A mixed-intelligence reflectarray-assisted integrated sensing and communication method and system

Abstract

The application discloses a kind of based on mixed intelligent reflecting surface auxiliary's integrated sensing method, it is related to wireless communication technical field, including: establishing integrated sensing system, the system includes a dual-function base station, a mixed intelligent reflecting surface, K communication users and L perception targets;Communication model and perception model are constructed, according to the communication model and perception model, establish the optimization problem of maximum L perception targets in minimum perception beam gain under the constraint of maximum total transmit power of dual-function base station, communication quality constraint of communication user, maximum total transmit power constraint of mixed intelligent reflecting surface and noise power constraint;The optimization problem is transformed and solved based on alternating variable iterative optimization algorithm, and the final optimal solution of optimization problem is obtained.The application can maximize the minimum perception beam gain of perception target, and improve the sensing ability of system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, and more particularly to a sensing and communication integration method based on hybrid intelligent reflecting surface assistance. BACKGROUND

[0002] Intelligent reflecting surface (IRS) is composed of a large number of passive elements, each of which can independently cause phase changes. By adjusting the phase of the elements, the reflection of the signal can be controlled, which can expand the coverage of the sensing area in non-line-of-sight paths and improve the sensing accuracy. Pure passive IRS has the advantages of low power consumption, low cost, easy deployment, etc. However, pure passive IRS can only reflect incident signals and cannot amplify signals, resulting in significant attenuation of signals after reflection, especially in long-distance and non-line-of-sight environments. Active IRS can effectively alleviate the above problems. Active IRS not only reflects incident signals according to the required phase shift of the system, but also amplifies reflected signals, thereby effectively compensating for path loss, which can bring significant performance improvement even in the case of occluded line-of-sight links. It can also expand the coverage, effectively enhance the signal coverage, reduce the blind area, and improve the coverage of the communication and sensing system. However, these advantages are accompanied by higher power consumption and cost, and the active amplifier elements will introduce certain thermal noise, which will have a negative impact on system performance.

[0003] Hybrid active-passive IRS combines the advantages of passive and active IRS. Through a switch, hybrid IRS elements can transition between active and passive modes according to real-time environment and requirements. In weak signal strength or non-line-of-sight environments, active IRS can amplify signals and enhance coverage; in strong signal or line-of-sight environments, passive mode can maintain low power consumption and reduce unnecessary energy consumption to meet the actual needs of specific applications. This hybrid mode not only integrates the advantages of active IRS, but also to some extent avoids the disadvantages of active IRS. Therefore, hybrid active-passive IRS is an extremely attractive technology in future networks.

[0004] Studies have shown that the introduction of hybrid active-passive IRS can bring considerable performance improvement to the system, but the current research direction mainly focuses on the joint optimization of base station beamforming and IRS reflection coefficient, without considering the position optimization of hybrid IRS active elements. The current position of hybrid IRS active elements is usually randomly selected and pre-set, and has not been optimized by algorithm, which has a large gap with the performance upper limit of IRS. SUMMARY

[0005] The present application provides a sensing and communication integration method based on hybrid intelligent reflecting surface assistance to improve the sensing ability of the system.

[0006] To solve the above technical problems, the technical scheme of the present application is as follows:

[0007] The present application provides a hybrid intelligent reflecting surface assisted integrated communication and sensing method, comprising:

[0008] An integrated communication and sensing system is established, which includes a dual-function base station, a hybrid intelligent reflecting surface, K communication users and L sensing targets;

[0009] A communication model is established according to the signal transmission tasks between the dual-function base station, the hybrid intelligent reflecting surface and the K communication users, and a sensing model is established according to the sensing tasks of the dual-function base station and the hybrid intelligent reflecting surface on the L sensing targets;

[0010] According to the communication model and the sensing model, an optimization problem is established to maximize the minimum sensing beam gain of the L sensing targets under the constraints of the maximum total transmit power of the dual-function base station, the communication quality of the communication users, the maximum total transmit power of the hybrid intelligent reflecting surface and the noise power;

[0011] Based on the alternating variable iterative optimization algorithm, the optimization problem is transformed and solved to obtain the final optimal solution of the optimization problem.

[0012] Preferably, the hybrid intelligent reflecting surface includes N a active reflecting elements and N-N a passive reflecting elements, 1≤N a ≤N, N represents the number of reflecting elements of the hybrid intelligent reflecting surface; the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflecting surface are set.

[0013] Preferably, the communication model is established according to the signal transmission tasks between the dual-function base station, the hybrid intelligent reflecting surface and the K communication users, comprising:

[0014] There is no obstacle between the dual-function base station and the K communication users, the dual-function base station generates a transmit signal, the transmit signal is transmitted to each user through a first channel, and at the same time, after being reflected by the hybrid intelligent reflecting surface, it is transmitted to each user through a second channel;

[0015] The received signal of the kth communication user is determined by the following formula:

[0016]

