepresents the path loss of the kth user under the direct path, K represents the total number of users, [H M,R ] k represents the channel matrix between the kth user and the reconfigurable smart surface; for a uniform linear array with an array element spacing of half a wavelength λ / 2, the array response is

[0015]

[0016] in l represents the number of elements in the uniform linear array, so we have is the array response vector at the kth user, and

[0017]

[0018] For a uniform planar array with an element spacing of half a wavelength, the array response is

[0019]

[0020] Where N x Represents the number of array elements in the x-axis direction of a uniform array located on the xoy plane, N y represents the number of elements in the y-axis direction of the uniform array, so we have is the array response vector received by the reconfigurable smart surface from the kth user, and

[0021]

[0022] S13, the channel between the reconfigurable intelligent surface and the base station is obtained as

[0023]

[0024] where υ R,B represents the receiving angle at the base station, and denote the azimuth and elevation angles of the reflection at the reconfigurable smart surface, γ R,B represents the path loss of the direct path between the reconfigurable smart surface and the base station, and and The array response vectors represent the base station receiving angle and the reconfigurable smart surface reflection angle, and is the corresponding array flow type. Then, using the above formula and taking into account the reconfigurable smart surface, the total channel matrix for,

[0025]

[0026] in is the phase control matrix of the reconfigurable smart surface. At this time, it is assumed that the reconfigurable smart surface is composed of a series of discrete phase controllers, that is, Ω = diag(ω), where and It is assumed here that the reconfigurable smart surface is a perfect reflection surface, that is, let a=1.

[0027] Furthermore, step S2 specifically includes:

[0028] S21, indicates the channel matrix H R,B can be regarded as prior information and then used for the channel matrix H M,R This method is based on the location-aware channel estimation technique, which requires that during the bth time slot, the transmitted signal Satisfy the conditions Where I represents the identity matrix, In order to distinguish it from the total number of time slots B in the location awareness processing flow discussed in the previous section, represents the total number of time slots. At the same time, for the convenience of subsequent discussion, it is assumed that each time slot contains K snapshots. Based on these assumptions, the location awareness process also needs to be divided into two stages.

[0029] In the first phase, from the first time slot to the time slots, the phase-controlled matrix of the reconfigurable smart surface always remains unchanged at Ω1, so from the first time slot to the The received signal of the bth time slot among the time slots as follows,

[0030]

[0031] in is a noise signal. So The received signal of the time slot as follows,

[0032]

[0033] in

[0034] S22. In the second stage, from time slot to The phase-controlled matrix of the reconfigurable smart surface changes once in each time slot, which also allows the reconfigurable smart surface to have enough time to respond to the phase change. time slot to The received signal of the bth time slot among the time slots as follows,

[0035]

[0036] in As defined in the above formula, Ω b represents the reconfigurable smart surface phase-controlled matrix corresponding to the b-th time slot, and

[0037] In getting the time slot to The received signal of the bth time slot among the time slots After that, you need to eliminate term, that is, multiply the right by one Then multiply right by one because and are all full rank, so it is easy to know that the matrix It must be a full-rank matrix (Footnote: According to Property 1, in the process of constructing the channel matrix, the variable The order of arrangement is important for solving With θ M,R The correspondence between them has no effect. Because this property states that although the variable The order of may change the order of accumulation of channel matrices between each user and the reconfigurable smart surface, but this will not affect the final channel matrix H M,R Therefore, when performing channel estimation, once the variable The order of can be directly calculated based on this No additional angle matching is required. ); then eliminate A(υ R,B ) term, that is, multiply the above formula by one on the left because Then we can get Right now,

[0038]

[0039] in At this time, y b For vectorization, we have

[0040]

[0041] So the time slot to The received signal of the time slot for,

[0042]

[0043] in

[0044] S23. In the first stage, the transmission angles of K user terminals need to be solved using the data received at the base station. Since H R,B As a priori knowledge is known, this prior knowledge can be used to further reduce the amount of calculation of the atomic norm in channel estimation. Transformed into the following form,

[0045]

[0046] in So we can build an SDP optimization model. Similarly, due to the non-convexity of rank, we can use traces instead of ranks to obtain the trace minimization problem, as follows:

[0047]

[0048] So it can be further written as the following optimization problem:

[0049]

[0050] in is a regularization parameter, generally set to By solving the above formula, we can get the Toeplitz matrix Through Solve to get the transmission angle of the user end The solution algorithm can be either Root-MUSIC or ESPRIT algorithm.

