Method and device for reconstructing intelligent surface-assisted symbiotic radio system

Abstract

The invention provides a method and a device for reconstructing an intelligent surface-assisted symbiotic radio system. The method comprises the following steps: establishing a model for reconstructing the intelligent surface-assisted symbiotic radio system; a novel transmission framework structure combining a reconstructed intelligent surface and symbiotic radio is provided; two situations are proposed, the first situation is that a system base station adopts an all-digital antenna, the second situation is that the system base station adopts a dynamic metasurface antenna, and two optimization problems are proposed accordingly. For an optimization problem under the condition of a full-digital antenna, an alternating optimization method is adopted, an original optimization problem is decomposed into two sub-problems which are independently solved, and finally the problem is solved by utilizing a positive semi-definite relaxation method; and for the optimization problem under the condition of the dynamic metasurface antenna, an alternate optimization method is also adopted, a slack variable simplification problem is introduced, and finally, a manifold optimization method is adopted for solving.

Description

Technical Field

[0001] The present invention belongs to the field of communication technologies, and particularly relates to a method and apparatus for a reconfigurable intelligent surface-assisted coexisting radio system. Background Art

[0002] A reconfigurable intelligent surface (RIS) is a promising solution proposed in recent years to address communication link blockages in wireless communication scenarios and improve the performance of wireless communication systems. It is a flat panel composed of a large number of low-cost electronic components and a central controller that controls all reflecting elements and adjusts the required signal phases without any dedicated radio frequency processing. It can be easily installed on various building surfaces to integrate direct and reflected links to improve the overall performance of the wireless communication system.

[0003] Coexisting radio is an emerging wireless communication technology that optimizes spectrum utilization and energy efficiency through the coexisting relationship between a primary communication system and passive devices. Coexisting radio usually combines environmental reflectors or backscatter communication technologies to achieve resource sharing and collaborative work. Due to the characteristics of passive communication and low-complexity deployment of coexisting radio, it is very suitable for Internet of Things devices with limited power consumption and large-scale deployment requirements. Summary of the Invention

[0004] The present invention proposes a reconfigurable intelligent surface-assisted coexisting radio system, which has characteristics such as low power consumption, high precision, and low complexity.

[0005] In a first aspect, a method and apparatus for a reconfigurable intelligent surface-assisted coexisting radio system includes:

[0006] S1: Establish a system model of a base station, a reconfigurable intelligent surface, Internet of Things devices, a sensing target, and a primary user, and propose a new transmission framework structure that combines a reconfigurable intelligent surface and coexisting radio.

[0007] S2: According to the different types of base station antennas in the system model, two optimization problems are proposed, namely, jointly optimizing the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, the phase shift matrix of the reconfigurable intelligent surface, and the analog signal precoding matrix to minimize the base station power of the system.

[0008] S3: Divide the problem into two cases. When the system base station uses all-digital antennas, introduce auxiliary variables, apply the Schur complement to the Fisher information matrix, and decompose the original problem into two sub-problems by combining alternating optimization. Then convert the sub-problems into semidefinite programming problems to obtain the optimal solutions. When the system uses dynamic metasurface antennas, also use the alternating optimization method, and finally use the manifold optimization method to obtain the optimal solutions for the sub-problems.

[0009] Preferably, the step S1 specifically includes:

[0010] The system includes 1 N-antenna base station, 1 single-antenna primary communication user, 1 sensing target, multiple single-antenna Internet of Things devices, and 1 reconfigurable intelligent surface equipped with M reflection elements.

[0011] Preferably, the step S2 specifically includes:

[0012] Model the channels in the system as near-field channels. For the optimization problem in the case of all-digital antennas, under the constraints of communication quality, the Cramer-Rao bound of unknown parameters, and the reconfigurable intelligent surface, the expression of the minimum power of the system is as follows:

[0013]

[0014] s.t.γ p,s ≥γ s , (5a)

[0015] γ i,s ≥γ s , (5b)

[0016] γ i,c ≥γ c , (5c)

[0017] 0≤β m ≤1,0≤θ m ≤2π, (5d)

[0018] CRB≤C min ,(5e)

[0019] Among them, γ p,s ,γ i,s ,γ i,c represents the signal-to-interference-plus-noise ratio of the corresponding communication channel in problem (5), β m ,θ m represent the reflection amplitude and phase of the m-th reflection element of the reconfigurable intelligent surface, CRB represents the Cramer-Rao bound of unknown parameters, γ s ,γ c ,C minRepresents the bounds of the corresponding variables. (5a-c) represent the communication quality constraints of the system, (5d) represents the constraints for reconfiguring the intelligent surface, and (5e) is the effectiveness constraint for unknown parameter estimation.