[0017] Where y k represents the received signal of the kth communication user, represents the set of communication users, represents the equivalent channel vector from the dual-function base station to the kth communication user, H2denotes a channel matrix of the second channel from the dual-functional base station to the hybrid intelligent reflecting surface, H2denotes a reflection coefficient matrix of the hybrid intelligent reflecting surface, where β n and φ n are the reflection amplitude and phase of the nth reflection element of the hybrid intelligent reflecting surface, respectively, β n ≥ 0, H2denotes a set of reflection elements of the hybrid intelligent reflecting surface, the reflection amplitude of the N a active reflection elements is 0 ≤ β n ≤ β max , β max denotes a maximum threshold of the reflection amplitude, the reflection amplitude of the N-N a passive reflection elements is 1, Q = diag(q1, q2, …, q n , …, q N ) denotes a mode scheduling matrix of the hybrid intelligent reflecting surface, q n ∈ {0, 1}, q n = 0 for the passive reflection elements, and q n = 1 for the active reflection elements; the additive white Gaussian noise caused by the active reflection elements of the hybrid intelligent reflecting surface, denotes the additive white noise at the receiving end of the kth communication user, denotes the noise power of the kth communication user; x denotes the transmitting signal of the dual-functional base station, and denote the fading channel vector of the first channel from the dual-functional base station to the kth communication user and the fading channel vector of the second channel from the hybrid intelligent reflecting surface to the kth communication user, respectively, where M denotes the number of antennas configured by the dual-functional base station, M > 1.

[0018] Preferably, the method for determining the transmitting signal of the dual-functional base station comprises:

[0019]

[0020] where x denotes the transmitting signal of the dual-functional base station, x0denotes the zero-mean dedicated sensing signal of the dual-functional base station; w k denotes the transmitting beamforming vector; s k denotes the information symbol sent by the dual-functional base station to the kth communication user.

[0021] Preferably, the method for establishing the perception model according to the perception tasks of the dual-functional base station and the hybrid intelligent reflecting surface on the L perception targets comprises:

[0022] The dual-function base station has an obstacle between the dual-function base station and L perception targets, the dual-function base station generates a transmission signal, after reflection by a hybrid intelligent reflecting surface, each perception target is perceived through a third channel;

[0023] The perception model of the perception target is determined by the following formula:

[0024]

[0025] Wherein, represents a cascaded channel vector from the dual-function base station to the lth perception target via the hybrid intelligent reflecting surface, represents an array response matrix of the hybrid intelligent reflecting surface, is determined by the following formula:

[0026]

[0027] Wherein, represents the azimuth angle of the lth perception target relative to the hybrid intelligent reflecting surface, represents the elevation angle of the lth perception target relative to the hybrid intelligent reflecting surface, d x and d y respectively represent the spacing between two consecutive reflecting elements of the intelligent reflecting surface in the x-axis and y-axis, and λ represents the wavelength of the radio frequency transmission signal of the dual-function base station, represents the Kronecker product, N x and N y respectively represent the number of elements of the hybrid intelligent reflecting surface in the x-axis and y-axis directions, N=N x ×N y .

[0028] Preferably, the optimization problem of maximizing the minimum perception beam gain in the L perception targets under the constraints of the maximum total transmission power of the dual-function base station, the communication quality constraint of the communication user, the maximum total transmission power constraint of the hybrid intelligent reflecting surface and the noise power constraint is established according to the communication model and the perception model, comprising:

[0029]

[0030] 1-q n ≤β n ≤q n β max +1-q n (h)

[0031]

[0032] Wherein, P0 represents the maximum total transmission power of the dual-function base station, represents the perception signal covariance matrix of the dual-function base station, denotes the maximum amplification power of the hybrid intelligent reflecting surface, SINR k denotes the signal-to-noise ratio of the kth communication user, denotes the hybrid intelligent reflecting surface noise power generated at the lth sensing target, denotes the hybrid intelligent reflecting surface noise power threshold, Γ k denotes the preset user signal-to-noise ratio threshold, denotes the sensing target set; formula (a) denotes the maximum minimum sensing beam gain optimization objective function, formula (b) denotes the maximum total transmission power limit of the integrated sensing and communication dual-function base station; formula (c) denotes the positive semi-definite requirement of the sensing signal covariance of the base station; formula (d) denotes that the signal-to-noise ratio of the kth communication user must be greater than the preset user signal-to-noise ratio threshold; formula (e) denotes that the hybrid intelligent reflecting surface noise power generated at the lth sensing target cannot exceed the preset threshold; formula (f) denotes the amplification power constraint of the hybrid intelligent reflecting surface; formula (g) denotes the reflection phase shift coefficient limit of the hybrid intelligent reflecting surface; formula (h) and (i) respectively denote the reflection amplitude limits of the passive reflecting element and the active reflecting element: for the passive reflecting element, q n = 0, 0 ≤ β n ≤ 1; for the active reflecting element, q n = 1, 0 ≤ β n ≤ β max .

[0033] Preferably, the alternating variable iteration optimization algorithm transforms the optimization objective to obtain the final optimal solution of the optimization problem, which includes:

[0034] S4.1: fixing the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflecting surface, optimizing the transmission beamforming vector and the sensing signal covariance matrix, and transforming the optimization problem into a first sub-optimization problem;

[0035] Solving the first sub-optimization problem by using an interior point method to obtain the current optimal solution of the transmission beamforming vector and the sensing signal covariance matrix;

[0036] S4.2: fixing the current optimal solution of the transmission beamforming vector and the sensing signal covariance matrix, optimizing the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflecting surface, and transforming the optimization problem into a second sub-optimization problem;

[0037] Solving the second sub-optimization problem by using a convex solver to obtain the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflecting surface;

[0038] S4.3: Calculate the growth of the beamforming gain of the current optimization target and the beamforming gain of the last optimization target, compare it with the preset minimum tolerance limit, when the growth is greater than the minimum tolerance limit, execute step S4.4; otherwise, execute step S4.5;

[0039] S4.4: Fix the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflector, repeat the above steps S4.1-S4.3;

[0040] S4.5: Take the current optimal solution of the transmit beamforming vector corresponding to the current optimization target, the current optimal solution of the sensing signal covariance matrix, the current optimal solution of the mode scheduling matrix of the hybrid intelligent reflector, and the current optimal solution of the reflection coefficient matrix of the hybrid intelligent reflector as the final optimal solution of the optimization problem, and calculate the corresponding minimum sensing beamforming gain.