[0051] S24. In the second stage, the channel matrix H between the base station and the reconfigurable smart surface needs to be restored using the received signal at the base station. R,B At this time, it is also necessary to use the total channel matrix between the reconfigurable smart surface and all user terminals obtained in the first stage So we can get as follows,

[0052]

[0053] Then we can get If we want to recover the channel matrix H between the base station and the reconfigurable smart surface R,B , we need to construct H R,B The decoupled atomic set is as follows:

[0054]

[0055] Therefore, an equivalent SDP optimization model can be constructed, and the same logic is as follows:

[0056]

[0057] So it can be further written as the following optimization problem:

[0058]

[0059] where κ N is a regularization parameter, generally set to By solving the above formula, we can get the Toeplitz matrix and And the channel matrix Through and Solve and obtain the reflection angle of the reconfigurable smart surface and the receiving angle at the base station This solution algorithm can be Root-MUSIC or ESPRIT algorithm; in this case, the path loss between the reconfigurable smart surface and the base station needs to be obtained. as follows,

[0060]

[0061] Furthermore, step S3 specifically includes:

[0062] S31. Considering that the incident angle and reflection angle of the reconfigurable smart surface are known, it becomes more convenient to design the phase control matrix of the reconfigurable smart surface. Compared with other methods, this study does not need to solve the complex phase control matrix optimization function. For the kth user, a simplified process can be used to obtain the optimal phase control matrix Ω * .

[0063]

[0064] Furthermore, step S4 is specifically as follows:

[0065] S41. For distance estimation, a single slope model of path loss and distance function is considered, and its model formula can be expressed as follows:

[0066]

[0067] Where P r Represents the received power, P t represents the transmit power, K is a constant coefficient that depends on the antenna characteristics and the average channel loss, and is generally set to d r is the reference distance of the antenna, usually assumed to be d r =1m, is the actual distance, is the path loss exponent, which is usually assumed to be in free space. So the path loss can be expressed as,

[0068]

[0069] Then the solution (1-[ρ M,R ] k ) and (1-[γ R,B ] k ) into the above formula to obtain the distance between each transmitter and receiver. After successfully estimating the distance between the user and the reconfigurable smart surface and the distance between the reconfigurable smart surface and the base station, combined with the previously calculated incident angle and reflection angle of the reconfigurable smart surface, the relative positions of the base station, the reconfigurable smart surface and the user in the three-dimensional coordinate system can be deduced using geometric relationships through these distance and angle information, so that the base station knows the location of the user and the distance between them.

[0070] The beneficial effects of the present invention are as follows: a passive reconfigurable smart surface is adopted. By splitting the channel involving the reconfigurable smart surface into two sub-channels for solution, and then introducing an appropriate phase control matrix, the total channel matrix from the user end through the reconfigurable smart surface to the base station can be obtained, thereby realizing super-resolution channel estimation. In addition, a relationship model between path loss and distance is established, so that the base station can obtain the user's location information. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a flow chart of a method for integrating synaesthesia based on location perception assisted by RIS according to an embodiment of the present invention;

[0072] Figure 2 is a schematic diagram of an uplink communication system assisted by a reconfigurable smart surface in an embodiment;

[0073] Figure 3 is a schematic diagram of the pilot frame structure of the uplink in the embodiment;

[0074] Figure 4 is a schematic diagram of incident signal and reflected signal angles of a reconfigurable smart surface in an embodiment;