[0020] The optimization problem in the case of a dynamic meta-surface antenna is

[0021]

[0022] s.t. γ p,s ≥γ s , (6a)

[0023] γ i,s ≥γ s , (6b)

[0024] γ i,c ≥γ c , (6c)

[0025] 0 ≤ β m ≤ 1, 0 ≤ θ m ≤ 2π, (6d)

[0026] CRB ≤ C min , (6e)

[0027]

[0028] where f i,j is an element in the analog precoding matrix of the dynamic meta-surface antenna, representing a non-zero phase shift. (6f) is the constraint for the analog precoding matrix of the dynamic meta-surface antenna.

[0029] Preferably, the step S3 specifically includes:

[0030] Since the constraints in problem (5) contain multiple coupled variables and the CRB matrix, problem (5) is non-convex. To overcome the non-convexity of this problem, auxiliary variables are first introduced to transform the CRB constraint, then the Schur complement is applied to the transformed constraint, and then the alternating optimization algorithm is used to decompose the problem, and the sub-problems are reduced to semi-definite programming problems for solution.

[0031] Since the constraints in problem (6) are similar to those in problem (5), the alternating optimization method is also used to decompose the problem into two sub-problems. The solution method for one sub-problem is similar to that of problem (5), and the other sub-problem is solved using the manifold optimization method after transformation.

[0032] An apparatus for a reconfigurable intelligent surface-assisted coexisting radio system, which includes:

[0033] A model establishment module, configured to establish a downlink system model of the reconfigurable intelligent surface-assisted coexisting radio system;

[0034] An equation construction module for minimizing the base station power of the system by jointly optimizing the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix of the reconfigurable intelligent surface in the cases of full digital antennas and dynamic metasurface antennas. In the case of dynamic metasurface antennas, the analog signal precoding matrix also needs to be optimized;

[0035] An iterative processing module for decomposing the planned problem into two sub-problems that can be solved separately, simplifying the problem by introducing slack variables, and finally converting the problem in the case of full digital antennas into a semidefinite programming problem for solution. The problem in the case of dynamic metasurface antennas is finally solved using the manifold optimization method to obtain the optimal solution of the minimum base station power of the system under different antenna types.

[0036] The modeling module includes:

[0037] A first modeling unit for equipping the base station with N antennas, the primary communication user with 1 antenna, one sensing target, multiple single-antenna Internet of Things devices, and a reconfigurable intelligent surface equipped with M reflection units;

[0038] A second modeling unit for establishing the near-field channels between the reconfigurable intelligent surface and the base station, users, and Internet of Things devices by considering the incident angle and emission angle of the reconfigurable intelligent surface to obtain effective channel state information.

[0039] In the case of full digital antennas, the equation construction module jointly optimizes the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix of the reconfigurable intelligent surface to obtain the optimization equation for the minimum base station power of the system:

[0040]

[0041] s.t.γ p,s ≥γ s , (7a)

[0042] γ i,s ≥γ s , (7b)

[0043] γ i,c ≥γ c , (7c)

[0044] 0≤β m ≤1,0≤θ m , (7d)

[0045] CRB≤C min , (7e)

[0046] In the case of a dynamic meta - surface antenna, the optimization equation for minimizing the base - station power of the system is obtained by jointly optimizing the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, the phase - shift matrix of the reconfigurable intelligent surface, and the analog - signal precoding matrix:

[0047]

[0048] s.t.γ p,s ≥γ s , (8a)

[0049] γ i,s ≥γ s , (8b)

[0050] γ i,c ≥γ c , (8c)

[0051] 0≤β m ≤1,0≤θ m ≤2π, (8d)

[0052] CRB≤C min , (8e)

[0053]

[0054] As can be seen from the above technical solutions, by decomposing the original problem into two sub - problems that can be solved separately, and jointly optimizing the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, the phase - shift matrix of the reconfigurable intelligent surface, and the analog - signal precoding matrix, the base - station power of the system can be minimized. This can also improve the spectral efficiency and the computational complexity is relatively low. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for the description of the embodiments or the prior art. Obviously, the following 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.