[0041] Preferably, in S4.1, the first sub-optimization problem is solved by using an interior point method to obtain the current optimal solution of the transmit beamforming vector and the sensing signal covariance matrix, which includes:

[0042] Introducing a beamforming matrix for the kth communication user The first sub-optimization problem is relaxed to a convex semidefinite programming problem for solving the transmit beamforming vector and the sensing signal covariance matrix by using a semidefinite relaxation technique to remove the rank-one constraint, and then an interior point method is used to solve it to obtain the current optimal transmit beamforming vector and the sensing signal covariance matrix.

[0043] Preferably, in S4.2, the second sub-optimization problem is solved by using a convex solver to obtain the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflector, which includes:

[0044] Definition Introducing a matrix And relaxing the optimization variable And limiting the Z optimization variable according to the following conditions:

[0045]

[0046] In the above conditions, Z=QUQ is satisfied H =QUQ, where u i,j And z i,j Respectively, the elements of the i-th row and j-th column of matrix U and Z, q i,i Indicates the i-th diagonal element of the i-th row and i-th column of matrix Q, i.e. the i-th diagonal element, It is an arbitrary constant; when the i-th diagonal element of Q is 0, equations (j), (k), (l), and (m) guarantee that all elements in the i-th row and i-th column of z are zero; equations (n) and (o) guarantee that the remaining elements of Z are zero. i,j =u i,j ;

[0047] In the optimization problem, QUQ in equations (d), (e), and (f) H Substituting Z, we rewrite equation (i) as the following equality constraint:

[0048]

[0049] The penalty method is used to solve the non-convex binary constraint (i1), the equality constraint is transformed into a penalty term added to the optimization objective function, the upper bound of the penalty term in the objective function is constructed using the continuous convex approximation technique, the second optimization subproblem is transformed into a convex function, the second sub-optimization problem is transformed into a convex optimization problem, and the mode scheduling matrix and reflection coefficient matrix of the current optimal hybrid intelligent reflector are obtained by using a standard convex solver.

[0050] The present invention also provides a synsensory integration system based on a hybrid intelligent reflective surface for use in the above method, the system comprising the following modules:

[0051] The system construction module is used to establish an integrated sensing system, which includes a dual-function base station, a hybrid intelligent reflector, K communication users, and L sensing targets.

[0052] The model building module is used to establish a communication model based on the signal transmission task between the dual-function base station, the hybrid intelligent reflector and K communication users, and to establish a perception model based on the perception task of the dual-function base station and the hybrid intelligent reflector for L perception targets.

[0053] The optimization problem construction module is used to establish an optimization problem based on the communication model and the sensing model, which satisfies the constraints of the maximum total transmit power of the dual-function base station, the communication quality of the communication user, the maximum total transmit power of the hybrid intelligent reflector, and the noise power constraint, to maximize the minimum sensing beam gain among L sensing targets.

[0054] The optimization problem-solving module is used to transform and solve the optimization problem based on the alternating variable iterative optimization algorithm to obtain the final optimal solution to the optimization problem.

[0055] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0056] The application provides a method for communication and sensing integration based on hybrid intelligent reflecting surface assistance, which comprises the following steps: first, establishing a communication and sensing integration system, wherein the system comprises a dual-function base station, a hybrid intelligent reflecting surface, K communication users and L sensing targets; then, establishing a communication model and a sensing model, and according to the communication model and the sensing model, establishing an optimization problem of maximizing the minimum sensing beam gain of the L sensing targets under certain constraints; and based on an alternating variable iterative optimization algorithm, the optimization problem is transformed and solved to obtain the final optimal solution of the optimization problem. The application can maximize the minimum sensing beam gain of the sensing targets and improve the sensing capability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 A flowchart of the method for communication and sensing integration based on hybrid intelligent reflecting surface assistance according to Embodiment 1 is shown in the figure.

[0058] Figure 2 A system block diagram of the method for communication and sensing integration based on hybrid intelligent reflecting surface assistance according to Embodiment 2 is shown in the figure.

[0059] Figure 3 A structural schematic diagram of the communication and sensing integration system based on hybrid intelligent reflecting surface assistance according to Embodiment 3 is shown in the figure. DETAILED DESCRIPTION

[0060] The accompanying drawings are only used for illustrative purposes and should not be construed as limiting the patent;

[0061] In order to better illustrate the embodiments, some components in the drawings may be omitted, enlarged or reduced, and do not represent the actual size of the product;

[0062] For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0063] The technical solutions of the application will be further described below in combination with the drawings and embodiments.