[0075] Figure 5 is a comparison of channel estimation performance between a user and a reconfigurable smart surface in an embodiment;

[0076] Figure 6 is a performance comparison diagram of path gain from a user to a reconfigurable smart surface in an embodiment, and its performance can reflect the accuracy of distance estimation. DETAILED DESCRIPTION

[0077] The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0078] Example

[0079] A RIS-assisted synaesthesia integration method based on location perception, such as Figure 1 , characterized in that it comprises the following steps,

[0080] S1. According to the Saleh-Valenzuela channel model, a channel model from the user to the base station through the smart metasurface is constructed, such as Figure 2 ,Using the prior knowledge between the base station and the reconfigurable smart surface, the channel model from the user to the base station through the smart metasurface can be decomposed into two sub-channel estimation problems, so as to solve the sub-channel between the user and the reconfigurable smart metasurface separately;

[0081] S2, the channel matrix solution problem of the sub-channel from the user to the base station through the smart metasurface can be transformed into an atomic norm minimization problem, and then a parameter is solved in the first stage, and the parameter is eliminated in the second stage and other parameters are solved, such as Figure 3 ;

[0082] S3. After obtaining the parameters of each channel, the phase control matrix of the reconfigurable intelligent metasurface for a single user can be obtained through the incident angle information and reflection angle information of the reconfigurable intelligent metasurface, such as Figure 4 ;

[0083] S4. After obtaining the two sub-channel matrices and the phase control matrix, they can be combined to construct a channel matrix from the user to the base station through the intelligent metasurface. Then, according to the obtained path gain, they are substituted into the single slope model to obtain the three-dimensional coordinates of the user and the base station with the reconfigurable intelligent metasurface as the origin, and then the relative position of the user and the base station can be obtained.

[0084] In this embodiment, step S1 is specifically:

[0085] S11. Consider a hybrid reconfigurable intelligent metasurface-assisted MIMO system, which includes a base station, a reconfigurable intelligent metasurface and multiple users. The base station and the users are respectively configured with N array elements. B and N M The uniform linear array of the reconfigurable smart surface is configured with N array elements. R The uniform array is configured, and the spacing between the array elements of each array is half a wavelength. It is assumed that there is a control link between the reconfigurable smart surface and the base station, and the direct path between the user and the base station does not exist due to being blocked.

[0086] S12. When performing channel modeling, the SV channel model is used to describe the channel characteristics between mobile users and reconfigurable smart surfaces. This model uses the angle of arrival, angle of departure, and corresponding path loss as basic parameters. Given that the goal is to achieve channel estimation and positioning of mobile users, this model pays special attention to the contribution of the direct path and ignores other multipath effects. In the current research scenario, since only the direct path is considered, the corresponding delay is zero, so the path gain can be simplified to a real number. Based on this assumption, the channel model expression between the mobile user and the reconfigurable smart surface can be derived as follows:

[0087]

[0088] where [θ M,R ] k represents the transmission angle of the kth user, and denote the azimuth and elevation angles of the kth user incident on the reconfigurable smart surface, [ρ M,R ] k represents the path loss of the kth user under the direct path, K represents the total number of users, [H M,R ] k represents the channel matrix between the kth user and the reconfigurable smart surface; for a uniform linear array with an array element spacing of half a wavelength λ / 2, the array response is

[0089]

[0090] in l represents the number of elements in the uniform linear array, so we have is the array response vector at the kth user, and

[0091]

[0092] For a uniform planar array with an element spacing of half a wavelength, the array response is

[0093]

[0094] Where N x Represents the number of array elements in the x-axis direction of a uniform array located on the xoy plane, N y represents the number of elements in the y-axis direction of the uniform array, so we have is the array response vector received by the reconfigurable smart surface from the kth user, and

[0095]

[0096] S13, similar to S12, the channel between the reconfigurable smart surface and the base station can be obtained as

[0097]

[0098] where υ R,B represents the receiving angle at the base station, and denote the azimuth and elevation angles of the reflection at the reconfigurable smart surface, γ R,B represents the path loss of the direct path between the reconfigurable smart surface and the base station, and and represent the base station receiving angle and the reconfigurable smart surface, respectively. and is the corresponding array flow pattern.