[0056] Figure 1 is a schematic flowchart of a method and device for a reconfigurable intelligent - surface - assisted co - existing radio system provided by an embodiment of the present invention;

[0057] Figure 2 is a system - model diagram of an embodiment of the present invention;

[0058] Figure 3 is a relationship diagram between the number of iterations of the optimization - problem algorithm proposed in an embodiment of the present invention and the base - station power;

[0059] Figure 4 is the normalized received signal energy map achieved by near-field beamforming in the physical space in the embodiments of the present invention;

[0060] Figure 5 is the normalized spectrum of the sensing target in the embodiments of the present invention;

[0061] Figure 6 is the relationship diagram between the central distance between the base station and the reconfigurable intelligent surface and the base station power of the system provided in the embodiments of the present invention.

[0062] Figure 7 is the component block diagram of the embodiments of the present invention. Detailed implementation manners

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0064] S1: Establish a system model of a base station, a reconfigurable intelligent surface, Internet of Things devices, a primary communication user, and a sensing target;

[0065] S2: In the case of all-digital antennas, jointly optimize the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix of the reconfigurable intelligent surface to minimize the optimization problem of the system base station power. In the case of dynamic metasurface antennas, the analog signal precoding matrix also needs to be optimized;

[0066] S3: For the optimization problem in the case of all-digital antennas, use the alternating optimization method to transform all sub-problems into semi-definite programming problems for solution; for the optimization problem in the case of dynamic metasurface antennas, also use the alternating optimization method, introduce slack variables to simplify the problem, and finally use the manifold optimization method for solution.

[0067] Specifically, step S1 includes:

[0068] The method described in this embodiment is applied to the system model as shown in Figure 2 The system parameters include: a base station equipped with N = 10 antennas, 1 sensing target, K = 5 Internet of Things devices with single antennas, a reconfigurable intelligent surface equipped with M = 10 reflection units, and 1 primary communication user. The initial transmission power of the base station is 19 dBm, the noise of all nodes is σ 2 =-120 dBm, the loss power of the reconfigurable intelligent surface is 0.1 mW, and the communication constraint of the base station is γs = 15 dB, and the communication constraint of the Internet of Things device is γ c = 10 dB. The carrier frequency is f c = 28 GHz, and the carrier wavelength is λ = 1.07 cm.

[0069] Furthermore, step S2 includes: Figure 2 is the system model and transmission framework followed by the embodiments of the present invention. According to the different types of base station antennas, the ways to solve the problems in the embodiments of the present invention are also different. First, introduce how to solve the problem of minimizing the base station power proposed by the present invention when the base station antenna is a fully digital antenna.

[0070] In this embodiment, the channel between the base station and the primary communication user is h u , the channel between the base station and the reconfigurable intelligent surface is h br , the channel between the base station and the Internet of Things device is h IoT , the channel between the reconfigurable intelligent surface and the primary communication user is h ru , and the channel between the reconfigurable intelligent surface and the Internet of Things device is h ri . The near-field round-trip channel matrix for target sensing is G.

[0071] The signal transmitted by the base station can be expressed as

[0072] x(l) = wp(l) + s(l) (9)

[0073] p(l) is the normalized data symbol, s(l) is the sensing signal, and w is the precoding vector of p(l). The echo signal received by the base station regarding the sensing target is

[0074] y s (l) = Gx(l) + n s (l) (10)

[0075] is the additive white noise corresponding to the channel.

[0076] The covariance matrix of the signal transmitted by the base station is as follows,

[0077] R x = E[x(l)x H (l)] = ww H + R s (11)

[0078] where R s = E[s(l)s H (l)] is the covariance matrix of the sensing signal.

[0079] The Cramér-Rao bound is used as the performance metric for target sensing,

[0080]

[0081] During the current symbol period of the Internet of Things (IoT) device transmission, within the \(l\)-th base station symbol period, the signal received by the primary communication user can be expressed as

[0082] y u (l) = (h u + h ru Θh br c)wp(l)+(h u + h ru Θh br c)s(l)+ n u (l)(13)

[0083] where \(n(l)\) is the additive white Gaussian noise of the corresponding channel, \(\Theta=\text{diag}(\theta_1,...,\theta M ), is the phase shift matrix, \(\mu m \in[0,1]\) is the reflection amplitude, and \(v m \in[0,2\pi)\) is the reflection angle.

[0084] Within the \(l\)-th base station symbol period, the signal vector received by the IoT device is

[0085]

[0086] where \(n(l)\) is the additive white Gaussian noise of the corresponding channel, and \(c\) is the signal transmitted by the RIS.