[0064] Embodiment 1

[0065] The embodiment provides a method for communication and sensing integration based on hybrid intelligent reflecting surface assistance, as shown in the figure, which comprises the following steps: Figure 1

[0066] S1: establishing a communication and sensing integration system, wherein the system comprises a dual-function base station, a hybrid intelligent reflecting surface, K communication users and L sensing targets;

[0067] S2: establishing a communication model according to the signal transmission task between the dual-function base station, the hybrid intelligent reflecting surface and the K communication users, and establishing a sensing model according to the sensing task of the dual-function base station and the hybrid intelligent reflecting surface on the L sensing targets;

[0068] ​S3: Based on the communication model and the sensing model, establish an optimization problem to maximize the minimum sensing beam gain among L sensing targets under the constraints of the maximum total transmit power of the dual-function base station, the communication quality of the communication user, the maximum total transmit power of the hybrid intelligent reflector, and the noise power.

[0069] S4: The optimization problem is transformed and solved using the alternating variable iterative optimization algorithm to obtain the final optimal solution to the optimization problem.

[0070] In the specific implementation process, this embodiment establishes a communication model for communication users and a perception model for sensing users. Under the constraints of maximum total transmit power of dual-function base stations, communication quality of communication users, maximum total transmit power of hybrid intelligent reflectors, and noise power, an optimization problem is established and solved to maximize the minimum sensing beam gain among L sensing targets. This can maximize the minimum sensing beam gain of the sensing targets in the integrated sensing system.

[0071] Example 2

[0072] This embodiment provides a synesthetic integration method based on hybrid intelligent reflective surface assistance, including:

[0073] S1: Establish a sensor-integrated system, which includes a dual-function base station, a hybrid intelligent reflective surface, K communication users, and L sensing targets;

[0074] like Figure 2 The diagram shown is a system block diagram of the sensing integration method described in this embodiment. It consists of a dual-function base station, a hybrid intelligent reflector, communication users, and sensing targets. There are L sensing targets and K single-antenna communication users (CUs). The base station is equipped with M antennas, where M>1.

[0075] With the assistance of a hybrid smart reflector, the base station is responsible for transmitting communication signals to K single-antenna communication users and dedicated sensing signals to L targets. The hybrid smart reflector is a system consisting of N=N... x ×N y A uniform planar array (UPA) consisting of N reflective elements, where N x and N y These represent the number of reflective elements along the x-axis and y-axis of the hybrid smart reflector, respectively. The hybrid smart reflector comprises N... a One active reflective element and NN a There are 1 passive reflective elements, 1 ≤ N a ≤N, where N represents the number of reflective elements in the hybrid intelligent reflector; the mode scheduling matrix and reflection coefficient matrix of the hybrid intelligent reflector are set.

[0076] S2: a communication model is established according to the signal transmission task between the dual-function base station, the hybrid intelligent reflecting surface and the K communication users, and a perception model is established according to the perception task of the dual-function base station and the hybrid intelligent reflecting surface on the L perception targets;

[0077] There is no obstacle between the dual-function base station and the K communication users, the dual-function base station generates a transmission signal, the transmission signal is transmitted to each user through a first channel, and after being reflected by the hybrid intelligent reflecting surface, the transmission signal is transmitted to each user through a second channel;

[0078] The received signal of the kth communication user is determined by the following formula:

[0079]

[0080] wherein y k represents the received signal of the kth communication user, represents a set of communication users, represents an equivalent channel vector from the dual-function base station to the kth communication user, represents a channel matrix of the second channel from the dual-function base station to the hybrid intelligent reflecting surface, represents a reflection coefficient matrix of the hybrid intelligent reflecting surface, wherein β n and φ n ∈(0,2π] are the reflection amplitude and phase of the nth reflection element of the hybrid intelligent reflecting surface, respectively, β n ≥0, represents a set of reflection elements of the hybrid intelligent reflecting surface, N a The reflection amplitude of the N n active reflection elements is 0≤β max ≤β max , β a represents a maximum threshold of the reflection amplitude, N-N n The reflection amplitude of the Q passive reflection elements is 1, Q=diag(q1,q2,…,q N ) represents a mode scheduling matrix of the hybrid intelligent reflecting surface, q n ∈{0,1}, For passive reflection elements, q n =0, and for active reflection elements, q n =1; The additive white Gaussian noise caused by the active reflection elements of the hybrid intelligent reflecting surface, represents the additive white noise at the receiving end of the kth communication user, represents the noise power of the kth communication user; x represents the transmission signal of the dual-function base station, and respectively represent the fading channel vector of the first channel from the dual-function base station to the kth communication user and the fading channel vector of the second channel from the hybrid intelligent reflecting surface to the kth communication user, where M represents the number of antennas configured by the dual-function base station, M > 1.

[0081] The method for determining the transmission signal of the dual-function base station comprises:

[0082]

[0083] wherein x represents the transmission signal of the dual-function base station, x0 represents the zero-mean dedicated sensing signal of the dual-function base station; w k represents the transmission beamforming vector; s k represents the information symbol sent by the dual-function base station to the kth communication user.

[0084] Considering that the sensing signal of the base station is preset and known at the communication user, the signal-to-noise ratio of the kth communication user after eliminating the interference brought by x0 is:

[0085]

[0086] In this case, the total transmission power of the dual-function base station is:

[0087]

[0088] wherein P BS represents the transmission power of the dual-function base station, represents the sensing signal covariance matrix of the dual-function base station, [·] H represents the conjugate transpose.