[0099] Then, using the above formula and taking into account the reconfigurable smart surface, the total channel matrix for,

[0100]

[0101] in is the phase control matrix of the reconfigurable smart surface. At this time, it is assumed that the reconfigurable smart surface is composed of a series of discrete phase controllers, that is, Ω = diag(ω), where and It is assumed here that the reconfigurable smart surface is a perfect reflection surface, that is, let a=1.

[0102] In this embodiment, step S2 is specifically:

[0103] S21, indicates the channel matrix H R,B can be regarded as prior information and then used for the channel matrix H M,R This method is based on the location-aware channel estimation technique, which requires that during the bth time slot, the transmitted signal Satisfy the conditions Where I represents the identity matrix, In order to distinguish it from the total number of time slots B in the location awareness processing flow discussed in the previous section, represents the total number of time slots. At the same time, for the convenience of subsequent discussion, it is assumed that each time slot contains K snapshots. Based on these assumptions, the location awareness process also needs to be divided into two stages.

[0104] In the first phase, from the first time slot to the time slots, the phase-controlled matrix of the reconfigurable smart surface always remains unchanged at Ω1, so from the first time slot to the The received signal of the bth time slot among the time slots as follows,

[0105]

[0106] in is a noise signal. So The received signal of the time slot as follows,

[0107]

[0108] in

[0109] S22. In the second stage, from time slot to The phase-controlled matrix of the reconfigurable smart surface changes once in each time slot, which also allows the reconfigurable smart surface to have enough time to respond to the phase change. time slot to The received signal of the bth time slot among the time slots as follows,

[0110]

[0111] in As defined in the above formula, Ω b represents the reconfigurable smart surface phase-controlled matrix corresponding to the b-th time slot, and

[0112] In getting the time slot to The received signal of the bth time slot among the time slots After that, you need to eliminate term, that is, multiply the right by one Then multiply right by one because and are all full rank, so it is easy to know that the matrix It must be a full-rank matrix (Footnote: According to Property 1, in the process of constructing the channel matrix, the variable The order of arrangement is important for solving With θ M,R The correspondence between them has no effect. Because this property states that although the variable The order of may change the order of accumulation of channel matrices between each user and the reconfigurable smart surface, but this will not affect the final channel matrix H M,R Therefore, when performing channel estimation, once the variable The order of can be directly calculated based on this No additional angle matching is required. ); then eliminate A(υ R,B ) term, that is, multiply the above formula by one on the left because Then we can get Right now,

[0113]

[0114] in At this time, y b For vectorization, we have

[0115]

[0116] So the time slot to The received signal of the time slot for,

[0117]

[0118] in

[0119] S23. In the first stage, the transmission angles of K user terminals need to be solved using the data received at the base station. Since H R,B As a priori knowledge is known, this prior knowledge can be used to further reduce the amount of calculation of the atomic norm in channel estimation. Transformed into the following form,

[0120]

[0121] in So we can build an SDP optimization model. Similarly, due to the non-convexity of rank, we can use traces instead of ranks to obtain the trace minimization problem, as follows:

[0122]

[0123] So it can be further written as the following optimization problem:

[0124]

[0125] in is a regularization parameter, generally set to By solving the above formula, we can get the Toeplitz matrix Through Solve to get the transmission angle of the user end The solution algorithm can be either Root-MUSIC or ESPRIT algorithm.