[0087] The average signal-to-interference-plus-noise ratio (SINR) for the IoT device to decode the signal \(c\) is

[0088]

[0089] The average SINR for the IoT device to decode the signal \(s\) is

[0090]

[0091] The average SINR for the primary communication user to decode the signal \(s\) is

[0092]

[0093] Based on the above information, an optimization problem can be formulated for the case of all-digital antennas. Jointly optimize the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix of the intelligent reconfigurable intelligent surface to minimize the base station power of the system.

[0094]

[0095] s.t. \(\gamma p,s≥γ s , (18a)

[0096] γ i,s ≥γ s , (18b)

[0097] γ i,c ≥γ c , (18c)

[0098] 0 ≤ β m ≤ 1, 0 ≤ θ m ≤ 2π, (18d)

[0099] CRB ≤ C min , (18e)

[0100] Introduce an auxiliary variable U and transform the constraint (18e) into the following form:

[0101] Tr{U -1} ≤ C min (19a)

[0102]

[0103] Apply the Schur complement to (19b) to obtain

[0104]

[0105] So the problem (18) becomes

[0106]

[0107] s.t. Tr{U -1} ≤ C min , U ≥ 0 (21a)

[0108]

[0109] (19a)-(19c) (21b)

[0110] Adopt alternating optimization, fix Θ, optimize R x , w, and at the same time use the SDR method to transform the quadratic optimization variable in the constraint into a linear optimization variable, obtaining the following sub-problem

[0111]

[0112] s.t. Tr{U -1} ≤ C min , U ≥ 0 (22a)

[0113]

[0114]

[0115]

[0116] rank(W) = 1(22d)

[0117] By transforming the problem into a semidefinite programming problem to solve the rank-1 constraint, tools such as CVX can then be directly used for direct solution. After obtaining R x , the optimal solution of w, substituting it into the original problem (18), the optimal value of Θ can be obtained.

[0118] Now introduce how to solve the base station power minimization problem proposed by the present invention when the base station antenna is a dynamic metasurface antenna.

[0119] The signal transmitted by the base station in the case of a dynamic metasurface antenna can be expressed as

[0120] x DMA (l) = HF DMA (w BB p(l) + s(l))(23)

[0121] where F DMA is the analog signal precoding matrix of the dynamic metasurface antenna, w BB is the digital signal precoding matrix of the dynamic metasurface antenna, L e is the number of elements on each microstrip line, α i is the waveguide attenuation coefficient, β i is the wave number, is the position of the l-th element on the i-th microstrip line.

[0122] The covariance matrix of the signal transmitted by the base station in the case of a dynamic metasurface antenna can be expressed as

[0123]

[0124] Combining the above information, an optimization problem in the case of a dynamic metasurface antenna can be proposed. Jointly optimize the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix of the reconfigurable intelligent surface and the analog signal precoding matrix, so as to minimize the base station power.

[0125]

[0126] (19a)-(19e)(25b) Adopting the alternating optimization method, the following problem is obtained,

[0127]

[0128]

[0129] Vectorize the matrix to obtain

[0130]

[0131] where r = vec(R DMA ), f = vec(F DMA ),

[0132] Finally, make Transform the problem into

[0133]

[0134] s.t. |b l | = 1(28a)

[0135] This problem can be directly solved by the manifold optimization method. Substituting the obtained optimal solution F DMA into problem (25) can obtain a problem similar to problem (18), and the solution method is the same as that of problem (18), which will not be elaborated here.

[0136] As can be seen from the above technical solutions, the present invention provides a method for minimizing the base station power in a reconfigurable intelligent surface-assisted coexisting radio system, which calculates the minimum power of the system base station by jointly optimizing the covariance matrix of the sensing signal, the precoding vector of the normalized data symbol transmitted by the base station, the phase shift matrix of the reconfigurable intelligent surface, and the precoding matrix of the analog signal.

[0137] The system configuration parameters of this embodiment are as follows:

[0138]

[0139]

[0140] Figure 3 Shows the relationship between the number of iterations of the optimization problem algorithm proposed in the embodiment of the present invention and the base station power. In the first few iterations, the base station power will decrease significantly, but when it drops to a certain value, the base station power will tend to be stable.

[0141] Figure 4 Shows the normalized received signal energy map realized by near-field beam focusing in physical space, indicating that the near-field beam focusing energy distribution is concentrated, suitable for short-distance high-precision communication, and can achieve high-precision positioning and energy transfer in both the angle and distance dimensions.