[0089] The total output power of the active reflecting element in the hybrid intelligent reflecting surface is:

[0090]

[0091] wherein p ris represents the total output power of the active reflecting element in the hybrid intelligent reflecting surface, Q = diag(q1, q2, …, q n ,…, q N ) represents the mode scheduling matrix of the hybrid intelligent reflecting surface, q n = 1 represents that the nth element is the active reflecting element of the hybrid intelligent reflecting surface, q n = 0 represents that the nth element is the passive reflecting element of the hybrid intelligent reflecting surface. is the reflection coefficient matrix of the hybrid intelligent reflecting surface, wherein β n ≥ 0, n ∈ {1, …, N} and φ nand a phase of the nth reflection element of the hybrid intelligent reflecting surface, N is the number of reflection elements of the hybrid intelligent reflecting surface, N a The reflection amplitude of the N active reflection elements is 0≤β n ≤β max , β max represents a maximum threshold of the reflection amplitude, N-N a The reflection amplitude of the passive reflection elements is 1.

[0092] The embodiment perceives L potential targets, and the line-of-sight link between the base station and the perceived target is blocked by an obstacle, so the virtual link created by the intelligent reflecting surface is used to perceive the target. The application will use the beam pattern gain at the potential target in the position of interest as the performance metric of perception.

[0093] There are obstacles between the dual-function base station and L perceived targets, and the dual-function base station generates a transmission signal, which is reflected by the hybrid intelligent reflecting surface and then perceives each perceived target through a third channel;

[0094] The perception model of the perceived target is determined by the following formula:

[0095]

[0096] wherein, represents the concatenated channel vector from the dual-function base station to the lth perceived target via the hybrid intelligent reflecting surface, represents the array response matrix of the hybrid intelligent reflecting surface, is determined by the following formula:

[0097]

[0098] wherein, represents the azimuth angle of the lth perceived target relative to the hybrid intelligent reflecting surface, represents the elevation angle of the lth perceived target relative to the hybrid intelligent reflecting surface, d x and d y respectively represent the spacing between two consecutive reflection elements of the intelligent reflecting surface in the x-axis and y-axis, and λ represents the wavelength of the radio frequency transmission signal of the dual-function base station, represents the Kronecker product.

[0099] The hybrid intelligent reflecting surface noise power generated at the lth perceived target is:

[0100]

[0101] ​S3: according to the communication model and the perception model, an optimization problem of maximizing the minimum perception beam gain in the L perception targets under the constraints of the maximum total transmit power of the dual-function base station, the communication user communication quality constraint, the maximum total transmit power constraint of the hybrid intelligent reflecting surface, and the noise power constraint is established;

[0102]

[0103] 1-q n ≤β n ≤q n β max +1-q n (h)

[0104]

[0105] wherein P0 represents the maximum total transmit power of the dual-function base station, represents the perception signal covariance matrix of the dual-function base station, represents the maximum amplification power of the hybrid intelligent reflecting surface, SINR k represents the signal-to-noise ratio of the kth communication user, represents the noise power of the hybrid intelligent reflecting surface generated at the lth perception target, represents the noise power threshold of the hybrid intelligent reflecting surface, Γ k represents the preset user signal-to-noise ratio threshold, represents the perception target set; formula (a) represents the maximum minimum perception beam gain optimization objective function, formula (b) represents the maximum total transmit power limit of the integrated dual-function base station; formula (c) represents the semi-positive requirement of the perception signal covariance of the base station; formula (d) represents that the signal-to-noise ratio of the kth communication user must be greater than the preset user signal-to-noise ratio threshold; formula (e) represents that the noise power of the hybrid intelligent reflecting surface generated at the lth perception target cannot exceed the preset threshold; formula (f) represents the amplification power constraint of the hybrid intelligent reflecting surface; formula (g) represents the reflection phase shift coefficient limit of the hybrid intelligent reflecting surface; formula (h), (i) respectively represent the reflection amplitude limit of the passive reflecting element and the active reflecting element: for the passive reflecting element, q n = 0, 0 ≤ β n ≤ 1; for the active reflecting element, q n = 1, 0 ≤ β n ≤ β max .

[0106] S4: the optimization problem is solved by transformation based on the alternating variable iterative optimization algorithm, and the final optimal solution of the optimization problem is obtained.

[0107] The transmit beamforming vector, the sensing signal covariance matrix, the reflection coefficient matrix of the hybrid intelligent reflecting surface and the mode scheduling matrix are coupled in the optimization problem. The non-convex constraint that each component of the mode scheduling matrix can only be 0 or 1 makes it difficult to solve optimally. The optimization problem is a non-convex optimization problem. In order to solve this non-convex problem, the problem is first decomposed into two sub-problems, and then an alternating variable iterative optimization technique is used for solving:

[0108] S4.1: Fixing the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflecting surface, the transmit beamforming vector and the sensing signal covariance matrix are optimized, and the optimization problem is converted into a first sub-optimization problem;

[0109] The first sub-optimization problem is solved by using an interior point method to obtain the current optimal solution of the transmit beamforming vector and the sensing signal covariance matrix;

[0110] The beamforming matrix of the kth communication user is introduced rank(W k )≤1, the rank-one constraint is removed by using a semidefinite relaxation technique, the first sub-optimization problem is relaxed into a convex semidefinite programming problem for solving the transmit beamforming vector and the sensing signal covariance matrix, and then an interior point method is used to solve to obtain the current optimal transmit beamforming vector and sensing signal covariance matrix.