[0126] S24. In the second stage, the channel matrix H between the base station and the reconfigurable smart surface needs to be restored using the received signal at the base station. R,B At this time, it is also necessary to use the total channel matrix between the reconfigurable smart surface and all user terminals obtained in the first stage So we can get as follows,

[0127]

[0128] Then we can get If we want to recover the channel matrix H between the base station and the reconfigurable smart surface R,B , we need to construct H R,B The decoupled atomic set is as follows:

[0129]

[0130] Therefore, an equivalent SDP optimization model can be constructed, and the same logic is as follows:

[0131]

[0132] So it can be further written as the following optimization problem:

[0133]

[0134] where κ N is a regularization parameter, generally set to By solving the above formula, we can get the Toeplitz matrix and And the channel matrix Through and Solve and obtain the reflection angle of the reconfigurable smart surface and the receiving angle at the base station This solution algorithm can be Root-MUSIC or ESPRIT algorithm; in this case, the path loss between the reconfigurable smart surface and the base station needs to be obtained. as follows,

[0135]

[0136] In this embodiment, step S3 is specifically:

[0137] S31. Considering that the incident angle and reflection angle of the reconfigurable smart surface are known, it becomes more convenient to design the phase control matrix of the reconfigurable smart surface. Compared with other methods, this study does not need to solve the complex phase control matrix optimization function. For the kth user, a simplified process can be used to obtain the optimal phase control matrix Ω * .

[0138]

[0139] In this embodiment, step S4 is specifically as follows:

[0140] S41. For distance estimation, a single slope model of path loss and distance function is considered, and its model formula can be expressed as follows:

[0141]

[0142] Where P r Represents the received power, P t represents the transmit power, K is a constant coefficient that depends on the antenna characteristics and the average channel loss, and is generally set to d r is the reference distance of the antenna, usually assumed to be d r =1m, is the actual distance, is the path loss exponent, which is usually assumed to be in free space. So the path loss can be expressed as,

[0143]

[0144] Then the above solution (1-[ρ M,R ] k ) and (1-[γ R,B ] k ) into the above formula to obtain the distance between each transmitter and receiver. After successfully estimating the distance between the user and the reconfigurable smart surface and the distance between the reconfigurable smart surface and the base station, combined with the previously calculated incident angle and reflection angle of the reconfigurable smart surface, the relative positions of the base station, the reconfigurable smart surface and the user in the three-dimensional coordinate system can be deduced using geometric relationships through these distance and angle information, so that the base station knows the location of the user and the distance between them.

[0145] The channel estimation and positioning method in the scene based on the area array passive reconfigurable intelligent surface of the embodiment is verified by simulation experiments as follows.

[0146] Simulation example: Select N B =N M =12, NR =36, the number of users is set to K=3, all receiving angles and departure angles are randomly and uniformly located in [0,π] and the intervals between angles are greater than the minimum angle interval required by the atomic norm, the path loss can be set to any real number in (0,1), and the signal-to-noise ratio is defined as SNR=10log 10 (1 / σ 2 ). When verifying the performance of the proposed algorithm in the location awareness scenario, it is noted that the number of snapshots in the first stage is set to 30 snapshots, while the number of snapshots in the second stage is 120 snapshots. Since the phased matrix changes once every K=3 snapshots, the amount of data in the second stage is only equivalent to 40 snapshots, and the number of pilots required in the second stage will also increase as K increases. The performance comparison between the embodiment method and the orthogonal basis matching algorithm of the existing method is shown in the figure. Figure 5 and Figure 6 As shown, the ordinate represents the size of the estimation error, and the abscissa represents the SNR. It can be seen from the figure that the embodiment method has better performance than the existing method.