[0142] Figure 5The normalized spectrum of the sensed target in the embodiment of the present invention is given. This figure intuitively shows the positioning effect of the sensed target in the near-field channel through the normalization algorithm. The spike in the spectrum in the figure indicates that this position corresponds to the strongest response of the sensed signal, reflecting the true position of the sensed target.

[0143] Figure 6 It is a graph showing the relationship between the central distance between the base station and the reconfigurable intelligent surface and the base station power of the system provided by the embodiment of the present invention. It can be seen that as the distance increases, the base station needs to have a greater power to meet the communication quality constraint, which is in line with the actual situation.

[0144] In the embodiment, the model establishment specifically includes:

[0145] A model establishment module for establishing a reconfigurable intelligent surface-assisted coexisting radio system model; including a base station with N antennas, a primary communication user with a single antenna, a reconfigurable intelligent surface equipped with M reflection units, multiple single-antenna Internet of Things devices, and a sensed target.

[0146] In this example, the equation construction module specifically includes:

[0147] An equation construction module jointly optimizes the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix of the reconfigurable intelligent surface in the case of full-digital antennas and dynamic meta-surface antennas, constructs an optimization problem for minimizing the base station power of the system, and also optimizes the analog signal precoding matrix in the case of dynamic meta-surface antennas;

[0148] The optimization problem in the case of full-digital antennas,

[0149]

[0150] s.t.γ p,s ≥γ s , (29a)

[0151] γ i,s ≥γ s , (29b)

[0152] γ i,c ≥γ c , (29c)

[0153] 0≤β m ≤1,0≤θ m ≤2π, (29d)

[0154] CRB≤C min , (29e)

[0155] In problem (29), (29a)-(29c) are the communication quality constraints of the system, (29d) is the constraint for reconfiguring the intelligent surface, and (29e) is the Cramer-Rao bound constraint.

[0156] Optimization problem in the case of dynamic metasurface antennas

[0157]

[0158] s.t. γ p,s ≥γ s , (30a)

[0159] γ i,s ≥γ s , (30b)

[0160] γ i,c ≥γ c , (30c)

[0161] 0 ≤ β m ≤ 1, 0 ≤ θ m ≤ 2π, (30d)

[0162] CRB ≤ C min , (30e)

[0163]

[0164] (30f) is the constraint for the analog precoding matrix of the dynamic metasurface antenna.

[0165] In this example, the iterative solution module specifically includes:

[0166] The iterative solution module is used to split the original non-convex problem in the two antenna cases into two separately solvable sub-problems by using methods such as the alternating optimization method, the SDR method, and the manifold optimization method, and calculate the optimal solution of the system base station power minimization problem by using convex optimization tools such as CVX.

Claims

1. A method for reconstructing an intelligent surface-assisted coexisting radio system, characterized in that, The method includes: S1: Establish a reconfigurable intelligent surface-assisted coexisting radio system model, and propose a new transmission framework structure that combines reconfigurable intelligent surfaces and coexisting radios; S2: Propose two optimization problems. In the case of fully digital antennas and dynamic metasurface antennas, optimize the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix of the reconfigurable intelligent surface to minimize the base station power of the system. In the case of dynamic metasurface antennas, also optimize the analog signal precoding matrix; S3: For the optimization problem in the case of fully digital antennas, use the alternating optimization method to transform all sub-problems into semi-definite programming problems for solution; for the optimization problem in the case of dynamic metasurface antennas, also use the alternating optimization method, introduce slack variables to simplify the problem, and finally use the manifold optimization method for solution.

2. The method according to claim 1, wherein The specific steps of step S1 include: The system includes a base station with N antennas, a primary communication user with a single antenna, a sensing target, multiple single-antenna Internet of Things devices, and a reconfigurable intelligent surface equipped with M reflection units, and a new transmission framework structure that combines reconfigurable intelligent surfaces and coexisting radios is proposed.

3. The method according to claim 1, wherein The specific steps of step S2 include: Formulate the objective functions of the two problems, and solve the problems by jointly optimizing the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix of the reconfigurable intelligent surface. In the case of dynamic metasurface antennas, also optimize the analog signal precoding matrix.