[0111] S4.2: Fixing the current optimal solution of the transmit beamforming vector and the sensing signal covariance matrix, the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflecting surface are optimized, and the optimization problem is converted into a second sub-optimization problem;

[0112] The second sub-optimization problem is solved by using a convex solver to obtain the current optimal solution of the mode scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflecting surface;

[0113] On the basis of obtaining the transmit communication signal transmission beam and the sensing signal covariance matrix of the base station, the matrix is introduced and the relaxation optimization variable is limited according to the following conditions:

[0114]

[0115] In the above conditions, Z=QUQ H =QUQ, where u i,j and z i,j represent the elements of the i-th row and the j-th column of the matrix U and Z, q i,i represents the i-th diagonal element of the matrix Q, i.e., the i-th diagonal element, is an arbitrary constant; when the i-th diagonal element of Q is zero, equations (j), (k), (l), (m) guarantee that all elements of the i-th row and column of Z are zero; equations (n), (o) guarantee that the remaining elements of Z are i,j = u i,j ;

[0116] Substitute Z for QUQin equations (d), (e), (f) in the optimization problem, and rewrite equation (i) as the following equality constraint: H

[0117]

[0118] It is inferred that the value of q n is either 1 or 0, otherwise q n (q n -1)>0 always holds. According to this property, a penalty method is used to solve the non-convex binary constraint (ii), which converts the equality constraint into a penalty term added to the optimization objective function, and a successive convex approximation (SCA) technique is used to construct an upper bound of the penalty term in the objective function, which converts the second optimization sub-problem into a convex function, and a standard convex solver (e.g., CVX) is used to solve it, and if the rank of the current optimal solution of the second optimization sub-problem is greater than 1, a series of Gaussian randomization is implemented to obtain the best rank-one solution, and then the pattern scheduling matrix and the reflection coefficient matrix of the current optimal hybrid intelligent reflector are obtained.

[0119] S4.3: Calculate the growth of the beam gain of the current optimization objective and the beam gain of the last optimization objective, and compare it with the preset minimum tolerance limit, and when the growth is greater than the minimum tolerance limit, execute step S4.4; otherwise, execute step S4.5;

[0120] S4.4: Fix the current optimal solution of the pattern scheduling matrix and the reflection coefficient matrix of the hybrid intelligent reflector, and repeat the above steps S4.1-S4.3;

[0121] S4.5: Take the current optimal solution of the current optimization objective corresponding to the beamforming vector, the current optimal solution of the perception signal covariance matrix, the current optimal solution of the pattern scheduling matrix of the hybrid intelligent reflector, and the current optimal solution of the reflection coefficient matrix of the hybrid intelligent reflector as the final optimal solution of the optimization problem, and calculate the corresponding minimum perception beam gain.

[0122] ​On the basis of the two optimization sub-problems, and by using an alternating optimization algorithm to realize system performance optimization, the solution of the problem obtained by each alternating optimization needs to meet the maximum total transmit power limit of the base station; the semi-positive definiteness of the sensing signal covariance matrix; the minimum signal-to-noise ratio at the communication user to guarantee the communication quality; the hybrid intelligent reflecting surface noise power limit to achieve good radar sensing effect; the amplification power constraint of the hybrid intelligent reflecting surface; the amplification amplitude and reflecting phase constraint of the intelligent reflecting surface.

[0123] Embodiment 3

[0124] The embodiment provides a mixed intelligent reflecting surface assisted integrated communication and sensing system, as shown in the figure, comprising the following modules: Figure 3

[0125] A system construction module is configured to establish an integrated communication and sensing system, wherein the system comprises a dual-function base station, a mixed intelligent reflecting surface, K communication users and L sensing targets.

[0126] A model construction module is configured to establish a communication model according to the signal transmission task among the dual-function base station, the mixed intelligent reflecting surface and the K communication users, and establish a sensing model according to the sensing task of the dual-function base station and the mixed intelligent reflecting surface on the L sensing targets.

[0127] An optimization problem construction module is configured to establish an optimization problem of maximizing the minimum sensing beam gain in the L sensing targets under the constraints of the maximum total transmit power of the dual-function base station, the communication quality of the communication user, the maximum total transmit power of the mixed intelligent reflecting surface and the noise power according to the communication model and the sensing model.

[0128] An optimization problem solving module is configured to transform and solve the optimization problem based on an alternating variable iterative optimization algorithm to obtain the final optimal solution of the optimization problem.

[0129] The same or similar reference signs correspond to the same or similar components;

[0130] The terms used to describe the positional relationship in the drawings are only used for exemplary illustration, and should not be understood as a limitation on the patent;

[0131] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not intended to limit the implementation modes of the present application. For those skilled in the art, other different forms of changes or variations can be made on the basis of the above description. Here, it is not necessary and impossible to enumerate all the implementation modes. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the claims of the present application.​