[0147] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A RIS-assisted location-aware synaesthesia integration method, characterized by: The following steps are included: S1. Based on the Saleh-Valenzuela channel model, a channel model from the user to the base station through the smart metasurface is constructed. By using the prior knowledge between the base station and the reconfigurable smart surface, the channel model from the user to the base station through the smart metasurface is decomposed into two sub-channel estimation problems, thereby solving the sub-channel between the user and the reconfigurable smart metasurface separately. S2, convert the channel matrix solution problem of the sub-channel from the user to the base station through the smart metasurface into an atomic norm minimization problem, then solve one parameter in the first stage, eliminate the parameter in the second stage and solve other parameters; S3. After obtaining the parameters of each channel, the phase control matrix of the reconfigurable intelligent metasurface for a single user is obtained through the incident angle information and reflection angle information of the reconfigurable intelligent metasurface; S4. After obtaining the two sub-channel matrices and the phase control matrix, the channel matrix from the user to the base station through the intelligent supersurface is constructed by combination. Then, the obtained path gain is substituted into the single slope model to obtain the three-dimensional coordinates of the user and the base station with the reconfigurable intelligent supersurface as the origin, and then the relative position of the user and the base station is obtained.

2. The RIS-assisted location-aware synaesthesia integration method according to claim 1, characterized in that: Step S1 specifically comprises: S11. Consider a hybrid reconfigurable intelligent metasurface-assisted MIMO system, which includes a base station, a reconfigurable intelligent metasurface and multiple users. The base station and the users are respectively configured with N array elements. B and N M The uniform linear array of the reconfigurable smart surface is configured with N array elements. R The uniform array is configured, and the spacing between the array elements of each array is half a wavelength; it is assumed that there is a control link between the reconfigurable smart surface and the base station, and the direct path between the user and the base station does not exist due to being blocked; S12. When performing channel modeling, an SV channel model is used to describe the channel characteristics between the mobile user and the reconfigurable smart surface; the model uses the arrival angle, departure angle and corresponding path loss as basic parameters; Since the goal is to achieve channel estimation and positioning of mobile users, this model pays special attention to the contribution of the direct path and ignores other multipath effects. In the previous research scenario, since only the direct path is considered, the corresponding delay is zero, so the path gain is simplified to a real number. Based on this assumption, the channel model expression between the mobile user and the reconfigurable smart surface is derived as follows: where [θ M,R ] k represents the transmission angle of the kth user, and denote the azimuth and elevation angles of the kth user incident on the reconfigurable smart surface, [ρ M,R [ k represents the path loss of the kth user under the direct path, K represents the total number of users, [H M,R ] k represents the channel matrix between the kth user and the reconfigurable smart surface; for a uniform linear array with an array element spacing of half a wavelength λ2, the array response is in l represents the number of elements in the uniform linear array, so we have is the array response vector at the kth user, and For a uniform planar array, when the array element spacing is half a wavelength, the array response is Where N x Represents the number of array elements in the x-axis direction of a uniform array located on the xoy plane, N y represents the number of elements in the y-axis direction of the uniform array, so we have is the array response vector received by the reconfigurable smart surface from the kth user, and S13, the channel between the reconfigurable intelligent surface and the base station is obtained as where υ R,B represents the receiving angle at the base station, and denote the azimuth and elevation angles of the reflection at the reconfigurable smart surface, γ R,B represents the path loss of the direct path between the reconfigurable smart surface and the base station, and and The array response vectors represent the base station receiving angle and the reconfigurable smart surface reflection angle, and is the corresponding array flow type; then considering the reconfigurable smart surface, the total channel matrix for, in is the phase control matrix of the reconfigurable smart surface. At this time, it is assumed that the reconfigurable smart surface is composed of a series of discrete phase controllers, that is, Ω = diag(ω), where and a∈[0,1], assuming that the reconfigurable smart surface is a perfect reflection surface, that is, let a=1.