4. The method according to claim 1, wherein The specific steps of step S3 include: For the optimization problem in the case of fully digital antennas, use the alternating optimization method, introduce auxiliary variables and slack variables, and transform the two decomposed sub-problems into semi-definite programming problems for solution. Then the initial objective problem in the case of fully digital antennas is: s.t.γ p,s ≥γ s , (1a) γ i,s ≥γ s , (1b) γ i,c ≥γ c , (1c) 0 ≤ β m ≤ 1, 0 ≤ θ m ≤ 2π, (1d) CRB ≤ C min , (1e) where, γ p,s , γ i,s , γ i,c represents the signal-to-interference-plus-noise ratio of the corresponding communication channel in problem (1), β m , θ m represents the reflection amplitude and phase of the m-th reflecting element of the reconfigurable intelligent surface, CRB represents the Cramér-Rao bound of the unknown parameters, γ s , γ c , C min represents the bounds of the corresponding variables; (1a-c) represent the communication quality constraints of the system, (1d) represents the constraints of the reconfigurable intelligent surface, and (1e) is the effectiveness constraint for the estimation of unknown parameters; For the optimization problem in the case of dynamic metasurface antennas, use the alternating optimization method, introduce slack variables to simplify the problem, and finally use the manifold optimization method for solution. The initial objective problem in the case of dynamic metasurface antennas is: s.t.γ p,s ≥γ s , (2a) γ i,s ≥γ s , (2b) γ i,c ≥γ c , (2c) 0 ≤ β m ≤ 1, 0 ≤ θ m ≤ 2π, (2d) CRB ≤ C min , (2e) where f i,j is an element in the analog precoding matrix of the dynamic metasurface antenna, representing a non-zero phase shift; (2f) is a constraint on the analog precoding matrix of the dynamic metasurface antenna.

5. A device for reconstructing an intelligent surface-assisted coexisting radio system, characterized in that, It includes: A model establishment module for establishing a downlink system model of a reconfigurable intelligent surface-assisted coexisting radio system; An equation construction module for minimizing the base station power of the system by jointly optimizing the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix of the reconfigurable intelligent surface in the case of fully digital antennas and dynamic metasurface antennas. In the case of dynamic metasurface antennas, also optimize the analog signal precoding matrix; An iterative processing module for decomposing the planned problems into two separately solvable sub-problems, simplifying the problems by introducing slack variables, and finally transforming the problems in the case of fully digital antennas into semi-definite programming problems for solution, and using the manifold optimization method for solution of the problems in the case of dynamic metasurface antennas, so as to obtain the optimal solutions of the minimum base station power of the system under different antenna types; The model establishment module includes: A first modeling unit for equipping the base station with N antennas, the primary communication user with a single antenna, a sensing target, multiple single-antenna Internet of Things devices, and a reconfigurable intelligent surface equipped with M reflection units; The second modeling unit considers the incident angle and emission angle of the reconfigurable intelligent surface, establishes the near-field channels between the reconfigurable intelligent surface and the base station, users, and Internet of Things devices, and obtains effective channel state information.

6. The device according to claim 5, characterized in that It includes: The equation construction module. In the case of all-digital antennas, jointly optimize the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix of the reconfigurable intelligent surface to obtain the optimization equation for the minimum power of the system base station: s.t.γ p,s ≥γ s , (3a) γ i,s ≥γ s , (3b) γ i,c ≥γ c , (3c) 0 ≤ β m ≤ 1, 0 ≤ θ m ≤ 2π, (3d) CRB ≤ C min , (3e) In the case of dynamic metasurface antennas, jointly optimize the covariance matrix of the sensing signal, the precoding vector of the normalized data symbols transmitted by the base station, and the phase shift matrix and analog signal precoding matrix of the reconfigurable intelligent surface to obtain the optimization equation for the minimum power of the system base station: s.t.γ p,s ≥γ s , (4a) γ i,s ≥γ s , (4b) γ i,c ≥γ c , (4c) 0 ≤ β m ≤ 1, 0 ≤ θ m ≤ 2π, (4d) CRB ≤ C min , (4e) 7. The device according to claim 5, characterized in that, It includes: The iterative processing module. For the optimization problem in the case of all-digital antennas, use the alternating optimization method to decompose the original optimization problem into two sub-problems that can be solved separately, and convert them into semi-definite programming problems for solution; For the optimization problem in the case of dynamic metasurface antennas, also use the alternating optimization method, introduce slack variables to simplify the problem, and finally use the manifold optimization method to solve the problem, so as to obtain the optimal solutions of the minimum power of the system in both cases.

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