Claims

1. A synesthetic integration method based on hybrid intelligent reflective surfaces, characterized in that, include: Establish a sensing-integrated system, which includes a dual-function base station, a hybrid intelligent reflective surface, K communication users, and L sensing targets; A communication model is established based on the signal transmission task between the dual-function base station, the hybrid intelligent reflector, and K communication users; a perception model is established based on the perception task of the dual-function base station and the hybrid intelligent reflector for L sensing targets. Based on the communication model and the sensing model, an optimization problem is established to maximize the minimum sensing beam gain among L sensing targets under the constraints of maximum total transmit power of dual-function base stations, communication quality of communication users, maximum total transmit power of hybrid intelligent reflectors, and noise power. The optimization problem is transformed and solved using an alternating variable iterative optimization algorithm to obtain the final optimal solution to the optimization problem. The hybrid intelligent reflective surface includes N a One active reflective element and NN a There are 1 passive reflective elements, 1 ≤ N a ≤N, where N represents the number of reflective elements in the hybrid intelligent reflector; set the mode scheduling matrix and reflection coefficient matrix of the hybrid intelligent reflector; The optimization problem of maximizing the minimum sensing beam gain among L sensing targets, based on the communication model and sensing model, and satisfying the constraints of maximum total transmit power of dual-function base stations, communication quality of communication users, maximum total transmit power of hybrid intelligent reflectors, and noise power, includes: 1-q n ≤β n ≤q n β max +1-q n (h) Wherein, P0 represents the maximum total transmit power of the dual-function base station. This represents the covariance matrix of the sensed signals of a dual-function base station. SINR represents the maximum amplification power of the hybrid intelligent reflector. k This represents the signal-to-noise ratio of the k-th communication user. This represents the hybrid intelligent reflective surface noise power generated at the l-th sensing target. Γ represents the noise power threshold of the hybrid smart reflector. k This indicates the preset user signal-to-noise ratio threshold. Let represent the set of sensing targets; Equation (a) represents the objective function for optimizing the maximum and minimum sensing beam gain; Equation (b) represents the maximum total transmit power limit of the integrated sensing dual-function base station; Equation (c) represents the positive semi-definite requirement of the sensing signal covariance of the base station; Equation (d) represents that the signal-to-noise ratio of the k-th communication user must be greater than the preset user signal-to-noise ratio threshold; Equation (e) represents that the noise power of the hybrid intelligent reflector generated at the l-th sensing target cannot exceed the preset threshold; Equation (f) represents the amplification power constraint of the hybrid intelligent reflector; Equation (g) represents the reflection phase shift coefficient limit of the hybrid intelligent reflector; Equations (h) and (i) represent the reflection amplitude limits of the passive and active reflector elements, respectively: for the passive reflector element, q n =0, 0≤β n ≤1; for active reflective elements, q n =1, 0≤β n ≤β max .

2. The integrated sensing method based on hybrid intelligent reflective surface assistance according to claim 1, characterized in that, The establishment of a communication model based on the signal transmission task between the dual-function base station, the hybrid intelligent reflector, and the K communication users includes: There are no obstacles between the dual-function base station and the K communication users. The dual-function base station generates a transmission signal. The transmission signal is transmitted to each user through the first channel and is also reflected by the hybrid intelligent reflective surface and transmitted to each user through the second channel. The received signal of the k-th communication user is determined by the following formula: Among them, y k This represents the received signal of the k-th communication user. Represents a set of communication users. This represents the equivalent channel vector from the dual-function base station to the k-th communication user. This represents the channel matrix from the dual-function base station to the second channel of the hybrid smart reflector. Let β represent the reflection coefficient matrix of the hybrid smart reflector. n and φ n ∈(0,2π] represents the reflection amplitude and phase of the nth reflecting element of the hybrid intelligent reflective surface, respectively, and β n ≥0, N represents the set of reflective elements of a hybrid intelligent reflective surface. a The reflection amplitude of each active reflector is 0 ≤ β n ≤β max , β max This represents the maximum threshold for reflection amplitude, NN a The reflection amplitude of each passive reflector is 1, Q = diag(q1,q2,…,q n ,…,q N ) represents the mode scheduling matrix of the hybrid intelligent reflector, q n ∈{0,1}, For a passive reflective element, q n =0, for active reflective elements, q n =1; Additive white Gaussian noise caused by the active reflective element of the hybrid smart reflective surface. This represents the additive white noise at the receiver for the k-th communication user. Let represent the noise power of the k-th communication user; x represents the transmitted signal of the dual-function base station. and Let M and M represent the fading channel vectors of the first channel from the dual-function base station to the k-th communication user and the second channel from the hybrid smart reflector to the k-th communication user, respectively, where M represents the number of antennas configured in the dual-function base station, and M>

1.

3. The integrated sensing method based on hybrid intelligent reflective surface assistance according to claim 2, characterized in that, The method for determining the transmitted signal of the dual-function base station includes: Where x represents the transmitted signal of the dual-function base station, and x0 represents the zero-mean dedicated sensing signal of the dual-function base station; w k Represents the transmitted beamforming vector; s k This represents the information symbol sent by the dual-function base station to the k-th communication user.

4. The integrated sensing method based on hybrid intelligent reflective surface assistance according to claim 1, characterized in that, The step of establishing a perception model based on the perception task of the dual-function base station and the hybrid intelligent reflector surface for each L-sensing target includes: There are obstacles between the dual-function base station and the L sensing targets. The dual-function base station generates a transmission signal, which is reflected by the hybrid intelligent reflective surface and then senses each sensing target through the third channel. The perception model of the perceived target is determined by the following formula: in, This represents the cascaded channel vector from the dual-function base station to the l-th sensing target via the hybrid intelligent reflector. This represents the array response matrix of the hybrid smart reflective surface. Determined by the following formula: in, Let represent the azimuth angle of the l-th sensing target relative to the hybrid intelligent reflective surface. d represents the pitch angle of the l-th sensing target relative to the hybrid intelligent reflector. x and d y These represent the distances between the two consecutive reflective elements of the intelligent reflective surface along the x-axis and y-axis, respectively, and λ represents the wavelength of the radio frequency transmission signal of the dual-function base station. N represents the Kronecker product. x and N y These represent the number of components along the x-axis and y-axis of the hybrid intelligent reflective surface, respectively, where N = N x ×N y .