3. The RIS-assisted location-aware synaesthesia integration method according to claim 2, characterized in that: Specifically, step S2 includes: S21, channel matrix H R,B can be regarded as prior information and then used in the channel matrix H M,R It is a channel estimation technique based on location awareness, which requires that during the bth time slot, the signal is transmitted Satisfy the conditions Where I represents the identity matrix, represents the total number of time slots; assuming that each time slot contains K snapshots; based on these assumptions, the location awareness process also needs to be divided into two stages; In the first phase, from the first time slot to the time slots, the phase-controlled matrix of the reconfigurable smart surface always remains unchanged at Ω1, so from the first time slot to the The received signal of the bth time slot among the time slots as follows, in is a noise signal; so The received signal of the time slot as follows, in S22. In the second stage, from time slot to The phase-controlled matrix of the reconfigurable smart surface changes once in each time slot, which also allows the reconfigurable smart surface to have enough time to respond to the phase change. time slot to The received signal of the bth time slot among the time slots as follows, Where Ω b represents the reconfigurable smart surface phase-controlled matrix corresponding to the b-th time slot, and In getting the time slot to The received signal of the bth time slot among the time slots After that, eliminate term, that is, multiply the right by one Then multiply right by one because and are all full rank, so it is easy to know that the matrix It must be a full rank matrix; then eliminate A(υ R,B ) term, that is, multiply by one because Then we get Right now, in At this time, y b For vectorization, we have So the time slot to The received signal of the time slot for, in S23. In the first stage, the transmission angles of K user terminals are solved using the data received at the base station. Since H R,B As a priori knowledge, we use this priori knowledge to further reduce the amount of calculation of the atomic norm in channel estimation. Transformed into the following form, in So the SDP optimization model is constructed. Similarly, due to the non-convexity of rank, the trace minimization problem obtained by using trace instead of rank is as follows: So it can be further written as the following optimization problem: in is a regularization parameter, generally set to By solving the Toeplitz matrix Through Solve to get the transmission angle of the user end This solution algorithm is the Root-MUSIC or ESPRIT algorithm; S24. In the second stage, the channel matrix H between the base station and the reconfigurable smart surface is restored using the received signal at the base station. R,B At this time, the total channel matrix between the reconfigurable smart surface and all user terminals obtained in the first stage is also used So we get as follows, Then get If we want to recover the channel matrix H between the base station and the reconfigurable smart surface R,B , first construct H R,B The decoupled atomic set is as follows: So an equivalent SDP optimization model is constructed, and the same logic is as follows: So it can be further written as the following optimization problem: where κ N is a regularization parameter, set to By solving the Toeplitz matrix and And the channel matrix Through and Solve and obtain the reflection angle of the reconfigurable smart surface and the receiving angle at the base station This solution algorithm is Root-MUSIC or ESPRIT algorithm; at this time, the path loss between the reconfigurable smart surface and the base station is obtained as follows, 4. The RIS-assisted location-aware synaesthesia integration method according to claim 3, characterized in that: Step S3 specifically comprises: S31. Considering that the incident angle and reflection angle of the reconfigurable smart surface are known, it becomes more convenient to design the phase control matrix of the reconfigurable smart surface without solving the complex phase control matrix optimization function. For the kth user, a simplified process is used to obtain the optimal phase control matrix Ω * , 5. The RIS-assisted location-aware synaesthesia integration method according to claim 4, characterized in that: Step S4 specifically includes: S41. For distance estimation, a single slope model of path loss and distance function is considered, and its model formula is expressed as follows: Where P r Represents the received power, P t represents the transmit power, K is a constant coefficient that depends on the antenna characteristics and the average channel loss and is set to d r is the reference distance of the antenna, assuming d r =1m, is the actual distance, is the path loss exponent, assuming propagation in free space The path loss is then expressed as, The solution (1-[ρ M,R ] k ) and (1-[γ R,B ] k ) is substituted into the above formula to obtain the distance d between each transmitting end and receiving end respectively; after successfully estimating the distance between the user and the reconfigurable smart surface and the distance between the reconfigurable smart surface and the base station, combined with the incident angle and reflection angle of the reconfigurable smart surface calculated previously, through these distance and angle information, the relative positions of the base station, the reconfigurable smart surface and the user in the three-dimensional coordinate system are derived using geometric relationships, so that the base station knows the location of the user and the distance between them.

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