5. The integrated sensing method based on hybrid intelligent reflective surface assistance according to claim 1, characterized in that, The method of transforming and solving the optimization objective based on the alternating variable iterative optimization algorithm to obtain the final optimal solution to the optimization problem includes: S4.1: Fix the mode scheduling matrix and reflection coefficient matrix of the hybrid intelligent reflector, optimize the transmitted beamforming vector and the covariance matrix of the sensed signal, and transform the optimization problem into the first sub-optimization problem; The first sub-optimization problem is solved using the interior point method to obtain the current optimal solution for the transmitted beamforming vector and the covariance matrix of the sensed signal; S4.2: The current optimal solution for the fixed beamforming vector and the covariance matrix of the sensing signal is used to optimize the mode scheduling matrix and the reflection coefficient matrix of the hybrid smart reflector, transforming the optimization problem into a second sub-optimization problem; The second sub-optimization problem is solved using a convex solver to obtain the current optimal solution for the mode scheduling matrix and reflection coefficient matrix of the hybrid intelligent reflector. S4.3: Calculate the increase in beam gain of the current optimization target compared to the increase in beam gain of the previous optimization target, and compare it with the preset minimum tolerance limit. If the increase is greater than the minimum tolerance limit, proceed to step S4.4; otherwise, proceed to step S4.

5. S4.4: Find the current optimal solution for the mode scheduling matrix and reflection coefficient matrix of the fixed hybrid intelligent reflector, and repeat steps S4.1-S4.3 above; S4.5: Take the current optimal solution of the beamforming vector corresponding to the current optimization objective, the current optimal solution of the sensing signal covariance matrix, the current optimal solution of the mode scheduling matrix of the hybrid intelligent reflector, and the current optimal solution of the reflection coefficient matrix of the hybrid intelligent reflector as the final optimal solution of the optimization problem, and calculate the corresponding minimum sensing beam gain.

6. The integrated sensing method based on hybrid intelligent reflective surface assistance according to claim 5, characterized in that, In step S4.1, the step of solving the first sub-optimization problem using the interior-point method to obtain the current optimal solution for the transmitted beamforming vector and the sensing signal covariance matrix includes: Introduce a beamforming matrix for the k-th communication user rank(W k If )≤1, the rank-one constraint is removed using semidefinite relaxation techniques, and the first sub-optimization problem is relaxed to a convex semidefinite programming problem of solving the transmit beamforming vector and the covariance matrix of the sensing signal. Then, the interior point method is used to solve for the current optimal transmit beamforming vector and the covariance matrix of the sensing signal.

7. The synesthetic integration method based on hybrid intelligent reflective surface assistance according to claim 5, characterized in that, In step S4.2, the step of using a convex solver to solve the second sub-optimization problem and obtain the current optimal solution for the mode scheduling matrix and reflection coefficient matrix of the hybrid intelligent reflector includes: definition Introducing matrices and relaxation optimization variables The optimization variables for Z are constrained according to the following conditions: Under the above conditions, Z = QUQ is satisfied. H =QUQ, where u i,j and z i,j Let q represent the elements in the i-th row and j-th column of matrices U and Z, respectively. i,i Let represent the element in the i-th row and i-th column of matrix Q, that is, the i-th diagonal element. It is an arbitrary constant; when the i-th diagonal element of Q is 0, equations (j), (k), (l), and (m) guarantee that all elements in the i-th row and i-th column of Z are zero; equations (n) and (o) guarantee that the remaining elements z of Z are zero. i,j =u i,j ; In the optimization problem, QUQ in equations (d), (e), and (f) H Substituting Z, we rewrite equation (i) as the following equality constraint: The penalty method is used to solve the non-convex binary constraint (i1), the equality constraint is transformed into a penalty term added to the optimization objective function, the upper bound of the penalty term in the objective function is constructed using the continuous convex approximation technique, the second optimization subproblem is transformed into a convex function, the second sub-optimization problem is transformed into a convex optimization problem, and the mode scheduling matrix and reflection coefficient matrix of the current optimal hybrid intelligent reflector are obtained by using a standard convex solver.

8. A sensory integration system based on hybrid intelligent reflective surface assistance, used to implement the method described in any one of claims 1-7, characterized in that, include: The system construction module is used to establish an integrated sensing system, which includes a dual-function base station, a hybrid intelligent reflector, K communication users, and L sensing targets. The model building module is used to establish a communication model based on the signal transmission task between the dual-function base station, the hybrid intelligent reflector and K communication users, and to establish a perception model based on the perception task of the dual-function base station and the hybrid intelligent reflector for L perception targets. The optimization problem construction module is used to establish an optimization problem based on the communication model and the sensing model, which satisfies the constraints of the maximum total transmit power of the dual-function base station, the communication quality of the communication user, the maximum total transmit power of the hybrid intelligent reflector, and the noise power constraint, to maximize the minimum sensing beam gain among L sensing targets. The optimization problem-solving module is used to transform and solve the optimization problem based on the alternating variable iterative optimization algorithm to obtain the final optimal solution to the optimization problem.

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